public void Rbm_ClassifierDeepTest()
{
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(0.01, 1000, new int[] { 10, 5, 2 });
RbmDeepScore score = api.Run() as RbmDeepScore;
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 };
RbmDeepResult result = api.Algorithm.Predict(testData, api.Context) as RbmDeepResult;
//
// 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()[1].HiddenNodesPredictions[0] == result.Results[1].ToArray()[1].HiddenNodesPredictions[0] &&
result.Results[0].ToArray()[1].HiddenNodesPredictions[1] == result.Results[1].ToArray()[1].HiddenNodesPredictions[1]);
// Here is test for second class.
Assert.True(result.Results[2].ToArray()[1].HiddenNodesPredictions[0] == result.Results[3].ToArray()[1].HiddenNodesPredictions[0] &&
result.Results[2].ToArray()[1].HiddenNodesPredictions[1] == result.Results[3].ToArray()[1].HiddenNodesPredictions[1]);
}
public void DigitRecognitionDeepTest(int iterations, double learningRate, int[] layers)
{
Debug.WriteLine($"{iterations}-{String.Join("", layers)}");
LearningApi api = new LearningApi(getDescriptorForRbmTwoClassesClassifier(4096));
// Initialize data provider
// TODO: Describe Digit Dataset.
api.UseCsvDataProvider(Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\DigitDataset.csv"), ',', false, 0);
api.UseDefaultDataMapper();
api.UseDeepRbm(learningRate, iterations, layers);
Stopwatch watch = new Stopwatch();
watch.Start();
RbmDeepScore score = api.Run() as RbmDeepScore;
watch.Stop();
var testData = RbmHandwrittenDigitUnitTests.ReadData(Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\predictiondigitdata.csv"));
var result = api.Algorithm.Predict(testData, api.Context) as RbmDeepResult;
var accList = new double[result.Results.Count];
var predictions = new double[result.Results.Count][];
var predictedHiddenNodes = new double[result.Results.Count][];
var Time = watch.ElapsedMilliseconds / 1000;
int i = 0;
foreach (var item in result.Results)
{
predictions[i] = item.First().VisibleNodesPredictions;
predictedHiddenNodes[i] = item.Last().HiddenNodesPredictions;
accList[i] = testData[i].GetHammingDistance(predictions[i]);
i++;
}
RbmHandwrittenDigitUnitTests.WriteDeepResult(iterations, layers, accList, Time * 1000, predictedHiddenNodes);
/// write predicted hidden nodes.......
RbmHandwrittenDigitUnitTests.WriteOutputMatrix(iterations, layers, predictions, testData);
}
public void smileyTestDeepRbm(int iterations, double learningRate, int[] layers)
{
LearningApi api = new LearningApi(getDescriptorForRbm(1600));
// Initialize data provider
api.UseCsvDataProvider(Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\Smiley.csv"), ',', false, 0);
api.UseDefaultDataMapper();
api.UseDeepRbm(learningRate, iterations, layers);
Stopwatch watch = new Stopwatch();
watch.Start();
RbmDeepScore score = api.Run() as RbmDeepScore;
watch.Stop();
var testData = ReadData(Path.Combine(Directory.GetCurrentDirectory(), @"RestrictedBolzmannMachine2\Data\SmileyTest.csv"));
var result = api.Algorithm.Predict(testData, api.Context) as RbmDeepResult;
var accList = new double[result.Results.Count];
var predictions = new double[result.Results.Count][];
var predictedHiddenNodes = new double[result.Results.Count][];
var Time = watch.ElapsedMilliseconds / 1000;
int i = 0;
foreach (var item in result.Results)
{
predictions[i] = item.First().VisibleNodesPredictions;
predictedHiddenNodes[i] = item.Last().HiddenNodesPredictions;
accList[i] = testData[i].GetHammingDistance(predictions[i]);
i++;
}
var ValTest = calcDelta(predictions, testData);
var lossTest = ValTest / (layers.First());
Debug.WriteLine($"lossTest: {lossTest}");
WriteDeepResult(iterations, layers, accList, Time / 60.0, predictedHiddenNodes);
/// write predicted hidden nodes.......
WriteOutputMatrix(iterations, layers, predictions, testData);
}
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);
}