public void NN_backpropagation_generic_rprop_3L_gnb_all_training_samples()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(1, 2, .5, 1500);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.AddHiddenLayer(1, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Sigmoid());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int count = 0;
for (int row = 0; row < 10; row++)
{
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
if (SupportFunctions.DoubleCompare(value,
_trainingData[_indexTargetAttribute][row]))
{
count++;
}
}
//Due to random weights
Assert.IsTrue(count >= 5);
}
public BuildGenericDeepNN()
{
_buildGenericRprop =
new BuildGenericBackPropagationRprop();
_buildGenericRprop.SetParameters(0, 2);
NumberOfUnitsHiddenLayer = -1;
_scalingFactor = 2;
_noOfHiddenLayers = 2; //1 Output, 2 Hidden
}
public void NN_backpropagation_generic_rprop_invalid_layer_throws_exception()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(1, 1, .5, 1500);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.AddHiddenLayer(1, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Sigmoid());
}
public void NN_backpropagation_generic_rprop_one_hidden_pythagoras_rmse_data_1()
{
Init_dataset_pythagoras();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(0, 1, .02, 20000, .1);//,.005,2000);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
/*
* model.SetWeight(1, 0, 0, .53);
* model.SetWeight(1, 1, 0, .53);
* model.SetWeight(1, 2, 0, .53);
*
* model.SetWeight(1, 0, 1, .53);
* model.SetWeight(1, 1, 1, .53);
* model.SetWeight(1, 2, 1, .53);
*
* model.SetWeight(2, 0, 0, .53);
* model.SetWeight(2, 1, 0, .53);
* model.SetWeight(2, 2, 0, .53);
*/
System.Diagnostics.Debug.WriteLine("Weight[1][0][0]:" + model.GetWeight(1, 0, 0));
System.Diagnostics.Debug.WriteLine("Weight[1][1][0]:" + model.GetWeight(1, 1, 0));
System.Diagnostics.Debug.WriteLine("Weight[1][2][0]:" + model.GetWeight(1, 2, 0));
System.Diagnostics.Debug.WriteLine("Weight[1][0][1]:" + model.GetWeight(1, 0, 1));
System.Diagnostics.Debug.WriteLine("Weight[1][1][1]:" + model.GetWeight(1, 1, 1));
System.Diagnostics.Debug.WriteLine("Weight[1][2][1]:" + model.GetWeight(1, 2, 1));
System.Diagnostics.Debug.WriteLine("Weight[2][0][1]:" + model.GetWeight(2, 0, 0));
System.Diagnostics.Debug.WriteLine("Weight[2][1][1]:" + model.GetWeight(2, 1, 0));
System.Diagnostics.Debug.WriteLine("Weight[2][2][1]:" + model.GetWeight(2, 2, 0));
int row = 0;
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
Assert.IsTrue(value <= 1.69);
}
public void NN_backpropagation_generic_rprop_missing_layer_throws_exception()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(1, 3, .5, 1500);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.AddHiddenLayer(1, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Sigmoid());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
}
public void NN_backpropagation_generic_rprop_no_hidden_pythagoras_rmse()
{
Init_dataset_pythagoras();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(0, 0, .3, 1);
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < 10);
}
public void NN_backpropagation_generic_rprop_jason_simple_rmse()
{
Init_dataset_jason_linear_regression();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(0, 1, .01, 10000);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Linear());
ModelBackPropagationBase model =
(ModelBackPropagationBase)build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
double value = model.GetModelRMSE(_trainingData);
Assert.IsTrue(value < .61 && value > 0);
}
public void NN_backpropagation_generic_rprop_gnb_single_training_sample_class_0()
{
initData_dataset_gaussian_naive_bayes_jason_example();
BuildGenericBackPropagationRprop build =
new BuildGenericBackPropagationRprop();
build.SetParameters(1, 1);
build.AddHiddenLayer(0, 2, new Sigmoid());
build.SetOutputLayerActivationFunction(new Sigmoid());
ModelBackPropagationBase model =
(ModelBackPropagationBase )build.BuildModel(
_trainingData, _attributeHeaders,
_indexTargetAttribute);
int row = 0;
double[] data = GetSingleTrainingRowDataForTest(row);
double value = model.RunModelForSingleData(data);
Assert.AreEqual(value,
_trainingData[_indexTargetAttribute][row]);
}