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 ClassifyTest()
{
// Create some sample input data instances. This is the same
// data used in the Gutierrez-Osuna's example available on:
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
double[][] inputs =
{
// Class 0
new double[] { 4, 1 },
new double[] { 2, 4 },
new double[] { 2, 3 },
new double[] { 3, 6 },
new double[] { 4, 4 },
// Class 1
new double[] { 9, 10 },
new double[] { 6, 8 },
new double[] { 9, 5 },
new double[] { 8, 7 },
new double[] { 10, 8 }
};
int[] output =
{
0, 0, 0, 0, 0, // The first five are from class 0
1, 1, 1, 1, 1 // The last five are from class 1
};
// Now we can chose a kernel function to
// use, such as a linear kernel function.
IKernel kernel = new Linear();
// Then, we will create a KDA using this linear kernel.
var kda = new KernelDiscriminantAnalysis(inputs, output, kernel);
kda.Compute(); // Compute the analysis
// Now we can project the data into KDA space:
double[][] projection = kda.Transform(inputs);
// Or perform classification using:
int[] results = kda.Classify(inputs);
// Test the classify method
for (int i = 0; i < 5; i++)
{
int expected = 0;
int actual = results[i];
Assert.AreEqual(expected, actual);
}
for (int i = 5; i < 10; i++)
{
int expected = 1;
int actual = results[i];
Assert.AreEqual(expected, actual);
}
}
/// <summary>
/// Launched when the user clicks the "Run analysis" button.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Save any pending changes
dgvAnalysisSource.EndEdit();
if (dgvAnalysisSource.DataSource == null)
{
MessageBox.Show("Please load some data using File > Open!");
return;
}
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvAnalysisSource.DataSource as DataTable).ToMatrix(out columnNames);
// Create and compute a new Simple Descriptive Analysis
sda = new DescriptiveAnalysis(sourceMatrix, columnNames);
sda.Compute();
// Show the descriptive analysis on the screen
dgvDistributionMeasures.DataSource = sda.Measures;
// Create the kernel function
IKernel kernel = createKernel();
// Get the input values (the two first columns)
double[,] inputs = sourceMatrix.GetColumns(0, 1);
// Get only the associated labels (last column)
int[] outputs = sourceMatrix.GetColumn(2).ToInt32();
// Creates the Kernel Discriminant Analysis of the given data
kda = new KernelDiscriminantAnalysis(inputs, outputs, kernel);
// Keep only the important components
kda.Threshold = (double)numThreshold.Value;
kda.Compute(); // Finally, compute the analysis!
if (kda.Discriminants.Count < 2)
{
MessageBox.Show("Could not gather enough components to create"
+ "create a 2D plot. Please try a smaller threshold value.");
return;
}
// Perform the transformation of the data using two components
double[,] result = kda.Transform(inputs, 2);
// Create a new plot with the original Z column
double[,] points = result.InsertColumn(sourceMatrix.GetColumn(2));
// Create output scatter plot
outputScatterplot.DataSource = points;
createMappingScatterplot(graphMapFeature, points);
// Create output table
dgvProjectionResult.DataSource = new ArrayDataView(points, columnNames);
// Populates components overview with analysis data
dgvFeatureVectors.DataSource = new ArrayDataView(kda.DiscriminantMatrix);
dgvScatterBetween.DataSource = new ArrayDataView(kda.ScatterBetweenClass);
dgvScatterWithin.DataSource = new ArrayDataView(kda.ScatterWithinClass);
dgvScatterTotal.DataSource = new ArrayDataView(kda.ScatterMatrix);
dgvPrincipalComponents.DataSource = kda.Discriminants;
distributionView.DataSource = kda.Discriminants;
cumulativeView.DataSource = kda.Discriminants;
// Populates classes information
dgvClasses.DataSource = kda.Classes;
lbStatus.Text = "Good! Feel free to browse the other tabs to see what has been found.";
}
public void ComputeTest3()
{
// Schölkopf KPCA toy example
double[][] inputs = scholkopf().ToArray();
int[] output = Matrix.Expand(new int[,] { { 1 }, { 2 }, { 3 } }, new int[] { 30, 30, 30 }).GetColumn(0);
IKernel kernel = new Gaussian(0.2);
KernelDiscriminantAnalysis target = new KernelDiscriminantAnalysis(inputs, output, kernel);
target.Compute();
double[][] actual = target.Transform(inputs, 2);
double[][] expected1 =
{
new double[] { 1.2785801485080475, 0.20539157505913622},
new double[] { 1.2906613255489541, 0.20704272225753775},
new double[] { 1.2978134597266808, 0.20802649628632208},
};
double[][] actual1 = actual.Submatrix(0, 2, 0, 1);
Assert.IsTrue(Matrix.IsEqual(actual1, expected1, 0.0000001));
// Assert the result equals the transformation of the input
double[][] result = target.Result.ToArray();
double[][] projection = target.Transform(inputs);
Assert.IsTrue(Matrix.IsEqual(result, projection));
}
public void ThresholdTest()
{
double[,] inputs =
{
{ 1 },
{ 2 },
{ 3 },
};
int[] output = { 0, 1, 1 };
IKernel kernel = new Gaussian(0.1);
KernelDiscriminantAnalysis target = new KernelDiscriminantAnalysis(inputs, output, kernel);
bool thrown = false;
try { target.Threshold = -1; }
catch (ArgumentOutOfRangeException) { thrown = true; }
Assert.IsTrue(thrown);
thrown = false;
try { target.Threshold = 1.1; }
catch (ArgumentOutOfRangeException) { thrown = true; }
Assert.IsTrue(thrown);
target.Threshold = 1.0;
target.Compute();
Assert.AreEqual(2, target.Classes.Count);
Assert.AreEqual(0, target.Classes[0].Number);
Assert.AreEqual(1, target.Classes[0].Indices.Length);
Assert.AreEqual(0, target.Classes[0].Indices[0]);
Assert.AreEqual(1, target.Classes[1].Number);
Assert.AreEqual(2, target.Classes[1].Indices.Length);
Assert.AreEqual(1, target.Classes[1].Indices[0]);
Assert.AreEqual(2, target.Classes[1].Indices[1]);
Assert.AreEqual(0, target.CumulativeProportions.Length);
Assert.AreEqual(3, target.DiscriminantMatrix.GetLength(0)); // dimension
Assert.AreEqual(0, target.DiscriminantMatrix.GetLength(1)); // components kept
Assert.AreEqual(0, target.DiscriminantProportions.Length);
Assert.AreEqual(0, target.Discriminants.Count);
}
public void ConstructorTest1()
{
double[,] inputs =
{
{ 1, 1, },
{ 2, 2, },
{ 3, 3, },
};
int[] output = { 1, 2, 3 };
IKernel kernel = new Gaussian(0.1);
KernelDiscriminantAnalysis target = new KernelDiscriminantAnalysis(inputs, output, kernel);
Assert.AreEqual(3, target.Classes.Count);
Assert.AreEqual(0, target.Classes[0].Index);
Assert.AreEqual(1, target.Classes[1].Index);
Assert.AreEqual(2, target.Classes[2].Index);
Assert.AreEqual(1, target.Classes[0].Number);
Assert.AreEqual(2, target.Classes[1].Number);
Assert.AreEqual(3, target.Classes[2].Number);
Assert.AreEqual(output, target.Classifications);
Assert.AreEqual(kernel, target.Kernel);
Assert.AreEqual(1e-4, target.Regularization);
Assert.AreEqual(inputs, target.Source);
Assert.AreEqual(0.001, target.Threshold);
Assert.IsNull(target.CumulativeProportions);
Assert.IsNull(target.DiscriminantMatrix);
Assert.IsNull(target.DiscriminantProportions);
Assert.IsNull(target.Discriminants);
Assert.IsNull(target.Eigenvalues);
Assert.IsNull(target.Result);
Assert.IsNull(target.ScatterBetweenClass);
Assert.IsNull(target.ScatterMatrix);
Assert.IsNull(target.ScatterWithinClass);
Assert.IsNull(target.StandardDeviations);
Assert.IsNull(target.Means);
}
public void ComputeTest()
{
double[,] inputs =
{
{ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
{ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
};
int[] output = { 0, 1 };
IKernel kernel = new Gaussian(0.1);
KernelDiscriminantAnalysis target = new KernelDiscriminantAnalysis(inputs, output, kernel);
target.Threshold = 0;
target.Compute();
double[,] actual = target.Transform(inputs);
double[,] expected =
{
{ 1.0, -1.0},
{ -1.0, -1.0},
};
Assert.IsTrue(Matrix.IsEqual(actual, expected));
}
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);
}
public void ClassifyTest1()
{
// Create some sample input data
// This is the same data used in the example by Gutierrez-Osuna
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
double[,] inputs =
{
{ 4, 1 }, // Class 1
{ 2, 4 },
{ 2, 3 },
{ 3, 6 },
{ 4, 4 },
{ 9, 10 }, // Class 2
{ 6, 8 },
{ 9, 5 },
{ 8, 7 },
{ 10, 8 }
};
int[] output =
{
1, 1, 1, 1, 1, // Class labels for the input vectors
2, 2, 2, 2, 2
};
// Create a new Linear Discriminant Analysis object
KernelDiscriminantAnalysis lda = new KernelDiscriminantAnalysis(inputs, output, new Linear());
// Compute the analysis
lda.Compute();
// Test the classify method
for (int i = 0; i < 5; i++)
{
int expected = 1;
int actual = lda.Classify(inputs.GetRow(i));
Assert.AreEqual(expected, actual);
}
for (int i = 5; i < 10; i++)
{
int expected = 2;
int actual = lda.Classify(inputs.GetRow(i));
Assert.AreEqual(expected, actual);
}
}
public void ComputeTest2()
{
// Scholkopf KPCA toy example
double[,] inputs =
{
#region Scholkopf KPCA Toy Example
{ -0.383504649, -0.162495898 }, { -0.437316092, -0.087483818 },
{ -0.492491985, -0.127135841 }, { -0.46483931, -0.437745429 },
{ -0.569651254, -0.227378242 }, { -0.330385752, -0.232293992 },
{ -0.494094022, -0.168201208 }, { -0.320292822, -0.251117221 },
{ -0.473593147, -0.200204135 }, { -0.412832671, -0.039348904 },
{ -0.644617154, -0.115235137 }, { -0.570116535, -0.173189919 },
{ -0.375401788, -0.292348909 }, { -0.5638977, -0.207049939 },
{ -0.442264978, -0.185210865 }, { -0.536002963, -0.255709364 },
{ -0.513557629, -0.23367057 }, { -0.634933848, -0.158477254 },
{ -0.62704499, -0.044218646 }, { -0.401542973, -0.44442989 },
{ -0.504488061, -0.309819539 }, { -0.579894452, -0.087735214 },
{ -0.576517243, -0.141833274 }, { -0.41382651, -0.22713543 },
{ -0.505622512, -0.158580869 }, { -0.448652183, -0.297781423 },
{ -0.460331913, -0.302146617 }, { -0.424378103, -0.168231202 },
{ -0.459951398, -0.04838922 }, { -0.634138072, -0.125056755 },
{ -0.050770039, 0.603988485 }, { 0.088529945, 0.351715749 },
{ -0.024809355, 0.715865471 }, { -0.0726249, 0.497372053 },
{ -0.04450403, 0.715348699 }, { -0.061291112, 0.521354339 },
{ -0.020914408, 0.663480859 }, { 0.056214783, 0.682040976 },
{ -0.106392289, 0.582397349 }, { 0.035158895, 0.656247387 },
{ 0.113299993, 0.587255712 }, { 0.014999425, 0.655417156 },
{ 0.070314405, 0.490265568 }, { -0.005241158, 0.52686986 },
{ 0.201849612, 0.740473192 }, { 0.09241594, 0.537978579 },
{ -0.18141147, 0.623714877 }, { 0.003497332, 0.441315301 },
{ -0.180786206, 0.559851519 }, { 0.102819255, 0.522930773 },
{ 0.039460031, 0.573731949 }, { 0.063940564, 0.697648954 },
{ 0.087421289, 0.697781504 }, { 0.175240173, 0.717002111 },
{ -0.032005083, 0.615931086 }, { -0.013741381, 0.649952085 },
{ 0.061576963, 0.494462493 }, { 0.097789407, 0.55492568 },
{ -0.111534771, 0.727037824 }, { -0.055002145, 0.68986936 },
{ 0.54387051, 0.05261625 }, { 0.375265568, -0.018445412 },
{ 0.532466692, 0.019878283 }, { 0.539007041, 0.159042684 },
{ 0.459486168, 0.003219164 }, { 0.529231488, 0.088916367 },
{ 0.756591024, -0.129915249 }, { 0.454218436, 0.11825731 },
{ 0.338917299, 0.181747171 }, { 0.233047622, -0.058430213 },
{ 0.424030335, -0.101067382 }, { 0.432527914, -0.096049831 },
{ 0.382831281, 0.069115958 }, { 0.703293002, -0.075861821 },
{ 0.596848105, -0.009697173 }, { 0.5670292, -0.140694905 },
{ 0.542014604, 0.103081246 }, { 0.212724873, -0.07598744 },
{ 0.668587408, 0.087412723 }, { 0.502792455, 0.0761127 },
{ 0.409796942, -0.016592345 }, { 0.294674251, 0.030090744 },
{ 0.50890863, -0.032246733 }, { 0.708709913, -0.036841128 },
{ 0.536511846, 0.114789528 }, { 0.584610553, 0.004143026 },
{ 0.481546234, -0.109804965 }, { 0.603071442, 0.156672375 },
{ 0.347237735, -0.104842345 }, { 0.596493896, 0.042272368 },
#endregion
};
int[] output = Matrix.Expand(new int[,] { { 1 }, { 2 }, { 3 } }, new int[] { 30, 30, 30 }).GetColumn(0);
IKernel kernel = new Gaussian(0.2);
KernelDiscriminantAnalysis target = new KernelDiscriminantAnalysis(inputs, output, kernel);
target.Compute();
double[,] actual = target.Transform(inputs, 2);
double[,] expected1 =
{
{ 1.2785801485080475, 0.20539157505913622},
{ 1.2906613255489541, 0.20704272225753775},
{ 1.2978134597266808, 0.20802649628632208},
};
double[,] actual1 = actual.Submatrix(0, 2, 0, 1);
Assert.IsTrue(Matrix.IsEqual(actual1, expected1, 0.0000001));
// Assert the result equals the transformation of the input
double[,] result = target.Result;
double[,] projection = target.Transform(inputs);
Assert.IsTrue(Matrix.IsEqual(result, projection));
}
public void large_transform_few_components()
{
int n = 100;
double[][] data = Jagged.Random(n, n);
int[] labels = Vector.Random(n, 0, 10);
var kda = new KernelDiscriminantAnalysis();
var target = kda.Learn(data, labels);
var expected = kda.Transform(data, 2);
Assert.AreEqual(n, expected.Rows());
Assert.AreEqual(2, expected.Columns());
kda.NumberOfOutputs = 2;
target = kda.Learn(data, labels);
var actual = target.First.Transform(data);
Assert.AreEqual(n, actual.Rows());
Assert.AreEqual(2, actual.Columns());
Assert.IsTrue(actual.IsEqual(expected));
}
public void scholkopf_new_method()
{
// Schölkopf KPCA toy example
double[][] inputs = scholkopf().ToJagged();
int[] output = Matrix.Expand(new int[,] { { 0 }, { 1 }, { 2 } }, new int[] { 30, 30, 30 }).GetColumn(0);
IKernel kernel = new Gaussian(0.2);
var target = new KernelDiscriminantAnalysis(kernel);
var cls = target.Learn(inputs, output);
double[][] actual = target.Transform(inputs, 2);
double[][] expected1 =
{
new double[] { 1.2785801485080475, 0.20539157505913622},
new double[] { 1.2906613255489541, 0.20704272225753775},
new double[] { 1.2978134597266808, 0.20802649628632208},
};
double[][] actual1 = actual.Submatrix(0, 2, 0, 1);
Assert.IsTrue(Matrix.IsEqual(actual1, expected1, 0.0000001));
Assert.IsNull(target.Result);
int[] actual2 = target.Classify(inputs);
Assert.IsTrue(Matrix.IsEqual(actual2, output));
int[] actual4 = cls.Decide(inputs);
Assert.IsTrue(Matrix.IsEqual(actual4, output));
int[] actual3 = new int[inputs.Length];
double[][] scores = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
actual3[i] = target.Classify(inputs[i], out scores[i]);
Assert.IsTrue(Matrix.IsEqual(actual3, output));
scores = scores.Get(0, 5, null);
double[][] expected = new double[][] {
new double[] { -6.23928931356786E-06, -5.86731829543872, -4.76988430445096 },
new double[] { -9.44593697210785E-05, -5.92312597750504, -4.82189359956088 },
new double[] { -0.000286839977573986, -5.95629842504978, -4.85283341267476 },
new double[] { -4.38986003009456E-05, -5.84990179343448, -4.75189423787298 },
new double[] { -0.000523817959022851, -5.77534144986199, -4.683120454667 }
};
Assert.IsTrue(Matrix.IsEqual(scores, expected, 1e-6));
}
public void ClassifyTest()
{
// Create some sample input data instances. This is the same
// data used in the Gutierrez-Osuna's example available on:
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
double[][] inputs =
{
// Class 0
new double[] { 4, 1 },
new double[] { 2, 4 },
new double[] { 2, 3 },
new double[] { 3, 6 },
new double[] { 4, 4 },
// Class 1
new double[] { 9, 10 },
new double[] { 6, 8 },
new double[] { 9, 5 },
new double[] { 8, 7 },
new double[] { 10, 8 }
};
int[] output =
{
0, 0, 0, 0, 0, // The first five are from class 0
1, 1, 1, 1, 1 // The last five are from class 1
};
// Now we can chose a kernel function to
// use, such as a linear kernel function.
IKernel kernel = new Linear();
// Then, we will create a KDA using this linear kernel.
var kda = new KernelDiscriminantAnalysis(inputs, output, kernel);
kda.Compute(); // Compute the analysis
// Now we can project the data into KDA space:
double[][] projection = kda.Transform(inputs);
double[][] classifierProjection = kda.Classifier.First.Transform(inputs);
Assert.IsTrue(projection.IsEqual(classifierProjection));
// Or perform classification using:
int[] results = kda.Classify(inputs);
string str = projection.ToCSharp();
double[][] expected = new double[][] {
new double[] { 80.7607049998409, -5.30485371541545E-06, 6.61304584781419E-06, 4.52807990036774E-06, -3.44409628150189E-06, 3.69094504515388E-06, -1.33641000168438E-05, -0.000132874977040842, -0.000261921590627878, 1.22137997452386 },
new double[] { 67.6629612351861, 6.80622743409742E-06, -8.48466262226566E-06, -5.80961187779394E-06, 4.4188405141643E-06, -4.73555212510135E-06, 1.71463925084936E-05, 0.000170481102685471, 0.000336050342774286, -1.5670535522193 },
new double[] { 59.8679301679674, 4.10375477777336E-06, -5.11575246520124E-06, -3.50285421113483E-06, 2.66430090034575E-06, -2.85525936627451E-06, 1.03382660725515E-05, 0.00010279007663172, 0.000202618589039361, -0.944841112367518 },
new double[] { 101.494441852779, 1.02093411395998E-05, -1.27269939227403E-05, -8.71441780958548E-06, 6.62826077091339E-06, -7.10332818965043E-06, 2.57195887591877E-05, 0.000255721654028207, 0.000504075514164981, -2.35058032832894 },
new double[] { 104.145798201497, 2.80256425000402E-06, -3.49368461627364E-06, -2.39219308895144E-06, 1.81952256639306E-06, -1.94993321933623E-06, 7.06027928387698E-06, 7.01981011275166E-05, 0.000138373670580449, -0.645257345031474 },
new double[] { 242.123077020588, 9.00824221261587E-06, -1.12297005614437E-05, -7.689192102589E-06, 5.84846541151762E-06, -6.26764250277745E-06, 2.26937548148953E-05, 0.000225636753569347, 0.000444772512580016, -2.07404146617259 },
new double[] { 171.808759436683, 9.60879168943052E-06, -1.19783472456447E-05, -8.2018049702981E-06, 6.23836308744075E-06, -6.68548535731617E-06, 2.42066717959233E-05, 0.000240679203812988, 0.000474424013376051, -2.21231089725078 },
new double[] { 203.147921684494, -4.5041210583463E-06, 5.61485022387842E-06, 3.8445962076139E-06, -2.92423269243614E-06, 3.13382127359318E-06, -1.13468773577097E-05, -0.000112818376692303, -0.000222386256126583, 1.03702073308629 },
new double[] { 200.496565335776, 2.90265583302585E-06, -3.61845908969372E-06, -2.47762852723099E-06, 1.88450551963371E-06, -2.01957368695105E-06, 7.31243213181187E-06, 7.27051762225983E-05, 0.000143315587422421, -0.668302250211177 },
new double[] { 244.774433369306, 1.60146531058558E-06, -1.99639123366069E-06, -1.36696743169296E-06, 1.0397271781315E-06, -1.11424755644407E-06, 4.03444536090092E-06, 4.01132006970784E-05, 7.90706689741683E-05, -0.368718482875124 }
};
Assert.IsTrue(expected.IsEqual(projection, 1e-6));
// Test the classify method
for (int i = 0; i < 5; i++)
{
int actual = results[i];
Assert.AreEqual(0, actual);
}
for (int i = 5; i < 10; i++)
{
int actual = results[i];
Assert.AreEqual(1, actual);
}
}
private void buildModel()
{
if (independentVls == null) getMatrix();
kda = new Accord.Statistics.Analysis.KernelDiscriminantAnalysis(independentVls, dependentVls, kernel);
Console.WriteLine("Building model");
kda.Compute();
Console.WriteLine("Finished model");
meanValues = kda.Means;
stdValues = kda.StandardDeviations;
}