Small changes in test file. Add more tests.

README.md

@@ -3,4 +3,4 @@
 Simple multilayer perceptron c++ implementation.
 
 
-David Nogueira, 2016.03.26
+David Nogueira, 2016.11.01

src/MLPTest.cpp

@@ -35,7 +35,8 @@ UNIT(LearnAND) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
 
@@ -73,7 +74,8 @@ UNIT(LearnNAND) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
 
@@ -111,7 +113,8 @@ UNIT(LearnOR) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
 
@@ -149,7 +152,8 @@ UNIT(LearnNOR) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
 
@@ -185,7 +189,8 @@ UNIT(LearnXOR) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 50'000, 0.25);
 
@@ -219,7 +224,83 @@ UNIT(LearnNOT) {
 
   size_t num_examples = training_sample_set_with_bias.size();
   size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
-  MLP my_mlp(num_features, 1, 1, 2, false);
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
+  //Train MLP
+  my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
+
+  for (const auto & training_sample : training_sample_set_with_bias) {
+    std::vector<double>  output;
+    my_mlp.GetOutput(training_sample.input_vector(), &output);
+    bool predicted_output = output[0] > 0.5 ? true : false;
+    bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
+    ASSERT_TRUE(predicted_output == correct_output);
+  }
+  std::cout << "Trained with success." << std::endl;
+  std::cout << std::endl;
+}
+
+UNIT(LearnX1) {
+  std::cout << "Train X1 function with mlp." << std::endl;
+
+  std::vector<TrainingSample> training_set =
+  {
+    { { 0, 0 },{ 0.0 } },
+    { { 0, 1 },{ 0.0 } },
+    { { 1, 0 },{ 1.0 } },
+    { { 1, 1 },{ 1.0 } }
+  };
+  bool bias_already_in = false;
+  std::vector<TrainingSample> training_sample_set_with_bias(training_set);
+  //set up bias
+  if (!bias_already_in) {
+    for (auto & training_sample_with_bias : training_sample_set_with_bias) {
+      training_sample_with_bias.AddBiasValue(1);
+    }
+  }
+
+  size_t num_examples = training_sample_set_with_bias.size();
+  size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
+  //Train MLP
+  my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);
+
+  for (const auto & training_sample : training_sample_set_with_bias) {
+    std::vector<double>  output;
+    my_mlp.GetOutput(training_sample.input_vector(), &output);
+    bool predicted_output = output[0] > 0.5 ? true : false;
+    bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
+    ASSERT_TRUE(predicted_output == correct_output);
+  }
+  std::cout << "Trained with success." << std::endl;
+  std::cout << std::endl;
+}
+
+
+UNIT(LearnX2) {
+  std::cout << "Train X2 function with mlp." << std::endl;
+
+  std::vector<TrainingSample> training_set =
+  {
+    { { 0, 0 },{ 0.0 } },
+    { { 0, 1 },{ 1.0 } },
+    { { 1, 0 },{ 0.0 } },
+    { { 1, 1 },{ 1.0 } }
+  };
+  bool bias_already_in = false;
+  std::vector<TrainingSample> training_sample_set_with_bias(training_set);
+  //set up bias
+  if (!bias_already_in) {
+    for (auto & training_sample_with_bias : training_sample_set_with_bias) {
+      training_sample_with_bias.AddBiasValue(1);
+    }
+  }
+
+  size_t num_examples = training_sample_set_with_bias.size();
+  size_t num_features = training_sample_set_with_bias[0].GetInputVectorSize();
+  size_t num_outputs = training_sample_set_with_bias[0].GetOutputVectorSize();
+  MLP my_mlp(num_features, num_outputs, 1, 2, false);
   //Train MLP
   my_mlp.UpdateMiniBatch(training_sample_set_with_bias, 0.5, 500, 0.25);