public void DistanceTest()
{
Polynomial target = new Polynomial(1);
double[] x = new double[] { 1, 1 };
double[] y = new double[] { 1, 1 };
double expected = 0;
double actual = target.Distance(x, y);
Assert.AreEqual(expected, actual);
x = new double[] { 0.5, 2.0 };
y = new double[] { 1.3, -0.2 };
expected = 5.48;
actual = target.Distance(x, y);
Assert.AreEqual(expected, actual);
target = new Polynomial(3);
x = new double[] { 9.4, 22.1 };
y = new double[] { -6.21, 4 };
expected = 192981940.60611719;
actual = target.Distance(x, y);
Assert.AreEqual(expected, actual);
}
public void ComputeTest5()
{
var dataset = SequentialMinimalOptimizationTest.yinyang;
double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
int[] labels = dataset.GetColumn(2).ToInt32();
var kernel = new Polynomial(2, 1);
Accord.Math.Tools.SetupGenerator(0);
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
var smo = new LinearCoordinateDescent(machine, projection, labels)
{
Complexity = 1000000,
Tolerance = 1e-15
};
double error = smo.Run();
Assert.AreEqual(1000000.0, smo.Complexity, 1e-15);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = Math.Sign(machine.Compute(projection[i]));
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(6, matrix.FalseNegatives);
Assert.AreEqual(7, matrix.FalsePositives);
Assert.AreEqual(44, matrix.TruePositives);
Assert.AreEqual(43, matrix.TrueNegatives);
}
public void DistanceTest()
{
Polynomial dense = new Polynomial(3);
SparsePolynomial target = new SparsePolynomial(3);
double[] sx = { 1, -0.555556, 2, +0.250000, 3, -0.864407, 4, -0.916667 };
double[] sy = { 1, -0.666667, 2, -0.166667, 3, -0.864407, 4, -0.916667 };
double[] sz = { 1, -0.944444, 3, -0.898305, 4, -0.916667 };
double[] dx = { -0.555556, +0.250000, -0.864407, -0.916667 };
double[] dy = { -0.666667, -0.166667, -0.864407, -0.916667 };
double[] dz = { -0.944444, +0.000000, -0.898305, -0.916667 };
double expected, actual;
expected = dense.Distance(dx, dy);
actual = target.Distance(sx, sy);
Assert.AreEqual(expected, actual, 1e-10);
expected = dense.Distance(dx, dz);
actual = target.Distance(sx, sz);
Assert.AreEqual(expected, actual, 1e-10);
expected = dense.Distance(dy, dz);
actual = target.Distance(sy, sz);
Assert.AreEqual(expected, actual, 1e-10);
}
public void FunctionTest()
{
Polynomial target = new Polynomial(1, 0);
double[] x = new double[] { 1, 1 };
double[] y = new double[] { 1, 1 };
double expected = 2;
double actual = target.Function(x, y);
Assert.AreEqual(expected, actual);
x = new double[] { 0.5, 2.0 };
y = new double[] { 1.3, -0.2 };
expected = 0.25;
actual = target.Function(x, y);
Assert.AreEqual(expected, actual);
target = new Polynomial(3, 0);
x = new double[] { 9.4, 22.1 };
y = new double[] { -6.21, 4 };
expected = 27070.26085757601;
actual = target.Function(x, y);
Assert.AreEqual(expected, actual, 0.0001);
}
public void FunctionTest()
{
Polynomial target = new Polynomial(1, 0);
double[] x = new double[] { 1, 1 };
double[] y = new double[] { 1, 1 };
double expected = 2;
double actual = target.Function(x, y);
Assert.AreEqual(expected, actual);
x = new double[] { 0.5, 2.0 };
y = new double[] { 1.3, -0.2 };
expected = 0.25;
actual = target.Function(x, y);
Assert.AreEqual(expected, actual);
target = new Polynomial(3, 0);
x = new double[] { 9.4, 22.1 };
y = new double[] { -6.21, 4 };
expected = System.Math.Pow(x.InnerProduct(y), 3);
actual = target.Function(x, y);
Assert.AreEqual(expected, actual, 0.0001);
}
public void ComputeTest5()
{
var dataset = SequentialMinimalOptimizationTest.yinyang;
var inputs = dataset.Submatrix(null, 0, 1).ToArray();
var labels = dataset.GetColumn(2).ToInt32();
var kernel = new Polynomial(2, 0);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.UseComplexityHeuristic = true;
double error = smo.Run();
Assert.AreEqual(0.2, error);
Assert.AreEqual(0.11714451552090824, smo.Complexity);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = Math.Sign(machine.Compute(inputs[i]));
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(20, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(30, matrix.TruePositives);
Assert.AreEqual(50, matrix.TrueNegatives);
}
{
Accord.Math.Tools.SetupGenerator(0);
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
var smo = new LinearNewtonMethod(machine, projection, labels);
smo.UseComplexityHeuristic = true;
double error = smo.Run();
Assert.AreEqual(0.18, error);
Assert.AreEqual(0.11714451552090821, smo.Complexity, 1e-15);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
actual[i] = Math.Sign(machine.Compute(projection[i]));
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(17, matrix.FalseNegatives);
Assert.AreEqual(1, matrix.FalsePositives);
Assert.AreEqual(33, matrix.TruePositives);
Assert.AreEqual(49, matrix.TrueNegatives);
}
}
//// Do training for all existing trained Data
public SVM(string TrainedDataInputFile)
{
_engine = new TesseractEngine(@"./tessdata3", "eng", EngineMode.TesseractAndCube);
_engine.SetVariable("tessedit_char_whitelist", "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ");
_engine.SetVariable("tessedit_char_blacklist", "¢§+~»~`!@#$%^&*()_+-={}[]|\\:\";\'<>?,./");
string[] TrainedData = Directory.GetFiles(TrainedDataInputFile, "*.png");
double[][] inputs = new double[TrainedData.Length][]; ///
double[] InputArray = new double[784];
int[] Outputs = new int[TrainedData.Length];
for (int i = 0; i < TrainedData.Length; i++)
{
string filename = Path.GetFileNameWithoutExtension(TrainedData[i]);
Bitmap TrainingImage = new Bitmap(TrainedData[i]);
string[] split = filename.Split('.');
for (int j = 0; j < 28; j++)
{
for (int k = 0; k < 28; k++)
{
if ((!TrainingImage.GetPixel(j, k).Name.Equals("ffffffff")))
InputArray[j * 28 + k] = 1;
else
InputArray[j * 28 + k] = 0;
}
}
inputs[i] = InputArray;
Outputs[i] = Convert.ToInt32(split[0]);
InputArray = new double[784];
}
IKernel kernel;
kernel = new Polynomial(2, 0);
ksvm = new MulticlassSupportVectorMachine(784, kernel, 2);
MulticlassSupportVectorLearning ml = new MulticlassSupportVectorLearning(ksvm, inputs, Outputs);
double complexity = 1; ///// set these three parameters Carefuly later
double epsilon = 0.001;
double tolerance = 0.2;
ml.Algorithm = (svm, classInputs, classOutputs, i, j) =>
{
var smo = new SequentialMinimalOptimization(svm, classInputs, classOutputs);
smo.Complexity = complexity; /// Cost parameter for SVM
smo.Epsilon = epsilon;
smo.Tolerance = tolerance;
return smo;
};
// Train the machines. It should take a while.
double error = ml.Run();
}
public void Aprender(IDadosSinaisEstaticos dados)
{
var kernel = new Polynomial(degree: 3, constant: 1);
svm = new MulticlassSupportVectorMachine(QuantidadeIndeterminadaDeCaracteristicas, kernel, dados.QuantidadeClasses);
var teacher = new MulticlassSupportVectorLearning(svm, dados.CaracteristicasSinais, dados.IdentificadoresSinais)
{
Algorithm = (machine, classInputs, classOutputs, j, k) =>
new SequentialMinimalOptimization(machine, classInputs, classOutputs)
{
Complexity = 1
}
};
teacher.Run();
}
public void LearnTest()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor =
{
-1,
1,
1,
-1
};
var kernel = new Polynomial(2, 0.0);
double[][] augmented = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
augmented[i] = kernel.Transform(inputs[i]);
SupportVectorMachine machine = new SupportVectorMachine(augmented[0].Length);
// Create the Least Squares Support Vector Machine teacher
var learn = new LinearDualCoordinateDescent(machine, augmented, xor);
// Run the learning algorithm
double error = learn.Run();
Assert.AreEqual(0, error);
int[] output = augmented.Apply(p => Math.Sign(machine.Compute(p)));
for (int i = 0; i < output.Length; i++)
Assert.AreEqual(System.Math.Sign(xor[i]), System.Math.Sign(output[i]));
}
public void ExpandReverseDistanceTest()
{
for (int i = 1; i <= 10; i++)
{
Polynomial kernel = new Polynomial(i, 0);
var x = new double[] { 0.5, 2.0 };
var y = new double[] { 1.3, -0.2 };
var phi_x = kernel.Transform(x);
var phi_y = kernel.Transform(y);
//int expected_size = (int)System.Math.Pow(x.Length, i);
//Assert.AreEqual(phi_x.Length, phi_y.Length);
//Assert.AreEqual(phi_x.Length, expected_size);
double d = Distance.SquareEuclidean(x, y);
double phi_d = kernel.ReverseDistance(phi_x, phi_y);
Assert.AreEqual(phi_d, d, 1e-6);
Assert.IsFalse(double.IsNaN(phi_d));
Assert.IsFalse(double.IsNaN(d));
}
}
public void ExpandDistanceTest()
{
for (int i = 1; i <= 10; i++)
{
Polynomial kernel = new Polynomial(i, 0);
var x = new double[] { 0.5, 2.0 };
var y = new double[] { 1.3, -0.2 };
var phi_x = kernel.Transform(x);
var phi_y = kernel.Transform(y);
double d1 = Distance.SquareEuclidean(phi_x, phi_y);
double d2 = kernel.Distance(x, y);
double d3 = Accord.Statistics.Tools.Distance(kernel, x, y);
Assert.AreEqual(d1, d2, 1e-4);
Assert.AreEqual(d1, d3, 1e-4);
Assert.IsFalse(double.IsNaN(d1));
Assert.IsFalse(double.IsNaN(d2));
Assert.IsFalse(double.IsNaN(d3));
}
}
public void FunctionTest2()
{
// Tested against R's kernlab
double[][] data =
{
new double[] { 5.1, 3.5, 1.4, 0.2 },
new double[] { 5.0, 3.6, 1.4, 0.2 },
new double[] { 4.9, 3.0, 1.4, 0.2 },
new double[] { 5.8, 4.0, 1.2, 0.2 },
new double[] { 4.7, 3.2, 1.3, 0.2 },
};
// rbf <- polydot(3)
Polynomial kernel = new Polynomial(degree: 3, constant: 1);
// Compute the kernel matrix
double[,] actual = new double[5, 5];
for (int i = 0; i < 5; i++)
for (int j = 0; j < 5; j++)
actual[i, j] = kernel.Function(data[i], data[j]);
double[,] expected =
{
{ 70240.51, 69426.53, 57022.17, 99252.85, 55002.06 },
{ 69426.53, 68719.48, 56181.89, 98099.75, 54353.80 },
{ 57022.17, 56181.89, 46694.89, 80286.11, 44701.08 },
{ 99252.85, 98099.75, 80286.11, 141583.69, 77635.89 },
{ 55002.06, 54353.80, 44701.08, 77635.89, 43095.88 },
};
// Assert both are equal
for (int i = 0; i < 5; i++)
for (int j = 0; j < 5; j++)
Assert.AreEqual(expected[i, j], actual[i, j], 1e-2);
}
private void btnRunAnalysis_Click(object sender, EventArgs e)
{
if (dgvAnalysisSource.Rows.Count == 0)
{
MessageBox.Show("Please load the training data before clicking this button");
return;
}
lbStatus.Text = "Gathering data. This may take a while...";
Application.DoEvents();
// Extract inputs and outputs
int rows = dgvAnalysisSource.Rows.Count;
double[][] input = Jagged.Zeros(rows, 32 * 32);
int[] output = new int[rows];
for (int i = 0; i < rows; i++)
{
input.SetRow(i, (double[])dgvAnalysisSource.Rows[i].Cells["colTrainingFeatures"].Value);
output[i] = (int)dgvAnalysisSource.Rows[i].Cells["colTrainingLabel"].Value;
}
// Create the chosen Kernel with given parameters
IKernel kernel;
if (rbGaussian.Checked)
kernel = new Gaussian((double)numSigma.Value);
else
kernel = new Polynomial((int)numDegree.Value, (double)numConstant.Value);
// Create the Kernel Discriminant Analysis using the selected Kernel
kda = new KernelDiscriminantAnalysis(kernel)
{
Threshold = (double)numThreshold.Value,
Regularization = (double)numRegularization.Value
};
lbStatus.Text = "Computing the analysis. This may take a significant amount of time...";
Application.DoEvents();
// Compute the analysis.
kda.Learn(input, output);
// Show information about the analysis in the form
dgvPrincipalComponents.DataSource = kda.Discriminants;
dgvFeatureVectors.DataSource = new ArrayDataView(kda.DiscriminantVectors);
dgvClasses.DataSource = kda.Classes;
// Create the component graphs
distributionView.DataSource = kda.Discriminants;
cumulativeView.DataSource = kda.Discriminants;
lbStatus.Text = "Analysis complete. Click Classify to test the analysis.";
btnClassify.Enabled = true;
}
public void ComputeTest1()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
IKernel kernel = new Polynomial(2);
var msvm = new MultilabelSupportVectorMachine(5, kernel, 4);
var smo = new MultilabelSupportVectorLearning(msvm, inputs, outputs);
smo.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs);
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
double error = smo.Run();
Assert.AreEqual(0, error);
int[] evals = new int[inputs.Length];
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double[] responses; msvm.Compute(inputs[i], out responses);
int actual; responses.Max(out actual);
Assert.AreEqual(expected, actual);
evals[i] = msvm.GetLastKernelEvaluations();
}
for (int i = 0; i < evals.Length; i++)
Assert.AreEqual(msvm.SupportVectorUniqueCount, evals[i]);
}
public void TransformTest()
{
var inputs = yinyang.Submatrix(null, 0, 1).ToArray();
var labels = yinyang.GetColumn(2).ToInt32();
ConfusionMatrix actual, expected;
SequentialMinimalOptimization a, b;
var kernel = new Polynomial(2, 0);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
a = new SequentialMinimalOptimization(machine, inputs, labels);
a.UseComplexityHeuristic = true;
a.Run();
int[] values = new int[labels.Length];
for (int i = 0; i < values.Length; i++)
values[i] = Math.Sign(machine.Compute(inputs[i]));
expected = new ConfusionMatrix(values, labels);
}
{
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
b = new SequentialMinimalOptimization(machine, projection, labels);
b.UseComplexityHeuristic = true;
b.Run();
int[] values = new int[labels.Length];
for (int i = 0; i < values.Length; i++)
values[i] = Math.Sign(machine.Compute(projection[i]));
actual = new ConfusionMatrix(values, labels);
}
Assert.AreEqual(a.Complexity, b.Complexity, 1e-15);
Assert.AreEqual(expected.TrueNegatives, actual.TrueNegatives);
Assert.AreEqual(expected.TruePositives, actual.TruePositives);
Assert.AreEqual(expected.FalseNegatives, actual.FalseNegatives);
Assert.AreEqual(expected.FalsePositives, actual.FalsePositives);
}
private void button1_Click(object sender, EventArgs e)
{
// Finishes and save any pending changes to the given data
dgvAnalysisSource.EndEdit();
if (dgvAnalysisSource.DataSource == null) return;
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvAnalysisSource.DataSource as DataTable).ToMatrix(out sourceColumns);
// Creates a new Simple Descriptive Analysis
sda = new DescriptiveAnalysis(sourceMatrix, sourceColumns);
sda.Compute();
dgvDistributionMeasures.DataSource = sda.Measures;
// Populates statistics overview tab with analysis data
dgvDistributionMeasures.DataSource = sda.Measures;
IKernel kernel;
if (rbGaussian.Checked)
{
kernel = new Gaussian((double)numSigma.Value);
}
else
{
kernel = new Polynomial((int)numDegree.Value, (double)numConstant.Value);
}
// Get only the input values (exclude the class label indicator column)
double[,] data = sourceMatrix.Submatrix(null, startColumn: 0, endColumn: 1);
// Get only the associated labels
int[] labels = sourceMatrix.GetColumn(2).ToInt32();
// Creates the Kernel Discriminant Analysis of the given source
kda = new KernelDiscriminantAnalysis(data, labels, kernel);
// Keep all components
kda.Threshold = (double)numThreshold.Value;
// Computes the analysis
kda.Compute();
// Perform the transformation of the data using two components
double[,] result = kda.Transform(data, 2);
// Create a new plot with the original Z column
double[,] points = result.InsertColumn(sourceMatrix.GetColumn(2));
// Create output scatter plot
outputScatterplot.DataSource = points;
CreateScatterplot(graphMapFeature, points);
// Create output table
dgvProjectionResult.DataSource = new ArrayDataView(points, sourceColumns);
// Populates components overview with analysis data
dgvFeatureVectors.DataSource = new ArrayDataView(kda.DiscriminantMatrix);
dgvPrincipalComponents.DataSource = kda.Discriminants;
dgvScatterBetween.DataSource = new ArrayDataView(kda.ScatterBetweenClass);
dgvScatterWithin.DataSource = new ArrayDataView(kda.ScatterWithinClass);
dgvScatterTotal.DataSource = new ArrayDataView(kda.ScatterMatrix);
// Populates classes information
dgvClasses.DataSource = kda.Classes;
CreateComponentCumulativeDistributionGraph(graphCurve);
CreateComponentDistributionGraph(graphShare);
}
private void button1_Click(object sender, EventArgs e)
{
// Finishes and save any pending changes to the given data
dgvAnalysisSource.EndEdit();
if (dgvAnalysisSource.DataSource == null) return;
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvAnalysisSource.DataSource as DataTable).ToMatrix(out sourceColumns);
int rows = sourceMatrix.GetLength(0);
int cols = sourceMatrix.GetLength(1);
// Creates a new Simple Descriptive Analysis
sda = new DescriptiveAnalysis(sourceMatrix, sourceColumns);
sda.Compute();
// Populates statistics overview tab with analysis data
dgvDistributionMeasures.DataSource = sda.Measures;
IKernel kernel;
if (rbGaussian.Checked)
{
kernel = new Gaussian((double)numSigma.Value);
}
else
{
kernel = new Polynomial((int)numDegree.Value, (double)numConstant.Value);
}
// Get only the input values (exclude the class label indicator column)
double[,] data = sourceMatrix.Submatrix(null, startColumn: 0, endColumn: 1);
// Get only the associated labels
int[] labels = sourceMatrix.GetColumn(2).ToInt32();
// Creates the Kernel Principal Component Analysis of the given source
kpca = new KernelPrincipalComponentAnalysis(data, kernel,
(AnalysisMethod)cbMethod.SelectedValue);
kpca.Center = cbCenter.Checked;
// Compute the analysis
kpca.Compute();
double[,] result;
if (kpca.Components.Count >= 2)
{
// Perform the transformation of the data using two components
result = kpca.Transform(data, 2);
}
else
{
result = kpca.Transform(data, 1);
result = result.InsertColumn(Matrix.Vector(result.GetLength(0), 0.0));
}
// Create a new plot with the original Z column
double[,] points = result.InsertColumn(sourceMatrix.GetColumn(2));
// Create output scatter plot
outputScatterplot.DataSource = points;
CreateScatterplot(graphMapFeature, points);
// Create output table
dgvProjectionResult.DataSource = new ArrayDataView(points, sourceColumns);
dgvReversionSource.DataSource = new ArrayDataView(kpca.Result);
// Populates components overview with analysis data
dgvFeatureVectors.DataSource = new ArrayDataView(kpca.ComponentMatrix);
dgvPrincipalComponents.DataSource = kpca.Components;
dgvProjectionComponents.DataSource = kpca.Components;
dgvReversionComponents.DataSource = kpca.Components;
numComponents.Maximum = kpca.Components.Count;
numNeighbor.Maximum = kpca.Result.GetLength(0);
numNeighbor.Value = System.Math.Min(10, numNeighbor.Maximum);
CreateComponentCumulativeDistributionGraph(graphCurve);
CreateComponentDistributionGraph(graphShare);
}
public void ComputeTest1()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
IKernel kernel = new Polynomial(2);
var msvm = new MulticlassSupportVectorMachine(5, kernel, 4);
var smo = new MulticlassSupportVectorLearning(msvm, inputs, outputs);
smo.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs);
double error = smo.Run();
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Elimination);
Assert.AreEqual(expected, actual);
}
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Voting);
Assert.AreEqual(expected, actual);
}
}
private void btnRunTraining_Click(object sender, EventArgs e)
{
if (dgvTrainingSource.Rows.Count == 0)
{
MessageBox.Show("Please load the training data before clicking this button");
return;
}
lbStatus.Text = "Gathering data. This may take a while...";
Application.DoEvents();
// Extract inputs and outputs
int rows = dgvTrainingSource.Rows.Count;
double[][] input = new double[rows][];
int[] output = new int[rows];
for (int i = 0; i < rows; i++)
{
input[i] = (double[])dgvTrainingSource.Rows[i].Cells["colTrainingFeatures"].Value;
output[i] = (int)dgvTrainingSource.Rows[i].Cells["colTrainingLabel"].Value;
}
// Create the chosen Kernel with given parameters
IKernel kernel;
if (rbGaussian.Checked)
{
kernel = new Gaussian((double)numSigma.Value);
}
else
{
if (numDegree.Value == 1)
kernel = new Linear((double)numConstant.Value);
else
kernel = new Polynomial((int)numDegree.Value, (double)numConstant.Value);
}
// Create the Multi-class Support Vector Machine using the selected Kernel
ksvm = new MulticlassSupportVectorMachine(1024, kernel, 10);
// Create the learning algorithm using the machine and the training data
MulticlassSupportVectorLearning ml = new MulticlassSupportVectorLearning(ksvm, input, output);
// Extract training parameters from the interface
double complexity = (double)numComplexity.Value;
double tolerance = (double)numTolerance.Value;
int cacheSize = (int)numCache.Value;
SelectionStrategy strategy = (SelectionStrategy)cbStrategy.SelectedItem;
// Configure the learning algorithm
ml.Algorithm = (svm, classInputs, classOutputs, i, j) =>
{
var smo = new SequentialMinimalOptimization(svm, classInputs, classOutputs);
smo.Complexity = complexity;
smo.Tolerance = tolerance;
smo.CacheSize = cacheSize;
smo.Strategy = strategy;
if (kernel is Linear) smo.Compact = true;
return smo;
};
lbStatus.Text = "Training the classifiers. This may take a (very) significant amount of time...";
Application.DoEvents();
Stopwatch sw = Stopwatch.StartNew();
// Train the machines. It should take a while.
double error = ml.Run();
sw.Stop();
lbStatus.Text = String.Format(
"Training complete ({0}ms, {1}er). Click Classify to test the classifiers.",
sw.ElapsedMilliseconds, error);
btnClassifyVoting.Enabled = true;
btnClassifyElimination.Enabled = true;
btnCalibration.Enabled = true;
// Populate the information tab with the machines
dgvMachines.Rows.Clear();
int k = 1;
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < i; j++, k++)
{
var machine = ksvm[i, j];
int sv = machine.SupportVectors == null ? 0 : machine.SupportVectors.Length;
int c = dgvMachines.Rows.Add(k, i + "-vs-" + j, sv, machine.Threshold);
dgvMachines.Rows[c].Tag = machine;
}
}
// approximate size in bytes =
// number of support vectors *
// number of doubles in a support vector *
// size of double
int bytes = ksvm.SupportVectorUniqueCount * 1024 * sizeof(double);
float megabytes = bytes / (1024 * 1024);
lbSize.Text = String.Format("{0} ({1} MB)", ksvm.SupportVectorUniqueCount, megabytes);
}
public void TransformTest_Quadratic()
{
double[][] data =
{
new double[] { 5.1, 3.5, 1.4, 0.2 },
new double[] { 5.0, 3.6, 1.4, 0.2 },
new double[] { 4.9, 3.0, 1.4, 0.2 },
new double[] { 5.8, 4.0, 1.2, 0.2 },
new double[] { 4.7, 3.2, 1.3, 0.2 },
};
var target = new Polynomial(2);
var quadratic = new Quadratic();
double[][] expected = data.Apply(quadratic.Transform);
double[][] actual = data.Apply(target.Transform);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
}
public void KernelFunctionCacheConstructorTest7()
{
double[][] inputs =
{
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 },
};
IKernel kernel = new Polynomial(2);
int cacheSize = inputs.Length;
KernelFunctionCache target = new KernelFunctionCache(kernel, inputs, cacheSize);
Assert.AreEqual(3, target.Size);
Assert.AreEqual(0, target.Hits);
Assert.AreEqual(0, target.Misses);
// upper half
for (int i = 0; i < inputs.Length; i++)
{
for (int j = i + 1; j < inputs.Length; j++)
{
double expected = kernel.Function(inputs[i], inputs[j]);
double actual = target.GetOrCompute(i, j);
Assert.AreEqual(expected, actual);
}
}
var lruList1 = target.GetLeastRecentlyUsedList();
Assert.AreEqual(3, target.Misses);
Assert.AreEqual(0, target.Hits);
Assert.AreEqual(1.0, target.Usage);
// upper half, backwards
for (int i = inputs.Length - 1; i >= 0; i--)
{
for (int j = inputs.Length - 1; j >= i; j--)
{
double expected = kernel.Function(inputs[i], inputs[j]);
double actual = target.GetOrCompute(j, i);
Assert.AreEqual(expected, actual);
}
}
var lruList2 = target.GetLeastRecentlyUsedList();
Assert.IsTrue(lruList2.SequenceEqual(lruList1.Reverse()));
Assert.AreEqual(3, target.Misses);
Assert.AreEqual(3, target.Hits);
Assert.AreEqual(1.0, target.Usage);
}
public void multiclass_precomputed_matrix_smo()
{
#region doc_precomputed
// Let's say we have the following data to be classified
// into three possible classes. Those are the samples:
//
double[][] trainInputs =
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 1, 0 }, // 0
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 0 }, // 1
new double[] { 1, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
new double[] { 1, 0, 1, 1 }, // 2
new double[] { 1, 1, 0, 1 }, // 2
new double[] { 0, 1, 1, 1 }, // 2
new double[] { 1, 1, 1, 1 }, // 2
};
int[] trainOutputs = // those are the training set class labels
{
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2,
};
// Let's chose a kernel function
Polynomial kernel = new Polynomial(2);
// Get the kernel matrix for the training set
double[][] K = kernel.ToJagged(trainInputs);
// Create a pre-computed kernel
var pre = new Precomputed(K);
// Create a one-vs-one learning algorithm using SMO
var teacher = new MulticlassSupportVectorLearning<Precomputed, int>()
{
Learner = (p) => new SequentialMinimalOptimization<Precomputed, int>()
{
Kernel = pre
}
};
#if DEBUG
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Learn a machine
var machine = teacher.Learn(pre.Indices, trainOutputs);
// Compute the machine's prediction for the training set
int[] trainPrediction = machine.Decide(pre.Indices);
// Evaluate prediction error for the training set using mean accuracy (mAcc)
double trainingError = new ZeroOneLoss(trainOutputs).Loss(trainPrediction);
// Now let's compute the machine's prediction for a test set
double[][] testInputs = // test-set inputs
{
// input output
new double[] { 0, 1, 1, 0 }, // 0
new double[] { 0, 1, 0, 0 }, // 0
new double[] { 0, 0, 0, 1 }, // 1
new double[] { 1, 1, 1, 1 }, // 2
};
int[] testOutputs = // those are the test set class labels
{
0, 0, 1, 2,
};
// Compute precomputed matrix between train and testing
pre.Values = kernel.ToJagged2(trainInputs, testInputs);
// Update the kernel
machine.Kernel = pre;
// Compute the machine's prediction for the test set
int[] testPrediction = machine.Decide(pre.Indices);
// Evaluate prediction error for the training set using mean accuracy (mAcc)
double testError = new ZeroOneLoss(testOutputs).Loss(testPrediction);
#endregion
Assert.AreEqual(0, trainingError);
Assert.AreEqual(0, testError);
// Create a one-vs-one learning algorithm using SMO
var teacher2 = new MulticlassSupportVectorLearning<Polynomial>()
{
Learner = (p) => new SequentialMinimalOptimization<Polynomial>()
{
Kernel = kernel
}
};
#if DEBUG
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
// Learn a machine
var expected = teacher2.Learn(trainInputs, trainOutputs);
Assert.AreEqual(4, expected.NumberOfInputs);
Assert.AreEqual(3, expected.NumberOfOutputs);
Assert.AreEqual(0, machine.NumberOfInputs);
Assert.AreEqual(3, machine.NumberOfOutputs);
var machines = Enumerable.Zip(machine, expected, (a,b) => Tuple.Create(a.Value, b.Value));
foreach (var pair in machines)
{
var a = pair.Item1;
var e = pair.Item2;
Assert.AreEqual(0, a.NumberOfInputs);
Assert.AreEqual(2, a.NumberOfOutputs);
Assert.AreEqual(4, e.NumberOfInputs);
Assert.AreEqual(2, e.NumberOfOutputs);
Assert.IsTrue(a.Weights.IsEqual(e.Weights));
}
}
public void TransformTest_Linear()
{
double[][] data =
{
new double[] { 5.1, 3.5, 1.4, 0.2 },
new double[] { 5.0, 3.6, 1.4, 0.2 },
new double[] { 4.9, 3.0, 1.4, 0.2 },
new double[] { 5.8, 4.0, 1.2, 0.2 },
new double[] { 4.7, 3.2, 1.3, 0.2 },
};
var target = new Polynomial(1);
var linear = new Linear(constant: 1);
Assert.AreEqual(target.Constant, linear.Constant);
double[][] expected = data.Apply(x => linear.Transform(x));
double[][] actual = data.Apply(target.Transform);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
}
public void RevertTest3()
{
string path = @"..\..\..\..\Unit Tests\Accord.Tests.Statistics\Resources\examples.xls";
// Create a new reader, opening a given path
ExcelReader reader = new ExcelReader(path);
// Afterwards, we can query the file for all
// worksheets within the specified workbook:
string[] sheets = reader.GetWorksheetList();
// Finally, we can request an specific sheet:
DataTable table = reader.GetWorksheet("Wikipedia");
// Now, we have loaded the Excel file into a DataTable. We
// can go further and transform it into a matrix to start
// running other algorithms on it:
double[,] matrix = table.ToMatrix();
IKernel kernel = new Polynomial(2);
// Create analysis
KernelPrincipalComponentAnalysis target = new KernelPrincipalComponentAnalysis(matrix,
kernel, AnalysisMethod.Center, centerInFeatureSpace: true);
target.Compute();
double[,] forward = target.Result;
double[,] reversion = target.Revert(forward);
Assert.IsTrue(!reversion.HasNaN());
}
public void ComputeTest2()
{
double[][] input =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] output =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
IKernel kernel = new Polynomial(2);
int classes = 4;
int inputs = 5;
// Create the Multi-class Support Vector Machine using the selected Kernel
var msvm = new MulticlassSupportVectorMachine(inputs, kernel, classes);
// Create the learning algorithm using the machine and the training data
var ml = new MulticlassSupportVectorLearning(msvm, input, output);
// Configure the learning algorithm
ml.Algorithm = (svm, classInputs, classOutputs, i, j) =>
{
var smo = new SequentialMinimalOptimization(svm, classInputs, classOutputs)
{
Complexity = 1
};
return smo;
};
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
// Executes the training algorithm
double error = ml.Run();
Assert.AreEqual(6, msvm.GetLastKernelEvaluations());
int[] evals = new int[input.Length];
int[] evalexp = { 8, 8, 7, 7, 7, 7, 6, 6 };
#if NET35
AForge.Parallel.For(0, input.Length, i =>
#else
Parallel.For(0, input.Length, i =>
#endif
{
double[] data = input[i];
double[] responses;
int num = msvm.Compute(data, MulticlassComputeMethod.Elimination, out responses);
Assert.AreEqual(output[i], num);
evals[i] = msvm.GetLastKernelEvaluations();
});
for (int i = 0; i < evals.Length; i++)
Assert.AreEqual(evals[i], evalexp[i]);
#if NET35
AForge.Parallel.For(0, input.Length, i =>
#else
Parallel.For(0, input.Length, i =>
#endif
{
double[] data = input[i];
double[] responses;
int num = msvm.Compute(data, MulticlassComputeMethod.Voting, out responses);
Assert.AreEqual(output[i], num);
evals[i] = msvm.GetLastKernelEvaluations();
});
for (int i = 0; i < evals.Length; i++)
Assert.AreEqual(msvm.SupportVectorUniqueCount, evals[i]);
}
public void ComputeTest1()
{
double[][] inputs =
{
new double[] { 1, 4, 2, 0, 1 },
new double[] { 1, 3, 2, 0, 1 },
new double[] { 3, 0, 1, 1, 1 },
new double[] { 3, 0, 1, 0, 1 },
new double[] { 0, 5, 5, 5, 5 },
new double[] { 1, 5, 5, 5, 5 },
new double[] { 1, 0, 0, 0, 0 },
new double[] { 1, 0, 0, 0, 0 },
};
int[] outputs =
{
0, 0,
1, 1,
2, 2,
3, 3,
};
IKernel kernel = new Polynomial(2);
var msvm = new MulticlassSupportVectorMachine(5, kernel, 4);
var smo = new MulticlassSupportVectorLearning(msvm, inputs, outputs);
smo.Algorithm = (svm, classInputs, classOutputs, i, j) =>
new SequentialMinimalOptimization(svm, classInputs, classOutputs)
{
Complexity = 1
};
Assert.AreEqual(0, msvm.GetLastKernelEvaluations());
double error = smo.Run();
Assert.AreEqual(6, msvm.GetLastKernelEvaluations());
int[] evals = new int[inputs.Length];
int[] evalexp = { 8, 8, 7, 7, 7, 7, 6, 6 };
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Elimination);
Assert.AreEqual(expected, actual);
evals[i] = msvm.GetLastKernelEvaluations();
}
for (int i = 0; i < evals.Length; i++)
Assert.AreEqual(evals[i], evalexp[i]);
for (int i = 0; i < inputs.Length; i++)
{
double expected = outputs[i];
double actual = msvm.Compute(inputs[i], MulticlassComputeMethod.Voting);
Assert.AreEqual(expected, actual);
evals[i] = msvm.GetLastKernelEvaluations();
}
for (int i = 0; i < evals.Length; i++)
Assert.AreEqual(msvm.SupportVectorUniqueCount, evals[i], 1);
}
public void GridsearchConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Example binary data
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor = // xor labels
{
-1, 1, 1, -1
};
// Declare the parameters and ranges to be searched
GridSearchRange[] ranges =
{
new GridSearchRange("complexity", new double[] { 0.00000001, 5.20, 0.30, 0.50 } ),
new GridSearchRange("degree", new double[] { 1, 10, 2, 3, 4, 5 } ),
new GridSearchRange("constant", new double[] { 0, 1, 2 } )
};
// Instantiate a new Grid Search algorithm for Kernel Support Vector Machines
var gridsearch = new GridSearch<KernelSupportVectorMachine>(ranges);
// Set the fitting function for the algorithm
gridsearch.Fitting = delegate(GridSearchParameterCollection parameters, out double error)
{
// The parameters to be tried will be passed as a function parameter.
int degree = (int)parameters["degree"].Value;
double constant = parameters["constant"].Value;
double complexity = parameters["complexity"].Value;
// Use the parameters to build the SVM model
Polynomial kernel = new Polynomial(degree, constant);
KernelSupportVectorMachine ksvm = new KernelSupportVectorMachine(kernel, 2);
// Create a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(ksvm, inputs, xor);
smo.Complexity = complexity;
// Measure the model performance to return as an out parameter
error = smo.Run();
return ksvm; // Return the current model
};
// Declare some out variables to pass to the grid search algorithm
GridSearchParameterCollection bestParameters; double minError;
// Compute the grid search to find the best Support Vector Machine
KernelSupportVectorMachine bestModel = gridsearch.Compute(out bestParameters, out minError);
// A linear kernel can't solve the xor problem.
Assert.AreNotEqual((int)bestParameters["degree"].Value, 1);
// The minimum error should be zero because the problem is well-known.
Assert.AreEqual(minError, 0.0);
Assert.IsNotNull(bestModel);
Assert.IsNotNull(bestParameters);
Assert.AreEqual(bestParameters.Count, 3);
}
/// <summary>
/// Estimates a suitable value for SMO's Complexity parameter C.
/// </summary>
///
private void btnEstimateC_Click(object sender, EventArgs e)
{
// Extract inputs
int rows = dgvTrainingSource.Rows.Count;
double[][] input = new double[rows][];
for (int i = 0; i < rows; i++)
input[i] = (double[])dgvTrainingSource.Rows[i].Cells["colTrainingFeatures"].Value;
IKernel kernel;
if (rbGaussian.Checked)
kernel = new Gaussian((double)numSigma.Value);
else
kernel = new Polynomial((int)numDegree.Value, (double)numConstant.Value);
numComplexity.Value = (decimal)SequentialMinimalOptimization.EstimateComplexity(kernel, input);
}
private IKernel getKernel()
{
IKernel kernel;
if (rbGaussian.Checked)
{
kernel = new Gaussian((double)numSigma.Value);
}
else if (rbPolynomial.Checked)
{
kernel = new Polynomial((int)numDegree.Value, (double)numSigAlpha.Value);
}
else if (rbLaplacian.Checked)
{
kernel = new Laplacian((double)numLaplacianSigma.Value);
}
else if (rbSigmoid.Checked)
{
kernel = new Sigmoid((double)numSigAlpha.Value, (double)numSigB.Value);
}
else throw new Exception();
return kernel;
}
