3-layer sigmoidal neural networks

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diff --git a/test/train-sets/3parity b/test/train-sets/3parity
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+-1 |f  1:1 2:-1 3:-1
+-1 |f  1:-1 2:1 3:-1
+1 |f  1:1 2:1 3:-1
+-1 |f  1:-1 2:-1 3:1
+1 |f  1:1 2:-1 3:1
+1 |f  1:-1 2:1 3:1
+-1 |f  1:1 2:1 3:1
+1 |f  1:-1 2:-1 3:-1
diff --git a/test/train-sets/ref/3parity.stderr b/test/train-sets/ref/3parity.stderr
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+Num weight bits = 16
+learning rate = 10
+initial_t = 1
+power_t = 0.5
+decay_learning_rate = 1
+randomly initializing neural network output weights and hidden bias
+creating cache_file = train-sets/3parity.cache
+Reading from train-sets/3parity
+num sources = 1
+average    since         example     example  current  current  current
+loss       last          counter      weight    label  predict features
+1.550870   1.550870            3         3.0   1.0000  -1.0000        4
+1.919601   2.288332            6         6.0   1.0000   0.7762        4
+2.011137   2.120980           11        11.0   1.0000  -1.0000        4
+2.154878   2.298620           22        22.0   1.0000   0.3713        4
+2.354256   2.553635           44        44.0  -1.0000   1.0000        4
+2.286332   2.216827           87        87.0  -1.0000   1.0000        4
+2.222494   2.158657          174       174.0   1.0000   0.8935        4
+1.716414   1.210335          348       348.0  -1.0000  -0.9598        4
+1.368982   1.021549          696       696.0   1.0000   0.9744        4
+1.151838   0.934694         1392      1392.0   1.0000   1.0000        4
+0.976327   0.800816         2784      2784.0   1.0000   1.0000        4
+0.756642   0.536958         5568      5568.0   1.0000   1.0000        4
+0.378355   0.000000        11135     11135.0  -1.0000  -1.0000        4
+
+finished run
+number of examples = 16000
+weighted example sum = 1.6e+04
+weighted label sum = 0
+average loss = 0.2633
+best constant = -6.25e-05
+total feature number = 64000
diff --git a/test/train-sets/ref/3parity.stdout b/test/train-sets/ref/3parity.stdout
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index 00000000..e69de29b
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+++ b/test/train-sets/ref/3parity.stdout