merged pull from JohnLangford/master, fixed mf

130 files changed, 154864 insertions, 3663 deletions

diff --git a/.gitignore b/.gitignore
index 95c5aac6..adbd7a0d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -43,3 +43,4 @@ vowpalwabbit/active_interactor
 vowpalwabbit/vw
 cluster/spanning_tree
 *.d
+*.prev
diff --git a/Makefile.am b/Makefile.am
index 6416a14c..5caf86b6 100644
--- a/Makefile.am
+++ b/Makefile.am
@@ -18,7 +18,7 @@ noinst_HEADERS = vowpalwabbit/accumulate.h vowpalwabbit/oaa.h		\
        vowpalwabbit/v_array.h vowpalwabbit/lda_core.h			\
        vowpalwabbit/v_hashmap.h vowpalwabbit/loss_functions.h		\
        vowpalwabbit/network.h vowpalwabbit/wap.h vowpalwabbit/noop.h	\
-       vowpalwabbit/nn.h vowpalwabbit/learner.h
+       vowpalwabbit/nn.h vowpalwabbit/learner.h vowpalwabbit/bs.h vowpalwabbit/topk.h
 
 ACLOCAL_AMFLAGS = -I acinclude.d
 
diff --git a/README b/README
index c47f5d7b..7d60680b 100644
--- a/README
+++ b/README
@@ -7,7 +7,6 @@ license as described in the file LICENSE.
 This is the vowpal wabbit fast online learning code.  For Windows, look at README.windows.txt
 
 You can download the latest version from here:
-
 https://github.com/JohnLangford/vowpal_wabbit/wiki/Download
 
 Alternatively, the very latest version is available here:
diff --git a/cluster/single_machine b/cluster/single_machine
index 3688711d..a8e812e1 100755
--- a/cluster/single_machine
+++ b/cluster/single_machine
@@ -2,6 +2,8 @@
 # A simplest-possible example of parallel code at work on a single machine.
 ./spanning_tree
 # This isn't actually useful---to do something useful, you would need to partition the dataset amongst the VW instances
-../vowpalwabbit/vw --total 2 --node 0 --unique_id 0 -d ../test/train-sets/0001.dat --span_server localhost > node_0 2>&1 &
-../vowpalwabbit/vw --total 2 --node 1 --unique_id 0 -d ../test/train-sets/0001.dat --span_server localhost 
+head -n 100 ../test/train-sets/0001.dat > head_data
+tail -n 100 ../test/train-sets/0001.dat > tail_data
+../vowpalwabbit/vw --total 2 --node 0 --unique_id 0 -d head_data --cache_file c0 -k --passes 100 --span_server localhost --holdout_off > node_0 2>&1 &
+../vowpalwabbit/vw --total 2 --node 1 --unique_id 0 -d tail_data --cache_file c1 -k --passes 100 --span_server localhost --holdout_off
 killall spanning_tree
diff --git a/configure.ac b/configure.ac
index e8869793..98c557d7 100644
--- a/configure.ac
+++ b/configure.ac
@@ -1,4 +1,4 @@
-AC_INIT([vowpal_wabbit], [7.3.1], [jl@hunch.net])
+AC_INIT([vowpal_wabbit], [7.3.2], [jl@hunch.net])
 AC_DEFINE([PACKAGE_URL],["https://github.com/JohnLangford/vowpal_wabbit"],[project url])
 AM_CONFIG_HEADER(vowpalwabbit/config.h)
 AM_INIT_AUTOMAKE()
diff --git a/cs_test/Program.cs b/cs_test/Program.cs
index 0c7e4b02..5197f037 100644
--- a/cs_test/Program.cs
+++ b/cs_test/Program.cs
@@ -6,16 +6,20 @@ using System.IO;
 

 using Microsoft.Research.MachineLearning;

 using System.Runtime.InteropServices;

+using System.Diagnostics;

 

 namespace cs_test

 {

+

     class Program

     {

         static void Main(string[] args)

         {

             //RunFeaturesTest();

             //RunParserTest();

-            RunParserTest2();

+            //RunSpeedTest();

+            RunFlatExampleTestEx();

+            //RunVWParce_and_VWLearn();

         }

 

         private static void RunFeaturesTest()

@@ -90,7 +94,10 @@ namespace cs_test
             IntPtr example = IntPtr.Zero;

             while (IntPtr.Zero != (example = VowpalWabbitInterface.GetExample(vw)))

             {

-                example = VowpalWabbitInterface.GetExample(vw);

+                IntPtr labelPtr = VowpalWabbitInterface.GetLabel(vw, example);

+                VowpalWabbitInterface.LABEL label = new VowpalWabbitInterface.LABEL();

+                label = (VowpalWabbitInterface.LABEL)Marshal.PtrToStructure(labelPtr, typeof(VowpalWabbitInterface.LABEL));

+

                 count++;

                 int featureSpaceLen = 0;

                 IntPtr featureSpacePtr = VowpalWabbitInterface.ExportExample(vw, example, ref featureSpaceLen);

@@ -113,67 +120,225 @@ namespace cs_test
                 }

                 VowpalWabbitInterface.ReleaseFeatureSpace(featureSpacePtr, featureSpaceLen);

 

+                float score = VowpalWabbitInterface.Learn(vw, example);

+                VowpalWabbitInterface.FinishExample(vw, example);

+            }

+

+            VowpalWabbitInterface.EndParser(vw);

+            VowpalWabbitInterface.Finish(vw);

+        }

+

+        private static void RunSpeedTest()

+        {

+            Console.WriteLine(DateTime.Now.Millisecond + DateTime.Now.Second * 1000 + DateTime.Now.Minute * 60 * 1000);

+

+            //IntPtr vw = VowpalWabbitInterface.Initialize("--ngram 2 --skips 4 -l 0.25 -b 22 -d rcv1.train.raw.txt -f out");

+            //IntPtr vw = VowpalWabbitInterface.Initialize("-d rcv1.train.raw.txt -b 22 --ngram 2 --skips 4 -l 0.25 -c");

+            //IntPtr vw = VowpalWabbitInterface.Initialize("-d rcv1.train.raw.txt -c");

+            IntPtr vw = VowpalWabbitInterface.Initialize("-d vw.dat");

+

+            VowpalWabbitInterface.StartParser(vw, false);

+

+            int count = 0;

+            IntPtr example = IntPtr.Zero;

+            Stopwatch s = Stopwatch.StartNew();

+            while (IntPtr.Zero != (example = VowpalWabbitInterface.GetExample(vw)))

+            {

+                count++;

 

                 float score = VowpalWabbitInterface.Learn(vw, example);

                 VowpalWabbitInterface.FinishExample(vw, example);

             }

+            s.Stop();

 

+            long t1 = s.ElapsedMilliseconds;

             VowpalWabbitInterface.EndParser(vw);

 

             VowpalWabbitInterface.Finish(vw);

+

+            Console.WriteLine(DateTime.Now.Millisecond + DateTime.Now.Second * 1000 + DateTime.Now.Minute * 60 * 1000);

+

+            Debug.WriteLine("RunSpeedTest Elapsed Time: {0} ms", s.ElapsedMilliseconds);

+            Console.WriteLine("RunSpeedTest Elapsed Time: {0} ms", s.ElapsedMilliseconds);

         }

 

+        //private static void RunFlatExampleTest()

+        //{

+        //    IntPtr vw = VowpalWabbitInterface.Initialize("-q st -d 0002.dat -f out");

 

-        private static void RunParserTest2()

-        {

-            long maxEx = 10; //long.MaxValue

+        //    VowpalWabbitInterface.StartParser(vw, false);

+

+        //    int count = 0;

+        //    IntPtr example = IntPtr.Zero;

+        //    while (IntPtr.Zero != (example = VowpalWabbitInterface.GetExample(vw)))

+        //    {

+        //        count++;

+        //        // make example flat

+        //        IntPtr flatec = VowpalWabbitInterface.Flatten_Example(vw, example);

+        //        VowpalWabbitInterface.FinishExample(vw, example);

+        //    }

+

+        //    VowpalWabbitInterface.EndParser(vw);

+        //    VowpalWabbitInterface.Finish(vw);

+        //}

 

-            //            IntPtr vw = VowpalWabbitInterface.Initialize("-q st -d 0002.dat -f out");

-            IntPtr vw = VowpalWabbitInterface.Initialize("-q st -d 0002.dat -b 18 --hash strings -f out --ring_size 74748 --examples 10");

+        private static void RunFlatExampleTestEx()

+        {

+            IntPtr vw = VowpalWabbitInterface.Initialize("-q st -d rcv1.train.raw.txt -f out");

+            //IntPtr vw = VowpalWabbitInterface.Initialize("-q st -d 0002.dat -f out");

 

             VowpalWabbitInterface.StartParser(vw, false);

 

+            uint stride = VowpalWabbitInterface.Get_Stride(vw);

+            uint mask = (1 << 18) - 1;

+

             int count = 0;

             IntPtr example = IntPtr.Zero;

-            while (count < maxEx)

+            while (IntPtr.Zero != (example = VowpalWabbitInterface.GetExample(vw)))

             {

-                example = VowpalWabbitInterface.GetExample(vw);

-                if (IntPtr.Zero == example)

-                    break;

-

                 count++;

+                IntPtr flatec = IntPtr.Zero;

+                // make example flat

+                flatec = VowpalWabbitInterface.Flatten_Example(vw, example);

+                if (IntPtr.Zero == flatec)

+                {

+                    continue;

+                }

+                VowpalWabbitInterface.FLAT_RAW_EXAMPLE flat = new VowpalWabbitInterface.FLAT_RAW_EXAMPLE();

+                flat = (VowpalWabbitInterface.FLAT_RAW_EXAMPLE)Marshal.PtrToStructure(flatec, typeof(VowpalWabbitInterface.FLAT_RAW_EXAMPLE));

 

+                // Get IntPtr data

+                VowpalWabbitInterface.FLAT_EXAMPLE flatEx = new VowpalWabbitInterface.FLAT_EXAMPLE();

 

-                //int featureSpaceLen = 0;

-                //IntPtr featureSpacePtr = VowpalWabbitInterface.ExportExample(vw, example, ref featureSpaceLen);

+                flatEx.final_prediction = flat.final_prediction;

+                flatEx.example_counter = flat.example_counter;

+                flatEx.ft_offset = flat.ft_offset;

+                flatEx.global_weight = flat.global_weight;

+                flatEx.num_features = flat.num_features;

 

-                //VowpalWabbitInterface.FEATURE_SPACE[] featureSpace = new VowpalWabbitInterface.FEATURE_SPACE[featureSpaceLen];

-                //int featureSpace_size = Marshal.SizeOf(typeof(VowpalWabbitInterface.FEATURE_SPACE));

+                flatEx.ld = new VowpalWabbitInterface.LABEL();

+                flatEx.ld = (VowpalWabbitInterface.LABEL)Marshal.PtrToStructure(flat.ld, typeof(VowpalWabbitInterface.LABEL));

+                if (flat.tag_len > 0)

+                {

+                    flatEx.tag = new byte[flat.tag_len];

+                    Marshal.Copy(flat.tag, flatEx.tag, 0, flat.tag_len);

+                }

 

-                //for (int i = 0; i < featureSpaceLen; i++)

-                //{

-                //    IntPtr curfeatureSpacePos = new IntPtr(featureSpacePtr.ToInt32() + i * featureSpace_size);

-                //    featureSpace[i] = (VowpalWabbitInterface.FEATURE_SPACE)Marshal.PtrToStructure(curfeatureSpacePos, typeof(VowpalWabbitInterface.FEATURE_SPACE));

+                IList<int> indices = new List<int>();

+                if (flat.num_features > 0)

+                {

+                    flatEx.feature_map = new VowpalWabbitInterface.FEATURE[flat.num_features];

+                    int feature_size = Marshal.SizeOf(typeof(VowpalWabbitInterface.FEATURE));

+                    for (int i = 0; i < (int)flat.num_features; i++)

+                    {

+                        IntPtr curfeaturePos = new IntPtr(flat.feature_map.ToInt32() + i * feature_size);

+                        flatEx.feature_map[i] = (VowpalWabbitInterface.FEATURE)Marshal.PtrToStructure(curfeaturePos, typeof(VowpalWabbitInterface.FEATURE));

 

-                //    VowpalWabbitInterface.FEATURE[] feature = new VowpalWabbitInterface.FEATURE[featureSpace[i].len];

-                //    int feature_size = Marshal.SizeOf(typeof(VowpalWabbitInterface.FEATURE));

-                //    for (int j = 0; j < featureSpace[i].len; j++)

-                //    {

-                //        IntPtr curfeaturePos = new IntPtr((featureSpace[i].features.ToInt32() + j * feature_size));

-                //        feature[j] = (VowpalWabbitInterface.FEATURE)Marshal.PtrToStructure(curfeaturePos, typeof(VowpalWabbitInterface.FEATURE));

-                //    }

-                //}

-                //VowpalWabbitInterface.ReleaseFeatureSpace(featureSpacePtr, featureSpaceLen);

+                        int val = ((int)(flatEx.feature_map[i].weight_index / stride)) & (int)mask;

+                        indices.Add(val);

 

+                        flatEx.feature_map[i].weight_index = (uint)val;

+                    }

+                }

 

-                //float score = VowpalWabbitInterface.Learn(vw, example);

-                //VowpalWabbitInterface.FinishExample(vw, example);

+                VowpalWabbitInterface.FreeFlattenExample(flatec);

+                VowpalWabbitInterface.FinishExample(vw, example);

             }

 

             VowpalWabbitInterface.EndParser(vw);

-

             VowpalWabbitInterface.Finish(vw);

         }

 

+        public class VWInstanceEx

+        {

+            public VowpalWabbitInterface.FEATURE_SPACE[] featureSpace;

+

+            public VWInstanceEx(IntPtr vw, IntPtr ex)

+            {

+                if (IntPtr.Zero == vw ||

+                    IntPtr.Zero == ex)

+                    return;

+

+                int featureSpaceLen = 0;

+                IntPtr featureSpacePtr = VowpalWabbitInterface.ExportExample(vw, ex, ref featureSpaceLen);

+

+                this.featureSpace = new VowpalWabbitInterface.FEATURE_SPACE[featureSpaceLen];

+                int featureSpace_size = Marshal.SizeOf(typeof(VowpalWabbitInterface.FEATURE_SPACE));

+

+                for (int i = 0; i < featureSpaceLen; i++)

+                {

+                    IntPtr curfeatureSpacePos = new IntPtr(featureSpacePtr.ToInt32() + i * featureSpace_size);

+                    this.featureSpace[i] = (VowpalWabbitInterface.FEATURE_SPACE)Marshal.PtrToStructure(curfeatureSpacePos, typeof(VowpalWabbitInterface.FEATURE_SPACE));

+

+                    VowpalWabbitInterface.FEATURE[] feature = new VowpalWabbitInterface.FEATURE[this.featureSpace[i].len];

+                    int feature_size = Marshal.SizeOf(typeof(VowpalWabbitInterface.FEATURE));

+                    for (int j = 0; j < this.featureSpace[i].len; j++)

+                    {

+                        IntPtr curfeaturePos = new IntPtr((this.featureSpace[i].features.ToInt32() + j * feature_size));

+                        feature[j] = (VowpalWabbitInterface.FEATURE)Marshal.PtrToStructure(curfeaturePos, typeof(VowpalWabbitInterface.FEATURE));

+                    }

+                }

+

+                VowpalWabbitInterface.ReleaseFeatureSpace(featureSpacePtr, featureSpaceLen);

+            }

+        }

+        private static void RunVWParce_and_VWLearn()

+        {

+            // parse and cache

+            IntPtr vw0 = VowpalWabbitInterface.Initialize(@"-d rcv1.train.raw.txt -c");

+            VowpalWabbitInterface.StartParser(vw0, false);

+

+            long instanceCount = 0;

+            VWInstanceEx[] vwInstanceExs = new VWInstanceEx[781266];

+            Stopwatch s = Stopwatch.StartNew();

+            while (instanceCount < 781266)

+            {

+                IntPtr example = VowpalWabbitInterface.GetExample(vw0);

+

+                if (IntPtr.Zero == example)

+                    break;

+

+                vwInstanceExs[instanceCount] = new VWInstanceEx(vw0, example);

+                VowpalWabbitInterface.FinishExample(vw0, example);

+                instanceCount++;

+            }

+            VowpalWabbitInterface.EndParser(vw0);

+            VowpalWabbitInterface.Finish(vw0);

+

+            // learn

+            instanceCount = 0;

+            IntPtr vw = VowpalWabbitInterface.Initialize(@"--quiet --random_seed 276518665 -f C:\\Users\\niruc\\AppData\\Local\\Temp\\outl1.tmp --readable_model C:\\Users\\niruc\\AppData\\Local\\Temp\\outl2.tmp");

+            foreach (VWInstanceEx vwInstanceEx in vwInstanceExs)

+            {

+                VowpalWabbitInterface.FEATURE_SPACE[] featureSpace = new VowpalWabbitInterface.FEATURE_SPACE[vwInstanceEx.featureSpace.Length];

+                GCHandle[] pinnedsFeatures = new GCHandle[vwInstanceEx.featureSpace.Length];

+                for (int i = 0; i < vwInstanceEx.featureSpace.Length; i++)

+                {

+                    pinnedsFeatures[i] = GCHandle.Alloc(vwInstanceEx.featureSpace[i].features, GCHandleType.Pinned);

+                    featureSpace[i].features = pinnedsFeatures[i].AddrOfPinnedObject();

+                }

+                GCHandle pinnedFeatureSpace = GCHandle.Alloc(featureSpace, GCHandleType.Pinned);

+                IntPtr featureSpacePtr = pinnedFeatureSpace.AddrOfPinnedObject();

+

+                IntPtr importedExample = VowpalWabbitInterface.ImportExample(vw, featureSpacePtr, vwInstanceEx.featureSpace.Length);

+                VowpalWabbitInterface.Learn(vw, importedExample);

+                VowpalWabbitInterface.FinishExample(vw, importedExample);

+

+                for (int i = 0; i < vwInstanceEx.featureSpace.Length; i++)

+                {

+                    pinnedsFeatures[i].Free();

+                }

+                pinnedFeatureSpace.Free();

+                importedExample = IntPtr.Zero;

+

+                instanceCount++;

+            }

+            VowpalWabbitInterface.Finish(vw);

+

+            Debug.WriteLine("Elapsed Time: {0} ms", s.ElapsedMilliseconds);

+            Console.WriteLine("Elapsed Time: {0} ms", s.ElapsedMilliseconds);

+

+        }

     }

 }

+

diff --git a/cs_test/VowpalWabbitInterface.cs b/cs_test/VowpalWabbitInterface.cs
index ee92f5f0..37e38f6a 100644
--- a/cs_test/VowpalWabbitInterface.cs
+++ b/cs_test/VowpalWabbitInterface.cs
@@ -9,7 +9,7 @@ namespace Microsoft.Research.MachineLearning
     public sealed class VowpalWabbitInterface

     {

         [StructLayout(LayoutKind.Sequential)]

-        internal struct FEATURE_SPACE

+        public struct FEATURE_SPACE

         {

             public byte name;

             public IntPtr features;     // points to a FEATURE[]

@@ -23,6 +23,51 @@ namespace Microsoft.Research.MachineLearning
             public uint weight_index;

         }

 

+        [StructLayout(LayoutKind.Sequential)]

+        public struct LABEL

+        {

+            public float label;

+            public float weight;

+            public float initial;

+        }

+

+        [StructLayout(LayoutKind.Sequential)]

+        public struct FLAT_RAW_EXAMPLE

+        {

+            public IntPtr ld;

+            public float final_prediction;  

+

+            //byte[] tag;//An identifier for the example.

+            public int tag_len;

+            public IntPtr tag;//An identifier for the example. 

+

+            public UInt64 example_counter;

+            public UInt32 ft_offset;

+            public float global_weight;

+

+            public UInt64 num_features;//precomputed, cause it's fast&easy.  

+

+            //FEATURE[] feature_map; //map to store sparse feature vectors  

+            public int feature_map_len;

+            public IntPtr feature_map; //map to store sparse feature vectors  

+        }

+

+        [StructLayout(LayoutKind.Sequential)]

+        public struct FLAT_EXAMPLE

+        {

+            public LABEL ld;

+            public float final_prediction;

+

+            public byte[] tag;//An identifier for the example. 

+

+            public UInt64 example_counter;

+            public UInt32 ft_offset;

+            public float global_weight;

+

+            public UInt64 num_features;//precomputed, cause it's fast&easy.  

+            public FEATURE[] feature_map; //map to store sparse feature vectors  

+        }

+

         [DllImport("libvw.dll", EntryPoint = "VW_Initialize", CallingConvention = CallingConvention.StdCall)]

         public static extern IntPtr Initialize([MarshalAs(UnmanagedType.LPWStr)]string arguments);

 

@@ -51,6 +96,9 @@ namespace Microsoft.Research.MachineLearning
         [DllImport("libvw.dll", EntryPoint = "VW_GetExample", CallingConvention = CallingConvention.StdCall)]

         public static extern IntPtr GetExample(IntPtr parser);

 

+        [DllImport("libvw.dll", EntryPoint = "VW_GetLabel", CallingConvention = CallingConvention.StdCall)]

+        public static extern IntPtr GetLabel(IntPtr vw, IntPtr example);

+

         [DllImport("libvw.dll", EntryPoint = "VW_FinishExample", CallingConvention = CallingConvention.StdCall)]

         public static extern void FinishExample(IntPtr vw, IntPtr example);

         

@@ -67,9 +115,19 @@ namespace Microsoft.Research.MachineLearning
         public static extern void AddLabel(IntPtr example, float label=float.MaxValue, float weight=1, float initial=0);

 

         [DllImport("libvw.dll", EntryPoint = "VW_Get_Weight", CallingConvention = CallingConvention.StdCall)]

-        public static extern void Get_Weight(IntPtr vw, UInt32 index);

+        public static extern float Get_Weight(IntPtr vw, UInt32 index, UInt32 offset);

 

         [DllImport("libvw.dll", EntryPoint = "VW_Num_Weights", CallingConvention = CallingConvention.StdCall)]

         public static extern UInt32 Num_Weights(IntPtr vw);

+

+        [DllImport("libvw.dll", EntryPoint = "VW_Get_Stride", CallingConvention = CallingConvention.StdCall)]

+        public static extern UInt32 Get_Stride(IntPtr vw);

+

+        [DllImport("libvw.dll", EntryPoint = "VW_FlattenExample", CallingConvention = CallingConvention.StdCall)]

+        public static extern IntPtr Flatten_Example(IntPtr vw, IntPtr example);

+

+        [DllImport("libvw.dll", EntryPoint = "VW_FreeFlattenExample", CallingConvention = CallingConvention.StdCall)]

+        public static extern IntPtr FreeFlattenExample(IntPtr fec);

+    

     }

 }

diff --git a/cs_test/cs_test.csproj b/cs_test/cs_test.csproj
index e6261580..d0903697 100644
--- a/cs_test/cs_test.csproj
+++ b/cs_test/cs_test.csproj
@@ -65,6 +65,7 @@
     <CodeAnalysisIgnoreBuiltInRuleSets>false</CodeAnalysisIgnoreBuiltInRuleSets>

     <CodeAnalysisRuleDirectories>;C:\Program Files (x86)\Microsoft Visual Studio 10.0\Team Tools\Static Analysis Tools\FxCop\\Rules</CodeAnalysisRuleDirectories>

     <CodeAnalysisIgnoreBuiltInRules>false</CodeAnalysisIgnoreBuiltInRules>

+    <UseVSHostingProcess>false</UseVSHostingProcess>

   </PropertyGroup>

   <PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Debug|AnyCPU'">

     <DebugSymbols>true</DebugSymbols>

diff --git a/demo/random-noise/distrib.r b/demo/random-noise/distrib.r
new file mode 100755
index 00000000..93bea239
--- /dev/null
+++ b/demo/random-noise/distrib.r
@@ -0,0 +1,75 @@
+#!/usr/bin/Rscript
+# --vanilla
+#
+# distrib.r:
+#   utility to plot distribution/density of a numeric data-set column
+#
+#   Usage:
+#       distrib.r data_file ["optional chart title string"]
+#   where data_file contains the numeric vector, a number per line.
+#
+# -- where to look for R libraries
+# .libPaths(c('~/local/lib/R',
+#             '/usr/lib/R/library',
+#             '/usr/lib/R/site-library'
+# ))
+suppressPackageStartupMessages(library(ggplot2))
+
+ratio = 1.61803398875
+W = 4
+H = W / ratio
+DPI = 200
+FONTSIZE = 9 
+MyGray = 'grey50'
+
+title.theme   = element_text(family="FreeSans", face="bold.italic",
+                            size=FONTSIZE-2)
+x.title.theme = element_text(family="FreeSans", face="bold.italic",
+                            size=FONTSIZE-2, vjust=-0.1)
+y.title.theme = element_text(family="FreeSans", face="bold.italic",
+                           size=FONTSIZE-2, angle=90, vjust=0.2)
+x.axis.theme  = element_text(family="FreeSans", face="bold",
+                            size=FONTSIZE-2, colour=MyGray)
+y.axis.theme  = element_text(family="FreeSans", face="bold",
+                            size=FONTSIZE-2, colour=MyGray)
+legend.theme  = element_text(family="FreeSans", face="bold.italic",
+                            size=FONTSIZE-1, colour="black")
+
+eprintf <- function(...) cat(sprintf(...), sep='', file=stderr())
+
+argv <- commandArgs(trailingOnly = TRUE)
+
+csvfile <- argv[1]
+title <- ifelse(! is.na(argv[2]),
+                argv[2],
+                'vw demo: random expression distribution')
+Ys   <- read.csv(csvfile, header=F, col.names='Ys')
+
+d <- data.frame(Ys=Ys)
+
+Y_labels <- function(yrange) {
+    the.min <- as.integer(floor(yrange[1]))
+    the.max <- as.integer(ceiling(yrange[2] + 1))
+    seq(from=the.min, to=the.max, by=1)
+}
+
+# geom_histogram(binwidth=.5, alpha=.5, position="identity")
+# geom_histogram(fill='#3377ff',
+#        binwidth=.01, alpha=.4, stat='density') +
+g <- ggplot(data=d, aes(x=Ys)) +
+        geom_density(fill='#3377ff', alpha=0.4, lwd=0.2) +
+        scale_x_continuous(breaks=Y_labels(range(Ys))) +
+        ggtitle(title) +
+        xlab(NULL) +
+        theme(
+            plot.title=title.theme,
+            axis.title.y=y.title.theme,
+            axis.title.x=x.title.theme,
+            axis.text.x=x.axis.theme,
+            axis.text.y=y.axis.theme
+        )
+
+pngfile <- sprintf("%s.density.png", csvfile)
+ggsave(g, file=pngfile, width=W, height=H, dpi=DPI)
+
+
diff --git a/library/Makefile b/library/Makefile
index df9a86fd..9a0f71ce 100644
--- a/library/Makefile
+++ b/library/Makefile
@@ -9,11 +9,14 @@ ifeq ($(UNAME), Darwin)
 BOOST_PROGRAM_OPTIONS = boost_program_options-mt
 endif
 
-all: ezexample_predict ezexample_train library_example recommend
+all: ezexample_predict ezexample_train library_example recommend 
 
 ezexample_predict: ezexample_predict.cc ../vowpalwabbit/libvw.a ezexample.h
 	g++ -g -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l pthread
 
+ezexample_predict_threaded: ezexample_predict_threaded.cc ../vowpalwabbit/libvw.a ezexample.h
+	g++ -g -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l boost_thread
+
 ezexample_train: ezexample_train.cc ../vowpalwabbit/libvw.a ezexample.h
 	g++ -g -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l pthread
 
@@ -21,4 +24,7 @@ library_example: library_example.cc ../vowpalwabbit/libvw.a
 	g++ -g -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l pthread
 
 recommend: recommend.cc ../vowpalwabbit/libvw.a ezexample.h
-	g++ -g -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l pthread
+	g++ -g -O3 -o $@ $< -L ../vowpalwabbit -l vw -l allreduce -l $(BOOST_PROGRAM_OPTIONS) -l z -l pthread
+
+clean:
+	rm -f *.o ezexample_predict ezexample_train library_example recommend ezexample_predict_threaded
diff --git a/library/ezexample.h b/library/ezexample.h
index 145e4b9a..48152891 100644
--- a/library/ezexample.h
+++ b/library/ezexample.h
@@ -17,6 +17,7 @@ public: vw_namespace(const char c) : namespace_letter(c) {}
 class ezexample {
  private:
   vw*vw_ref;
+  vw*vw_par_ref;   // an extra parser if we're multithreaded
   bool is_multiline;
 
   char str[2];
@@ -35,16 +36,17 @@ class ezexample {
   ezexample & operator=(const ezexample & ex);
 
   example* get_new_example() {
-    example* new_ec = VW::new_unused_example(*vw_ref);
-    vw_ref->p->lp->default_label(new_ec->ld);
+    example* new_ec = VW::new_unused_example(*vw_par_ref);
+    vw_par_ref->p->lp->default_label(new_ec->ld);
     return new_ec;
   }
 
  public:
 
   // REAL FUNCTIONALITY
-  ezexample(vw*this_vw, bool multiline=false) {
+  ezexample(vw*this_vw, bool multiline=false, vw*this_vw_parser=NULL) {
     vw_ref = this_vw;
+    vw_par_ref = (this_vw_parser == NULL) ? this_vw : this_vw_parser;
     is_multiline = multiline;
 
     str[0] = 0; str[1] = 0;
@@ -59,17 +61,17 @@ class ezexample {
     for (size_t i=0; i<256; i++) ns_exists[i] = false;
 
     if (vw_ref->add_constant)
-      VW::add_constant_feature(*this_vw, ec);
+      VW::add_constant_feature(*vw_ref, ec);
 
     example_changed_since_prediction = true;
   }
 
   ~ezexample() {
     if (ec->in_use)
-      VW::finish_example(*vw_ref, ec);
+      VW::finish_example(*vw_par_ref, ec);
     for (example**ecc=example_copies.begin; ecc!=example_copies.end; ecc++)
       if ((*ecc)->in_use)
-        VW::finish_example(*vw_ref, *ecc);
+        VW::finish_example(*vw_par_ref, *ecc);
     example_copies.erase();
     free(example_copies.begin);
   }
@@ -130,8 +132,8 @@ class ezexample {
   inline fid addf(fid fint, float v) { return addf(current_ns, fint, v); }
 
   inline ezexample& set_label(string label) {
-    VW::parse_example_label(*vw_ref, *ec, label);
-    ec->global_weight = vw_ref->p->lp->get_weight(ec->ld);
+    VW::parse_example_label(*vw_par_ref, *ec, label);
+    ec->global_weight = vw_par_ref->p->lp->get_weight(ec->ld);
     example_changed_since_prediction = true;
     return *this;
   }
@@ -160,7 +162,7 @@ class ezexample {
   }
 
   float predict() {
-    static example* empty_example = is_multiline ? VW::read_example(*vw_ref, (char*)"") : NULL;
+    static example* empty_example = is_multiline ? VW::read_example(*vw_par_ref, (char*)"") : NULL;
     if (example_changed_since_prediction) {
       mini_setup_example();
       vw_ref->learn(ec);
@@ -182,7 +184,7 @@ class ezexample {
       // we need to make a copy
       example* copy = get_new_example();
       assert(ec->in_use);
-      VW::copy_example_data(copy, ec, vw_ref->p->lp->label_size, vw_ref->p->lp->copy_label);
+      VW::copy_example_data(vw_ref->audit, copy, ec, vw_par_ref->p->lp->label_size, vw_par_ref->p->lp->copy_label);
       assert(copy->in_use);
       vw_ref->learn(copy);
       example_copies.push_back(copy);
@@ -197,11 +199,12 @@ class ezexample {
   }
 
   void finish() {
-    static example* empty_example = is_multiline ? VW::read_example(*vw_ref, (char*)"") : NULL;
+    static example* empty_example = is_multiline ? VW::read_example(*vw_par_ref, (char*)"") : NULL;
     if (is_multiline) {
       vw_ref->learn(empty_example);
       for (example**ecc=example_copies.begin; ecc!=example_copies.end; ecc++)
-        VW::finish_example(*vw_ref, *ecc);
+        if ((*ecc)->in_use)
+          VW::finish_example(*vw_par_ref, *ecc);
       example_copies.erase();
     }
   }
diff --git a/library/ezexample_predict.cc b/library/ezexample_predict.cc
index f550b8b8..0aa95941 100644
--- a/library/ezexample_predict.cc
+++ b/library/ezexample_predict.cc
@@ -8,7 +8,6 @@ using namespace std;
 int main(int argc, char *argv[])

 {

   // INITIALIZE WITH WHATEVER YOU WOULD PUT ON THE VW COMMAND LINE -- THIS READS IN A MODEL FROM train.w

-  //  vw vw = VW::initialize("--hash all -q st --noconstant -i train.w -t --quiet --csoaa_ldf s");

   vw* vw = VW::initialize("-t -i train.w -q st --hash all --noconstant --csoaa_ldf s --quiet");

 

   {

diff --git a/library/ezexample_predict_threaded.cc b/library/ezexample_predict_threaded.cc
new file mode 100644
index 00000000..a45d76ca
--- /dev/null
+++ b/library/ezexample_predict_threaded.cc
@@ -0,0 +1,149 @@
+#include <stdio.h>

+#include "../vowpalwabbit/vw.h"

+#include "ezexample.h"

+

+#include <boost/thread/thread.hpp>

+

+using namespace std;

+

+int runcount = 100;

+

+class Worker

+{

+public:

+  Worker(vw & instance, string & vw_init_string, vector<double> & ref)

+    : m_vw(instance)

+    , m_referenceValues(ref)

+    , vw_init_string(vw_init_string)

+  { }

+

+  void operator()()

+  {

+    m_vw_parser = VW::initialize(vw_init_string);

+    if (m_vw_parser == NULL) {

+      cerr << "cannot initialize vw parser" << endl;

+      exit(-1);

+    }

+

+    int errorCount = 0;

+    for (int i = 0; i < runcount; ++i)

+    {

+      vector<double>::iterator it = m_referenceValues.begin();

+      ezexample ex(&m_vw, false, m_vw_parser);

+      

+      ex(vw_namespace('s'))

+        ("p^the_man")

+        ("w^the")

+        ("w^man")

+        (vw_namespace('t'))

+        ("p^le_homme")

+        ("w^le")

+        ("w^homme");

+      ex.set_label("1");

+      if (*it != ex()) { cerr << "fail!" << endl; ++errorCount; }

+      //if (*it != pred) { cerr << "fail!" << endl; ++errorCount; }

+      //VW::finish_example(m_vw, vec2);

+      ++it;

+

+      --ex;   // remove the most recent namespace

+      ex(vw_namespace('t'))

+        ("p^un_homme")

+        ("w^un")

+        ("w^homme");

+      ex.set_label("1");

+      if (*it != ex()) { cerr << "fail!" << endl; ++errorCount; }

+      ++it;

+

+      --ex;   // remove the most recent namespace

+      // add features with explicit ns

+      ex('t', "p^un_homme")

+        ('t', "w^un")

+        ('t', "w^homme");

+      ex.set_label("1");

+      if (*it != ex()) { cerr << "fail!" << endl; ++errorCount; }

+      ++it;

+

+      //cout << "."; cout.flush();

+    }

+    cerr << "error count = " << errorCount << endl;

+    VW::finish(*m_vw_parser);

+    m_vw_parser = NULL;

+  }

+

+private:

+  vw & m_vw;

+  vw * m_vw_parser;

+  vector<double> & m_referenceValues;

+  string & vw_init_string;

+};

+

+int main(int argc, char *argv[])

+{

+  if (argc != 3)

+  {

+    cerr << "need two args: threadcount runcount" << endl;

+    return 1;

+  }

+  int threadcount = atoi(argv[1]);

+  runcount = atoi(argv[2]);

+  // INITIALIZE WITH WHATEVER YOU WOULD PUT ON THE VW COMMAND LINE -- THIS READS IN A MODEL FROM train.w

+  string vw_init_string_all    = "-t --csoaa_ldf s --quiet -q st --noconstant --hash all -i train.w";

+  string vw_init_string_parser = "-t --csoaa_ldf s --quiet -q st --noconstant --hash all --noop";   // this needs to have enough arguments to get the parser right

+  vw*vw = VW::initialize(vw_init_string_all);

+  vector<double> results;

+

+  // HAL'S SPIFFY INTERFACE USING C++ CRAZINESS

+  {

+    ezexample ex(vw, false);

+    ex(vw_namespace('s'))

+      ("p^the_man")

+      ("w^the")

+      ("w^man")

+      (vw_namespace('t'))

+      ("p^le_homme")

+      ("w^le")

+      ("w^homme");

+    ex.set_label("1");

+    results.push_back(ex.predict());

+    cerr << "should be near zero = " << ex.predict() << endl;

+

+    --ex;   // remove the most recent namespace

+    ex(vw_namespace('t'))

+      ("p^un_homme")

+      ("w^un")

+      ("w^homme");

+    ex.set_label("1");

+    results.push_back(ex.predict());

+    cerr << "should be near one  = " << ex.predict() << endl;

+

+    --ex;   // remove the most recent namespace

+    // add features with explicit ns

+    ex('t', "p^un_homme")

+      ('t', "w^un")

+      ('t', "w^homme");

+    ex.set_label("1");

+    results.push_back(ex.predict());

+    cerr << "should be near one  = " << ex.predict() << endl;

+  }

+

+  if (threadcount == 0)

+  {

+    Worker w(*vw, vw_init_string_parser, results);

+    w();

+  }

+  else

+  {

+    boost::thread_group tg;

+    for (int t = 0; t < threadcount; ++t)

+    {

+      cerr << "starting thread " << t << endl;

+      boost::thread * pt = tg.create_thread(Worker(*vw, vw_init_string_parser, results));

+    }

+    tg.join_all();

+    cerr << "finished!" << endl;

+  }

+

+

+  // AND FINISH UP

+  VW::finish(*vw);

+}

diff --git a/library/library_example.cc b/library/library_example.cc
index 76b9e1fc..7227b61e 100644
--- a/library/library_example.cc
+++ b/library/library_example.cc
@@ -52,7 +52,7 @@ int main(int argc, char *argv[])
     {

       cout << "namespace = " << pfs[i].name;

       for (size_t j = 0; j < pfs[i].len; j++)

-	cout << " " << pfs[i].fs[j].weight_index << ":" << pfs[i].fs[j].x << ":" << VW::get_weight(*model2, pfs[i].fs[j].weight_index);

+	cout << " " << pfs[i].fs[j].weight_index << ":" << pfs[i].fs[j].x << ":" << VW::get_weight(*model2, pfs[i].fs[j].weight_index, 0);

       cout << endl;

     }   

 

diff --git a/library/recommend.cc b/library/recommend.cc
index f4b7909d..e45fad7a 100644
--- a/library/recommend.cc
+++ b/library/recommend.cc
@@ -38,22 +38,12 @@ void progress()
         fprintf(stderr, "%12d %8d %8d %8d %12d %s %s\n", pairs, users, items, recs, skipped, userfilename.c_str(), itemfilename.c_str());
 }
 
-unsigned hash_ber(char *in, size_t len) {
-        unsigned hashv = 0;
-        while (len--)  hashv = ((hashv) * 33) + *in++;
-        return hashv;
-}
-unsigned hash_fnv(char *in, size_t len) {
-        unsigned hashv = 2166136261UL;
-        while(len--) hashv = (hashv * 16777619) ^ *in++;
-        return hashv;
-}
 #define MASK(u,b) ( u & ((1UL << b) - 1))
 #define NUM_HASHES 2
 void get_hashv(char *in, size_t len, unsigned *out) {
         assert(NUM_HASHES==2);
-        out[0] = MASK(hash_ber(in,len),b);
-        out[1] = MASK(hash_fnv(in,len),b);
+        out[0] = MASK(uniform_hash(in,len,1),b);
+        out[1] = MASK(uniform_hash(in,len,2),b);
 }
 
 #define BIT_TEST(c,i) (c[i/8] & (1 << (i % 8)))
diff --git a/library/train.sh b/library/train.sh
index 538f7e5b..ce118f04 100755
--- a/library/train.sh
+++ b/library/train.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
 rm -f train.cache train.w
-../vowpalwabbit/vw -c -d train -f train.w -q st --passes 100 --hash all --noconstant
-../vowpalwabbit/vw -t -d train -i train.w -p train.pred --noconstant 
+../vowpalwabbit/vw -c -d train -f train.w -q st --passes 100 --hash all --noconstant --csoaa_ldf m
+../vowpalwabbit/vw -t -d train -i train.w -p train.pred --noconstant  --csoaa_ldf m
 
diff --git a/library/train.w b/library/train.w
index f6f1b100..be1820ff 100644
--- a/library/train.w
+++ b/library/train.w
diff --git a/test/Makefile b/test/Makefile
new file mode 100644
index 00000000..8c15655b
--- /dev/null
+++ b/test/Makefile
@@ -0,0 +1,2 @@
+test:
+	cd ..; make test
diff --git a/test/RunTests b/test/RunTests
index 0cc9b0f4..8d2aaf2d 100755
--- a/test/RunTests
+++ b/test/RunTests
@@ -19,6 +19,8 @@ my $LDA;
 my $Diff = 'diff';
 my $Cat = 'cat';
 
+my $diff_opts = '-N --minimal --side-by-side --suppress-common-lines --ignore-all-space --strip-trailing-cr -W 150';
+
 my @ToTest = ();
 
 sub usage(@) {
@@ -225,7 +227,7 @@ sub next_test() {
         die "$0: test $TestNo: command is undefined\n";
     }
     unless (defined $out_ref) {
-        die "$0: test $TestNo: stdout ref: undefined\n";
+        warn "$0: test $TestNo: stdout ref: undefined (assuming empty)\n";
     }
     unless (defined $err_ref) {
         die "$0: test $TestNo: stderr ref: undefined\n";
@@ -286,7 +288,9 @@ sub lenient_array_compare($$) {
 sub diff_lenient_float($$) {
     my ($reffile, $outfile) = @_;
     my $status = 0;
-    my $diff_opts = '-N --minimal --side-by-side --suppress-common-lines --ignore-all-space --strip-trailing-cr';
+
+    my $diff_opts = '-N --minimal --side-by-side --suppress-common-lines --ignore-all-space --strip-trailing-cr -W 200';
+
     my $tmpf = 'lenient-diff.tmp';
     system("$Diff $diff_opts $reffile $outfile >$tmpf");
     $status = $? >> 8;
@@ -325,6 +329,7 @@ sub touch(@) {
 sub diff($$) {
     my ($reffile, $outfile) = @_;
     my $status = 0;
+    $reffile = '' unless (defined $reffile);
 
     # Special case, empty file w/o reference is not considered a failure.
     # This is a most common case with stdout.
@@ -348,7 +353,7 @@ sub diff($$) {
     }
 
     # Actually run the diff
-    my $diff_cmd = "$Diff -N $reffile $outfile";
+    my $diff_cmd = "$Diff $diff_opts $reffile $outfile";
     system("$diff_cmd >diff.tmp");
     $status = $? >> 8;
     if (-s 'diff.tmp') {
@@ -616,13 +621,13 @@ __DATA__
     pred-sets/ref/0002c.predict
 
 # Test 9: label-dependent features with csoaa_ldf
-{VW} -k -c -d train-sets/cs_test.ldf -p cs_test.ldf.csoaa.predict --passes 10 --invariant --csoaa_ldf multiline
+{VW} -k -c -d train-sets/cs_test.ldf -p cs_test.ldf.csoaa.predict --passes 10 --invariant --csoaa_ldf multiline --holdout_off
     train-sets/ref/cs_test.ldf.csoaa.stdout
     train-sets/ref/cs_test.ldf.csoaa.stderr
     train-sets/ref/cs_test.ldf.csoaa.predict
 
 # Test 10: label-dependent features with wap_ldf
-{VW} -k -c -d train-sets/cs_test.ldf -p cs_test.ldf.wap.predict --passes 10 --invariant --wap_ldf multiline
+{VW} -k -c -d train-sets/cs_test.ldf -p cs_test.ldf.wap.predict --passes 10 --invariant --wap_ldf multiline --holdout_off
     train-sets/ref/cs_test.ldf.wap.stdout
     train-sets/ref/cs_test.ldf.wap.stderr
     train-sets/ref/cs_test.ldf.wap.predict
@@ -638,12 +643,12 @@ __DATA__
     train-sets/ref/multiclass.stderr
 
 # Test 13: Run searn on wsj_small for 12 passes, 4 passes per policy, extra features
-{VW} -k -c -d train-sets/wsj_small.dat.gz --passes 12 --invariant --searn_passes_per_policy 4 --searn_task sequence --searn 5 --searn_sequencetask_history 2 --searn_sequencetask_bigrams --searn_sequencetask_features 1 --quiet
+{VW} -k -c -d train-sets/wsj_small.dat.gz --passes 12 --invariant --searn_passes_per_policy 4 --searn_task sequence --searn 5 --searn_history 2 --searn_bigrams --searn_features 1 --quiet --holdout_off
     train-sets/ref/searn_wsj.stdout
     train-sets/ref/searn_wsj.stderr
 
 # Test 14: Run searn (wap) on wsj_small for 2 passes, 1 pass per policy, extra features
-{VW} -k -b 19 -c -d train-sets/wsj_small.dat.gz --passes 2 --invariant --searn_passes_per_policy 1 --searn_task sequence --searn 5 --wap 5 --searn_sequencetask_history 2 --searn_sequencetask_bigrams --searn_sequencetask_features 1 --quiet
+{VW} -k -b 19 -c -d train-sets/wsj_small.dat.gz --passes 2 --invariant --searn_passes_per_policy 1 --searn_task sequence --searn 5 --wap 5 --searn_history 2 --searn_bigrams --searn_features 1 --quiet --holdout_off
     train-sets/ref/searn_wsj2.dat.stdout
     train-sets/ref/searn_wsj2.dat.stderr
 
@@ -663,7 +668,7 @@ __DATA__
     train-sets/ref/wiki1K.stderr
 
 # Test 18: Run searn on seq_small for 12 passes, 4 passes per policy
-{VW} -k -c -d train-sets/seq_small --passes 12 --invariant --searn_passes_per_policy 4 --searn 4 --searn_task sequence --quiet
+{VW} -k -c -d train-sets/seq_small --passes 12 --invariant --searn_passes_per_policy 4 --searn 4 --searn_task sequence --quiet --holdout_off
     train-sets/ref/searn_small.stdout
     train-sets/ref/searn_small.stderr
 
@@ -692,3 +697,55 @@ __DATA__
 {VW} -i movielens.reg -t -d test-sets/ml100k_small_test
     test-sets/ref/ml100k_small.stdout
     test-sets/ref/ml100k_small.stderr
+
+# Test 24: active-learning -- training
+{VW} -k --active_simulation --active_mellowness 0.000001 -d train-sets/rcv1_small.dat -l 10 --initial_t 10
+    train-sets/ref/active-simulation.t24.stderr
+
+# Test 25: bagging -- training regressor
+{VW} -d train-sets/0002.dat -f models/bs.reg.model --bs 4 -p bs.reg.predict 
+    train-sets/ref/bs.reg.stdout
+    train-sets/ref/bs.reg.stderr
+    train-sets/ref/bs.reg.predict
+
+# Test 26: bagging -- predicting with bagged regressor
+{VW} -d train-sets/0002.dat -i models/bs.prreg.model -p bs.prreg.predict -t
+    train-sets/ref/bs.prreg.stdout
+    train-sets/ref/bs.prreg.stderr
+    train-sets/ref/bs.prreg.predict
+
+# Test 27: bagging -- binary classifiers
+{VW} -d train-sets/0001.dat -f models/bs.vote.model --bs 4 --bs_type vote -p bs.vote.predict 
+    train-sets/ref/bs.vote.stdout
+    train-sets/ref/bs.vote.stderr
+    train-sets/ref/bs.vote.predict
+
+# Test 28: bagging -- predict with bagged classifier
+{VW} -d train-sets/0001.dat -i models/bs.prvote.model -p bs.prvote.predict -t
+    train-sets/ref/bs.prvote.stdout
+    train-sets/ref/bs.prvote.stderr
+    train-sets/ref/bs.prvote.predict
+
+# Test 29: affix features
+{VW} -d train-sets/affix_test.dat -k -c --passes 10 --holdout_off --affix -2
+    train-sets/ref/affix_test.stdout
+    train-sets/ref/affix_test.stderr
+
+# Test 30: train --l1 regularized model
+{VW} -d train-sets/0001.dat -f models/mask.model --invert_hash mask.predict --l1 0.01
+    train-sets/ref/mask.stderr
+    
+# Test 31: train model using --feature_mask
+{VW} -d train-sets/0001.dat --invert_hash remask.predict --feature_mask models/mask.model
+    train-sets/ref/remask.stderr
+
+# Test 32: train model for topk recommender
+{VW} -d train-sets/topk.vw -f topk.model -q MF --passes 100 --cache_file topk-train.cache -k --holdout_off 
+    train-sets/ref/topk-train.stdout
+    train-sets/ref/topk-train.stderr
+
+# Test 33: train model for topk recommender
+{VW} -d train-sets/topk.vw -i topk.model --top 2 -p topk.predict
+    train-sets/ref/topk-rec.stdout
+    train-sets/ref/topk-rec.stderr
+    train-sets/ref/topk-rec.predict
diff --git a/test/pred-sets/ref/0022.stderr b/test/pred-sets/ref/0022.stderr
index 88dccb2e..f3d05bbe 100644
--- a/test/pred-sets/ref/0022.stderr
+++ b/test/pred-sets/ref/0022.stderr
@@ -13,7 +13,8 @@ loss       last          counter      weight    label  predict features
 0.000000   0.000000            6         6.0   1.0000   1.0000        4
 
 finished run
-number of examples = 8
+number of examples per pass = 8
+passes used = 1
 weighted example sum = 8
 weighted label sum = 0
 average loss = 0
diff --git a/test/test-sets/ref/0001.stderr b/test/test-sets/ref/0001.stderr
index 68ea7315..61715757 100644
--- a/test/test-sets/ref/0001.stderr
+++ b/test/test-sets/ref/0001.stderr
@@ -20,7 +20,8 @@ loss       last          counter      weight    label  predict features
 0.000000   0.000000          174       174.0   1.0000   1.0000      140
 
 finished run
-number of examples = 200
+number of examples per pass = 200
+passes used = 1
 weighted example sum = 200
 weighted label sum = 91
 average loss = 0
diff --git a/test/test-sets/ref/0002b.stderr b/test/test-sets/ref/0002b.stderr
index cbb87372..3d44f2be 100644
--- a/test/test-sets/ref/0002b.stderr
+++ b/test/test-sets/ref/0002b.stderr
@@ -26,7 +26,8 @@ loss       last          counter      weight    label  predict features
 0.013838   0.015817        44537     44537.0   0.4905   0.5615       15
 
 finished run
-number of examples = 74746
+number of examples per pass = 74746
+passes used = 1
 weighted example sum = 6.952e+04
 weighted label sum = 3.511e+04
 average loss = 0.01197
diff --git a/test/test-sets/ref/0002c.stderr b/test/test-sets/ref/0002c.stderr
index cd99e74a..f83c4c6a 100644
--- a/test/test-sets/ref/0002c.stderr
+++ b/test/test-sets/ref/0002c.stderr
@@ -26,10 +26,11 @@ loss       last          counter      weight    label  predict features
 0.011797   0.015520        44537     44537.0   0.4905   0.3865       15
 
 finished run
-number of examples = 74746
-weighted example sum = 69521
-weighted label sum = 35113.3
-average loss = 0.0106127
-best constant = 0.505067
-best constant's loss = 0.249974
+number of examples per pass = 74746
+passes used = 1
+weighted example sum = 6.952e+04
+weighted label sum = 3.511e+04
+average loss = 0.01063
+best constant = 0.5051
+best constant's loss = 0.25
 total feature number = 1119986
diff --git a/test/test-sets/ref/ml100k_small.stderr b/test/test-sets/ref/ml100k_small.stderr
index 32bf174f..971206e3 100644
--- a/test/test-sets/ref/ml100k_small.stderr
+++ b/test/test-sets/ref/ml100k_small.stderr
@@ -15,7 +15,8 @@ loss       last          counter      weight    label  predict features
 1.178384   0.889414           22        22.0   3.0000   3.3272       23
 
 finished run
-number of examples = 30
+number of examples per pass = 30
+passes used = 1
 weighted example sum = 30
 weighted label sum = 110
 average loss = 1.399
diff --git a/test/train-sets/affix_test.dat b/test/train-sets/affix_test.dat
new file mode 100644
index 00000000..87d59702
--- /dev/null
+++ b/test/train-sets/affix_test.dat
@@ -0,0 +1,6 @@
+-1 | happiness
++1 | happier
+-1 | ugliness
++1 | uglier
+-1 | fastidiousness
++1 | faster
diff --git a/test/train-sets/ref/0001.stderr b/test/train-sets/ref/0001.stderr
index 782c3385..99a964aa 100644
--- a/test/train-sets/ref/0001.stderr
+++ b/test/train-sets/ref/0001.stderr
@@ -23,7 +23,8 @@ loss       last          counter      weight    label  predict features
 0.069038   0.000000         1392      1392.0   0.0000   0.0000       62
 
 finished run
-number of examples = 1600
+number of examples per pass = 200
+passes used = 8
 weighted example sum = 1600
 weighted label sum = 728
 average loss = 0.06006
diff --git a/test/train-sets/ref/0002.stderr b/test/train-sets/ref/0002.stderr
index 3031b4d3..4670e2ae 100644
--- a/test/train-sets/ref/0002.stderr
+++ b/test/train-sets/ref/0002.stderr
@@ -25,10 +25,11 @@ loss       last          counter      weight    label  predict features
 0.001101   0.001310        44537     44537.0   0.4905   0.4683       15
 
 finished run
-number of examples = 74746
-weighted example sum = 6.952e+04
-weighted label sum = 3.511e+04
-average loss = 0.001248
-best constant = 0.5051
-best constant's loss = 0.25
+number of examples per pass = 74746
+passes used = 1
+weighted example sum = 69521
+weighted label sum = 35113.3
+average loss = 0.00124849
+best constant = 0.505067
+best constant's loss = 0.249974
 total feature number = 1119986
diff --git a/test/train-sets/ref/0002a.stderr b/test/train-sets/ref/0002a.stderr
index 5259753e..974ad712 100644
--- a/test/train-sets/ref/0002a.stderr
+++ b/test/train-sets/ref/0002a.stderr
@@ -26,7 +26,8 @@ loss       last          counter      weight    label  predict features
 0.000821   0.000839        44537     44537.0   0.4905   0.4853      197
 
 finished run
-number of examples = 74746
+number of examples per pass = 74746
+passes used = 1
 weighted example sum = 6.952e+04
 weighted label sum = 3.511e+04
 average loss = 0.0008412
diff --git a/test/train-sets/ref/0002c.stderr b/test/train-sets/ref/0002c.stderr
index 39230217..87543a60 100644
--- a/test/train-sets/ref/0002c.stderr
+++ b/test/train-sets/ref/0002c.stderr
@@ -25,7 +25,8 @@ loss       last          counter      weight    label  predict features
 0.000724   0.000647        44537     44537.0   0.4905   0.4846       15
 
 finished run
-number of examples = 74746
+number of examples per pass = 74746
+passes used = 1
 weighted example sum = 6.952e+04
 weighted label sum = 3.511e+04
 average loss = 0.0008729
diff --git a/test/train-sets/ref/active-simulation.t24.stderr b/test/train-sets/ref/active-simulation.t24.stderr
new file mode 100644
index 00000000..40c0ffc7
--- /dev/null
+++ b/test/train-sets/ref/active-simulation.t24.stderr
@@ -0,0 +1,33 @@
+Num weight bits = 18
+learning rate = 10
+initial_t = 10
+power_t = 0.5
+using no cache
+Reading datafile = train-sets/rcv1_small.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+1.000000   1.000000            1       490.2   1.0000   0.0000       50
+1.005870   1.244963            2       502.2  -1.0000   0.1158      103
+1.007812   1.170191            3       508.2  -1.0000   0.0818       56
+1.005833   0.000000            4       509.2  unknown  -0.0461      134
+1.003861   0.000000            5       510.2  unknown  -0.0659      130
+1.003885   1.016081            6       511.2   1.0000  -0.0080       30
+1.003828   0.974701            7       512.2  -1.0000  -0.0127       81
+1.001872   0.000000            8       513.2  unknown  -0.0531      145
+0.876856   0.540072          141       703.7  -1.0000  -0.1940       82
+0.640553   0.399695          540      1394.2  -1.0000   0.1056       34
+0.439186   0.237403         1382      2785.4   1.0000   0.1403      191
+0.344554   0.249822         2668      5567.9  unknown   0.8667       35
+0.284067   0.223691         5591     11146.0   1.0000  -0.1930      121
+
+finished run
+number of examples per pass = 10000
+passes used = 1
+weighted example sum = 19090.5
+weighted label sum = -1355
+average loss = 0.240509
+best constant = -0.136917
+total feature number = 779394
+total queries = 889
+
diff --git a/test/train-sets/ref/affix_test.stderr b/test/train-sets/ref/affix_test.stderr
new file mode 100644
index 00000000..f3b1d437
--- /dev/null
+++ b/test/train-sets/ref/affix_test.stderr
@@ -0,0 +1,24 @@
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+decay_learning_rate = 1
+creating cache_file = train-sets/affix_test.dat.cache
+Reading datafile = train-sets/affix_test.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+1.008092   1.008092            3         3.0  -1.0000  -0.2249        3
+0.808355   0.608619            6         6.0   1.0000   0.2517        3
+0.496722   0.122761           11        11.0  -1.0000  -0.7206        3
+0.259290   0.021858           22        22.0   1.0000   0.9391        3
+0.129920   0.000549           44        44.0   1.0000   0.9947        3
+
+finished run
+number of examples per pass = 6
+passes used = 10
+weighted example sum = 60
+weighted label sum = 0
+average loss = 0.0952751
+best constant = 0
+total feature number = 180
diff --git a/test/train-sets/ref/affix_test.stdout b/test/train-sets/ref/affix_test.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/affix_test.stdout
diff --git a/test/train-sets/ref/bs.prreg.predict b/test/train-sets/ref/bs.prreg.predict
new file mode 100644
index 00000000..a25e5ed0
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+++ b/test/train-sets/ref/bs.prreg.predict
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diff --git a/test/train-sets/ref/bs.prreg.stderr b/test/train-sets/ref/bs.prreg.stderr
new file mode 100644
index 00000000..0bf878a8
--- /dev/null
+++ b/test/train-sets/ref/bs.prreg.stderr
@@ -0,0 +1,36 @@
+only testing
+Num weight bits = 18
+learning rate = 10
+initial_t = 1
+power_t = 0.5
+predictions = bs.prreg.predict
+using no cache
+Reading datafile = train-sets/0002.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.286797   0.286797            3         3.0   0.5498   0.0000       15
+0.271153   0.255508            6         6.0   0.2681   0.0000       15
+0.310037   0.356697           11        11.0   0.4315   0.0000       15
+0.270070   0.230104           22        22.0   0.5519   0.0000       15
+0.303996   0.337922           44        44.0   0.5514   0.0000       15
+0.284588   0.264729           87        87.0   0.5140   0.0000       15
+0.294585   0.304582          174       174.0   0.5596   0.0000       15
+0.290007   0.285429          348       348.0   0.5475   0.0000       15
+0.278993   0.267978          696       696.0   0.3421   0.0000       15
+0.286305   0.293618         1392      1392.0   0.4996   0.0000       15
+0.274909   0.263514         2784      2784.0   0.5090   0.0000       15
+0.263663   0.252417         5568      5568.0   0.6413   0.0000       15
+0.266312   0.268961        11135     11135.0   0.3869   0.0000       15
+0.260244   0.254175        22269     22269.0   0.5063   0.0000       15
+0.251980   0.243716        44537     44537.0   0.4905   0.0000       15
+
+finished run
+number of examples per pass = 74746
+passes used = 1
+weighted example sum = 69521
+weighted label sum = 35113.3
+average loss = 0.260912
+best constant = 0.505067
+best constant's loss = 0.249974
+total feature number = 1119986
diff --git a/test/train-sets/ref/bs.prreg.stdout b/test/train-sets/ref/bs.prreg.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/bs.prreg.stdout
diff --git a/test/train-sets/ref/bs.prvote.predict b/test/train-sets/ref/bs.prvote.predict
new file mode 100644
index 00000000..b3621d49
--- /dev/null
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diff --git a/test/train-sets/ref/bs.prvote.stderr b/test/train-sets/ref/bs.prvote.stderr
new file mode 100644
index 00000000..4fca68f7
--- /dev/null
+++ b/test/train-sets/ref/bs.prvote.stderr
@@ -0,0 +1,28 @@
+only testing
+Num weight bits = 18
+learning rate = 10
+initial_t = 1
+power_t = 0.5
+predictions = bs.prvote.predict
+using no cache
+Reading datafile = train-sets/0001.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.333333   0.333333            3         3.0   0.0000   0.0000       57
+0.333333   0.333333            6         6.0   1.0000   0.0000       31
+0.272727   0.200000           11        11.0   0.0000   0.0000       38
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+0.356322   0.348837           87        87.0   1.0000   0.0000       61
+0.459770   0.563218          174       174.0   1.0000   0.0000       26
+
+finished run
+number of examples per pass = 200
+passes used = 1
+weighted example sum = 200
+weighted label sum = 91
+average loss = 0.455
+best constant = 0.452261
+best constant's loss = 0.247721
+total feature number = 15482
diff --git a/test/train-sets/ref/bs.prvote.stdout b/test/train-sets/ref/bs.prvote.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/bs.prvote.stdout
diff --git a/test/train-sets/ref/bs.reg.predict b/test/train-sets/ref/bs.reg.predict
new file mode 100644
index 00000000..afe3dbf5
--- /dev/null
+++ b/test/train-sets/ref/bs.reg.predict
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+0.266573 CFT/20091028 0.214433 0.316945
+0.117909 SRS/20091028 0.076769 0.171165
+0.481479 MOO/20091028 0.441819 0.546767
+0.338794 BIV/20091028 0.300795 0.366759
+0.213899 VXX/20091028 0.118671 0.288751
+0.514447 IYM/20091028 0.467244 0.597763
+0.455344 IFN/20091028 0.417756 0.500228
+0.533524 SLV/20091028 0.484309 0.585647
+0.509377 TAO/20091028 0.484672 0.538745
+0.570018 PGF/20091028 0.550095 0.592505
+0.592394 IYR/20091028 0.568322 0.614456
+0.102752 QID/20091028 0.052755 0.169671
+0.547519 THD/20091028 0.497752 0.584075
+0.570998 IJS/20091028 0.527576 0.610512
+0.577951 VB/20091028 0.527532 0.626508
+0.339313 EDV/20091028 0.291709 0.391531
+0.524107 IEZ/20091028 0.455348 0.588348
+0.553343 VTV/20091028 0.518059 0.585766
+0.574239 IJR/20091028 0.540819 0.609165
+0.320858 UCO/20091028 0.271501 0.348731
+0.551687 JNK/20091028 0.538094 0.560179
+0.588878 IWN/20091028 0.577600 0.603661
+0.565939 VV/20091028 0.559464 0.569271
+0.499604 UGL/20091028 0.493369 0.502830
+0.682195 UWM/20091028 0.645362 0.711622
+0.651977 IWC/20091028 0.626421 0.673097
+0.597365 EWA/20091028 0.563731 0.642140
+0.585730 IVV/20091028 0.555340 0.613066
+0.586810 SPY/20091028 0.558084 0.616239
+0.433708 TFI/20091028 0.408216 0.462946
+0.596480 VEA/20091028 0.580425 0.624587
+0.594784 QQQQ/20091028 0.581294 0.609501
+0.588274 UYG/20091028 0.568073 0.623003
+0.558500 OIH/20091028 0.550015 0.570453
+0.514795 GXC/20091028 0.496007 0.527107
+0.575526 SSO/20091028 0.562884 0.582219
+0.587890 XLI/20091028 0.580336 0.605217
+0.602411 GML/20091028 0.597758 0.611425
+0.577804 ROM/20091028 0.567545 0.583945
+0.556883 FXC/20091028 0.549640 0.575860
+0.155382 DOG/20091028 0.122047 0.199692
+0.507715 IYE/20091028 0.476219 0.541873
+0.194941 SKF/20091028 0.156165 0.240660
+0.292921 SHY/20091028 0.270420 0.326095
+0.447794 DBA/20091028 0.411342 0.502703
+0.527230 RSP/20091028 0.499859 0.573101
+0.513762 DBS/20091028 0.496543 0.551939
+0.565284 IBB/20091028 0.540488 0.608222
+0.551587 KCE/20091028 0.528453 0.589582
+0.540895 PKN/20091028 0.523523 0.564936
+0.523817 TNA/20091028 0.514436 0.545302
+0.512584 FAS/20091028 0.505184 0.529460
+0.531552 FXE/20091028 0.504085 0.557442
+0.533192 HYG/20091028 0.505165 0.553872
+0.551867 IWS/20091028 0.534645 0.563564
+0.298966 FXP/20091028 0.285730 0.320306
+0.341483 MBB/20091028 0.322010 0.381876
+0.544284 RFG/20091028 0.527107 0.556036
+0.539759 EPU/20091028 0.526323 0.550687
+0.243532 UUP/20091028 0.219111 0.287456
+0.477383 AGQ/20091028 0.402456 0.519297
+0.570258 SOXX/20091028 0.459238 0.649630
+0.244107 FAZ/20091028 0.116399 0.406325
+0.571437 VBK/20091028 0.512120 0.612733
+0.548310 RPG/20091028 0.507377 0.570481
+0.520811 EWH/20091028 0.503702 0.534826
+0.283059 TZA/20091028 0.171743 0.380631
+0.530834 SGG/20091028 0.505302 0.555177
+0.539751 KOL/20091028 0.506675 0.564570
+0.578678 EWY/20091028 0.552678 0.602385
+0.582814 PRF/20091028 0.564365 0.594492
+0.400192 TLH/20091028 0.374496 0.430404
+0.572985 EPP/20091028 0.547063 0.593852
+0.528793 XLE/20091028 0.505897 0.544731
+0.580885 EWN/20091028 0.566537 0.588543
+0.435872 SHM/20091028 0.399278 0.463038
+0.522871 FXI/20091028 0.504139 0.537617
+0.577190 EWS/20091028 0.567563 0.588653
+0.564687 IDU/20091028 0.551551 0.575579
+0.354456 VXZ/20091028 0.314709 0.372653
+0.574309 IVE/20091028 0.560571 0.586262
+0.527449 DGP/20091028 0.511107 0.535460
+0.619916 GMF/20091028 0.549395 0.658603
+0.632223 IWR/20091028 0.571191 0.660729
+0.595382 RKH/20091028 0.560225 0.610460
+0.411323 TIP/20091028 0.379374 0.445706
+0.577887 URE/20091028 0.561449 0.587280
+0.427176 DBO/20091028 0.406681 0.459102
+0.578911 IOO/20091028 0.560618 0.602277
+0.578149 DBV/20091028 0.558390 0.598824
+0.589256 EFA/20091028 0.569567 0.618552
+0.605848 BGU/20091028 0.570170 0.675785
+0.592953 EFV/20091028 0.575388 0.615773
+0.580703 IWB/20091028 0.562115 0.605731
+0.589861 IYF/20091028 0.571730 0.612317
+0.407875 YCS/20091028 0.391956 0.423428
+0.549589 DXJ/20091028 0.540950 0.569718
+0.588545 IWO/20091028 0.580908 0.599947
+0.453198 DBC/20091028 0.438918 0.470022
+0.240956 RWM/20091028 0.189230 0.296179
+0.563637 VBR/20091028 0.543908 0.601693
+0.284905 MZZ/20091028 0.197241 0.345120
+0.540193 IWD/20091028 0.522364 0.553708
+0.559827 PCY/20091028 0.542314 0.568593
+0.545784 EWI/20091028 0.533537 0.554034
+0.546638 IJH/20091028 0.531702 0.557554
+0.547265 EEM/20091028 0.532894 0.556257
+0.516287 EWM/20091028 0.500184 0.522600
+0.529657 SDY/20091028 0.513737 0.538363
+0.548692 ILF/20091028 0.533604 0.562335
+0.458485 JJG/20091028 0.428888 0.499023
+0.414229 TBT/20091028 0.404611 0.421277
+0.581315 XLF/20091028 0.573419 0.587182
+0.538410 ERX/20091028 0.517544 0.554580
+0.318497 SHV/20091028 0.281142 0.357952
+0.545150 EWX/20091028 0.517738 0.569221
+0.332633 EFZ/20091028 0.311657 0.358790
+0.413323 FXB/20091028 0.393553 0.428250
+0.526310 PHO/20091028 0.514050 0.539870
+0.524077 IGE/20091028 0.505563 0.544702
+0.363205 BGZ/20091028 0.348276 0.377099
+0.530968 UDN/20091028 0.507838 0.555114
+0.411602 CSJ/20091028 0.402613 0.420417
+0.542056 GXG/20091028 0.516212 0.578726
+0.555172 USD/20091028 0.537791 0.565786
+0.522654 EWD/20091028 0.511283 0.535292
+0.538826 EWJ/20091028 0.530333 0.543280
+0.569031 BRF/20091028 0.558596 0.582632
+0.586217 VEU/20091028 0.576357 0.593296
+0.559578 XLU/20091028 0.550831 0.569941
+0.498935 JJC/20091028 0.494588 0.503919
+0.601489 FGD/20091028 0.597174 0.606416
+0.558430 FXF/20091028 0.544589 0.569577
+0.517366 LQD/20091028 0.502456 0.528595
+0.596623 SCZ/20091028 0.591514 0.599362
+0.570504 IYW/20091028 0.567636 0.574926
+0.575230 VPL/20091028 0.573184 0.577584
+0.580303 DGS/20091028 0.578031 0.585157
+0.562519 ICF/20091028 0.555280 0.570446
+0.556577 DVY/20091028 0.555148 0.558534
+0.573710 IEO/20091028 0.568640 0.578504
+0.571388 VOT/20091028 0.566862 0.578439
+0.446465 CIU/20091028 0.421411 0.460055
+0.571841 EWG/20091028 0.563842 0.578416
+0.553662 EWT/20091028 0.549402 0.559057
+0.508010 GSG/20091028 0.497855 0.517723
+0.456661 KRE/20091028 0.444409 0.464095
+0.576060 LVL/20091028 0.569084 0.580262
+0.545548 UNG/20091028 0.507834 0.566877
+0.505325 MUB/20091028 0.476947 0.522061
+0.553433 VT/20091028 0.530354 0.570056
+0.523874 DAG/20091028 0.508421 0.554966
+0.516132 PPH/20091028 0.489847 0.527686
+0.585469 VSS/20091028 0.565330 0.598838
+0.503734 DBB/20091028 0.481166 0.528973
+0.537210 XLP/20091028 0.510481 0.553292
+0.586461 IJT/20091028 0.570879 0.596768
+0.593877 EWZ/20091028 0.571087 0.618078
+0.600610 PBW/20091028 0.577142 0.621540
+0.434227 FXY/20091028 0.398229 0.453048
+0.549991 IYZ/20091028 0.534603 0.567043
+0.590793 MVV/20091028 0.578694 0.602135
+0.572441 VUG/20091028 0.555058 0.581746
+0.457997 PST/20091028 0.447289 0.469414
+0.331409 PSQ/20091028 0.315996 0.350369
+0.557659 VNQ/20091028 0.543425 0.573640
+0.442505 IEI/20091028 0.424111 0.455904
+0.573971 EWW/20091028 0.556202 0.591499
+0.579732 IWP/20091028 0.569033 0.590213
+0.570521 IWV/20091028 0.561884 0.577959
+0.555205 DIG/20091028 0.539505 0.564186
+0.569611 VTI/20091028 0.562505 0.576704
+0.555828 FXA/20091028 0.539741 0.562309
+0.575300 NLR/20091028 0.565933 0.583167
+0.463107 AGG/20091028 0.453252 0.472186
+0.544849 BWX/20091028 0.539400 0.549528
+0.538903 IAU/20091028 0.527869 0.548194
+0.553992 XLV/20091028 0.542345 0.566270
+0.583694 XOP/20091028 0.570326 0.600436
+0.581508 EZU/20091028 0.570619 0.598449
+0.568472 JXI/20091028 0.560799 0.581229
+0.572469 XBI/20091028 0.553651 0.590621
+0.571574 IYG/20091028 0.561341 0.584607
+0.588414 SLX/20091028 0.572151 0.602805
+0.558229 HAO/20091028 0.547248 0.573354
+0.613958 EZA/20091028 0.605023 0.620450
+0.597664 XLY/20091028 0.588767 0.613512
+0.461790 IEF/20091028 0.454787 0.473603
+0.607945 DEM/20091028 0.598711 0.618812
+0.579531 IVW/20091028 0.574206 0.587305
+0.617524 UYM/20091028 0.601508 0.633054
+0.593229 IXC/20091028 0.565121 0.606756
+0.574097 PFF/20091029 0.555455 0.609100
+0.535463 WIP/20091029 0.522385 0.549599
+0.476631 GCC/20091029 0.465534 0.505652
+0.531181 AAXJ/20091029 0.500012 0.576474
+0.527497 VWO/20091029 0.511883 0.554738
+0.420738 EEV/20091029 0.405305 0.439468
+0.557706 GDX/20091029 0.507757 0.580560
+0.516806 RTH/20091029 0.471048 0.536420
+0.548190 MXI/20091029 0.513403 0.577272
+0.511529 EWU/20091029 0.475346 0.531220
+0.429736 SH/20091029 0.389764 0.466687
+0.564707 EDC/20091029 0.518670 0.601658
+0.536585 ERY/20091029 0.477747 0.586307
+0.480742 SDS/20091029 0.447545 0.512339
+0.535327 OEF/20091029 0.520912 0.553719
+0.520768 IYT/20091029 0.486076 0.599108
+0.507654 BIL/20091029 0.487242 0.547067
+0.505819 GLL/20091029 0.471480 0.524398
+0.417665 EDZ/20091029 0.323781 0.510885
+0.557776 IWM/20091029 0.471254 0.614171
+0.529968 VXF/20091029 0.483888 0.600359
+0.543002 IJJ/20091029 0.518513 0.567836
+0.528693 PIN/20091029 0.521253 0.549229
+0.536122 XLB/20091029 0.496113 0.573286
+0.490325 ECH/20091029 0.472663 0.510614
+0.477508 TYH/20091029 0.448538 0.500002
+0.519357 VAW/20091029 0.480741 0.538243
+0.538199 DBP/20091029 0.532180 0.543673
+0.515561 XME/20091029 0.494749 0.543168
+0.502420 VO/20091029 0.483495 0.531048
+0.510635 RSX/20091029 0.488258 0.524074
+0.537166 EWC/20091029 0.523052 0.555921
+0.479725 TUR/20091029 0.473911 0.484311
+0.517771 VYM/20091029 0.474189 0.541053
+0.477452 FCG/20091029 0.452118 0.503089
+0.512486 VGT/20091029 0.500845 0.527514
+0.516261 EWQ/20091029 0.493875 0.538555
+0.502607 IEV/20091029 0.495771 0.507403
+0.507958 XLK/20091029 0.482401 0.528148
+0.486810 EFG/20091029 0.482587 0.497801
+0.498512 BKF/20091029 0.484581 0.511093
+0.466759 KIE/20091029 0.452372 0.487975
+0.510387 EEB/20091029 0.496582 0.531274
+0.516005 IJK/20091029 0.478290 0.538124
+0.444974 DUG/20091029 0.389797 0.513568
+0.399840 TWM/20091029 0.364886 0.449665
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+0.514528 BSV/20091029 0.454779 0.539981
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+0.514099 DIA/20091029 0.475117 0.539983
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+0.499011 BND/20091029 0.481646 0.546932
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+0.488957 AMJ/20091029 0.459298 0.548862
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+0.536630 PZA/20091029 0.518349 0.548159
+0.498638 VGK/20091029 0.470642 0.520844
+0.503560 RWX/20091029 0.488901 0.521505
+0.490044 JJA/20091029 0.422649 0.530392
+0.516153 FXD/20091029 0.473671 0.556653
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+0.464061 DTO/20091029 0.450655 0.475096
+0.528015 EWP/20091029 0.507171 0.541347
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+0.582222 ERY/20091113 0.557380 0.599869
+0.478774 SDS/20091113 0.464038 0.495911
+0.497714 OEF/20091113 0.492013 0.501513
+0.540795 IYT/20091113 0.532342 0.548446
+0.539768 BIL/20091113 0.528132 0.550975
+0.480843 GLL/20091113 0.469912 0.491520
+0.448786 EDZ/20091113 0.433795 0.460686
+0.518439 IWM/20091113 0.515391 0.520968
+0.513685 VXF/20091113 0.508543 0.519857
+0.517004 IJJ/20091113 0.516468 0.517978
+0.529119 PIN/20091113 0.513839 0.542673
+0.522158 XLB/20091113 0.518492 0.529749
+0.513044 ECH/20091113 0.500909 0.530738
+0.372859 TYH/20091113 0.369694 0.376626
+0.528682 VAW/20091113 0.525254 0.532406
+0.496811 DBP/20091113 0.491719 0.506282
+0.549456 XME/20091113 0.545488 0.557801
+0.518975 VO/20091113 0.514401 0.521415
+0.496328 RSX/20091113 0.491645 0.501317
+0.497063 EWC/20091113 0.492258 0.503071
+0.494617 TUR/20091113 0.488920 0.498226
+0.523718 VYM/20091113 0.515246 0.530573
+0.456388 FCG/20091113 0.449762 0.470435
+0.508280 VGT/20091113 0.504722 0.515577
+0.482268 EWQ/20091113 0.477001 0.488068
+0.476285 IEV/20091113 0.472529 0.481427
+0.512538 XLK/20091113 0.503654 0.519051
+0.494818 EFG/20091113 0.484708 0.501762
+0.501622 BKF/20091113 0.494244 0.507062
+0.497166 KIE/20091113 0.486039 0.504715
+0.509566 EEB/20091113 0.505467 0.515000
+0.501804 IJK/20091113 0.494558 0.511964
+0.548256 DUG/20091113 0.528518 0.566110
+0.440447 TWM/20091113 0.431751 0.455156
+0.512956 MDY/20091113 0.505046 0.517866
+0.500045 ACWI/20091113 0.496483 0.505878
+0.509963 BSV/20091113 0.500737 0.518946
+0.535512 DDM/20091113 0.528957 0.541140
+0.507075 DIA/20091113 0.501827 0.511401
+0.507701 TLT/20091113 0.488248 0.521379
+0.459640 DXD/20091113 0.435112 0.487739
+0.474687 XHB/20091113 0.467472 0.484658
+0.477934 VDE/20091113 0.475730 0.480680
+0.513467 BND/20091113 0.499428 0.525688
+0.505849 EMB/20091113 0.499683 0.513985
+0.674015 SCO/20091113 0.668649 0.679231
+0.528669 AMJ/20091113 0.509866 0.536791
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+0.526209 PZA/20091113 0.515572 0.535062
+0.476528 VGK/20091113 0.473431 0.480491
+0.462549 RWX/20091113 0.452791 0.471708
+0.524595 JJA/20091113 0.517815 0.529924
+0.521145 FXD/20091113 0.517552 0.526601
+0.450392 XES/20091113 0.434822 0.457692
+0.514147 VIG/20091113 0.511285 0.517717
+0.417255 DZZ/20091113 0.412269 0.427785
+0.493024 VFH/20091113 0.490158 0.497736
+0.665188 DTO/20091113 0.657865 0.677030
+0.466250 EWP/20091113 0.462010 0.471697
+0.509742 FDN/20091113 0.504289 0.522432
+0.544722 INP/20091113 0.524655 0.557647
+0.445570 TYP/20091113 0.430457 0.471310
+0.538577 RWR/20091113 0.529194 0.545925
+0.512218 KBE/20091113 0.508432 0.515793
+0.520567 EUO/20091113 0.512356 0.525459
+0.514871 IWF/20091113 0.512459 0.516672
+0.439507 SMN/20091113 0.427963 0.452074
+0.554427 SMH/20091113 0.547198 0.572964
+0.506204 XRT/20091113 0.500333 0.510280
+0.418129 USO/20091113 0.394024 0.446080
+0.522596 DJP/20091113 0.508183 0.535383
+0.497790 CFT/20091113 0.493044 0.503829
+0.391093 SRS/20091113 0.370595 0.400809
+0.586625 MOO/20091113 0.579350 0.594422
+0.495576 BIV/20091113 0.486794 0.506973
+0.345488 VXX/20091113 0.331225 0.359303
+0.521306 IYM/20091113 0.518433 0.526010
+0.509721 IFN/20091113 0.505252 0.514551
+0.514916 SLV/20091113 0.489924 0.543826
+0.536610 TAO/20091113 0.534852 0.538046
+0.548594 PGF/20091113 0.543500 0.551761
+0.545595 IYR/20091113 0.533334 0.560239
+0.427076 QID/20091113 0.403181 0.446268
+0.510262 THD/20091113 0.499325 0.519045
+0.536510 IJS/20091113 0.532204 0.541066
+0.528387 VB/20091113 0.524350 0.533051
+0.497287 EDV/20091113 0.474861 0.515425
+0.457317 IEZ/20091113 0.449307 0.462208
+0.524270 VTV/20091113 0.517856 0.529144
+0.524953 IJR/20091113 0.514561 0.531031
+0.317248 UCO/20091113 0.301209 0.335567
+0.526355 JNK/20091113 0.521179 0.530350
+0.535129 IWN/20091113 0.526263 0.541781
+0.523561 VV/20091113 0.517653 0.527804
+0.520237 UGL/20091113 0.493530 0.536651
+0.553397 UWM/20091113 0.535318 0.573357
+0.539587 IWC/20091113 0.530765 0.548096
+0.479004 EWA/20091113 0.473455 0.484564
+0.524254 IVV/20091113 0.519849 0.533207
+0.527107 SPY/20091113 0.520952 0.533624
+0.525192 TFI/20091113 0.520477 0.530748
+0.491540 VEA/20091113 0.486239 0.497724
+0.513299 QQQQ/20091113 0.508060 0.516920
+0.483996 UYG/20091113 0.478352 0.492035
+0.448332 OIH/20091113 0.447111 0.451395
+0.495183 GXC/20091113 0.484520 0.505005
+0.536904 SSO/20091113 0.531528 0.542826
+0.527666 XLI/20091113 0.517110 0.541010
+0.549761 GML/20091113 0.542681 0.560463
+0.527450 ROM/20091113 0.521622 0.534605
+0.502510 FXC/20091113 0.489083 0.515419
+0.484725 DOG/20091113 0.465546 0.503474
+0.479313 IYE/20091113 0.472627 0.482814
+0.502404 SKF/20091113 0.491601 0.509530
+0.487289 SHY/20091113 0.482910 0.489924
+0.541438 DBA/20091113 0.532806 0.546009
+0.524325 RSP/20091113 0.514710 0.535035
+0.510025 DBS/20091113 0.496132 0.518568
+0.521019 IBB/20091113 0.511794 0.536909
+0.467474 KCE/20091113 0.463402 0.476426
+0.512200 PKN/20091113 0.509387 0.516617
+0.444108 TNA/20091113 0.439431 0.451514
+0.487365 FAS/20091113 0.474644 0.510377
+0.487332 FXE/20091113 0.482197 0.496432
+0.530197 HYG/20091113 0.523628 0.535195
+0.531866 IWS/20091113 0.525230 0.538422
+0.541250 FXP/20091113 0.527900 0.550045
+0.486896 MBB/20091113 0.476736 0.491241
+0.518126 RFG/20091113 0.512412 0.527124
+0.517716 EPU/20091113 0.511195 0.523706
+0.531427 UUP/20091113 0.519557 0.541956
+0.524773 AGQ/20091113 0.505484 0.535334
+0.579428 SOXX/20091113 0.574749 0.584925
+0.491906 FAZ/20091113 0.477208 0.508214
+0.531645 VBK/20091113 0.525312 0.538039
+0.514506 RPG/20091113 0.511305 0.518263
+0.519925 EWH/20091113 0.516859 0.524501
+0.381452 TZA/20091113 0.371597 0.393337
+0.548804 SGG/20091113 0.542407 0.553550
+0.535335 KOL/20091113 0.531678 0.540512
+0.559498 EWY/20091113 0.553840 0.566418
+0.526825 PRF/20091113 0.523983 0.528041
+0.499337 TLH/20091113 0.485641 0.513759
+0.496369 EPP/20091113 0.487527 0.502686
+0.481481 XLE/20091113 0.475803 0.485284
+0.499083 EWN/20091113 0.496573 0.501993
+0.500661 SHM/20091113 0.498034 0.503095
+0.463805 FXI/20091113 0.455456 0.469365
+0.535796 EWS/20091113 0.533277 0.538448
+0.562925 IDU/20091113 0.552617 0.572907
+0.514052 VXZ/20091113 0.507776 0.519818
+0.532977 IVE/20091113 0.529085 0.537631
+0.507833 DGP/20091113 0.501091 0.513820
+0.523138 GMF/20091113 0.517023 0.529223
+0.526548 IWR/20091113 0.521663 0.533023
+0.486585 RKH/20091113 0.483439 0.491459
+0.501829 TIP/20091113 0.498280 0.505086
+0.603436 URE/20091113 0.594271 0.614880
+0.447917 DBO/20091113 0.436937 0.466300
+0.504843 IOO/20091113 0.497373 0.513583
+0.475189 DBV/20091113 0.466793 0.491210
+0.494036 EFA/20091113 0.488048 0.501762
+0.478440 BGU/20091113 0.461983 0.503775
+0.482567 EFV/20091113 0.473846 0.487194
+0.529681 IWB/20091113 0.521181 0.536385
+0.504301 IYF/20091113 0.495069 0.510177
+0.475261 YCS/20091113 0.466546 0.491549
+0.534527 DXJ/20091113 0.533090 0.537809
+0.534101 IWO/20091113 0.526111 0.538146
+0.504682 DBC/20091113 0.498231 0.512088
+0.465873 RWM/20091113 0.460330 0.474673
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+0.456291 MZZ/20091113 0.453606 0.461065
+0.526995 IWD/20091113 0.519406 0.530564
+0.516349 PCY/20091113 0.510506 0.523733
+0.464937 EWI/20091113 0.457207 0.473039
+0.522067 IJH/20091113 0.514904 0.531012
+0.517895 EEM/20091113 0.513268 0.524704
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+0.521903 SDY/20091113 0.511450 0.531693
+0.535041 ILF/20091113 0.528606 0.540947
+0.527009 JJG/20091113 0.518554 0.535576
+0.496715 TBT/20091113 0.472739 0.518301
+0.488441 XLF/20091113 0.485592 0.492114
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+0.478252 SHV/20091113 0.468100 0.489591
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+0.504782 EFZ/20091113 0.499871 0.508592
+0.472755 FXB/20091113 0.465580 0.478280
+0.493311 PHO/20091113 0.483411 0.503991
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+0.426767 BGZ/20091113 0.421980 0.429475
+0.488464 UDN/20091113 0.472740 0.498554
+0.491692 CSJ/20091113 0.479625 0.497812
+0.558793 GXG/20091113 0.553353 0.568031
+0.616141 USD/20091113 0.597889 0.645786
+0.451492 EWD/20091113 0.446258 0.457160
+0.531367 EWJ/20091113 0.527298 0.540893
+0.560014 BRF/20091113 0.555598 0.567417
+0.490430 VEU/20091113 0.481857 0.496799
+0.567852 XLU/20091113 0.563560 0.571960
+0.543004 JJC/20091113 0.533824 0.549764
+0.499885 FGD/20091113 0.495761 0.504651
+0.483466 FXF/20091113 0.474328 0.486605
+0.492615 LQD/20091113 0.489222 0.495656
+0.472017 SCZ/20091113 0.465940 0.475932
+0.508489 IYW/20091113 0.503909 0.514283
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+0.523704 DGS/20091113 0.517056 0.528217
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+0.464736 IEO/20091113 0.449200 0.475148
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+0.550630 KRE/20091113 0.536872 0.563014
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+0.502300 XLP/20091113 0.498612 0.504139
+0.522560 IJT/20091113 0.513329 0.527260
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+0.553914 PBW/20091113 0.541928 0.560705
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+0.515388 VUG/20091113 0.512274 0.519241
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+0.478206 PCY/20091117 0.472147 0.490373
+0.455191 EWI/20091117 0.444261 0.467507
+0.507858 IJH/20091117 0.498645 0.512986
+0.491640 EEM/20091117 0.486815 0.495461
+0.481136 EWM/20091117 0.477213 0.486354
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+0.488292 JJG/20091117 0.480425 0.494691
+0.549229 TBT/20091117 0.542648 0.560025
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+0.486223 FXB/20091117 0.475381 0.498026
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+0.538851 EWJ/20091117 0.532630 0.543649
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+0.460129 IXC/20091117 0.457954 0.462548
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+0.488557 AAXJ/20091118 0.484150 0.493255
+0.496759 VWO/20091118 0.487543 0.509257
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+0.485957 MXI/20091118 0.480529 0.497231
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+0.506278 IWM/20091118 0.501383 0.512815
+0.508534 VXF/20091118 0.503161 0.512710
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+0.491692 XLK/20091118 0.488424 0.493260
+0.475549 EFG/20091118 0.469610 0.480314
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+0.478952 TWM/20091118 0.470336 0.488231
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+0.520550 DXD/20091118 0.509645 0.529875
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diff --git a/test/train-sets/ref/bs.reg.stderr b/test/train-sets/ref/bs.reg.stderr
new file mode 100644
index 00000000..95dcae6d
--- /dev/null
+++ b/test/train-sets/ref/bs.reg.stderr
@@ -0,0 +1,35 @@
+final_regressor = models/bs.reg.model
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+predictions = bs.reg.predict
+using no cache
+Reading datafile = train-sets/0002.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.181886   0.181886            3         3.0   0.5498   0.2896       15
+0.106220   0.030554            6         6.0   0.2681   0.0000       15
+0.097672   0.087415           11        11.0   0.4315   0.0000       15
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+0.035977   0.031420           87        87.0   0.5140   0.3388       15
+0.023001   0.010025          174       174.0   0.5596   0.5893       15
+0.013429   0.003858          348       348.0   0.5475   0.5442       15
+0.007462   0.001494          696       696.0   0.3421   0.3213       15
+0.004128   0.000794         1392      1392.0   0.4996   0.4936       15
+0.002262   0.000395         2784      2784.0   0.5090   0.5122       15
+0.001315   0.000368         5568      5568.0   0.6413   0.5793       15
+0.000889   0.000463        11135     11135.0   0.3869   0.4435       15
+0.000652   0.000416        22269     22269.0   0.5063   0.5157       15
+0.000644   0.000636        44537     44537.0   0.4905   0.4893       15
+
+finished run
+number of examples per pass = 74746
+passes used = 1
+weighted example sum = 69521
+weighted label sum = 0
+average loss = 0.000812178
+best constant = 0
+total feature number = 1119986
diff --git a/test/train-sets/ref/bs.reg.stdout b/test/train-sets/ref/bs.reg.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/bs.reg.stdout
diff --git a/test/train-sets/ref/bs.vote.predict b/test/train-sets/ref/bs.vote.predict
new file mode 100644
index 00000000..560f0d24
--- /dev/null
+++ b/test/train-sets/ref/bs.vote.predict
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+-1.000000 0.236033 0.459578
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+-1.000000 0.321507 0.375389
+-1.000000 0.165551 0.244836
+-1.000000 0.476566 0.620723
+-1.000000 0.294528 0.318210
+-1.000000 0.715140 0.845894
+-1.000000 0.556829 0.692226
+-1.000000 0.695677 0.767937
+-1.000000 0.427762 0.542853
+-1.000000 0.381689 0.470247
+-1.000000 0.437735 0.513902
+-1.000000 0.303374 0.444996
+-1.000000 0.201594 0.447449
+-1.000000 0.335906 0.586852
+-1.000000 0.566305 0.799865
+-1.000000 0.551280 0.675520
+-1.000000 0.227184 0.381761
+-1.000000 0.535517 0.737854
+-1.000000 0.462763 0.651277
+-1.000000 0.208805 0.454696
+-1.000000 0.196710 0.286818
+-1.000000 0.271469 0.292503
+-1.000000 0.259612 0.431565
+-1.000000 0.448857 0.662813
+-1.000000 0.410403 0.555561
+-1.000000 0.276979 0.446122
+-1.000000 0.252549 0.430267
+-1.000000 0.471164 0.648070
diff --git a/test/train-sets/ref/bs.vote.stderr b/test/train-sets/ref/bs.vote.stderr
new file mode 100644
index 00000000..d44e0805
--- /dev/null
+++ b/test/train-sets/ref/bs.vote.stderr
@@ -0,0 +1,27 @@
+final_regressor = models/bs.vote.model
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+predictions = bs.vote.predict
+using no cache
+Reading datafile = train-sets/0001.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.333333   0.333333            3         3.0   0.0000   0.0000       57
+0.333333   0.333333            6         6.0   1.0000   0.0000       31
+0.636364   1.000000           11        11.0   0.0000  -1.0000       38
+0.818182   1.000000           22        22.0   1.0000  -1.0000       88
+0.909091   1.000000           44        44.0   0.0000  -1.0000       66
+0.954023   1.000000           87        87.0   1.0000  -1.0000       61
+0.977011   1.000000          174       174.0   1.0000  -1.0000       26
+
+finished run
+number of examples per pass = 200
+passes used = 1
+weighted example sum = 200
+weighted label sum = 0
+average loss = 0.98
+best constant = 0
+total feature number = 15482
diff --git a/test/train-sets/ref/bs.vote.stdout b/test/train-sets/ref/bs.vote.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/bs.vote.stdout
diff --git a/test/train-sets/ref/cs_test.ldf.csoaa.stderr b/test/train-sets/ref/cs_test.ldf.csoaa.stderr
index 2cbf45a9..4860463c 100644
--- a/test/train-sets/ref/cs_test.ldf.csoaa.stderr
+++ b/test/train-sets/ref/cs_test.ldf.csoaa.stderr
@@ -15,9 +15,10 @@ loss       last          counter      weight    label  predict features
 0.363636   0.363636         22     22.0    known        0        3
 
 finished run
-number of examples = 30
+number of examples per pass = 3
+passes used = 10
 weighted example sum = 30
 weighted label sum = 0
-average loss = 0.3667
-best constant = -0.03448
+average loss = 0.366667
+best constant = -0.0344828
 total feature number = 210
diff --git a/test/train-sets/ref/cs_test.ldf.wap.stderr b/test/train-sets/ref/cs_test.ldf.wap.stderr
index 320df83b..4744648b 100644
--- a/test/train-sets/ref/cs_test.ldf.wap.stderr
+++ b/test/train-sets/ref/cs_test.ldf.wap.stderr
@@ -15,9 +15,10 @@ loss       last          counter      weight    label  predict features
 0.363636   0.363636         22     22.0    known        0        3
 
 finished run
-number of examples = 30
+number of examples per pass = 3
+passes used = 10
 weighted example sum = 30
 weighted label sum = 0
-average loss = 0.3667
-best constant = -0.03448
+average loss = 0.366667
+best constant = -0.0344828
 total feature number = 210
diff --git a/test/train-sets/ref/mask.stderr b/test/train-sets/ref/mask.stderr
new file mode 100644
index 00000000..91c2d5f9
--- /dev/null
+++ b/test/train-sets/ref/mask.stderr
@@ -0,0 +1,28 @@
+final_regressor = models/mask.model
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+using l1 regularization = 0.01
+using no cache
+Reading datafile = train-sets/0001.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.345659   0.345659            3         3.0   0.0000   0.1460       57
+0.315263   0.284867            6         6.0   1.0000   0.0793       31
+0.245089   0.160881           11        11.0   0.0000   0.1793       38
+0.269415   0.293741           22        22.0   1.0000   0.4167       88
+0.236489   0.203563           44        44.0   0.0000   0.2383       66
+0.235436   0.234359           87        87.0   1.0000   0.2071       61
+0.248760   0.262084          174       174.0   1.0000   0.3526       26
+
+finished run
+number of examples per pass = 200
+passes used = 1
+weighted example sum = 200
+weighted label sum = 91
+average loss = 0.241252
+best constant = 0.455
+best constant's loss = 0.247975
+total feature number = 15482
diff --git a/test/train-sets/ref/ml100k_small.stderr b/test/train-sets/ref/ml100k_small.stderr
index 2fa77b6c..740b0549 100644
--- a/test/train-sets/ref/ml100k_small.stderr
+++ b/test/train-sets/ref/ml100k_small.stderr
@@ -22,7 +22,8 @@ loss       last          counter      weight    label  predict features
 2.182642   1.286485          348       348.0   5.0000   3.7808       23
 
 finished run
-number of examples = 474
+number of examples per pass = 237
+passes used = 2
 weighted example sum = 474
 weighted label sum = 1666
 average loss = 1.945
diff --git a/test/train-sets/ref/multiclass.stderr b/test/train-sets/ref/multiclass.stderr
index 2c0ed74b..6ac163be 100644
--- a/test/train-sets/ref/multiclass.stderr
+++ b/test/train-sets/ref/multiclass.stderr
@@ -16,7 +16,8 @@ loss       last          counter      weight    label  predict features
 0.126437   0.000000         87     87.0          7        7        2
 
 finished run
-number of examples = 100
+number of examples per pass = 10
+passes used = 10
 weighted example sum = 100
 weighted label sum = 0
 average loss = 0.11
diff --git a/test/train-sets/ref/oaa.stderr b/test/train-sets/ref/oaa.stderr
index 27f5ae72..fb3fedef 100644
--- a/test/train-sets/ref/oaa.stderr
+++ b/test/train-sets/ref/oaa.stderr
@@ -16,7 +16,8 @@ loss       last          counter      weight    label  predict features
 0.160920   0.000000         87     87.0          7        7        2
 
 finished run
-number of examples = 100
+number of examples per pass = 10
+passes used = 10
 weighted example sum = 100
 weighted label sum = 0
 average loss = 0.14
diff --git a/test/train-sets/ref/rcv1_small.stderr b/test/train-sets/ref/rcv1_small.stderr
index 3124dd30..846914c3 100644
--- a/test/train-sets/ref/rcv1_small.stderr
+++ b/test/train-sets/ref/rcv1_small.stderr
@@ -21,12 +21,11 @@ num sources = 1
  9 0.29526   	0.00000   	0.00011   	 0.562531  	0.101449  	          	          	2.45273   	1.00000   
 10 0.29486   	0.00000   	0.00003   	 0.559816  	0.115043  	          	          	0.80446   	1.00000   
 11 0.29475   	0.00000   	0.00001   	 0.504319  	0.006255  	          	          	0.09031   	1.00000   
-12 0.29473   	0.00000   	0.00000   	 0.565762  	0.132422  	          	          	0.03132   	1.00000   
 
 finished run
 number of examples = 200000
-weighted example sum = 2e+05
-weighted label sum = -1.272e+04
-average loss = 0.2951
+weighted example sum = 200000
+weighted label sum = -12720
+average loss = 0.199705
 best constant = -0.0636
 total feature number = 15587880
diff --git a/test/train-sets/ref/rcv1_small.stdout b/test/train-sets/ref/rcv1_small.stdout
index 17dc62b1..21b0a927 100644
--- a/test/train-sets/ref/rcv1_small.stdout
+++ b/test/train-sets/ref/rcv1_small.stdout
@@ -1,6 +1,3 @@
 
 Termination condition reached in pass 11: decrease in loss less than 0.100%.
 If you want to optimize further, decrease termination threshold.
-
-Termination condition reached in pass 12: decrease in loss less than 0.100%.
-If you want to optimize further, decrease termination threshold.
diff --git a/test/train-sets/ref/remask.stderr b/test/train-sets/ref/remask.stderr
new file mode 100644
index 00000000..cb276a1a
--- /dev/null
+++ b/test/train-sets/ref/remask.stderr
@@ -0,0 +1,26 @@
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+using no cache
+Reading datafile = train-sets/0001.dat
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.317229   0.317229            3         3.0   0.0000   0.5262       57
+0.270263   0.223297            6         6.0   1.0000   0.2209       31
+0.217912   0.155090           11        11.0   0.0000   0.2572       38
+0.242425   0.266938           22        22.0   1.0000   0.7009       88
+0.218850   0.195276           44        44.0   0.0000   0.3510       66
+0.214091   0.209220           87        87.0   1.0000   0.4271       61
+0.194782   0.175474          174       174.0   1.0000   0.0736       26
+
+finished run
+number of examples per pass = 200
+passes used = 1
+weighted example sum = 200
+weighted label sum = 91
+average loss = 0.189489
+best constant = 0.455
+best constant's loss = 0.247975
+total feature number = 15482
diff --git a/test/train-sets/ref/searn_small.stderr b/test/train-sets/ref/searn_small.stderr
index 5a58b363..e69de29b 100644
--- a/test/train-sets/ref/searn_small.stderr
+++ b/test/train-sets/ref/searn_small.stderr
@@ -1,2 +0,0 @@
-average    since      sequence         example            current label      current predicted  current   cur   cur         predic.        examples
-loss       last        counter          weight          sequence prefix        sequence prefix features  pass   pol            made          gener.
diff --git a/test/train-sets/ref/searn_wsj.stderr b/test/train-sets/ref/searn_wsj.stderr
index 5a58b363..e69de29b 100644
--- a/test/train-sets/ref/searn_wsj.stderr
+++ b/test/train-sets/ref/searn_wsj.stderr
@@ -1,2 +0,0 @@
-average    since      sequence         example            current label      current predicted  current   cur   cur         predic.        examples
-loss       last        counter          weight          sequence prefix        sequence prefix features  pass   pol            made          gener.
diff --git a/test/train-sets/ref/searn_wsj2.dat.stderr b/test/train-sets/ref/searn_wsj2.dat.stderr
index 5a58b363..e69de29b 100644
--- a/test/train-sets/ref/searn_wsj2.dat.stderr
+++ b/test/train-sets/ref/searn_wsj2.dat.stderr
@@ -1,2 +0,0 @@
-average    since      sequence         example            current label      current predicted  current   cur   cur         predic.        examples
-loss       last        counter          weight          sequence prefix        sequence prefix features  pass   pol            made          gener.
diff --git a/test/train-sets/ref/topk-rec.predict b/test/train-sets/ref/topk-rec.predict
new file mode 100644
index 00000000..bafd7cee
--- /dev/null
+++ b/test/train-sets/ref/topk-rec.predict
@@ -0,0 +1,9 @@
+2.000016  J-A 
+2.999566  J-J 
+
+1.000159  M-A 
+2.999230  M-M 
+
+2.000115  A-J 
+2.999882  A-A 
+
diff --git a/test/train-sets/ref/topk-rec.stderr b/test/train-sets/ref/topk-rec.stderr
new file mode 100644
index 00000000..c6a7d46e
--- /dev/null
+++ b/test/train-sets/ref/topk-rec.stderr
@@ -0,0 +1,22 @@
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+predictions = topk.predict
+using no cache
+Reading datafile = train-sets/topk.vw
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+0.000000   0.000000            3         3.0   2.0000   2.0000        4
+0.000000   0.000000            6         6.0   3.0000   2.9992        4
+0.000000   0.000000           11        11.0   3.0000   2.9999        4
+
+finished run
+number of examples per pass = 12
+passes used = 1
+weighted example sum = 12
+weighted label sum = 0
+average loss = 1.09104e-07
+best constant = 0
+total feature number = 39
diff --git a/test/train-sets/ref/topk-rec.stdout b/test/train-sets/ref/topk-rec.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/topk-rec.stdout
diff --git a/test/train-sets/ref/topk-train.stderr b/test/train-sets/ref/topk-train.stderr
new file mode 100644
index 00000000..15eb0016
--- /dev/null
+++ b/test/train-sets/ref/topk-train.stderr
@@ -0,0 +1,30 @@
+creating quadratic features for pairs: MF 
+final_regressor = topk.model
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+decay_learning_rate = 1
+creating cache_file = topk-train.cache
+Reading datafile = train-sets/topk.vw
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+3.928039   3.928039            3         3.0   2.0000   0.3865        4
+3.505718   3.083397            6         6.0   3.0000   0.0000        4
+2.476008   1.240357           11        11.0   3.0000   0.9632        4
+1.684657   0.893305           22        22.0   1.0000   1.4393        4
+1.174029   0.663401           44        44.0  unknown   0.7909        1
+0.741233   0.298372           87        87.0   2.0000   2.1010        4
+0.430471   0.119709          174       174.0   3.0000   2.4709        4
+0.225430   0.020390          348       348.0  unknown   0.9266        1
+0.113289   0.001148          696       696.0  unknown   0.9384        1
+
+finished run
+number of examples per pass = 12
+passes used = 100
+weighted example sum = 1200
+weighted label sum = 1500
+average loss = 0.0657109
+best constant = 1.66667
+total feature number = 3900
diff --git a/test/train-sets/ref/topk-train.stdout b/test/train-sets/ref/topk-train.stdout
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/test/train-sets/ref/topk-train.stdout
diff --git a/test/train-sets/ref/xxor.stderr b/test/train-sets/ref/xxor.stderr
index 70bb4422..7feaa7b0 100644
--- a/test/train-sets/ref/xxor.stderr
+++ b/test/train-sets/ref/xxor.stderr
@@ -21,7 +21,8 @@ loss       last          counter      weight    label  predict features
 0.011501   0.000000          696       696.0   0.0000   0.0000        5
 
 finished run
-number of examples = 800
+number of examples per pass = 8
+passes used = 100
 weighted example sum = 800
 weighted label sum = 400
 average loss = 0.01001
diff --git a/test/train-sets/ref/zero.stderr b/test/train-sets/ref/zero.stderr
index b77baa49..19f235b5 100644
--- a/test/train-sets/ref/zero.stderr
+++ b/test/train-sets/ref/zero.stderr
@@ -12,7 +12,6 @@ creating cache_file = train-sets/zero.dat.cache
 Reading datafile = train-sets/zero.dat
 num sources = 1
  1 0.00000   	0.00000   	0.00000   	          	          	          	0.00000   	0.00000   	0.00000   
- 3 0.00000   	0.00000   	0.00000   	 -nan      	-nan      	
 
 finished run
 number of examples = 25
diff --git a/test/train-sets/ref/zero.stdout b/test/train-sets/ref/zero.stdout
index 97924984..86e1e556 100644
--- a/test/train-sets/ref/zero.stdout
+++ b/test/train-sets/ref/zero.stdout
@@ -1,2 +1 @@
 Derivative 0 detected.
-Derivative 0 detected.
diff --git a/test/train-sets/topk.vw b/test/train-sets/topk.vw
new file mode 100644
index 00000000..73f686ea
--- /dev/null
+++ b/test/train-sets/topk.vw
@@ -0,0 +1,12 @@
+3 'J-J|M J |F J
+0 'J-M|M J |F M
+2 'J-A|M J |F A
+
+0 'M-J|M M |F J
+3 'M-M|M M |F M
+1 'M-A|M M |F A
+
+2 'A-J|M A |F J
+1 'A-M|M A |F M
+3 'A-A|M A |F A
+
diff --git a/utl/vw-csv2bin b/utl/vw-csv2bin
new file mode 100755
index 00000000..627a2e8e
--- /dev/null
+++ b/utl/vw-csv2bin
@@ -0,0 +1,125 @@
+#!/usr/bin/python
+# 2013 Eric Whyne 
+# http://www.datamungeblog.com
+import re
+import sys, getopt
+
+infilename = ''
+outfilename = ''
+pregex = 'Y(es)?|T(rue)?|\+?1'
+nregex = 'No?|F(alse)?|0|-1'
+category_index = 0
+delimeter = ','
+
+def printhelp():
+    print "\n" + sys.argv[0] + """ converts csv data into vw binary classifier training data.
+    
+Options: 
+    -h                 Print this help.
+    -i <filename>      input file, if not defined will use stdin
+    -o <filename>      output file, if not defined will use stdout
+    -p <regex>		   regex identifying positive side of binary classification
+                        if not defined will use 'Y(es)?|T(rue)?|+?1'
+    -n <regex>         regex identifying negative side of binary classification
+                        if not defined will use 'No?|F(alse)?|0|-1'
+    -c <integer>       csv column which binary classification resides
+                        if not defined will use 0
+    -d <delimeter>     specifies boundaries used to separate csv columns
+                         if not defined will use ',' 
+                        
+Examples:
+    cat data.csv | ./vw-csv2bin -c 14 -p '>' -n '<' > training.vw
+    ./vw-csv2bin -i data.csv -o training.vw -d '\\t' -c 14 -p '>' -n '<'
+"""
+
+try:
+   opts, args = getopt.getopt(sys.argv[1:],"hi:o:p:n:c:d:")
+except getopt.GetoptError:
+   printhelp()
+   sys.exit(2)
+for opt, arg in opts:
+  if opt == '-h':
+    printhelp()
+    sys.exit()
+  elif opt == '-i':
+    infilename = arg  
+  elif opt == '-o':
+    outfilename = arg
+  elif opt == '-p':
+    pregex = arg
+  elif opt == '-n':
+    nregex = arg
+  elif opt == '-c':
+    category_index = int(arg)
+  elif opt == '-d':
+    delimeter = arg
+
+if infilename:
+    infile = open (infilename,'r')
+else:
+    infile = sys.stdin
+if outfilename:
+    outfile = open (outfilename,'w')
+
+for line in infile:
+  line = re.sub('\||:|\s', '', line) # Remove vertical bar, colon, space, and newline; unsupported by vw file format
+  data = line.split(delimeter)
+  category = data.pop(category_index)
+  if re.search(nregex, category): # regex for negative category
+    category = "-1"
+  elif re.search(pregex, category): # regex for positive category
+    category = '1'
+  else:
+    sys.exit("Regex did not match a record, exiting.\nPostive Regex: " + pregex + "\nNegative Regex: "+ nregex + "\nRecord:\n" + line)
+  outline = category + " | "
+  colnum = 0
+  for col in data:
+    colstr = str(colnum)
+    #col = re.sub(r'\s','',col) # remove all whitespace
+    if re.search('^[0-9.]*$', col): # If the feature is a number, then give it a label
+      outline = outline + "f" + colstr + ":" + col + " "
+    else: # If the feature is a string, then let vw handle it directly
+      outline = outline + col + ' '
+    colnum = colnum + 1
+  outline = outline + "\n"
+  if outfilename:
+    outfile.write(outline)
+  else:
+    sys.stdout.write(outline)
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+     
diff --git a/utl/vw-validate.html b/utl/vw-validate.html
index 9f102e4d..a23d1f12 100644
--- a/utl/vw-validate.html
+++ b/utl/vw-validate.html
@@ -68,10 +68,10 @@ p {
   }
 
   function summarizeString(s) {
-    if (s.length < 80) {
+    if (s.length < 120) {
       return s;
     }
-    return s.substring(0, 6) + "..." + s.substring(s.length - 6);
+    return s.substring(0, 16) + "&hellip; " + s.substring(s.length - 16);
   }
   
   function isInteger(s) {
@@ -103,106 +103,195 @@ p {
         addExampleFeedback(i, "Empty example, probably a dangling newline!");
         continue;
       }
-      
       addExampleFeedback(i, "Example &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExample) + "</span>&rdquo;.");
 
-      var labelVersusFeatMatch = /^([^|]*\S+)( )?\|(.*)/.exec(vwExample);
-      if (labelVersusFeatMatch != null) {
-        var vwExLabel = labelVersusFeatMatch[1].split(" ");
-        var vwExLabelBarSpace = (labelVersusFeatMatch[2] == " ");
-        var vwExNamespaces = labelVersusFeatMatch[3].split("|");
-        
-        if (vwExLabel.length > 4) {
-          addExampleFeedback(i, "Too many elements in the label section!");
+      var vwExPipeIdx = vwExample.indexOf("|");
+      if (vwExPipeIdx == null) {
+        addExampleFeedback(i, "Missing pipe ("|") separator in example.");
+        continue;
+      }
+      var vwExPrefix = vwExample.substr(0, vwExPipeIdx);
+      var vwExNamespaces = vwExample.substr(vwExPipeIdx + 1);
+
+      var vwExLabel = null;
+      var vwExImp = null;
+      var vwExBase = null;
+      var vwExTag = null;
+      
+      var isLegacyExTag = false;
+
+      var vwExPrefixMatch = /^([^' ]+) ([^' ]+) ([^' ]+) '([^' ]+) $/.exec(vwExPrefix);
+      if (vwExPrefixMatch != null) {
+        addExampleFeedback(i, "Found &ldquo;&lt;label&gt; &lt;importance&gt; [base] ['tag] |&hellip;&rdquo; prefix format.");
+        vwExLabel = vwExPrefixMatch[1];
+        vwExImp = vwExPrefixMatch[2];
+        vwExBase = vwExPrefixMatch[3];
+        vwExTag = vwExPrefixMatch[4];
+      }
+      else {
+        var vwExPrefixMatch = /^([^' ]+) ([^' ]+) ([^' ]+) ([^' ]+)$/.exec(vwExPrefix);
+        if (vwExPrefixMatch != null) {
+          addExampleFeedback(i, "Found &ldquo;&lt;label&gt; &lt;importance&gt; [base] [tag]|&hellip;&rdquo; prefix format.");
+          vwExLabel = vwExPrefixMatch[1];
+          vwExImp = vwExPrefixMatch[2];
+          vwExBase = vwExPrefixMatch[3];
+          vwExTag = vwExPrefixMatch[4];
+          isLegacyExTag = true;
         }
         else {
-          addExampleFeedback(i, "Example label / response / class is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExLabel[0]) + "</span>&rdquo;.");
-          
-          if ((vwExLabel.length >= 2) && isFloat(vwExLabel[1])) {
-            addExampleFeedback(i, "Importance weight is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExLabel[1]) + "</span>&rdquo;.");
+          var vwExPrefixMatch = /^([^' ]+) ([^' ]+) '([^' ]+) $/.exec(vwExPrefix);
+          if (vwExPrefixMatch != null) {
+            addExampleFeedback(i, "Found &ldquo;&lt;label&gt; [importance] ['tag] |&hellip;&rdquo; prefix format.");
+            vwExLabel = vwExPrefixMatch[1];
+            vwExImp = vwExPrefixMatch[2];
+            vwExTag = vwExPrefixMatch[3];
           }
-          if ((vwExLabel.length >= 3) && isFloat(vwExLabel[2])) {
-            addExampleFeedback(i, "Initial prediction is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExLabel[2]) + "</span>&rdquo;.");
-          }
-          
-          if (! isFloat(vwExLabel[vwExLabel.length - 1])) {
-            var vwExTag = vwExLabel[vwExLabel.length - 1];
-            if (vwExTag.match(/^'/)) {
-              vwExTag = vwExTag.substring(1);
-              addExampleFeedback(i, "Tag is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExTag) + "</span>&rdquo;.");
+          else {
+            var vwExPrefixMatch = /^([^' ]+) ([^' ]+) ([^' ]+)$/.exec(vwExPrefix);
+            if (vwExPrefixMatch != null) {
+              addExampleFeedback(i, "Found &ldquo;&lt;label&gt; [importance] [tag]|&hellip;&rdquo; prefix format.");
+              vwExLabel = vwExPrefixMatch[1];
+              vwExImp = vwExPrefixMatch[2];
+              vwExTag = vwExPrefixMatch[3];
+              isLegacyExTag = true;
             }
             else {
-              if (vwExLabelBarSpace) {
-                addExampleFeedback(i, "Badly formatted example, with an extra space before the pipe, and tag in the legacy format should be preceded by a single quote!");
+              var vwExPrefixMatch = /^([^' ]+) '([^' ]+) $/.exec(vwExPrefix);
+              if (vwExPrefixMatch != null) {
+                addExampleFeedback(i, "Found &ldquo;[label] ['tag] |&hellip;&rdquo; prefix format.");
+                vwExLabel = vwExPrefixMatch[1];
+                vwExTag = vwExPrefixMatch[2];
               }
               else {
-                addExampleFeedback(i, "Tag is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExTag) + "</span>&rdquo; in the legacy format, should be preceded by a single quote!");
+                var vwExPrefixMatch = /^([^' ]+) ([^' ]+)$/.exec(vwExPrefix);
+                if (vwExPrefixMatch != null) {
+                  addExampleFeedback(i, "Found &ldquo;[label] [tag]|&hellip;&rdquo; prefix format.");
+                  vwExLabel = vwExPrefixMatch[1];
+                  vwExTag = vwExPrefixMatch[2];
+                }
+                else {
+                  var vwExPrefixMatch = /^([^' ]+) $/.exec(vwExPrefix);
+                  if (vwExPrefixMatch != null) {
+                    addExampleFeedback(i, "Found &ldquo;[label] |&hellip;&rdquo; prefix format.");
+                    vwExLabel = vwExPrefixMatch[1];
+                  }
+                  else {
+                    var ambPrefixEls = vwExPrefix.trim().split(" ");
+                    addExampleFeedback(i, "Found &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExPrefix) + "</span>&rdquo; ambiguous prefix with " + ambPrefixEls.length + " elements.");
+                    vwExLabel = ambPrefixEls[0];
+                    if (ambPrefixEls.length > 1) {
+                      vwExImp = ambPrefixEls[1];
+                    }
+                    if (ambPrefixEls.length > 2) {
+                      vwExTag = ambPrefixEls[2];
+                      if (vwExTag.match(/^'/)) {
+                        vwExTag = vwExTag.substr(1);
+                      }
+                    }
+                  }
+                }
               }
             }
           }
         }
+      }
+
+      addExampleFeedback(i, "Example label / response / class is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExLabel) + "</span>&rdquo;.");
+
+      if (vwExImp == null) {
+        addExampleFeedback(i, "Example has default &ldquo;<span class=\"vwFeedbackMsgExample\">1.0</span>&rdquo; importance weight.");
+      }
+      else {
+        if (isFloat(vwExImp)) {
+          addExampleFeedback(i, "Importance weight is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExImp) + "</span>&rdquo;.");
+        }
+        else {
+          addExampleFeedback(i, "Importance weight &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExImp) + "</span>&rdquo; must be a float!");
+        }
+      }
+
+      if (vwExBase == null) {
+        addExampleFeedback(i, "Example has default &ldquo;<span class=\"vwFeedbackMsgExample\">0</span>&rdquo; base.");
+      }
+      else {
+        if (isFloat(vwExBase)) {
+          addExampleFeedback(i, "Base is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExBase) + "</span>&rdquo;.");
+        }
+        else {
+          addExampleFeedback(i, "Base &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExBase) + "</span>&rdquo; must be a float!");
+        }
+      }
+
+      if (vwExTag != null) {
+        if (isLegacyExTag) {
+          addExampleFeedback(i, "Tag is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExTag) + "</span>&rdquo; in the legacy format, should be preceded by a single quote!");
+        }
+        else {
+          addExampleFeedback(i, "Tag is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExTag) + "</span>&rdquo;.");
+        }
+      }
+
+      var vwExNamespaceEls = vwExNamespaces.split(" |");
+      var isDefaultUsed = false;
+      for (var j=0; j < vwExNamespaceEls.length; j++) {
+        var vwExNamespace = vwExNamespaceEls[j];
         
-        var isDefaultUsed = false;
-        for (var j=0; j < vwExNamespaces.length; j++) {
-          var vwExNamespace = vwExNamespaces[j];
-          
-          var namespaceMatch = /^(\S*) (.*)/.exec(vwExNamespace);
-          if (namespaceMatch != null) {
-            var vwExNamespace = namespaceMatch[1];
-            var vwExNamespaceFeatures = namespaceMatch[2].split(" ");
-            if (vwExNamespace != "") {
-              var vwExNamespaceName = null;
-              var vwExNamespaceScale = null;
-              if (vwExNamespace.match(/:/)) {
-                vwExNamespaceName = vwExNamespace.split(":")[0];
-                vwExNamespaceScale = vwExNamespace.split(":")[1];
-              }
-              else {
-                vwExNamespaceName = vwExNamespace;
-              }
-              addExampleNamespaceFeedback(i, j, "Namespace found with name &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExNamespaceName) + "</span>&rdquo;.");
-              if (vwExNamespaceScale != null) {
-                addExampleNamespaceFeedback(i, j, "Scale of namespace is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExNamespaceScale[1]) + "</span>&rdquo;.");
-              }
+        var namespaceMatch = /^(\S*) (.*)/.exec(vwExNamespace);
+        if (namespaceMatch != null) {
+          var vwExNamespace = namespaceMatch[1];
+          var vwExNamespaceFeatures = namespaceMatch[2].split(" ");
+          if (vwExNamespace != "") {
+            var vwExNamespaceName = null;
+            var vwExNamespaceScale = null;
+            if (vwExNamespace.match(/:/)) {
+              vwExNamespaceName = vwExNamespace.split(":")[0];
+              vwExNamespaceScale = vwExNamespace.split(":")[1];
             }
             else {
-              if (isDefaultUsed) {
-                addExampleNamespaceFeedback(i, j, "Default namespace used more than once!");
-              }
-              else {
-                addExampleNamespaceFeedback(i, j, "Using default namespace.");
-                isDefaultUsed = true;
-              }
+              vwExNamespaceName = vwExNamespace;
             }
-            
-            addExampleNamespaceFeedback(i, j, "Found " + vwExNamespaceFeatures.length + " feature(s).");
-            for (var k=0; k < vwExNamespaceFeatures.length; k++) {
-              var vwFeature = vwExNamespaceFeatures[k].split(":");
-              addExampleNamespaceFeatureFeedback(i, j, k, "Label &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwFeature[0]) + "</span>&rdquo;.");
-              if (isInteger(vwFeature[0])) {
-                addExampleNamespaceFeatureFeedback(i, j, k, "Label is an integer, and will be used instead of the feature label hashing.");
-              }
-
-              switch (vwFeature.length) {
-              case 1:
-                addExampleNamespaceFeatureFeedback(i, j, k, "Using default value of 1 for feature.");
-                break;
-              case 2:
-                addExampleNamespaceFeatureFeedback(i, j, k, "Value &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwFeature[1]) + "</span>&rdquo;.");
-                break;
-              default:
-                addExampleNamespaceFeatureFeedback(i, j, k, "Badly formatted feature, too many colon : sections!");
-              }
+            addExampleNamespaceFeedback(i, j, "Namespace found with name &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExNamespaceName) + "</span>&rdquo;.");
+            if (vwExNamespaceScale == null) {
+              addExampleNamespaceFeedback(i, j, "Using default scale of &ldquo;<span class=\"vwFeedbackMsgExample\">1.0</span>&rdquo; for namespace.");
+            }
+            else {
+              addExampleNamespaceFeedback(i, j, "Scale of namespace is &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwExNamespaceScale) + "</span>&rdquo;.");
             }
-            
           }
           else {
-            addExampleNamespaceFeedback(i, j, "Badly formed namespace.");
+            if (isDefaultUsed) {
+              addExampleNamespaceFeedback(i, j, "Default namespace used more than once!");
+            }
+            else {
+              addExampleNamespaceFeedback(i, j, "Using default namespace.");
+              isDefaultUsed = true;
+            }
           }
+          
+          addExampleNamespaceFeedback(i, j, "Found " + vwExNamespaceFeatures.length + " feature(s).");
+          for (var k=0; k < vwExNamespaceFeatures.length; k++) {
+            var vwFeature = vwExNamespaceFeatures[k].split(":");
+            addExampleNamespaceFeatureFeedback(i, j, k, "Label &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwFeature[0]) + "</span>&rdquo;.");
+            if (isInteger(vwFeature[0])) {
+              addExampleNamespaceFeatureFeedback(i, j, k, "Label is an integer, and will be used instead of the feature label hashing.");
+            }
+
+            switch (vwFeature.length) {
+            case 1:
+              addExampleNamespaceFeatureFeedback(i, j, k, "Using default value of &ldquo;<span class=\"vwFeedbackMsgExample\">1</span>&rdquo; for feature.");
+              break;
+            case 2:
+              addExampleNamespaceFeatureFeedback(i, j, k, "Value &ldquo;<span class=\"vwFeedbackMsgExample\">" + summarizeString(vwFeature[1]) + "</span>&rdquo;.");
+              break;
+            default:
+              addExampleNamespaceFeatureFeedback(i, j, k, "Badly formatted feature, too many colon : sections!");
+            }
+          }
+          
+        }
+        else {
+          addExampleNamespaceFeedback(i, j, "Badly formed namespace.");
         }
-      }
-      else {
-        addExampleFeedback(i, "Badly formed example.");
       }
       
       addFeedback("&nbsp;");
@@ -218,9 +307,10 @@ p {
     <h3>Vowpal Wabbit Data Format Validation</h3>
     <p><small>by <a href="http://blog.someben.com/">Ben Gimpert</a></small>  </p>
     
-    <p style="width: 80%; margin-top: 1em;">Not sure if your training data is in the right format for Vowpal Wabbit? Not seeing the results you expect from Vowpal Wabbit, in general? Paste a few lines of your training data into this text box, and then hit the &ldquo;Validate&rdquo; button:</p>
+    <p style="width: 80%; margin-top: 1em;">Not sure if your training data is in the right format for Vowpal Wabbit? Not seeing the results you expect from Vowpal Wabbit, in general? Paste a few lines of your training data into this text box, and then hit the &ldquo;Validate&rdquo; button below. Note that the only unambiguous prefixes for training examples have exactly one or four elements.</p>
     
-    <textarea id="vwData" style="width: 80%; height: 8em; font-family: monospace; margin-top: 1.5em; margin-bottom: 1.5em;">1 1.0 |MetricFeatures:3.28 height:1.5 length:2.0 |Says black with white stripes |OtherFeatures NumberOfLegs:4.0 HasStripes
+    <textarea id="vwData" style="width: 80%; height: 12em; font-family: monospace; margin-top: 1.5em; margin-bottom: 1.5em;">1 1.0 |MetricFeatures:3.28 height:1.5 length:2.0 |Says black with white stripes |OtherFeatures NumberOfLegs:4.0 HasStripes
+1 1.0 zebra|MetricFeatures:3.28 height:1.5 length:2.0 |Says black with white stripes |OtherFeatures NumberOfLegs:4.0 HasStripes
 1 2 'tag| a:2 b:3
 1 2 'tag | a:2 b:3
 1 2 tag | a:2 b:3
diff --git a/utl/vw-varinfo b/utl/vw-varinfo
index 91bf360e..9901d3e5 100755
--- a/utl/vw-varinfo
+++ b/utl/vw-varinfo
@@ -142,6 +142,7 @@ sub get_args {
                             |--initial_regressor
                             |-f
                             |--final_regressor
+                            |--feature_mask
                             |-r
                             |--raw_predictions
                             |--cache_file
diff --git a/vowpalwabbit/Makefile.am b/vowpalwabbit/Makefile.am
index 7c0ebf8c..d846fc09 100644
--- a/vowpalwabbit/Makefile.am
+++ b/vowpalwabbit/Makefile.am
@@ -4,7 +4,7 @@ liballreduce_la_SOURCES = allreduce.cc
 
 bin_PROGRAMS = vw active_interactor
 
-libvw_la_SOURCES = hash.cc global_data.cc io_buf.cc parse_regressor.cc parse_primitives.cc unique_sort.cc cache.cc rand48.cc simple_label.cc oaa.cc ect.cc autolink.cc binary.cc csoaa.cc cb.cc wap.cc beam.cc searn.cc searn_sequencetask.cc parse_example.cc sparse_dense.cc network.cc parse_args.cc accumulate.cc gd.cc lda_core.cc mf.cc bfgs.cc noop.cc example.cc parser.cc loss_functions.cc sender.cc nn.cc 
+libvw_la_SOURCES = hash.cc global_data.cc io_buf.cc parse_regressor.cc parse_primitives.cc unique_sort.cc cache.cc rand48.cc simple_label.cc oaa.cc ect.cc autolink.cc binary.cc csoaa.cc cb.cc wap.cc beam.cc searn.cc searn_sequencetask.cc parse_example.cc sparse_dense.cc network.cc parse_args.cc accumulate.cc gd.cc learner.cc lda_core.cc bfgs.cc noop.cc example.cc parser.cc loss_functions.cc sender.cc nn.cc bs.cc topk.cc mf.cc
 
 # accumulate.cc uses all_reduce
 libvw_la_LIBADD = liballreduce.la
diff --git a/vowpalwabbit/accumulate.cc b/vowpalwabbit/accumulate.cc
index 3b79fa17..a2ac239c 100644
--- a/vowpalwabbit/accumulate.cc
+++ b/vowpalwabbit/accumulate.cc
@@ -46,18 +46,15 @@ void accumulate_avg(vw& all, string master_location, regressor& reg, size_t o) {
   size_t stride = all.reg.stride;

   float* local_grad = new float[length];

   weight* weights = reg.weight_vector;

-  float numnodes = 1.;

-  all_reduce(&numnodes, 1, master_location, all.unique_id, all.total, all.node, all.socks);

-  for(uint32_t i = 0;i < length;i++) 

-    {

-      local_grad[i] = weights[stride*i+o];

-    }

+  float numnodes = (float)all.total;

 

+  for(uint32_t i = 0;i < length;i++) 

+    local_grad[i] = weights[stride*i+o];

+  

   all_reduce(local_grad, length, master_location, all.unique_id, all.total, all.node, all.socks);

   for(uint32_t i = 0;i < length;i++) 

-    {

-      weights[stride*i+o] = local_grad[i]/numnodes;

-    }

+    weights[stride*i+o] = local_grad[i]/numnodes;

+

   delete[] local_grad;

 }

 

@@ -80,26 +77,55 @@ void accumulate_weighted_avg(vw& all, string master_location, regressor& reg) {
     cerr<<"Weighted averaging is implemented only for adaptive gradient, use accumulate_avg instead\n";

     return;

   }

-  uint32_t length = 1 << all.num_bits; //This is size of gradient

+  uint32_t length = 1 << all.num_bits; //This is the number of parameters

   size_t stride = all.reg.stride;

   weight* weights = reg.weight_vector;

   float* local_weights = new float[length];

 

   for(uint32_t i = 0;i < length;i++) 

-    local_weights[i] = sqrt(weights[stride*i+1]*weights[stride*i+1]);

+    local_weights[i] = weights[stride*i+1];

   

+  //find weighting for average

   all_reduce(local_weights, length, master_location, all.unique_id, all.total, all.node, all.socks);

 

-  for(uint32_t i = 0;i < length;i++) 

+  for(uint32_t i = 0;i < length;i++) //Compute weighted versions 

     if(local_weights[i] > 0) {

-      float ratio = sqrt(weights[stride*i+1]*weights[stride*i+1])/local_weights[i];

-      weights[stride*i] *= ratio;

-      weights[stride*i+1] *= ratio;

+      float ratio = weights[stride*i+1]/local_weights[i];

+      local_weights[i] = weights[stride*i] * ratio;

+      weights[stride*i+1] *= ratio; //A crude max

+      if (stride > 2)

+	weights[stride*i+2] *= ratio; //A crude max

     }

     else 

-      weights[stride*i] = 0; 

+      local_weights[i] = 0; 

+

+  //Find weighted average weight

+  all_reduce(local_weights, length, master_location, all.unique_id, all.total, all.node, all.socks);

 

-  all_reduce(weights, (int)stride*length, master_location, all.unique_id, all.total, all.node, all.socks);

+  for(uint32_t i = 0;i < length;i++) 

+    {

+      weights[stride*i] = local_weights[i];

+      local_weights[i] = weights[stride*i+1];

+    }

+

+  //Find weighted average for adaptation

+  all_reduce(local_weights, length, master_location, all.unique_id, all.total, all.node, all.socks);

+

+  for(uint32_t i = 0;i < length;i++) 

+    {

+      weights[stride*i+1] = local_weights[i];

+      if (stride > 2)

+	local_weights[i] = weights[stride*i+2];

+    }

+

+  if (stride > 2)

+    {

+      //Find weighted average for normalization

+      all_reduce(local_weights, length, master_location, all.unique_id, all.total, all.node, all.socks);

+      

+      for(uint32_t i = 0;i < length;i++) 

+	weights[stride*i+2] = local_weights[i];

+    }

 

   delete[] local_weights;

 }

diff --git a/vowpalwabbit/autolink.cc b/vowpalwabbit/autolink.cc
index 1d6fb0fc..55fb50eb 100644
--- a/vowpalwabbit/autolink.cc
+++ b/vowpalwabbit/autolink.cc
@@ -9,10 +9,9 @@ namespace ALINK {
   struct autolink {
     uint32_t d;
     uint32_t stride;
-    learner base;
   };
 
-  void learn(void* d, example* ec)
+  void learn(void* d, learner& base, example* ec)
   {
     autolink* b = (autolink*)d;
 
@@ -20,7 +19,7 @@ namespace ALINK {
     float weight = ((label_data*)ec->ld)->weight;
     ((label_data*)ec->ld)->label = FLT_MAX;
     ((label_data*)ec->ld)->weight = 0;
-    b->base.learn(ec);
+    base.learn(ec);
     ((label_data*)ec->ld)->label = label;
     ((label_data*)ec->ld)->weight = weight;
     float base_pred = ec->final_prediction;
@@ -37,41 +36,16 @@ namespace ALINK {
 	  base_pred *= ec->final_prediction;
 	}
     ec->total_sum_feat_sq += sum_sq;
-    b->base.learn(ec);
+    base.learn(ec);
    
     ec->atomics[autolink_namespace].erase();
     ec->indices.pop();
     ec->total_sum_feat_sq -= sum_sq;
   }
   
-  void finish(void* d)
-  {
-    autolink* b = (autolink*)d;
-    b->base.finish();
-    free(b);
-  }
-
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn(d, ec);
-	    return_simple_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
-  }
-
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     autolink* data = (autolink*)calloc(1,sizeof(autolink));
-    data->base = all.l;
     data->d = (uint32_t)vm["autolink"].as<size_t>();
     data->stride = all.reg.stride;
     
@@ -82,7 +56,6 @@ namespace ALINK {
 	all.options_from_file.append(ss.str());
       }
 
-    learner l(data, drive, learn, finish, all.l.sl);
-    return l;
+    return new learner(data, learn, all.l);
   }
 }
diff --git a/vowpalwabbit/autolink.h b/vowpalwabbit/autolink.h
index d91e3ce8..42d52f39 100644
--- a/vowpalwabbit/autolink.h
+++ b/vowpalwabbit/autolink.h
@@ -2,6 +2,6 @@
 #ifndef AUTOLINK
 #define AUTOLINK
 namespace ALINK {
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
 }
 #endif
diff --git a/vowpalwabbit/bfgs.cc b/vowpalwabbit/bfgs.cc
index b5c10a79..91938847 100644
--- a/vowpalwabbit/bfgs.cc
+++ b/vowpalwabbit/bfgs.cc
@@ -361,6 +361,7 @@ void bfgs_iter_middle(vw& all, bfgs& b, float* mem, double* rho, double* alpha,
     }
   }
 
+
   coef_j = alpha[0] - rho[0] * y_r;
   mem = mem0;
   w = w0;
@@ -433,6 +434,7 @@ double add_regularization(vw& all, bfgs& b, float regularization)
 	ret += 0.5*b.regularizers[2*i]*delta_weight*delta_weight;
       }
     }
+
   return ret;
 }
 
@@ -489,7 +491,7 @@ void preconditioner_to_regularizer(vw& all, bfgs& b, float regularization)
     for(uint32_t i = 0; i < length; i++) 
       b.regularizers[2*i] = weights[stride*i+W_COND] + b.regularizers[2*i];
   for(uint32_t i = 0; i < length; i++) 
-    b.regularizers[2*i+1] = weights[stride*i];
+      b.regularizers[2*i+1] = weights[stride*i];
 }
 
 void zero_state(vw& all)
@@ -583,8 +585,14 @@ int process_pass(vw& all, bfgs& b) {
 		  if (all.l2_lambda > 0.)
 		    b.loss_sum += add_regularization(all, b, all.l2_lambda);
 		  if (!all.quiet){
-                    if(!all.holdout_set_off && b.current_pass >= 1)
-                      fprintf(stderr, "%2lu h%-10.5f\t", (long unsigned int)b.current_pass+1, all.sd->holdout_sum_loss_since_last_pass / all.sd->weighted_holdout_examples_since_last_pass);
+                    if(!all.holdout_set_off && b.current_pass >= 1){
+                      if(all.sd->holdout_sum_loss_since_last_pass == 0. && all.sd->weighted_holdout_examples_since_last_pass == 0.){
+                        fprintf(stderr, "%2lu ", (long unsigned int)b.current_pass+1);
+                        fprintf(stderr, "h unknown    ");
+                      }                      
+                      else
+                        fprintf(stderr, "%2lu h%-10.5f\t", (long unsigned int)b.current_pass+1, all.sd->holdout_sum_loss_since_last_pass / all.sd->weighted_holdout_examples_since_last_pass);
+                    }
                     else
                       fprintf(stderr, "%2lu %-10.5f\t", (long unsigned int)b.current_pass+1, b.loss_sum / b.importance_weight_sum);
                   }
@@ -706,7 +714,7 @@ int process_pass(vw& all, bfgs& b) {
       {
 	if(all.span_server != "")
 	  accumulate(all, all.span_server, all.reg, W_COND); //Accumulate preconditioner
-	preconditioner_to_regularizer(all, b, all.l2_lambda);
+	//preconditioner_to_regularizer(all, b, all.l2_lambda);
       }
     ftime(&b.t_end_global);
     b.net_time = (int) (1000.0 * (b.t_end_global.time - b.t_start_global.time) + (b.t_end_global.millitm - b.t_start_global.millitm)); 
@@ -718,6 +726,7 @@ int process_pass(vw& all, bfgs& b) {
 
 void process_example(vw& all, bfgs& b, example *ec)
  {
+
   label_data* ld = (label_data*)ec->ld;
   if (b.first_pass)
     b.importance_weight_sum += ld->weight;
@@ -751,46 +760,82 @@ void process_example(vw& all, bfgs& b, example *ec)
     update_preconditioner(all, ec);//w[3]
  }
 
-void learn(void* d, example* ec)
+void end_pass(void*d)
 {
   bfgs* b = (bfgs*)d;
   vw* all = b->all;
-  assert(ec->in_use);
+  
+  if (b->current_pass <= b->final_pass) 
+  {
+       if(b->current_pass < b->final_pass)
+       { 
+          int status = process_pass(*all, *b);
+
+          //reaching the max number of passes regardless of convergence 
+          if(b->final_pass == b->current_pass)
+          {
+             cerr<<"Maximum number of passes reached. ";
+             if(!b->output_regularizer)
+                cerr<<"If you want to optimize further, increase the number of passes\n";
+             if(b->output_regularizer)
+             { 
+               cerr<<"\nRegular model file has been created. "; 
+               cerr<<"Output feature regularizer file is created only when the convergence is reached. Try increasing the number of passes for convergence\n";
+               b->output_regularizer = false;
+             }
+
+          } 
+          
+          //attain convergence before reaching max iterations 
+	   if (status != LEARN_OK && b->final_pass > b->current_pass) {
+	      b->final_pass = b->current_pass;
+	   }
+
+	   if (b->output_regularizer && b->final_pass == b->current_pass) {
+	     zero_preconditioner(*all);
+	     b->preconditioner_pass = true;
+	   }
+
+	   if(!all->holdout_set_off)
+	   {
+	     if(summarize_holdout_set(*all, b->no_win_counter))
+               finalize_regressor(*all, all->final_regressor_name); 
+	     if(b->early_stop_thres == b->no_win_counter)
+	     { 
+               all-> early_terminate = true;
+               cerr<<"Early termination reached w.r.t. holdout set error";
+             }
+
+	   } 
+           
+       }else{//reaching convergence in the previous pass
+        if(b->output_regularizer) 
+           preconditioner_to_regularizer(*all, *b, (*all).l2_lambda);
+        b->current_pass ++;
+      }   
+                
+  }
+}
 
-  if (ec->end_pass && b->current_pass <= b->final_pass) 
-      {
-	int status = process_pass(*all, *b);
-	if (status != LEARN_OK && b->final_pass > b->current_pass) {
-	  b->final_pass = b->current_pass;
-	}
-	if (b->output_regularizer && b->current_pass== b->final_pass) {
-	  zero_preconditioner(*all);
-	  b->preconditioner_pass = true;
-	}
-        if(!all->holdout_set_off)
-        {
-          if(summarize_holdout_set(*all, b->no_win_counter))
-            finalize_regressor(*all, all->final_regressor_name); 
-          if(b->early_stop_thres == b->no_win_counter)
-            all-> early_terminate = true;
-        }         
-      }
+void learn(void* d, learner& base, example* ec)
+{
+  bfgs* b = (bfgs*)d;
+  vw* all = b->all;
+  assert(ec->in_use);
 
   if (b->current_pass <= b->final_pass)
-    if (!command_example(all,ec))
-      {
-	if(ec->test_only)
-          { 
-            label_data* ld = (label_data*)ec->ld;
-            ec->final_prediction = bfgs_predict(*all,ec); 
-            ec->loss = all->loss->getLoss(all->sd, ec->final_prediction, ld->label) * ld->weight;
-            all->sd->holdout_sum_loss_since_last_pass += ec->loss;
-          }
-        else if (test_example(ec))
-	  ec->final_prediction = bfgs_predict(*all,ec);//w[0]
-	else
-	  process_example(*all, *b, ec);
-      }
+    {
+      if(ec->test_only)
+	{ 
+	  label_data* ld = (label_data*)ec->ld;
+	  ec->final_prediction = bfgs_predict(*all,ec); 
+	  ec->loss = all->loss->getLoss(all->sd, ec->final_prediction, ld->label) * ld->weight;
+	}
+      else if (test_example(ec))
+	ec->final_prediction = bfgs_predict(*all,ec);//w[0]
+      else
+	process_example(*all, *b, ec);
+    }
 }
 
 void finish(void* d)
@@ -801,11 +846,11 @@ void finish(void* d)
   free(b->mem);
   free(b->rho);
   free(b->alpha);
-  free(b);
 }
 
 void save_load_regularizer(vw& all, bfgs& b, io_buf& model_file, bool read, bool text)
 {
+
   char buff[512];
   int c = 0;
   uint32_t stride = all.reg.stride;
@@ -854,6 +899,7 @@ void save_load_regularizer(vw& all, bfgs& b, io_buf& model_file, bool read, bool
 
 void save_load(void* d, io_buf& model_file, bool read, bool text)
 {
+
   bfgs* b = (bfgs*)d;
   vw* all = b->all;
 
@@ -900,7 +946,9 @@ void save_load(void* d, io_buf& model_file, bool read, bool text)
       reset_state(*all, *b, false);
     }
 
-  bool reg_vector = b->output_regularizer || all->per_feature_regularizer_input.length() > 0;
+  //bool reg_vector = b->output_regularizer || all->per_feature_regularizer_input.length() > 0;
+  bool reg_vector = (b->output_regularizer && !read) || (all->per_feature_regularizer_input.length() > 0 && read);
+    
   if (model_file.files.size() > 0)
     {
       char buff[512];
@@ -916,39 +964,13 @@ void save_load(void* d, io_buf& model_file, bool read, bool text)
     }
 }
 
-void drive(vw* all, void* d)
-{
-  bfgs* b = (bfgs*)d;
-
-  example* ec = NULL;
-
-  b->first_hessian_on = true;
-  b->backstep_on = true;
-
-  while ( true )
-    {
-     if(all-> early_terminate)
-        {
-          all->p->done = true;
-          all->final_regressor_name = "";//skip finalize_regressor
-          all->text_regressor_name = "";
-          all->per_feature_regularizer_output = "";
-          all->per_feature_regularizer_text = "";
-          return;
-        }
-      else if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-	{
-	  learn(b, ec);
-	  return_simple_example(*all, ec);
-	}
-      else if (parser_done(all->p))
-	return;
-      else 
-	;//busywait when we have predicted on all examples but not yet trained on all.
-    }
-}
+  void init_driver(void* data)
+  {
+    bfgs* b = (bfgs*)data;
+    b->backstep_on = true;
+  }
 
-void setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
 {
   bfgs* b = (bfgs*)calloc(1,sizeof(bfgs));
   b->all = &all;
@@ -957,6 +979,7 @@ void setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::va
   b->first_pass = true;
   b->gradient_pass = true;
   b->preconditioner_pass = true;
+  b->backstep_on = false;
   b->final_pass=all.numpasses;  
   b->no_win_counter = 0;
   b->early_stop_thres = 3;
@@ -968,13 +991,6 @@ void setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::va
       b->early_stop_thres = vm["early_terminate"].as< size_t>();     
   }
   
-  sl_t sl = {b, save_load};
-  learner t(b,drive,learn,finish,sl);
-  all.l = t;
-
-  all.bfgs = true;
-  all.reg.stride = 4;
-  
   if (vm.count("hessian_on") || all.m==0) {
     all.hessian_on = true;
   }
@@ -993,5 +1009,16 @@ void setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::va
       cout << "you must make at least 2 passes to use BFGS" << endl;
       throw exception();
     }
+
+  all.bfgs = true;
+  all.reg.stride = 4;
+
+  learner* l = new learner(b,learn, save_load, all.reg.stride);
+  l->set_save_load(save_load);
+  l->set_init_driver(init_driver);
+  l->set_end_pass(end_pass);
+  l->set_finish(finish);
+
+  return l;
 }
 }
diff --git a/vowpalwabbit/bfgs.h b/vowpalwabbit/bfgs.h
index 8ce28ac2..42ae2a6b 100644
--- a/vowpalwabbit/bfgs.h
+++ b/vowpalwabbit/bfgs.h
@@ -8,7 +8,7 @@ license as described in the file LICENSE.
 #include "gd.h"
 
 namespace BFGS {
-  void setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
 }
 
 #endif
diff --git a/vowpalwabbit/binary.cc b/vowpalwabbit/binary.cc
index 290962c6..76e4b09f 100644
--- a/vowpalwabbit/binary.cc
+++ b/vowpalwabbit/binary.cc
@@ -2,47 +2,23 @@
 #include "vw.h"
 
 namespace BINARY {
-  struct binary {
-    learner base;
-  };
-
-  void learn(void* d, example* ec)
+  void learn(void* d, learner& base, example* ec)
   {
-    binary* b = (binary*)d;
-    b->base.learn(ec);
+    base.learn(ec);
     
     float prediction = -1;
     if ( ec->final_prediction > 0)
       prediction = 1;
     ec->final_prediction = prediction;
-  }
-
-  void finish(void* d)
-  {
-    binary* b = (binary*)d;
-    b->base.finish();
-    free(b);
-  }
 
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn(d, ec);
-	    OAA::output_example(*all, ec);
-	    VW::finish_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
+    label_data* ld = (label_data*)ec->ld;
+    if (ld->label == ec->final_prediction)
+      ec->loss = 0.;
+    else
+      ec->loss = 1.;
   }
 
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     if (!vm_file.count("binary")) 
       {
@@ -52,9 +28,9 @@ namespace BINARY {
       }
 
     all.sd->binary_label = true;
-    binary* data = (binary*)calloc(1,sizeof(binary));
-    data->base = all.l;
-    learner l(data, drive, learn, finish, all.l.sl);
+
+    learner* l = new learner(NULL, learn, all.l);
+
     return l;
   }
 }
diff --git a/vowpalwabbit/binary.h b/vowpalwabbit/binary.h
index de336b2c..1121bf07 100644
--- a/vowpalwabbit/binary.h
+++ b/vowpalwabbit/binary.h
@@ -2,6 +2,6 @@
 #ifndef BIN
 #define BIN
 namespace BINARY {
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
 }
 #endif
diff --git a/vowpalwabbit/bs.cc b/vowpalwabbit/bs.cc
new file mode 100644
index 00000000..f683d3a3
--- /dev/null
+++ b/vowpalwabbit/bs.cc
@@ -0,0 +1,298 @@
+/*
+Copyright (c) by respective owners including Yahoo!, Microsoft, and
+individual contributors. All rights reserved.  Released under a BSD (revised)
+license as described in the file LICENSE.
+ */
+#include <float.h>
+#include <math.h>
+#include <stdio.h>
+#include <sstream>
+#include <numeric>
+#include <vector>
+
+#include "bs.h"
+#include "cache.h"
+#include "v_hashmap.h"
+#include "vw.h"
+#include "rand48.h"
+
+using namespace std;
+
+namespace BS {
+
+  struct bs{
+    uint32_t B; //number of bootstrap rounds
+    size_t bs_type;
+    float lb;
+    float ub;
+    vector<double> pred_vec;
+    vw* all;
+  };
+
+  float weight_gen()//sampling from Poisson with rate 1
+  { 
+    float temp = frand48();
+    if(temp<=0.3678794411714423215955) return 0.;
+    if(temp<=0.735758882342884643191)  return 1.;
+    if(temp<=0.919698602928605803989)  return 2.;
+    if(temp<=0.9810118431238461909214) return 3.;
+    if(temp<=0.9963401531726562876545) return 4.;
+    if(temp<=0.9994058151824183070012) return 5.;
+    if(temp<=0.9999167588507119768923) return 6.;
+    if(temp<=0.9999897508033253583053) return 7.;
+    if(temp<=0.9999988747974020309819) return 8.;
+    if(temp<=0.9999998885745216612793) return 9.;
+    if(temp<=0.9999999899522336243091) return 10.;
+    if(temp<=0.9999999991683892573118) return 11.;
+    if(temp<=0.9999999999364022267287) return 12;
+    if(temp<=0.999999999995480147453) return 13.;
+    if(temp<=0.9999999999996999989333) return 14.;
+    if(temp<=0.9999999999999813223654) return 15.;
+    if(temp<=0.9999999999999989050799) return 16.;
+    if(temp<=0.9999999999999999393572) return 17.;
+    if(temp<=0.999999999999999996817)  return 18.;
+    if(temp<=0.9999999999999999998412) return 19.;
+    return 20.;
+  }
+
+  void bs_predict_mean(vw& all, example* ec, vector<double> &pred_vec)
+  {
+    ec->final_prediction = (float)accumulate(pred_vec.begin(), pred_vec.end(), 0.0)/pred_vec.size();
+    ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ((label_data*)ec->ld)->label) * ((label_data*)ec->ld)->weight;    
+  }
+
+  void bs_predict_vote(vw& all, example* ec, vector<double> &pred_vec)
+  { //majority vote in linear time
+    unsigned int counter = 0;
+    float current_label = 1.;
+    for(unsigned int i=0; i<pred_vec.size(); i++)
+    {
+      if(pred_vec[i] == current_label)
+        counter++;
+      else
+      { 
+        if (counter == 0)
+        {
+          counter = 1;
+          current_label = (float)pred_vec[i];
+        }
+        else
+          counter--;
+      }
+       
+      if(counter==0)
+        current_label = -1;
+    }
+    if(counter == 0)//no majority exists
+    {
+      ec->final_prediction = -1;
+      ec->loss = 1.;
+      return;
+    }
+    //will output majority if it exists
+    ec->final_prediction = current_label;
+    if (ec->final_prediction == ((label_data*)ec->ld)->label)
+      ec->loss = 0.;
+    else
+      ec->loss = 1.;
+  }
+
+  void print_result(int f, float res, float weight, v_array<char> tag, float lb, float ub)
+  {
+    if (f >= 0)
+    {
+      char temp[30];
+      sprintf(temp, "%f", res);
+      std::stringstream ss;
+      ss << temp;
+      print_tag(ss, tag);
+      ss << ' ';
+      sprintf(temp, "%f", lb);
+      ss << temp;
+      ss << ' ';
+      sprintf(temp, "%f", ub);
+      ss << temp;
+      ss << '\n';
+      ssize_t len = ss.str().size();
+#ifdef _WIN32
+	  ssize_t t = _write(f, ss.str().c_str(), (unsigned int)len);
+#else
+	  ssize_t t = write(f, ss.str().c_str(), (unsigned int)len);
+#endif
+      if (t != len)
+        cerr << "write error" << endl;
+    }    
+  }
+
+  void output_example(vw& all, bs* d, example* ec)
+  {
+    label_data* ld = (label_data*)ec->ld;
+    
+    if(ec->test_only)
+      {
+	all.sd->weighted_holdout_examples += ld->weight;//test weight seen
+	all.sd->weighted_holdout_examples_since_last_dump += ld->weight;
+	all.sd->weighted_holdout_examples_since_last_pass += ld->weight;
+	all.sd->holdout_sum_loss += ec->loss;
+	all.sd->holdout_sum_loss_since_last_dump += ec->loss;
+	all.sd->holdout_sum_loss_since_last_pass += ec->loss;//since last pass
+      }
+    else
+      {
+	all.sd->weighted_examples += ld->weight;
+	all.sd->sum_loss += ec->loss;
+	all.sd->sum_loss_since_last_dump += ec->loss;
+	all.sd->total_features += ec->num_features;
+	all.sd->example_number++;
+      }
+    
+    if(all.final_prediction_sink.size() != 0)//get confidence interval only when printing out predictions
+    {
+      d->lb = FLT_MAX;
+      d->ub = -FLT_MAX;
+      for (unsigned i = 0; i < d->pred_vec.size(); i++)
+      {
+        if(d->pred_vec[i] > d->ub)
+          d->ub = (float)d->pred_vec[i];
+        if(d->pred_vec[i] < d->lb)
+          d->lb = (float)d->pred_vec[i];
+      }
+    }
+
+    for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
+      BS::print_result(*sink, ec->final_prediction, 0, ec->tag, d->lb, d->ub);
+  
+    print_update(all, ec);
+  }
+
+  void learn(void* data, learner& base, example* ec)
+  {
+    bs* d = (bs*)data;
+    vw* all = d->all;
+    bool shouldOutput = all->raw_prediction > 0;
+
+    float weight_temp = ((label_data*)ec->ld)->weight;
+  
+    string outputString;
+    stringstream outputStringStream(outputString);
+    d->pred_vec.clear();
+
+    for (size_t i = 1; i <= d->B; i++)
+      {
+        ((label_data*)ec->ld)->weight = weight_temp * weight_gen();
+
+        base.learn(ec, i-1);
+
+        d->pred_vec.push_back(ec->final_prediction);
+
+        if (shouldOutput) {
+          if (i > 1) outputStringStream << ' ';
+          outputStringStream << i << ':' << ec->partial_prediction;
+        }
+      }	
+
+    ((label_data*)ec->ld)->weight = weight_temp;
+
+    switch(d->bs_type)
+    {
+      case BS_TYPE_MEAN:
+        bs_predict_mean(*all, ec, d->pred_vec);
+        break;
+      case BS_TYPE_VOTE:
+        bs_predict_vote(*all, ec, d->pred_vec);
+        break;
+      default:
+        std::cerr << "Unknown bs_type specified: " << d->bs_type << ". Exiting." << endl;
+        throw exception();
+    }
+
+    if (shouldOutput) 
+      all->print_text(all->raw_prediction, outputStringStream.str(), ec->tag);
+
+  }
+
+  void finish_example(vw& all, void* d, example* ec)
+  {
+    BS::output_example(all, (bs*)d, ec);
+    VW::finish_example(all, ec);
+  }
+
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  {
+    bs* data = (bs*)calloc(1, sizeof(bs));
+    data->ub = FLT_MAX;
+    data->lb = -FLT_MAX;
+
+    po::options_description desc("BS options");
+    desc.add_options()
+      ("bs_type", po::value<string>(), "prediction type {mean,vote}");
+
+    po::parsed_options parsed = po::command_line_parser(opts).
+      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
+      options(desc).allow_unregistered().run();
+    opts = po::collect_unrecognized(parsed.options, po::include_positional);
+    po::store(parsed, vm);
+    po::notify(vm);
+
+    po::parsed_options parsed_file = po::command_line_parser(all.options_from_file_argc,all.options_from_file_argv).
+      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
+      options(desc).allow_unregistered().run();
+    po::store(parsed_file, vm_file);
+    po::notify(vm_file);
+
+    if( vm_file.count("bs") ) {
+      data->B = (uint32_t)vm_file["bs"].as<size_t>();
+      if( vm.count("bs") && (uint32_t)vm["bs"].as<size_t>() != data->B )
+        std::cerr << "warning: you specified a different number of samples through --bs than the one loaded from predictor. Pursuing with loaded value of: " << data->B << endl;
+    }
+    else {
+      data->B = (uint32_t)vm["bs"].as<size_t>();
+
+      //append bs with number of samples to options_from_file so it is saved to regressor later
+      std::stringstream ss;
+      ss << " --bs " << data->B;
+      all.options_from_file.append(ss.str());
+    }
+
+    if (vm.count("bs_type") || vm_file.count("bs_type"))
+    {
+      std::string type_string;
+
+      if(vm_file.count("bs_type")) {
+        type_string = vm_file["bs_type"].as<std::string>();
+        if( vm.count("bs_type") && type_string.compare(vm["bs_type"].as<string>()) != 0)
+          cerr << "You specified a different --bs_type than the one loaded from regressor file. Pursuing with loaded value of: " << type_string << endl;
+      }
+      else {
+        type_string = vm["bs_type"].as<std::string>();
+
+        all.options_from_file.append(" --bs_type ");
+        all.options_from_file.append(type_string);
+      }
+
+      if (type_string.compare("mean") == 0) { 
+        data->bs_type = BS_TYPE_MEAN;
+      }
+      else if (type_string.compare("vote") == 0) {
+        data->bs_type = BS_TYPE_VOTE;
+      }
+      else {
+        std::cerr << "warning: bs_type must be in {'mean','vote'}; resetting to mean." << std::endl;
+        data->bs_type = BS_TYPE_MEAN;
+      }
+    }
+    else {
+      //by default use mean
+      data->bs_type = BS_TYPE_MEAN;
+      all.options_from_file.append(" --bs_type mean");
+    }
+
+    data->pred_vec.reserve(data->B);
+    data->all = &all;
+
+    learner* l = new learner(data, learn, all.l, data->B);
+    l->set_finish_example(finish_example);
+
+    return l;
+  }
+}
diff --git a/vowpalwabbit/bs.h b/vowpalwabbit/bs.h
new file mode 100644
index 00000000..cfca0595
--- /dev/null
+++ b/vowpalwabbit/bs.h
@@ -0,0 +1,28 @@
+/*
+Copyright (c) by respective owners including Yahoo!, Microsoft, and
+individual contributors. All rights reserved.  Released under a BSD
+license as described in the file LICENSE.
+ */
+#ifndef BS_H
+#define BS_H
+
+#define BS_TYPE_MEAN 0
+#define BS_TYPE_VOTE 1
+
+#include "io_buf.h"
+#include "parse_primitives.h"
+#include "global_data.h"
+#include "example.h"
+#include "parse_args.h"
+#include "v_hashmap.h"
+#include "simple_label.h"
+
+namespace BS
+{
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  void print_result(int f, float res, float weight, v_array<char> tag, float lb, float ub);
+  
+  void output_example(vw& all, example* ec, float lb, float ub);
+}
+
+#endif
diff --git a/vowpalwabbit/cb.cc b/vowpalwabbit/cb.cc
index 1325be29..482edaf3 100644
--- a/vowpalwabbit/cb.cc
+++ b/vowpalwabbit/cb.cc
@@ -17,20 +17,16 @@ license as described in the file LICENSE.
 namespace CB
 {
   struct cb {
-    uint32_t increment;
     size_t cb_type;
     CSOAA::label cb_cs_ld; 
     float avg_loss_regressors;
     size_t nb_ex_regressors;
     float last_pred_reg;
     float last_correct_cost;
-    bool first_print_call;
 
     float min_cost;
     float max_cost;
 
-    learner base;
-    
     cb_class* known_cost;
     vw* all;
   };
@@ -283,18 +279,14 @@ namespace CB
 
   void call_scorer(vw& all, cb& c, example* ec, uint32_t index)
   {
-    uint32_t desired_increment = c.increment * (2*index-1);
-   
     float old_min = all.sd->min_label;
     //all.sd->min_label = c.min_cost;
     float old_max = all.sd->max_label;
     //all.sd->max_label = c.max_cost;
-    update_example_indicies(all.audit, ec, desired_increment);
-    all.scorer.learn(ec);
+    all.scorer->learn(ec, 2*(index)-1);
     all.sd->min_label = old_min;
     all.sd->max_label = old_max;
-    update_example_indicies(all.audit, ec, -desired_increment);
-  }
+   }
   
   float get_cost_pred(vw& all, cb& c, example* ec, uint32_t index)
   {
@@ -340,7 +332,7 @@ namespace CB
         wc.wap_value = 0.;
       
         //get cost prediction for this action
-        wc.x = get_cost_pred(all, c,ec,i);
+        wc.x = get_cost_pred(all, c, ec, i-1);
 	if (wc.x < min)
 	  {
 	    min = wc.x;
@@ -369,7 +361,7 @@ namespace CB
         wc.wap_value = 0.;
       
         //get cost prediction for this action
-        wc.x = get_cost_pred(all, c,ec,cl->weight_index);
+        wc.x = get_cost_pred(all, c, ec, cl->weight_index - 1);
 	if (wc.x < min || (wc.x == min && cl->weight_index < argmin))
 	  {
 	    min = wc.x;
@@ -408,7 +400,7 @@ namespace CB
         wc.wap_value = 0.;
 
         //get cost prediction for this label
-        wc.x = get_cost_pred(all, c,ec,i);
+        wc.x = get_cost_pred(all, c,ec, all.sd->k + i - 1);
         wc.weight_index = i;
         wc.partial_prediction = 0.;
         wc.wap_value = 0.;
@@ -433,7 +425,7 @@ namespace CB
         wc.wap_value = 0.;
 
         //get cost prediction for this label
-        wc.x = get_cost_pred(all,c,ec,cl->weight_index);
+        wc.x = get_cost_pred(all, c, ec, all.sd->k + cl->weight_index - 1);
         wc.weight_index = cl->weight_index;
         wc.partial_prediction = 0.;
         wc.wap_value = 0.;
@@ -475,17 +467,11 @@ namespace CB
     }
   }
 
-  void learn(void* d, example* ec) {
+  void learn(void* d, learner& base, example* ec) {
     cb* c = (cb*)d;
     vw* all = c->all;
     CB::label* ld = (CB::label*)ec->ld;
 
-    if (command_example(all, ec))
-      {
-	c->base.learn(ec);
-	return;
-      }
-
     //check if this is a test example where we just want a prediction
     if( CB::is_test_label(ld) )
     {
@@ -493,7 +479,7 @@ namespace CB
       cb_test_to_cs_test_label(*all,ec,c->cb_cs_ld);
 
        ec->ld = &c->cb_cs_ld;
-       c->base.learn(ec);
+       base.learn(ec);
        ec->ld = ld;
        return;
     }
@@ -523,19 +509,18 @@ namespace CB
     if (c->cb_type != CB_TYPE_DM)
       {
 	ec->ld = &c->cb_cs_ld;
-	c->base.learn(ec);
+	base.learn(ec);
 	ec->ld = ld;
       }
   }
 
-  void print_update(vw& all, cb& c, bool is_test, example *ec)
+  void init_driver(void*)
   {
-    if( c.first_print_call )
-    {
-      fprintf(stderr, "*estimate* *estimate*                                                avglossreg last pred  last correct\n");
-      c.first_print_call = false;
-    }
+    fprintf(stderr, "*estimate* *estimate*                                                avglossreg last pred  last correct\n");
+  }
 
+  void print_update(vw& all, cb& c, bool is_test, example *ec)
+  {
     if (all.sd->weighted_examples > all.sd->dump_interval && !all.quiet && !all.bfgs)
       {
         char label_buf[32];
@@ -544,7 +529,33 @@ namespace CB
         else
           sprintf(label_buf," known");
 
-        fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8lu %8lu   %-10.6f %-10.6f %-10.6f\n",
+        if(!all.holdout_set_off && all.current_pass >= 1)
+        {
+          if(all.sd->holdout_sum_loss == 0. && all.sd->weighted_holdout_examples == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss/all.sd->weighted_holdout_examples);
+
+          if(all.sd->holdout_sum_loss_since_last_dump == 0. && all.sd->weighted_holdout_examples_since_last_dump == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss_since_last_dump/all.sd->weighted_holdout_examples_since_last_dump);
+        
+          fprintf(stderr, "%8ld %8.1f   %s %8lu %8lu   %-10.6f %-10.6f %-10.6f h\n",
+	      (long int)all.sd->example_number,
+	      all.sd->weighted_examples,
+	      label_buf,
+              (long unsigned int)ec->final_prediction,
+              (long unsigned int)ec->num_features,
+              c.avg_loss_regressors,
+              c.last_pred_reg,
+              c.last_correct_cost);
+
+          all.sd->weighted_holdout_examples_since_last_dump = 0;
+          all.sd->holdout_sum_loss_since_last_dump = 0.0;
+        }
+        else
+          fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8lu %8lu   %-10.6f %-10.6f %-10.6f\n",
                 all.sd->sum_loss/all.sd->weighted_examples,
                 all.sd->sum_loss_since_last_dump / (all.sd->weighted_examples - all.sd->old_weighted_examples),
                 (long int)all.sd->example_number,
@@ -566,8 +577,6 @@ namespace CB
   {
     CB::label* ld = (CB::label*)ec->ld;
 
-    all.sd->weighted_examples += 1.;
-    all.sd->total_features += ec->num_features;
     float loss = 0.;
     if (!CB::is_test_label(ld))
       {//need to compute exact loss
@@ -597,16 +606,31 @@ namespace CB
         loss = chosen_loss;
       }
 
-    all.sd->sum_loss += loss;
-    all.sd->sum_loss_since_last_dump += loss;
-  
+    if(ec->test_only)
+    {
+      all.sd->weighted_holdout_examples += ec->global_weight;//test weight seen
+      all.sd->weighted_holdout_examples_since_last_dump += ec->global_weight;
+      all.sd->weighted_holdout_examples_since_last_pass += ec->global_weight;
+      all.sd->holdout_sum_loss += loss;
+      all.sd->holdout_sum_loss_since_last_dump += loss;
+      all.sd->holdout_sum_loss_since_last_pass += loss;//since last pass
+    }
+    else
+    {
+      all.sd->sum_loss += loss;
+      all.sd->sum_loss_since_last_dump += loss;
+      all.sd->weighted_examples += 1.;
+      all.sd->total_features += ec->num_features;
+      all.sd->example_number++;
+    }
+
     for (size_t i = 0; i<all.final_prediction_sink.size(); i++)
       {
         int f = all.final_prediction_sink[i];
         all.print(f, ec->final_prediction, 0, ec->tag);
       }
   
-    all.sd->example_number++;
+
 
     print_update(all, c, CB::is_test_label((CB::label*)ec->ld), ec);
   }
@@ -614,34 +638,20 @@ namespace CB
   void finish(void* d)
   {
     cb* c=(cb*)d;
-    c->base.finish();
     c->cb_cs_ld.costs.delete_v();
-    free(c);
   }
 
-  void drive(vw* all, void* d)
+  void finish_example(vw& all, void* data, example* ec)
   {
-    cb* c = (cb*)d;
-    example* ec = NULL;
-    while ( true )
-    {
-      if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-      {
-        learn(d, ec);
-	if (!command_example(&all, ec))
-	  output_example(*all, *c, ec);
-	VW::finish_example(*all, ec);
-      }
-      else if (parser_done(all->p))
-	return;
-    }
+    cb* c = (cb*)data;
+    output_example(all, *c, ec);
+    VW::finish_example(all, ec);
   }
 
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     cb* c = (cb*)calloc(1, sizeof(cb));
     c->all = &all;
-    c->first_print_call = true;
     c->min_cost = 0.;
     c->max_cost = 1.;
     po::options_description desc("CB options");
@@ -674,7 +684,9 @@ namespace CB
       ss << " --cb " << nb_actions;
       all.options_from_file.append(ss.str());
     }
+    all.sd->k = nb_actions;
 
+    size_t problem_multiplier = 2;//default for DR
     if (vm.count("cb_type") || vm_file.count("cb_type"))
     {
       std::string type_string;
@@ -691,39 +703,39 @@ namespace CB
         all.options_from_file.append(type_string);
       }
 
-      c->increment = all.weights_per_problem * all.reg.stride;
-
-      if (type_string.compare("dr") == 0) { 
+      if (type_string.compare("dr") == 0) 
         c->cb_type = CB_TYPE_DR;
-        all.weights_per_problem *= nb_actions * 2;
-      }
-      else if (type_string.compare("dm") == 0) {
-        c->cb_type = CB_TYPE_DM;
-        all.weights_per_problem *= nb_actions * 2;
-      }
-      else if (type_string.compare("ips") == 0) {
-        c->cb_type = CB_TYPE_IPS;
-        all.weights_per_problem *= nb_actions;
-      }
+      else if (type_string.compare("dm") == 0)
+	{
+	  c->cb_type = CB_TYPE_DM;
+	  problem_multiplier = 1;
+	}
+      else if (type_string.compare("ips") == 0)
+	{
+	  c->cb_type = CB_TYPE_IPS;
+	  problem_multiplier = 1;
+	}
       else {
         std::cerr << "warning: cb_type must be in {'ips','dm','dr'}; resetting to dr." << std::endl;
         c->cb_type = CB_TYPE_DR;
-        all.weights_per_problem *= nb_actions * 2;
       }
     }
     else {
       //by default use doubly robust
       c->cb_type = CB_TYPE_DR;
-      all.weights_per_problem *= nb_actions * 2;
       all.options_from_file.append(" --cb_type dr");
     }
 
     *(all.p->lp) = CB::cb_label_parser; 
 
-    all.sd->k = nb_actions;
+    learner* l = new learner(c, learn, all.l, problem_multiplier);
+    l->set_finish_example(finish_example); 
+    l->set_init_driver(init_driver);
+    l->set_finish(finish);
+    // preserve the increment of the base learner since we are
+    // _adding_ to the number of problems rather than multiplying.
+    l->increment = all.l->increment; 
 
-    learner l(c, drive, learn, finish, all.l.sl);
-    c->base = all.l;
     return l;
   }
 }
diff --git a/vowpalwabbit/cb.h b/vowpalwabbit/cb.h
index e2f60382..62870016 100644
--- a/vowpalwabbit/cb.h
+++ b/vowpalwabbit/cb.h
@@ -32,7 +32,7 @@ namespace CB {
     v_array<cb_class> costs;
   };
 
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
 
   size_t read_cached_label(shared_data* sd, void* v, io_buf& cache);
   void cache_label(void* v, io_buf& cache);
diff --git a/vowpalwabbit/constant.h b/vowpalwabbit/constant.h
index 4dd431fe..9b6d5089 100644
--- a/vowpalwabbit/constant.h
+++ b/vowpalwabbit/constant.h
@@ -8,5 +8,6 @@ license as described in the file LICENSE.
 const int quadratic_constant = 27942141;
 const int cubic_constant = 21791;
 const int cubic_constant2 = 37663;
+const int affix_constant = 13903957;
 const int constant = 11650396;
 #endif
diff --git a/vowpalwabbit/csoaa.cc b/vowpalwabbit/csoaa.cc
index 6f62646a..e3b42341 100644
--- a/vowpalwabbit/csoaa.cc
+++ b/vowpalwabbit/csoaa.cc
@@ -19,8 +19,6 @@ using namespace std;
 
 namespace CSOAA {
   struct csoaa{
-    uint32_t csoaa_increment;
-    learner base;
     vw* all;
   };
 
@@ -44,9 +42,9 @@ namespace CSOAA {
 	}
       break;
     default:
-      cerr << "example with a wierd name.  What is ";
+      cerr << "example with a wierd name.  What is '";
       cerr.write(s.begin, s.end - s.begin);
-      cerr << "\n";
+      cerr << "'?\n";
     }
   }
 
@@ -133,7 +131,6 @@ namespace CSOAA {
   void delete_label(void* v)
   {
     label* ld = (label*)v;
-    //cerr << "ld=[size=" << ld->costs.size() << " begin=" << ld->costs.begin <<  "|" << ld->costs[0].weight_index << ":" << ld->costs[0].x << "...]";
     ld->costs.delete_v();
   }
 
@@ -155,7 +152,7 @@ namespace CSOAA {
   {
     label* ld = (label*)v;
 
-    //cerr << "csoaa::parse_label words.size()=" << words.size() << endl;
+    //v_array<substring> parse_name; // TODO: make the parser thread safe so we don't have to do this here!
     ld->costs.erase();
     for (unsigned int i = 0; i < words.size(); i++) {
       wclass f = {0.,0,0.,0.};
@@ -189,7 +186,7 @@ namespace CSOAA {
             else
               cerr << "invalid cost specification: " << f.weight_index << endl;
           } else 
-            cerr << "malformed cost specification!" << endl;
+            cerr << "malformed cost specification on '" << (p->parse_name[0].begin) << "'" << endl;
         }
         ld->costs.push_back(f);
       }
@@ -218,7 +215,30 @@ namespace CSOAA {
         else
           sprintf(label_buf," known");
 
-        fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8lu %8lu\n",
+        if(!all.holdout_set_off && all.current_pass >= 1)
+        {
+          if(all.sd->holdout_sum_loss == 0. && all.sd->weighted_holdout_examples == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss/all.sd->weighted_holdout_examples);
+
+          if(all.sd->holdout_sum_loss_since_last_dump == 0. && all.sd->weighted_holdout_examples_since_last_dump == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss_since_last_dump/all.sd->weighted_holdout_examples_since_last_dump);
+        
+          fprintf(stderr, "%8ld %8.1f   %s %8lu %8lu h\n",
+                (long int)all.sd->example_number,
+                all.sd->weighted_examples,
+                label_buf,
+                (long unsigned int)ec->final_prediction,
+                (long unsigned int)ec->num_features);
+
+          all.sd->weighted_holdout_examples_since_last_dump = 0;
+          all.sd->holdout_sum_loss_since_last_dump = 0.0;
+        }
+        else
+          fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8lu %8lu\n",
                 all.sd->sum_loss/all.sd->weighted_examples,
                 all.sd->sum_loss_since_last_dump / (all.sd->weighted_examples - all.sd->old_weighted_examples),
                 (long int)all.sd->example_number,
@@ -236,8 +256,7 @@ namespace CSOAA {
   void output_example(vw& all, example* ec)
   {
     label* ld = (label*)ec->ld;
-    all.sd->weighted_examples += 1.;
-    all.sd->total_features += ec->num_features;
+
     float loss = 0.;
     if (!is_test_label(ld))
       {//need to compute exact loss
@@ -257,9 +276,24 @@ namespace CSOAA {
         loss = chosen_loss - min;
       }
 
-    all.sd->sum_loss += loss;
-    all.sd->sum_loss_since_last_dump += loss;
-  
+    if(ec->test_only)
+      {
+        all.sd->weighted_holdout_examples += ec->global_weight;//test weight seen
+        all.sd->weighted_holdout_examples_since_last_dump += ec->global_weight;
+        all.sd->weighted_holdout_examples_since_last_pass += ec->global_weight;
+        all.sd->holdout_sum_loss += loss;
+        all.sd->holdout_sum_loss_since_last_dump += loss;
+        all.sd->holdout_sum_loss_since_last_pass += loss;//since last pass
+     }
+    else
+      {
+        all.sd->weighted_examples += 1.;
+        all.sd->total_features += ec->num_features;
+        all.sd->sum_loss += loss;
+        all.sd->sum_loss_since_last_dump += loss;    
+        all.sd->example_number++;
+      }
+
     for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
       all.print((int)*sink, ec->final_prediction, 0, ec->tag);
 
@@ -275,25 +309,16 @@ namespace CSOAA {
       all.print_text(all.raw_prediction, outputStringStream.str(), ec->tag);
     }
 
-    all.sd->example_number++;
-
     print_update(all, is_test_label((label*)ec->ld), ec);
   }
 
-  void learn(void* d, example* ec) {
+  void learn(void* d, learner& base, example* ec) {
     csoaa* c = (csoaa*)d;
     vw* all = c->all;
     label* ld = (label*)ec->ld;
 
-    if (command_example(all, ec))
-      {
-	c->base.learn(ec);
-	return;
-      }
-
     size_t prediction = 1;
     float score = FLT_MAX;
-    uint32_t current_increment = 0;
     for (wclass *cl = ld->costs.begin; cl != ld->costs.end; cl ++)
       {
         uint32_t i = cl->weight_index;
@@ -313,14 +338,7 @@ namespace CSOAA {
 
 	ec->ld = &simple_temp;
 
-        uint32_t desired_increment = c->csoaa_increment * (i-1);
-
-        if (desired_increment != current_increment) {
-	  update_example_indicies(all->audit, ec, desired_increment - current_increment);
-          current_increment = desired_increment;
-        }
-
-	c->base.learn(ec);
+	base.learn(ec, i);
         cl->partial_prediction = ec->partial_prediction;
 	if (ec->partial_prediction < score || (ec->partial_prediction == score && i < prediction)) {
           score = ec->partial_prediction;
@@ -330,37 +348,15 @@ namespace CSOAA {
       }
     ec->ld = ld;
     ec->final_prediction = (float)prediction;
-    if (current_increment != 0)
-      update_example_indicies(all->audit, ec, -current_increment);
   }
 
-  void finish(void* d)
+  void finish_example(vw& all, void*, example* ec)
   {
-    csoaa* c=(csoaa*)d;
-    c->base.finish();
-    free(c);
+    output_example(all, ec);
+    VW::finish_example(all, ec);
   }
 
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn(d, ec);
-            output_example(*all, ec);
-            if (ec->in_use)
-              VW::finish_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
- }
-
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     csoaa* c=(csoaa*)calloc(1,sizeof(csoaa));
     c->all = &all;
@@ -381,13 +377,10 @@ namespace CSOAA {
     }
 
     *(all.p->lp) = cs_label_parser;
-    c->csoaa_increment = all.weights_per_problem * all.reg.stride;
-    all.weights_per_problem *= nb_actions;
-    c->base=all.l;
     all.sd->k = nb_actions;
 
-    learner l(c, drive, learn, finish, all.l.sl);
-    c->base = all.l;
+    learner* l = new learner(c, learn, all.l, nb_actions);
+    l->set_finish_example(finish_example);
     return l;
   }
 
@@ -410,12 +403,13 @@ namespace CSOAA_AND_WAP_LDF {
 
     size_t read_example_this_loop;
     bool need_to_clear;
-    bool is_singleline;
     bool is_wap;
     bool first_pass;
+    bool treat_as_classifier;
     float csoaa_example_t;
-    learner base;
     vw* all;
+
+    learner* base;
   };
 
 namespace LabelDict { 
@@ -449,7 +443,7 @@ namespace LabelDict {
     bool is_lab = ec_is_label_definition(l.ec_seq[0]);
     for (size_t i=1; i<l.ec_seq.size(); i++) {
       if (is_lab != ec_is_label_definition(l.ec_seq[i])) {
-        if (!((i == l.ec_seq.size()-1) && (OAA::example_is_newline(l.ec_seq[i])))) {
+        if (!((i == l.ec_seq.size()-1) && (example_is_newline(l.ec_seq[i])))) {
           cerr << "error: mixed label definition and examples in ldf data!" << endl;
           throw exception();
         }
@@ -624,7 +618,7 @@ namespace LabelDict {
     ec->indices.decr();
   }
 
-  void make_single_prediction(vw& all, ldf& l, example*ec, size_t*prediction, float*min_score) {
+  void make_single_prediction(vw& all, ldf& l, learner& base, example*ec, size_t*prediction, float*min_score, float*min_cost, float*max_cost) {
     label   *ld = (label*)ec->ld;
     v_array<CSOAA::wclass> costs = ld->costs;
     label_data simple_label;
@@ -638,7 +632,7 @@ namespace LabelDict {
       LabelDict::add_example_namespace_from_memory(l, ec, costs[j].weight_index);
       
       ec->ld = &simple_label;
-      l.base.learn(ec); // make a prediction
+      base.learn(ec); // make a prediction
       costs[j].partial_prediction = ec->partial_prediction;
 
       if (ec->partial_prediction < *min_score) {
@@ -646,6 +640,9 @@ namespace LabelDict {
         *prediction = costs[j].weight_index;
       }
 
+      if (min_cost && (costs[j].x < *min_cost)) *min_cost = costs[j].x;
+      if (max_cost && (costs[j].x > *max_cost)) *max_cost = costs[j].x;
+
       LabelDict::del_example_namespace_from_memory(l, ec, costs[j].weight_index);
     }
 
@@ -654,15 +651,13 @@ namespace LabelDict {
 
 
 
-  void do_actual_learning_wap(vw& all, ldf& l, size_t start_K)
+  void do_actual_learning_wap(vw& all, ldf& l, learner& base, size_t start_K)
   {
     size_t K = l.ec_seq.size();
     bool   isTest = CSOAA::example_is_test(l.ec_seq[start_K]);
     size_t prediction = 0;
     float  min_score = FLT_MAX;
 
-    v_hashmap<size_t,float> hit_labels(8, 0., NULL);
-
     for (size_t k=start_K; k<K; k++) {
       example *ec = l.ec_seq.begin[k];
 
@@ -675,7 +670,7 @@ namespace LabelDict {
         throw exception();
       }
 
-      make_single_prediction(all, l, ec, &prediction, &min_score);
+      make_single_prediction(all, l, base, ec, &prediction, &min_score, NULL, NULL);
     }
 
     // do actual learning
@@ -726,7 +721,7 @@ namespace LabelDict {
               simple_label.weight = value_diff;
               ec1->partial_prediction = 0.;
               subtract_example(all, ec1, ec2);
-              l.base.learn(ec1);
+              base.learn(ec1);
               unsubtract_example(all, ec1);
               
               LabelDict::del_example_namespace_from_memory(l, ec2, costs2[j2].weight_index);
@@ -743,13 +738,15 @@ namespace LabelDict {
     }
   }
 
-  void do_actual_learning_oaa(vw& all, ldf& l, size_t start_K)
+  void do_actual_learning_oaa(vw& all, ldf& l, learner& base, size_t start_K)
   {
     size_t K = l.ec_seq.size();
     size_t prediction = 0;
     bool   isTest = CSOAA::example_is_test(l.ec_seq[start_K]);
     float  min_score = FLT_MAX;
-
+    float  min_cost  = FLT_MAX;
+    float  max_cost  = -FLT_MAX;
+    
     for (size_t k=start_K; k<K; k++) {
       example *ec = l.ec_seq.begin[k];
       if (CSOAA::example_is_test(ec) != isTest) {
@@ -760,7 +757,7 @@ namespace LabelDict {
         cerr << "warning: example headers at position " << k << ": can only have in initial position!" << endl;
         throw exception();
       }
-      make_single_prediction(all, l, ec, &prediction, &min_score);
+      make_single_prediction(all, l, base, ec, &prediction, &min_score, &min_cost, &max_cost);
     }
 
     // do actual learning
@@ -778,13 +775,28 @@ namespace LabelDict {
         if (all.training && !isTest) {
           float example_t = ec->example_t;
           ec->example_t = l.csoaa_example_t;
+
           simple_label.initial = 0.;
-          simple_label.label = costs[j].x;
           simple_label.weight = 1.;
+          if (!l.treat_as_classifier) { // treat like regression
+            simple_label.label = costs[j].x;
+          } else { // treat like classification
+            if (costs[j].x <= min_cost) {
+              simple_label.label = -1.;
+              simple_label.weight = max_cost - min_cost;
+            } else {
+              simple_label.label = 1.;
+              simple_label.weight = costs[j].x - min_cost;
+            }
+          }
+          // TODO: check the example->done and ec->partial_prediction = costs[j].partial_prediciton here
+
           ec->ld = &simple_label;
-          ec->partial_prediction = 0.;
+          //ec->partial_prediction = costs[j].partial_prediction;
+          //cerr << "[" << ec->partial_prediction << "," << ec->done << "]";
+          //ec->done = false;
           LabelDict::add_example_namespace_from_memory(l, ec, costs[j].weight_index);
-          l.base.learn(ec);
+          base.learn(ec);
           LabelDict::del_example_namespace_from_memory(l, ec, costs[j].weight_index);
           ec->example_t = example_t;
         }
@@ -805,7 +817,7 @@ namespace LabelDict {
   }
 
 
-  void do_actual_learning(vw& all, ldf& l)
+  void do_actual_learning(vw& all, ldf& l, learner& base)
   {
     if (l.ec_seq.size() <= 0) return;  // nothing to do
 
@@ -836,8 +848,8 @@ namespace LabelDict {
     }
 
     /////////////////////// learn
-    if (l.is_wap) do_actual_learning_wap(all, l, start_K);
-    else        do_actual_learning_oaa(all, l, start_K);
+    if (l.is_wap) do_actual_learning_wap(all, l, base, start_K);
+    else          do_actual_learning_oaa(all, l, base, start_K);
     
     /////////////////////// remove header
     if (start_K > 0)
@@ -851,7 +863,7 @@ namespace LabelDict {
     label* ld = (label*)ec->ld;
     v_array<CSOAA::wclass> costs = ld->costs;
 
-    if (OAA::example_is_newline(ec)) return;
+    if (example_is_newline(ec)) return;
     if (LabelDict::ec_is_example_header(ec)) return;
     if (LabelDict::ec_is_label_definition(ec)) return;
 
@@ -916,144 +928,106 @@ namespace LabelDict {
     l.ec_seq.erase();
   }
 
+  void end_pass(void* data)
+  {
+    ldf* l=(ldf*)data;
+    l->first_pass = false;
+  }
 
-  void learn_singleline(vw& all, ldf& l, example*ec) {
-    if (command_example(&all, ec))
-      {
-	l.base.learn(ec);
-	return;
-      }
-    
-    if ((!all.training) || CSOAA::example_is_test(ec)) {
+  void learn(void* data, learner& base, example *ec) 
+  {
+    ldf* l=(ldf*)data;
+    vw* all = l->all;
+    l->base = &base;
+
+    if ((!all->training) || CSOAA::example_is_test(ec)) {
       size_t prediction = 0;
       float  min_score = FLT_MAX;
-      make_single_prediction(all, l, ec, &prediction, &min_score);
-    } else {
-      l.ec_seq.erase();
-      l.ec_seq.push_back(ec);
-      do_actual_learning(all,l);
-      l.ec_seq.erase();
+      make_single_prediction(*all, *l, base, ec, &prediction, &min_score, NULL, NULL);
     }
-  }
-
-  void learn_multiline(vw& all, ldf& l, example *ec) {
-    if (OAA::example_is_newline(ec) || l.ec_seq.size() >= all.p->ring_size - 2 || command_example(&all,ec)) {
-      if (l.ec_seq.size() >= all.p->ring_size - 2 && l.first_pass)
+    if (example_is_newline(ec) || l->ec_seq.size() >= all->p->ring_size - 2) {
+      if (l->ec_seq.size() >= all->p->ring_size - 2 && l->first_pass)
         cerr << "warning: length of sequence at " << ec->example_counter << " exceeds ring size; breaking apart" << endl;
 	
-      do_actual_learning(all, l);
+      do_actual_learning(*all, *l, base);
+
+      if (!LabelDict::ec_seq_is_label_definition(*l, l->ec_seq) && l->ec_seq.size() > 0)
+        global_print_newline(*all);
 
-      if (!LabelDict::ec_seq_is_label_definition(l, l.ec_seq) && l.ec_seq.size() > 0)
-        global_print_newline(all);
       if (ec->in_use)
-        VW::finish_example(all, ec);
-      l.need_to_clear = true;
+        VW::finish_example(*all, ec);
+      l->need_to_clear = true;
     } else if (LabelDict::ec_is_label_definition(ec)) {
-      if (l.ec_seq.size() > 0)
+      if (l->ec_seq.size() > 0)
         cerr << "warning: label definition encountered in data block -- ignoring data!" << endl;
-      learn_singleline(all, l, ec);
+    
+      if (!((!all->training) || CSOAA::example_is_test(ec))) {
+        l->ec_seq.erase();
+        l->ec_seq.push_back(ec);
+        do_actual_learning(*all, *l, base);
+        l->ec_seq.erase();
+      }
+
       if (ec->in_use)
-        VW::finish_example(all, ec);
+        VW::finish_example(*all, ec);
     } else {
-      l.ec_seq.push_back(ec);
+      l->ec_seq.push_back(ec);
     }
     
-    if (l.need_to_clear) {
-      output_example_seq(all, l);
-      clear_seq(all, l);
-      l.need_to_clear = false;
+    if (l->need_to_clear) {
+      output_example_seq(*all, *l);
+      clear_seq(*all, *l);
+      l->need_to_clear = false;
     }
-
-    if (command_example(&all, ec))
-      {
-	if (ec->end_pass)
-	  l.first_pass = false;
-
-	l.base.learn(ec);
-	return;
-      }
-  }
-
-  void learn(void* d, example*ec) {
-    ldf* l = (ldf*)d;
-    vw* all = l->all;
-    if (l->is_singleline) learn_singleline(*all,*l, ec);
-    else learn_multiline(*all,*l, ec);
   }
 
   void finish(void* d)
   {
     ldf* l=(ldf*)d;
     vw* all = l->all;
-    l->base.finish();
     clear_seq(*all, *l);
     l->ec_seq.delete_v();
     LabelDict::free_label_features(*l);
   }
 
-  void drive_ldf_singleline(vw& all, ldf& l) {
-    example* ec = NULL;
-    while (true) {
-      if ((ec = VW::get_example(all.p)) != NULL) { //semiblocking operation.
-
-        if (LabelDict::ec_is_example_header(ec)) {
-          cerr << "error: example headers not allowed in ldf singleline mode" << endl;
-          throw exception();
-        }
-        learn_singleline(all, l, ec);
-        if (! LabelDict::ec_is_label_definition(ec)) {
-          all.sd->weighted_examples += 1;
-          all.sd->example_number++;
-        }
-        bool hit_loss = false;
-        output_example(all, ec, hit_loss);
-        if (ec->in_use)
-          VW::finish_example(all, ec);
-      } else if (parser_done(all.p)) {
-        return;
-      }
+  void finish_example(vw& all, void*, example* ec)
+  {
+    if (! LabelDict::ec_is_label_definition(ec)) {
+      all.sd->weighted_examples += 1;
+      all.sd->example_number++;
     }
+    bool hit_loss = false;
+    output_example(all, ec, hit_loss);
+    VW::finish_example(all, ec);
   }
 
-  void drive_ldf_multiline(vw& all, ldf& l) {
-    example* ec = NULL;
-    l.read_example_this_loop = 0;
-    l.need_to_clear = false;
-    while (true) {
-      if ((ec = VW::get_example(all.p)) != NULL) { // semiblocking operation
-        learn_multiline(all, l, ec);
-        if (l.need_to_clear) {
-	  if (l.ec_seq.size() > 0)
-	    output_example_seq(all, l);
-          clear_seq(all, l);
-          l.need_to_clear = false;
-        }
-      } else if (parser_done(all.p)) {
-        do_actual_learning(all, l);
-        output_example_seq(all, l);
-        clear_seq(all, l);
-	l.ec_seq.delete_v();
-        return;
-      }
+  void finish_multiline_example(vw& all, void* data, example* ec)
+  {
+    ldf* l=(ldf*)data;
+    if (l->need_to_clear) {
+      if (l->ec_seq.size() > 0)
+	output_example_seq(all, *l);
+          clear_seq(all, *l);
+          l->need_to_clear = false;
     }
   }
 
-  void drive(vw* all, void* d)
+  void end_examples(void* data)
   {
-    ldf* l = (ldf*)d;
-    if (l->is_singleline)
-      drive_ldf_singleline(*all, *l);
-    else
-      drive_ldf_multiline(*all,*l);
+    ldf* l=(ldf*)data;
+    vw* all = l->all;
+    do_actual_learning(*all, *l, *(l->base));
+    output_example_seq(*all, *l);
+    clear_seq(*all, *l);
+    l->ec_seq.delete_v();
   }
-  
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     ldf* ld = (ldf*)calloc(1, sizeof(ldf));
 
     ld->all = &all;
     ld->need_to_clear = true;
-    ld->is_singleline = true;
     ld->first_pass = true;
  
     string ldf_arg;
@@ -1062,7 +1036,7 @@ namespace LabelDict {
       
       if(vm.count("csoaa_ldf") && ldf_arg.compare(vm["csoaa_ldf"].as<string>()) != 0) {
         ldf_arg = vm["csoaa_ldf"].as<string>();
-        std::cerr << "warning: you specified a different ldf argument through --csoaa_ldf than the one loaded from regressor. Pursuing with loaded value of: " << ldf_arg << endl;
+        //std::cerr << "warning: you specified a different ldf argument through --csoaa_ldf than the one loaded from regressor. Proceeding with value of: " << ldf_arg << endl;
       }
     }
     else if( vm.count("csoaa_ldf") ){
@@ -1076,7 +1050,7 @@ namespace LabelDict {
       
       if(vm.count("wap_ldf") && ldf_arg.compare(vm["wap_ldf"].as<string>()) != 0) {
         ldf_arg = vm["csoaa_ldf"].as<string>();
-        std::cerr << "warning: you specified a different value for --wap_ldf than the one loaded from regressor. Pursuing with loaded value of: " << ldf_arg << endl;
+        //std::cerr << "warning: you specified a different value for --wap_ldf than the one loaded from regressor. Proceeding with value of: " << ldf_arg << endl;
       }
     }
     else {
@@ -1090,23 +1064,32 @@ namespace LabelDict {
 
     all.sd->k = (uint32_t)-1;
 
-    if (ldf_arg.compare("singleline") == 0 || ldf_arg.compare("s") == 0)
-      ld->is_singleline = true;
-    else if (ldf_arg.compare("multiline") == 0 || ldf_arg.compare("m") == 0)
-      ld->is_singleline = false;
+    ld->treat_as_classifier = false;
+    if (ldf_arg.compare("multiline") == 0 || ldf_arg.compare("m") == 0) {
+      ld->treat_as_classifier = false;
+    } else if (ldf_arg.compare("multiline-classifier") == 0 || ldf_arg.compare("mc") == 0) {
+      ld->treat_as_classifier = true;
+    }
     else {
-      cerr << "ldf requires either [s]ingleline or [m]ultiline argument" << endl;
+      cerr << "ldf requires either m/multiline or mc/multiline-classifier at the end" << endl;
       throw exception();
     }
 
+    all.p->emptylines_separate_examples = true; // TODO: check this to be sure!!!  !ld->is_singleline;
+
     if (all.add_constant) {
       all.add_constant = false;
     }
     ld->label_features.init(256, v_array<feature>(), LabelDict::size_t_eq);
     ld->label_features.get(1, 94717244);
-    
-    learner l(ld, drive, learn, finish, all.l.sl);
-    ld->base = all.l;
+
+    ld->read_example_this_loop = 0;
+    ld->need_to_clear = false;
+    learner* l = new learner(ld, learn, all.l);
+    l->set_finish_example(finish_multiline_example); 
+    l->set_finish(finish);
+    l->set_end_examples(end_examples); 
+    l->set_end_pass(end_pass);
     return l;
   }
 
diff --git a/vowpalwabbit/csoaa.h b/vowpalwabbit/csoaa.h
index 1ba2ad9d..690c829a 100644
--- a/vowpalwabbit/csoaa.h
+++ b/vowpalwabbit/csoaa.h
@@ -28,9 +28,9 @@ namespace CSOAA {
     v_array<wclass> costs;
   };
   
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
 
-  void output_example(vw& all, example* ec);
+  void finish_example(vw& all, void*, example* ec);
   size_t read_cached_label(shared_data* sd, void* v, io_buf& cache);
   void cache_label(void* v, io_buf& cache);
   void default_label(void* v);
@@ -51,7 +51,7 @@ namespace CSOAA {
 namespace CSOAA_AND_WAP_LDF {
   typedef CSOAA::label label;
 
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
   void global_print_newline(vw& all);
   void output_example(vw& all, example* ec, bool&hit_loss);
 
diff --git a/vowpalwabbit/ect.cc b/vowpalwabbit/ect.cc
index 41b93da3..83b89492 100644
--- a/vowpalwabbit/ect.cc
+++ b/vowpalwabbit/ect.cc
@@ -50,11 +50,8 @@ namespace ECT
     
     uint32_t last_pair;
     
-    uint32_t increment;
-    
     v_array<bool> tournaments_won;
 
-    learner base;
     vw* all;
   };
 
@@ -97,10 +94,10 @@ namespace ECT
     cout << endl;
   }
 
-  void create_circuit(vw& all, ect& e, uint32_t max_label, uint32_t eliminations)
+  size_t create_circuit(vw& all, ect& e, uint32_t max_label, uint32_t eliminations)
   {
     if (max_label == 1)
-      return;
+      return 0;
 
     v_array<v_array<uint32_t > > tournaments;
 
@@ -184,14 +181,11 @@ namespace ECT
     
     if ( max_label > 1)
       e.tree_height = final_depth(eliminations);
-    
-    if (e.last_pair > 0) {
-      all.weights_per_problem *= (e.last_pair + (eliminations-1));
-      e.increment = (uint32_t) all.length() / all.weights_per_problem * all.reg.stride;
-    }
+
+    return e.last_pair + (eliminations-1);
   }
 
-  float ect_predict(vw& all, ect& e, example* ec)
+  float ect_predict(vw& all, ect& e, learner& base, example* ec)
   {
     if (e.k == (size_t)1)
       return 1;
@@ -206,18 +200,10 @@ namespace ECT
       {
         if ((finals_winner | (((size_t)1) << i)) <= e.errors)
           {// a real choice exists
-            uint32_t offset = 0;
-	  
             uint32_t problem_number = e.last_pair + (finals_winner | (((uint32_t)1) << i)) - 1; //This is unique.
-	    offset = problem_number*e.increment;
-	  
-            update_example_indicies(all.audit, ec,offset);
-            ec->partial_prediction = 0;
 	  
-            e.base.learn(ec);
+            base.learn(ec, problem_number);
 	  
-            update_example_indicies(all.audit, ec,-offset);
-	    
 	    float pred = ec->final_prediction;
 	    if (pred > 0.)
               finals_winner = finals_winner | (((size_t)1) << i);
@@ -227,15 +213,9 @@ namespace ECT
     uint32_t id = e.final_nodes[finals_winner];
     while (id >= e.k)
       {
-	uint32_t offset = (id-e.k)*e.increment;
-	
-	ec->partial_prediction = 0;
-	update_example_indicies(all.audit, ec,offset);
-	e.base.learn(ec);
-	float pred = ec->final_prediction;
-	update_example_indicies(all.audit, ec,-offset);
+	base.learn(ec, id - e.k);
 
-	if (pred > 0.)
+	if (ec->final_prediction > 0.)
 	  id = e.directions[id].right;
 	else
 	  id = e.directions[id].left;
@@ -251,7 +231,7 @@ namespace ECT
     return false;
   }
 
-  void ect_train(vw& all, ect& e, example* ec)
+  void ect_train(vw& all, ect& e, learner& base, example* ec)
   {
     if (e.k == 1)//nothing to do
       return;
@@ -261,7 +241,7 @@ namespace ECT
 
     e.tournaments_won.erase();
 
-    uint32_t id = e.directions[(uint32_t)(mc->label)-1].winner;
+    uint32_t id = e.directions[mc->label - 1].winner;
     bool left = e.directions[id].left == mc->label - 1;
     do
       {
@@ -273,17 +253,10 @@ namespace ECT
 	simple_temp.weight = mc->weight;
 	ec->ld = &simple_temp;
 	
-	uint32_t offset = (id-e.k)*e.increment;
-	
-	update_example_indicies(all.audit, ec,offset);
-	
-	ec->partial_prediction = 0;
-	e.base.learn(ec);
+	base.learn(ec, id-e.k);
 	simple_temp.weight = 0.;
-	ec->partial_prediction = 0;
-	e.base.learn(ec);//inefficient, we should extract final prediction exactly.
+	base.learn(ec, id-e.k);//inefficient, we should extract final prediction exactly.
 	float pred = ec->final_prediction;
-	update_example_indicies(all.audit, ec,-offset);
 
 	bool won = pred*simple_temp.label > 0;
 
@@ -335,14 +308,7 @@ namespace ECT
 	      
                 uint32_t problem_number = e.last_pair + j*(1 << (i+1)) + (1 << i) -1;
 		
-                uint32_t offset = problem_number*e.increment;
-	      
-                update_example_indicies(all.audit, ec,offset);
-                ec->partial_prediction = 0;
-	      
-				e.base.learn(ec);
-		
-                update_example_indicies(all.audit, ec,-offset);
+		base.learn(ec, problem_number);
 		
 		float pred = ec->final_prediction;
 		if (pred > 0.)
@@ -357,25 +323,19 @@ namespace ECT
       }
   }
 
-  void learn(void* d, example* ec)
+  void learn(void* d, learner& base, example* ec)
   {
     ect* e=(ect*)d;
     vw* all = e->all;
     
-    if (command_example(all, ec))
-      {
-	e->base.learn(ec);
-	return;
-      }
-
     OAA::mc_label* mc = (OAA::mc_label*)ec->ld;
     if (mc->label == 0 || (mc->label > e->k && mc->label != (uint32_t)-1))
       cout << "label " << mc->label << " is not in {1,"<< e->k << "} This won't work right." << endl;
-    float new_label = ect_predict(*all, *e, ec);
+    float new_label = ect_predict(*all, *e, base, ec);
     ec->ld = mc;
     
     if (mc->label != (uint32_t)-1 && all->training)
-      ect_train(*all, *e, ec);
+      ect_train(*all, *e, base, ec);
     ec->ld = mc;
     
     ec->final_prediction = new_label;
@@ -384,7 +344,6 @@ namespace ECT
   void finish(void* d)
   {
     ect* e = (ect*)d;
-    e->base.finish();
     for (size_t l = 0; l < e->all_levels.size(); l++)
       {
 	for (size_t t = 0; t < e->all_levels[l].size(); t++)
@@ -402,27 +361,7 @@ namespace ECT
     e->tournaments_won.delete_v();
   }
   
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn(d, ec);
-            OAA::output_example(*all, ec);
-	    VW::finish_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-          {
-            return;
-          }
-        else 
-          ;
-      }
-  }
-
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     ect* data = (ect*)calloc(1, sizeof(ect));
     po::options_description desc("ECT options");
@@ -476,11 +415,13 @@ namespace ECT
     }
 
     *(all.p->lp) = OAA::mc_label_parser;
-    create_circuit(all, *data, data->k, data->errors+1);
+    size_t wpp = create_circuit(all, *data, data->k, data->errors+1);
     data->all = &all;
     
-    learner l(data, drive, learn, finish, all.l.sl);
-    data->base = all.l;
+    learner* l = new learner(data, learn, all.l, wpp);
+    l->set_finish_example(OAA::finish_example);
+    l->set_finish(finish);
+
     return l;
   }
 }
diff --git a/vowpalwabbit/ect.h b/vowpalwabbit/ect.h
index eb944bf5..62425110 100644
--- a/vowpalwabbit/ect.h
+++ b/vowpalwabbit/ect.h
@@ -11,7 +11,7 @@ license as described in the file LICENSE.
 
 namespace ECT
 {
-  learner setup(vw&, std::vector<std::string>&, po::variables_map&, po::variables_map& vm_file);
+  learner* setup(vw&, std::vector<std::string>&, po::variables_map&, po::variables_map& vm_file);
 }
 
 #endif
diff --git a/vowpalwabbit/example.cc b/vowpalwabbit/example.cc
index 02be2286..d62b65a7 100644
--- a/vowpalwabbit/example.cc
+++ b/vowpalwabbit/example.cc
@@ -7,12 +7,86 @@ license as described in the file LICENSE.
 #include "parse_primitives.h"
 #include "v_array.h"
 #include "example.h"
+#include "simple_label.h"  
+#include "gd.h"  
+#include "global_data.h"  
+  
+void vec_store(vw& all, void* p, float fx, uint32_t fi) {  
+  feature f = {fx, fi};
+  (*(v_array<feature>*) p).push_back(f);  
+}  
+  
+int compare_feature(const void* p1, const void* p2) {  
+  feature* f1 = (feature*) p1;  
+  feature* f2 = (feature*) p2;  
+  return (f1->weight_index - f2->weight_index);  
+}  
+  
+float collision_cleanup(v_array<feature>& feature_map) {  
+    
+ int pos = 0;  
+ float sum_sq = 0.;  
+  
+ for(uint32_t i = 1;i < feature_map.size();i++) {  
+    if(feature_map[i].weight_index == feature_map[pos].weight_index)   
+      feature_map[pos].x += feature_map[i].x;  
+    else {  
+      sum_sq += feature_map[pos].x*feature_map[pos].x;  
+      feature_map[++pos] = feature_map[i];            
+    }  
+  }  
+  sum_sq += feature_map[pos].x*feature_map[pos].x;  
+  feature_map.end = &(feature_map[pos]);    
+  feature_map.end++;  
+  return sum_sq;  
+}  
+
+namespace VW {
+
+flat_example* flatten_example(vw& all, example *ec) 
+{  
+	flat_example* fec = (flat_example*) calloc(1,sizeof(flat_example));  
+	fec->ld = ec->ld;
+	fec->final_prediction = ec->final_prediction;  
+
+	fec->tag_len = ec->tag.size();
+	if (fec->tag_len >0)
+	{
+		fec->tag = ec->tag.begin;
+	}
+
+	fec->example_counter = ec->example_counter;  
+	fec->ft_offset = ec->ft_offset;  
+	fec->global_weight = ec->global_weight;  
+	fec->num_features = ec->num_features;  
+    
+	v_array<feature> feature_map; //map to store sparse feature vectors  
+	GD::foreach_feature<vec_store>(all, ec, &feature_map); 
+	qsort(feature_map.begin, feature_map.size(), sizeof(feature), compare_feature);  
+    
+	fec->feature_map_len = feature_map.size();
+	if (fec->feature_map_len > 0)
+	{
+		fec->feature_map = feature_map.begin;
+	}
+
+	return fec;  
+}
+
+void free_flatten_example(flat_example* fec) 
+{  
+	if (fec)
+		free(fec);
+}
+
+}
 
 example *alloc_example(size_t label_size)
 {
   example* ec = (example*)calloc(1, sizeof(example));
   if (ec == NULL) return NULL;
   ec->ld = calloc(1, label_size);
+  if (ec->ld == NULL) { free(ec); return NULL; }
   ec->in_use = true;
   ec->ft_offset = 0;
   //  std::cerr << "  alloc_example.indices.begin=" << ec->indices.begin << " end=" << ec->indices.end << " // ld = " << ec->ld << "\t|| me = " << ec << std::endl;
@@ -49,13 +123,20 @@ void dealloc_example(void(*delete_label)(void*), example&ec)
   ec.indices.delete_v();
 }
 
-feature copy_feature(feature src) {
-  feature f = { src.x, src.weight_index };
-  return f;
+audit_data copy_audit_data(audit_data &src) {
+  audit_data dst;
+  dst.space = (char*)calloc(strlen(dst.space)+1, sizeof(char));
+  strcpy(dst.space, src.space);
+  dst.feature = (char*)calloc(strlen(dst.feature)+1, sizeof(char));
+  strcpy(dst.feature, src.feature);
+  dst.weight_index = src.weight_index;
+  dst.x = src.x;
+  dst.alloced = src.alloced;
+  return dst;
 }
 
 namespace VW {
-  void copy_example_data(example* &dst, example* src, size_t label_size, void(*copy_label)(void*&,void*))
+  void copy_example_data(bool audit, example* &dst, example* src, size_t label_size, void(*copy_label)(void*&,void*))
 {
   if (!src->ld) {
     if (dst->ld) free(dst->ld);  // TODO: this should be a delete_label, really
@@ -67,6 +148,7 @@ namespace VW {
     } else if (copy_label) {
       copy_label(dst->ld, src->ld);
     } else {
+      dst->ld = (void*)malloc(label_size);
       memcpy(dst->ld, src->ld, label_size);
     }
   }
@@ -78,9 +160,13 @@ namespace VW {
 
   copy_array(dst->indices, src->indices);
   for (size_t i=0; i<256; i++)
-    copy_array(dst->atomics[i], src->atomics[i], copy_feature);
+    copy_array(dst->atomics[i], src->atomics[i]);
   dst->ft_offset = src->ft_offset;
 
+  if (audit)
+    for (size_t i=0; i<256; i++)
+      copy_array(dst->audit_features[i], src->audit_features[i], copy_audit_data);
+  
   dst->num_features = src->num_features;
   dst->partial_prediction = src->partial_prediction;
   copy_array(dst->topic_predictions, src->topic_predictions);
@@ -89,45 +175,14 @@ namespace VW {
   dst->eta_global = src->eta_global;
   dst->global_weight = src->global_weight;
   dst->example_t = src->example_t;
-  for (size_t i=0; i<256; i++)
-    dst->sum_feat_sq[i] = src->sum_feat_sq[i];
+  memcpy(dst->sum_feat_sq, src->sum_feat_sq, 256 * sizeof(float));
   dst->total_sum_feat_sq = src->total_sum_feat_sq;
   dst->revert_weight = src->revert_weight;
+  dst->test_only = src->test_only;
+  dst->end_pass = src->end_pass;
   dst->sorted = src->sorted;
   dst->in_use = src->in_use;
   dst->done = src->done;
 }
 }
 
-void update_example_indicies(bool audit, example* ec, uint32_t amount) { 
-  ec->ft_offset += amount; }
-
-#include "global_data.h"
-void save_predictor(vw& all, string reg_name, size_t current_pass);
-
-bool command_example(void* a, example* ec) 
-{
-  vw* all=(vw*)a;
-  if(ec->end_pass) // the end-of-pass example
-    return true;
-
-  if (ec->indices.size() > 1) // one nonconstant feature.
-    return false;
-
-  if (ec->tag.size() >= 4 && !strncmp((const char*) ec->tag.begin, "save", 4) && all->current_command != ec->example_counter)
-    {//save state
-      string final_regressor_name = all->final_regressor_name;
-      
-      if ((ec->tag).size() >= 6 && (ec->tag)[4] == '_')
-	final_regressor_name = string(ec->tag.begin+5, (ec->tag).size()-5);
-      
-      if (!all->quiet)
-	cerr << "saving regressor to " << final_regressor_name << endl;
-      save_predictor(*all, final_regressor_name, 0);
-      
-      all->current_command = ec->example_counter;
-      
-      return true;
-    }
-  return false;
-}
diff --git a/vowpalwabbit/example.h b/vowpalwabbit/example.h
index 1602da26..3d572214 100644
--- a/vowpalwabbit/example.h
+++ b/vowpalwabbit/example.h
@@ -9,6 +9,15 @@ license as described in the file LICENSE.
 #include <stdint.h>
 #include "v_array.h"
 
+const size_t wap_ldf_namespace  = 126;
+const size_t history_namespace  = 127;
+const size_t constant_namespace = 128;
+const size_t nn_output_namespace  = 129;
+const size_t autolink_namespace  = 130;
+const size_t neighbor_namespace  = 131;
+const size_t affix_namespace     = 132;
+const size_t spelling_namespace  = 133;
+
 struct feature {
   float x;
   uint32_t weight_index;
@@ -59,10 +68,38 @@ struct example // core example datatype.
   bool done; //set to false by setup_example()
 };
 
+ struct vw;  
+ 
+namespace VW {
+struct flat_example 
+{
+	void* ld;  
+	simple_prediction final_prediction;  
+
+	size_t tag_len;
+	char* tag;//An identifier for the example.  
+
+	size_t example_counter;  
+	uint32_t ft_offset;  
+	float global_weight;
+
+	size_t num_features;//precomputed, cause it's fast&easy.  
+	size_t feature_map_len;
+	feature* feature_map; //map to store sparse feature vectors  
+};
+flat_example* flatten_example(vw& all, example *ec);
+void free_flatten_example(flat_example* fec);
+}
+
 example *alloc_example(size_t);
 void dealloc_example(void(*delete_label)(void*), example&);
 
-void update_example_indicies(bool audit, example* ec, uint32_t amount);
-bool command_example(void*a, example* ec); 
+inline int example_is_newline(example* ec)
+{
+  // if only index is constant namespace or no index
+  return ((ec->indices.size() == 0) || 
+          ((ec->indices.size() == 1) &&
+           (ec->indices.last() == constant_namespace)));
+}
 
 #endif
diff --git a/vowpalwabbit/gd.cc b/vowpalwabbit/gd.cc
index fa4237b5..f1f9078e 100644
--- a/vowpalwabbit/gd.cc
+++ b/vowpalwabbit/gd.cc
@@ -45,6 +45,7 @@ namespace GD
     vw* all;
   };
   void predict(vw& all, gd& g, example* ex);
+  void local_predict(vw& all, gd& g, example* ex);
   void sync_weights(vw& all);
   
   struct train_data {
@@ -65,7 +66,7 @@ namespace GD
       total_weight = ec->example_t;
 
     if(!all.holdout_set_off)
-      total_weight -= all.sd->weighted_holdout_examples; //exclude weights from test_only examples   
+      total_weight -= (float)all.sd->weighted_holdout_examples; //exclude weights from test_only examples   
     
     float avg_norm = all.normalized_sum_norm_x / total_weight;
     if (sqrt_norm) avg_norm = sqrt(avg_norm);
@@ -128,100 +129,101 @@ inline void specialized_update(vw& all, void* dat, float x, uint32_t fi)
   }
 }
 
-void learn(void* d, example* ec)
+  void end_pass(void* d)
+  {
+    gd* g = (gd*)d;
+    vw* all = g->all;
+    
+    sync_weights(*all);
+    if(all->span_server != "") {
+      if(all->adaptive)
+	accumulate_weighted_avg(*all, all->span_server, all->reg);
+      else 
+        accumulate_avg(*all, all->span_server, all->reg, 0);	      
+    }
+    
+    all->eta *= all->eta_decay_rate;
+    if (all->save_per_pass)
+      save_predictor(*all, all->final_regressor_name, all->current_pass);   
+    
+    all->current_pass++;
+    
+    if(!all->holdout_set_off)
+      {
+        if(summarize_holdout_set(*all, g->no_win_counter))
+          finalize_regressor(*all, all->final_regressor_name);
+        if((g->early_stop_thres == g->no_win_counter) &&
+           ((all->check_holdout_every_n_passes <= 1) ||
+            ((all->current_pass % all->check_holdout_every_n_passes) == 0)))
+          all-> early_terminate = true;
+      }   
+  }
+
+void learn(void* d, learner& base, example* ec)
 {
   gd* g = (gd*)d;
   vw* all = g->all;
+  label_data* ld = (label_data*)ec->ld;
 
   assert(ec->in_use);
-  if (ec->end_pass)
-    { 
-      sync_weights(*all);
-      if(all->span_server != "") {
-	if(all->adaptive)
-	  accumulate_weighted_avg(*all, all->span_server, all->reg);
-	else 
-	  accumulate_avg(*all, all->span_server, all->reg, 0);	      
-      }
-      
-      all->eta *= all->eta_decay_rate;
-      if (all->save_per_pass)
-	save_predictor(*all, all->final_regressor_name, all->current_pass);   
-      
-      all->current_pass++;
 
-      if(!all->holdout_set_off)
-      {
-        if(summarize_holdout_set(*all, g->no_win_counter))
-          finalize_regressor(*all, all->final_regressor_name); 
-        if(g->early_stop_thres == g->no_win_counter)
-          all-> early_terminate = true;
-      }   
-    }
-  
-  if (!command_example(all, ec))
-    { 
-      predict(*all,*g,ec);
+  predict(*all,*g,ec);
+
+  if ((all->holdout_set_off || !ec->test_only) && ld->weight > 0)
+    {
+      local_predict(*all, *g, ec);
 
-      if (all->holdout_set_off || !ec->test_only)
-      {
       if (ec->eta_round != 0.)
-	{ 
+	{
           if(all->power_t == 0.5) { 
             if (all->adaptive) {
               if (all->normalized_updates){ 
                 if (g->feature_mask_off) 
                   generic_train<specialized_update<true, true, true> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
                 else
                   generic_train<specialized_update<true, true, false> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
               }
               else {
                 if (g->feature_mask_off) 
                   generic_train<specialized_update<true, false, true> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
                 else
                   generic_train<specialized_update<true, false, false> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
               }
             }              
             else { //for adaptive 
               if (all->normalized_updates){ 
                 if (g->feature_mask_off) 
                   generic_train<specialized_update<false, true, true> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
                 else
                   generic_train<specialized_update<false, true, false> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
               }
               else {
                 if (g->feature_mask_off) 
                   generic_train<specialized_update<false, false, true> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
                 else
                   generic_train<specialized_update<false, false, false> >
-                    (*all,ec,ec->eta_round,true);
+                    (*all,ec,(float)ec->eta_round,true);
               }  
             }
           }//end of power_t
           else{
             if (g->feature_mask_off)
-              generic_train<general_update<true> >(*all,ec,ec->eta_round,false);
+              generic_train<general_update<true> >(*all,ec,(float)ec->eta_round,false);
             else
-              generic_train<general_update<false> >(*all,ec,ec->eta_round,false);
+              generic_train<general_update<false> >(*all,ec,(float)ec->eta_round,false);
           }  
 	  if (all->sd->contraction < 1e-10)  // updating weights now to avoid numerical instability
 	    sync_weights(*all);
 	}
-      }
     }
 }
-  void finish(void* d)
-{
-  gd* g = (gd*)d;
-  free(g);
-}
 
 void sync_weights(vw& all) {
   if (all.sd->gravity == 0. && all.sd->contraction == 1.)  // to avoid unnecessary weight synchronization
@@ -512,7 +514,7 @@ float compute_norm(vw& all, example* &ec)
       total_weight = ec->example_t;
 
     if(!all.holdout_set_off)
-      total_weight -= all.sd->weighted_holdout_examples; //exclude weights from test_only examples   
+      total_weight -= (float)all.sd->weighted_holdout_examples; //exclude weights from test_only examples   
     
     all.normalized_sum_norm_x += ld->weight * nd.norm_x;
     
@@ -533,10 +535,6 @@ float compute_norm(vw& all, example* &ec)
 {
   label_data* ld = (label_data*)ec->ld;
 
-  all.set_minmax(all.sd, ld->label);
-
-  ec->final_prediction = finalize_prediction(all, ec->partial_prediction * (float)all.sd->contraction);
-  
   if(g.active_simulation){
     float k = ec->example_t - ld->weight;
     ec->revert_weight = all.loss->getRevertingWeight(all.sd, ec->final_prediction, all.eta/powf(k,all.power_t));
@@ -553,19 +551,15 @@ float compute_norm(vw& all, example* &ec)
   if(all.active)
     t = (float)all.sd->weighted_unlabeled_examples;
   else
-    t = ec->example_t;
+    t = (float)(ec->example_t - all.sd->weighted_holdout_examples);
 
   ec->eta_round = 0;
-  if (ec->test_only)//if this is a holdout example
-  {
+
+  if (ld->label != FLT_MAX)
     ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ld->label) * ld->weight;
-    all.sd->holdout_sum_loss += ec->loss;
-    all.sd->holdout_sum_loss_since_last_dump += ec->loss;
-    all.sd->holdout_sum_loss_since_last_pass += ec->loss;//since last pass
-  }
-  else if (ld->label != FLT_MAX)
+
+  if (ld->label != FLT_MAX && !ec->test_only)
     {
-      ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ld->label) * ld->weight;
       if (all.training && ec->loss > 0.)
         {
 	  float eta_t;
@@ -603,7 +597,7 @@ float compute_norm(vw& all, example* &ec)
 
           eta_t = all.eta * norm * ld->weight;
           if(!all.adaptive) eta_t *= powf(t,-all.power_t);
-
+          
           float update = 0.f;
           if( all.invariant_updates )
             update = all.loss->getUpdate(ec->final_prediction, ld->label, eta_t, norm);
@@ -624,8 +618,6 @@ float compute_norm(vw& all, example* &ec)
   else if(all.active)
     ec->revert_weight = all.loss->getRevertingWeight(all.sd, ec->final_prediction, all.eta/powf(t,all.power_t));
 
-  if ((all.audit && !ec->test_only) || all.hash_inv)
-    print_audit_features(all, ec);
 }
 
   void predict(vw& all, gd& g, example* ex)
@@ -636,7 +628,7 @@ float compute_norm(vw& all, example* &ec)
     label_data* ld = (label_data*)ex->ld;
     float prediction;
 
-    if (all.training && all.normalized_updates && ld->label != FLT_MAX && (all.holdout_set_off || !ex->test_only)) {
+    if (all.training && all.normalized_updates && ld->label != FLT_MAX) {
       if( all.power_t == 0.5 ) {
 	if (all.reg_mode % 2)
 	  prediction = inline_predict<vec_add_trunc_rescale>(all, ex);
@@ -658,9 +650,15 @@ float compute_norm(vw& all, example* &ec)
     }
 
     ex->partial_prediction = prediction;
+
+    all.set_minmax(all.sd, ld->label);
+
+    ex->final_prediction = finalize_prediction(all, ex->partial_prediction * (float)all.sd->contraction);
+
+    if ((all.audit && !ex->test_only) || all.hash_inv)
+      print_audit_features(all, ex);
   }
 
-  local_predict(all, g, ex);
   ex->done = true;
 }
 
@@ -710,8 +708,9 @@ void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text)
 	}
       else// write binary or text
 	{
-	  v = &(all.reg.weight_vector[stride*i]);
-	  if (*v != 0.)
+                      
+         v = &(all.reg.weight_vector[stride*i]);
+	 if (*v != 0.)
 	    {
 	      c++;
 	      char buff[512];
@@ -892,33 +891,7 @@ void save_load(void* data, io_buf& model_file, bool read, bool text)
     }
 }
 
-void driver(vw* all, void* data)
-{
-  example* ec = NULL;
-  
-  while ( true )
-    {
-     if(all-> early_terminate)
-        {
-          all->p->done = true;
-          all->final_regressor_name = "";//skip finalize_regressor
-          all->text_regressor_name = "";
-          all->inv_hash_regressor_name = "";
-          return;
-        }
-     else if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-	{
-	  learn(data, ec);
-	  return_simple_example(*all, ec);
-	}
-      else if (parser_done(all->p))
-	return;
-      else 
-	;//busywait when we have predicted on all examples but not yet trained on all.
-    }
-}
-
-learner setup(vw& all, po::variables_map& vm)
+learner* setup(vw& all, po::variables_map& vm)
 {
   gd* g = (gd*)calloc(1, sizeof(gd));
   g->all = &all;
@@ -940,9 +913,8 @@ learner setup(vw& all, po::variables_map& vm)
       g->early_stop_thres = vm["early_terminate"].as< size_t>();     
   }
     
-  sl_t sl = {g,save_load};
-  learner ret(g,driver,learn,finish,sl);
-
+  learner* ret = new learner(g,learn, save_load, all.reg.stride);
+  ret->set_end_pass(end_pass);
   return ret;
 }
 }
diff --git a/vowpalwabbit/gd.h b/vowpalwabbit/gd.h
index dfcab384..699c5faf 100644
--- a/vowpalwabbit/gd.h
+++ b/vowpalwabbit/gd.h
@@ -15,6 +15,7 @@ license as described in the file LICENSE.
 #include "parse_regressor.h"
 #include "parser.h"
 #include "sparse_dense.h"
+#include "v_array.h"
 
 namespace GD{
 void print_result(int f, float res, v_array<char> tag);
@@ -26,7 +27,7 @@ void train_offset_example(regressor& r, example* ex, size_t offset);
 void compute_update(example* ec);
 void offset_train(regressor &reg, example* &ec, float update, size_t offset);
 void train_one_example_single_thread(regressor& r, example* ex);
-learner setup(vw& all, po::variables_map& vm);
+learner* setup(vw& all, po::variables_map& vm);
 void save_load_regressor(vw& all, io_buf& model_file, bool read, bool text);
 void output_and_account_example(example* ec);
 
@@ -47,13 +48,13 @@ void output_and_account_example(example* ec);
      
      for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++) {
        if (ec->atomics[(int)(*i)[0]].size() > 0) {
-	 v_array<feature> temp = ec->atomics[(int)(*i)[0]];
-	 for (; temp.begin != temp.end; temp.begin++)
-	   {
-	     uint32_t halfhash = quadratic_constant * (temp.begin->weight_index + offset);
-	     foreach_feature<T>(all, dat, ec->atomics[(int)(*i)[1]].begin, ec->atomics[(int)(*i)[1]].end, 
-				halfhash, temp.begin->x);
-	   }
+		v_array<feature> temp = ec->atomics[(int)(*i)[0]];
+		 for (; temp.begin != temp.end; temp.begin++)
+		   {
+			 uint32_t halfhash = quadratic_constant * (temp.begin->weight_index + offset);
+			 foreach_feature<T>(all, dat, ec->atomics[(int)(*i)[1]].begin, ec->atomics[(int)(*i)[1]].end, 
+					halfhash, temp.begin->x);
+		   }
        }
      }
      
@@ -64,9 +65,9 @@ void output_and_account_example(example* ec);
 	 v_array<feature> temp2 = ec->atomics[(int)(*i)[1]];
 	 for (; temp2.begin != temp2.end; temp2.begin++) {
 	   
-	   uint32_t halfhash = cubic_constant2 * (cubic_constant * (temp1.begin->weight_index + offset) + temp2.begin->weight_index + offset);
-	   float mult = temp1.begin->x * temp2.begin->x;
-	   return foreach_feature<T>(all, dat, ec->atomics[(int)(*i)[2]].begin, ec->atomics[(int)(*i)[2]].end, halfhash, mult);
+     uint32_t halfhash = cubic_constant2 * (cubic_constant * (temp1.begin->weight_index + offset) + temp2.begin->weight_index + offset);
+	 float mult = temp1.begin->x * temp2.begin->x;
+     foreach_feature<T>(all, dat, ec->atomics[(int)(*i)[2]].begin, ec->atomics[(int)(*i)[2]].end, halfhash, mult);
 	 }
        }
      }
diff --git a/vowpalwabbit/gd_mf.cc b/vowpalwabbit/gd_mf.cc
new file mode 100644
index 00000000..727646f9
--- /dev/null
+++ b/vowpalwabbit/gd_mf.cc
@@ -0,0 +1,302 @@
+/*
+Copyright (c) by respective owners including Yahoo!, Microsoft, and
+individual contributors. All rights reserved.  Released under a BSD (revised)
+license as described in the file LICENSE.
+ */
+#include <fstream>
+#include <float.h>
+#ifdef _WIN32
+#include <winsock2.h>
+#else
+#include <netdb.h>
+#endif
+#include <string.h>
+#include <stdio.h>
+#include "parse_example.h"
+#include "constant.h"
+#include "sparse_dense.h"
+#include "gd.h"
+#include "cache.h"
+#include "simple_label.h"
+#include "rand48.h"
+#include "vw.h"
+
+using namespace std;
+
+namespace GDMF {
+  struct gdmf {
+    vw* all;
+  };
+
+void mf_local_predict(example* ec, regressor& reg);
+
+float mf_inline_predict(vw& all, example* &ec)
+{
+  float prediction = all.p->lp->get_initial(ec->ld);
+
+  // clear stored predictions
+  ec->topic_predictions.erase();
+
+  float linear_prediction = 0;
+  // linear terms
+  for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
+    GD::foreach_feature<vec_add>(all, &linear_prediction, ec->atomics[*i].begin, ec->atomics[*i].end);
+
+  // store constant + linear prediction
+  // note: constant is now automatically added
+  ec->topic_predictions.push_back(linear_prediction);
+  
+  prediction += linear_prediction;
+
+  // interaction terms
+  for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++) 
+    {
+      if (ec->atomics[(int)(*i)[0]].size() > 0 && ec->atomics[(int)(*i)[1]].size() > 0)
+	{
+	  for (uint32_t k = 1; k <= all.rank; k++)
+	    {
+	      // x_l * l^k
+	      // l^k is from index+1 to index+all.rank
+	      //float x_dot_l = sd_offset_add(weights, mask, ec->atomics[(int)(*i)[0]].begin, ec->atomics[(int)(*i)[0]].end, k);
+              float x_dot_l = 0;
+	      GD::foreach_feature<vec_add>(all, &x_dot_l, ec->atomics[(int)(*i)[0]].begin, ec->atomics[(int)(*i)[0]].end, k);
+	      // x_r * r^k
+	      // r^k is from index+all.rank+1 to index+2*all.rank
+	      //float x_dot_r = sd_offset_add(weights, mask, ec->atomics[(int)(*i)[1]].begin, ec->atomics[(int)(*i)[1]].end, k+all.rank);
+              float x_dot_r = 0;
+	      GD::foreach_feature<vec_add>(all, &x_dot_r, ec->atomics[(int)(*i)[1]].begin, ec->atomics[(int)(*i)[1]].end, k+all.rank);
+
+	      prediction += x_dot_l * x_dot_r;
+
+	      // store prediction from interaction terms
+	      ec->topic_predictions.push_back(x_dot_l);
+	      ec->topic_predictions.push_back(x_dot_r);
+	    }
+	}
+    }
+
+  if (all.triples.begin() != all.triples.end()) {
+    cerr << "cannot use triples in matrix factorization" << endl;
+    throw exception();
+  }
+    
+  // ec->topic_predictions has linear, x_dot_l_1, x_dot_r_1, x_dot_l_2, x_dot_r_2, ... 
+
+  return prediction;
+}
+
+void mf_inline_train(vw& all, example* &ec, float update)
+{
+      weight* weights = all.reg.weight_vector;
+      size_t mask = all.reg.weight_mask;
+      label_data* ld = (label_data*)ec->ld;
+
+      // use final prediction to get update size
+      // update = eta_t*(y-y_hat) where eta_t = eta/(3*t^p) * importance weight
+      float eta_t = all.eta/pow(ec->example_t,all.power_t) / 3.f * ld->weight;
+      update = all.loss->getUpdate(ec->final_prediction, ld->label, eta_t, 1.); //ec->total_sum_feat_sq);
+
+      float regularization = eta_t * all.l2_lambda;
+
+      // linear update
+      for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
+	sd_offset_update(weights, mask, ec->atomics[*i].begin, ec->atomics[*i].end, 0, update, regularization);
+      
+      // quadratic update
+      for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++) 
+	{
+	  if (ec->atomics[(int)(*i)[0]].size() > 0 && ec->atomics[(int)(*i)[1]].size() > 0)
+	    {
+
+	      // update l^k weights
+	      for (size_t k = 1; k <= all.rank; k++)
+		{
+		  // r^k \cdot x_r
+		  float r_dot_x = ec->topic_predictions[2*k];
+		  // l^k <- l^k + update * (r^k \cdot x_r) * x_l
+		  sd_offset_update(weights, mask, ec->atomics[(int)(*i)[0]].begin, ec->atomics[(int)(*i)[0]].end, k, update*r_dot_x, regularization);
+		}
+
+	      // update r^k weights
+	      for (size_t k = 1; k <= all.rank; k++)
+		{
+		  // l^k \cdot x_l
+		  float l_dot_x = ec->topic_predictions[2*k-1];
+		  // r^k <- r^k + update * (l^k \cdot x_l) * x_r
+		  sd_offset_update(weights, mask, ec->atomics[(int)(*i)[1]].begin, ec->atomics[(int)(*i)[1]].end, k+all.rank, update*l_dot_x, regularization);
+		}
+
+	    }
+	}
+  if (all.triples.begin() != all.triples.end()) {
+    cerr << "cannot use triples in matrix factorization" << endl;
+    throw exception();
+  }
+
+}  
+
+void mf_print_offset_features(vw& all, example* &ec, size_t offset)
+{
+  weight* weights = all.reg.weight_vector;
+  size_t mask = all.reg.weight_mask;
+  for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
+    if (ec->audit_features[*i].begin != ec->audit_features[*i].end)
+      for (audit_data *f = ec->audit_features[*i].begin; f != ec->audit_features[*i].end; f++)
+	{
+	  cout << '\t' << f->space << '^' << f->feature << ':' << f->weight_index <<"(" << ((f->weight_index + offset) & mask)  << ")" << ':' << f->x;
+
+	  cout << ':' << weights[(f->weight_index + offset) & mask];
+	}
+    else
+      for (feature *f = ec->atomics[*i].begin; f != ec->atomics[*i].end; f++)
+	{
+	  size_t index = (f->weight_index + offset) & all.reg.weight_mask;
+	  
+	  cout << "\tConstant:";
+	  cout << (index/all.reg.stride & all.parse_mask) << ':' << f->x;
+	  cout  << ':' << trunc_weight(weights[index], (float)all.sd->gravity) * (float)all.sd->contraction;
+	}
+  for (vector<string>::iterator i = all.pairs.begin(); i != all.pairs.end();i++) 
+    if (ec->atomics[(int)(*i)[0]].size() > 0 && ec->atomics[(int)(*i)[1]].size() > 0)
+      {
+	/* print out nsk^feature:hash:value:weight:nsk^feature^:hash:value:weight:prod_weights */
+	for (size_t k = 1; k <= all.rank; k++)
+	  {
+	    for (audit_data* f = ec->audit_features[(int)(*i)[0]].begin; f!= ec->audit_features[(int)(*i)[0]].end; f++)
+	      for (audit_data* f2 = ec->audit_features[(int)(*i)[1]].begin; f2!= ec->audit_features[(int)(*i)[1]].end; f2++)
+		{
+		  cout << '\t' << f->space << k << '^' << f->feature << ':' << ((f->weight_index+k)&mask) 
+		       <<"(" << ((f->weight_index + offset +k) & mask)  << ")" << ':' << f->x;
+		  cout << ':' << weights[(f->weight_index + offset + k) & mask];
+		  
+		  cout << ':' << f2->space << k << '^' << f2->feature << ':' << ((f2->weight_index+k+all.rank)&mask) 
+		       <<"(" << ((f2->weight_index + offset +k+all.rank) & mask)  << ")" << ':' << f2->x;
+		  cout << ':' << weights[(f2->weight_index + offset + k+all.rank) & mask];
+		  
+		  cout << ':' <<  weights[(f->weight_index + offset + k) & mask] * weights[(f2->weight_index + offset + k + all.rank) & mask];
+		}
+	  }
+      }
+  if (all.triples.begin() != all.triples.end()) {
+    cerr << "cannot use triples in matrix factorization" << endl;
+    throw exception();
+  }
+  cout << endl;
+}
+
+void mf_print_audit_features(vw& all, example* ec, size_t offset)
+{
+  print_result(all.stdout_fileno,ec->final_prediction,-1,ec->tag);
+  mf_print_offset_features(all, ec, offset);
+}
+
+void mf_local_predict(vw& all, example* ec)
+{
+  label_data* ld = (label_data*)ec->ld;
+  all.set_minmax(all.sd, ld->label);
+
+  ec->final_prediction = GD::finalize_prediction(all, ec->partial_prediction);
+
+  if (ld->label != FLT_MAX)
+    {
+      ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ld->label) * ld->weight;
+    }
+
+  if (all.audit)
+    mf_print_audit_features(all, ec, 0);
+}
+
+float mf_predict(vw& all, example* ex)
+{
+  float prediction = mf_inline_predict(all, ex);
+
+  ex->partial_prediction = prediction;
+  mf_local_predict(all, ex);
+
+  return ex->final_prediction;
+}
+
+  void save_load(void* d, io_buf& model_file, bool read, bool text)
+{
+  vw* all = ((gdmf*)d)->all;
+  uint32_t length = 1 << all->num_bits;
+  uint32_t stride = all->reg.stride;
+
+  if(read)
+    {
+      initialize_regressor(*all);
+      if(all->random_weights)
+	for (size_t j = 0; j < all->reg.stride*length; j++)
+	  all->reg.weight_vector[j] = (float) (0.1 * frand48()); 
+    }
+
+  if (model_file.files.size() > 0)
+    {
+      uint32_t i = 0;
+      uint32_t text_len;
+      char buff[512];
+      size_t brw = 1;
+
+      do 
+	{
+	  brw = 0;
+	  size_t K = all->rank*2+1;
+	  
+	  text_len = sprintf(buff, "%d ", i);
+	  brw += bin_text_read_write_fixed(model_file,(char *)&i, sizeof (i),
+					   "", read,
+					   buff, text_len, text);
+	  if (brw != 0)
+	    for (uint32_t k = 0; k < K; k++)
+	      {
+		uint32_t ndx = stride*i+k;
+		
+		weight* v = &(all->reg.weight_vector[ndx]);
+		text_len = sprintf(buff, "%f ", *v);
+		brw += bin_text_read_write_fixed(model_file,(char *)v, sizeof (*v),
+						 "", read,
+						 buff, text_len, text);
+		
+	      }
+	  if (text)
+	    brw += bin_text_read_write_fixed(model_file,buff,0,
+					     "", read,
+					     "\n",1,text);
+	  
+	  if (!read)
+	    i++;
+	}  
+      while ((!read && i < length) || (read && brw >0));
+    }
+}
+
+void end_pass(void* d)
+{
+  vw* all = ((gdmf*)d)->all;
+
+   all->eta *= all->eta_decay_rate;
+   if (all->save_per_pass)
+     save_predictor(*all, all->final_regressor_name, all->current_pass);
+   
+   all->current_pass++;
+}
+
+  void learn(void* d, learner& base, example* ec)
+  {
+    vw* all = ((gdmf*)d)->all;
+ 
+    mf_predict(*all,ec);
+    if (all->training && ((label_data*)(ec->ld))->label != FLT_MAX)
+      mf_inline_train(*all, ec, ec->eta_round);
+  }
+
+  learner* setup(vw& all)
+  {
+    gdmf* data = (gdmf*)calloc(1,sizeof(gdmf)); 
+    data->all = &all;
+    learner* l = new learner(data,learn, save_load, all.reg.stride);
+    l->set_end_pass(end_pass);
+
+    return l;
+  }
+}
diff --git a/vowpalwabbit/global_data.cc b/vowpalwabbit/global_data.cc
index 4f2eecff..27255512 100644
--- a/vowpalwabbit/global_data.cc
+++ b/vowpalwabbit/global_data.cc
@@ -204,7 +204,7 @@ void noop_mm(shared_data* sd, float label)
 
 void vw::learn(example* ec)
 {
-  this->l.learn(ec);
+  this->l->learn(ec);
 }
 
 void compile_gram(vector<string> grams, uint32_t* dest, char* descriptor, bool quiet)
@@ -239,13 +239,12 @@ vw::vw()
   sd->max_label = 1.;
   
   p = new_parser();
+  p->emptylines_separate_examples = false;
   p->lp = (label_parser*)malloc(sizeof(label_parser));
   *(p->lp) = simple_label;
 
   reg_mode = 0;
-
   current_pass = 0;
-  current_command = 0;
 
   bfgs = false;
   hessian_on = false;
@@ -264,8 +263,6 @@ vw::vw()
 
   set_minmax = set_mm;
 
-  weights_per_problem = 1;
-
   power_t = 0.5;
   eta = 0.5; //default learning rate for normalized adaptive updates, this is switched to 10 by default for the other updates (see parse_args.cc)
   numpasses = 1;
@@ -307,6 +304,8 @@ vw::vw()
     {
       ngram[i] = 0;
       skips[i] = 0;
+      affix_features[i] = 0;
+      spelling_features[i] = 0;
     }
 
   //by default use invariant normalized adaptive updates
@@ -333,6 +332,8 @@ vw::vw()
   do_reset_source = false;
   holdout_set_off = true;
   holdout_period = 10;
+  holdout_after = 0;
+  check_holdout_every_n_passes = 1;
   early_terminate = false;
 
   max_examples = (size_t)-1;
diff --git a/vowpalwabbit/global_data.h b/vowpalwabbit/global_data.h
index 736552f7..cfa95db4 100644
--- a/vowpalwabbit/global_data.h
+++ b/vowpalwabbit/global_data.h
@@ -127,15 +127,14 @@ struct vw {
 
   node_socks socks;
 
-  learner l;//the top level leaner
-  learner scorer;//a scoring function
+  learner* l;//the top level leaner
+  learner* scorer;//a scoring function
 
   void learn(example*);
 
   void (*set_minmax)(shared_data* sd, float label);
 
   size_t current_pass;
-  size_t current_command;
 
   uint32_t num_bits; // log_2 of the number of features.
   bool default_bits;
@@ -160,9 +159,9 @@ struct vw {
   int options_from_file_argc;
 
   bool searn;
-  void* /*ImperativeSearn::searn_struct*/ searnstr;
+  void* /*Searn::searn*/ searnstr;
 
-  uint32_t weights_per_problem; //this stores the current number of "weight vector" required by the based learner, which is used to compute offsets when composing reductions
+  uint32_t wpp; 
 
   int stdout_fileno;
 
@@ -192,6 +191,8 @@ struct vw {
   std::vector<std::string> skip_strings; // triples of features to cross.
   uint32_t ngram[256];//ngrams to generate.
   uint32_t skips[256];//skips in ngrams.
+  uint32_t affix_features[256]; // affixes to generate (up to 8 per namespace)
+  bool     spelling_features[256]; // generate spelling features for which namespace
   bool audit;//should I print lots of debugging information?
   bool quiet;//Should I suppress updates?
   bool training;//Should I train if label data is available?
@@ -207,6 +208,8 @@ struct vw {
   bool holdout_set_off;
   bool early_terminate;
   uint32_t holdout_period;
+  uint32_t holdout_after;
+  size_t check_holdout_every_n_passes;  // default: 1, but searn might want to set it higher if you spend multiple passes learning a single policy
 
   float normalized_sum_norm_x;
   size_t normalized_idx; //offset idx where the norm is stored (1 or 2 depending on whether adaptive is true)
@@ -232,7 +235,6 @@ struct vw {
   size_t unique_id; //unique id for each node in the network, id == 0 means extra io.
   size_t total; //total number of nodes
   size_t node; //node id number
-  bool is_noop; // are we a noop learner?
 
   void (*print)(int,float,float,v_array<char>);
   void (*print_text)(int, string, v_array<char>);
@@ -266,6 +268,7 @@ void noop_mm(shared_data*, float label);
 void print_lda_result(vw& all, int f, float* res, float weight, v_array<char> tag);
 void get_prediction(int sock, float& res, float& weight);
 void compile_gram(vector<string> grams, uint32_t* dest, char* descriptor, bool quiet);
+int print_tag(std::stringstream& ss, v_array<char> tag);
 
 #endif
  
diff --git a/vowpalwabbit/io_buf.h b/vowpalwabbit/io_buf.h
index f07bd5ea..e8b8157c 100644
--- a/vowpalwabbit/io_buf.h
+++ b/vowpalwabbit/io_buf.h
@@ -52,6 +52,7 @@ class io_buf {
   }
 
   virtual int open_file(const char* name, bool stdin_off, int flag=READ){
+
     int ret = -1;
     switch(flag){
     case READ:
@@ -93,6 +94,7 @@ class io_buf {
       std::cerr << "Unknown file operation. Something other than READ/WRITE specified" << std::endl;
       ret = -1;
     }
+
     return ret;
   }
 
diff --git a/vowpalwabbit/lda_core.cc b/vowpalwabbit/lda_core.cc
index d959de59..55d6ca12 100644
--- a/vowpalwabbit/lda_core.cc
+++ b/vowpalwabbit/lda_core.cc
@@ -25,8 +25,28 @@ license as described in the file LICENSE.
 #include "vw.h"

 

 namespace LDA {

+

+class index_feature {

+public:

+  uint32_t document;

+  feature f;

+  bool operator<(const index_feature b) const { return f.weight_index < b.f.weight_index; }

+};

+

   struct lda {

     v_array<float> Elogtheta;

+    v_array<float> decay_levels;

+    v_array<float> total_new;

+    v_array<example* > examples;

+    v_array<float> total_lambda;

+    v_array<int> doc_lengths;

+    v_array<float> digammas;

+    v_array<float> v;

+    std::vector<index_feature> sorted_features;

+

+    bool total_lambda_init;

+    

+    double example_t;

     vw* all;

   };

   

@@ -484,15 +504,6 @@ v_array<float> old_gamma;
   return score / doc_length;

 }

 

-class index_feature {

-public:

-  uint32_t document;

-  feature f;

-  bool operator<(const index_feature b) const { return f.weight_index < b.f.weight_index; }

-};

-

-std::vector<index_feature> sorted_features;

-

 size_t next_pow2(size_t x) {

   int i = 0;

   x = x > 0 ? x - 1 : 0;

@@ -503,7 +514,7 @@ size_t next_pow2(size_t x) {
   return ((size_t)1) << i;

 }

 

-  void save_load(void* d, io_buf& model_file, bool read, bool text)

+void save_load(void* d, io_buf& model_file, bool read, bool text)

 {

   lda* l = (lda*)d;

   vw* all = l->all;

@@ -565,171 +576,151 @@ size_t next_pow2(size_t x) {
     }

 }

 

-

-  void drive(vw* all, void* d)

-{

-  v_array<float>* Elogtheta = &((lda*)d)->Elogtheta;

-  regressor reg = all->reg;

-  example* ec = NULL;

-

-  v_array<float> total_lambda;

-  v_array<float> total_new;

-  v_array<example* > examples;

-  v_array<int> doc_lengths;

-  v_array<float> digammas;

-  v_array<float> v;

-  v.resize(all->lda*all->minibatch);

-  

-  total_lambda.erase();

-

-  for (size_t k = 0; k < all->lda; k++)

-    total_lambda.push_back(0.f);

-  size_t stride = all->reg.stride;

-  weight* weights = reg.weight_vector;

-

-  for (size_t i =0; i <= all->reg.weight_mask;i+=stride)

-    for (size_t k = 0; k < all->lda; k++)

-      total_lambda[k] += weights[i+k];

-

-  v_array<float> decay_levels;

-  decay_levels.push_back(0.f);

-  double example_t = all->initial_t;

-  while ( true )

-    {

-      example_t++;

-      total_new.erase();

-      for (size_t k = 0; k < all->lda; k++)

-	total_new.push_back(0.f);

-

-      sorted_features.resize(0);

-

-      float eta = -1;

-      float minuseta = -1;

-      examples.erase();

-      doc_lengths.erase();

-      size_t batch_size = all->minibatch;

-      for (size_t d = 0; d < batch_size; d++)

-	{

-          doc_lengths.push_back(0);

-	  if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.

-	    {

-	      examples.push_back(ec);

-              for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) {

-                feature* f = ec->atomics[*i].begin;

-                for (; f != ec->atomics[*i].end; f++) {

-                  index_feature temp = {(uint32_t)d, *f};

-                  sorted_features.push_back(temp);

-                  doc_lengths[d] += (int)f->x;

-                }

-              }

-	    }

-	  else if (parser_done(all->p))

-	    batch_size = d;

-	  else

-	    d--;

-	}

-

-      sort(sorted_features.begin(), sorted_features.end());

-

-      eta = all->eta * powf((float)example_t, - all->power_t);

-      minuseta = 1.0f - eta;

-      eta *= all->lda_D / batch_size;

-      decay_levels.push_back(decay_levels.last() + log(minuseta));

-

-      digammas.erase();

-      float additional = (float)(all->length()) * all->lda_rho;

-      for (size_t i = 0; i<all->lda; i++) {

-	digammas.push_back(mydigamma(total_lambda[i] + additional));

+  void learn_batch(lda& l)

+  {

+    float eta = -1;

+    float minuseta = -1;

+

+    if (l.total_lambda.size() == 0)

+      {

+	for (size_t k = 0; k < l.all->lda; k++)

+	  l.total_lambda.push_back(0.f);

+	

+	size_t stride = l.all->reg.stride;

+	for (size_t i =0; i <= l.all->reg.weight_mask;i+=stride)

+	  for (size_t k = 0; k < l.all->lda; k++)

+	    l.total_lambda[k] += l.all->reg.weight_vector[i+k];

       }

-      

-      size_t last_weight_index = -1;

-      for (index_feature* s = &sorted_features[0]; s <= &sorted_features.back(); s++)

-	{

-	  if (last_weight_index == s->f.weight_index)

-	    continue;

-	  last_weight_index = s->f.weight_index;

-	  float* weights_for_w = &(weights[s->f.weight_index & all->reg.weight_mask]);

-          float decay = fmin(1.0, exp(decay_levels.end[-2] - decay_levels.end[(int)(-1-example_t+weights_for_w[all->lda])]));

-	  float* u_for_w = weights_for_w + all->lda+1;

-

-	  weights_for_w[all->lda] = (float)example_t;

-	  for (size_t k = 0; k < all->lda; k++)

-	    {

-	      weights_for_w[k] *= decay;

-	      u_for_w[k] = weights_for_w[k] + all->lda_rho;

-	    }

-	  myexpdigammify_2(*all, u_for_w, digammas.begin);

-	}

-

-      for (size_t d = 0; d < batch_size; d++)

-	{

-          float score = lda_loop(*all, *Elogtheta, &v[d*all->lda], weights, examples[d],all->power_t);

-          if (all->audit)

-	    GD::print_audit_features(*all, examples[d]);

-          // If the doc is empty, give it loss of 0.

-          if (doc_lengths[d] > 0) {

-            all->sd->sum_loss -= score;

-            all->sd->sum_loss_since_last_dump -= score;

-          }

-	  return_simple_example(*all, examples[d]);

-	}

 

-      for (index_feature* s = &sorted_features[0]; s <= &sorted_features.back();)

-	{

-	  index_feature* next = s+1;

-	  while(next <= &sorted_features.back() && next->f.weight_index == s->f.weight_index)

-	    next++;

-

-	  float* word_weights = &(weights[s->f.weight_index & all->reg.weight_mask]);

-	  for (size_t k = 0; k < all->lda; k++) {

-	    float new_value = minuseta*word_weights[k];

-	    word_weights[k] = new_value;

-	  }

-

-	  for (; s != next; s++) {

-	    float* v_s = &v[s->document*all->lda];

-	    float* u_for_w = &weights[(s->f.weight_index & all->reg.weight_mask) + all->lda + 1];

-	    float c_w = eta*find_cw(*all, u_for_w, v_s)*s->f.x;

-	    for (size_t k = 0; k < all->lda; k++) {

-	      float new_value = u_for_w[k]*v_s[k]*c_w;

-	      total_new[k] += new_value;

- 	      word_weights[k] += new_value;

-	    }

+    l.example_t++;

+    l.total_new.erase();

+    for (size_t k = 0; k < l.all->lda; k++)

+      l.total_new.push_back(0.f);

+    

+    size_t batch_size = l.examples.size();

+   

+    sort(l.sorted_features.begin(), l.sorted_features.end());

+    

+    eta = l.all->eta * powf((float)l.example_t, - l.all->power_t);

+    minuseta = 1.0f - eta;

+    eta *= l.all->lda_D / batch_size;

+    l.decay_levels.push_back(l.decay_levels.last() + log(minuseta));

+    

+    l.digammas.erase();

+    float additional = (float)(l.all->length()) * l.all->lda_rho;

+    for (size_t i = 0; i<l.all->lda; i++) {

+      l.digammas.push_back(mydigamma(l.total_lambda[i] + additional));

+    }

+    

+    

+    weight* weights = l.all->reg.weight_vector;

+    

+    size_t last_weight_index = -1;

+    for (index_feature* s = &l.sorted_features[0]; s <= &l.sorted_features.back(); s++)

+      {

+	if (last_weight_index == s->f.weight_index)

+	  continue;

+	last_weight_index = s->f.weight_index;

+	float* weights_for_w = &(weights[s->f.weight_index & l.all->reg.weight_mask]);

+	float decay = fmin(1.0, exp(l.decay_levels.end[-2] - l.decay_levels.end[(int)(-1 - l.example_t+weights_for_w[l.all->lda])]));

+	float* u_for_w = weights_for_w + l.all->lda+1;

+	

+	weights_for_w[l.all->lda] = (float)l.example_t;

+	for (size_t k = 0; k < l.all->lda; k++)

+	  {

+	    weights_for_w[k] *= decay;

+	    u_for_w[k] = weights_for_w[k] + l.all->lda_rho;

 	  }

+	myexpdigammify_2(*l.all, u_for_w, l.digammas.begin);

+      }

+    

+    for (size_t d = 0; d < batch_size; d++)

+      {

+	float score = lda_loop(*l.all, l.Elogtheta, &(l.v[d*l.all->lda]), weights, l.examples[d],l.all->power_t);

+	if (l.all->audit)

+	  GD::print_audit_features(*l.all, l.examples[d]);

+	// If the doc is empty, give it loss of 0.

+	if (l.doc_lengths[d] > 0) {

+	  l.all->sd->sum_loss -= score;

+	  l.all->sd->sum_loss_since_last_dump -= score;

 	}

-      for (size_t k = 0; k < all->lda; k++) {

-	total_lambda[k] *= minuseta;

-	total_lambda[k] += total_new[k];

+	return_simple_example(*l.all, NULL, l.examples[d]);

       }

-

-      if (parser_done(all->p))

-	{

-	  for (size_t i = 0; i < all->length(); i++) {

-	    weight* weights_for_w = & (weights[i*all->reg.stride]);

-            float decay = fmin(1.0, exp(decay_levels.last() - decay_levels.end[(int)(-1-example_t+weights_for_w[all->lda])]));

-	    for (size_t k = 0; k < all->lda; k++) {

-	      weights_for_w[k] *= decay;

-            }

+    

+    for (index_feature* s = &l.sorted_features[0]; s <= &l.sorted_features.back();)

+      {

+	index_feature* next = s+1;

+	while(next <= &l.sorted_features.back() && next->f.weight_index == s->f.weight_index)

+	  next++;

+	

+	float* word_weights = &(weights[s->f.weight_index & l.all->reg.weight_mask]);

+	for (size_t k = 0; k < l.all->lda; k++) {

+	  float new_value = minuseta*word_weights[k];

+	  word_weights[k] = new_value;

+	}

+	

+	for (; s != next; s++) {

+	  float* v_s = &(l.v[s->document*l.all->lda]);

+	  float* u_for_w = &weights[(s->f.weight_index & l.all->reg.weight_mask) + l.all->lda + 1];

+	  float c_w = eta*find_cw(*l.all, u_for_w, v_s)*s->f.x;

+	  for (size_t k = 0; k < l.all->lda; k++) {

+	    float new_value = u_for_w[k]*v_s[k]*c_w;

+	    l.total_new[k] += new_value;

+	    word_weights[k] += new_value;

 	  }

-

-	  return;

 	}

+      }

+    for (size_t k = 0; k < l.all->lda; k++) {

+      l.total_lambda[k] *= minuseta;

+      l.total_lambda[k] += l.total_new[k];

     }

-}

 

-  void learn(void*, example*)

+    l.sorted_features.resize(0);

+    

+    l.examples.erase();

+    l.doc_lengths.erase();

+  }

+  

+  void learn(void* d, learner& base, example* ec) 

   {

-    cout << "LDA can't be used as a reduction" << endl;

+    lda* l = (lda*)d;

+    size_t num_ex = l->examples.size();

+    l->examples.push_back(ec);

+    l->doc_lengths.push_back(0);

+    for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) {

+      feature* f = ec->atomics[*i].begin;

+      for (; f != ec->atomics[*i].end; f++) {

+	index_feature temp = {(uint32_t)num_ex, *f};

+	l->sorted_features.push_back(temp);

+	l->doc_lengths[num_ex] += (int)f->x;

+      }

+    }

+    if (++num_ex == l->all->minibatch)

+      learn_batch(*l);

   }

 

-  void finish(void*d) {

-    free(d);

+void end_examples(void* d)

+{

+  lda* l = (lda*)d;

+

+  learn_batch(*l);

+

+  for (size_t i = 0; i < l->all->length(); i++) {

+    weight* weights_for_w = & (l->all->reg.weight_vector[i*l->all->reg.stride]);

+    float decay = fmin(1.0, exp(l->decay_levels.last() - l->decay_levels.end[(int)(-1- l->example_t +weights_for_w[l->all->lda])]));

+    for (size_t k = 0; k < l->all->lda; k++) 

+      weights_for_w[k] *= decay;

   }

+}

 

-learner setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm)

+  void finish_example(vw& all, void*, example*ec)

+{}

+

+learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm)

 {

   lda* ld = (lda*)calloc(1,sizeof(lda));

   ld->all = &all;

+  ld->example_t = all.initial_t;

 

   po::options_description desc("LDA options");

   desc.add_options()

@@ -761,9 +752,16 @@ learner setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm)
     size_t minibatch2 = next_pow2(all.minibatch);

     all.p->ring_size = all.p->ring_size > minibatch2 ? all.p->ring_size : minibatch2;

   }

-

-  sl_t sl = {ld, save_load};

-  learner l(ld, drive, learn, finish, sl);

+  

+  ld->v.resize(all.lda*all.minibatch);

+  

+  ld->decay_levels.push_back(0.f);

+  

+  learner* l = new learner(ld, learn, save_load, all.reg.stride);

+  l->set_save_load(save_load);

+  l->set_finish_example(finish_example);

+  l->set_end_examples(end_examples);  

+  

   return l;

 }

 }

diff --git a/vowpalwabbit/lda_core.h b/vowpalwabbit/lda_core.h
index 241c59cd..fe3b9684 100644
--- a/vowpalwabbit/lda_core.h
+++ b/vowpalwabbit/lda_core.h
@@ -7,7 +7,7 @@ license as described in the file LICENSE.
 #define LDA_CORE_H
 
 namespace LDA{
-  learner setup(vw&, std::vector<std::string>&, po::variables_map&);
+  learner* setup(vw&, std::vector<std::string>&, po::variables_map&);
 }
 
 #endif
diff --git a/vowpalwabbit/learner.cc b/vowpalwabbit/learner.cc
new file mode 100644
index 00000000..f11bdcc0
--- /dev/null
+++ b/vowpalwabbit/learner.cc
@@ -0,0 +1,60 @@
+#include "global_data.h"
+#include "parser.h"
+#include "learner.h"
+
+void save_predictor(vw& all, string reg_name, size_t current_pass);
+
+namespace LEARNER
+{
+  void generic_driver(vw* all)
+  {
+    example* ec = NULL;
+
+    all->l->init_driver();
+    while ( true )
+      {
+	if(all->early_terminate)
+	  {
+ 	    all->p->done = true;
+ 	    return;
+	  }
+	else if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
+	  {
+	    if (ec->indices.size() > 1) // one nonconstant feature.
+	      {
+		all->l->learn(ec);
+		all->l->finish_example(*all, ec);
+	      }
+	    else if (ec->end_pass)
+	      {
+		all->l->end_pass();
+		VW::finish_example(*all,ec);
+	      }
+	    else if (ec->tag.size() >= 4 && !strncmp((const char*) ec->tag.begin, "save", 4))
+	      {//save state
+
+		string final_regressor_name = all->final_regressor_name;
+		
+		if ((ec->tag).size() >= 6 && (ec->tag)[4] == '_')
+		  final_regressor_name = string(ec->tag.begin+5, (ec->tag).size()-5);
+		
+		if (!all->quiet)
+		  cerr << "saving regressor to " << final_regressor_name << endl;
+		save_predictor(*all, final_regressor_name, 0);
+		
+		VW::finish_example(*all,ec);
+	      }
+	    else 
+	      {
+		all->l->learn(ec);
+		all->l->finish_example(*all, ec);
+	      }
+	  }
+	else if (parser_done(all->p))
+	  {
+	    all->l->end_examples();
+	    return;
+	  }
+      }
+  }
+}
diff --git a/vowpalwabbit/learner.h b/vowpalwabbit/learner.h
index 70d3fa84..9577249e 100644
--- a/vowpalwabbit/learner.h
+++ b/vowpalwabbit/learner.h
@@ -8,34 +8,155 @@ license as described in the file LICENSE.
 // This is the interface for a learning algorithm
 struct vw;
 
-struct sl_t {
-  void* sldata;
-  void (*save_loader)(void* sldata, io_buf&, bool read, bool text);
+struct learner;
+
+struct func_data {
+  void* data;
+  learner* base;
+  void (*func)(void* data);
 };
 
-struct learner {
-private:
+inline func_data tuple_dbf(void* data, learner* base, void (*func)(void* data))
+{
+  func_data foo;
+  foo.data = data;
+  foo.base = base;
+  foo.func = func;
+  return foo;
+}
+
+struct learn_data {
+  void* data;
+  learner* base;
+  void (*learn_f)(void* data, learner& base, example*);
+};
+
+struct save_load_data{
+  void* data;
+  learner* base;
+  void (*save_load_f)(void*, io_buf&, bool read, bool text);
+};
+
+struct finish_example_data{
   void* data;
-  void (*driver)(vw* all, void* data);
-  void (*learn_f)(void* data, example*);
-  void (*finisher)(void* data);
+  learner* base;
+  void (*finish_example_f)(vw&, void* data, example*);
+};
+
+void return_simple_example(vw& all, void*, example* ec);
+
+#include<iostream>
+using namespace std;
 
+namespace LEARNER
+{
+  void generic_driver(vw* all);
+
+  inline void generic_sl(void*, io_buf&, bool, bool) {}
+  inline void generic_learner(void* data, learner& base, example*)
+  { cout << "calling generic learner\n";}
+  inline void generic_func(void* data) {}
+
+  const save_load_data generic_save_load_fd = {NULL, NULL, generic_sl};
+  const learn_data generic_learn_fd = {NULL, NULL, generic_learner};
+  const func_data generic_func_fd = {NULL, NULL, generic_func};
+}
+
+struct learner {
+private:
+  func_data init_fd;
+  learn_data learn_fd;
+  finish_example_data finish_example_fd;
+  save_load_data save_load_fd;
+  func_data end_pass_fd;
+  func_data end_examples_fd;
+  func_data finisher_fd;
+  
 public:
-  sl_t sl;
+  size_t weights; //this stores the number of "weight vectors" required by the learner.
+  size_t increment;
+
+  //called once for each example.  Must work under reduction.
+  inline void learn(example* ec, size_t i=0) 
+  { 
+    ec->ft_offset += (uint32_t)(increment*i);
+    learn_fd.learn_f(learn_fd.data, *learn_fd.base, ec);
+    ec->ft_offset += - (uint32_t)(increment*i);
+  }
+
+  //called anytime saving or loading needs to happen. Autorecursive.
+  inline void save_load(io_buf& io, bool read, bool text) { save_load_fd.save_load_f(save_load_fd.data, io, read, text); if (save_load_fd.base) save_load_fd.base->save_load(io, read, text); }
+  inline void set_save_load(void (*sl)(void*, io_buf& io, bool read, bool text))
+  { save_load_fd.save_load_f = sl; 
+    save_load_fd.data = learn_fd.data; 
+    save_load_fd.base = learn_fd.base;}
+
+  //called to clean up state.  Autorecursive.
+  void set_finish(void (*f)(void*)) { finisher_fd = tuple_dbf(learn_fd.data,learn_fd.base,f); }
+  inline void finish() 
+  { if (finisher_fd.data) 
+      {finisher_fd.func(finisher_fd.data); free(finisher_fd.data); } 
+    if (finisher_fd.base) finisher_fd.base->finish(); }
+
+  void end_pass(){ 
+    end_pass_fd.func(end_pass_fd.data);
+    if (end_pass_fd.base) end_pass_fd.base->end_pass(); }//autorecursive
+  void set_end_pass(void (*ep)(void*)) {end_pass_fd = tuple_dbf(learn_fd.data, learn_fd.base, ep);}
+
+  //called after parsing of examples is complete.  Autorecursive.
+  void end_examples() 
+  { end_examples_fd.func(end_examples_fd.data); 
+    if (end_examples_fd.base) end_examples_fd.base->end_examples(); } 
+  void set_end_examples(void (*ee)(void*)) {end_examples_fd = tuple_dbf(learn_fd.data,learn_fd.base,ee);}
+
+  //Called at the beginning by the driver.  Explicitly not recursive.
+  void init_driver() { init_fd.func(init_fd.data);}
+  void set_init_driver(void (*id)(void*)) { init_fd = tuple_dbf(learn_fd.data,learn_fd.base, id); }
+
+  //called after learn example for each example.  Explicitly not recursive.
+  inline void finish_example(vw& all, example* ec) { finish_example_fd.finish_example_f(all, finish_example_fd.data, ec);}
+  void set_finish_example(void (*ef)(vw& all, void*, example*))
+  {finish_example_fd.data = learn_fd.data;
+    finish_example_fd.finish_example_f = ef;}
 
-  inline void learn(example* ec) { learn_f(data,ec); }
-  inline void finish() { finisher(data); }
-  inline void drive(vw* all) { driver(all, data); }
-  inline void save_load(io_buf& io, bool read, bool text) { sl.save_loader(sl.sldata, io, read, text); }
+  void driver(vw* all) {LEARNER::generic_driver(all);}
 
-  learner(void *dat, void (*d)(vw* all, void* data), void (*l)(void* data, example*),   void (*f)(void* data), sl_t s)
+  inline learner()
   {
-    data = dat;
-    driver = d;
-    learn_f = l;
-    finisher = f;
-    sl = s;
+    weights = 1;
+    increment = 1;
+
+    learn_fd = LEARNER::generic_learn_fd;
+    finish_example_fd.data = NULL;
+    finish_example_fd.finish_example_f = return_simple_example;
+    end_pass_fd = LEARNER::generic_func_fd;
+    end_examples_fd = LEARNER::generic_func_fd;
+    init_fd = LEARNER::generic_func_fd;
+    finisher_fd = LEARNER::generic_func_fd;
+    save_load_fd = LEARNER::generic_save_load_fd;
+  }
+
+  inline learner(void *dat, void (*l)(void*, learner&, example*), void (*sl)(void*, io_buf& io, bool read, bool text), size_t params_per_weight)
+  { // the constructor for all learning algorithms.
+    *this = learner();
+
+    learn_fd.data = dat;
+    learn_fd.learn_f = l;
+    set_save_load(sl);
+    increment = params_per_weight;
+  }
+
+  inline learner(void *dat, void (*l)(void*, learner&, example*), learner* base, size_t ws = 1) 
+  { //the reduction constructor.
+    *this = *base;
+    
+    learn_fd.learn_f = l;
+    learn_fd.data = dat;
+    learn_fd.base = base;
+
+    increment = base->increment * base->weights;
+    weights = ws;
   }
-  learner() {}
 };
+
 #endif
diff --git a/vowpalwabbit/main.cc b/vowpalwabbit/main.cc
index ccf34afc..e31be2e9 100644
--- a/vowpalwabbit/main.cc
+++ b/vowpalwabbit/main.cc
@@ -44,10 +44,10 @@ int main(int argc, char *argv[])
 
   VW::start_parser(*all);
 
-  all->l.drive(all);
+  all->l->driver(all);
 
   VW::end_parser(*all);
-  
+
   ftime(&t_end);
   double net_time = (int) (1000.0 * (t_end.time - t_start.time) + (t_end.millitm - t_start.millitm)); 
   if(!all->quiet && all->span_server != "")
@@ -77,10 +77,22 @@ int main(int argc, char *argv[])
     {
       cerr.precision(6);
       cerr << endl << "finished run";
-      cerr << endl << "number of examples = " << all->sd->example_number;
+      if(all->current_pass == 0)
+        cerr << endl << "number of examples = " << all->sd->example_number;
+      else{
+        cerr << endl << "number of examples per pass = " << all->sd->example_number / all->current_pass;
+        cerr << endl << "passes used = " << all->current_pass;
+      }
       cerr << endl << "weighted example sum = " << all->sd->weighted_examples;
       cerr << endl << "weighted label sum = " << all->sd->weighted_labels;
-      cerr << endl << "average loss = " << all->sd->sum_loss / all->sd->weighted_examples;
+      if(all->holdout_set_off)
+      {
+        cerr << endl << "average loss = " << all->sd->sum_loss / all->sd->weighted_examples;
+      }  
+      else
+      {
+        cerr << endl << "average loss = " << all->sd->holdout_best_loss << " h";
+      }
       cerr << endl << "best constant = " << best_constant;
       if (all->sd->min_label == 0. && all->sd->max_label == 1. && best_constant < 1. && best_constant > 0.)
 	cerr << endl << "best constant's loss = " << constant_loss;
diff --git a/vowpalwabbit/mf.cc b/vowpalwabbit/mf.cc
index 5320d2ed..a7972726 100644
--- a/vowpalwabbit/mf.cc
+++ b/vowpalwabbit/mf.cc
@@ -31,7 +31,6 @@ using namespace std;
 namespace MF {
 
 struct mf {
-  learner base;
   vector<string> pairs;
 
   uint32_t rank;
@@ -51,20 +50,20 @@ struct mf {
   vw* all;
 };
 
-void inline_predict(mf* data, vw* all, example* &ec) {
+void inline_predict(mf* data, vw* all, learner& base, example* &ec) {
 
   float prediction = 0;
-  data->sub_predictions.erase();
+  data->sub_predictions.resize(2*all->rank+1, true);
 
   // set weight to 0 to indicate test example (predict only)
   float weight = ((label_data*) ec->ld)->weight;
   ((label_data*) ec->ld)->weight = 0;
 
   // predict from linear terms
-  data->base.learn(ec);
+  base.learn(ec);
 
   // store linear prediction
-  data->sub_predictions.push_back(ec->partial_prediction);
+  data->sub_predictions[0] = ec->partial_prediction;
   prediction += ec->partial_prediction;
 
   // store namespace indices
@@ -73,32 +72,30 @@ void inline_predict(mf* data, vw* all, example* &ec) {
   // add interaction terms to prediction
   for (vector<string>::iterator i = data->pairs.begin(); i != data->pairs.end(); i++) {
     if (ec->atomics[(int) (*i)[0]].size() > 0 && ec->atomics[(int) (*i)[1]].size() > 0) {
-      for (size_t k = 1; k <= all->rank; k++) {
-	// set example to left namespace only
-	ec->indices.erase();
-	ec->indices.push_back((int) (*i)[0]);
 
+      // set example to left namespace only
+      ec->indices.erase();
+      ec->indices.push_back((int) (*i)[0]);
+
+      for (size_t k = 1; k <= all->rank; k++) {
 	// compute l^k * x_l using base learner
-	update_example_indicies(all->audit, ec, data->increment*k);
-	data->base.learn(ec);
-	float x_dot_l = ec->partial_prediction;
-	data->sub_predictions.push_back(ec->partial_prediction);
-	update_example_indicies(all->audit, ec, -data->increment*k);
+	base.learn(ec, k);
+	data->sub_predictions[2*k-1] = ec->partial_prediction;
+      }
 
-	// set example to right namespace only
-	ec->indices.erase();
-	ec->indices.push_back((int) (*i)[1]);
+      // set example to right namespace only
+      ec->indices.erase();
+      ec->indices.push_back((int) (*i)[1]);
 
+      for (size_t k = 1; k <= all->rank; k++) {
 	// compute r^k * x_r using base learner
-	update_example_indicies(all->audit, ec, data->increment*(k + data->rank));
-	data->base.learn(ec);
-	float x_dot_r = ec->partial_prediction;
-	data->sub_predictions.push_back(ec->partial_prediction);
-	update_example_indicies(all->audit, ec, -data->increment*(k + data->rank));
-
-	// accumulate prediction
-	prediction += (x_dot_l * x_dot_r);
+	base.learn(ec, k + all->rank);
+	data->sub_predictions[2*k] = ec->partial_prediction;
       }
+
+      // accumulate prediction
+      for (size_t k = 1; k <= all->rank; k++)
+	prediction += (data->sub_predictions[2*k-1] * data->sub_predictions[2*k]);
     }
   }
   // restore namespace indices and label
@@ -113,23 +110,18 @@ void inline_predict(mf* data, vw* all, example* &ec) {
 }
 
 
-void learn(void* d, example* ec) {
+void learn(void* d, learner& base, example* ec) {
   mf* data = (mf*) d;
   vw* all = data->all;
 
-  if (command_example(all, ec)) {
-    data->base.learn(ec);
-    return;
-  }
-
   // predict with current weights
-  inline_predict(data, all, ec);
+  inline_predict(data, all, base, ec);
 
   // force base learner to use precomputed prediction
   ec->precomputed_prediction = true;
 
   // update linear weights
-  data->base.learn(ec);
+  base.learn(ec);
 
   // store namespace indices
   copy_array(data->indices, ec->indices);
@@ -138,41 +130,43 @@ void learn(void* d, example* ec) {
   // looping over all pairs of non-empty namespaces
   for (vector<string>::iterator i = data->pairs.begin(); i != data->pairs.end(); i++) {
     if (ec->atomics[(int) (*i)[0]].size() > 0 && ec->atomics[(int) (*i)[1]].size() > 0) {
-      for (size_t k = 1; k <= all->rank; k++) {
-	// set example to left namespace only
-	ec->indices.erase();
-	ec->indices.push_back((int) (*i)[0]);
 
-	// store feature values for left namespace
-	copy_array(data->temp_features, ec->atomics[(int) (*i)[0]]);
+      // set example to left namespace only
+      ec->indices.erase();
+      ec->indices.push_back((int) (*i)[0]);
+
+      // store feature values in left namespace
+      copy_array(data->temp_features, ec->atomics[(int) (*i)[0]]);
+
+      for (size_t k = 1; k <= all->rank; k++) {
 
 	// multiply features in left namespace by r^k * x_r
 	for (feature* f = ec->atomics[(int) (*i)[0]].begin; f != ec->atomics[(int) (*i)[0]].end; f++)
 	  f->x *= data->sub_predictions[2*k];
 
 	// update l^k using base learner
-	update_example_indicies(all->audit, ec, data->increment*k);
-	data->base.learn(ec);
-	update_example_indicies(all->audit, ec, -data->increment*k);
+	base.learn(ec, k);
 
-	// restore left namespace features
+	// restore left namespace features (undoing multiply)
 	copy_array(ec->atomics[(int) (*i)[0]], data->temp_features);
+      }
 
-	// set example to right namespace only
-	ec->indices.erase();
-	ec->indices.push_back((int) (*i)[1]);
 
-	// store feature values for right namespace
-	copy_array(data->temp_features, ec->atomics[(int) (*i)[1]]);
+      // set example to right namespace only
+      ec->indices.erase();
+      ec->indices.push_back((int) (*i)[1]);
+
+      // store feature values for right namespace
+      copy_array(data->temp_features, ec->atomics[(int) (*i)[1]]);
+
+      for (size_t k = 1; k <= all->rank; k++) {
 
 	// multiply features in right namespace by l^k * x_l
 	for (feature* f = ec->atomics[(int) (*i)[1]].begin; f != ec->atomics[(int) (*i)[1]].end; f++)
 	  f->x *= data->sub_predictions[2*k-1];
 
 	// update r^k using base learner
-	update_example_indicies(all->audit, ec, data->increment*(k + data->rank));
-	data->base.learn(ec);
-	update_example_indicies(all->audit, ec, -data->increment*(k + data->rank));
+	base.learn(ec, k + all->rank);
 
 	// restore right namespace features
 	copy_array(ec->atomics[(int) (*i)[1]], data->temp_features);
@@ -189,34 +183,17 @@ void finish(void* data) {
   mf* o = (mf*) data;
   // restore global pairs
   o->all->pairs = o->pairs;
-  o->base.finish();
 
   // clean up local v_arrays
   o->indices.delete_v();
   o->sub_predictions.delete_v();
-  delete o;
 }
 
-void drive(vw* all, void* d) {
-  example* ec = NULL;
-
-  while (true) {
-    if ((ec = VW::get_example(all->p)) != NULL) //blocking operation.
-      {
-	learn(d, ec);
-	return_simple_example(*all, ec);
-      } else if (parser_done(all->p))
-      return;
-    else
-      ; //busywait when we have predicted on all examples but not yet trained on all.
-  }
-}
 
-learner setup(vw& all, po::variables_map& vm) {
+learner* setup(vw& all, po::variables_map& vm) {
   mf* data = new mf;
 
   // copy global data locally
-  data->base = all.l;
   data->all = &all;
   data->rank = all.rank;
 
@@ -225,17 +202,14 @@ learner setup(vw& all, po::variables_map& vm) {
   data->pairs = all.pairs;
   all.pairs.clear();
 
-  // set index increment between weights
-  data->increment = all.reg.stride * all.weights_per_problem;
-  all.weights_per_problem *= data->rank;
-
   // initialize weights randomly
   if(!vm.count("initial_regressor"))
     {
       for (size_t j = 0; j < (all.reg.weight_mask + 1) / all.reg.stride; j++)
 	all.reg.weight_vector[j*all.reg.stride] = (float) (0.1 * frand48());
     }
-  learner ret(data, drive,learn, finish, all.l.sl);
-  return ret;
+  learner* l = new learner(data, learn, all.l, 2*data->rank+1);
+  l->set_finish(finish);
+  return l;
 }
 }
diff --git a/vowpalwabbit/mf.h b/vowpalwabbit/mf.h
index f90647b5..f3442262 100644
--- a/vowpalwabbit/mf.h
+++ b/vowpalwabbit/mf.h
@@ -13,6 +13,6 @@ license as described in the file LICENSE.
 #include "gd.h"
 
 namespace MF{
-  learner setup(vw& all, po::variables_map& vm);
+  learner* setup(vw& all, po::variables_map& vm);
 }
 #endif
diff --git a/vowpalwabbit/nn.cc b/vowpalwabbit/nn.cc
index 6d43bba7..277689b3 100644
--- a/vowpalwabbit/nn.cc
+++ b/vowpalwabbit/nn.cc
@@ -25,16 +25,15 @@ namespace NN {
   
   struct nn {
     uint32_t k;
-    uint32_t increment;
     loss_function* squared_loss;
     example output_layer;
+    size_t increment;
     bool dropout;
     uint64_t xsubi;
     uint64_t save_xsubi;
     bool inpass;
     bool finished_setup;
 
-    learner base;
     vw* all;
   };
 
@@ -69,69 +68,121 @@ namespace NN {
                           uint32_t          offset)
     {
       for (feature* x = f.begin; x != f.end; ++x)
+	x->weight_index += offset;
+    }
+
+  void finish_setup (nn& n, vw& all)
+  {
+    bool initialize = true;
+
+    // TODO: output_layer audit
+
+    memset (&n.output_layer, 0, sizeof (n.output_layer));
+    n.output_layer.indices.push_back(nn_output_namespace);
+    feature output = {1., nn_constant*all.reg.stride};
+
+    for (unsigned int i = 0; i < n.k; ++i)
+      {
+        n.output_layer.atomics[nn_output_namespace].push_back(output);
+        initialize &= (all.reg.weight_vector[output.weight_index & all.reg.weight_mask] == 0);
+        ++n.output_layer.num_features;
+        output.weight_index += (uint32_t)n.increment;
+      }
+
+    if (! n.inpass) 
+      {
+        n.output_layer.atomics[nn_output_namespace].push_back(output);
+        initialize &= (all.reg.weight_vector[output.weight_index & all.reg.weight_mask] == 0);
+        ++n.output_layer.num_features;
+      }
+
+    n.output_layer.in_use = true;
+
+    if (initialize) {
+      // output weights
+
+      float sqrtk = sqrt ((float)n.k);
+      for (feature* x = n.output_layer.atomics[nn_output_namespace].begin; 
+           x != n.output_layer.atomics[nn_output_namespace].end; 
+           ++x)
+        {
+          weight* w = &all.reg.weight_vector[x->weight_index & all.reg.weight_mask];
+
+          w[0] = (float) (frand48 () - 0.5) / sqrtk;
+
+          // prevent divide by zero error
+          if (n.dropout && all.normalized_updates)
+            w[all.normalized_idx] = 1e-4f;
+        }
+
+      // hidden biases
+
+      unsigned int weight_index = constant * all.reg.stride;
+
+      for (unsigned int i = 0; i < n.k; ++i)
         {
-          x->weight_index += offset;
+          weight_index += (uint32_t)n.increment;
+          all.reg.weight_vector[weight_index & all.reg.weight_mask] = (float) (frand48 () - 0.5);
         }
     }
 
-  void finish_setup (nn* n, vw& all);
+    n.finished_setup = true;
+  }
 
-  void learn_with_output(vw& all, nn& n, example* ec, bool shouldOutput)
+  void end_pass(void* d)
   {
-    if (! n.finished_setup)
-      finish_setup (&n, all);
+    nn* n = (nn*)d;
+    
+    if (n->all->bfgs)
+      n->xsubi = n->save_xsubi;
+  }
 
-    if (ec->end_pass) {
-      if (all.bfgs)
-        n.xsubi = n.save_xsubi;
-    }
+  void learn(void* d, learner& base, example* ec)
+  {
+    nn* n = (nn*)d;
+    bool shouldOutput = n->all->raw_prediction > 0;
 
-    if (command_example(&all, ec)) {
-      n.base.learn(ec);
-      return;
-    }
+    if (! n->finished_setup)
+      finish_setup (*n, *(n->all));
 
     label_data* ld = (label_data*)ec->ld;
     float save_label = ld->label;
-    void (*save_set_minmax) (shared_data*, float) = all.set_minmax;
+    void (*save_set_minmax) (shared_data*, float) = n->all->set_minmax;
     float save_min_label;
     float save_max_label;
-    float dropscale = n.dropout ? 2.0f : 1.0f;
-    loss_function* save_loss = all.loss;
+    float dropscale = n->dropout ? 2.0f : 1.0f;
+    loss_function* save_loss = n->all->loss;
 
-    float* hidden_units = (float*) alloca (n.k * sizeof (float));
-    bool* dropped_out = (bool*) alloca (n.k * sizeof (bool));
+    float* hidden_units = (float*) alloca (n->k * sizeof (float));
+    bool* dropped_out = (bool*) alloca (n->k * sizeof (bool));
   
     string outputString;
     stringstream outputStringStream(outputString);
 
-    all.set_minmax = noop_mm;
-    all.loss = n.squared_loss;
-    save_min_label = all.sd->min_label;
-    all.sd->min_label = hidden_min_activation;
-    save_max_label = all.sd->max_label;
-    all.sd->max_label = hidden_max_activation;
+    n->all->set_minmax = noop_mm;
+    n->all->loss = n->squared_loss;
+    save_min_label = n->all->sd->min_label;
+    n->all->sd->min_label = hidden_min_activation;
+    save_max_label = n->all->sd->max_label;
+    n->all->sd->max_label = hidden_max_activation;
     ld->label = FLT_MAX;
-    for (unsigned int i = 0; i < n.k; ++i)
+    for (unsigned int i = 0; i < n->k; ++i)
       {
-        update_example_indicies(all.audit, ec, n.increment);
-
-        n.base.learn(ec);
+        base.learn(ec, i);
         hidden_units[i] = ec->final_prediction;
 
-        dropped_out[i] = (n.dropout && merand48 (n.xsubi) < 0.5);
+        dropped_out[i] = (n->dropout && merand48 (n->xsubi) < 0.5);
 
         if (shouldOutput) {
           if (i > 0) outputStringStream << ' ';
           outputStringStream << i << ':' << ec->partial_prediction << ',' << fasttanh (hidden_units[i]);
         }
       }
-    update_example_indicies(all.audit, ec, -n.k * n.increment);
     ld->label = save_label;
-    all.loss = save_loss;
-    all.set_minmax = save_set_minmax;
-    all.sd->min_label = save_min_label;
-    all.sd->max_label = save_max_label;
+    n->all->loss = save_loss;
+    n->all->set_minmax = save_set_minmax;
+    n->all->sd->min_label = save_min_label;
+    n->all->sd->max_label = save_max_label;
 
     bool converse = false;
     float save_partial_prediction = 0;
@@ -140,104 +191,102 @@ namespace NN {
 
 CONVERSE: // That's right, I'm using goto.  So sue me.
 
-    n.output_layer.total_sum_feat_sq = 1;
-    n.output_layer.sum_feat_sq[nn_output_namespace] = 1;
+    n->output_layer.total_sum_feat_sq = 1;
+    n->output_layer.sum_feat_sq[nn_output_namespace] = 1;
 
-    for (unsigned int i = 0; i < n.k; ++i)
+    for (unsigned int i = 0; i < n->k; ++i)
       {
         float sigmah = 
           (dropped_out[i]) ? 0.0f : dropscale * fasttanh (hidden_units[i]);
-        n.output_layer.atomics[nn_output_namespace][i].x = sigmah;
+        n->output_layer.atomics[nn_output_namespace][i].x = sigmah;
 
-        n.output_layer.total_sum_feat_sq += sigmah * sigmah;
-        n.output_layer.sum_feat_sq[nn_output_namespace] += sigmah * sigmah;
+        n->output_layer.total_sum_feat_sq += sigmah * sigmah;
+        n->output_layer.sum_feat_sq[nn_output_namespace] += sigmah * sigmah;
       }
 
-    if (n.inpass) {
+    if (n->inpass) {
       // TODO: this is not correct if there is something in the 
       // nn_output_namespace but at least it will not leak memory
       // in that case
 
-      update_atomics_indices (n.output_layer.atomics[nn_output_namespace], -ec->ft_offset);
+      update_atomics_indices (n->output_layer.atomics[nn_output_namespace], -ec->ft_offset);
       ec->indices.push_back (nn_output_namespace);
       v_array<feature> save_nn_output_namespace = ec->atomics[nn_output_namespace];
-      ec->atomics[nn_output_namespace] = n.output_layer.atomics[nn_output_namespace];
-      ec->sum_feat_sq[nn_output_namespace] = n.output_layer.sum_feat_sq[nn_output_namespace];
-      ec->total_sum_feat_sq += n.output_layer.sum_feat_sq[nn_output_namespace];
-      n.base.learn(ec);
-      n.output_layer.partial_prediction = ec->partial_prediction;
-      n.output_layer.loss = ec->loss;
-      ec->total_sum_feat_sq -= n.output_layer.sum_feat_sq[nn_output_namespace];
+      ec->atomics[nn_output_namespace] = n->output_layer.atomics[nn_output_namespace];
+      ec->sum_feat_sq[nn_output_namespace] = n->output_layer.sum_feat_sq[nn_output_namespace];
+      ec->total_sum_feat_sq += n->output_layer.sum_feat_sq[nn_output_namespace];
+      base.learn(ec, n->k);
+      n->output_layer.partial_prediction = ec->partial_prediction;
+      n->output_layer.loss = ec->loss;
+      ec->total_sum_feat_sq -= n->output_layer.sum_feat_sq[nn_output_namespace];
       ec->sum_feat_sq[nn_output_namespace] = 0;
       ec->atomics[nn_output_namespace] = save_nn_output_namespace;
       ec->indices.pop ();
-      update_atomics_indices (n.output_layer.atomics[nn_output_namespace], ec->ft_offset);
+      update_atomics_indices (n->output_layer.atomics[nn_output_namespace], ec->ft_offset);
     }
     else {
-      n.output_layer.ld = ec->ld;
-      n.output_layer.partial_prediction = 0;
-      n.output_layer.eta_round = ec->eta_round;
-      n.output_layer.eta_global = ec->eta_global;
-      n.output_layer.global_weight = ec->global_weight;
-      n.output_layer.example_t = ec->example_t;
-      n.base.learn(&n.output_layer);
-      n.output_layer.ld = 0;
+      n->output_layer.ld = ec->ld;
+      n->output_layer.partial_prediction = 0;
+      n->output_layer.eta_round = ec->eta_round;
+      n->output_layer.eta_global = ec->eta_global;
+      n->output_layer.global_weight = ec->global_weight;
+      n->output_layer.example_t = ec->example_t;
+      base.learn(&n->output_layer, n->k);
+      n->output_layer.ld = 0;
     }
 
-    n.output_layer.final_prediction = GD::finalize_prediction (all, n.output_layer.partial_prediction);
+    n->output_layer.final_prediction = GD::finalize_prediction (*(n->all), n->output_layer.partial_prediction);
 
     if (shouldOutput) {
-      outputStringStream << ' ' << n.output_layer.partial_prediction;
-      all.print_text(all.raw_prediction, outputStringStream.str(), ec->tag);
+      outputStringStream << ' ' << n->output_layer.partial_prediction;
+      n->all->print_text(n->all->raw_prediction, outputStringStream.str(), ec->tag);
     }
 
-    if (all.training && ld->label != FLT_MAX) {
-      float gradient = all.loss->first_derivative(all.sd, 
-                                                  n.output_layer.final_prediction,
+    if (n->all->training && ld->label != FLT_MAX) {
+      float gradient = n->all->loss->first_derivative(n->all->sd, 
+                                                  n->output_layer.final_prediction,
                                                   ld->label);
 
       if (fabs (gradient) > 0) {
-        all.loss = n.squared_loss;
-        all.set_minmax = noop_mm;
-        save_min_label = all.sd->min_label;
-        all.sd->min_label = hidden_min_activation;
-        save_max_label = all.sd->max_label;
-        all.sd->max_label = hidden_max_activation;
-
-        for (unsigned int i = 0; i < n.k; ++i) {
-          update_example_indicies (all.audit, ec, n.increment);
+        n->all->loss = n->squared_loss;
+        n->all->set_minmax = noop_mm;
+        save_min_label = n->all->sd->min_label;
+        n->all->sd->min_label = hidden_min_activation;
+        save_max_label = n->all->sd->max_label;
+        n->all->sd->max_label = hidden_max_activation;
+
+        for (unsigned int i = 0; i < n->k; ++i) {
           if (! dropped_out[i]) {
             float sigmah = 
-              n.output_layer.atomics[nn_output_namespace][i].x / dropscale;
+              n->output_layer.atomics[nn_output_namespace][i].x / dropscale;
             float sigmahprime = dropscale * (1.0f - sigmah * sigmah);
-            float nu = all.reg.weight_vector[n.output_layer.atomics[nn_output_namespace][i].weight_index & all.reg.weight_mask];
+            float nu = n->all->reg.weight_vector[n->output_layer.atomics[nn_output_namespace][i].weight_index & n->all->reg.weight_mask];
             float gradhw = 0.5f * nu * gradient * sigmahprime;
 
-            ld->label = GD::finalize_prediction (all, hidden_units[i] - gradhw);
+            ld->label = GD::finalize_prediction (*(n->all), hidden_units[i] - gradhw);
             if (ld->label != hidden_units[i]) 
-              n.base.learn(ec);
+              base.learn(ec, i);
           }
         }
-        update_example_indicies (all.audit, ec, -n.k*n.increment);
 
-        all.loss = save_loss;
-        all.set_minmax = save_set_minmax;
-        all.sd->min_label = save_min_label;
-        all.sd->max_label = save_max_label;
+        n->all->loss = save_loss;
+        n->all->set_minmax = save_set_minmax;
+        n->all->sd->min_label = save_min_label;
+        n->all->sd->max_label = save_max_label;
       }
     }
 
     ld->label = save_label;
 
     if (! converse) {
-      save_partial_prediction = n.output_layer.partial_prediction;
-      save_final_prediction = n.output_layer.final_prediction;
-      save_ec_loss = n.output_layer.loss;
+      save_partial_prediction = n->output_layer.partial_prediction;
+      save_final_prediction = n->output_layer.final_prediction;
+      save_ec_loss = n->output_layer.loss;
     }
 
-    if (n.dropout && ! converse)
+    if (n->dropout && ! converse)
       {
-        for (unsigned int i = 0; i < n.k; ++i)
+        for (unsigned int i = 0; i < n->k; ++i)
           {
             dropped_out[i] = ! dropped_out[i];
           }
@@ -251,44 +300,23 @@ CONVERSE: // That's right, I'm using goto.  So sue me.
     ec->loss = save_ec_loss;
   }
 
-  void learn(void* d,example* ec) {
-    nn* n = (nn*)d;
-    vw* all = n->all;
-    learn_with_output(*all, *n, ec, false);
-  }
-
-  void drive_nn(vw *all, void* d)
+  void finish_example(vw& all, void*, example* ec)
   {
-    nn* n = (nn*)d;
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn_with_output(*all, *n, ec, all->raw_prediction > 0);
-            int save_raw_prediction = all->raw_prediction;
-            all->raw_prediction = -1;
-            return_simple_example(*all, ec);
-            all->raw_prediction = save_raw_prediction;
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
+    int save_raw_prediction = all.raw_prediction;
+    all.raw_prediction = -1;
+    return_simple_example(all, NULL, ec);
+    all.raw_prediction = save_raw_prediction;
   }
 
   void finish(void* d)
   {
     nn* n =(nn*)d;
-    n->base.finish();
     delete n->squared_loss;
     free (n->output_layer.indices.begin);
     free (n->output_layer.atomics[nn_output_namespace].begin);
-    free(n);
   }
 
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     nn* n = (nn*)calloc(1,sizeof(nn));
     n->all = &all;
@@ -371,11 +399,6 @@ CONVERSE: // That's right, I'm using goto.  So sue me.
                 << (all.training ? "training" : "testing") 
                 << std::endl;
 
-    n->base = all.l;
-
-    n->increment = all.reg.stride * all.weights_per_problem;
-    all.weights_per_problem *= n->k + 1;
-
     n->finished_setup = false;
     n->squared_loss = getLossFunction (0, "squared", 0);
 
@@ -385,66 +408,12 @@ CONVERSE: // That's right, I'm using goto.  So sue me.
       n->xsubi = vm["random_seed"].as<size_t>();
 
     n->save_xsubi = n->xsubi;
+    learner* l = new learner(n, learn, all.l, n->k+1);
+    l->set_finish(finish);
+    l->set_finish_example(finish_example);
+    l->set_end_pass(end_pass);
+    n->increment = l->increment;//Indexing of output layer is odd.
 
-    learner l(n,drive_nn,learn,finish,all.l.sl);
     return l;
   }
-
-  void finish_setup (nn* n, vw& all)
-  {
-    bool initialize = true;
-
-    // TODO: output_layer audit
-
-    memset (&n->output_layer, 0, sizeof (n->output_layer));
-    n->output_layer.indices.push_back(nn_output_namespace);
-    feature output = {1., nn_constant*all.reg.stride};
-
-    for (unsigned int i = 0; i < n->k; ++i)
-      {
-        n->output_layer.atomics[nn_output_namespace].push_back(output);
-        initialize &= (all.reg.weight_vector[output.weight_index & all.reg.weight_mask] == 0);
-        ++n->output_layer.num_features;
-        output.weight_index += n->increment;
-      }
-
-    if (! n->inpass) 
-      {
-        n->output_layer.atomics[nn_output_namespace].push_back(output);
-        initialize &= (all.reg.weight_vector[output.weight_index & all.reg.weight_mask] == 0);
-        ++n->output_layer.num_features;
-      }
-
-    n->output_layer.in_use = true;
-
-    if (initialize) {
-      // output weights
-
-      float sqrtk = sqrt ((float)n->k);
-      for (feature* x = n->output_layer.atomics[nn_output_namespace].begin; 
-           x != n->output_layer.atomics[nn_output_namespace].end; 
-           ++x)
-        {
-          weight* w = &all.reg.weight_vector[x->weight_index & all.reg.weight_mask];
-
-          w[0] = (float) (frand48 () - 0.5) / sqrtk;
-
-          // prevent divide by zero error
-          if (n->dropout && all.normalized_updates)
-            w[all.normalized_idx] = 1e-4f;
-        }
-
-      // hidden biases
-
-      unsigned int weight_index = constant * all.reg.stride;
-
-      for (unsigned int i = 0; i < n->k; ++i)
-        {
-          weight_index += n->increment;
-          all.reg.weight_vector[weight_index & all.reg.weight_mask] = (float) (frand48 () - 0.5);
-        }
-    }
-
-    n->finished_setup = true;
-  }
 }
diff --git a/vowpalwabbit/nn.h b/vowpalwabbit/nn.h
index a2d66b96..e8e47506 100644
--- a/vowpalwabbit/nn.h
+++ b/vowpalwabbit/nn.h
@@ -11,7 +11,7 @@ license as described in the file LICENSE.
 
 namespace NN
 {
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
 }
 
 #endif
diff --git a/vowpalwabbit/noop.cc b/vowpalwabbit/noop.cc
index b8a72155..6cb3c44f 100644
--- a/vowpalwabbit/noop.cc
+++ b/vowpalwabbit/noop.cc
@@ -3,36 +3,13 @@ Copyright (c) by respective owners including Yahoo!, Microsoft, and
 individual contributors. All rights reserved.  Released under a BSD (revised)
 license as described in the file LICENSE.
  */
-// This is a function which does nothing with examples.  Used when VW is used as a compressor.
+// This is a learner which does nothing with examples.  Used when VW is used as a compressor.
 
-#include "example.h"
-#include "parser.h"
-#include "gd.h"
-#include "simple_label.h"
 #include "vw.h"
 
 namespace NOOP {
-  void learn(void* d, example*ec) {}
-  void finish(void* d) {}
-
-  void save_load(void* d, io_buf& model_file, bool read, bool text) {}
-  
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    
-    while ( !parser_done(all->p)){
-      ec = VW::get_example(all->p);
-      if (ec != NULL)
-	return_simple_example(*all, ec);
-    }
-  }
-  
-  learner setup(vw& all)
+  learner* setup(vw& all)
   {
-    sl_t sl = {NULL,save_load};
-    all.is_noop = true;
-    learner l(NULL,drive,learn,finish,sl);
-    return l;
+    return new learner();
   }
 }
diff --git a/vowpalwabbit/noop.h b/vowpalwabbit/noop.h
index 8d903464..fdae726c 100644
--- a/vowpalwabbit/noop.h
+++ b/vowpalwabbit/noop.h
@@ -5,5 +5,5 @@ license as described in the file LICENSE.
 */
 
 namespace NOOP {
-  learner setup(vw&);
+  learner* setup(vw&);
 }
diff --git a/vowpalwabbit/oaa.cc b/vowpalwabbit/oaa.cc
index c79eb889..78398422 100644
--- a/vowpalwabbit/oaa.cc
+++ b/vowpalwabbit/oaa.cc
@@ -20,15 +20,12 @@ namespace OAA {
 
   struct oaa{
     uint32_t k;
-    uint32_t increment;
-    uint32_t total_increment;
-    learner base;
     vw* all;
   };
 
-  char* bufread_label(mc_label* ld, char* c)
+    char* bufread_label(mc_label* ld, char* c)
   {
-    ld->label = *(float *)c;
+    ld->label = *(uint32_t *)c;
     c += sizeof(ld->label);
     ld->weight = *(float *)c;
     c += sizeof(ld->weight);
@@ -60,7 +57,7 @@ namespace OAA {
 
   char* bufcache_label(mc_label* ld, char* c)
   {
-    *(float *)c = ld->label;
+    *(uint32_t *)c = ld->label;
     c += sizeof(ld->label);
     *(float *)c = ld->weight;
     c += sizeof(ld->weight);
@@ -78,7 +75,7 @@ namespace OAA {
   void default_label(void* v)
   {
     mc_label* ld = (mc_label*) v;
-    ld->label = -1;
+    ld->label = (uint32_t)-1;
     ld->weight = 1.;
   }
 
@@ -94,17 +91,22 @@ namespace OAA {
     case 0:
       break;
     case 1:
-      ld->label = (float)int_of_substring(words[0]);
+      ld->label = int_of_substring(words[0]);
       ld->weight = 1.0;
       break;
     case 2:
-      ld->label = (float)int_of_substring(words[0]);
+      ld->label = int_of_substring(words[0]);
       ld->weight = float_of_substring(words[1]);
       break;
     default:
       cerr << "malformed example!\n";
       cerr << "words.size() = " << words.size() << endl;
     }
+    if (ld->label == 0)
+      {
+	cout << "label 0 is not allowed for multiclass.  Valid labels are {1,k}" << endl;
+	throw exception();
+      }
   }
 
   void print_update(vw& all, example *ec)
@@ -118,7 +120,30 @@ namespace OAA {
         else
           sprintf(label_buf,"%8ld",(long int)ld->label);
 
-        fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8ld %8lu\n",
+        if(!all.holdout_set_off && all.current_pass >= 1)
+        {
+          if(all.sd->holdout_sum_loss == 0. && all.sd->weighted_holdout_examples == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss/all.sd->weighted_holdout_examples);
+
+          if(all.sd->holdout_sum_loss_since_last_dump == 0. && all.sd->weighted_holdout_examples_since_last_dump == 0.)
+            fprintf(stderr, " unknown   ");
+          else
+	    fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss_since_last_dump/all.sd->weighted_holdout_examples_since_last_dump);
+
+            fprintf(stderr, "%8ld %8.1f   %s %8ld %8lu h\n",
+	      (long int)all.sd->example_number,
+	      all.sd->weighted_examples,
+	      label_buf,
+	      (long int)ec->final_prediction,
+	      (long unsigned int)ec->num_features);
+
+          all.sd->weighted_holdout_examples_since_last_dump = 0;
+          all.sd->holdout_sum_loss_since_last_dump = 0.0;
+        }
+        else
+          fprintf(stderr, "%-10.6f %-10.6f %8ld %8.1f   %s %8ld %8lu\n",
                 all.sd->sum_loss/all.sd->weighted_examples,
                 all.sd->sum_loss_since_last_dump / (all.sd->weighted_examples - all.sd->old_weighted_examples),
                 (long int)all.sd->example_number,
@@ -135,46 +160,74 @@ namespace OAA {
 
   void output_example(vw& all, example* ec)
   {
-    if (command_example(&all,ec))
-      return;
-
     mc_label* ld = (mc_label*)ec->ld;
-    all.sd->weighted_examples += ld->weight;
-    all.sd->total_features += ec->num_features;
+
     size_t loss = 1;
-    if (ld->label == ec->final_prediction)
+    if (ld->label == (uint32_t)ec->final_prediction)
       loss = 0;
-    all.sd->sum_loss += loss;
-    all.sd->sum_loss_since_last_dump += loss;
-  
+
+    if(ec->test_only)
+    {
+      all.sd->weighted_holdout_examples += ec->global_weight;//test weight seen
+      all.sd->weighted_holdout_examples_since_last_dump += ec->global_weight;
+      all.sd->weighted_holdout_examples_since_last_pass += ec->global_weight;
+      all.sd->holdout_sum_loss += loss;
+      all.sd->holdout_sum_loss_since_last_dump += loss;
+      all.sd->holdout_sum_loss_since_last_pass += loss;//since last pass
+    }
+    else
+    {
+      all.sd->weighted_examples += ld->weight;
+      all.sd->total_features += ec->num_features;
+      all.sd->sum_loss += loss;
+      all.sd->sum_loss_since_last_dump += loss;
+      all.sd->example_number++;
+    }
+ 
     for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
       all.print(*sink, ec->final_prediction, 0, ec->tag);
-  
-    all.sd->example_number++;
 
-    print_update(all, ec);
+    OAA::print_update(all, ec);
   }
 
-  void learn_with_output(oaa* d, example* ec, bool shouldOutput)
-  {
-    vw* all = d->all;
-    if (command_example(all,ec))
-      {
-	d->base.learn(ec);
-	return;
+  void label_to_array(void*label, v_array<uint32_t>&out) {
+    mc_label*l = (mc_label*)label;
+    if (l->label == (uint32_t)-1)
+      out.erase();
+    else {
+      if (out.size() == 1) out[0] = l->label;
+      else {
+        out.erase();
+        out.push_back( l->label );
       }
+    }
+  }
+
+  void finish_example(vw& all, void*, example* ec)
+  {
+    output_example(all, ec);
+    VW::finish_example(all, ec);
+  }
+
+  void learn(void* d, learner& base, example* ec)
+  {
+    oaa* o=(oaa*)d;
+
+    vw* all = o->all;
+
+    bool shouldOutput = all->raw_prediction > 0;
 
     mc_label* mc_label_data = (mc_label*)ec->ld;
     float prediction = 1;
     float score = INT_MIN;
   
-    if (mc_label_data->label == 0 || (mc_label_data->label > d->k && mc_label_data->label != (uint32_t)-1))
-      cout << "label " << mc_label_data->label << " is not in {1,"<< d->k << "} This won't work right." << endl;
-  
+    if (mc_label_data->label == 0 || (mc_label_data->label > o->k && mc_label_data->label != (uint32_t)-1))
+      cout << "label " << mc_label_data->label << " is not in {1,"<< o->k << "} This won't work right." << endl;
+    
     string outputString;
     stringstream outputStringStream(outputString);
 
-    for (size_t i = 1; i <= d->k; i++)
+    for (size_t i = 1; i <= o->k; i++)
       {
         label_data simple_temp;
         simple_temp.initial = 0.;
@@ -184,9 +237,7 @@ namespace OAA {
           simple_temp.label = -1;
         simple_temp.weight = mc_label_data->weight;
         ec->ld = &simple_temp;
-        if (i != 1)
-          update_example_indicies(all->audit, ec, d->increment);
-        d->base.learn(ec);
+        base.learn(ec, i-1);
         if (ec->partial_prediction > score)
           {
             score = ec->partial_prediction;
@@ -202,43 +253,12 @@ namespace OAA {
       }	
     ec->ld = mc_label_data;
     ec->final_prediction = prediction;
-    update_example_indicies(all->audit, ec, -d->total_increment);
 
     if (shouldOutput) 
       all->print_text(all->raw_prediction, outputStringStream.str(), ec->tag);
   }
 
-  void learn(void* d, example* ec) {
-    learn_with_output((oaa*)d, ec, false);
-  }
-
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-            learn_with_output((oaa*)d, ec, all->raw_prediction > 0);
-	    if (!command_example(all, ec))
-	      output_example(*all, ec);
-	    VW::finish_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
-  }
-
-  void finish(void* data)
-  {    
-    oaa* o=(oaa*)data;
-    o->base.finish();
-    free(o);
-  }
-
-  learner setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     oaa* data = (oaa*)calloc(1, sizeof(oaa));
     //first parse for number of actions
@@ -258,11 +278,10 @@ namespace OAA {
 
     data->all = &all;
     *(all.p->lp) = mc_label_parser;
-    data->increment = all.reg.stride * all.weights_per_problem;
-    all.weights_per_problem *= data->k;
-    data->total_increment = data->increment*(data->k-1);
-    data->base = all.l;
-    learner l(data, drive, learn, finish, all.l.sl);
+
+    learner* l = new learner(data, learn, all.l, data->k);
+    l->set_finish_example(finish_example);
+
     return l;
   }
 }
diff --git a/vowpalwabbit/oaa.h b/vowpalwabbit/oaa.h
index b9a94cd3..d4318253 100644
--- a/vowpalwabbit/oaa.h
+++ b/vowpalwabbit/oaa.h
@@ -17,11 +17,11 @@ namespace OAA
 {
 
   struct mc_label {
-    float label;
+    uint32_t label;
     float weight;
   };
   
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
   
   size_t read_cached_label(shared_data*, void* v, io_buf& cache);
   void cache_label(void* v, io_buf& cache);
@@ -36,22 +36,14 @@ namespace OAA
                                         NULL,
 					sizeof(mc_label)};
   
-  void output_example(vw& all, example* ec);
+  void finish_example(vw& all, void*, example* ec);
 
-  inline int example_is_newline(example* ec)
-  {
-    // if only index is constant namespace or no index
-    return ((ec->indices.size() == 0) || 
-            ((ec->indices.size() == 1) &&
-             (ec->indices.last() == constant_namespace)));
-  }
+  void label_to_array(void*label, v_array<uint32_t>&out);
 
   inline int example_is_test(example* ec)
   {
     return (((OAA::mc_label*)ec->ld)->label == (uint32_t)-1);
   }
-
-
 }
 
 #endif
diff --git a/vowpalwabbit/parse_args.cc b/vowpalwabbit/parse_args.cc
index 91d2bb98..983b1fe1 100644
--- a/vowpalwabbit/parse_args.cc
+++ b/vowpalwabbit/parse_args.cc
@@ -16,6 +16,8 @@ license as described in the file LICENSE.
 #include "global_data.h"
 #include "nn.h"
 #include "oaa.h"
+#include "bs.h"
+#include "topk.h"
 #include "ect.h"
 #include "csoaa.h"
 #include "wap.h"
@@ -51,6 +53,47 @@ bool valid_ns(char c)
     return true;
 }
 
+
+void parse_affix_argument(vw&all, string str) {
+  if (str.length() == 0) return;
+  char*cstr = new char[str.length()+1];
+  strcpy(cstr, str.c_str());
+
+  char*p = strtok(cstr, ",");
+  while (p != 0) {
+    char*q = p;
+    uint16_t prefix = 1;
+    if (q[0] == '+') { q++; }
+    else if (q[0] == '-') { prefix = 0; q++; }
+    if ((q[0] < '1') || (q[0] > '7')) {
+      cerr << "malformed affix argument (length must be 1..7): " << p << endl;
+      throw exception();
+    }
+    uint16_t len = (uint16_t)(q[0] - '0');
+    uint16_t ns = (uint16_t)' ';  // default namespace
+    if (q[1] != 0) {
+      if (valid_ns(q[1]))
+        ns = (uint16_t)q[1];
+      else {      
+        cerr << "malformed affix argument (invalid namespace): " << p << endl;
+        throw exception();
+      }
+      if (q[2] != 0) {
+        cerr << "malformed affix argument (too long): " << p << endl;
+        throw exception();
+      }
+    }
+
+    uint16_t afx = (len << 1) | (prefix & 0x1);
+    all.affix_features[ns] <<= 4;
+    all.affix_features[ns] |=  afx;
+    
+    p = strtok(NULL, ",");
+  }
+  
+  delete cstr;
+}
+
 vw* parse_args(int argc, char *argv[])
 {
   po::options_description desc("VW options");
@@ -66,6 +109,9 @@ vw* parse_args(int argc, char *argv[])
     ("active_simulation", "active learning simulation mode")
     ("active_mellowness", po::value<float>(&(all->active_c0)), "active learning mellowness parameter c_0. Default 8")
     ("binary", "report loss as binary classification on -1,1")
+    ("bs", po::value<size_t>(), "bootstrap mode with k rounds by online importance resampling")
+    ("top", po::value<size_t>(), "top k recommendation")
+    ("bs_type", po::value<string>(), "bootstrap mode - currently 'mean' or 'vote'")
     ("autolink", po::value<size_t>(), "create link function with polynomial d")
     ("sgd", "use regular stochastic gradient descent update.")
     ("adaptive", "use adaptive, individual learning rates.")
@@ -73,8 +119,7 @@ vw* parse_args(int argc, char *argv[])
     ("normalized", "use per feature normalized updates")
     ("exact_adaptive_norm", "use current default invariant normalized adaptive update rule")
     ("audit,a", "print weights of features")
-    ("bit_precision,b", po::value<size_t>(),
-     "number of bits in the feature table")
+    ("bit_precision,b", po::value<size_t>(), "number of bits in the feature table")
     ("bfgs", "use bfgs optimization")
     ("cache,c", "Use a cache.  The default is <data>.cache")
     ("cache_file", po::value< vector<string> >(), "The location(s) of cache_file.")
@@ -102,6 +147,7 @@ vw* parse_args(int argc, char *argv[])
     ("hessian_on", "use second derivative in line search")
     ("holdout_off", "no holdout data in multiple passes")
     ("holdout_period", po::value<uint32_t>(&(all->holdout_period)), "holdout period for test only, default 10")
+    ("holdout_after", po::value<uint32_t>(&(all->holdout_after)), "holdout after n training examples, default off (disables holdout_period)")
     ("version","Version information")
     ("ignore", po::value< vector<unsigned char> >(), "ignore namespaces beginning with character <arg>")
     ("keep", po::value< vector<unsigned char> >(), "keep namespaces beginning with character <arg>")
@@ -146,8 +192,7 @@ vw* parse_args(int argc, char *argv[])
     ("early_terminate", po::value<size_t>(), "Specify the number of passes tolerated when holdout loss doesn't decrease before early termination, default is 3")
     ("save_resume", "save extra state so learning can be resumed later with new data")
     ("sendto", po::value< vector<string> >(), "send examples to <host>")
-    ("searn", po::value<size_t>(), "use searn, argument=maximum action id")
-    ("searnimp", po::value<size_t>(), "use searn, argument=maximum action id or 0 for LDF")
+    ("searn", po::value<size_t>(), "use searn, argument=maximum action id or 0 for LDF")
     ("testonly,t", "Ignore label information and just test")
     ("loss_function", po::value<string>()->default_value("squared"), "Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.")
     ("quantile_tau", po::value<float>()->default_value(0.5), "Parameter \\tau associated with Quantile loss. Defaults to 0.5")
@@ -158,7 +203,9 @@ vw* parse_args(int argc, char *argv[])
 
     ("sort_features", "turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes")
     ("ngram", po::value< vector<string> >(), "Generate N grams")
-    ("skips", po::value< vector<string> >(), "Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram.");
+    ("skips", po::value< vector<string> >(), "Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram.")
+    ("affix", po::value<string>(), "generate prefixes/suffixes of features; argument '+2a,-3b,+1' means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace")
+    ("spelling", po::value< vector<string> >(), "compute spelling features for a give namespace (use '_' for default namespace)");
 
   //po::positional_options_description p;
   // Be friendly: if -d was left out, treat positional param as data file
@@ -185,8 +232,11 @@ vw* parse_args(int argc, char *argv[])
   if(vm.count("holdout_off"))
       all->holdout_set_off = true;
 
-  all->l = GD::setup(*all, vm);
-  all->scorer = all->l;
+  if(!all->holdout_set_off && (vm.count("output_feature_regularizer_binary") || vm.count("output_feature_regularizer_text")))
+  {
+      all->holdout_set_off = true;
+      cerr<<"Making holdout_set_off=true since output regularizer specified\n";
+  }   
 
   all->data_filename = "";
 
@@ -274,8 +324,12 @@ vw* parse_args(int argc, char *argv[])
       }
     }
   }
+
+  all->l = GD::setup(*all, vm);
+  all->scorer = all->l;
+
   if (vm.count("bfgs") || vm.count("conjugate_gradient")) 
-    BFGS::setup(*all, to_pass_further, vm, vm_file);
+    all->l = BFGS::setup(*all, to_pass_further, vm, vm_file);
 
   if (vm.count("version") || argc == 1) {
     /* upon direct query for version -- spit it out to stdout */
@@ -306,6 +360,18 @@ vw* parse_args(int argc, char *argv[])
       all->skip_strings = vm["skips"].as<vector<string> >();
       compile_gram(all->skip_strings, all->skips, (char*)"skips", all->quiet);
     }
+
+  if (vm.count("affix")) {
+    parse_affix_argument(*all, vm["affix"].as<string>());
+  }
+
+  if (vm.count("spelling")) {
+    vector<string> spelling_ns = vm["spelling"].as< vector<string> >();
+    for (size_t id=0; id<spelling_ns.size(); id++)
+      if (spelling_ns[id][0] == '_') all->spelling_features[' '] = true;
+      else all->spelling_features[(size_t)spelling_ns[id][0]] = true;
+  }
+  
   if (vm.count("bit_precision"))
     {
       all->default_bits = false;
@@ -338,8 +404,6 @@ vw* parse_args(int argc, char *argv[])
   if(vm.count("sort_features"))
     all->p->sort_features = true;
 
-  
-
   if (vm.count("quadratic"))
     {
       all->pairs = vm["quadratic"].as< vector<string> >();
@@ -558,7 +622,6 @@ vw* parse_args(int argc, char *argv[])
   if(vm.count("quantile_tau"))
     loss_parameter = vm["quantile_tau"].as<float>();
 
-  all->is_noop = false;
   if (vm.count("noop")) 
     all->l = NOOP::setup(*all);
   
@@ -603,8 +666,11 @@ vw* parse_args(int argc, char *argv[])
   }
 
   if (vm.count("raw_predictions")) {
-    if (!all->quiet)
+    if (!all->quiet) {
       cerr << "raw predictions = " <<  vm["raw_predictions"].as< string >() << endl;
+      if (vm.count("binary") || vm_file.count("binary"))
+        cerr << "Warning: --raw has no defined value when --binary specified, expect no output" << endl;
+    }
     if (strcmp(vm["raw_predictions"].as< string >().c_str(), "stdout") == 0)
       all->raw_prediction = 1;//stdout
     else
@@ -628,8 +694,9 @@ vw* parse_args(int argc, char *argv[])
     all->l = SENDER::setup(*all, vm, all->pairs);
 
   // load rest of regressor
-  all->l.save_load(io_temp, true, false);
+  all->l->save_load(io_temp, true, false);
   io_temp.close_file();
+
   //load the mask model, might be different from -i
   parse_mask_regressor_args(*all, vm);
 
@@ -645,7 +712,7 @@ vw* parse_args(int argc, char *argv[])
   all->reg_mode += (all->l2_lambda > 0.) ? 2 : 0;
   if (!all->quiet)
     {
-      if (all->reg_mode %2)
+      if (all->reg_mode %2 && !vm.count("bfgs"))
 	cerr << "using l1 regularization = " << all->l1_lambda << endl;
       if (all->reg_mode > 1)
 	cerr << "using l2 regularization = " << all->l2_lambda << endl;
@@ -661,8 +728,11 @@ vw* parse_args(int argc, char *argv[])
   if (all->rank != 0) 
     all->l = MF::setup(*all, vm);
 
-  if(vm.count("autolink") || vm_file.count("autolinnk") ) 
+  if(vm.count("autolink") || vm_file.count("autolink") ) 
     all->l = ALINK::setup(*all, to_pass_further, vm, vm_file);
+
+  if(vm.count("top") || vm_file.count("top") ) 
+    all->l = TOPK::setup(*all, to_pass_further, vm, vm_file);
   
   if (vm.count("binary") || vm_file.count("binary"))
     all->l = BINARY::setup(*all, to_pass_further, vm, vm_file);
@@ -723,11 +793,8 @@ vw* parse_args(int argc, char *argv[])
     got_cb = true;
   }
 
+  all->searnstr = NULL;
   if (vm.count("searn") || vm_file.count("searn") ) { 
-    if (vm.count("searnimp") || vm_file.count("searnimp")) {
-      cerr << "fail: cannot have both --searn and --searnimp" << endl;
-      throw exception();
-    }
     if (!got_cs && !got_cb) {
       if( vm_file.count("searn") ) vm.insert(pair<string,po::variable_value>(string("csoaa"),vm_file["searn"]));
       else vm.insert(pair<string,po::variable_value>(string("csoaa"),vm["searn"]));
@@ -735,26 +802,18 @@ vw* parse_args(int argc, char *argv[])
       all->l = CSOAA::setup(*all, to_pass_further, vm, vm_file);  // default to CSOAA unless others have been specified
       got_cs = true;
     }
+    all->searnstr = (Searn::searn*)calloc(1, sizeof(Searn::searn));
     all->l = Searn::setup(*all, to_pass_further, vm, vm_file);
   }
 
-  if (vm.count("searnimp") || vm_file.count("searnimp") ) { 
-    if (!got_cs && !got_cb) {
-      if( vm_file.count("searnimp") ) vm.insert(pair<string,po::variable_value>(string("csoaa"),vm_file["searnimp"]));
-      else vm.insert(pair<string,po::variable_value>(string("csoaa"),vm["searnimp"]));
-      
-      all->l = CSOAA::setup(*all, to_pass_further, vm, vm_file);  // default to CSOAA unless others have been specified
-      got_cs = true;
-    }
-    all->searnstr = (ImperativeSearn::searn*)calloc(1, sizeof(ImperativeSearn::searn));
-    all->l = ImperativeSearn::setup(*all, to_pass_further, vm, vm_file);
-  }
-
   if (got_cb && got_mc) {
     cerr << "error: doesn't make sense to do both MC learning and CB learning" << endl;
     throw exception();
   }
 
+  if(vm.count("bs") || vm_file.count("bs") ) 
+    all->l = BS::setup(*all, to_pass_further, vm, vm_file);
+
   if (to_pass_further.size() > 0) {
     bool is_actually_okay = false;
 
@@ -788,11 +847,12 @@ vw* parse_args(int argc, char *argv[])
 
   parse_source_args(*all, vm, all->quiet,all->numpasses);
 
-  // force stride * weights_per_problem to be divisible by 2 to avoid 32-bit overflow
+  // force stride * weights_per_problem to be a power of 2 to avoid 32-bit overflow
   uint32_t i = 0;
-  while (all->reg.stride * all->weights_per_problem  > (uint32_t)(1 << i))
+  size_t params_per_problem = all->l->increment * all->l->weights;
+  while (params_per_problem > (uint32_t)(1 << i))
     i++;
-  all->weights_per_problem = (1 << i) / all->reg.stride;
+  all->wpp = (1 << i) / all->reg.stride;
 
   return all;
 }
@@ -873,10 +933,9 @@ namespace VW {
   void finish(vw& all)
   {
     finalize_regressor(all, all.final_regressor_name);
-    all.l.finish();
+    all.l->finish();
     if (all.reg.weight_vector != NULL)
       free(all.reg.weight_vector);
-    if (all.searnstr != NULL) free(all.searnstr);
     free_parser(all);
     finalize_source(all.p);
     free(all.p->lp);
diff --git a/vowpalwabbit/parse_example.cc b/vowpalwabbit/parse_example.cc
index ddbd09c7..d4f673bf 100644
--- a/vowpalwabbit/parse_example.cc
+++ b/vowpalwabbit/parse_example.cc
@@ -11,6 +11,7 @@ license as described in the file LICENSE.
 #include "cache.h"
 #include "unique_sort.h"
 #include "global_data.h"
+#include "constant.h"
 
 using namespace std;
 
@@ -74,6 +75,9 @@ public:
   parser* p;
   example* ae;
   uint32_t weights_per_problem;
+  uint32_t* affix_features;
+  bool* spelling_features;
+  v_array<char> spelling;
   
   ~TC_parser(){ }
   
@@ -133,6 +137,68 @@ public:
 	audit_data ad = {copy(base),feature_v.begin,word_hash,v,true};
 	ae->audit_features[index].push_back(ad);
       }
+      if ((affix_features[index] > 0) && (feature_name.end != feature_name.begin)) {
+        if (ae->atomics[affix_namespace].size() == 0)
+          ae->indices.push_back(affix_namespace);
+        uint32_t affix = affix_features[index];
+        while (affix > 0) {
+          bool is_prefix = affix & 0x1;
+          uint32_t len   = (affix >> 1) & 0x7;
+          substring affix_name = { feature_name.begin, feature_name.end };
+          if (affix_name.end > affix_name.begin + len) {
+            if (is_prefix)
+              affix_name.end = affix_name.begin + len;
+            else
+              affix_name.begin = affix_name.end - len;
+          }
+          word_hash = p->hasher(affix_name,(uint32_t)channel_hash) * (affix_constant + (affix & 0xF) * quadratic_constant);
+          feature f2 = { v, (uint32_t) word_hash * weights_per_problem };
+          ae->sum_feat_sq[affix_namespace] += v*v;
+          ae->atomics[affix_namespace].push_back(f2);
+          if (audit) {
+            v_array<char> affix_v;
+            if (index != ' ') affix_v.push_back(index);
+            affix_v.push_back(is_prefix ? '+' : '-');
+            affix_v.push_back('0' + len);
+            affix_v.push_back('=');
+            push_many(affix_v, affix_name.begin, affix_name.end - affix_name.begin);
+            affix_v.push_back('\0');
+            audit_data ad = {copy((char*)"affix"),affix_v.begin,word_hash,v,true};
+            ae->audit_features[affix_namespace].push_back(ad);
+          }
+          
+          affix >>= 4;
+        }
+      }
+      if (spelling_features[index]) {
+        if (ae->atomics[spelling_namespace].size() == 0)
+          ae->indices.push_back(spelling_namespace);
+        //v_array<char> spelling;
+        spelling.erase();
+        for (char*c = feature_name.begin; c!=feature_name.end; ++c) {
+          char d = 0;
+          if      ((*c >= '0') && (*c <= '9')) d = '0';
+          else if ((*c >= 'a') && (*c <= 'z')) d = 'a';
+          else if ((*c >= 'A') && (*c <= 'Z')) d = 'A';
+          else if  (*c == '.')                 d = '.';
+          else                                 d = '#';
+          if ((spelling.size() == 0) || (spelling.last() != d))
+            spelling.push_back(d);
+        }
+        substring spelling_ss = { spelling.begin, spelling.end };
+        size_t word_hash = hashstring(spelling_ss, (uint32_t)channel_hash);
+        feature f2 = { v, (uint32_t) word_hash * weights_per_problem };
+        ae->sum_feat_sq[spelling_namespace] += v*v;
+        ae->atomics[spelling_namespace].push_back(f2);
+        if (audit) {
+          v_array<char> spelling_v;
+          if (index != ' ') { spelling_v.push_back(index); spelling_v.push_back('_'); }
+          push_many(spelling_v, spelling_ss.begin, spelling_ss.end - spelling_ss.begin);
+          spelling_v.push_back('\0');
+          audit_data ad = {copy((char*)"spelling"),spelling_v.begin,word_hash,v,true};
+          ae->audit_features[spelling_namespace].push_back(ad);
+        }
+      }
     }
   }
   
@@ -190,8 +256,7 @@ public:
       cout << "malformed example !\n'|' , space or EOL expected after : \"" << std::string(beginLine, reading_head - beginLine).c_str() << "\"" << endl;
     }
   }
-  
-  
+    
   inline void nameSpace(){
     cur_channel_v = 1.0;
     base = NULL;
@@ -242,7 +307,9 @@ public:
     this->endLine = endLine;
     this->p = all.p;
     this->ae = ae;
-    this->weights_per_problem = all.weights_per_problem;
+    this->weights_per_problem = all.wpp;
+    this->affix_features = all.affix_features;
+    this->spelling_features = all.spelling_features;
     audit = all.audit || all.hash_inv;
     listNameSpace();
   }
diff --git a/vowpalwabbit/parse_primitives.cc b/vowpalwabbit/parse_primitives.cc
index 6a090d9d..ef515de5 100644
--- a/vowpalwabbit/parse_primitives.cc
+++ b/vowpalwabbit/parse_primitives.cc
@@ -12,13 +12,13 @@ license as described in the file LICENSE.
 
 using namespace std;
 
-void tokenize(char delim, substring s, v_array<substring>& ret)
+void tokenize(char delim, substring s, v_array<substring>& ret, bool allow_empty)
 {
   ret.erase();
   char *last = s.begin;
   for (; s.begin != s.end; s.begin++) {
     if (*s.begin == delim) {
-      if (s.begin != last)
+      if (allow_empty || (s.begin != last))
 	{
 	  substring temp = {last, s.begin};
 	  ret.push_back(temp);
@@ -26,7 +26,7 @@ void tokenize(char delim, substring s, v_array<substring>& ret)
       last = s.begin+1;
     }
   }
-  if (s.begin != last)
+  if (allow_empty || (s.begin != last))
     {
       substring final = {last, s.begin};
       ret.push_back(final);
diff --git a/vowpalwabbit/parse_primitives.h b/vowpalwabbit/parse_primitives.h
index 6d2c1562..90a4f9e4 100644
--- a/vowpalwabbit/parse_primitives.h
+++ b/vowpalwabbit/parse_primitives.h
@@ -98,12 +98,13 @@ struct parser {
   uint32_t in_pass_counter;
   example* examples;
   uint64_t used_index;
+  bool emptylines_separate_examples; // true if you want to have holdout computed on a per-block basis rather than a per-line basis
   MUTEX examples_lock;
   CV example_available;
   CV example_unused;
   MUTEX output_lock;
   CV output_done;
-
+  
   bool done;
   v_array<size_t> gram_mask;
 
@@ -120,7 +121,7 @@ struct parser {
 };
 
 //chop up the string into a v_array of substring.
-void tokenize(char delim, substring s, v_array<substring> &ret);
+void tokenize(char delim, substring s, v_array<substring> &ret, bool allow_empty=false);
 
 inline char* safe_index(char *start, char v, char *max)
 {
@@ -203,7 +204,15 @@ inline float float_of_substring(substring s)
 
 inline int int_of_substring(substring s)
 {
-  return atoi(std::string(s.begin, s.end-s.begin).c_str());
+  char* endptr = s.end;
+  int i = strtol(s.begin,&endptr,10);
+  if (endptr == s.begin && s.begin != s.end)  
+    {
+      std::cout << "warning: " << std::string(s.begin, s.end-s.begin).c_str() << " is not a good int, replacing with 0" << std::endl;
+      i = 0;
+    }
+
+  return i;
 }
 
 #endif
diff --git a/vowpalwabbit/parse_regressor.cc b/vowpalwabbit/parse_regressor.cc
index a5d0e523..f30a53e7 100644
--- a/vowpalwabbit/parse_regressor.cc
+++ b/vowpalwabbit/parse_regressor.cc
@@ -48,6 +48,7 @@ const size_t buf_size = 512;
 
 void save_load_header(vw& all, io_buf& model_file, bool read, bool text)
 {
+
   char buff[buf_size];
   char buff2[buf_size];
   uint32_t text_len;
@@ -167,7 +168,7 @@ void save_load_header(vw& all, io_buf& model_file, bool read, bool text)
 				buff,text_len, text);
       for (size_t i = 0; i < ngram_len; i++)
 	{
-	  char ngram[3];
+	  char ngram[3] = {0,0,0};
 	  if (!read) {
 	    text_len = sprintf(buff, "%s ", all.ngram_strings[i].c_str());
 	    memcpy(ngram, all.ngram_strings[i].c_str(), min(3, all.ngram_strings[i].size()));
@@ -195,7 +196,7 @@ void save_load_header(vw& all, io_buf& model_file, bool read, bool text)
 				buff,text_len, text);
       for (size_t i = 0; i < skip_len; i++)
 	{
-	  char skip[3];
+	  char skip[3] = {0,0,0};
 	  if (!read) {
 	    text_len = sprintf(buff, "%s ", all.skip_strings[i].c_str());
 	    memcpy(skip, all.skip_strings[i].c_str(), min(3, all.skip_strings[i].size()));
@@ -239,7 +240,7 @@ void dump_regressor(vw& all, string reg_name, bool as_text)
   io_temp.open_file(start_name.c_str(), all.stdin_off, io_buf::WRITE);
   
   save_load_header(all, io_temp, false, as_text);
-  all.l.save_load(io_temp, false, as_text);
+  all.l->save_load(io_temp, false, as_text);
 
   io_temp.flush(); // close_file() should do this for me ...
   io_temp.close_file();
@@ -260,22 +261,25 @@ void save_predictor(vw& all, string reg_name, size_t current_pass)
 
 void finalize_regressor(vw& all, string reg_name)
 {
-  if (all.per_feature_regularizer_output.length() > 0)
-    dump_regressor(all, all.per_feature_regularizer_output, false);
-  else
-    dump_regressor(all, reg_name, false);
-  if (all.per_feature_regularizer_text.length() > 0)
-    dump_regressor(all, all.per_feature_regularizer_text, true);
-  else{
-    dump_regressor(all, all.text_regressor_name, true);
-    all.print_invert = true;
-    dump_regressor(all, all.inv_hash_regressor_name, true);
-    all.print_invert = false;
+  if (!all.early_terminate){
+    if (all.per_feature_regularizer_output.length() > 0)
+      dump_regressor(all, all.per_feature_regularizer_output, false);
+    else
+      dump_regressor(all, reg_name, false);
+    if (all.per_feature_regularizer_text.length() > 0)
+      dump_regressor(all, all.per_feature_regularizer_text, true);
+    else{
+      dump_regressor(all, all.text_regressor_name, true);
+      all.print_invert = true;
+      dump_regressor(all, all.inv_hash_regressor_name, true);
+      all.print_invert = false;
+    }
   }
 }
 
 void parse_regressor_args(vw& all, po::variables_map& vm, io_buf& io_temp)
 {
+
   if (vm.count("final_regressor")) {
     all.final_regressor_name = vm["final_regressor"].as<string>();
     if (!all.quiet)
@@ -287,11 +291,22 @@ void parse_regressor_args(vw& all, po::variables_map& vm, io_buf& io_temp)
   vector<string> regs;
   if (vm.count("initial_regressor") || vm.count("i"))
     regs = vm["initial_regressor"].as< vector<string> >();
-  
-  if (regs.size() > 0)
+
+  if (vm.count("input_feature_regularizer"))
+    regs.push_back(vm["input_feature_regularizer"].as<string>());
+
+  if (regs.size() > 0) {
     io_temp.open_file(regs[0].c_str(), all.stdin_off, io_buf::READ);
+    if (!all.quiet) {
+        //cerr << "initial_regressor = " << regs[0] << endl;
+      if (regs.size() > 1) {
+        cerr << "warning: ignoring remaining " << (regs.size() - 1) << " initial regressors" << endl;
+      }
+    }
+  }
 
   save_load_header(all, io_temp, true, false);
+
 }
 
 void parse_mask_regressor_args(vw& all, po::variables_map& vm){
@@ -310,16 +325,31 @@ void parse_mask_regressor_args(vw& all, po::variables_map& vm){
         return;
       }
     }
+
     //all other cases, including from different file, or -i does not exist, need to read in the mask file
     io_buf io_temp_mask;
     io_temp_mask.open_file(mask_filename.c_str(), false, io_buf::READ);
     save_load_header(all, io_temp_mask, true, false);
-    all.l.save_load(io_temp_mask, true, false);
+    all.l->save_load(io_temp_mask, true, false);
     io_temp_mask.close_file();
     for (size_t j = 0; j < length; j++){	 
       if(all.reg.weight_vector[j*all.reg.stride] != 0.)
         all.reg.weight_vector[j*all.reg.stride + all.feature_mask_idx] = 1.;
     }
+
+    // Deal with the over-written header from initial regressor
+    if (vm.count("initial_regressor")) {
+      vector<string> init_filename = vm["initial_regressor"].as< vector<string> >();
+
+      // Load original header again.
+      io_buf io_temp;
+      io_temp.open_file(init_filename[0].c_str(), false, io_buf::READ);
+      save_load_header(all, io_temp, true, false);
+      io_temp.close_file();
+    } else {
+      // If no initial regressor, just clear out the options loaded from the header.
+      all.options_from_file.assign("");
+    }
   }
 }
 
diff --git a/vowpalwabbit/parser.cc b/vowpalwabbit/parser.cc
index 0a10dbce..bd59cd80 100644
--- a/vowpalwabbit/parser.cc
+++ b/vowpalwabbit/parser.cc
@@ -139,10 +139,13 @@ void handle_sigterm (int)
   got_sigterm = true;
 }
 
-bool is_test_only(uint32_t counter, uint32_t period, bool holdout_off)
+bool is_test_only(uint32_t counter, uint32_t period, uint32_t after, bool holdout_off)
 {
   if(holdout_off) return false;
-  return (counter % period == 0);
+  if (after == 0) // hold out by period
+    return (counter % period == 0);
+  else // hold out by position
+    return (counter+1 >= after);
 }
 
 parser* new_parser()
@@ -719,7 +722,7 @@ bool parse_atomic_example(vw& all, example* ae, bool do_read = true)
   if (all.p->write_cache) 
     {
       all.p->lp->cache_label(ae->ld,*(all.p->output));
-      cache_features(*(all.p->output), ae, all.parse_mask);
+      cache_features(*(all.p->output), ae, (uint32_t)all.parse_mask);
     }
 
   return true;
@@ -738,10 +741,13 @@ void setup_example(vw& all, example* ae)
   ae->num_features = 0;
   ae->total_sum_feat_sq = 0;
   ae->done = false;
+  ae->loss = 0.;
   
   ae->example_counter = (size_t)(all.p->parsed_examples + 1);
-  all.p->in_pass_counter++;
-  ae->test_only = is_test_only(all.p->in_pass_counter, all.holdout_period, all.holdout_set_off);
+  if ((!all.p->emptylines_separate_examples) || example_is_newline(ae))
+    all.p->in_pass_counter++;
+
+  ae->test_only = is_test_only(all.p->in_pass_counter, all.holdout_period, all.holdout_after, all.holdout_set_off);
   ae->global_weight = all.p->lp->get_weight(ae->ld);
   all.sd->t += ae->global_weight;
   ae->example_t = (float)all.sd->t;
@@ -769,7 +775,9 @@ void setup_example(vw& all, example* ae)
   if (all.add_constant) {
     //add constant feature
     ae->indices.push_back(constant_namespace);
-    feature temp = {1,(uint32_t) (constant * all.weights_per_problem)};
+    //jntj: weights_per_problem no longer available
+    //feature temp = {1,(uint32_t) (constant * all.weights_per_problem)};
+    feature temp = {1,(uint32_t) (constant)};
     ae->atomics[constant_namespace].push_back(temp);
     ae->total_sum_feat_sq++;
   }
@@ -915,19 +923,19 @@ namespace VW{
     int fs_count = 0;
     for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++)
       {
-	fs_ptr[fs_count].name = *i;
-	fs_ptr[fs_count].len = ec->atomics[*i].size();
-	fs_ptr[fs_count].fs = new feature[fs_ptr[fs_count].len];
+		fs_ptr[fs_count].name = *i;
+		fs_ptr[fs_count].len = ec->atomics[*i].size();
+		fs_ptr[fs_count].fs = new feature[fs_ptr[fs_count].len];
 	
-	int f_count = 0;
-	for (feature *f = ec->atomics[*i].begin; f != ec->atomics[*i].end; f++)
-	  {
-	    feature t = *f;
-	    t.weight_index /= all.reg.stride;
-	    fs_ptr[fs_count].fs[f_count] = t;
-	    f_count++;
-	  }
-	fs_count++;
+		int f_count = 0;
+		for (feature *f = ec->atomics[*i].begin; f != ec->atomics[*i].end; f++)
+		  {
+			feature t = *f;
+			t.weight_index /= all.reg.stride;
+			fs_ptr[fs_count].fs[f_count] = t;
+			f_count++;
+		  }
+		fs_count++;
       }
     return fs_ptr;
   }
@@ -1007,13 +1015,18 @@ void *main_parse_loop(void *in)
 {
 	vw* all = (vw*) in;
 	size_t example_number = 0;  // for variable-size batch learning algorithms
+
+
 	while(!all->p->done)
 	  {
-	    example* ae = get_unused_example(*all);
-	   if (!all->do_reset_source && example_number != all->pass_length && all->max_examples > example_number
-		   && parse_atomic_example(*all, ae) )  
-	     setup_example(*all, ae);
-	   else
+            example* ae = get_unused_example(*all);
+	    if (!all->do_reset_source && example_number != all->pass_length && all->max_examples > example_number
+		   && parse_atomic_example(*all, ae) )
+	     {
+	       setup_example(*all, ae);
+	       example_number++;
+	     }
+	    else
 	     {
 	       reset_source(*all, all->num_bits);
 	       all->do_reset_source = false;
@@ -1032,11 +1045,11 @@ void *main_parse_loop(void *in)
 			 }
 	       example_number = 0;
 	     }
-	   example_number++;
 	   mutex_lock(&all->p->examples_lock);
 	   all->p->parsed_examples++;
 	   condition_variable_signal_all(&all->p->example_available);
 	   mutex_unlock(&all->p->examples_lock);
+
 	  }  
 	return NULL;
 }
@@ -1067,6 +1080,11 @@ example* get_example(parser* p)
     }
   }
 }
+
+label_data* get_label(example* ec)
+{
+	return (label_data*)(ec->ld);
+}
 }
 
 void initialize_examples(vw& all)
diff --git a/vowpalwabbit/parser.h b/vowpalwabbit/parser.h
index 6f6f5401..7abc944a 100644
--- a/vowpalwabbit/parser.h
+++ b/vowpalwabbit/parser.h
@@ -11,12 +11,6 @@ license as described in the file LICENSE.
 #include "example.h"
 #include "vw.h"
 
-const size_t wap_ldf_namespace  = 126;
-const size_t history_namespace  = 127;
-const size_t constant_namespace = 128;
-const size_t nn_output_namespace  = 129;
-const size_t autolink_namespace  = 130;
-
 parser* new_parser();
 #include <boost/program_options.hpp>
 namespace po = boost::program_options;
diff --git a/vowpalwabbit/searn.cc b/vowpalwabbit/searn.cc
index 3507dbfc..f4081713 100644
--- a/vowpalwabbit/searn.cc
+++ b/vowpalwabbit/searn.cc
@@ -31,7 +31,7 @@ namespace SearnUtil
   using namespace std;
 
   string   audit_feature_space("history");
-  uint32_t history_constant    = 8290741;
+  uint32_t history_constant    = 8290743;
   uint32_t example_number = 0;
 
 
@@ -62,16 +62,6 @@ namespace SearnUtil
       free(ptr);
   }
 
-  void add_policy_offset(vw&all, example *ec, uint32_t increment, uint32_t policy)
-  {
-    update_example_indicies(all.audit, ec, policy * increment);
-  }
-
-  void remove_policy_offset(vw&all, example *ec, uint32_t increment, uint32_t policy)
-  {
-    update_example_indicies(all.audit, ec, -policy * increment);
-  }
-
   int random_policy(uint64_t seed, float beta, bool allow_current_policy, int current_policy, bool allow_optimal, bool reset_seed)
   {
     if(reset_seed) //reset_seed is false for contextual bandit, so that we only reset the seed if the base learner is not a contextual bandit learner, as this breaks the exploration.
@@ -96,6 +86,7 @@ namespace SearnUtil
     } else {
       float r = frand48();
       pid = 0;
+    
       if (r > beta) {
         r -= beta;
         while ((r > 0) && (pid < num_valid_policies-1)) {
@@ -104,7 +95,6 @@ namespace SearnUtil
         }
       }
     }
-
     // figure out which policy pid refers to
     if (allow_optimal && (pid == num_valid_policies-1))
       return -1; // this is the optimal policy
@@ -116,54 +106,54 @@ namespace SearnUtil
     return pid;
   }
 
-  void add_history_to_example(vw&all, history_info *hinfo, example* ec, history h)
-  {
-    size_t v0, v, max_string_length = 0;
-    size_t total_length = max(hinfo->features, hinfo->length);
+  const float history_value = 1.;
 
-    if (total_length == 0) return;
+  void add_history_to_example(vw&all, history_info &hinfo, example* ec, history h)
+  {
+    uint64_t v0, v1, v, max_string_length = 0;
+    uint32_t wpp = all.wpp * all.reg.stride;
+    if (hinfo.length == 0) return;
     if (h == NULL) {
       cerr << "error: got empty history in add_history_to_example" << endl;
       throw exception();
     }
 
     if (all.audit) {
-      max_string_length = max((int)(ceil( log10((float)total_length+1) )),
-                              (int)(ceil( log10((float)MAX_ACTION_ID+1) ))) + 1;
+      max_string_length = max((int)(ceil( log10((float)hinfo.length+1) )),
+                              (int)(ceil( log10((float)10000000000+1) ))) + 1;   // max action id
     }
 
-    for (size_t t=1; t<=total_length; t++) {
-      v0 = (h[hinfo->length-t] * quadratic_constant + t) * quadratic_constant + history_constant;
-
+    for (uint32_t t=1; t<=hinfo.length; t++) {
+      v0 = (h[hinfo.length-t] * quadratic_constant + t) * history_constant;
       // add the basic history features
-      feature temp = {1., (uint32_t) ( (2*v0) & all.parse_mask )};
+      feature temp = {history_value, (uint32_t) ( (v0*wpp) & all.reg.weight_mask )};
       ec->atomics[history_namespace].push_back(temp);
 
       if (all.audit) {
-        audit_data a_feature = { NULL, NULL, (uint32_t)((2*v0) & all.parse_mask), 1., true };
+        audit_data a_feature = { NULL, NULL, (uint32_t)((v0*wpp) & all.reg.weight_mask), history_value, true };
         a_feature.space = (char*)calloc_or_die(audit_feature_space.length()+1, sizeof(char));
         strcpy(a_feature.space, audit_feature_space.c_str());
 
         a_feature.feature = (char*)calloc_or_die(5 + 2*max_string_length, sizeof(char));
-        sprintf(a_feature.feature, "ug@%d=%d", (int)t, (int)h[hinfo->length-t]);
+        sprintf(a_feature.feature, "ug@%d=%d", (int)t, (int)h[hinfo.length-t]);
 
         ec->audit_features[history_namespace].push_back(a_feature);
       }
 
       // add the bigram features
-      if ((t > 1) && hinfo->bigrams) {
-        v0 = ((v0 - history_constant) * quadratic_constant + h[hinfo->length-t+1]) * quadratic_constant + history_constant;
+      if ((t > 1) && hinfo.bigrams) {
+        v1 = (v0 * cubic_constant + h[hinfo.length-t+1]) * history_constant;
 
-        feature temp = {1., (uint32_t) ( (2*v0) & all.parse_mask )};
+        feature temp = {history_value, (uint32_t) ( (v1*wpp) & all.reg.weight_mask )};
         ec->atomics[history_namespace].push_back(temp);
 
         if (all.audit) {
-          audit_data a_feature = { NULL, NULL, (uint32_t)((2*v0) & all.parse_mask), 1., true };
+          audit_data a_feature = { NULL, NULL, (uint32_t)((v1*wpp) & all.reg.weight_mask), history_value, true };
           a_feature.space = (char*)calloc_or_die(audit_feature_space.length()+1, sizeof(char));
           strcpy(a_feature.space, audit_feature_space.c_str());
 
           a_feature.feature = (char*)calloc_or_die(6 + 3*max_string_length, sizeof(char));
-          sprintf(a_feature.feature, "bg@%d=%d-%d", (int)t-1, (int)h[hinfo->length-t], (int)h[hinfo->length-t+1]);
+          sprintf(a_feature.feature, "bg@%d=%d-%d", (int)t-1, (int)h[hinfo.length-t], (int)h[hinfo.length-t+1]);
 
           ec->audit_features[history_namespace].push_back(a_feature);
         }
@@ -173,7 +163,7 @@ namespace SearnUtil
 
     string fstring;
 
-    if (hinfo->features > 0) {
+    if (hinfo.features > 0) {
       for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) {
         int feature_index = 0;
         for (feature* f = ec->atomics[*i].begin; f != ec->atomics[*i].end; f++) {
@@ -190,39 +180,39 @@ namespace SearnUtil
 
           v = f->weight_index + history_constant;
 
-          for (size_t t=1; t<=hinfo->features; t++) {
-            v0 = (h[hinfo->length-t] * quadratic_constant + t) * quadratic_constant;
+          for (uint32_t t=1; t<=hinfo.features; t++) {
+            v0 = (h[hinfo.length-t] * quadratic_constant + t) * history_constant;
           
             // add the history/feature pair
-            feature temp = {1., (uint32_t) ( (2*(v0 + v)) & all.parse_mask )};
+            feature temp = {history_value, (uint32_t) ( ((v0 + v)*wpp) & all.reg.weight_mask )};
             ec->atomics[history_namespace].push_back(temp);
 
             if (all.audit) {
-              audit_data a_feature = { NULL, NULL, (uint32_t)((2*(v+v0)) & all.parse_mask), 1., true };
+              audit_data a_feature = { NULL, NULL, (uint32_t)(((v+v0)*wpp) & all.reg.weight_mask), history_value, true };
               a_feature.space = (char*)calloc_or_die(audit_feature_space.length()+1, sizeof(char));
               strcpy(a_feature.space, audit_feature_space.c_str());
 
               a_feature.feature = (char*)calloc_or_die(8 + 2*max_string_length + fstring.length(), sizeof(char));
-              sprintf(a_feature.feature, "ug+f@%d=%d=%s", (int)t, (int)h[hinfo->length-t], fstring.c_str());
+              sprintf(a_feature.feature, "ug+f@%d=%d=%s", (int)t, (int)h[hinfo.length-t], fstring.c_str());
 
               ec->audit_features[history_namespace].push_back(a_feature);
             }
 
 
             // add the bigram
-            if ((t > 0) && hinfo->bigram_features) {
-              v0 = (v0 + h[hinfo->length-t+1]) * quadratic_constant;
+            if ((t > 0) && hinfo.bigram_features) {
+              v1 = (v0 * cubic_constant + h[hinfo.length-t+1]) * history_constant;
 
-              feature temp = {1., (uint32_t) ( (2*(v + v0)) & all.parse_mask )};
+              feature temp = {history_value, (uint32_t) ( ((v + v1)*wpp) & all.reg.weight_mask )};
               ec->atomics[history_namespace].push_back(temp);
 
               if (all.audit) {
-                audit_data a_feature = { NULL, NULL, (uint32_t)((2*(v+v0)) & all.parse_mask), 1., true };
+                audit_data a_feature = { NULL, NULL, (uint32_t)(((v+v1)*wpp) & all.reg.weight_mask), history_value, true };
                 a_feature.space = (char*)calloc_or_die(audit_feature_space.length()+1, sizeof(char));
                 strcpy(a_feature.space, audit_feature_space.c_str());
 
                 a_feature.feature = (char*)calloc_or_die(9 + 3*max_string_length + fstring.length(), sizeof(char));
-                sprintf(a_feature.feature, "bg+f@%d=%d-%d=%s", (int)t-1, (int)h[hinfo->length-t], (int)h[hinfo->length-t+1], fstring.c_str());
+                sprintf(a_feature.feature, "bg+f@%d=%d-%d=%s", (int)t-1, (int)h[hinfo.length-t], (int)h[hinfo.length-t+1], fstring.c_str());
 
                 ec->audit_features[history_namespace].push_back(a_feature);
               }
@@ -234,15 +224,14 @@ namespace SearnUtil
     }
 
     ec->indices.push_back(history_namespace);
-    ec->sum_feat_sq[history_namespace] += ec->atomics[history_namespace].size();
+    ec->sum_feat_sq[history_namespace] += ec->atomics[history_namespace].size() * history_value;
     ec->total_sum_feat_sq += ec->sum_feat_sq[history_namespace];
     ec->num_features += ec->atomics[history_namespace].size();
   }
 
-  void remove_history_from_example(vw&all, history_info *hinfo, example* ec)
+  void remove_history_from_example(vw&all, history_info &hinfo, example* ec)
   {
-    size_t total_length = max(hinfo->features, hinfo->length);
-    if (total_length == 0) return;
+    if (hinfo.length == 0) return;
 
     if (ec->indices.size() == 0) {
       cerr << "internal error (bug): trying to remove history, but there are no namespaces!" << endl;
@@ -255,7 +244,7 @@ namespace SearnUtil
     }
 
     ec->num_features -= ec->atomics[history_namespace].size();
-    ec->total_sum_feat_sq -= ec->sum_feat_sq[history_namespace];
+    ec->total_sum_feat_sq -= ec->sum_feat_sq[history_namespace] * history_value;
     ec->sum_feat_sq[history_namespace] = 0;
     ec->atomics[history_namespace].erase();
     if (all.audit) {
@@ -274,1159 +263,119 @@ namespace SearnUtil
     ec->indices.decr();
   }
 
-}
-
-namespace Searn
-{
-  // rollout
-  struct rollout_item {
-    state st;
-    bool  is_finished;
-    bool  alive;
-    size_t hash;
-  };
-
-    // debug stuff
-  const bool PRINT_DEBUG_INFO =0;
-  const bool PRINT_UPDATE_EVERY_EXAMPLE =0;
-    
-  struct searn {
-    // task stuff
-    search_task task;
-    bool is_singleline;
-    bool is_ldf;
-    bool has_hash;
-    bool constrainted_actions;
-    size_t input_label_size;
-    
-    // options
-    size_t max_action;
-    size_t max_rollout;
-    size_t passes_per_policy;     //this should be set to the same value as --passes for dagger
-    float  beta;
-    float gamma;
-    bool   do_recombination;
-    bool   allow_current_policy; //this should be set to true for dagger
-    bool   rollout_oracle; //if true then rollout are performed using oracle instead (optimal approximation discussed in searn's paper). this should be set to true for dagger
-    bool   adaptive_beta; //used to implement dagger through searn. if true, beta = 1-(1-alpha)^n after n updates, and policy is mixed with oracle as \pi' = (1-beta)\pi^* + beta \pi
-    float  alpha; //parameter used to adapt beta for dagger (see above comment), should be in (0,1)
-    bool   rollout_all_actions;  //by default we rollout all actions. This is set to false when searn is used with a contextual bandit base learner, where we rollout only one sampled action
-    
-    // memory
-    rollout_item* rollout;
-    v_array<example*> ec_seq;
-    example** global_example_set;
-    example* empty_example;
-    OAA::mc_label empty_label;
-    CSOAA::label loss_vector;
-    CB::label loss_vector_cb;
-    v_array<void*>old_labels;
-    v_array<OAA::mc_label>new_labels;
-    CSOAA::label testall_labels;
-    CSOAA::label allowed_labels;
-    CB::label testall_labels_cb;
-    CB::label allowed_labels_cb;
-    // we need a hashmap that maps from STATES to ACTIONS
-    v_hashmap<state,action> *past_states;
-    v_array<state> unfreed_states;
-    
-    // tracking of example
-    size_t read_example_this_loop;
-    size_t read_example_last_id;
-    size_t passes_since_new_policy;
-    size_t read_example_last_pass;
-    size_t total_examples_generated;
-    size_t total_predictions_made;
-    size_t searn_num_features;
-    
-    // variables
-    uint32_t current_policy;
-    uint32_t total_number_of_policies;
-    uint32_t increment; //for policy offset
-    
-    learner base;
-    vw* all;
-  };
-
-  void drive(vw* all, void*d);
-  
-  void simple_print_example_features(vw&all, example *ec)
-  {
-    for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
-      {
-        feature* end = ec->atomics[*i].end;
-        for (feature* f = ec->atomics[*i].begin; f!= end; f++) {
-          cerr << "\t" << f->weight_index << ":" << f->x << ":" << all.reg.weight_vector[f->weight_index & all.reg.weight_mask];
-        }
-      }
-    cerr << endl;
-  }
-
-  void simple_print_costs(CSOAA::label *c)
-  {
-    for (CSOAA::wclass *f = c->costs.begin; f != c->costs.end; f++) {
-      clog << "\t" << f->weight_index << ":" << f->x << "::" << f->partial_prediction;
-    }
-    clog << endl;
-  }
-
-  bool should_print_update(vw& all)
-  {
-    //uncomment to print out final loss after all examples processed
-    //commented for now so that outputs matches make test
-    //if( parser_done(all.p)) return true;
-
-    if (!(all.sd->weighted_examples > all.sd->dump_interval && !all.quiet && !all.bfgs)) {
-      if (!PRINT_UPDATE_EVERY_EXAMPLE) return false;
-    }
-    return true;
-  }
-
-  std::vector<action> empty_action_vector = std::vector<action>();
-
-  void to_short_string(string in, size_t max_len, char*out) {
-    for (size_t i=0; i<max_len; i++) {
-      if (i >= in.length())
-        out[i] = ' ';
-      else if ((in[i] == '\n') || (in[i] == '\t'))    // TODO: maybe catch other characters?
-        out[i] = ' ';
-      else
-        out[i] = in[i];
-    }
-
-    if (in.length() > max_len) { 
-      out[max_len-2] = '.'; 
-      out[max_len-1] = '.'; 
-    }
-    out[max_len] = 0;
-  }
-
-  bool will_global_print_label(vw& all, searn& s)
-  {
-    if (!s.task.to_string) return false;
-    if (all.final_prediction_sink.size() == 0) return false;
-    return true;
-  }
-
-  void global_print_label(vw& all, searn& s, example*ec, state s0, std::vector<action> last_action_sequence)
-  {
-    if (!s.task.to_string) return;
-    if (all.final_prediction_sink.size() == 0) return;
-
-    string str = s.task.to_string(s0, false, last_action_sequence);
-    for (size_t i=0; i<all.final_prediction_sink.size(); i++) {
-      int f = all.final_prediction_sink[i];
-      all.print_text(f, str, ec->tag);
-    }
-  }
-
-  void print_update(vw& all, searn& s, state s0, std::vector<action> last_action_sequence)
-  {
-    if (!should_print_update(all))
-      return;
-
-    char true_label[21];
-    char pred_label[21];
-    if (s.task.to_string) {
-      to_short_string(s.task.to_string(s0, true , empty_action_vector ), 20, true_label);
-      to_short_string(s.task.to_string(s0, false, last_action_sequence), 20, pred_label);
-    } else {
-      to_short_string("", 20, true_label);
-      to_short_string("", 20, pred_label);
-    }
-
-    fprintf(stderr, "%-10.6f %-10.6f %8ld %15f   [%s] [%s] %8lu %5d %5d %15lu %15lu\n",
-            all.sd->sum_loss/all.sd->weighted_examples,
-            all.sd->sum_loss_since_last_dump / (all.sd->weighted_examples - all.sd->old_weighted_examples),
-            (long int)all.sd->example_number,
-            all.sd->weighted_examples,
-            true_label,
-            pred_label,
-            (long unsigned int)s.searn_num_features,
-            (int)s.read_example_last_pass,
-            (int)s.current_policy,
-            (long unsigned int)s.total_predictions_made,
-            (long unsigned int)s.total_examples_generated);
-
-    all.sd->sum_loss_since_last_dump = 0.0;
-    all.sd->old_weighted_examples = all.sd->weighted_examples;
-    all.sd->dump_interval *= 2;
-  }
+  // size_t predict_with_history(vw&vw, example*ec, v_array<uint32_t>* ystar, history_info& hinfo, size_t*history) {
+  //   add_history_to_example(vw, hinfo, ec, history);
+  //   size_t prediction = ((Searn::searn*)vw.searnstr)->predict(vw, &ec, 0, NULL, ystar);
+  //   remove_history_from_example(vw, hinfo, ec);
+  //   return prediction;
+  // }
 
-  void clear_seq(vw&all, searn& s)
-  {
-    if (s.ec_seq.size() > 0) 
-      for (example** ecc=s.ec_seq.begin; ecc!=s.ec_seq.end; ecc++) {
-	VW::finish_example(all, *ecc);
-      }
-    s.ec_seq.erase();
-  }
-
-  void free_unfreed_states(searn& s)
-  {
-    while (!s.unfreed_states.empty()) {
-      state st = s.unfreed_states.pop();
-      s.task.finish(st);
-    }
-  }
-
-  void initialize_memory(searn& s)
-  {
-    // initialize searn's memory
-    s.rollout = (rollout_item*)SearnUtil::calloc_or_die(s.max_action, sizeof(rollout_item));
-    s.global_example_set = (example**)SearnUtil::calloc_or_die(s.max_action, sizeof(example*));
-
-    for (uint32_t k=1; k<=s.max_action; k++) {
-      CSOAA::wclass cost = { FLT_MAX, k, 1., 0. };
-      s.testall_labels.costs.push_back(cost);
-      CB::cb_class cost_cb = { FLT_MAX, k, 0. };
-      s.testall_labels_cb.costs.push_back(cost_cb);
-    }
-
-    s.empty_example = alloc_example(sizeof(OAA::mc_label));
-    OAA::default_label(s.empty_example->ld);
-    //    cerr << "create: empty_example->ld = " << empty_example->ld << endl;
-    s.empty_example->in_use = true;
-  }
-  
-  void free_memory(vw&all, searn& s)
-  {
-    dealloc_example(NULL, *s.empty_example);
-    free(s.empty_example);
-
-    SearnUtil::free_it(s.rollout);
-
-    s.loss_vector.costs.delete_v();
-
-    s.old_labels.delete_v();
-
-    s.new_labels.delete_v();
-
-    free_unfreed_states(s);
-    s.unfreed_states.delete_v();
-
-    s.ec_seq.delete_v();
-
-    SearnUtil::free_it(s.global_example_set);
-
-    s.testall_labels.costs.delete_v();
-    s.testall_labels_cb.costs.delete_v();
-    s.allowed_labels.costs.delete_v();
-    s.allowed_labels_cb.costs.delete_v();
-
-    if (s.do_recombination) {
-      delete s.past_states;
-      s.past_states = NULL;
-    }
-  }
+}
 
 
+namespace Searn {
+  const char INIT_TEST  = 0;
+  const char INIT_TRAIN = 1;
+  const char LEARN      = 2;
 
-  void learn(void*d, example *ec)
-  {
-    //vw*all = (vw*)in;
-    // TODO
-  }
+  const bool PRINT_DEBUG_INFO =0;
+  const bool PRINT_UPDATE_EVERY_EXAMPLE =0;
+  const bool PRINT_UPDATE_EVERY_PASS =0;
+  const bool PRINT_CLOCK_TIME =0;
 
-  void finish(void* d)
-  {
-    searn* s = (searn*)d;
-    vw* all = s->all;
-    s->base.finish();
-    // free everything
-    if (s->task.finalize != NULL)
-      s->task.finalize();
-    free_memory(*all,*s);
-    free(s);
-  }
+  inline bool isLDF(searn& srn) { return (srn.A == 0); }
 
-  learner setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  int choose_policy(searn& srn, bool allow_current, bool allow_optimal)
   {
-    searn* s = (searn*)calloc(1,sizeof(searn));
-
-    s->max_action           = 1;
-    s->max_rollout          = INT_MAX;
-    s->passes_per_policy    = 1;     //this should be set to the same value as --passes for dagger
-    s->beta                 = 0.5;
-    s->gamma                = 1.;
-    s->alpha                = 0.001f; //parameter used to adapt beta for dagger (see above comment), should be in (0,1)
-    s->rollout_all_actions  = true;  //by default we rollout all actions. This is set to false when searn is used with a contextual bandit base learner, where we rollout only one sampled action
-    s->ec_seq = v_array<example*>();
-    s->loss_vector.costs = v_array<CSOAA::wclass>();
-    s->loss_vector_cb.costs = v_array<CB::cb_class>();
-    s->old_labels = v_array<void*>();
-    s->new_labels = v_array<OAA::mc_label>();
-    s->testall_labels.costs = v_array<CSOAA::wclass>();
-    s->allowed_labels.costs = v_array<CSOAA::wclass>();
-    s->testall_labels_cb.costs = v_array<CB::cb_class>();
-    s->allowed_labels_cb.costs = v_array<CB::cb_class>();
-    // we need a hashmap that maps from STATES to ACTIONS
-    s->unfreed_states = v_array<state>();
-    s->total_number_of_policies = 1;
-
-    po::options_description desc("Searn options");
-    desc.add_options()
-      ("searn_task", po::value<string>(), "the searn task")
-      ("searn_rollout", po::value<size_t>(), "maximum rollout length")
-      ("searn_passes_per_policy", po::value<size_t>(), "maximum number of datapasses per policy")
-      ("searn_beta", po::value<float>(), "interpolation rate for policies")
-      ("searn_gamma", po::value<float>(), "discount rate for policies")
-      ("searn_recombine", "allow searn labeling to use the current policy")
-      ("searn_allow_current_policy", "allow searn labeling to use the current policy")
-      ("searn_rollout_oracle", "allow searn/dagger to do rollouts with the oracle when estimating cost-to-go")
-      ("searn_as_dagger", po::value<float>(), "sets options to make searn operate as dagger. parameter is the sliding autonomy rate (rate at which beta tends to 1).")
-      ("searn_total_nb_policies", po::value<size_t>(), "if we are going to train the policies through multiple separate calls to vw, we need to specify this parameter and tell vw how many policies are eventually going to be trained");
-
-    po::options_description add_desc_file("Searn options only available in regressor file");
-    add_desc_file.add_options()
-      ("searn_trained_nb_policies", po::value<size_t>(), "the number of trained policies in the regressor file");
-
-    po::options_description desc_file;
-    desc_file.add(desc).add(add_desc_file);
-
-    po::parsed_options parsed = po::command_line_parser(opts).
-      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
-      options(desc).allow_unregistered().run();
-    opts = po::collect_unrecognized(parsed.options, po::include_positional);
-    po::store(parsed, vm);
-    po::notify(vm);
-
-    po::parsed_options parsed_file = po::command_line_parser(all.options_from_file_argc, all.options_from_file_argv).
-      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
-      options(desc_file).allow_unregistered().run();
-    po::store(parsed_file, vm_file);
-    po::notify(vm_file);
-  
-    std::string task_string;
-    if(vm_file.count("searn_task")) {//we loaded searn task flag from regressor file    
-      task_string = vm_file["searn_task"].as<std::string>();
-      if(vm.count("searn_task") && task_string.compare(vm["searn_task"].as<std::string>()) != 0 )
-      {
-        std::cerr << "warning: specified --searn_task different than the one loaded from regressor. Pursuing with loaded value of: " << task_string << endl;
-      }
-    }
-    else {
-      if (vm.count("searn_task") == 0) {
-        cerr << "must specify --searn_task" << endl;
-        throw exception();
-      }
-      task_string = vm["searn_task"].as<std::string>();
-
-      //append the searn task to options_from_file so it is saved in the regressor file later
-      all.options_from_file.append(" --searn_task ");
-      all.options_from_file.append(task_string);
-    }
-
-    if (task_string.compare("sequence") == 0) {
-      s->task.final = SequenceTask::final;
-      s->task.loss = SequenceTask::loss;
-      s->task.step = SequenceTask::step; 
-      s->task.oracle = SequenceTask::oracle;
-      s->task.copy = SequenceTask::copy;
-      s->task.finish = SequenceTask::finish; 
-      s->task.searn_label_parser = OAA::mc_label_parser;
-      s->task.is_test_example = SequenceTask::is_test_example;
-      s->input_label_size = sizeof(OAA::mc_label);
-      s->task.start_state = NULL;
-      s->task.start_state_multiline = SequenceTask::start_state_multiline;
-      if (1) {
-        s->task.cs_example = SequenceTask::cs_example;
-        s->task.cs_ldf_example = NULL;
-      } else {
-        s->task.cs_example = NULL;
-        s->task.cs_ldf_example = SequenceTask::cs_ldf_example;
-      }
-      s->task.initialize = SequenceTask::initialize;
-      s->task.finalize = NULL;
-      s->task.equivalent = SequenceTask::equivalent;
-      s->task.hash = SequenceTask::hash;
-      s->task.allowed = SequenceTask::allowed;
-      s->task.to_string = SequenceTask::to_string;
-    } else {
-      std::cerr << "error: unknown search task '" << task_string << "'" << std::endl;
-      throw exception();
-    }
-
-    *(all.p->lp)=s->task.searn_label_parser;
-
-    if(vm_file.count("searn")) { //we loaded searn flag from regressor file 
-      s->max_action = vm_file["searn"].as<size_t>();
-      if( vm.count("searn") && vm["searn"].as<size_t>() != s->max_action )
-        std::cerr << "warning: you specified a different number of actions through --searn than the one loaded from predictor. Pursuing with loaded value of: " << s->max_action << endl;
-    }
-    else {
-      s->max_action = vm["searn"].as<size_t>();
-
-      //append searn with nb_actions to options_from_file so it is saved to regressor later
-      std::stringstream ss;
-      ss << " --searn " << s->max_action;
-      all.options_from_file.append(ss.str());
-    }
-
-    if(vm_file.count("searn_beta")) { //we loaded searn_beta flag from regressor file 
-      s->beta = vm_file["searn_beta"].as<float>();
-      if (vm.count("searn_beta") && vm["searn_beta"].as<float>() != s->beta )
-        std::cerr << "warning: you specified a different value through --searn_beta than the one loaded from predictor. Pursuing with loaded value of: " << s->beta << endl;
-
-    }
-    else {
-      if (vm.count("searn_beta")) s->beta = vm["searn_beta"].as<float>();
-
-      //append searn_beta to options_from_file so it is saved in the regressor file later
-      std::stringstream ss;
-      ss << " --searn_beta " << s->beta;
-      all.options_from_file.append(ss.str());
-    }
-
-    if (vm.count("searn_rollout"))                 s->max_rollout          = vm["searn_rollout"].as<size_t>();
-    if (vm.count("searn_passes_per_policy"))       s->passes_per_policy    = vm["searn_passes_per_policy"].as<size_t>();
-      
-    if (vm.count("searn_gamma"))                   s->gamma                = vm["searn_gamma"].as<float>();
-    if (vm.count("searn_norecombine"))             s->do_recombination     = false;
-    if (vm.count("searn_allow_current_policy"))    s->allow_current_policy = true;
-    if (vm.count("searn_rollout_oracle"))    	   s->rollout_oracle       = true;
-
-    //check if the base learner is contextual bandit, in which case, we dont rollout all actions.
-    if ( vm.count("cb") || vm_file.count("cb") ) s->rollout_all_actions = false;
-
-    //if we loaded a regressor with -i option, --searn_trained_nb_policies contains the number of trained policies in the file
-    // and --searn_total_nb_policies contains the total number of policies in the file
-    if ( vm_file.count("searn_total_nb_policies") )
-    {
-      s->current_policy = (uint32_t)vm_file["searn_trained_nb_policies"].as<size_t>();
-      s->total_number_of_policies = (uint32_t)vm_file["searn_total_nb_policies"].as<size_t>();
-      if (vm.count("searn_total_nb_policies") && (uint32_t)vm["searn_total_nb_policies"].as<size_t>() != s->total_number_of_policies)
-          std::cerr << "warning: --searn_total_nb_policies doesn't match the total number of policies stored in initial predictor. Using loaded value of: " << s->total_number_of_policies << endl;
-    }
-    else if (vm.count("searn_total_nb_policies"))
-    {
-      s->total_number_of_policies = (uint32_t)vm["searn_total_nb_policies"].as<size_t>();
-    }
-
-    if (vm.count("searn_as_dagger"))
-    {
-      //overide previously loaded options to set searn as dagger
-      s->allow_current_policy = true;
-      s->passes_per_policy = all.numpasses;
-      //s->rollout_oracle = true;
-      if( s->current_policy > 1 ) 
-        s->current_policy = 1;
-
-      //indicate to adapt beta for each update
-      s->adaptive_beta = true;
-      s->alpha = vm["searn_as_dagger"].as<float>();
-    }
-
-    if (s->beta <= 0 || s->beta >= 1) {
-      std::cerr << "warning: searn_beta must be in (0,1); resetting to 0.5" << std::endl;
-      s->beta = 0.5;
-    }
-
-    if (s->gamma <= 0 || s->gamma > 1) {
-      std::cerr << "warning: searn_gamma must be in (0,1); resetting to 1.0" << std::endl;
-      s->gamma = 1.0;
-    }
-
-    if (s->alpha < 0 || s->alpha > 1) {
-      std::cerr << "warning: searn_adaptive_beta must be in (0,1); resetting to 0.001" << std::endl;
-      s->alpha = 0.001f;
-    }
-
-    if (s->task.initialize != NULL)
-      if (!s->task.initialize(all, opts, vm, vm_file)) {
-        std::cerr << "error: task did not initialize properly" << std::endl;
-        throw exception();
-      }
-
-    // check to make sure task is valid and set up our variables
-    if (s->task.final  == NULL ||
-        s->task.loss   == NULL ||
-        s->task.step   == NULL ||
-        s->task.oracle == NULL ||
-        s->task.copy   == NULL ||
-        s->task.finish == NULL ||
-        ((s->task.start_state == NULL) == (s->task.start_state_multiline == NULL)) ||
-        ((s->task.cs_example  == NULL) == (s->task.cs_ldf_example        == NULL))) {
-      std::cerr << "error: searn task malformed" << std::endl;
-      throw exception();
-    }
-
-    s->is_singleline  = (s->task.start_state != NULL);
-    s->is_ldf         = (s->task.cs_example  == NULL);
-    s->has_hash       = (s->task.hash        != NULL);
-    s->constrainted_actions = (s->task.allowed != NULL);
-
-    if (s->do_recombination && (s->task.hash == NULL)) {
-      std::cerr << "warning: cannot do recombination when hashing is unavailable -- turning off recombination" << std::endl;
-      s->do_recombination = false;
-    }
-    if (s->do_recombination) {
-      // 0 is an invalid action
-      s->past_states = new v_hashmap<state,action>(1023, 0, s->task.equivalent);
-    }
-
-    if (s->is_ldf && !s->constrainted_actions) {
-      std::cerr << "error: LDF requires allowed" << std::endl;
-      throw exception();
-    }
-
-    all.searn = true;
-
-    //compute total number of policies we will have at end of training
-    // we add current_policy for cases where we start from an initial set of policies loaded through -i option
-    uint32_t tmp_number_of_policies = s->current_policy; 
-    if( all.training )
-	tmp_number_of_policies += (int)ceil(((float)all.numpasses) / ((float)s->passes_per_policy));
-
-    //the user might have specified the number of policies that will eventually be trained through multiple vw calls, 
-    //so only set total_number_of_policies to computed value if it is larger
-    if( tmp_number_of_policies > s->total_number_of_policies )
-    {
-	s->total_number_of_policies = tmp_number_of_policies;
-        if( s->current_policy > 0 ) //we loaded a file but total number of policies didn't match what is needed for training
-        {
-          std::cerr << "warning: you're attempting to train more classifiers than was allocated initially. Likely to cause bad performance." << endl;
-        }  
-    }
-
-    //current policy currently points to a new policy we would train
-    //if we are not training and loaded a bunch of policies for testing, we need to subtract 1 from current policy
-    //so that we only use those loaded when testing (as run_prediction is called with allow_current to true)
-    if( !all.training && s->current_policy > 0 )
-	s->current_policy--;
-
-    //std::cerr << "Current Policy: " << s->current_policy << endl;
-    //std::cerr << "Total Number of Policies: " << total_number_of_policies << endl;
-
-    std::stringstream ss1;
-    std::stringstream ss2;
-    ss1 << s->current_policy;
-    //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_trained_nb_policies
-    VW::cmd_string_replace_value(all.options_from_file,"--searn_trained_nb_policies", ss1.str()); 
-    ss2 << s->total_number_of_policies;
-    //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_total_nb_policies
-    VW::cmd_string_replace_value(all.options_from_file,"--searn_total_nb_policies", ss2.str());
-
-    all.weights_per_problem *= s->total_number_of_policies;
-    s->increment = ((uint32_t)all.length() / all.weights_per_problem) * all.reg.stride;
-    //cerr << "searn increment = " << s->increment << endl;
-
-    learner l(s, drive, learn, finish, all.l.sl);
-    s->base = all.l;
-    s->all = & all;
-    return l;
+    uint32_t seed = /* srn.read_example_last_id * 2147483 + */ (uint32_t)(srn.t * 2147483647);
+    return SearnUtil::random_policy(seed, srn.beta, allow_current, srn.current_policy, allow_optimal, srn.rollout_all_actions);
   }
 
-  uint32_t searn_predict(vw&all, searn& s, state s0, size_t step, bool allow_oracle, bool allow_current, v_array< pair<uint32_t,float> >* partial_predictions)  // TODO: partial_predictions
+  //CSOAA::label get_all_labels(searn& srn, size_t num_ec, v_array<uint32_t> *yallowed)
+  void get_all_labels(void*dst, searn& srn, size_t num_ec, v_array<uint32_t> *yallowed)
   {
-    int policy = SearnUtil::random_policy(s.read_example_last_id * 2147483 + step * 2147483647 /* s.has_hash ? s.task.hash(s0) : step */, s.beta, allow_current, (int)s.current_policy, allow_oracle, s.rollout_all_actions);
-    if (PRINT_DEBUG_INFO) { cerr << "predicing with policy " << policy << " (allow_oracle=" << allow_oracle << ", allow_current=" << allow_current << "), current_policy=" << s.current_policy << endl; }
-    if (policy == -1) {
-      return s.task.oracle(s0);
-    }
-
-    example *ec;
-
-    if (!s.is_ldf) {
-      s.task.cs_example(all, s0, ec, true);
-      SearnUtil::add_policy_offset(all, ec, s.increment, policy);
-
-      void* old_label = ec->ld;
-      if(s.rollout_all_actions) { //this means we have a cost-sensitive base learner
-        ec->ld = (void*)&s.testall_labels;
-        if (s.task.allowed != NULL) {  // we need to check which actions are allowed
-          s.allowed_labels.costs.erase();
-          bool all_allowed = true;
-          for (uint32_t k=1; k<=s.max_action; k++)
-            if (s.task.allowed(s0, k)) {
-              CSOAA::wclass cost = { FLT_MAX, k, 1., 0. };
-              s.allowed_labels.costs.push_back(cost);
-            } else
-              all_allowed = false;
-
-          if (!all_allowed)
-            ec->ld = (void*)&s.allowed_labels;
-        }
-      }
-      else { //if we have a contextual bandit base learner
-        ec->ld = (void*)&s.testall_labels_cb;
-        if (s.task.allowed != NULL) {  // we need to check which actions are allowed
-          s.allowed_labels_cb.costs.erase();
-          bool all_allowed = true;
-          for (uint32_t k=1; k<=s.max_action; k++)
-            if (s.task.allowed(s0, k)) {
-              CB::cb_class cost = { FLT_MAX, k, 0. };
-              s.allowed_labels_cb.costs.push_back(cost);
-            } else
-              all_allowed = false;
-
-          if (!all_allowed)
-            ec->ld = (void*)&s.allowed_labels_cb;
-        }
-      }
-      //cerr << "searn>";
-      //simple_print_example_features(all,ec);
-      s.base.learn(ec); 
-      s.total_predictions_made++;  
-      s.searn_num_features += ec->num_features;
-      uint32_t final_prediction = (uint32_t)ec->final_prediction;
-      ec->ld = old_label;
-
-      SearnUtil::remove_policy_offset(all, ec, s.increment, policy);
-      s.task.cs_example(all, s0, ec, false);
-
-      return final_prediction;
-    } else {  // is_ldf
-      //TODO: modify this to handle contextual bandit base learner with ldf
-      float best_prediction = 0;
-      uint32_t best_action = 0;
-      for (uint32_t action=1; action <= s.max_action; action++) {
-        if (!s.task.allowed(s0, action))
-          break;   // for LDF, there are no more actions
-
-        s.task.cs_ldf_example(all, s0, action, ec, true);
-        //cerr << "created example: " << ec << ", label: " << ec->ld << endl;
-        SearnUtil::add_policy_offset(all, ec, s.increment, policy);
-        s.base.learn(ec);  s.total_predictions_made++;  s.searn_num_features += ec->num_features;
-        //cerr << "base_learned on example: " << ec << endl;
-        s.empty_example->in_use = true;
-        s.base.learn(s.empty_example);
-        //cerr << "base_learned on empty example: " << s.empty_example << endl;
-        SearnUtil::remove_policy_offset(all, ec, s.increment, policy);
-
-        if (action == 1 || 
-            ec->partial_prediction < best_prediction) {
-          best_prediction = ec->partial_prediction;
-          best_action     = action;
+    if (srn.rollout_all_actions) { // dst should be a CSOAA::label*
+      CSOAA::label *ret = (CSOAA::label*)dst;
+      ret->costs.erase();
+      if (isLDF(srn)) {
+        for (uint32_t i=0; i<num_ec; i++) {
+          CSOAA::wclass cost = { FLT_MAX, i, 0., 0. };
+          ret->costs.push_back(cost);
         }
-        //cerr << "releasing example: " << ec << ", label: " << ec->ld << endl;
-        s.task.cs_ldf_example(all, s0, action, ec, false);
-      }
-
-      if (best_action < 1) {
-        std::cerr << "warning: internal error on search -- could not find an available action; quitting!" << std::endl;
-        throw exception();
-      }
-      return best_action;
-    }
-  }
-
-  float single_rollout(vw&all, searn& s, state s0, uint32_t action)
-  {
-    //first check if action is valid for current state
-    if( action < 1 || action > s.max_action || (s.task.allowed && !s.task.allowed(s0,action)) )
-    {
-	std::cerr << "warning: asked to rollout an unallowed action: " << action << "; not performing rollout." << std::endl;
-	return 0;
-    }
-    
-    //copy state and step it with current action
-    s.rollout[action-1].alive = true;
-    s.rollout[action-1].st = s.task.copy(s0);
-    s.task.step(s.rollout[action-1].st, action);
-    s.rollout[action-1].is_finished = s.task.final(s.rollout[action-1].st);
-    if (s.do_recombination) s.rollout[action-1].hash = s.task.hash(s.rollout[action-1].st);
-
-    //if not finished complete rollout
-    if (!s.rollout[action-1].is_finished) {
-      for (size_t step=1; step<s.max_rollout; step++) {
-        uint32_t act_tmp = 0;
-        if (s.do_recombination)
-          act_tmp = s.past_states->get(s.rollout[action-1].st, s.rollout[action-1].hash);
-
-        if (act_tmp == 0) {  // this means we didn't find it or we're not recombining
-          if( !s.rollout_oracle )
-            act_tmp = searn_predict(all, s, s.rollout[action-1].st, step, true, s.allow_current_policy, NULL);
-	  else
-            act_tmp = s.task.oracle(s.rollout[action-1].st);
-
-          if (s.do_recombination) {
-            // we need to make a copy of the state
-            state copy = s.task.copy(s.rollout[action-1].st);
-            s.past_states->put_after_get(copy, s.rollout[action-1].hash, act_tmp);
-            s.unfreed_states.push_back(copy);
+      } else { // is not LDF
+        if (yallowed == NULL) {
+          for (uint32_t i=1; i<=srn.A; i++) {
+            CSOAA::wclass cost = { FLT_MAX, i, 0., 0. };
+            ret->costs.push_back(cost);
           }
-        }          
-          
-        s.task.step(s.rollout[action-1].st, act_tmp);
-        s.rollout[action-1].is_finished = s.task.final(s.rollout[action-1].st);
-        if (s.do_recombination) s.rollout[action-1].hash = s.task.hash(s.rollout[action-1].st);
-        if (s.rollout[action-1].is_finished) break;
-      }
-    }
-
-    // finally, compute losses and free copies
-    float l = s.task.loss(s.rollout[action-1].st);
-    if ((l == FLT_MAX) && (!s.rollout[action-1].is_finished) && (s.max_rollout < INT_MAX)) {
-      std::cerr << "error: you asked for short rollouts, but your task does not support pre-final losses" << std::endl;
-      throw exception();
-    }
-    s.task.finish(s.rollout[action-1].st);
-
-    return l;
-  }
-
-  void parallel_rollout(vw&all, searn& s, state s0)
-  {
-    // first, make K copies of s0 and step them
-    bool all_finished = true;
-    for (size_t k=1; k<=s.max_action; k++) 
-      s.rollout[k-1].alive = false;
-    
-    for (uint32_t k=1; k<=s.max_action; k++) {
-      // in the case of LDF, we might run out of actions early
-      if (s.task.allowed && !s.task.allowed(s0, k)) {
-        if (s.is_ldf) break;
-        else continue;
-      }
-      s.rollout[k-1].alive = true;
-      s.rollout[k-1].st = s.task.copy(s0);
-      s.task.step(s.rollout[k-1].st, k);
-      s.rollout[k-1].is_finished = s.task.final(s.rollout[k-1].st);
-      if (s.do_recombination) s.rollout[k-1].hash = s.task.hash(s.rollout[k-1].st);
-      all_finished = all_finished && s.rollout[k-1].is_finished;
-    }
-
-    // now, complete all rollouts
-    if (!all_finished) {
-      for (size_t step=1; step<s.max_rollout; step++) {
-        all_finished = true;
-        for (size_t k=1; k<=s.max_action; k++) {
-          if (s.rollout[k-1].is_finished) continue;
-          
-          uint32_t action = 0;
-          if (s.do_recombination)
-            action = s.past_states->get(s.rollout[k-1].st, s.rollout[k-1].hash);
-
-          if (action == 0) {  // this means we didn't find it or we're not recombining
-            if( !s.rollout_oracle )
-              action = searn_predict(all, s, s.rollout[k-1].st, step, true, s.allow_current_policy, NULL);
-	    else
-              action = s.task.oracle(s.rollout[k-1].st);
-
-            if (s.do_recombination) {
-              // we need to make a copy of the state
-              state copy = s.task.copy(s.rollout[k-1].st);
-              s.past_states->put_after_get(copy, s.rollout[k-1].hash, action);
-              s.unfreed_states.push_back(copy);
-            }
-          }          
-          
-          s.task.step(s.rollout[k-1].st, action);
-          s.rollout[k-1].is_finished = s.task.final(s.rollout[k-1].st);
-          if (s.do_recombination) s.rollout[k-1].hash = s.task.hash(s.rollout[k-1].st);
-          all_finished = all_finished && s.rollout[k-1].is_finished;
-        }
-        if (all_finished) break;
-      }
-    }
-
-    // finally, compute losses and free copies
-    float min_loss = 0;
-    s.loss_vector.costs.erase();
-    for (uint32_t k=1; k<=s.max_action; k++) {
-      if (!s.rollout[k-1].alive)
-        break;
-
-      float l = s.task.loss(s.rollout[k-1].st);
-      if ((l == FLT_MAX) && (!s.rollout[k-1].is_finished) && (s.max_rollout < INT_MAX)) {
-        std::cerr << "error: you asked for short rollouts, but your task does not support pre-final losses" << std::endl;
-        throw exception();
-      }
-
-      CSOAA::wclass temp = { l, k, 1., 0. };
-      s.loss_vector.costs.push_back(temp);
-      if ((k == 1) || (l < min_loss)) { min_loss = l; }
-
-      s.task.finish(s.rollout[k-1].st);
-    }
-
-    // subtract the smallest loss
-    for (size_t k=1; k<=s.max_action; k++)
-      if (s.rollout[k-1].alive)
-        s.loss_vector.costs[k-1].x -= min_loss;
-  }
-
-  uint32_t uniform_exploration(searn& s, state s0, float& prob_sampled_action)
-  {
-    //find how many valid actions
-    size_t nb_allowed_actions = s.max_action;
-    if( s.task.allowed ) {  
-      for (uint32_t k=1; k<=s.max_action; k++) {
-        if( !s.task.allowed(s0,k) ) {
-          nb_allowed_actions--;
-          if (s.is_ldf) {
-            nb_allowed_actions = k-1;
-            break;
+        } else {
+          for (size_t i=0; i<yallowed->size(); i++) {
+            CSOAA::wclass cost = { FLT_MAX, (*yallowed)[i], 0., 0. };
+            ret->costs.push_back(cost);
           }
         }
       }
-    }
-
-    uint32_t action = (size_t)(frand48() * nb_allowed_actions) + 1;
-    if( s.task.allowed && nb_allowed_actions < s.max_action && !s.is_ldf) {
-      //need to adjust action to the corresponding valid action
-      for (uint32_t k=1; k<=action; k++) {
-        if( !s.task.allowed(s0,k) ) action++;
-      }
-    }
-    prob_sampled_action = (float) (1.0/nb_allowed_actions);
-    return action;
-  }
-
-  void get_contextual_bandit_loss_vector(vw&all, searn& s, state s0)
-  {
-    float prob_sampled = 1.;
-    uint32_t act = uniform_exploration(s, s0,prob_sampled);
-    float loss = single_rollout(all,s, s0,act);
-
-    s.loss_vector_cb.costs.erase();
-    for (uint32_t k=1; k<=s.max_action; k++) {
-      if( s.task.allowed && !s.task.allowed(s0,k))
-	break;
-      
-      CB::cb_class temp;
-      temp.x = FLT_MAX;
-      temp.weight_index = k;
-      temp.prob_action = 0.;
-      if( act == k ) {
-        temp.x = loss;
-        temp.prob_action = prob_sampled;
-      }
-      s.loss_vector_cb.costs.push_back(temp);
-    }
-  }
-
-  void generate_state_example(vw&all, searn& s, state s0)
-  {
-    // start by doing rollouts so we can get costs
-    s.loss_vector.costs.erase();
-    s.loss_vector_cb.costs.erase();
-    if( s.rollout_all_actions ) {
-      parallel_rollout(all, s, s0);      
-    }
-    else {
-      get_contextual_bandit_loss_vector(all, s, s0);
-    }
-
-    if (s.loss_vector.costs.size() <= 1 && s.loss_vector_cb.costs.size() == 0) {
-      // nothing interesting to do!
-      return;
-    }
-
-    // now, generate training examples
-    if (!s.is_ldf) {
-      s.total_examples_generated++;
-
-      example* ec;
-      s.task.cs_example(all, s0, ec, true);
-      void* old_label = ec->ld;
-
-      if(s.rollout_all_actions) 
-        ec->ld = (void*)&s.loss_vector;
-      else 
-        ec->ld = (void*)&s.loss_vector_cb;
- 
-      SearnUtil::add_policy_offset(all, ec, s.increment, s.current_policy);
-	  
-	  s.base.learn(ec);
-      SearnUtil::remove_policy_offset(all, ec, s.increment, s.current_policy);
-      ec->ld = old_label;
-      s.task.cs_example(all, s0, ec, false);
-    } else { // is_ldf
-      //TODO: support ldf with contextual bandit base learner
-      s.old_labels.erase();
-      s.new_labels.erase();
-
-      for (uint32_t k=1; k<=s.max_action; k++) {
-        if (s.rollout[k-1].alive) {
-          OAA::mc_label ld = { (float)k, s.loss_vector.costs[k-1].x };
-          s.new_labels.push_back(ld);
+    } else { // dst should be a CB::label*
+      CB::label *ret = (CB::label*)dst;
+      ret->costs.erase();
+      if (isLDF(srn)) {
+        for (uint32_t i=0; i<num_ec; i++) {
+          CB::cb_class cost = { FLT_MAX, i, 0. };
+          ret->costs.push_back(cost);
+        }
+      } else { // is not LDF
+        if (yallowed == NULL) {
+          for (uint32_t i=1; i<=srn.A; i++) {
+            CB::cb_class cost = { FLT_MAX, i, 0. };
+            ret->costs.push_back(cost);
+          }
         } else {
-          OAA::mc_label ld = { (float)k, 0. };
-          s.new_labels.push_back(ld);
+          for (size_t i=0; i<yallowed->size(); i++) {
+            CB::cb_class cost = { FLT_MAX, (*yallowed)[i], 0. };
+            ret->costs.push_back(cost);
+          }
         }
       }
-
-      //      cerr << "vvvvvvvvvvvvvvvvvvvvvvvvvvvv" << endl;
-
-      for (uint32_t k=1; k<=s.max_action; k++) {
-        if (!s.rollout[k-1].alive) break;
-
-        s.total_examples_generated++;
-
-        s.task.cs_ldf_example(all, s0, k, s.global_example_set[k-1], true);
-        s.old_labels.push_back(s.global_example_set[k-1]->ld);
-        s.global_example_set[k-1]->ld = (void*)(&s.new_labels[k-1]);
-        SearnUtil::add_policy_offset(all, s.global_example_set[k-1], s.increment, s.current_policy);
-        if (PRINT_DEBUG_INFO) { cerr << "add_policy_offset, s.max_action=" << s.max_action << ", total_number_of_policies=" << s.total_number_of_policies << ", current_policy=" << s.current_policy << endl;}
-        s.base.learn(s.global_example_set[k-1]);
-      }
-
-      //      cerr << "============================ (empty = " << s.empty_example << ")" << endl;
-      s.empty_example->in_use = true;
-      s.base.learn(s.empty_example);
-
-      for (uint32_t k=1; k<=s.max_action; k++) {
-        if (!s.rollout[k-1].alive) break;
-        SearnUtil::remove_policy_offset(all, s.global_example_set[k-1], s.increment, s.current_policy);
-        s.global_example_set[k-1]->ld = s.old_labels[k-1];
-        s.task.cs_ldf_example(all, s0, k, s.global_example_set[k-1], false);
-      }
-      //      cerr << "^^^^^^^^^^^^^^^^^^^^^^^^^^^^" << endl;
     }
-
   }
 
-  void run_prediction(vw&all, searn& s, state s0, bool allow_oracle, bool allow_current, bool track_actions, std::vector<action>* action_sequence)
+  uint32_t single_prediction_LDF(vw& all, learner& base, example** ecs, size_t num_ec, size_t pol)
   {
-    int step = 1;
-    while (!s.task.final(s0)) {
-      uint32_t action = searn_predict(all, s, s0, step, allow_oracle, allow_current, NULL);
-      if (track_actions)
-        action_sequence->push_back(action);
-
-      s.task.step(s0, action);
-      step++;
-    }
-  }
-
-  void do_actual_learning(vw&all, searn& s)
-  {
-    // there are two cases:
-    //   * is_singleline --> look only at ec_seq[0]
-    //   * otherwise     --> look at everything
-
-    if (s.ec_seq.size() == 0)
-      return;
-
-    // generate the start state
-    state s0;
-    if (s.is_singleline)
-      s.task.start_state(s.ec_seq[0], &s0);
-    else
-      s.task.start_state_multiline(s.ec_seq.begin, s.ec_seq.size(), &s0);
-
-    state s0copy = NULL;
-    bool  is_test = s.task.is_test_example(s.ec_seq.begin, s.ec_seq.size());
-    if (!is_test) {
-      s0copy = s.task.copy(s0);
-      all.sd->example_number++;
-      all.sd->total_features    += s.searn_num_features;
-      all.sd->weighted_examples += 1.;
-    }
-    bool will_print = is_test || should_print_update(all) || will_global_print_label(all, s);
-
-    s.searn_num_features = 0;
-    std::vector<action> action_sequence;
-
-    // if we are using adaptive beta, update it to take into account the latest updates
-    if( s.adaptive_beta ) s.beta = 1.f - powf(1.f - s.alpha,(float)s.total_examples_generated);
-    
-    run_prediction(all, s, s0, false, true, will_print, &action_sequence);
-    global_print_label(all, s, s.ec_seq[0], s0, action_sequence);
-
-    if (!is_test) {
-      float loss = s.task.loss(s0);
-      all.sd->sum_loss += loss;
-      all.sd->sum_loss_since_last_dump += loss;
-    }
-
-    print_update(all, s, s0, action_sequence);
-    
-    s.task.finish(s0);
-
-    if (is_test || !all.training)
-	{
-	  if (!s.is_singleline)
-		clear_seq(all, s);
-      return;
-	}
-
-    s0 = s0copy;
-
-    // training examples only get here
-    int step = 1;
-    while (!s.task.final(s0)) {
-      // if we are using adaptive beta, update it to take into account the latest updates
-      if( s.adaptive_beta ) s.beta = 1.f - powf(1.f - s.alpha,(float)s.total_examples_generated);
-
-      // first, make a prediction (we don't want to bias ourselves if
-      // we're using the current policy to predict)
-      uint32_t action = searn_predict(all, s, s0, step, true, s.allow_current_policy, NULL);
-
-      // generate training example for the current state
-      generate_state_example(all, s, s0);
-
-      // take the prescribed step
-      s.task.step(s0, action);
-
-      step++;
-    }
-    s.task.finish(s0);
-    if (s.do_recombination) {  // we need to free a bunch of memory
-      //      s.past_states->iter(&hm_free_state_copies);
-      free_unfreed_states(s);
-      s.past_states->clear();
-    }
-	if (!s.is_singleline)
-		clear_seq(all, s);
-  }
-
-  void process_next_example(vw&all, searn& s, example *ec)
-  {
-    bool is_real_example = true;
-
-    if (s.is_singleline) {
-      if (s.ec_seq.size() == 0)
-        s.ec_seq.push_back(ec);
-      else
-        s.ec_seq[0] = ec;
-
-      do_actual_learning(all, s);
-    } else {  
-      // is multiline
-      if (ec->end_pass || OAA::example_is_newline(ec) || s.ec_seq.size() >= all.p->ring_size - 2) { // give some wiggle room
-	if (s.ec_seq.size() >= all.p->ring_size - 2)
-        std::cerr << "warning: length of sequence at " << ec->example_counter << " exceeds ring size; breaking apart" << std::endl;
-        do_actual_learning(all, s);
-	
-	if (ec->end_pass)
-	  {
-	    is_real_example = false;
-	    s.read_example_last_pass++;
-	    s.passes_since_new_policy++;
-	    if (s.passes_since_new_policy >= s.passes_per_policy) {
-	      s.passes_since_new_policy = 0;
-	      if(all.training)
-		s.current_policy++;
-	      if (s.current_policy > s.total_number_of_policies) {
-		std::cerr << "internal error (bug): too many policies; not advancing" << std::endl;
-		s.current_policy = s.total_number_of_policies;
-	      }
-	      //reset searn_trained_nb_policies in options_from_file so it is saved to regressor file later
-	      std::stringstream ss;
-	      ss << s.current_policy;
-	      VW::cmd_string_replace_value(all.options_from_file,"--searn_trained_nb_policies", ss.str());
-	    }
-	  }
-	
-	VW::finish_example(all, ec);
-        is_real_example = false;
-      }
-      else
-	s.ec_seq.push_back(ec);
-    }
-    
-    // for both single and multiline
-    if (is_real_example) {
-      s.read_example_this_loop++;
-      s.read_example_last_id = ec->example_counter;
-    }
-  }
-
-  void drive(vw* all, void*d)
-  {
-    // initialize everything
-    searn* s = (searn*)d;
-
-    const char * header_fmt = "%-10s %-10s %8s %15s %24s %22s %8s %5s %5s %15s %15s\n";
-
-    fprintf(stderr, header_fmt, "average", "since", "sequence", "example",   "current label", "current predicted",  "current",  "cur", "cur", "predic.", "examples");
-    fprintf(stderr, header_fmt, "loss",  "last",  "counter",  "weight", "sequence prefix",   "sequence prefix", "features", "pass", "pol",    "made",   "gener.");
-    cerr.precision(5);
-
-    initialize_memory(*s);
-
-    example* ec = NULL;
-    s->read_example_this_loop = 0;
-    while (true) {
-      if ((ec = VW::get_example(all->p)) != NULL) { // semiblocking operation
-        process_next_example(*all, *s, ec);
-      } else if (parser_done(all->p)) {
-        if (!s->is_singleline)
-          do_actual_learning(*all, *s);
-        break;
-      }
-    }
-
-    if( all->training ) {
-      std::stringstream ss1;
-      std::stringstream ss2;
-      ss1 << s->current_policy;
-      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_trained_nb_policies
-      VW::cmd_string_replace_value(all->options_from_file,"--searn_trained_nb_policies", ss1.str()); 
-      ss2 << s->total_number_of_policies;
-      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_total_nb_policies
-      VW::cmd_string_replace_value(all->options_from_file,"--searn_total_nb_policies", ss2.str());
-    }
-  }
-
-
-}
-
-
-namespace ImperativeSearn {
-  const char INIT_TEST  = 0;
-  const char INIT_TRAIN = 1;
-  const char LEARN      = 2;
-
-  inline bool isLDF(searn& srn) { return (srn.A == 0); }
-
-  uint32_t choose_policy(searn& srn, bool allow_current, bool allow_optimal)
-  {
-    uint32_t seed = 0; // TODO: srn.read_example_last_id * 2147483 + srn.t * 2147483647;
-    return SearnUtil::random_policy(seed, srn.beta, allow_current, srn.current_policy, allow_optimal, srn.rollout_all_actions);
-  }
-
-  CSOAA::label get_all_labels(searn& srn, size_t num_ec, v_array<uint32_t> *yallowed)
-  {
-    if (isLDF(srn)) {
-		CSOAA::label ret;  // TODO: cache these!
-      for (uint32_t i=0; i<num_ec; i++) {
-        CSOAA::wclass cost = { FLT_MAX, i, 1., 0. };
-		ret.costs.push_back(cost);
-      }
-      return ret;
-    }
-    // is not LDF
-    if (yallowed == NULL) {
-      CSOAA::label ret;  // TODO: cache this!
-      for (uint32_t i=1; i<=srn.A; i++) {
-        CSOAA::wclass cost = { FLT_MAX, i, 1., 0. };
-        ret.costs.push_back(cost);
+    assert(pol > 0);
+    searn *srn = (searn*)all.searnstr;
+
+    // TODO: modify this to handle contextual bandid base learner with ldf
+    float best_prediction = 0;
+    uint32_t best_action = 0;
+    for (uint32_t action=0; action<num_ec; action++) {
+      base.learn(ecs[action], pol);
+      srn->total_predictions_made++;
+      srn->num_features += ecs[action]->num_features;
+      srn->empty_example->in_use = true;
+      base.learn(srn->empty_example);
+
+      if ((action == 0) || 
+          ecs[action]->partial_prediction < best_prediction) {
+        best_prediction = ecs[action]->partial_prediction;
+        best_action     = action;
       }
-      return ret;
     }
-    CSOAA::label ret;
-    for (size_t i=0; i<yallowed->size(); i++) {
-      CSOAA::wclass cost = { FLT_MAX, (*yallowed)[i], 1., 0. };
-      ret.costs.push_back(cost);
-    }
-    return ret;
-  }
 
-  uint32_t single_prediction_LDF(vw& all, example** ecs, size_t num_ec, size_t pol)
-  {
-    assert(pol > 0);
-    // TODO
-    return 0;
+    return best_action;
   }
 
-  uint32_t single_prediction_notLDF(vw& all, searn& srn, example* ec, CSOAA::label valid_labels, uint32_t pol)
+  uint32_t single_prediction_notLDF(vw& all, searn& srn, learner& base, example* ec, void*valid_labels, uint32_t pol)
   {
-    assert(pol > 0);
+    assert(pol >= 0);
 
     void* old_label = ec->ld;
-    ec->ld = (void*)&valid_labels;
-    SearnUtil::add_policy_offset(all, ec, srn.increment, pol);
+    ec->ld = valid_labels;
 
-    srn.base.learn(ec);
+    base.learn(ec, pol);
     srn.total_predictions_made++;
     srn.num_features += ec->num_features;
     uint32_t final_prediction = (uint32_t)ec->final_prediction;
 
-    SearnUtil::remove_policy_offset(all, ec, srn.increment, pol);
     ec->ld = old_label;
 
     return final_prediction;
@@ -1438,18 +387,26 @@ namespace ImperativeSearn {
     return opts[(size_t)(((float)opts.size()) * r)];
   }
 
-  uint32_t single_action(vw& all, searn& srn, example** ecs, size_t num_ec, CSOAA::label valid_labels, uint32_t pol, v_array<uint32_t> *ystar) {
+  uint32_t single_action(vw& all, searn& srn, learner& base, example** ecs, size_t num_ec, void*valid_labels, int pol, v_array<uint32_t> *ystar) {
     //cerr << "pol=" << pol << " ystar.size()=" << ystar->size() << " ystar[0]=" << ((ystar->size() > 0) ? (*ystar)[0] : 0) << endl;
-    if (pol == 0) { // optimal policy
-      if ((ystar == NULL) || (ystar->size() == 0))
-        return choose_random<CSOAA::wclass>(valid_labels.costs).weight_index;
-      else
+    if (pol == -1) { // optimal policy
+      if ((ystar == NULL) || (ystar->size() == 0)) { // TODO: choose according to current model!
+        if (srn.rollout_all_actions)
+          return choose_random<CSOAA::wclass>(((CSOAA::label*)valid_labels)->costs).weight_index;
+        else
+          return choose_random<CB::cb_class >(((CB::label   *)valid_labels)->costs).weight_index;
+      } else
         return choose_random<uint32_t>(*ystar);
     } else {        // learned policy
-      if (isLDF(srn))
-        return single_prediction_LDF(all, ecs, num_ec, pol);
-      else
-        return single_prediction_notLDF(all, srn, *ecs, valid_labels, pol);
+      if (!isLDF(srn)) {  // single example
+        if (srn.auto_history) add_history_to_example(all, srn.hinfo, *ecs, srn.rollout_action.begin+srn.t);
+        size_t action = single_prediction_notLDF(all, srn, base, *ecs, valid_labels, pol);
+        if (srn.auto_history) remove_history_from_example(all, srn.hinfo, *ecs);
+        return (uint32_t)action;
+      } else {
+        // TODO: auto-history for LDF
+        return single_prediction_LDF(all, base, ecs, num_ec, pol);
+      }
     }
   }
 
@@ -1460,6 +417,26 @@ namespace ImperativeSearn {
     srn.snapshot_data.erase();
   }
 
+  void* copy_labels(searn &srn, void* l) {
+    if (srn.rollout_all_actions) {
+      CSOAA::label *ret = new CSOAA::label();
+      v_array<CSOAA::wclass> costs = ((CSOAA::label*)l)->costs;
+      for (size_t i=0; i<costs.size(); i++) {
+        CSOAA::wclass c = { costs[i].x, costs[i].weight_index, costs[i].partial_prediction, costs[i].wap_value };
+        ret->costs.push_back(c);
+      }
+      return ret;
+    } else {
+      CB::label *ret = new CB::label();
+      v_array<CB::cb_class> costs = ((CB::label*)l)->costs;
+      for (size_t i=0; i<costs.size(); i++) {
+        CB::cb_class c = { costs[i].x, costs[i].weight_index, costs[i].prob_action };
+        ret->costs.push_back(c);
+      }
+      return ret;
+    }
+  }
+
   // if not LDF:
   //   *ecs should be a pointer to THE example
   //   num_ec == 0
@@ -1475,7 +452,7 @@ namespace ImperativeSearn {
   //   ystar:
   //     == NULL (or empty) means we don't know the oracle label
   //     otherwise          means the oracle could do any of the listed actions
-  uint32_t searn_predict(vw& all, example** ecs, size_t num_ec, v_array<uint32_t> *yallowed, v_array<uint32_t> *ystar)  // num_ec == 0 means normal example, >0 means ldf, yallowed==NULL means all allowed, ystar==NULL means don't know
+  uint32_t searn_predict(vw& all, learner& base, example** ecs, size_t num_ec, v_array<uint32_t> *yallowed, v_array<uint32_t> *ystar)  // num_ec == 0 means normal example, >0 means ldf, yallowed==NULL means all allowed, ystar==NULL means don't know
   {
     searn* srn=(searn*)all.searnstr;
 
@@ -1488,19 +465,27 @@ namespace ImperativeSearn {
     }
 
     if (srn->state == INIT_TEST) {
-      uint32_t pol = choose_policy(*srn, true, false);
-      CSOAA::label valid_labels = get_all_labels(*srn, num_ec, yallowed);
-      uint32_t a = single_action(all, *srn, ecs, num_ec, valid_labels, pol, ystar);
+      int pol = choose_policy(*srn, true, false);
+      //cerr << "(" << pol << ")";
+      get_all_labels(srn->valid_labels, *srn, num_ec, yallowed);
+      uint32_t a = single_action(all, *srn, base, ecs, num_ec, srn->valid_labels, pol, ystar);
+      //uint32_t a_opt = single_action(all, *srn, ecs, num_ec, valid_labels, -1, ystar);
+      //clog << "predict @" << srn->t << " pol=" << pol << " a=" << a << endl;
+      if (srn->auto_history) srn->rollout_action.push_back(a);
       srn->t++;
-      valid_labels.costs.erase(); valid_labels.costs.delete_v();
       return a;
     }
     if (srn->state == INIT_TRAIN) {
-      uint32_t pol = choose_policy(*srn, srn->allow_current_policy, true);
-      CSOAA::label valid_labels = get_all_labels(*srn, num_ec, yallowed);
-      uint32_t a = single_action(all, *srn, ecs, num_ec, valid_labels, pol, ystar);
+      int pol = choose_policy(*srn, srn->allow_current_policy, true);
+      get_all_labels(srn->valid_labels, *srn, num_ec, yallowed);
+      uint32_t a = single_action(all, *srn, base, ecs, num_ec, srn->valid_labels, pol, ystar);
+      //uint32_t a_opt = single_action(all, *srn, ecs, num_ec, valid_labels, -1, ystar);
+      //clog << "predict @" << srn->t << " pol=" << pol << " a=" << a << endl;
+      //assert((srn->current_policy == 0) || (a == a_opt));
+      //if (! ((srn->current_policy == 0) || (a == a_opt))) { cerr << "FAIL!!!"<<endl;}
       srn->train_action.push_back(a);
-      srn->train_labels.push_back(valid_labels);
+      srn->train_labels.push_back(copy_labels(*srn, srn->valid_labels));
+      if (srn->auto_history) srn->rollout_action.push_back(a);
       srn->t++;
       return a;
     }
@@ -1508,27 +493,54 @@ namespace ImperativeSearn {
       if (srn->t < srn->learn_t) {
         assert(srn->t < srn->train_action.size());
         srn->t++;
-        return srn->train_action[srn->t-1];
+        size_t a = srn->train_action[srn->t - 1];
+        return a;
       } else if (srn->t == srn->learn_t) {
         if (srn->learn_example_copy == NULL) {
           size_t num_to_copy = (num_ec == 0) ? 1 : num_ec;
           srn->learn_example_len = num_to_copy;
+          // TODO: move the calloc outside
           srn->learn_example_copy = (example**)SearnUtil::calloc_or_die(num_to_copy, sizeof(example*));
           for (size_t n=0; n<num_to_copy; n++) {
-            srn->learn_example_copy[n] = alloc_example(sizeof(CSOAA::label));
-            VW::copy_example_data(srn->learn_example_copy[n], ecs[n], sizeof(CSOAA::label), all.p->lp->copy_label);
+            if (srn->examples_dont_change)
+              srn->learn_example_copy[n] = ecs[n];
+            else {
+              srn->learn_example_copy[n] = alloc_example(sizeof(OAA::mc_label));
+              VW::copy_example_data(all.audit, srn->learn_example_copy[n], ecs[n], sizeof(OAA::mc_label), NULL);
+            }
           }
           //cerr << "copying example to " << srn->learn_example_copy << endl;
         }
+        srn->snapshot_is_equivalent_to_t = (size_t)-1;
+        srn->snapshot_could_match = true;
         srn->t++;
+        if (srn->auto_history) srn->rollout_action.push_back(srn->learn_a);
         return srn->learn_a;
-      } else {
-        uint32_t pol = choose_policy(*srn, srn->allow_current_policy, true);
-        CSOAA::label valid_labels = get_all_labels(*srn, num_ec, yallowed);
-        uint32_t a = single_action(all, *srn, ecs, num_ec, valid_labels, pol, ystar);
-        srn->t++;
-        valid_labels.costs.erase(); valid_labels.costs.delete_v();
-        return a;
+      } else { // t > learn_t
+        size_t this_a = 0;
+        if (srn->rollout_oracle) {
+          get_all_labels(srn->valid_labels, *srn, num_ec, yallowed);
+          this_a = single_action(all, *srn, base, ecs, num_ec, srn->valid_labels, -1, ystar);
+          srn->t++;
+          //valid_labels.costs.erase(); valid_labels.costs.delete_v();
+        } else if ((!srn->do_fastforward) || (!srn->snapshot_could_match) || (srn->snapshot_is_equivalent_to_t == ((size_t)-1))) { // we haven't converged, continue predicting
+          int pol = choose_policy(*srn, srn->allow_current_policy, true);
+          get_all_labels(srn->valid_labels, *srn, num_ec, yallowed);
+          this_a = single_action(all, *srn, base, ecs, num_ec, srn->valid_labels, pol, ystar);
+          //clog << "predict @" << srn->t << " pol=" << pol << " a=" << a << endl;
+          srn->t++;
+          //valid_labels.costs.erase(); valid_labels.costs.delete_v();
+
+          srn->snapshot_could_match = true;
+          srn->snapshot_is_equivalent_to_t = (size_t)-1;
+        } else {    // we can keep predicting using training trajectory
+          srn->snapshot_is_equivalent_to_t++;
+          srn->t = srn->snapshot_is_equivalent_to_t;
+          //clog << "restoring previous prediction @ " << (srn->t-1) << " = " << srn->train_action[srn->t-1] << endl;
+          this_a = srn->train_action[srn->t - 1];
+        }
+        if (srn->auto_history) srn->rollout_action.push_back(this_a);
+        return this_a;
       }
       assert(false);
     }
@@ -1552,22 +564,83 @@ namespace ImperativeSearn {
       srn->learn_loss += incr_loss;
   }
 
-  bool snapshot_linear_search(v_array<snapshot_item> a, size_t desired_t, size_t tag, size_t &pos) {
-    if (a.size() == 0) return false;
-    for (pos=a.size()-1; ; pos--) {
-      if ((a[pos].pred_step <= desired_t) && (tag == a[pos].tag))
+bool snapshot_binary_search_lt(v_array<snapshot_item> a, size_t desired_t, size_t tag, size_t &pos, size_t last_found_pos) {
+    size_t hi  = a.size();
+    if (hi == 0) return false;
+    if (last_found_pos + 1 < hi) {
+      pos = last_found_pos+1;
+      if ((a[pos].pred_step == desired_t) && (a[pos].tag == tag)) return true;
+      if (pos == hi - 1) { // this is the last item
+        if ((a[pos].pred_step < desired_t) && (a[pos].tag == tag)) return true;
+        else return false;
+      }
+      // not the last item
+      if ((a[pos].pred_step <= desired_t) && (a[pos].tag == tag) &&
+          ((a[pos+1].pred_step >  desired_t) ||
+           ((a[pos+1].pred_step == desired_t) && (a[pos+1].tag > tag))))
         return true;
+    }
+    pos = hi >> 1;
+    while (true) {
+      if (hi <= 5) break;
+      if ((a[pos].pred_step == desired_t) && (a[pos].tag == tag)) return true;
+      if ((a[pos].pred_step >  desired_t) ||
+          ((a[pos].pred_step == desired_t) && (a[pos].tag >  tag)))
+        hi = pos;
+      else break;
+      
+      pos = hi >> 1;
+    }
+
+    for (pos=hi-1; ; pos--) {
+      if ((a[pos].pred_step <= desired_t) && (tag == a[pos].tag)) return true;
       if (pos == 0) return false;
     }
     return false;
   }
 
-  void searn_snapshot(vw& all, size_t index, size_t tag, void* data_ptr, size_t sizeof_data)
+
+  bool snapshot_binary_search_eq(v_array<snapshot_item> a, size_t desired_index, size_t tag, size_t &pos, size_t last_found_pos) {
+    size_t lo  = 0;  // the answer is always >= lo
+    size_t hi  = a.size();  // the answer is always < hi
+    if (hi == 0) return false;
+
+    if (last_found_pos + 1 < hi) {
+      pos = last_found_pos+1;
+      if ((a[pos].index == desired_index) && (a[pos].tag == tag)) return true;
+    } else      
+      pos = (lo + hi) >> 1;
+    while (true) {
+      if (lo == hi) return false;
+      if ((a[pos].index == desired_index) && (a[pos].tag == tag)) return true;
+      if (hi - lo <= 5) break;
+      if ((a[pos].index <  desired_index) ||
+          ((a[pos].index == desired_index) && (a[pos].tag <  tag)))
+        lo = pos;
+      else
+        hi = pos;
+      pos = (lo + hi) >> 1;
+    }
+
+    for (pos=lo; pos<hi; pos++) {
+      if (a[pos].index > desired_index) return false;
+      if ((a[pos].index == desired_index) && (a[pos].tag == tag)) return true;
+    }
+    return false;
+  }
+
+  
+
+  void searn_snapshot(vw& all, size_t index, size_t tag, void* data_ptr, size_t sizeof_data, bool used_for_prediction)
   {
     searn* srn=(searn*)all.searnstr;
     if (! srn->do_snapshot) return;
+    assert(tag > 0);
 
-    //cerr << "snapshot called with:   { index=" << index << ", tag=" << tag << ", data_ptr=" << *(size_t*)data_ptr << ", t=" << srn->t << " }" << endl;
+    //if (srn->state == INIT_TRAIN) return;
+    //if (srn->state == LEARN) return;
+    
+    //clog << "snapshot called with:   { index=" << index << ", tag=" << tag << ", data_ptr=" << *(size_t*)data_ptr << ", t=" << srn->t << ", u4p=" << used_for_prediction << " }" << endl;
     
 
     if (srn->state == INIT_TEST) return;
@@ -1577,6 +650,18 @@ namespace ImperativeSearn {
            ((srn->snapshot_data.last().index == index) && (srn->snapshot_data.last().tag > tag)))) 
         cerr << "warning: trying to snapshot in a non-monotonic order! ignoring this snapshot" << endl;
       else {
+        // if we're doing auto-history and this is the first snapshot of a given index, we need to also snapshot the relevant piece of history
+        if (srn->auto_history &&
+            ((srn->snapshot_data.size() == 0) ||
+             ((srn->snapshot_data.size() > 0) && (srn->snapshot_data.last().index < index)))) {
+          size_t history_size = srn->hinfo.length * sizeof(uint32_t);
+          void* history_data = malloc(history_size);  // TODO: free these!
+          memcpy(history_data, srn->rollout_action.begin + srn->t, history_size);
+          snapshot_item item = { index, 0, history_data, history_size, srn->t };
+          srn->snapshot_data.push_back(item);
+          //cerr << "ss t=" << srn->t << " 
+        }
+
         void* new_data = malloc(sizeof_data);
         memcpy(new_data, data_ptr, sizeof_data);
         snapshot_item item = { index, tag, new_data, sizeof_data, srn->t };
@@ -1584,55 +669,185 @@ namespace ImperativeSearn {
       }
       return;
     }
-    if (srn->t > srn->learn_t) return;
 
-    //cerr << "index=" << index << " tag=" << tag << endl;
-
-    // otherwise, we're restoring snapshots -- we want to find the index of largest value that has .t<=learn_t
-    size_t i;
-    bool found;
-    found = snapshot_linear_search(srn->snapshot_data, srn->learn_t, tag, i);
-    if (!found) return;  // can't do anything
+    // ELSE, this is TEST mode
+    if (srn->t <= srn->learn_t) {  // RESTORE up to certain point
+      //cerr << "index=" << index << " tag=" << tag << endl;
+
+      // otherwise, we're restoring snapshots -- we want to find the index of largest value that has .t<=learn_t
+      size_t i;
+      bool found;
+      found = snapshot_binary_search_lt(srn->snapshot_data, srn->learn_t, tag, i, srn->snapshot_last_found_pos);
+      // size_t i2;
+      // bool found2;
+      // found2 = snapshot_linear_search_lt(srn->snapshot_data, srn->learn_t, tag, i2);
+      // assert(found == found2);
+      // assert(i == i2);
+      if (!found) return;  // can't do anything
+
+      if (srn->auto_history && (tag == 1)) {
+        assert((i > 0) && (srn->snapshot_data[i-1].pred_step == srn->learn_t) && (srn->snapshot_data[i-1].tag == 0));
+        // restore i-1 as history
+        snapshot_item item = srn->snapshot_data[i-1];
+        assert(item.data_size == srn->hinfo.length * sizeof(uint32_t));
+        if (srn->rollout_action.size() < srn->learn_t + srn->hinfo.length)
+          srn->rollout_action.resize(srn->learn_t + srn->hinfo.length);
+        //assert(srn->rollout_action.size() >= srn->learn_t + srn->hinfo.length);
+        memcpy(srn->rollout_action.begin + srn->learn_t, item.data_ptr, item.data_size);
+      }
+      
+      // if (srn->auto_history && (i > 0) && (srn->snapshot_data[i-1].pred_step == srn->learn_t) && (srn->snapshot_data[i-1].tag == 0)) {
+      //   // restore i-1 as history
+      //   snapshot_item item = srn->snapshot_data[i-1];
+      //   assert(item.data_size == srn->hinfo.length * sizeof(uint32_t));
+      //   memcpy(srn->rollout_action.begin + srn->learn_t, item.data_ptr, item.data_size);
+      // }
 
-    snapshot_item item = srn->snapshot_data[i];
 
-    /*
-    //cerr << "restoring snapshot @ " << item.pred_step << " (learn_t=" << srn->learn_t << ") with " << index << "." << tag << ", value=" << *(size_t*)item.data_ptr << endl;
-    cerr << "would restore snapshot: { index=" << item.index << ", tag=" << item.tag << ", data_ptr=";
-    if (item.tag == 1) { cerr << *(size_t*)item.data_ptr; }
-    if (item.tag == 2) {
+      
+      srn->snapshot_last_found_pos = i;
+      snapshot_item item = srn->snapshot_data[i];
+
+      /*
+      //cerr << "restoring snapshot @ " << item.pred_step << " (learn_t=" << srn->learn_t << ") with " << index << "." << tag << ", value=" << *(size_t*)item.data_ptr << endl;
+      cerr << "would restore snapshot: { index=" << item.index << ", tag=" << item.tag << ", data_ptr=";
+      if (item.tag == 1) { cerr << *(size_t*)item.data_ptr; }
+      if (item.tag == 2) {
       size_t *tmp = (size_t*)item.data_ptr;
       cerr << tmp[0];
+      }
+      if (item.tag == 3) { cerr << *(float*)item.data_ptr; }
+      cerr << ", pred_step=" << item.pred_step << ", moving from t=" << srn->t << " }" << endl;
+      */
+      assert(sizeof_data == item.data_size);
+
+      memcpy(data_ptr, item.data_ptr, sizeof_data);
+      srn->t = item.pred_step;
+    } else if (srn->do_fastforward) { // can we FAST FORWARD to end???
+      if (srn->rollout_oracle) return;
+      if (! srn->snapshot_could_match) return; // already hosed
+      if (! used_for_prediction) return; // we don't care if it matches or not
+      size_t i;
+      bool found;
+      found = snapshot_binary_search_eq(srn->snapshot_data, index, tag, i, srn->snapshot_last_found_pos);
+
+      // bool found2; size_t i2;
+      // found2 = snapshot_linear_search_eq(srn->snapshot_data, index, tag, i2);
+      // assert(found == found2);
+      // assert(i == i2);
+      
+      if (!found) return; // can't do anything
+
+      
+      srn->snapshot_last_found_pos = i;
+      //clog << "a" << index << "/" << tag << " ";
+      snapshot_item item = srn->snapshot_data[i];
+      bool matches = memcmp(item.data_ptr, data_ptr, sizeof_data) == 0;
+      if (matches) {
+        // TODO: make sure it's the right number of snapshots!!!
+        srn->snapshot_is_equivalent_to_t = item.pred_step;
+
+        if (srn->auto_history && (i>0) && (srn->snapshot_data[i-1].index == index) && (srn->snapshot_data[i-1].tag == 0)) {
+          item = srn->snapshot_data[i-1];
+          if (srn->rollout_action.size() >= srn->t + srn->hinfo.length) {
+            matches = memcmp(item.data_ptr, srn->rollout_action.begin + srn->t, sizeof_data) == 0;
+            if (!matches)
+              srn->snapshot_could_match = false;
+          }
+        }
+      } else {
+        srn->snapshot_could_match = false;
+      }
     }
-    if (item.tag == 3) { cerr << *(float*)item.data_ptr; }
-    cerr << ", pred_step=" << item.pred_step << ", moving from t=" << srn->t << " }" << endl;
-    */
-    assert(sizeof_data == item.data_size);
+  }
 
-    memcpy(data_ptr, item.data_ptr, sizeof_data);
-    srn->t = item.pred_step;
+  template<class T> bool v_array_contains(v_array<T> &A, T x) {
+    for (T* e = A.begin; e != A.end; ++e)
+      if (*e == x)
+        return true;
+    return false;
   }
 
+  inline bool cmp_size_t(const size_t a, const size_t b) { return a < b; }
+
   v_array<size_t> get_training_timesteps(vw& all, searn& srn)
   {
     v_array<size_t> timesteps;
-    for (size_t t=0; t<srn.T; t++)
-      timesteps.push_back(t);
+
+    if (srn.subsample_timesteps <= 0) {
+      for (size_t t=0; t<srn.T; t++)
+        timesteps.push_back(t);
+    } else if (srn.subsample_timesteps < 1) {
+      for (size_t t=0; t<srn.T; t++)
+        if (frand48() <= srn.subsample_timesteps)
+          timesteps.push_back(t);
+
+      if (timesteps.size() == 0) // ensure at least one
+        timesteps.push_back((size_t)(frand48() * srn.T));
+    } else {
+      while ((timesteps.size() < (size_t)srn.subsample_timesteps) &&
+             (timesteps.size() < srn.T)) {
+        size_t t = (size_t)(frand48() * (float)srn.T);
+        if (! v_array_contains(timesteps, t))
+          timesteps.push_back(t);
+      }
+      std::sort(timesteps.begin, timesteps.end, cmp_size_t);
+    }
+      
     return timesteps;
   }
 
-  void generate_training_example(vw& all, searn& srn, example** ec, size_t len, CSOAA::label labels, v_array<float> losses)
+  size_t labelset_size(searn&srn,void*l) {
+    if (srn.rollout_all_actions)
+      return ((CSOAA::label*)l)->costs.size();
+    else
+      return ((CB::label*)l)->costs.size();
+  }
+
+  size_t labelset_weight_index(searn&srn, void*l, size_t i) {
+    if (srn.rollout_all_actions)
+      return ((CSOAA::label*)l)->costs[i].weight_index;
+    else
+      return ((CB::label*)l)->costs[i].weight_index;
+  }
+
+  bool should_print_update(vw& all, bool hit_new_pass=false)
+  {
+    //uncomment to print out final loss after all examples processed
+    //commented for now so that outputs matches make test
+    //if( parser_done(all.p)) return true;
+
+    if (PRINT_UPDATE_EVERY_EXAMPLE) return true;
+    if (PRINT_UPDATE_EVERY_PASS && hit_new_pass) return true;
+    return (all.sd->weighted_examples > all.sd->dump_interval) && !all.quiet && !all.bfgs;
+  }
+
+  bool might_print_update(vw& all)
   {
-    assert(labels.costs.size() == losses.size());
-	for (size_t i=0; i<labels.costs.size(); i++)
-      labels.costs[i].x = losses[i];
+    // basically do should_print_update but check me and the next
+    // example because of off-by-ones
+
+    if (PRINT_UPDATE_EVERY_EXAMPLE) return true;
+    if (PRINT_UPDATE_EVERY_PASS) return true;
+    return (all.sd->weighted_examples + 1. >= all.sd->dump_interval) && !all.quiet && !all.bfgs;
+  }
+
+  void generate_training_example(vw& all, searn& srn, learner& base, example** ec, size_t len, void*labels, v_array<float> losses)
+  {
+    assert(labelset_size(srn, labels) == losses.size());
+    float min_loss = FLT_MAX;
+    for (size_t i=0; i<losses.size(); i++)
+      if (losses[i] < min_loss) min_loss = losses[i];
+    for (size_t i=0; i<losses.size(); i++)
+      if (srn.rollout_all_actions)
+        ((CSOAA::label*)labels)->costs[i].x = losses[i] - min_loss;
+      else
+        ((CB::label*)labels)->costs[i].x = losses[i] - min_loss;
 
     if (!isLDF(srn)) {
       void* old_label = ec[0]->ld;
-      ec[0]->ld = (void*)&labels;
-      SearnUtil::add_policy_offset(all, ec[0], srn.increment, srn.current_policy);
-      srn.base.learn(ec[0]);
-      SearnUtil::remove_policy_offset(all, ec[0], srn.increment, srn.current_policy);
+      ec[0]->ld = labels;
+      base.learn(ec[0], srn.current_policy);
       ec[0]->ld = old_label;
       srn.total_examples_generated++;
     } else { // isLDF
@@ -1640,10 +855,19 @@ namespace ImperativeSearn {
     }
   }
 
+  void clear_rollout_actions(searn&srn) {
+    srn.rollout_action.erase();
+    for (size_t t=0; t<srn.hinfo.length; t++)
+      srn.rollout_action.push_back(0);
+  }
+
+
   void train_single_example(vw& all, searn& srn, example**ec, size_t len)
   {
     // do an initial test pass to compute output (and loss)
-    //cerr << "======================================== INIT TEST ========================================" << endl;
+    // TODO: don't do this if we don't need it!
+    //clog << "======================================== INIT TEST (" << srn.current_policy << "," << srn.read_example_last_pass << ") ========================================" << endl;
+
     srn.state = INIT_TEST;
     srn.t = 0;
     srn.T = 0;
@@ -1653,81 +877,141 @@ namespace ImperativeSearn {
     srn.learn_loss = 0.f;
     srn.learn_example_copy = NULL;
     srn.learn_example_len  = 0;
-    srn.train_action.erase();
     srn.num_features = 0;
- 
-    srn.task->structured_predict(all, srn, ec, len, srn.pred_string, srn.truth_string);
-
-    if (srn.t == 0)
-      return;  // there was no data!
+    srn.train_action.erase();
+    if (srn.auto_history) clear_rollout_actions(srn);
+
+    srn.snapshot_is_equivalent_to_t = (size_t)-1;
+    srn.snapshot_could_match = false;
+    srn.snapshot_last_found_pos = (size_t)-1;
+
+    if ((all.final_prediction_sink.size() > 0) ||   // if we have to produce output, we need to run this
+        might_print_update(all) ||                  // if we have to print and update to stderr
+        (!all.training) ||                          // if we're just testing
+        (all.current_pass == 0) ||                  // we need error rates for progressive cost
+        (all.holdout_set_off) ||                    // no holdout
+        (ec[0]->test_only)                          // it's a holdout example
+        ) {
+
+      srn.should_produce_string = might_print_update(all) || (all.final_prediction_sink.size() > 0);
+      if (srn.should_produce_string) {  // TODO: don't do this all the time!!!
+        srn.truth_string->str("");  // erase contents
+        srn.pred_string->str("");
+      }
+      
+      assert(srn.truth_string != NULL);
+      srn.task->structured_predict(srn, ec, len,
+                                   srn.should_produce_string ? srn.pred_string  : NULL,
+                                   srn.should_produce_string ? srn.truth_string : NULL);
 
-    // do a pass over the data allowing oracle and snapshotting
-    //cerr << "======================================== INIT TRAIN ========================================" << endl;
-    srn.state = INIT_TRAIN;
-    srn.t = 0;
-    srn.loss_last_step = 0;
-    clear_snapshot(all, srn);
+      for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; ++sink)
+        all.print_text((int)*sink, srn.pred_string->str(), ec[0]->tag);
+    }
+    
+    if ((srn.t > 0) && all.training) {
+      if (srn.adaptive_beta)
+        srn.beta = 1.f - powf(1.f - srn.alpha, (float)srn.total_examples_generated);
+
+      // do a pass over the data allowing oracle and snapshotting
+      //clog << "======================================== INIT TRAIN (" << srn.current_policy << "," << srn.read_example_last_pass << ") ========================================" << endl;
+      srn.state = INIT_TRAIN;
+      srn.train_action.erase();
+      if (srn.auto_history)
+        clear_rollout_actions(srn);
+      srn.t = 0;
+      srn.loss_last_step = 0;
+      clear_snapshot(all, srn);
 
-    srn.task->structured_predict(all, srn, ec, len, NULL, NULL);
+      srn.snapshot_is_equivalent_to_t = (size_t)-1;
+      srn.snapshot_last_found_pos = (size_t)-1;
+      srn.snapshot_could_match = false;
+      srn.task->structured_predict(srn, ec, len, NULL, NULL);
 
-    if (srn.t == 0) {
-      clear_snapshot(all, srn);
-      return;  // there was no data
-    }
+      if (srn.t == 0) {
+        clear_snapshot(all, srn);
+        return;  // there was no data
+      }
 
-    srn.T = srn.t;
-
-    // generate training examples on which to learn
-    //cerr << "======================================== LEARN ========================================" << endl;
-    srn.state = LEARN;
-    v_array<size_t> tset = get_training_timesteps(all, srn);
-    for (size_t t=0; t<tset.size(); t++) {
-      CSOAA::label aset = srn.train_labels[t];
-      srn.learn_t = t;
-      srn.learn_losses.erase();
-
-      for (size_t i=0; i<aset.costs.size(); i++) {
-        if (aset.costs[i].weight_index == srn.train_action[srn.learn_t])
-          srn.learn_losses.push_back( srn.train_loss );
-        else {
-          srn.t = 0;
-          srn.learn_a = aset.costs[i].weight_index;
-          srn.loss_last_step = 0;
-          srn.learn_loss = 0.f;
-
-          //cerr << "learn_t = " << srn.learn_t << " || learn_a = " << srn.learn_a << endl;
-          srn.task->structured_predict(all, srn, ec, len, NULL, NULL);
-
-          srn.learn_losses.push_back( srn.learn_loss );
-          //cerr << "total loss: " << srn.learn_loss << endl;
+      srn.T = srn.t;
+
+      // generate training examples on which to learn
+      //clog << "======================================== LEARN (" << srn.current_policy << "," << srn.read_example_last_pass << ") ========================================" << endl;
+      srn.state = LEARN;
+      v_array<size_t> tset = get_training_timesteps(all, srn);
+      for (size_t tid=0; tid<tset.size(); tid++) {
+        size_t t = tset[tid];
+        void *aset = srn.train_labels[t];
+        srn.learn_t = t;
+        srn.learn_losses.erase();
+
+        for (size_t i=0; i<labelset_size(srn, aset); i++) {
+          if (srn.auto_history) {
+            // startup the rollout at the train actions
+            clear_rollout_actions(srn);
+            //srn.rollout_action.resize(srn.hinfo.length + srn.T);
+            push_many(srn.rollout_action, srn.train_action.begin, t);
+            //memcpy(srn.rollout_action.begin + srn.hinfo.length, srn.train_action.begin, srn.T * sizeof(uint32_t));
+          }
+          srn.snapshot_last_found_pos = (size_t)-1;
+
+          size_t this_index = labelset_weight_index(srn, aset, i);
+          if (this_index == srn.train_action[srn.learn_t])
+            srn.learn_losses.push_back( srn.train_loss );
+          else {
+            srn.t = 0;
+            srn.learn_a = (uint32_t)this_index;
+            srn.loss_last_step = 0;
+            srn.learn_loss = 0.f;
+
+            //clog << "learn_t = " << srn.learn_t << " || learn_a = " << srn.learn_a << endl;
+            srn.snapshot_is_equivalent_to_t = (size_t)-1;
+            srn.snapshot_could_match = true;
+            srn.task->structured_predict(srn, ec, len, NULL, NULL);
+
+            srn.learn_losses.push_back( srn.learn_loss );
+            //clog << "total loss: " << srn.learn_loss << endl;
+          }
         }
-      }
 
-      if (srn.learn_example_copy != NULL) {
-        generate_training_example(all, srn, srn.learn_example_copy, srn.learn_example_len, aset, srn.learn_losses);
+        if (srn.learn_example_copy != NULL) {
+          generate_training_example(all, srn, *srn.base_learner, srn.learn_example_copy, srn.learn_example_len, aset, srn.learn_losses);
 
-        for (size_t n=0; n<srn.learn_example_len; n++) {
-          dealloc_example(CSOAA::delete_label, *srn.learn_example_copy[n]);
-          free(srn.learn_example_copy[n]);
+          if (!srn.examples_dont_change)
+            for (size_t n=0; n<srn.learn_example_len; n++) {
+              dealloc_example(OAA::delete_label, *srn.learn_example_copy[n]);
+              free(srn.learn_example_copy[n]);
+            }
+          free(srn.learn_example_copy);
+          srn.learn_example_copy = NULL;
+          srn.learn_example_len  = 0;
+        } else {
+          cerr << "warning: searn did not generate an example for a given time-step" << endl;
         }
-        free(srn.learn_example_copy);
-        srn.learn_example_copy = NULL;
-        srn.learn_example_len  = 0;
-      } else {
-        cerr << "warning: searn did not generate an example for a given time-step" << endl;
       }
+      tset.erase(); tset.delete_v();
     }
-    tset.erase(); tset.delete_v();
-
+    
     clear_snapshot(all, srn);
     srn.train_action.erase();
     srn.train_action.delete_v();
+    srn.rollout_action.erase();
+    srn.rollout_action.delete_v();
     for (size_t i=0; i<srn.train_labels.size(); i++) {
-      srn.train_labels[i].costs.erase();
-      srn.train_labels[i].costs.delete_v();
+      if (srn.rollout_all_actions) {
+        ((CSOAA::label*)srn.train_labels[i])->costs.erase();
+        ((CSOAA::label*)srn.train_labels[i])->costs.delete_v();
+        delete ((CSOAA::label*)srn.train_labels[i]);
+      } else {
+        ((CB::label*)srn.train_labels[i])->costs.erase();
+        ((CB::label*)srn.train_labels[i])->costs.delete_v();
+        delete ((CB::label*)srn.train_labels[i]);
+      }
     }
     srn.train_labels.erase();
     srn.train_labels.delete_v();
+    
+    //clog << "======================================== DONE (" << srn.current_policy << "," << srn.read_example_last_pass << ") ========================================" << endl;
+
   }
 
 
@@ -1741,163 +1025,309 @@ namespace ImperativeSearn {
   }
 
   float safediv(float a,float b) { if (b == 0.f) return 0.f; else return (a/b); }
- 
-  void print_update(vw& all, searn& srn)
+
+  void to_short_string(string in, size_t max_len, char*out) {
+    for (size_t i=0; i<max_len; i++) {
+      if (i >= in.length())
+        out[i] = ' ';
+      else if ((in[i] == '\n') || (in[i] == '\t'))    // TODO: maybe catch other characters?
+        out[i] = ' ';
+      else
+        out[i] = in[i];
+    }
+
+    if (in.length() > max_len) { 
+      out[max_len-2] = '.'; 
+      out[max_len-1] = '.'; 
+    }
+    out[max_len] = 0;
+  }
+
+void print_update(vw& all, searn* srn)
   {
-    if (!Searn::should_print_update(all))
+    if (!srn->printed_output_header && !all.quiet) {
+      const char * header_fmt = "%-10s %-10s %8s %15s %24s %22s %8s %5s %5s %15s %15s\n";
+      fprintf(stderr, header_fmt, "average", "since", "sequence", "example",   "current label", "current predicted",  "current",  "cur", "cur", "predic.", "examples");
+      fprintf(stderr, header_fmt, "loss",  "last",  "counter",  "weight", "sequence prefix",   "sequence prefix", "features", "pass", "pol",    "made",   "gener.");
+      cerr.precision(5);
+      srn->printed_output_header = true;
+    }
+
+    if (!should_print_update(all, srn->hit_new_pass))
       return;
 
     char true_label[21];
     char pred_label[21];
-    Searn::to_short_string(srn.truth_string ? srn.truth_string->str() : "", 20, true_label);
-    Searn::to_short_string(srn.pred_string  ? srn.pred_string->str()  : "", 20, pred_label);
+    to_short_string(srn->truth_string->str(), 20, true_label);
+    to_short_string(srn->pred_string->str() , 20, pred_label);
+
+    float avg_loss = 0.;
+    float avg_loss_since = 0.;
+    if (!all.holdout_set_off && all.current_pass >= 1) {
+      avg_loss       = safediv((float)all.sd->holdout_sum_loss, (float)all.sd->weighted_holdout_examples);
+      avg_loss_since = safediv((float)all.sd->holdout_sum_loss_since_last_dump, (float)all.sd->weighted_holdout_examples_since_last_dump);
+
+      all.sd->weighted_holdout_examples_since_last_dump = 0;
+      all.sd->holdout_sum_loss_since_last_dump = 0.0;
+    } else {
+      avg_loss       = safediv((float)all.sd->sum_loss, (float)all.sd->weighted_examples);
+      avg_loss_since = safediv((float)all.sd->sum_loss_since_last_dump, (float) (all.sd->weighted_examples - all.sd->old_weighted_examples));
+    }
 
-    fprintf(stderr, "%-10.6f %-10.6f %8ld %15f   [%s] [%s] %8lu %5d %5d %15lu %15lu\n",
-            safediv((float)all.sd->sum_loss, (float)all.sd->weighted_examples),
-            safediv((float)all.sd->sum_loss_since_last_dump, (float) (all.sd->weighted_examples - all.sd->old_weighted_examples)),
+    fprintf(stderr, "%-10.6f %-10.6f %8ld %15f   [%s] [%s] %8lu %5d %5d %15lu %15lu",
+            avg_loss,
+            avg_loss_since,
             (long int)all.sd->example_number,
             all.sd->weighted_examples,
             true_label,
             pred_label,
-            (long unsigned int)srn.num_features,
-            (int)srn.read_example_last_pass,
-            (int)srn.current_policy,
-            (long unsigned int)srn.total_predictions_made,
-            (long unsigned int)srn.total_examples_generated);
+            (long unsigned int)srn->num_features,
+            (int)srn->read_example_last_pass,
+            (int)srn->current_policy,
+            (long unsigned int)srn->total_predictions_made,
+            (long unsigned int)srn->total_examples_generated);
+
+    if (PRINT_CLOCK_TIME) {
+      size_t num_sec = (size_t)(((float)(clock() - srn->start_clock_time)) / CLOCKS_PER_SEC);
+      fprintf(stderr, " %15lusec", num_sec);
+    }
+
+    if (!all.holdout_set_off && all.current_pass >= 1)
+      fprintf(stderr, " h");
+    
+    fprintf(stderr, "\n");
 
     all.sd->sum_loss_since_last_dump = 0.0;
     all.sd->old_weighted_examples = all.sd->weighted_examples;
     all.sd->dump_interval *= 2;
   }
 
+  void add_neighbor_features(searn& srn) {
+    size_t neighbor_constant = 8349204823;
+    if (srn.neighbor_features.size() == 0) return;
+    uint32_t wpp = srn.all->wpp * srn.all->reg.stride;
+
+    for (int32_t n=0; n<(int32_t)srn.ec_seq.size(); n++) {
+      example*me = srn.ec_seq[n];
+      //cerr << "o=" << me->num_features << endl;
+      for (int32_t*enc=srn.neighbor_features.begin; enc!=srn.neighbor_features.end; ++enc) {
+        int32_t offset = (*enc) >> 24;
+        size_t  old_ns = (*enc) & 0xFF;
+        size_t  enc_offset = wpp * ((2 * (size_t)(*enc)) + ((*enc < 0) ? 1 : 0));
+
+        // TODO: auditing :P
+        if ((n + offset >= 0) && (n + offset < (int32_t)srn.ec_seq.size())) { // we're okay on position
+          example*you = srn.ec_seq[n+offset];
+          size_t  you_size = you->atomics[old_ns].size();
+
+          if (you_size > 0) {
+            if (me->atomics[neighbor_namespace].size() == 0) {
+              me->indices.push_back(neighbor_namespace);
+            }
+
+            me->atomics[neighbor_namespace].resize(me->atomics[neighbor_namespace].size() + you_size + 1);
+            for (feature*f = you->atomics[old_ns].begin; f != you->atomics[old_ns].end; ++f) {
+              feature f2 = { (*f).x, (uint32_t)( ((*f).weight_index * neighbor_constant + enc_offset) & srn.all->reg.weight_mask ) };
+              me->atomics[neighbor_namespace].push_back(f2);
+            }
+            //push_many(me->atomics[neighbor_namespace], you->atomics[old_ns].begin, you_size);
+            //cerr << "copying " << you_size << " features" << endl;
+            me->sum_feat_sq[neighbor_namespace] += you->sum_feat_sq[old_ns];
+            me->total_sum_feat_sq += you->sum_feat_sq[old_ns];
+            me->num_features += you_size;
+          }
+        } else {
+          // TODO: add dummy features for <s> or </s>
+        }
+      }
+    }
+  }
+
+  void del_neighbor_features(searn& srn) {
+    if (srn.neighbor_features.size() == 0) return;
+
+    for (int32_t n=0; n<(int32_t)srn.ec_seq.size(); n++) {
+      example*me = srn.ec_seq[n];
+      //cerr << "n=" << me->num_features;
+      for (int32_t*enc=srn.neighbor_features.begin; enc!=srn.neighbor_features.end; ++enc) {
+        int32_t offset = (*enc) >> 24;
+        size_t  old_ns = (*enc) & 0xFF;
+
+        if ((n + offset >= 0) && (n + offset < (int32_t)srn.ec_seq.size())) { // we're okay on position
+          example*you = srn.ec_seq[n+offset];
+          size_t  you_size = you->atomics[old_ns].size();
+
+          if (you_size > 0) {
+            if (me->atomics[neighbor_namespace].size() == you_size) {
+              char last_idx = me->indices.pop();
+              assert(last_idx == (char)neighbor_namespace);
+              //cerr << "erasing new ns '" << (char)neighbor_namespace << "' of size " << me->atomics[neighbor_namespace].size() << endl;
+              me->atomics[neighbor_namespace].erase();
+            } else {
+              me->atomics[neighbor_namespace].end -= you_size;
+              //cerr << "erasing " << you_size << " features" << endl;
+            }
+            
+            me->sum_feat_sq[neighbor_namespace] -= you->sum_feat_sq[old_ns];
+            me->total_sum_feat_sq -= you->sum_feat_sq[old_ns];
+            me->num_features -= you_size;
+          }
+        } else {
+          // TODO: add dummy features for <s> or </s>
+        }
+      }
+      //cerr << " " << me->num_features << endl;
+    }
+  }
+
+
 
   void do_actual_learning(vw&all, searn& srn)
   {
     if (srn.ec_seq.size() == 0)
       return;  // nothing to do :)
 
-    if (Searn::should_print_update(all)) {
-      srn.truth_string = new stringstream();
-      srn.pred_string  = new stringstream();
-    }
-
+    add_neighbor_features(srn);
     train_single_example(all, srn, srn.ec_seq.begin, srn.ec_seq.size());
-    if (srn.test_loss >= 0.f) {
+    del_neighbor_features(srn);
+
+    if (srn.ec_seq[0]->test_only) {
+      all.sd->weighted_holdout_examples += 1.f;//test weight seen
+      all.sd->weighted_holdout_examples_since_last_dump += 1.f;
+      all.sd->weighted_holdout_examples_since_last_pass += 1.f;
+      all.sd->holdout_sum_loss += srn.test_loss;
+      all.sd->holdout_sum_loss_since_last_dump += srn.test_loss;
+      all.sd->holdout_sum_loss_since_last_pass += srn.test_loss;//since last pass
+    } else {
+      all.sd->weighted_examples += 1.f;
+      all.sd->total_features += srn.num_features;
       all.sd->sum_loss += srn.test_loss;
       all.sd->sum_loss_since_last_dump += srn.test_loss;
       all.sd->example_number++;
-      all.sd->total_features += srn.num_features;
-      all.sd->weighted_examples += 1.f;
-    }
-
-    print_update(all, srn);
-
-    if (srn.truth_string != NULL) {
-      delete srn.truth_string;
-      srn.truth_string = NULL;
-    }
-    if (srn.pred_string != NULL) {
-      delete srn.pred_string;
-      srn.pred_string = NULL;
     }
   }
 
-  void searn_learn(void*d, example*ec) {
+  void searn_learn(void*d, learner& base, example*ec) {
     searn *srn = (searn*)d;
     vw* all = srn->all;
-
+    srn->base_learner = &base;
     bool is_real_example = true;
-    if (ec->end_pass || OAA::example_is_newline(ec) || srn->ec_seq.size() >= all->p->ring_size - 2) { 
+    if (example_is_newline(ec) || srn->ec_seq.size() >= all->p->ring_size - 2) { 
       if (srn->ec_seq.size() >= all->p->ring_size - 2) { // give some wiggle room
 	std::cerr << "warning: length of sequence at " << ec->example_counter << " exceeds ring size; breaking apart" << std::endl;
-    }
+      }
 
       do_actual_learning(*all, *srn);
       clear_seq(*all, *srn);
-      if (ec->end_pass) {
-        srn->read_example_last_pass++;
-        srn->passes_since_new_policy++;
-        if (srn->passes_since_new_policy >= srn->passes_per_policy) {
-          srn->passes_since_new_policy = 0;
-          if(all->training)
-            srn->current_policy++;
-          if (srn->current_policy > srn->total_number_of_policies) {
-            std::cerr << "internal error (bug): too many policies; not advancing" << std::endl;
-            srn->current_policy = srn->total_number_of_policies;
-          }
-          //reset searn_trained_nb_policies in options_from_file so it is saved to regressor file later
-          std::stringstream ss;
-          ss << srn->current_policy;
-          VW::cmd_string_replace_value(all->options_from_file,"--searn_trained_nb_policies", ss.str());
-        }
-      }
+      srn->hit_new_pass = false;
       
-      VW::finish_example(*all, ec);
+      //VW::finish_example(*all, ec);
       is_real_example = false;
     } else {
       srn->ec_seq.push_back(ec);
     }
     
     if (is_real_example) {
-      srn->read_example_this_loop++;
       srn->read_example_last_id = ec->example_counter;
     }
   }
-  
-  void searn_drive(vw* all, void *d) {
+
+  void end_pass(void* d) {
     searn *srn = (searn*)d;
+    vw* all = srn->all;
+    srn->hit_new_pass = true;
+    srn->read_example_last_pass++;
+    srn->passes_since_new_policy++;
+    if (srn->passes_since_new_policy >= srn->passes_per_policy) {
+      srn->passes_since_new_policy = 0;
+      if(all->training)
+        srn->current_policy++;
+      if (srn->current_policy > srn->total_number_of_policies) {
+        std::cerr << "internal error (bug): too many policies; not advancing" << std::endl;
+        srn->current_policy = srn->total_number_of_policies;
+      }
+      //reset searn_trained_nb_policies in options_from_file so it is saved to regressor file later
+      std::stringstream ss;
+      ss << srn->current_policy;
+      VW::cmd_string_replace_value(all->options_from_file,"--searn_trained_nb_policies", ss.str());
+    }
+  }
 
-    const char * header_fmt = "%-10s %-10s %8s %15s %24s %22s %8s %5s %5s %15s %15s\n";
-    
-    fprintf(stderr, header_fmt, "average", "since", "sequence", "example",   "current label", "current predicted",  "current",  "cur", "cur", "predic.", "examples");
-    fprintf(stderr, header_fmt, "loss",  "last",  "counter",  "weight", "sequence prefix",   "sequence prefix", "features", "pass", "pol",    "made",   "gener.");
-    cerr.precision(5);
+  void finish_example(vw& all, void* d, example* ec) {
+    searn *srn = (searn*)d;
 
-    example* ec = NULL;
-    srn->read_example_this_loop = 0;
-    while (true) {
-      if ((ec = VW::get_example(all->p)) != NULL) { // semiblocking operation
-        searn_learn(d, ec);
-      } else if (parser_done(all->p)) {
-        do_actual_learning(*all, *srn);
-        break;
-      }
+    if (ec->end_pass || example_is_newline(ec) || srn->ec_seq.size() >= all.p->ring_size - 2) { 
+      print_update(all, srn);
+      VW::finish_example(all, ec);
     }
+  }
+
+  void end_examples(void* d) {
+    searn* srn = (searn*)d;
+    vw* all    = srn->all;
+
+    do_actual_learning(*all, *srn);
 
     if( all->training ) {
       std::stringstream ss1;
       std::stringstream ss2;
-      ss1 << (srn->current_policy+1);
-      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searnimp_trained_nb_policies
-      VW::cmd_string_replace_value(all->options_from_file,"--searnimp_trained_nb_policies", ss1.str()); 
+      ss1 << ((srn->passes_since_new_policy == 0) ? srn->current_policy : (srn->current_policy+1));
+      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_trained_nb_policies
+      VW::cmd_string_replace_value(all->options_from_file,"--searn_trained_nb_policies", ss1.str()); 
       ss2 << srn->total_number_of_policies;
-      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searnimp_total_nb_policies
-      VW::cmd_string_replace_value(all->options_from_file,"--searnimp_total_nb_policies", ss2.str());
+      //use cmd_string_replace_value in case we already loaded a predictor which had a value stored for --searn_total_nb_policies
+      VW::cmd_string_replace_value(all->options_from_file,"--searn_total_nb_policies", ss2.str());
     }
   }
 
   void searn_initialize(vw& all, searn& srn)
   {
-    srn.predict = searn_predict;
-    srn.declare_loss = searn_declare_loss;
-    srn.snapshot = searn_snapshot;
+    srn.predict_f = searn_predict;
+    srn.declare_loss_f = searn_declare_loss;
+    srn.snapshot_f = searn_snapshot;
 
+    srn.examples_dont_change = false;
+    
     srn.beta = 0.5;
     srn.allow_current_policy = false;
-    srn.rollout_all_actions = true;
+    srn.rollout_oracle = false;
+    srn.adaptive_beta = false;
+    srn.alpha = 1e-6f;
     srn.num_features = 0;
-    srn.current_policy = 1;
+    srn.current_policy = 0;
     srn.state = 0;
     srn.do_snapshot = true;
+    srn.do_fastforward = true;
+    srn.rollout_all_actions = true;
 
+    srn.neighbor_features_string = new string();
+    
     srn.passes_per_policy = 1;     //this should be set to the same value as --passes for dagger
 
-    srn.read_example_this_loop = 0;
+    srn.task = NULL;
+    srn.task_data = NULL;
+    
     srn.read_example_last_id = 0;
     srn.passes_since_new_policy = 0;
     srn.read_example_last_pass = 0;
     srn.total_examples_generated = 0;
     srn.total_predictions_made = 0;
+    srn.hit_new_pass = false;
+    srn.subsample_timesteps = 0.;
+    
+    srn.total_number_of_policies = 1;
+
+    srn.truth_string = new stringstream();
+    srn.pred_string  = new stringstream();
+    srn.should_produce_string = false;
+
+    srn.printed_output_header = false;
+
+    srn.auto_history = false;
+    
+    srn.empty_example = alloc_example(sizeof(OAA::mc_label));
+    OAA::default_label(srn.empty_example->ld);
+    srn.empty_example->in_use = true;
   }
 
   void searn_finish(void* d)
@@ -1906,23 +1336,53 @@ namespace ImperativeSearn {
     vw* all = srn->all;
     //cerr << "searn_finish" << endl;
 
+    delete srn->truth_string;
+    delete srn->pred_string;
+    delete srn->neighbor_features_string;
+    srn->neighbor_features.erase();
+    srn->neighbor_features.delete_v();
+    
+    if (srn->rollout_all_actions) { // dst should be a CSOAA::label*
+      ((CSOAA::label*)srn->valid_labels)->costs.erase();
+      ((CSOAA::label*)srn->valid_labels)->costs.delete_v();
+    } else {
+      ((CB::label*)srn->valid_labels)->costs.erase();
+      ((CB::label*)srn->valid_labels)->costs.delete_v();
+    }
+    
+    if (srn->rollout_all_actions) // labels are CSOAA
+      delete (CSOAA::label*)srn->valid_labels;
+    else // labels are CB
+      delete (CB::label*)srn->valid_labels;
+
+    dealloc_example(OAA::delete_label, *(srn->empty_example));
+    free(srn->empty_example);
+    
     srn->ec_seq.delete_v();
 
     clear_snapshot(*all, *srn);
     srn->snapshot_data.delete_v();
 
     for (size_t i=0; i<srn->train_labels.size(); i++) {
-      srn->train_labels[i].costs.erase();
-	  srn->train_labels[i].costs.delete_v();
+      if (srn->rollout_all_actions) {
+        ((CSOAA::label*)srn->train_labels[i])->costs.erase();
+        ((CSOAA::label*)srn->train_labels[i])->costs.delete_v();
+        delete ((CSOAA::label*)srn->train_labels[i]);
+      } else {
+        ((CB::label*)srn->train_labels[i])->costs.erase();
+        ((CB::label*)srn->train_labels[i])->costs.delete_v();
+        delete ((CB::label*)srn->train_labels[i]);
+      }
     }
     srn->train_labels.erase(); srn->train_labels.delete_v();
     srn->train_action.erase(); srn->train_action.delete_v();
+    srn->rollout_action.erase(); srn->rollout_action.delete_v();
     srn->learn_losses.erase(); srn->learn_losses.delete_v();
 
-    if (srn->task->finish != NULL)
-      srn->task->finish(*all);
-    if (srn->task->finish != NULL)
-      srn->base.finish();
+    if (srn->task->finish != NULL) {
+      srn->task->finish(*srn);
+      free(srn->task);
+    }
   }
 
   void ensure_param(float &v, float lo, float hi, float def, const char* string) {
@@ -1932,6 +1392,11 @@ namespace ImperativeSearn {
     }
   }
 
+  bool string_equal(string a, string b) { return a.compare(b) == 0; }
+  bool float_equal(float a, float b) { return fabs(a-b) < 1e-6; }
+  bool uint32_equal(uint32_t a, uint32_t b) { return a==b; }
+  bool size_equal(size_t a, size_t b) { return a==b; }
+
   template<class T> void check_option(T& ret, vw&all, po::variables_map& vm, po::variables_map& vm_file, const char* opt_name, bool default_to_cmdline, bool(*equal)(T,T), const char* mismatch_error_string, const char* required_error_string) {
     if (vm_file.count(opt_name)) { // loaded from regressor file
       ret = vm_file[opt_name].as<T>();
@@ -1943,7 +1408,7 @@ namespace ImperativeSearn {
     } else if (vm.count(opt_name)) {
       ret = vm[opt_name].as<T>();
       stringstream ss;
-      ss << " " << opt_name << " " << ret;
+      ss << " --" << opt_name << " " << ret;
       all.options_from_file.append(ss.str());
     } else if (strlen(required_error_string)>0) {
       std::cerr << required_error_string << endl;
@@ -1951,26 +1416,120 @@ namespace ImperativeSearn {
     }
   }  
 
-  bool string_equal(string a, string b) { return a.compare(b) == 0; }
-  bool float_equal(float a, float b) { return fabs(a-b) < 1e-6; }
-  bool uint32_equal(uint32_t a, uint32_t b) { return a==b; }
+  void check_option(bool& ret, vw&all, po::variables_map& vm, po::variables_map& vm_file, const char* opt_name, bool default_to_cmdline, const char* mismatch_error_string) {
+    if (vm_file.count(opt_name)) { // loaded from regressor file
+      ret = true;
+      if (!vm.count(opt_name)) {
+        if (default_to_cmdline)
+          ret = false;
+        std::cerr << mismatch_error_string << ret << endl;
+      }
+    } else if (vm.count(opt_name)) {
+      ret = true;
+      stringstream ss;
+      ss << " " << opt_name;
+      all.options_from_file.append(ss.str());
+    } else {
+      ret = false;
+    }
+  }  
+
+  void setup_searn_options(po::options_description& desc, vw&vw, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file) {
+    po::parsed_options parsed = po::command_line_parser(opts).
+      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
+      options(desc).allow_unregistered().run();
+    opts = po::collect_unrecognized(parsed.options, po::include_positional);
+    po::store(parsed, vm);
+    po::notify(vm);
+
+    po::parsed_options parsed_file = po::command_line_parser(vw.options_from_file_argc, vw.options_from_file_argv).
+      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
+      options(desc).allow_unregistered().run();
+    po::store(parsed_file, vm_file);
+    po::notify(vm_file);
+  }
+
 
-  learner setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  void handle_history_options(vw& vw, history_info &hinfo, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file) {
+    po::options_description desc("Searn[sequence] options");
+    desc.add_options()
+      ("searn_history",  po::value<size_t>(), "length of history to use")
+      ("searn_features", po::value<size_t>(), "length of history to pair with observed features")
+      ("searn_bigrams",                       "use bigrams from history")
+      ("searn_bigram_features",               "use bigrams from history paired with observed features");
+
+    setup_searn_options(desc, vw, opts, vm, vm_file);
+    
+    check_option<size_t>(hinfo.length, vw, vm, vm_file, "searn_history", false, size_equal,
+                         "warning: you specified a different value for --searn_history than the one loaded from regressor. proceeding with loaded value: ", "");
+    
+    check_option<size_t>(hinfo.features, vw, vm, vm_file, "searn_features", false, size_equal,
+                         "warning: you specified a different value for --searn_features than the one loaded from regressor. proceeding with loaded value: ", "");
+    
+    check_option        (hinfo.bigrams, vw, vm, vm_file, "searn_bigrams", false,
+                         "warning: you specified --searn_bigrams but that wasn't loaded from regressor. proceeding with loaded value: ");
+    
+    check_option        (hinfo.bigram_features, vw, vm, vm_file, "searn_bigram_features", false,
+                         "warning: you specified --searn_bigram_features but that wasn't loaded from regressor. proceeding with loaded value: ");
+  }
+
+  void parse_neighbor_features(searn&srn) {
+    srn.neighbor_features.erase();
+    size_t len = srn.neighbor_features_string->length();
+    if (len == 0) return;
+
+    char * cstr = new char [len+1];
+    strcpy(cstr, srn.neighbor_features_string->c_str());
+
+    char * p = strtok(cstr, ",");
+    v_array<substring> cmd;
+    while (p != 0) {
+      cmd.erase();
+      substring me = { p, p+strlen(p) };
+      tokenize(':', me, cmd, true);
+
+      int32_t posn = 0;
+      char ns = ' ';
+      if (cmd.size() == 1) {
+        posn = int_of_substring(cmd[0]);
+        ns   = ' ';
+      } else if (cmd.size() == 2) {
+        posn = int_of_substring(cmd[0]);
+        ns   = (cmd[1].end > cmd[1].begin) ? cmd[1].begin[0] : ' ';
+      } else {
+        cerr << "warning: ignoring malformed neighbor specification: '" << p << "'" << endl;
+      }
+      int32_t enc = (posn << 24) | (ns & 0xFF);
+      srn.neighbor_features.push_back(enc);
+      
+      p = strtok(NULL, ",");
+    }
+    cmd.erase();
+    cmd.delete_v();
+    
+    delete cstr;
+  }
+
+  learner* setup(vw&all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
   {
     searn* srn = (searn*)calloc(1,sizeof(searn));
     srn->all = &all;
 
     searn_initialize(all, *srn);
 
-    po::options_description desc("Imperative Searn options");
+    po::options_description desc("Searn options");
     desc.add_options()
       ("searn_task", po::value<string>(), "the searn task")
       ("searn_passes_per_policy", po::value<size_t>(), "maximum number of datapasses per policy")
       ("searn_beta", po::value<float>(), "interpolation rate for policies")
       ("searn_allow_current_policy", "allow searn labeling to use the current policy")
+      ("searn_rollout_oracle", "allow searn/dagger to do rollouts with the oracle when estimating cost-to-go")
+      ("searn_as_dagger", po::value<float>(), "sets options to make searn operate as dagger. parameter is the sliding autonomy rate (rate at which beta tends to 1).")
       ("searn_total_nb_policies", po::value<size_t>(), "if we are going to train the policies through multiple separate calls to vw, we need to specify this parameter and tell vw how many policies are eventually going to be trained")
-      ("searn_no_snapshot", "turn off snapshotting capabilities");
-
+      ("searn_no_snapshot", "turn off snapshotting capabilities")
+      ("searn_no_fastforward", "turn off fastforwarding (note: fastforwarding requires snapshotting)")
+      ("searn_subsample_timesteps", po::value<float>(), "instead of training at all timesteps, use a subset v. if v<=0, train everywhere. if v in (0,1), train on a random v% (>=1 always selected). if v>=1, train on precisely v steps per example")
+      ("searn_neighbor_features", po::value<string>(), "copy features from neighboring lines. argument looks like: '-1:a,+2' meaning copy previous line namespace a and next next line from namespace _unnamed_, where ',' separates them");
     po::options_description add_desc_file("Searn options only available in regressor file");
     add_desc_file.add_options()("searn_trained_nb_policies", po::value<size_t>(), "the number of trained policies in the regressor file");
 
@@ -1997,13 +1556,36 @@ namespace ImperativeSearn {
     check_option<string>(task_string, all, vm, vm_file, "searn_task", false, string_equal,
                          "warning: specified --searn_task different than the one loaded from regressor. using loaded value of: ",
                          "error: you must specify a task using --searn_task");
-    check_option<uint32_t>(srn->A, all, vm, vm_file, "searnimp", false, uint32_equal,
-                         "warning: you specified a different number of actions through --searnimp than the one loaded from predictor. using loaded value of: ", "");
+    check_option<size_t>(srn->A, all, vm, vm_file, "searn", false, size_equal,
+                         "warning: you specified a different number of actions through --searn than the one loaded from predictor. using loaded value of: ", "");
+    check_option<string>(*srn->neighbor_features_string, all, vm, vm_file, "searn_neighbor_features", false, string_equal,
+                         "warning: you specified a different feature structure with --searn_neighbor_features than the one loaded from predictor. using loaded value of: ", "");
 
+    parse_neighbor_features(*srn);
+    
+    if (vm.count("searn_subsample_timesteps"))     srn->subsample_timesteps  = vm["searn_subsample_timesteps"].as<float>();
     if (vm.count("searn_passes_per_policy"))       srn->passes_per_policy    = vm["searn_passes_per_policy"].as<size_t>();
     if (vm.count("searn_allow_current_policy"))    srn->allow_current_policy = true;
+    if (vm.count("searn_rollout_oracle"))          srn->rollout_oracle       = true;
     if (vm.count("searn_no_snapshot"))             srn->do_snapshot          = false;
+    if (vm.count("searn_no_fastforward"))          srn->do_fastforward       = false;
+    if (vm.count("searn_as_dagger")) {
+      srn->allow_current_policy = true;
+      srn->passes_per_policy = all.numpasses;
+      if (srn->current_policy > 1) srn->current_policy = 1;
+      srn->adaptive_beta = true;
+      srn->alpha = vm["searn_as_dagger"].as<float>();
+    }
 
+    //check if the base learner is contextual bandit, in which case, we dont rollout all actions.
+    if (vm.count("cb") || vm_file.count("cb")) {
+      srn->rollout_all_actions = false;
+      srn->valid_labels = new CB::label();
+    } else {
+      srn->rollout_all_actions = true;
+      srn->valid_labels = new CSOAA::label();
+    }
+    
     //if we loaded a regressor with -i option, --searn_trained_nb_policies contains the number of trained policies in the file
     // and --searn_total_nb_policies contains the total number of policies in the file
     if ( vm_file.count("searn_total_nb_policies") ) {
@@ -2015,15 +1597,17 @@ namespace ImperativeSearn {
       srn->total_number_of_policies = (uint32_t)vm["searn_total_nb_policies"].as<size_t>();
 
     ensure_param(srn->beta , 0.0, 1.0, 0.5, "warning: searn_beta must be in (0,1); resetting to 0.5");
+    ensure_param(srn->alpha, 0.0, 1.0, 1e-6f, "warning: searn_as_dagger must be in (0,1); resetting to 0.001");
 
     //compute total number of policies we will have at end of training
     // we add current_policy for cases where we start from an initial set of policies loaded through -i option
     uint32_t tmp_number_of_policies = srn->current_policy; 
     if( all.training )
-	tmp_number_of_policies += (int)ceil(((float)all.numpasses) / ((float)srn->passes_per_policy));
+      tmp_number_of_policies += (int)ceil(((float)all.numpasses) / ((float)srn->passes_per_policy));
 
     //the user might have specified the number of policies that will eventually be trained through multiple vw calls, 
     //so only set total_number_of_policies to computed value if it is larger
+    //clog << "current_policy=" << srn->current_policy << " tmp_number_of_policies=" << tmp_number_of_policies << " total_number_of_policies=" << srn->total_number_of_policies << endl;
     if( tmp_number_of_policies > srn->total_number_of_policies ) {
       srn->total_number_of_policies = tmp_number_of_policies;
       if( srn->current_policy > 0 ) //we loaded a file but total number of policies didn't match what is needed for training
@@ -2040,32 +1624,65 @@ namespace ImperativeSearn {
     ss1 << srn->current_policy;           VW::cmd_string_replace_value(all.options_from_file,"--searn_trained_nb_policies", ss1.str()); 
     ss2 << srn->total_number_of_policies; VW::cmd_string_replace_value(all.options_from_file,"--searn_total_nb_policies",   ss2.str());
 
-    all.weights_per_problem *= srn->total_number_of_policies;
-    srn->increment = ((uint32_t)all.length() / all.weights_per_problem) * all.reg.stride;
-
+    //clog << "searn increment = " << srn->increment <<  " " << all.reg.stride << endl;
+    //clog << "searn current_policy = " << srn->current_policy << " total_number_of_policies = " << srn->total_number_of_policies << endl;
+    
     if (task_string.compare("sequence") == 0) {
       searn_task* mytask = (searn_task*)calloc(1, sizeof(searn_task));
-      mytask->initialize = SequenceTask_Easy::initialize;
-      mytask->finish = SequenceTask_Easy::finish;
-      mytask->structured_predict = SequenceTask_Easy::structured_predict_v1;
-
+      mytask->initialize = SequenceTask::initialize;
+      mytask->finish = SequenceTask::finish;
+      mytask->structured_predict = SequenceTask::structured_predict;
+      all.p->emptylines_separate_examples = true;
+      srn->task = mytask;
+    } else if (task_string.compare("sequencespan") == 0) {
+      searn_task* mytask = (searn_task*)calloc(1, sizeof(searn_task));
+      mytask->initialize = SequenceSpanTask::initialize;
+      mytask->finish = SequenceSpanTask::finish;
+      mytask->structured_predict = SequenceSpanTask::structured_predict;
+      all.p->emptylines_separate_examples = true;
       srn->task = mytask;
     } else {
       cerr << "fail: unknown task for --searn_task: " << task_string << endl;
       throw exception();
     }
 
-    srn->task->initialize(all, srn->A);
+    // default to OAA labels unless the task wants to override this!
+    *(all.p->lp) = OAA::mc_label_parser; 
+    srn->task->initialize(*srn, srn->A, opts, vm, vm_file);
+
+    // set up auto-history if they want it
+    if (srn->auto_history) {
+      default_info(&srn->hinfo);
+
+      handle_history_options(all, srn->hinfo, opts, vm, vm_file);
+      
+      if (srn->hinfo.length < srn->hinfo.features)
+        srn->hinfo.length = srn->hinfo.features;
+      
+      if (srn->hinfo.length == 0)
+        srn->auto_history = false;
+    } else {
+      srn->hinfo.length = 0;
+      srn->hinfo.features = 0;
+      srn->hinfo.bigrams = false;
+      srn->hinfo.bigram_features = false;
+    }
+    
+    if (!srn->allow_current_policy) // if we're not dagger
+      all.check_holdout_every_n_passes = srn->passes_per_policy;
+
+    all.searnstr = srn;
+
+    srn->start_clock_time = clock();
+
+    learner* l = new learner(srn, searn_learn, all.l, srn->total_number_of_policies);
+    l->set_finish_example(finish_example);
+    l->set_end_examples(end_examples);
+    l->set_finish(searn_finish);
+    l->set_end_pass(end_pass);
     
-    learner l(srn, searn_drive, searn_learn, searn_finish, all.l.sl);
-    srn->base = all.l;
     return l;
   }
 }
-/*
-time ./vw --searn 45 --searn_task sequence -k -c -d ../test/train-sets/wsj_small.dat2.gz --passes 5 --searn_passes_per_policy 4
 
-old searn: 11.524 11.450 11.448 || 3.333 4.035
-new searn: 28.377 28.443 28.160 || 2.756 4.623
-snapshots: 27.681 28.495 27.838 || 2.756 4.623
-*/
+// ./vw -k -c -d train.f2.gz --passes 1 --searn_passes_per_policy 10 --searn_task sequence --searn 9 --searn_as_dagger 1e-6 --holdout_off -f foo --searn_neighbor_features -2,-1,1,2
diff --git a/vowpalwabbit/searn.h b/vowpalwabbit/searn.h
index b7e0f974..e00bce9a 100644
--- a/vowpalwabbit/searn.h
+++ b/vowpalwabbit/searn.h
@@ -12,20 +12,16 @@ license as described in the file LICENSE.
 #include "parse_primitives.h"
 #include "v_hashmap.h"
 #include "csoaa.h"
+#include <time.h>
 
 #define clog_print_audit_features(ec,reg) { print_audit_features(reg, ec); }
 
-
-#define MAX_ACTION_ID   10000
-
-typedef void*  state;
-typedef uint32_t action;     // actions are ONE-based
-typedef size_t* history;
+typedef uint32_t* history;
 
 namespace SearnUtil
 {
   struct history_info {
-    size_t length;          // was history_length
+    size_t length;          // was history_length, must be >= features
     bool   bigrams;         // was sequence_bigrams
     size_t features;        // was sequence_features
     bool   bigram_features; // was sequence_bigram_features
@@ -41,163 +37,15 @@ namespace SearnUtil
   void add_policy_offset(vw&, example*, uint32_t, uint32_t);
   void remove_policy_offset(vw&, example*, uint32_t, uint32_t);
  
-  void add_history_to_example(vw&, history_info*, example*, history);
-  void remove_history_from_example(vw&, history_info *, example*);
-}      
-      
-
-namespace Searn
-{
-  struct search_task {
-    /************************************************************************
-     ******************* REQUIRED FUNCTIONS FOR ALL TASKS *******************
-     ***********************************************************************/
-
-    // final(state) tells me whether state is a final(=goal) state or note
-    bool   (*final)(state);
-
-    // loss(state) on FINAL states gives the cumulative loss for that state.
-    // for non-final states, you can either (a) return MAX_FLT or, if
-    // possible, (b) return a partial loss up to this point
-    float  (*loss)(state);
-
-    // step(state, action) takes a state and an action and advances that
-    // state *in place* according to the action
-    void   (*step)(state, action);
-
-    // oracle(state) takes a state and tells me (some) oracle action that
-    // I can take from that state.
-    action (*oracle)(state);
-
-    // copy(src) copies the source state into the destination.  YOU
-    // are responsible for allocating the destination.  you can deallocate
-    // it in the finish function below
-    // note: if you provide hash or equivalent, we assume that a copied
-    // state will be equivalent to and have the same hash value as the
-    // original
-    state  (*copy)(state);
-
-    // finish(state) should de-allocate any memory associated with state.
-    // we will call finish precisely once per state created either through
-    // copy or through one of the start_state functions
-    void   (*finish)(state);
-
-    // you must provide a label parser for reading data
-    label_parser searn_label_parser;
-
-    // you must be able to tell us if an example is a test example or
-    // not.  we'll pass you a pointer to an example.  if you're multiline,
-    // we'll pass you all examples and the number of them; otherwise, we'll
-    // pass you one example and a value of "1"
-    bool (*is_test_example)(example**, size_t);
-
-    /************************************************************************
-     ******************* CHOOSE ONE OF THE FOLLOWING TWO ********************
-     ***********************************************************************/
-
-    // if you expect to read one example per line in the data file,
-    // you should provide an implementation of start_state(ec, state).
-    // this should take the example read from the file and generate a
-    // start state corresponding to that example.  you will need to allocate
-    // the memory for this state (will be freed in finish).
-    void   (*start_state)(example*, state*);
-
-    // if you expect to read multiple lines per example (with blank
-    // lines separating them), then implement this function.  you will
-    // be given an array of examples and the length of that array (=the
-    // number of lines) and you must return a state (see start_state).
-    void   (*start_state_multiline)(example**, size_t, state*);
-
-    /************************************************************************
-     ******************* CHOOSE ONE OF THE FOLLOWING TWO ********************
-     ***********************************************************************/
-
-    // if your application does not require label dependent features
-    // (aka the same set of actions are available at each time step
-    // and have the same semantics) then implement cs_example.  this
-    // takes a state, a reference to an example, and a flag indicating
-    // whether you are CREATING (true) or DESTROYING (false) the
-    // example.  if creating, then you should fill in the example
-    // reference with an example.  if you need to allocate memory to
-    // do this, you will have the opportunity to free it when
-    // cs_example is called with the flag set to false.  similarly,
-    // if you take the example from your example set (from
-    // start_state), then you should be sure to un-manipulate it in
-    // the destroy call.
-    void   (*cs_example)(vw&, state, example*&, bool);
-
-    // if you need label dependent features, then you will need to
-    // construct (and destroy) a separate example for each action.
-    // important: an example is constructed for EVERY action for a
-    // given state before any of them are destroyed, so don't reuse
-    // the same memory.  note: if you are using cs_ldf_example, you
-    // MUST provide the allowed() function, see below.
-    void   (*cs_ldf_example)(vw&, state, action, example*&, bool);
-
+  void add_history_to_example(vw&, history_info&, example*, history);
+  void remove_history_from_example(vw&, history_info&, example*);
 
-    /************************************************************************
-     ***************** FUNCTIONS REQUIRED FOR BEAM SEARCH *******************
-     ***********************************************************************/
-
-    // TODO: document
-    size_t (*bucket)(state);
-
-    /************************************************************************
-     ********************* (MOSTLY) OPTIONAL FUNCTIONS **********************
-     ***********************************************************************/
-
-    // your task might need to initialize some memory at startup or
-    // parse command line arguments: do that in initialize
-    bool   (*initialize)(vw&,std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
-
-    // your task might need to free some memory at the end of running:
-    // do that in finalize
-    void   (*finalize)();
-
-    // if you wish to _enable_ (but not force!) hypothesis recombination
-    // during rollouts, you'll need to provide an equivalence function
-    // between two states.  the semantics is: two states are equivalent
-    // if they are indistinguishable from the perspective of future
-    // predictions.  for instance, in a bigram model, once two states
-    // have the same most-recent-prediction, they become equivalent.
-    // if you provide this function, you MUST provide hash (see below)
-    bool   (*equivalent)(state, state);
-
-    // hash should take a state and return a hash value.  this is used
-    // in two ways.  FIRST, when we roll-out policies and need to choose
-    // a random policy (in searn), we will use the hash of the state
-    // as a seed to the PRNG.  if you don't give us a hash, we'll use
-    // the position in rollout, but this is not ideal if different steps
-    // can consume different amounts of the input.  SECOND, it is used
-    // to do efficient hypothesis recombination, in which case you must
-    // obey the semantics that equivalent(a,b) ==> hash(a)==hash(b) and,
-    // preferably, hash(a)!=hash(b) means that it's very likely that
-    // a and b are not equivalent.
-    size_t (*hash)(state);
+  size_t predict_with_history(vw&vw, example*ec, v_array<uint32_t>*ystar, history_info &hinfo, size_t*history);
+}      
 
-    // * in the case of LDF: allowed is a required function.  we will
-    // try actions 1, 2, ... and so on provided allowed(state,action)
-    // returns true.  once it returns false, we'll stop (and will not
-    // use the action id on which false was returned).
-    // * in the case of non-LDF: allowed is an optional function.  we
-    // will always try all actions 1, 2, ... max_action and you may
-    // return any of them as invalid
-    bool   (*allowed)(state, action);
+namespace Searn {
+  using namespace SearnUtil;
 
-    // to_string should return a string representation of some data.
-    // we'll give you a state and a bool specifying whether we want
-    // the TRUE output or the PREDICTED output.  in the case that we
-    // ask for the predicted output, we'll give you a sequence of
-    // actions corresponding to that prediction.  note: we make
-    // no guarantee that the state we give you is start or end or
-    // anything...
-    std::string (*to_string)(state, bool, std::vector<action>);
-  };
-
-  learner setup(vw&all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
-}
-
-namespace ImperativeSearn {
   struct snapshot_item {
     size_t index;
     size_t tag;
@@ -210,24 +58,49 @@ namespace ImperativeSearn {
 
   struct searn {
     // functions that you will call
-    uint32_t (*predict)(vw&,example**,size_t,v_array<uint32_t>*,v_array<uint32_t>*);
-    void     (*declare_loss)(vw&,size_t,float);   // <0 means it was a test example!
-    void     (*snapshot)(vw&,size_t,size_t,void*,size_t);
 
-    // structure that you must set
+    inline uint32_t predict(example** ecs, size_t ec_len, v_array<uint32_t>* yallowed, v_array<uint32_t>* ystar) // for LDF
+    { return this->predict_f(*this->all, *this->base_learner, ecs, ec_len, yallowed, ystar); }
+
+    inline uint32_t predict(example* ec, v_array<uint32_t>* yallowed, v_array<uint32_t>* ystar) // for not LDF
+    { return this->predict_f(*this->all, *this->base_learner, &ec, 0, yallowed, ystar); }
+
+    inline void     declare_loss(size_t predictions_since_last, float incr_loss)
+    { return this->declare_loss_f(*this->all, predictions_since_last, incr_loss); }
+
+    inline void     snapshot(size_t index, size_t tag, void* data_ptr, size_t sizeof_data, bool used_for_prediction)
+    { return this->snapshot_f(*this->all, index, tag, data_ptr, sizeof_data, used_for_prediction); }
+
+    // structure that you must set, and any associated data you want to store
     searn_task* task;
+    void* task_data;
+    bool auto_history;  // do you want us to automatically add history features?
+    bool examples_dont_change;  // set to true if you don't do any internal example munging
 
     // data that you should not look at.  ever.
-    uint32_t A;           // total number of actions, [1..A]; 0 means ldf
+    uint32_t (*predict_f)(vw&,learner&,example**,size_t,v_array<uint32_t>*,v_array<uint32_t>*);
+    void     (*declare_loss_f)(vw&,size_t,float);   // <0 means it was a test example!
+    void     (*snapshot_f)(vw&,size_t,size_t,void*,size_t,bool);
+    
+    size_t A;             // total number of actions, [1..A]; 0 means ldf
     char state;           // current state of learning
     size_t learn_t;       // when LEARN, this is the t at which we're varying a
     uint32_t learn_a;     //   and this is the a we're varying it to
+    size_t snapshot_is_equivalent_to_t;   // if we've finished snapshotting and are equiv up to this time step, then we can fast forward from there
+    bool snapshot_could_match;
+    size_t snapshot_last_found_pos;
     v_array<snapshot_item> snapshot_data;
-    v_array<uint32_t> train_action;  // which actions did we actually take in the train pass?
-    v_array< CSOAA::label > train_labels;  // which labels are valid at any given time
-
-    stringstream* pred_string;
-    stringstream* truth_string;
+    v_array<uint32_t> train_action;  // which actions did we actually take in the train (or test) pass?
+    v_array< void* > train_labels;  // which labels are valid at any given time
+    v_array<uint32_t> rollout_action; // for auto_history, we need a space other than train_action for rollouts
+    history_info hinfo;   // default history info for auto-history
+    string *neighbor_features_string;
+    v_array<int32_t> neighbor_features; // ugly encoding of neighbor feature requirements
+    
+    bool should_produce_string;
+    stringstream *pred_string;
+    stringstream *truth_string;
+    bool printed_output_header;
 
     size_t t;              // the current time step
     size_t T;              // the length of the (training) trajectory
@@ -242,35 +115,55 @@ namespace ImperativeSearn {
 
     float  beta;                  // interpolation rate
     bool   allow_current_policy;  // should the current policy be used for training? true for dagger
-    bool   rollout_all_actions;   // false for contextual bandits
+    bool   rollout_oracle; //if true then rollout are performed using oracle instead (optimal approximation discussed in searn's paper). this should be set to true for dagger
+    bool   adaptive_beta; //used to implement dagger through searn. if true, beta = 1-(1-alpha)^n after n updates, and policy is mixed with oracle as \pi' = (1-beta)\pi^* + beta \pi
+    bool   rollout_all_actions;   // by default we rollout all actions. This is set to false when searn is used with a contextual bandit base learner, where we rollout only one sampled action
+    float  alpha; //parameter used to adapt beta for dagger (see above comment), should be in (0,1)
     uint32_t current_policy;      // what policy are we training right now?
-    uint32_t increment;
+    float gamma;                  // for dagger
+
     size_t num_features;
     uint32_t total_number_of_policies;
     bool do_snapshot;
+    bool do_fastforward;
+    float subsample_timesteps;
 
-    size_t read_example_this_loop;
     size_t read_example_last_id;
     size_t passes_since_new_policy;
     size_t read_example_last_pass;
     size_t total_examples_generated;
     size_t total_predictions_made;
 
+    bool hit_new_pass;
+    
     size_t passes_per_policy;
 
     v_array<example*> ec_seq;
 
-    learner base;
+    learner* base_learner;
     vw* all;
+    void* valid_labels;
+    clock_t start_clock_time;
+    
+    example*empty_example;
   };
 
+  template<class T> void check_option(T& ret, vw&all, po::variables_map& vm, po::variables_map& vm_file, const char* opt_name, bool default_to_cmdline, bool(*equal)(T,T), const char* mismatch_error_string, const char* required_error_string);
+  void check_option(bool& ret, vw&all, po::variables_map& vm, po::variables_map& vm_file, const char* opt_name, bool default_to_cmdline, const char* mismatch_error_string);
+  bool string_equal(string a, string b);
+  bool float_equal(float a, float b);
+  bool uint32_equal(uint32_t a, uint32_t b);
+  bool size_equal(size_t a, size_t b);
+  void setup_searn_options(po::options_description& desc, vw&vw, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file);
+
+
   struct searn_task {
-    void (*initialize)(vw&,uint32_t&);
-    void (*finish)(vw&);
-    void (*structured_predict)(vw&, searn&, example**,size_t,stringstream*,stringstream*);
+    void (*initialize)(searn&,size_t&,std::vector<std::string>&, po::variables_map&, po::variables_map&);
+    void (*finish)(searn&);
+    void (*structured_predict)(searn&, example**,size_t,stringstream*,stringstream*);
   };
 
-  learner setup(vw&, std::vector<std::string>&, po::variables_map&, po::variables_map&);
+  learner* setup(vw&, std::vector<std::string>&, po::variables_map&, po::variables_map&);
   void searn_finish(void*);
   void searn_drive(void*);
   void searn_learn(void*,example*);
diff --git a/vowpalwabbit/searn_sequencetask.cc b/vowpalwabbit/searn_sequencetask.cc
index 148b56c8..070f4585 100644
--- a/vowpalwabbit/searn_sequencetask.cc
+++ b/vowpalwabbit/searn_sequencetask.cc
@@ -3,427 +3,104 @@ Copyright (c) by respective owners including Yahoo!, Microsoft, and
 individual contributors. All rights reserved.  Released under a BSD (revised)
 license as described in the file LICENSE.
  */
-#include <iostream>
-#include <float.h>
-#include <stdio.h>
-#include <math.h>
-#include <vector>
-#include "searn.h"
-#include "gd.h"
-#include "parser.h"
-#include "constant.h"
-#include "oaa.h"
-#include "csoaa.h"
 #include "searn_sequencetask.h"
-#include "vw.h"
 
 namespace SequenceTask {
-  SearnUtil::history_info hinfo;
-  uint32_t seq_max_action = 1;
-  size_t constant_pow_length = 0;
-  uint32_t increment = 0;  // this is just for fake LDF
+  using namespace Searn;
 
-  struct seq_state {
-    // global stuff -- common to any state in a trajectory
-    example** ec_start;
-    size_t    length;
-
-    // trajectory-specific stuff
-    size_t    pos;
-    history   predictions;
-    size_t    predictions_hash;
-    float     cum_loss;
-
-    // everything is zero based, so pos starts out at zero and is what
-    // we will predict NEXT.  this means that when pos==length we're
-    // done.
-  };
-
-  bool initialize(vw&all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
-  {
-    SearnUtil::default_info(&hinfo);
-
-    po::options_description desc("Searn[sequence] options");
-    desc.add_options()
-      ("searn_sequencetask_history",  po::value<size_t>(), "length of history to use")
-      ("searn_sequencetask_features", po::value<size_t>(), "length of history to pair with observed features")
-      ("searn_sequencetask_bigrams",                       "use bigrams from history")
-      ("searn_sequencetask_bigram_features",               "use bigrams from history paired with observed features");
-
-    po::parsed_options parsed = po::command_line_parser(opts).
-      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
-      options(desc).allow_unregistered().run();
-    opts = po::collect_unrecognized(parsed.options, po::include_positional);
-    po::store(parsed, vm);
-    po::notify(vm);
-
-    po::parsed_options parsed_file = po::command_line_parser(all.options_from_file_argc, all.options_from_file_argv).
-      style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
-      options(desc).allow_unregistered().run();
-    po::store(parsed_file, vm_file);
-    po::notify(vm_file);
-
-    if( vm_file.count("searn") ) { //we loaded a predictor file which contains the options we should use for the sequence task
-      //load all params from file
-      if(vm_file.count("searn_sequencetask_history"))
-        hinfo.length = vm_file["searn_sequencetask_history"].as<size_t>();
-
-      if( vm_file.count("searn_sequencetask_features") )
-        hinfo.features = vm_file["searn_sequencetask_features"].as<size_t>(); 
-
-      if( vm_file.count("searn_sequencetask_bigrams") )
-        hinfo.bigrams = true;
- 
-      if( vm_file.count("searn_sequencetask_bigram_features") )
-        hinfo.bigram_features = true;
-
-      //check if there is a mismatch between what was specified in command line and alert user
-      if( vm.count("searn_sequencetask_history") && hinfo.length != vm["searn_sequencetask_history"].as<size_t>() )
-        std::cerr << "warning: you specified a different value for --searn_sequencetask_history than the one loaded from regressor. Pursuing with loaded value: " << hinfo.length << endl;
-
-      if( vm.count("searn_sequencetask_features") && hinfo.features != vm["searn_sequencetask_features"].as<size_t>() )
-        std::cerr << "warning: you specified a different value for --searn_sequencetask_features than the one loaded from regressor. Pursuing with loaded value: " << hinfo.features << endl;
-
-      if( vm.count("searn_sequencetask_bigrams") && !hinfo.bigrams )
-        std::cerr << "warning: you specified --searn_sequencetask_bigrams but loaded regressor not using bigrams. Pursuing without bigrams." << endl;
-
-      if( vm.count("searn_sequencetask_bigram_features") && !hinfo.bigram_features )
-        std::cerr << "warning: you specified --searn_sequencetask_bigram_features but loaded regressor not using bigram_features. Pursuing without bigram_features." << endl;
-
-      seq_max_action = (uint32_t)vm_file["searn"].as<size_t>();
-    }    
-    else {
-      if (vm.count("searn_sequencetask_bigrams")) {
-        hinfo.bigrams = true;
-        all.options_from_file.append(" --searn_sequencetask_bigrams");
-      }
-
-      if (vm.count("searn_sequencetask_history")) { 
-        hinfo.length = vm["searn_sequencetask_history"].as<size_t>();
-
-        std::stringstream ss;
-        ss << " --searn_sequencetask_history " << hinfo.length;
-        all.options_from_file.append(ss.str());
-      }
-
-      if (vm.count("searn_sequencetask_bigram_features")) {
-        hinfo.bigram_features = true;
-        all.options_from_file.append(" --searn_sequencetask_bigram_features");
-      }
-
-      if (vm.count("searn_sequencetask_features")) {
-        hinfo.features = vm["searn_sequencetask_features"].as<size_t>();
-
-        std::stringstream ss;
-        ss << " --searn_sequencetask_features " << hinfo.features;
-        all.options_from_file.append(ss.str());
-      }
-
-      seq_max_action = (uint32_t)vm["searn"].as<size_t>();
-    }
-
-    constant_pow_length = 1;
-    for (size_t i=0; i < hinfo.length; i++)
-      constant_pow_length *= quadratic_constant;
-
-    increment = ((uint32_t)all.length() * all.reg.stride + 132489)/seq_max_action;
-
-    return true;
-  }
-
-  bool final(state s0)
-  {
-    seq_state* s = (seq_state*)s0;
-    return s->pos >= s->length;
-  }
-
-  float loss(state s0)
-  {
-    return ((seq_state*)s0)->cum_loss;
-  }
-
-  void step(state s0, action a)
-  {
-    seq_state* s = (seq_state*)s0;
-
-    s->cum_loss += (oracle(s0) == a) ? 0.0f : 1.0f;
-
-    if (hinfo.length > 0) {
-      size_t old_val = s->predictions[0];
-      s->predictions_hash -= old_val * constant_pow_length;
-      s->predictions_hash += a;
-      s->predictions_hash *= quadratic_constant;
-      for (size_t i=1; i<hinfo.length; i++)
-        s->predictions[i-1] = s->predictions[i];
-      s->predictions[hinfo.length-1] = a;
-    }
-
-    s->pos = s->pos + 1;
-  }
-
-  action oracle(state s0)
-  {
-    seq_state* s = (seq_state*)s0;
-    return (action)(((OAA::mc_label*)s->ec_start[s->pos]->ld)->label);
+  void initialize(searn& srn, size_t& num_actions, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file) {
+    srn.task_data            = new v_array<uint32_t>();
+    srn.auto_history         = true;  // automatically add history features to our examples, please
+    srn.examples_dont_change = true;  // we don't do any internal example munging
   }
 
-  state copy(state src0)
-  {
-    seq_state* src = (seq_state*)src0;
-    seq_state* dst = (seq_state*)SearnUtil::calloc_or_die(1, sizeof(seq_state));
-    //memcpy(dst, src, sizeof(seq_state));
-    dst->ec_start = src->ec_start;
-    dst->length   = src->length;
-    dst->pos      = src->pos;
-    dst->predictions_hash = src->predictions_hash;
-    dst->cum_loss = src->cum_loss;
-    dst->predictions = (history)SearnUtil::calloc_or_die(hinfo.length, sizeof(size_t));
-    for (size_t t=0; t<hinfo.length; t++)
-      dst->predictions[t] = src->predictions[t];
-
-    //    cerr << "copy returning s = " << dst << endl;
-    return (state)dst;
+  void finish(searn& srn) {
+    v_array<uint32_t> * y_star = (v_array<uint32_t>*) srn.task_data;
+    y_star->erase();
+    y_star->delete_v();
+    delete y_star;
   }
 
-  void finish(state s0)
-  {
-    seq_state* s = (seq_state*)s0;
-
-    //    cerr << "finish    with s = " << s << " and s->predictions = " << s->predictions << endl;
-    SearnUtil::free_it(s->predictions);
-    SearnUtil::free_it(s);
-  }
-
-  bool is_test_example(example**ec, size_t N) {
-    for (size_t n=0; n<N; n++) 
-      if (OAA::example_is_test(ec[n])) 
-	return 1;
-    return 0;
-  }
-
-
-  void start_state_multiline(example**ec, size_t len, state*s0)
-  {
-    seq_state* s = (seq_state*)SearnUtil::calloc_or_die(1, sizeof(seq_state));
-
-    s->ec_start = ec;
-    s->length   = len;
-    s->pos      = 0;
-    s->cum_loss = 0.;
-
-    s->predictions = (history)SearnUtil::calloc_or_die(hinfo.length, sizeof(size_t));
-    for (size_t t=0; t<hinfo.length; t++)
-      s->predictions[t] = 0;
-
-    s->predictions_hash = 0;
-
-    //    cerr << "ssml returning s = " << s << endl;
-
-    *s0 = s;
-  }
-
-
-  void cs_example(vw&all, state s0, example*&ec, bool create)
-  {
-    seq_state* s = (seq_state*)s0;
-    example* cur = s->ec_start[s->pos];
-    if (create) {
-      SearnUtil::add_history_to_example(all, &hinfo, cur, s->predictions);
-      ec = cur;
-    } else { // destroy
-      SearnUtil::remove_history_from_example(all, &hinfo, cur);
-      ec = NULL;
-    }
-  }
-
-  size_t hash(state s0)
-  {
-    seq_state* s = (seq_state*)s0;
-    return quadratic_constant * (s->pos + quadratic_constant * s->predictions_hash);
-  }
-  
-  bool equivalent(state a0, state b0)
-  {
-    seq_state* a = (seq_state*)a0;
-    seq_state* b = (seq_state*)b0;
-
-    if (a->pos != b->pos) return false;
-    if (a->predictions_hash != b->predictions_hash) return false;
-
-    for (size_t i=0; i<hinfo.length; i++)
-      if (a->predictions[i] != b->predictions[i])
-        return false;
-
-    return true;
-  }
-
-  using namespace std;
-  string to_string(state s0, bool return_truth, vector<action> actions)
-  {
-    seq_state* s = (seq_state*)s0;
-    stringstream ss;
-    size_t len = s->length;
+  void structured_predict(searn& srn, example**ec, size_t len, stringstream*output_ss, stringstream*truth_ss) {
+    v_array<uint32_t> * y_star = (v_array<uint32_t>*) srn.task_data;
+    float total_loss  = 0;
 
-    if (return_truth) {
-      for (size_t i=0; i<len; i++) {
-        size_t l = (size_t)(((OAA::mc_label*)s->ec_start[i]->ld)->label);
-        if (i > 0) ss << ' ';
-        ss << l;
-      }
-    } else {
-      for (size_t i=0; i<actions.size(); i++) {
-        if (i > len) {
-          cerr << "warning (searn_sequencetask.to_string): list of actions is too long!  truncating from " << actions.size() << " to " << len << endl;
-          break;
-        }
-        if (i > 0) ss << ' ';
-        ss << actions[i];
-      }
-      if (actions.size() < len) {
-        cerr << "warning (searn_sequencetask.to_string): list of actions is too short!  appending from " << actions.size() << " to " << len << endl;
-        for (size_t i=actions.size(); i<len; i++) {
-          if (i > 0) ss << ' ';
-          ss << '0';
-        }
-      }
-    }
+    for (size_t i=0; i<len; i++) {
+      srn.snapshot(i, 1, &i, sizeof(i), true);
+      srn.snapshot(i, 2, &total_loss, sizeof(total_loss), false);
 
-    return ss.str();
-  }
+      OAA::label_to_array(ec[i]->ld, *y_star);
 
-  // The following is just to test out LDF... we "fake" being an
-  // LDF-based task.
+      size_t prediction = srn.predict(ec[i], NULL, y_star);
 
-  bool allowed(state s, action a)
-  {
-    return ((a >= 1) && (a <= seq_max_action));
-  }
+      if (y_star->size() > 0)
+        total_loss += (float)(prediction != y_star->last());
 
-  void cs_ldf_example(vw& all, state s0, action a, example*&ec, bool create)
-  {
-    seq_state* s = (seq_state*)s0;
-    example* cur = s->ec_start[s->pos];
-    if (create) {
-      ec = alloc_example(sizeof(OAA::mc_label));
-      VW::copy_example_data(ec, cur, sizeof(OAA::mc_label), NULL);
-      OAA::default_label(ec->ld);
-      SearnUtil::add_history_to_example(all, &hinfo, ec, s->predictions);
-      update_example_indicies(all.audit, ec, increment * a);
-    } else {
-      dealloc_example(OAA::delete_label, *ec);
-      free(ec);
-      ec = NULL;
+      if (output_ss) (*output_ss) << prediction << ' ';
+      if (truth_ss ) (*truth_ss ) << ((y_star->size() == 0) ? '?' : y_star->last()) << ' ';
     }
+    srn.declare_loss(len, total_loss);
   }
 }
 
+namespace SequenceSpanTask {
+  using namespace Searn;
 
+  struct task_data {
+    v_array<uint32_t> y_allowed;
+    v_array<uint32_t> y_star;
+  };
 
-
-namespace SequenceTask_Easy {
-  using namespace ImperativeSearn;
-
-  SearnUtil::history_info hinfo;
-  v_array<size_t> yhat;
-
-  void initialize(vw& vw, uint32_t& num_actions) {
-    hinfo.length          = 1;
-    hinfo.bigrams         = false;
-    hinfo.features        = 0;
-    hinfo.bigram_features = false;
+  void initialize(searn& srn, size_t& num_actions, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file) {
+    task_data * my_task_data = new task_data();
+    my_task_data->y_allowed.erase();
+    my_task_data->y_allowed.push_back(1);
+    for (size_t l=2; l<num_actions; l+=2)
+      my_task_data->y_allowed.push_back(l);
+    my_task_data->y_allowed.push_back(1);  // push back an extra 1 that we can overwrite later if we want
+    
+    srn.task_data            = my_task_data;
+    srn.auto_history         = true;  // automatically add history features to our examples, please
+    srn.examples_dont_change = true;  // we don't do any internal example munging
   }
 
-  void finish(vw& vw) {
-    yhat.delete_v();
+  void finish(searn& srn) {
+    task_data * my_task_data = (task_data*)srn.task_data;
+    my_task_data->y_allowed.erase();    my_task_data->y_allowed.delete_v();
+    my_task_data->y_star.erase();       my_task_data->y_star.delete_v();
+    delete my_task_data;
   }
 
-  void get_oracle_labels(example*ec, v_array<uint32_t>*out) {
-    out->erase();
-    if (CSOAA::example_is_test(ec))
-      return;
-    CSOAA::label *lab = (CSOAA::label*)ec->ld;
-    float min_cost = lab->costs[0].x;
-    for (size_t l=1; l<lab->costs.size(); l++)
-      if (lab->costs[l].x < min_cost) min_cost = lab->costs[l].x;
-    
-    for (size_t l=0; l<lab->costs.size(); l++)
-      if (lab->costs[l].x <= min_cost)
-        out->push_back( lab->costs[l].weight_index );
-  }
-
-  void structured_predict_v1(vw& vw, searn& srn, example**ec, size_t len, stringstream*output_ss, stringstream*truth_ss) {
+  void structured_predict(searn& srn, example**ec, size_t len, stringstream*output_ss, stringstream*truth_ss) {
+    task_data * my_task_data = (task_data*)srn.task_data;
     float total_loss  = 0;
-    size_t history_length = max(hinfo.features, hinfo.length);
-    bool is_train = false;
-
-    yhat.erase();
-    yhat.resize(history_length + len, true); // pad the beginning with zeros for <s>
-
-    v_array<uint32_t> ystar;
+    uint32_t sys_tag = 1;
+    
     for (size_t i=0; i<len; i++) {
-      srn.snapshot(vw, i, 1, &i, sizeof(i));
-      srn.snapshot(vw, i, 2, yhat.begin+i, sizeof(size_t)*history_length);
-      srn.snapshot(vw, i, 3, &total_loss, sizeof(total_loss));
-      //cerr << "i=" << i << " --------------------------------------" << endl;
-
-      get_oracle_labels(ec[i], &ystar);
+      srn.snapshot(i, 1, &i, sizeof(i), true);
+      srn.snapshot(i, 2, &sys_tag, sizeof(sys_tag), true);
+      srn.snapshot(i, 3, &total_loss, sizeof(total_loss), false);
 
-      SearnUtil::add_history_to_example(vw, &hinfo, ec[i], yhat.begin+i);
-      yhat[i+history_length] = srn.predict(vw, &ec[i], 0, NULL, &ystar);
-      SearnUtil::remove_history_from_example(vw, &hinfo, ec[i]);
+      OAA::label_to_array(ec[i]->ld, my_task_data->y_star);
+      my_task_data->y_allowed[my_task_data->y_allowed.size()-1] = sys_tag;
+      size_t prediction = srn.predict(ec[i], &my_task_data->y_allowed, &my_task_data->y_star);
 
-      //cerr << "i=" << i << "\tpred=" << yhat.last() << endl;
-
-      if (!CSOAA::example_is_test(ec[i])) {
-        is_train = true;
-        if (yhat[i+history_length] != ystar.last())
-          total_loss += 1.0;
-      }
-    }
+      if (prediction == 1) sys_tag = 1;
+      else sys_tag = ((prediction % 2) == 0) ? (prediction+1) : prediction;
       
-    if (output_ss != NULL) {
-      for (size_t i=0; i<len; i++) {
-        if (i > 0) (*output_ss) << ' ';
-        (*output_ss) << yhat[i+history_length];
-      }
-    }
-    if (truth_ss != NULL) {
-      for (size_t i=0; i<len; i++) {
-        get_oracle_labels(ec[i], &ystar);
-        if (i > 0) (*truth_ss) << ' ';
-        if (ystar.size() > 0) (*truth_ss) << ystar[0];
-        else (*truth_ss) << '?';
-      }
+      if (my_task_data->y_star.size() > 0)
+        total_loss += (float)(prediction != my_task_data->y_star[0]);
+
+      if (output_ss) (*output_ss) << prediction << ' ';
+      if (truth_ss ) (*truth_ss ) << ((my_task_data->y_star.size() == 0) ? '?' : my_task_data->y_star[0]) << ' ';
     }
-    
-    ystar.erase();  ystar.delete_v();
-    srn.declare_loss(vw, len, is_train ? total_loss : -1.f);
+    srn.declare_loss(len, total_loss);
   }
+}
 
-  /*
-  void structured_predict_v2(vw& vw, example**ec, size_t len, string*output_str) {
-    float total_loss  = 0;
 
-    yhat.clear();
-    for (size_t n=0; n<hinfo.length; n++)
-      yhat.push_back(0);  // pad the beginning with zeros for <s>
 
-    for (int i=0; i<len; i++) {
-      vw.searn.snapshot(vw, i, 1, &i, sizeof(int));
-      vw.searn.snapshot(vw, i, 2, yhat.data()+i, sizeof(size_t)*hinfo.length);
 
-      size_t y = OAA::example_is_test(ec[i]) ? unknown_label : ((OAA::mc_label*)ec[i]->ld)->label;
 
-      SearnUtil::add_history_to_example(vw, &hinfo, ec[i], yhat.data()+i);
-      yhat.push_back( vw.searn.predict(vw, ec[i], y) );
-      SearnUtil::remove_history_from_example(vw, &hinfo, ec[i]);
 
-      if ((y != unknown_label) && (yhat.back != y))
-        total_loss += 1.0;
-    }
-      
-    vw.searn.declare_loss(vw, total_loss);
-  }
-  */
-}
 
diff --git a/vowpalwabbit/searn_sequencetask.h b/vowpalwabbit/searn_sequencetask.h
index 89f5c30d..c6a0596e 100644
--- a/vowpalwabbit/searn_sequencetask.h
+++ b/vowpalwabbit/searn_sequencetask.h
@@ -6,32 +6,18 @@ license as described in the file LICENSE.
 #ifndef SEARN_SEQUENCETASK_H
 #define SEARN_SEQUENCETASK_H
 
-#include "oaa.h"
-#include "parse_primitives.h"
 #include "searn.h"
 
 namespace SequenceTask {
-  bool   final(state);
-  float  loss(state);
-  void   step(state, action);
-  action oracle(state);
-  state  copy(state);
-  void   finish(state);
-  void   start_state_multiline(example**, size_t, state*);
-  bool   is_test_example(example**, size_t);
-  void   cs_example(vw&, state, example*&, bool);
-  bool   initialize(vw&,std::vector<std::string>&, po::variables_map&, po::variables_map&);
-  size_t hash(state);
-  bool   equivalent(state, state);
-  std::string to_string(state, bool, std::vector<action>);
-  void   cs_ldf_example(vw&, state, action, example*&, bool);
-  bool   allowed(state, action);
+  void initialize(Searn::searn&, size_t&, std::vector<std::string>&, po::variables_map&, po::variables_map&);
+  void finish(Searn::searn&);
+  void structured_predict(Searn::searn&, example**,size_t,stringstream*,stringstream*);
 }
 
-namespace SequenceTask_Easy {
-  void initialize(vw&, uint32_t&);
-  void finish(vw&);
-  void structured_predict_v1(vw&, ImperativeSearn::searn&, example**,size_t,stringstream*,stringstream*);
+namespace SequenceSpanTask {
+  void initialize(Searn::searn&, size_t&, std::vector<std::string>&, po::variables_map&, po::variables_map&);
+  void finish(Searn::searn&);
+  void structured_predict(Searn::searn&, example**,size_t,stringstream*,stringstream*);
 }
 
 
diff --git a/vowpalwabbit/sender.cc b/vowpalwabbit/sender.cc
index 8b91c33f..f0f5e256 100644
--- a/vowpalwabbit/sender.cc
+++ b/vowpalwabbit/sender.cc
@@ -27,8 +27,11 @@ using namespace std;
 namespace SENDER {
   struct sender {
     io_buf* buf;
-    learner base;
     int sd;
+    vw* all;
+    example** delay_ring;
+    size_t sent_index;
+    size_t received_index;
   };
 
   void open_sockets(sender& s, string host)
@@ -51,73 +54,59 @@ namespace SENDER {
   b->flush();
 }
 
-  void save_load(void* d, io_buf& model_file, bool read, bool text) {}
+void receive_result(sender& s)
+{
+  float res, weight;
+  get_prediction(s.sd,res,weight);
+  
+  example* ec=s.delay_ring[s.received_index++ % s.all->p->ring_size];
+  label_data* ld = (label_data*)ec->ld;
+  
+  ec->final_prediction = res;
+  
+  ec->loss = s.all->loss->getLoss(s.all->sd, ec->final_prediction, ld->label) * ld->weight;
+  
+  return_simple_example(*(s.all), NULL, ec);  
+}
+
+  void learn(void* d, learner& base, example* ec) 
+  { 
+    sender* s = (sender*)d;
+    if (s->received_index + s->all->p->ring_size - 1 == s->sent_index)
+      receive_result(*s);
+
+    label_data* ld = (label_data*)ec->ld;
+    s->all->set_minmax(s->all->sd, ld->label);
+    simple_label.cache_label(ld, *s->buf);//send label information.
+    cache_tag(*s->buf, ec->tag);
+    send_features(s->buf,ec, (uint32_t)s->all->parse_mask);
+    s->delay_ring[s->sent_index++ % s->all->p->ring_size] = ec;
+  }
+
+  void finish_example(vw& all, void*, example*ec)
+{}
 
-  void drive_send(vw* all, void* d)
+void end_examples(void* d)
 {
   sender* s = (sender*)d;
-  example* ec = NULL;
-  v_array<char> null_tag;
-  null_tag.erase();
-
-  example** delay_ring = (example**) calloc(all->p->ring_size, sizeof(example*));
-  size_t sent_index =0;
-  size_t received_index=0;
-
-  bool parser_finished = false;
-  while ( true )
-    {//this is a poor man's select operation.
-      if (received_index + all->p->ring_size == sent_index || (parser_finished & (received_index != sent_index)))
-	{
-	  float res, weight;
-	  get_prediction(s->sd,res,weight);
-	  
-	  ec=delay_ring[received_index++ % all->p->ring_size];
-	  label_data* ld = (label_data*)ec->ld;
-	  
-	  ec->final_prediction = res;
-	  
-	  ec->loss = all->loss->getLoss(all->sd, ec->final_prediction, ld->label) * ld->weight;
-	  
-	  return_simple_example(*all, ec);
-	}
-      else if ((ec = VW::get_example(all->p)) != NULL && !command_example(all,ec))//semiblocking operation.
-        {
-
-          label_data* ld = (label_data*)ec->ld;
-          all->set_minmax(all->sd, ld->label);
-	  simple_label.cache_label(ld, *s->buf);//send label information.
-	  cache_tag(*s->buf, ec->tag);
-	  send_features(s->buf,ec, all->parse_mask);
-	  delay_ring[sent_index++ % all->p->ring_size] = ec;
-        }
-      else if (parser_done(all->p))
-        { //close our outputs to signal finishing.
-	  parser_finished = true;
-	  if (received_index == sent_index)
-	    {
-	      shutdown(s->buf->files[0],SHUT_WR);
-	      s->buf->files.delete_v();
-	      s->buf->space.delete_v();
-	      free(delay_ring);
-	      return;
-	    }
-	}
-      else 
-	;
-    }
-  return;
+  //close our outputs to signal finishing.
+  while (s->received_index != s->sent_index)
+    receive_result(*s);
+  shutdown(s->buf->files[0],SHUT_WR);
 }
-  void learn(void* d, example*ec) { cout << "sender learn can not be used under reduction" << endl; }
+
   void finish(void* d) 
   { 
     sender* s = (sender*)d;
+    s->buf->files.delete_v();
+    s->buf->space.delete_v();
+    free(s->delay_ring);
     delete s->buf;
-    s->base.finish();
-    free(s);
   }
 
-  learner setup(vw& all, po::variables_map& vm, vector<string> pairs)
+  void save_load(void*, io_buf& io, bool read, bool text){}
+
+  learner* setup(vw& all, po::variables_map& vm, vector<string> pairs)
 {
   sender* s = (sender*)calloc(1,sizeof(sender));
   s->sd = -1;
@@ -127,9 +116,13 @@ namespace SENDER {
       open_sockets(*s, hosts[0]);
     }
 
-  s->base = all.l;
-  sl_t sl = {NULL, save_load};
-  learner l(s,drive_send,learn,finish,sl);
+  s->all = &all;
+  s->delay_ring = (example**) calloc(all.p->ring_size, sizeof(example*));
+
+  learner* l = new learner(s,learn, save_load, 1);
+  l->set_finish(finish);
+  l->set_finish_example(finish_example); 
+  l->set_end_examples(end_examples);
   return l;
 }
 
diff --git a/vowpalwabbit/sender.h b/vowpalwabbit/sender.h
index 033d8c2b..df098f8a 100644
--- a/vowpalwabbit/sender.h
+++ b/vowpalwabbit/sender.h
@@ -4,5 +4,5 @@ individual contributors. All rights reserved.  Released under a BSD
 license as described in the file LICENSE.
  */
 namespace SENDER{
-  learner setup(vw& all, po::variables_map& vm, vector<string> pairs);
+  learner* setup(vw& all, po::variables_map& vm, vector<string> pairs);
 }
diff --git a/vowpalwabbit/simple_label.cc b/vowpalwabbit/simple_label.cc
index 7bcdadb9..8906fc08 100644
--- a/vowpalwabbit/simple_label.cc
+++ b/vowpalwabbit/simple_label.cc
@@ -145,17 +145,24 @@ void print_update(vw& all, example *ec)
 	sprintf(label_buf,"%8.4f",ld->label);
       
       if(!all.holdout_set_off && all.current_pass >= 1){
-
-        fprintf(stderr, "%-10.6f %-10.6f %10ld %11.1f %s %8.4f %8lu h\n",
-	      all.sd->holdout_sum_loss/all.sd->weighted_holdout_examples,
-	      all.sd->holdout_sum_loss_since_last_dump / all.sd->weighted_holdout_examples_since_last_dump,
+        if(all.sd->holdout_sum_loss == 0. && all.sd->weighted_holdout_examples == 0.)
+          fprintf(stderr, " unknown   ");
+        else
+	  fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss/all.sd->weighted_holdout_examples);
+
+        if(all.sd->holdout_sum_loss_since_last_dump == 0. && all.sd->weighted_holdout_examples_since_last_dump == 0.)
+          fprintf(stderr, " unknown   ");
+        else
+	  fprintf(stderr, "%-10.6f " , all.sd->holdout_sum_loss_since_last_dump/all.sd->weighted_holdout_examples_since_last_dump);
+        
+        fprintf(stderr, "%10ld %11.1f %s %8.4f %8lu h\n",
 	      (long int)all.sd->example_number,
 	      all.sd->weighted_examples,
 	      label_buf,
 	      ec->final_prediction,
 	      (long unsigned int)ec->num_features);
 
-        all.sd->weighted_holdout_examples_since_last_dump = 0;
+        all.sd->weighted_holdout_examples_since_last_dump = 0.;
         all.sd->holdout_sum_loss_since_last_dump = 0.0;
       }
       else
@@ -183,14 +190,18 @@ void output_and_account_example(vw& all, example* ec)
     all.sd->weighted_holdout_examples += ec->global_weight;//test weight seen
     all.sd->weighted_holdout_examples_since_last_dump += ec->global_weight;
     all.sd->weighted_holdout_examples_since_last_pass += ec->global_weight;
+    all.sd->holdout_sum_loss += ec->loss;
+    all.sd->holdout_sum_loss_since_last_dump += ec->loss;
+    all.sd->holdout_sum_loss_since_last_pass += ec->loss;//since last pass
   }
   else
   {
+    if (ld->label != FLT_MAX)
+      all.sd->weighted_labels += ld->label * ld->weight;
     all.sd->weighted_examples += ld->weight;
-    all.sd->weighted_labels += ld->label == FLT_MAX ? 0 : ld->label * ld->weight;
-    all.sd->total_features += ec->num_features;
     all.sd->sum_loss += ec->loss;
     all.sd->sum_loss_since_last_dump += ec->loss;
+    all.sd->total_features += ec->num_features;
     all.sd->example_number++;
   }
   all.print(all.raw_prediction, ec->partial_prediction, -1, ec->tag);
@@ -211,21 +222,18 @@ void output_and_account_example(vw& all, example* ec)
 	all.print(f, ec->final_prediction, 0, ec->tag);
     }
 
-  
-
   print_update(all, ec);
 }
 
-void return_simple_example(vw& all, example* ec)
+void return_simple_example(vw& all, void*, example* ec)
 {
-  if (!command_example(&all, ec))
-    output_and_account_example(all, ec);
+  output_and_account_example(all, ec);
   VW::finish_example(all,ec);
 }
 
 bool summarize_holdout_set(vw& all, size_t& no_win_counter)
 {
-  float thisLoss = (all.sd->weighted_holdout_examples_since_last_pass > 0) ? (all.sd->holdout_sum_loss_since_last_pass / all.sd->weighted_holdout_examples_since_last_pass) : FLT_MAX;
+  float thisLoss = (all.sd->weighted_holdout_examples_since_last_pass > 0) ? (float)(all.sd->holdout_sum_loss_since_last_pass / all.sd->weighted_holdout_examples_since_last_pass) : FLT_MAX;
 
   all.sd->weighted_holdout_examples_since_last_pass = 0;
   all.sd->holdout_sum_loss_since_last_pass = 0;
diff --git a/vowpalwabbit/simple_label.h b/vowpalwabbit/simple_label.h
index e3651348..898df9be 100644
--- a/vowpalwabbit/simple_label.h
+++ b/vowpalwabbit/simple_label.h
@@ -17,7 +17,7 @@ struct label_data {
   float initial;
 };
 
-void return_simple_example(vw& all, example* ec);
+void return_simple_example(vw& all, void*, example* ec);
 
 size_t read_cached_simple_label(shared_data* sd, void* v, io_buf& cache);
 void cache_simple_label(void* v, io_buf& cache);
@@ -34,5 +34,6 @@ const label_parser simple_label = {default_simple_label, parse_simple_label,
 
 float query_decision(vw& all, example* ec, float k);
 bool summarize_holdout_set(vw& all, size_t& no_win_counter);
+void print_update(vw& all, example *ec);
 
 #endif
diff --git a/vowpalwabbit/topk.cc b/vowpalwabbit/topk.cc
new file mode 100644
index 00000000..69584c6f
--- /dev/null
+++ b/vowpalwabbit/topk.cc
@@ -0,0 +1,124 @@
+/*
+Copyright (c) by respective owners including Yahoo!, Microsoft, and
+individual contributors. All rights reserved.  Released under a BSD (revised)
+license as described in the file LICENSE.
+ */
+#include <float.h>
+#include <math.h>
+#include <stdio.h>
+#include <sstream>
+#include <numeric>
+#include <vector>
+#include <queue>
+
+#include "topk.h"
+#include "cache.h"
+#include "v_hashmap.h"
+#include "vw.h"
+
+using namespace std;
+
+typedef pair<float, v_array<char> > scored_example;
+
+struct compare_scored_examples
+{
+    bool operator()(scored_example const& a, scored_example const& b) const
+    {
+        return a.first > b.first;
+    }
+};
+
+namespace TOPK {
+
+  struct topk{
+    uint32_t B; //rec number
+    priority_queue<scored_example, vector<scored_example>, compare_scored_examples > pr_queue;
+    vw* all;
+  };
+
+  void print_result(int f, priority_queue<scored_example, vector<scored_example>, compare_scored_examples > &pr_queue)
+  {
+    if (f >= 0)
+    {
+      char temp[30];
+      std::stringstream ss;
+      scored_example tmp_example;
+      while(!pr_queue.empty())
+      {
+        tmp_example = pr_queue.top(); 
+        pr_queue.pop();       
+        sprintf(temp, "%f", tmp_example.first);
+        ss << temp;
+        ss << ' ';
+        print_tag(ss, tmp_example.second);
+        ss << ' ';
+        ss << '\n';      
+      }
+      ss << '\n';        
+      ssize_t len = ss.str().size();
+#ifdef _WIN32
+	  ssize_t t = _write(f, ss.str().c_str(), (unsigned int)len);
+#else
+	  ssize_t t = write(f, ss.str().c_str(), (unsigned int)len);
+#endif
+      if (t != len)
+        cerr << "write error" << endl;
+    }    
+  }
+
+  void output_example(vw& all, topk* d, example* ec)
+  {
+    label_data* ld = (label_data*)ec->ld;
+    
+    all.sd->weighted_examples += ld->weight;
+    all.sd->sum_loss += ec->loss;
+    all.sd->sum_loss_since_last_dump += ec->loss;
+    all.sd->total_features += ec->num_features;
+    all.sd->example_number++;
+ 
+    if (example_is_newline(ec))
+      for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
+        TOPK::print_result(*sink, d->pr_queue);
+       
+    print_update(all, ec);
+  }
+
+  void learn(void* data, learner& base, example* ec)
+  {
+    if (example_is_newline(ec)) return;//do not predict newline
+
+    topk* d = (topk*)data;
+
+    base.learn(ec);
+
+    if(d->pr_queue.size() < d->B)      
+      d->pr_queue.push(make_pair(ec->final_prediction, ec->tag));
+
+    else if(d->pr_queue.top().first < ec->final_prediction)
+    {
+      d->pr_queue.pop();
+      d->pr_queue.push(make_pair(ec->final_prediction, ec->tag));
+    }
+
+  }
+
+  void finish_example(vw& all, void* d, example* ec)
+  {
+    TOPK::output_example(all, (topk*)d, ec);
+    VW::finish_example(all, ec);
+  }
+
+  learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
+  {
+    topk* data = (topk*)calloc(1, sizeof(topk));
+
+    data->B = (uint32_t)vm["top"].as<size_t>();
+
+    data->all = &all;
+
+    learner* l = new learner(data, learn, all.l);
+    l->set_finish_example(finish_example);
+
+    return l;
+  }
+}
diff --git a/vowpalwabbit/topk.h b/vowpalwabbit/topk.h
new file mode 100644
index 00000000..d89aa4dc
--- /dev/null
+++ b/vowpalwabbit/topk.h
@@ -0,0 +1,22 @@
+/*
+Copyright (c) by respective owners including Yahoo!, Microsoft, and
+individual contributors. All rights reserved.  Released under a BSD
+license as described in the file LICENSE.
+ */
+#ifndef TOPK_H
+#define TOPK_H
+
+#include "io_buf.h"
+#include "parse_primitives.h"
+#include "global_data.h"
+#include "example.h"
+#include "parse_args.h"
+#include "v_hashmap.h"
+#include "simple_label.h"
+
+namespace TOPK
+{
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+}
+
+#endif
diff --git a/vowpalwabbit/v_array.h b/vowpalwabbit/v_array.h
index f9d73a91..9dbbbac9 100644
--- a/vowpalwabbit/v_array.h
+++ b/vowpalwabbit/v_array.h
@@ -91,10 +91,10 @@ template<class T> void copy_array(v_array<T>& dst, v_array<T> src)
   push_many(dst, src.begin, src.size());
 }
 
-template<class T> void copy_array(v_array<T>& dst, v_array<T> src, T(*copy_item)(T))
+template<class T> void copy_array(v_array<T>& dst, v_array<T> src, T(*copy_item)(T&))
 {
   dst.erase();
-  for (T*item = src.begin; item != src.end; item++)
+  for (T*item = src.begin; item != src.end; ++item)
     dst.push_back(copy_item(*item));
 }
 
diff --git a/vowpalwabbit/vw.h b/vowpalwabbit/vw.h
index 6f255858..56d1ae1d 100644
--- a/vowpalwabbit/vw.h
+++ b/vowpalwabbit/vw.h
@@ -9,6 +9,7 @@ license as described in the file LICENSE.
 #include "global_data.h"
 #include "example.h"
 #include "hash.h"
+#include "simple_label.h"
 
 namespace VW {
 
@@ -50,13 +51,14 @@ namespace VW {
   void parse_example_label(vw&all, example&ec, string label);
   example* new_unused_example(vw& all);
   example* get_example(parser* pf);
+  label_data* get_label(example*ec);
 
 	void add_constant_feature(vw& all, example*ec);
 	void add_label(example* ec, float label, float weight = 1, float base = 0);
   //notify VW that you are done with the example.
   void finish_example(vw& all, example* ec);
 
-	void copy_example_data(example*&, example*, size_t, void(*copy_example)(void*&,void*));
+  void copy_example_data(bool audit, example*&, example*, size_t, void(*copy_example)(void*&,void*));
 
 	// after export_example, must call releaseFeatureSpace to free native memory
   primitive_feature_space* export_example(vw& all, example* e, size_t& len);
@@ -90,12 +92,18 @@ namespace VW {
     return (uint32_t)(all.p->hasher(ss,u) & all.parse_mask);
   }
 
-  inline float get_weight(vw& all, uint32_t index) 
-  { return all.reg.weight_vector[(index * all.reg.stride) & all.reg.weight_mask];}
+  inline float get_weight(vw& all, uint32_t index, uint32_t offset) 
+  { return all.reg.weight_vector[((index * all.reg.stride + offset) & all.reg.weight_mask)];}
+
+  inline void set_weight(vw& all, uint32_t index, uint32_t offset, float value) 
+  { all.reg.weight_vector[((index * all.reg.stride + offset) & all.reg.weight_mask)] = value;}
 
   inline uint32_t num_weights(vw& all) 
   { return (uint32_t)all.length();}
 
+  inline uint32_t get_stride(vw& all) 
+  { return (uint32_t)all.reg.stride;}
+
 }
 
 #endif
diff --git a/vowpalwabbit/vw_static.vcxproj b/vowpalwabbit/vw_static.vcxproj
index 447969ba..1cc64831 100644
--- a/vowpalwabbit/vw_static.vcxproj
+++ b/vowpalwabbit/vw_static.vcxproj
@@ -204,7 +204,7 @@
   </Target>

   <ItemDefinitionGroup>

     <ClCompile>

-      <AdditionalIncludeDirectories>$(BoostIncludeDir);$(ZlibIncludeDir);./win32;%(AdditionalIncludeDirectories)</AdditionalIncludeDirectories>

+      <AdditionalIncludeDirectories>$(BoostIncludeDir);$(ZlibIncludeDir);./win32;%(AdditionalIncludeDirectories);</AdditionalIncludeDirectories>

       <PreprocessorDefinitions>ZLIB_WINAPI;%(PreprocessorDefinitions)</PreprocessorDefinitions>

       <AdditionalOptions>/D "_CRT_SECURE_NO_WARNINGS" %(AdditionalOptions)</AdditionalOptions>

       <InlineFunctionExpansion Condition="'$(Configuration)'=='Release'">AnySuitable</InlineFunctionExpansion>

@@ -256,6 +256,7 @@
     <ClInclude Include="nn.h" />

     <ClInclude Include="noop.h" />

     <ClInclude Include="oaa.h" />

+    <ClInclude Include="bs.h" />

     <ClInclude Include="parser.h" />

     <ClInclude Include="parse_args.h" />

     <ClInclude Include="parse_example.h" />

@@ -292,11 +293,13 @@
     <ClCompile Include="hash.cc" />

     <ClCompile Include="io_buf.cc" />

     <ClCompile Include="lda_core.cc" />

+    <ClCompile Include="learner.cc" />

     <ClCompile Include="loss_functions.cc" />

     <ClCompile Include="network.cc" />

     <ClCompile Include="nn.cc" />

     <ClCompile Include="noop.cc" />

     <ClCompile Include="oaa.cc" />

+    <ClCompile Include="bs.cc" />

     <ClCompile Include="parser.cc" />

     <ClCompile Include="parse_args.cc" />

     <ClCompile Include="parse_example.cc" />

@@ -314,4 +317,4 @@
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />

   <ImportGroup Label="ExtensionTargets">

   </ImportGroup>

-</Project>

+</Project>
diff --git a/vowpalwabbit/vwdll.cpp b/vowpalwabbit/vwdll.cpp
index a4f22ecf..b8766457 100644
--- a/vowpalwabbit/vwdll.cpp
+++ b/vowpalwabbit/vwdll.cpp
@@ -46,7 +46,7 @@ extern "C"
 			adjust_used_index(*pointer);
 			pointer->do_reset_source = true;
 			VW::start_parser(*pointer,false);
-			pointer->l.drive(pointer);
+			pointer->l->driver(pointer);
 			VW::end_parser(*pointer); 
 			}
 		else
@@ -108,6 +108,13 @@ extern "C"
 		return static_cast<VW_EXAMPLE>(VW::get_example(parser_pointer));
 	}
 
+	VW_DLL_MEMBER VW_LABEL VW_CALLING_CONV VW_GetLabel(VW_HANDLE handle, VW_EXAMPLE e)
+	{
+		vw * pointer = static_cast<vw*>(handle);
+		label_data* ld = VW::get_label(static_cast<example*>(e));
+		return static_cast<VW_LABEL>(ld);
+	}
+
 	VW_DLL_MEMBER void VW_CALLING_CONV VW_FinishExample(VW_HANDLE handle, VW_EXAMPLE e)
 	{
 		vw * pointer = static_cast<vw*>(handle);
@@ -158,10 +165,16 @@ extern "C"
 		return ex->final_prediction;
 	}
 
-	VW_DLL_MEMBER float VW_CALLING_CONV VW_Get_Weight(VW_HANDLE handle, size_t index)
+	VW_DLL_MEMBER float VW_CALLING_CONV VW_Get_Weight(VW_HANDLE handle, size_t index, size_t offset)
+	{
+		vw* pointer = static_cast<vw*>(handle);
+		return VW::get_weight(*pointer, (uint32_t) index, (uint32_t) offset);
+	}
+
+	VW_DLL_MEMBER void VW_CALLING_CONV VW_Set_Weight(VW_HANDLE handle, size_t index, size_t offset, float value)
 	{
 		vw* pointer = static_cast<vw*>(handle);
-		return VW::get_weight(*pointer, (uint32_t) index);
+		return VW::set_weight(*pointer, (uint32_t) index, (uint32_t)offset, value);
 	}
 
 	VW_DLL_MEMBER size_t VW_CALLING_CONV VW_Num_Weights(VW_HANDLE handle)
@@ -169,4 +182,23 @@ extern "C"
 		vw* pointer = static_cast<vw*>(handle);
 		return VW::num_weights(*pointer);
 	}
+
+	VW_DLL_MEMBER size_t VW_CALLING_CONV VW_Get_Stride(VW_HANDLE handle)
+	{
+		vw* pointer = static_cast<vw*>(handle);
+		return VW::get_stride(*pointer);
+	}
+
+	VW_DLL_MEMBER VW_FLAT_EXAMPLE VW_CALLING_CONV VW_FlattenExample(VW_HANDLE handle, VW_EXAMPLE e)
+	{
+		vw* pointer = static_cast<vw*>(handle);
+		example* ex = static_cast<example*>(e);
+		return static_cast<VW_FLAT_EXAMPLE>(VW::flatten_example(*pointer, ex));
+	}
+
+	VW_DLL_MEMBER void VW_CALLING_CONV VW_FreeFlattenExample(VW_FLAT_EXAMPLE fec)
+	{
+		VW::flat_example *p = reinterpret_cast<VW::flat_example*>(fec);
+		VW::free_flatten_example(p);
+	}
 }
diff --git a/vowpalwabbit/vwdll.h b/vowpalwabbit/vwdll.h
index 3754a071..00cbbe44 100644
--- a/vowpalwabbit/vwdll.h
+++ b/vowpalwabbit/vwdll.h
@@ -21,7 +21,9 @@ extern "C"
 
 	typedef void * VW_HANDLE;
 	typedef void * VW_EXAMPLE;
+	typedef void * VW_LABEL;
 	typedef void * VW_FEATURE_SPACE;
+	typedef void * VW_FLAT_EXAMPLE;
  
 	const VW_HANDLE INVALID_VW_HANDLE = NULL;
 	const VW_HANDLE INVALID_VW_EXAMPLE = NULL;
@@ -43,6 +45,7 @@ extern "C"
 	VW_DLL_MEMBER void VW_CALLING_CONV VW_EndParser(VW_HANDLE handle);
 
 	VW_DLL_MEMBER VW_EXAMPLE VW_CALLING_CONV VW_GetExample(VW_HANDLE handle);
+	VW_DLL_MEMBER VW_LABEL VW_CALLING_CONV VW_GetLabelExample(VW_HANDLE handle, VW_EXAMPLE e);
 	VW_DLL_MEMBER void VW_CALLING_CONV VW_FinishExample(VW_HANDLE handle, VW_EXAMPLE e);
 
 	VW_DLL_MEMBER size_t VW_CALLING_CONV VW_HashSpace(VW_HANDLE handle, const char16_t * s);
@@ -54,8 +57,12 @@ extern "C"
 	VW_DLL_MEMBER float VW_CALLING_CONV VW_Learn(VW_HANDLE handle, VW_EXAMPLE e);
 	VW_DLL_MEMBER void VW_CALLING_CONV VW_AddLabel(VW_EXAMPLE e, float label, float weight, float base);
 
-	VW_DLL_MEMBER float VW_CALLING_CONV VW_Get_Weight(VW_HANDLE handle, size_t index);
+	VW_DLL_MEMBER float VW_CALLING_CONV VW_Get_Weight(VW_HANDLE handle, size_t index, size_t offset);
 	VW_DLL_MEMBER size_t VW_CALLING_CONV VW_Num_Weights(VW_HANDLE handle);
+	VW_DLL_MEMBER size_t VW_CALLING_CONV VW_Get_Stride(VW_HANDLE handle);
+
+	VW_DLL_MEMBER VW_FLAT_EXAMPLE VW_CALLING_CONV VW_FlattenExample(VW_HANDLE handle, VW_EXAMPLE e);
+	VW_DLL_MEMBER void VW_CALLING_CONV VW_FreeFlattenExample(VW_FLAT_EXAMPLE fec);
 
 #ifdef __cplusplus	
 }
diff --git a/vowpalwabbit/wap.cc b/vowpalwabbit/wap.cc
index 9045981c..4bb9161d 100644
--- a/vowpalwabbit/wap.cc
+++ b/vowpalwabbit/wap.cc
@@ -19,8 +19,7 @@ using namespace std;
 
 namespace WAP {
   struct wap{
-    uint32_t increment;
-    learner base;
+    size_t increment; //wap does funky things with the increment, so we keep explicit access
     vw* all;
   };
   
@@ -29,7 +28,6 @@ namespace WAP {
     for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
       {
         size_t original_length = ec->atomics[*i].size();
-        //cerr << "original_length = " << original_length << endl;
         for (uint32_t j = 0; j < original_length; j++)
           {
             feature* f = &ec->atomics[*i][j];
@@ -38,9 +36,8 @@ namespace WAP {
             ec->atomics[*i].push_back(temp);
           }
         ec->sum_feat_sq[*i] *= 2;
-        //cerr << "final_length = " << ec->atomics[*i].size() << endl;
       }
-    if (all.audit)
+    if (all.audit || all.hash_inv)
       {
         for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
           if (ec->audit_features[*i].begin != ec->audit_features[*i].end)
@@ -62,12 +59,10 @@ namespace WAP {
                   f->weight_index += offset1;
                   ec->audit_features[*i].push_back(temp);
                 }
-              //cerr << "final_length = " << ec->audit_features[*i].size() << endl;
             }
       }
     ec->num_features *= 2;
     ec->total_sum_feat_sq *= 2;
-    //cerr << "total_sum_feat_sq = " << ec->total_sum_feat_sq << endl;
   }
 
   void unmirror_features(vw& all, example* ec, uint32_t offset1, uint32_t offset2)
@@ -80,7 +75,7 @@ namespace WAP {
           f->weight_index -= offset1;
         ec->sum_feat_sq[*i] /= 2;
       }
-    if (all.audit)
+    if (all.audit || all.hash_inv)
       {
         for (unsigned char* i = ec->indices.begin; i != ec->indices.end; i++) 
           if (ec->audit_features[*i].begin != ec->audit_features[*i].end)
@@ -130,7 +125,7 @@ namespace WAP {
   }
   v_array<float_wclass> vs;
 
-  void train(vw& all, wap& w, example* ec)
+  void train(vw& all, wap& w, learner& base, example* ec)
   {
     CSOAA::label* ld = (CSOAA::label*)ec->ld;
 
@@ -184,10 +179,10 @@ namespace WAP {
               uint32_t myi = (uint32_t)vs[i].ci.weight_index;
               uint32_t myj = (uint32_t)vs[j].ci.weight_index;
 
-              mirror_features(all, ec,(myi-1)*w.increment, (myj-1)*w.increment);
+              mirror_features(all, ec, (uint32_t)((myi-1)*w.increment), (uint32_t)((myj-1)*w.increment));
 
-              w.base.learn(ec);
-              unmirror_features(all, ec,(myi-1)*w.increment, (myj-1)*w.increment);
+              base.learn(ec);
+              unmirror_features(all, ec, (uint32_t)((myi-1)*w.increment), (uint32_t)((myj-1)*w.increment));
             }
         }
 
@@ -195,7 +190,7 @@ namespace WAP {
     ec->ld = ld;
   }
 
-  size_t test(vw& all, wap& w, example* ec)
+  size_t test(vw& all, wap& w, learner& base, example* ec)
   {
     size_t prediction = 1;
     float score = -FLT_MAX;
@@ -209,13 +204,8 @@ namespace WAP {
         simple_temp.weight = 0.;
         simple_temp.label = FLT_MAX;
         uint32_t myi = (uint32_t)cost_label->costs[i].weight_index;
-        if (myi!= 1)
-          update_example_indicies(all.audit, ec, w.increment*(myi-1));
-        ec->partial_prediction = 0.;
         ec->ld = &simple_temp;
-        w.base.learn(ec);
-        if (myi != 1)
-          update_example_indicies(all.audit, ec, -w.increment*(myi-1));
+        base.learn(ec, myi-1);
         if (ec->partial_prediction > score)
           {
             score = ec->partial_prediction;
@@ -226,52 +216,21 @@ namespace WAP {
     return prediction;
   }
 
-  void learn(void* d, example* ec)
+  void learn(void* d, learner& base, example* ec)
   {
     CSOAA::label* cost_label = (CSOAA::label*)ec->ld;
     wap* w = (wap*)d;
     vw* all = w->all;
     
-    if (command_example(all, ec))
-      {
-	w->base.learn(ec);
-	return;
-      }
-
-    size_t prediction = test(*all, *w, ec);
+    size_t prediction = test(*all, *w, base, ec);
     ec->ld = cost_label;
     
     if (cost_label->costs.size() > 0)
-      train(*all, *w, ec);
+      train(*all, *w, base, ec);
     ec->final_prediction = (float)prediction;
   }
   
-  void finish(void* d)
-  {
-    wap* w=(wap*)d;
-    w->base.finish();
-    free(w);
-  }
-  
-  void drive(vw* all, void* d)
-  {
-    example* ec = NULL;
-    while ( true )
-      {
-        if ((ec = VW::get_example(all->p)) != NULL)//semiblocking operation.
-          {
-	    learn(d, ec);
-            CSOAA::output_example(*all, ec);
-	    VW::finish_example(*all, ec);
-          }
-        else if (parser_done(all->p))
-	  return;
-        else 
-          ;
-      }
-  }
-  
-  learner setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file)
+  learner* setup(vw& all, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file)
   {
     wap* w=(wap*)calloc(1,sizeof(wap));
     w->all = &all;
@@ -293,11 +252,11 @@ namespace WAP {
     *(all.p->lp) = CSOAA::cs_label_parser;
 
     all.sd->k = (uint32_t)nb_actions;
-    all.weights_per_problem *= nb_actions;
-    w->increment = (uint32_t)((all.length()/ all.weights_per_problem) * all.reg.stride);
 
-    learner l(w, drive, learn, finish, all.l.sl);
-    w->base = all.l;
+    learner* l = new learner(w, learn, all.l, nb_actions);
+    l->set_finish_example(CSOAA::finish_example);
+    w->increment = l->increment;
+
     return l;
   }
 }
diff --git a/vowpalwabbit/wap.h b/vowpalwabbit/wap.h
index 94f8f37a..406c1579 100644
--- a/vowpalwabbit/wap.h
+++ b/vowpalwabbit/wap.h
@@ -10,7 +10,7 @@ license as described in the file LICENSE.
 #include "parse_args.h"
 
 namespace WAP {
-  learner setup(vw&, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
+  learner* setup(vw&, std::vector<std::string>&, po::variables_map& vm, po::variables_map& vm_file);
 }
 
 #endif