5 files changed, 97 insertions, 0 deletions

diff --git a/TensorMath.lua b/TensorMath.lua
index 682de23..e8cb97b 100644
--- a/TensorMath.lua
+++ b/TensorMath.lua
@@ -1088,6 +1088,14 @@ static void THTensor_random1__(THTensor *self, THGenerator *gen, long b)
             {name="long",default=100},
             {name="double",default=0},
             {name="double",default=0}})
+      
+      wrap("histc2",
+           cname("histc2"),
+           {{name=Tensor, default=true, returned=true},
+            {name=Tensor},
+            {name="long",default=100},
+            {name="double",default=0},
+            {name="double",default=0}})
 
       wrap("norm",
            cname("normall"),
diff --git a/doc/maths.md b/doc/maths.md
index 252b52d..433bf52 100755
--- a/doc/maths.md
+++ b/doc/maths.md
@@ -150,6 +150,43 @@ By default the elements are sorted into 100 equally spaced bins between the mini
 `y = torch.histc(x, n, min, max)` same as above with `n` bins and `[min, max]` as elements range.
 
 
+<a name="torch.histc2"></a>
+### [res] torch.histc2([res,] x [,nbins, min_value, max_value]) ###
+<a name="torch.histc2"></a>
+
+`y = torch.histc2(x)` returns the histogram of the elements in 2d tensor `x` along the last dimension.
+By default the elements are sorted into 100 equally spaced bins between the minimum and maximum values of `x`.
+
+`y = torch.histc(x, n)` same as above with `n` bins.
+
+`y = torch.histc(x, n, min, max)` same as above with `n` bins and `[min, max]` as elements range.
+
+```lua
+x =torch.Tensor(3, 6)
+
+> x[1] = torch.Tensor{ 2, 4, 2, 2, 5, 4 }
+> x[2] = torch.Tensor{ 3, 5, 1, 5, 3, 5 }
+> x[3] = torch.Tensor{ 3, 4, 2, 5, 5, 1 }
+
+> x
+ 2  4  2  2  5  4
+ 3  5  1  5  3  5
+ 3  4  2  5  5  1
+[torch.DoubleTensor of size 3x6]
+
+> torch.histc2(x, 5, 1, 5)
+ 0  3  0  2  1
+ 1  0  2  0  3
+ 1  1  1  1  2
+[torch.DoubleTensor of size 3x5]
+
+> y = torch.Tensor(1, 6):copy(x[1])
+
+> torch.histc2(y, 5)
+ 3  0  2  0  1
+[torch.DoubleTensor of size 1x5]
+```
+
 <a name="torch.linspace"></a>
 ### [res] torch.linspace([res,] x1, x2, [,n]) ###
 <a name="torch.linspace"></a>
diff --git a/lib/TH/generic/THTensorMath.c b/lib/TH/generic/THTensorMath.c
index e04d3b6..765f9c4 100644
--- a/lib/TH/generic/THTensorMath.c
+++ b/lib/TH/generic/THTensorMath.c
@@ -2538,5 +2538,44 @@ void THTensor_(histc)(THTensor *hist, THTensor *tensor, long nbins, real minvalu
   );
 }
 
+void THTensor_(histc2)(THTensor *hist, THTensor *tensor, long nbins, real minvalue, real maxvalue)
+{  
+  THArgCheck(THTensor_(nDimension)(tensor) < 3, 2, "invalid dimension %d, the input must be a 2d tensor", THTensor_(nDimension)(tensor));
+  
+  int dimension = 1;
+  THArgCheck(dimension >= 0 && dimension < THTensor_(nDimension)(tensor), 2, "invalid dimension %d",
+      dimension + TH_INDEX_BASE);
+
+  real minval;
+  real maxval;
+  real *h_data;
+
+  THTensor_(resize2d)(hist, tensor->size[0], nbins);
+  THTensor_(zero)(hist);
+
+  minval = minvalue;
+  maxval = maxvalue;
+  if (minval == maxval)
+  {
+    minval = THTensor_(minall)(tensor);
+    maxval = THTensor_(maxall)(tensor);
+  }
+  if (minval == maxval)
+  {
+    minval = minval - 1;
+    maxval = maxval + 1;
+  }
+
+  TH_TENSOR_DIM_APPLY2(real, tensor, real, hist, dimension, long i;
+                        for(i = 0; i < tensor_size; i++)
+                        {  
+                          if(tensor_data[i*tensor_stride] >= minval && tensor_data[i*tensor_stride] <= maxval) {
+                            const int bin = (int)((tensor_data[i*tensor_stride]-minval) / (maxval-minval) * nbins);
+                            hist_data[THMin(bin, nbins-1)] += 1;
+                          }
+                        }
+  );
+}
+
 #endif /* floating point only part */
 #endif
diff --git a/lib/TH/generic/THTensorMath.h b/lib/TH/generic/THTensorMath.h
index 87f1616..3908c27 100644
--- a/lib/TH/generic/THTensorMath.h
+++ b/lib/TH/generic/THTensorMath.h
@@ -163,6 +163,7 @@ TH_API void THTensor_(norm)(THTensor *r_, THTensor *t, real value, int dimension
 TH_API void THTensor_(renorm)(THTensor *r_, THTensor *t, real value, int dimension, real maxnorm);
 TH_API accreal THTensor_(dist)(THTensor *a, THTensor *b, real value);
 TH_API void THTensor_(histc)(THTensor *hist, THTensor *tensor, long nbins, real minvalue, real maxvalue);
+TH_API void THTensor_(histc2)(THTensor *hist, THTensor *tensor, long nbins, real minvalue, real maxvalue);
 
 TH_API accreal THTensor_(meanall)(THTensor *self);
 TH_API accreal THTensor_(varall)(THTensor *self);
diff --git a/test/test.lua b/test/test.lua
index 3eb119f..4ab2f4a 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1355,6 +1355,18 @@ function torchtest.histc()
    local z = torch.Tensor{ 0, 3, 0, 2, 1 }
    mytester:assertTensorEq(y,z,precision,'error in torch.histc')
 end
+function torchtest.histc2()
+   local x = torch.Tensor(3, 6)
+   x[1] = torch.Tensor{ 2, 4, 2, 2, 5, 4 }
+   x[2] = torch.Tensor{ 3, 5, 1, 5, 3, 5 }
+   x[3] = torch.Tensor{ 3, 4, 2, 5, 5, 1 }
+   local y = torch.histc2(x, 5, 1, 5) -- nbins, min, max
+   local z = torch.Tensor(3, 5)
+   z[1] = torch.Tensor{ 0, 3, 0, 2, 1 }
+   z[2] = torch.Tensor{ 1, 0, 2, 0, 3 }
+   z[3] = torch.Tensor{ 1, 1, 1, 1, 2 }
+   mytester:assertTensorEq(y,z,precision,'error in torch.histc2 in last dimension')
+end
 function torchtest.ones()
    local mx = torch.ones(msize,msize)
    local mxx = torch.Tensor()