public void correlation_success()
{
double[] mean = Measures.Mean(data, dimension: 0);
double[] stdDev = Measures.StandardDeviation(data);
double[,] cov = Measures.Correlation(data);
var actual = PrincipalComponentAnalysis.FromCorrelationMatrix(mean, stdDev, cov);
var expected = new PrincipalComponentAnalysis(data, AnalysisMethod.Standardize);
// Compute
actual.Compute();
expected.Compute();
// Transform
double[,] actualTransform = actual.Transform(data);
double[,] expectedTransform = expected.Transform(data);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actualTransform, expectedTransform, 0.01));
// Transform
double[,] image = actual.Transform(data);
double[,] reverse = actual.Revert(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(reverse, data, 0.01));
}
private void buildModel()
{
if (varCov == null)
{
getCov();
}
pca = PrincipalComponentAnalysis.FromCorrelationMatrix(MeanVector, StdVector, CorralationMatrix);
pca.Compute();
egVec = pca.ComponentMatrix;
prop = pca.ComponentProportions;
egVal = pca.Eigenvalues;
//Console.WriteLine("PCA method = " + pca.Method.ToString());
}
public void correlation_new_interface()
{
double[] mean = Measures.Mean(data, dimension: 0);
double[] stdDev = Measures.StandardDeviation(data);
double[][] cov = Measures.Correlation(data.ToJagged());
var actual = PrincipalComponentAnalysis.FromCorrelationMatrix(mean, stdDev, cov.ToMatrix());
var expected = new PrincipalComponentAnalysis(PrincipalComponentMethod.CorrelationMatrix)
{
Means = mean,
StandardDeviations = stdDev
};
// Compute
actual.Compute();
var transform = expected.Learn(cov);
// Transform
double[,] actualTransform = actual.Transform(data);
double[,] expectedTransform = expected.Transform(data);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actualTransform, expectedTransform, 0.01));
// Transform
double[,] image = actual.Transform(data);
double[,] reverse = actual.Revert(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(reverse, data, 1e-6));
// Transform
double[][] image2 = transform.Transform(data.ToJagged());
double[][] reverse2 = transform.Inverse().Transform(image2);
Assert.IsTrue(Matrix.IsEqual(reverse, reverse2, 1e-6));
Assert.IsTrue(Matrix.IsEqual(reverse2, data, 1e-6));
// Transform
double[][] reverse3 = actual.Revert(image2);
Assert.IsTrue(Matrix.IsEqual(reverse, reverse3, 1e-6));
Assert.IsTrue(Matrix.IsEqual(reverse3, data, 1e-6));
var a = transform.Transform(data.ToJagged()).ToMatrix();
Assert.IsTrue(Matrix.IsEqual(a, expectedTransform, 0.01));
}
public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.PCA)
{
egVec = new double[1, 1];
System.Windows.Forms.MessageBox.Show("Not a PCA Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
inpath = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
corr = new double[VariableFieldNames.Length, VariableFieldNames.Length];
egVec = new double[VariableFieldNames.Length, VariableFieldNames.Length];
n = System.Convert.ToInt32(sr.ReadLine());
meanVector = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
stdVector = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
string[] corrLg = sr.ReadLine().Split(new char[] { ',' });
prop = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
egVal = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
string[] egVecLg = sr.ReadLine().Split(new char[] { ',' });
for (int i = 0; i < VariableFieldNames.Length; i++)
{
for (int j = 0; j < VariableFieldNames.Length; j++)
{
int indexVl = (i * VariableFieldNames.Length) + j;
corr[i, j] = System.Convert.ToDouble(corrLg[indexVl]);
egVec[i, j] = System.Convert.ToDouble(egVecLg[indexVl]);
}
}
sr.Close();
}
pca = PrincipalComponentAnalysis.FromCorrelationMatrix(meanVector, stdVector, corr);
pca.Compute();
}