public void RBMDataSample1Test()
{
var dataPath = System.IO.Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\rbm_sample1.csv");
LearningApi api = new LearningApi(this.getDescriptorForRbm_sample1());
// Initialize data provider
api.UseCsvDataProvider(dataPath, ',', false, 1);
api.UseDefaultDataMapper();
api.UseRbm(0.2, 1000, 6, 3);
RbmResult score = api.Run() as RbmResult;
double[][] testData = new double[4][];
testData[0] = new double[] { 1, 1, 0, 0, 0, 0 };
testData[1] = new double[] { 0, 0, 0, 0, 1, 1 };
testData[2] = new double[] { 0, 1, 0, 0, 0, 0 };
testData[3] = new double[] { 0, 0, 0, 0, 1, 1 };
var result = api.Algorithm.Predict(testData, api.Context);
// NOT FINISHED.
//Assert.True(result[0] == 1);
//Assert.True(result[1] == 0);
//Assert.True(result[2] == 0);
//Assert.True(result[3] == 0);
//Assert.True(result[4] == 1);
//Assert.True(result[5] == 0);
}
public void CRbm_ClassifierTest()
{
var dataPath = System.IO.Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\rbm_twoclass_sample.csv");
LearningApi api = new LearningApi(this.getDescriptorForRbmTwoClassesClassifier());
// Initialize data provider
api.UseCsvDataProvider(dataPath, ';', false, 1);
api.UseDefaultDataMapper();
double[] featureVector = new double[] { 0.1, 0.2, 0.3 };
api.UseCRbm(featureVector, 0.01, 1000, 10, 2);
RbmResult score = api.Run() as RbmResult;
double[][] testData = new double[5][];
//
// This test data contains two patterns. One is grouped at left and one at almost right.
testData[0] = new double[] { 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 };
testData[1] = new double[] { 1, 0, 1, 0, 0, 0, 0, 0, 0, 0 };
testData[2] = new double[] { 0, 0, 0, 0, 0, 1, 1, 1, 0, 0 };
testData[3] = new double[] { 0, 0, 0, 0, 0, 1, 0, 1, 0, 0 };
// This will be classified as third class.
testData[4] = new double[] { 1, 1, 1, 0, 0, 1, 1, 1, 0, 0 };
var result = api.Algorithm.Predict(testData, api.Context) as RbmResult;
//
// 2 * BIT1 + BIT2 of [0] and [1] should be same.
// We don't know how RBM will classiffy data. We only expect that
// same or similar pattern of data will be assigned to the same class.
// Note, we have here two classes (two hiddne nodes).
// First and second data sample are of same class. Third and fourth are also of same class.
// Here we check first classs.
Assert.True(2 * result.HiddenNodesPredictions[0][0] + result.HiddenNodesPredictions[0][1] ==
2 * result.HiddenNodesPredictions[1][0] + result.HiddenNodesPredictions[1][1]);
// Here is test for second class.
Assert.True(2 * result.HiddenNodesPredictions[2][0] + result.HiddenNodesPredictions[2][1] ==
2 * result.HiddenNodesPredictions[3][0] + result.HiddenNodesPredictions[3][1]);
printVector("Weights", result.Weights);
}
public void Rbm_ClassifierTest2()
{
var dataPath = System.IO.Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\rbm_sample2.csv");
LearningApi api = new LearningApi(this.getDescriptorForRbmTwoClassesClassifier(21));
// Initialize data provider
api.UseCsvDataProvider(dataPath, ',', false, 1);
api.UseDefaultDataMapper();
api.UseDeepRbm(0.2, 10000, new int[] { 21, 9, 6, 2 });
RbmResult score = api.Run() as RbmResult;
var expectedResults = new Dictionary <int, List <double[]> >();
// All test data, which belong to the sam class.
List <double[]> testListClass1 = new List <double[]>();
List <double[]> testListClass2 = new List <double[]>();
//
// This test data contains two patterns. One is grouped at left and one at almost right.
// testListClass1 contains class 1
// testListClass2 contains class 2
testListClass1.Add(new double[] { 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
testListClass1.Add(new double[] { 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
testListClass1.Add(new double[] { 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
testListClass2.Add(new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 });
testListClass2.Add(new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0 });
testListClass2.Add(new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 });
expectedResults.Add(1, testListClass1);
expectedResults.Add(2, testListClass2);
validateClassificationResult(api, expectedResults);
}
public void Rbm_ClassifierTest()
{
var dataPath = System.IO.Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\rbm_twoclass_sample.csv");
LearningApi api = new LearningApi(this.getDescriptorForRbmTwoClassesClassifier(10));
// Initialize data provider
api.UseCsvDataProvider(dataPath, ';', false, 1);
api.UseDefaultDataMapper();
api.UseDeepRbm(0.2, 1000, new int[] { 10, 2 });
RbmResult score = api.Run() as RbmResult;
double[][] trainData = new double[6][];
// All test data, which belong to the sam class.
List <double[]> testListClass1 = new List <double[]>();
List <double[]> testListClass2 = new List <double[]>();
//
// This test data contains two patterns. One is grouped at left and one at almost right.
trainData[0] = new double[] { 1, 1, 1, 0, 0, 0, 0, 0, 0, 0 };
testListClass1.Add(trainData[0]);
trainData[1] = new double[] { 1, 0, 1, 0, 0, 0, 0, 0, 0, 0 };
testListClass1.Add(trainData[1]);
trainData[2] = new double[] { 0, 1, 1, 0, 0, 0, 0, 0, 0, 0 };
testListClass1.Add(trainData[2]);
trainData[3] = new double[] { 0, 0, 0, 0, 0, 1, 1, 1, 0, 0 };
testListClass2.Add(trainData[3]);
trainData[4] = new double[] { 0, 0, 0, 0, 0, 1, 0, 1, 0, 0 };
testListClass2.Add(trainData[4]);
trainData[5] = new double[] { 0, 0, 0, 0, 0, 1, 1, 0, 0, 0 };
testListClass2.Add(trainData[5]);
// This will be classified as third class.
//testData[4] = new double[] { 1, 1, 1, 0, 0, 1, 1, 1, 0, 0 };
RbmDeepResult result = api.Algorithm.Predict(trainData, api.Context) as RbmDeepResult;
var expectedResults = new Dictionary <int, List <double[]> >();
expectedResults.Add(1, testListClass1);
expectedResults.Add(2, testListClass2);
validateClassificationResult(api, expectedResults);
//
// 2 * BIT1 + BIT2 of [0] and [1] should be same.
// We don't know how RBM will classiffy data. We only expect that
// same or similar pattern of data will be assigned to the same class.
// Note, we have here two classes (two hiddne nodes).
// First and second data sample are of the same class.
// Third and fourth are also of same class. See data.
//// Here we check first classs.
//Assert.True(result.Results[0].ToArray()[0].HiddenNodesPredictions[0] == result.Results[1].ToArray()[0].HiddenNodesPredictions[0] &&
// result.Results[0].ToArray()[0].HiddenNodesPredictions[1] == result.Results[1].ToArray()[0].HiddenNodesPredictions[1]);
//// Here is test for second class.
//Assert.True(result.Results[2].ToArray()[0].HiddenNodesPredictions[0] == result.Results[3].ToArray()[0].HiddenNodesPredictions[0] &&
// result.Results[2].ToArray()[0].HiddenNodesPredictions[1] == result.Results[3].ToArray()[0].HiddenNodesPredictions[1]);
}
