//Protected methods (called by the public methods in parent class)
protected override void DoTrain(Bitmap[] charImgs)
{
double[][] input = convertDataToPCAInputFormat(charImgs);
pca = new PrincipalComponentAnalysis(input, AnalysisMethod.Center);
pca.Compute();
}
private PrincipalComponentAnalysis computePCA(double[][][] sequences)
{
PrincipalComponentAnalysis pca;
// Create combined array for computing PCA
int numTotalRows = sequences.Select(e => e.GetLength(0)).Sum();
double[][] pcaCombined = new double[numTotalRows][];
int total = 0;
for (int i = 0; i < sequences.GetLength(0); i++)
{
for (int j = 0; j < sequences[i].GetLength(0); j++)
{
pcaCombined[total + j] = sequences[i][j];
}
total += sequences[i].GetLength(0);
}
// PCA
double[,] pcaCombinedMulti = jaggedToMulti(pcaCombined);
pca = new PrincipalComponentAnalysis(pcaCombinedMulti);
pca.Compute();
return pca;
}
public static double[,] PCA(double[,] sourceMatrix)
{
// Creates the Principal Component Analysis of the given source
var pca = new PrincipalComponentAnalysis(sourceMatrix, AnalysisMethod.Center);
// Compute the Principal Component Analysis
pca.Compute();
var length1 = sourceMatrix.GetLength(0);
var length2 = sourceMatrix.GetLength(1);
// int sercount = rawSeries.Count();
// Creates a projection considering 80% of the information
// pca.Transform(sourceMatrix, 0.8f, true);
return pca.Transform(sourceMatrix, length1);
}
void btnCompute_Click(object sender, EventArgs e)
{
dataGridView2.Rows.Clear();
// Extract feature vectors
double[][] hands = extract();
// Create a new Principal Component Analysis object
pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Center,
ExplainedVariance = 0.95
};
// Compute it
pca.Learn(hands);
// Now we will plot the Eigenvectors as images
ArrayToImage reverse = new ArrayToImage(32, 32);
// For each Principal Component
for (int i = 0; i < pca.Components.Count; i++)
{
// We will extract its Eigenvector
double[] vector = pca.Components[i].Eigenvector;
// Normalize its values
reverse.Max = vector.Max();
reverse.Min = vector.Min();
// Then arrange each vector value as if it was a pixel
Bitmap eigenHand; reverse.Convert(vector, out eigenHand);
// This will give the Eigenhands
dataGridView2.Rows.Add(eigenHand, pca.Components[i].Proportion);
}
// Populate components overview with analysis data
dgvPrincipalComponents.DataSource = pca.Components;
distributionView.DataSource = pca.Components;
cumulativeView.DataSource = pca.Components;
btnCreateProjection.Enabled = true;
}
public void transform_more_columns_than_samples_new_interface()
{
// Lindsay's tutorial data
var datat = data.Transpose().ToJagged();
var target = new PrincipalComponentAnalysis();
// Compute
var regression = target.Learn(datat);
// Transform
double[][] actual = target.Transform(datat);
// Assert the scores equals the transformation of the input
Assert.IsNull(target.Result);
double[,] expected =
{
{ 0.50497524691810358, -0.00000000000000044408920985006262 },
{ -0.504975246918104, -0.00000000000000035735303605122226 }
};
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.01));
actual = target.Transform(datat);
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.01));
}
public void ConstructorTest2()
{
// Reproducing Lindsay Smith's "Tutorial on Principal Component Analysis"
// using the paper's original method. The tutorial can be found online
// at http://www.sccg.sk/~haladova/principal_components.pdf
// Step 1. Get some data
// ---------------------
double[,] data =
{
{ 2.5, 2.4 },
{ 0.5, 0.7 },
{ 2.2, 2.9 },
{ 1.9, 2.2 },
{ 3.1, 3.0 },
{ 2.3, 2.7 },
{ 2.0, 1.6 },
{ 1.0, 1.1 },
{ 1.5, 1.6 },
{ 1.1, 0.9 }
};
// Step 2. Subtract the mean
// -------------------------
// Note: The framework does this automatically
// when computing the covariance matrix. In this
// step we will only compute the mean vector.
double[] mean = Accord.Statistics.Tools.Mean(data);
// Step 3. Compute the covariance matrix
// -------------------------------------
double[,] covariance = Accord.Statistics.Tools.Covariance(data, mean);
// Create the analysis using the covariance matrix
var pca = PrincipalComponentAnalysis.FromCovarianceMatrix(mean, covariance);
// Compute it
pca.Compute();
// Step 4. Compute the eigenvectors and eigenvalues of the covariance matrix
//--------------------------------------------------------------------------
// Those are the expected eigenvalues, in descending order:
double[] eigenvalues = { 1.28402771, 0.0490833989 };
// And this will be their proportion:
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
// Those are the expected eigenvectors,
// in descending order of eigenvalues:
double[,] eigenvectors =
{
{ -0.677873399, -0.735178656 },
{ -0.735178656, 0.677873399 }
};
// Now, here is the place most users get confused. The fact is that
// the Eigenvalue decomposition (EVD) is not unique, and both the SVD
// and EVD routines used by the framework produces results which are
// numerically different from packages such as STATA or MATLAB, but
// those are correct.
// If v is an eigenvector, a multiple of this eigenvector (such as a*v, with
// a being a scalar) will also be an eigenvector. In the Lindsay case, the
// framework produces a first eigenvector with inverted signs. This is the same
// as considering a=-1 and taking a*v. The result is still correct.
// Retrieve the first expected eigenvector
double[] v = eigenvectors.GetColumn(0);
// Multiply by a scalar and store it back
eigenvectors.SetColumn(0, v.Multiply(-1));
// Everything is alright (up to the 9 decimal places shown in the tutorial)
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, threshold: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, threshold: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, threshold: 1e-8));
// Step 5. Deriving the new data set
// ---------------------------------
double[,] actual = pca.Transform(data);
// transformedData shown in pg. 18
double[,] expected = new double[, ]
{
{ 0.827970186, -0.175115307 },
{ -1.77758033, 0.142857227 },
{ 0.992197494, 0.384374989 },
{ 0.274210416, 0.130417207 },
{ 1.67580142, -0.209498461 },
{ 0.912949103, 0.175282444 },
{ -0.099109437, -0.349824698 },
{ -1.14457216, 0.046417258 },
{ -0.438046137, 0.017764629 },
{ -1.22382056, -0.162675287 },
};
// Everything is correct (up to 8 decimal places)
Assert.IsTrue(expected.IsEqual(actual, threshold: 1e-8));
}
public void learn_whiten_success()
{
#region doc_learn_1
// Below is the same data used on the excellent paper "Tutorial
// On Principal Component Analysis", by Lindsay Smith (2002).
double[][] data =
{
new double[] { 2.5, 2.4 },
new double[] { 0.5, 0.7 },
new double[] { 2.2, 2.9 },
new double[] { 1.9, 2.2 },
new double[] { 3.1, 3.0 },
new double[] { 2.3, 2.7 },
new double[] { 2.0, 1.6 },
new double[] { 1.0, 1.1 },
new double[] { 1.5, 1.6 },
new double[] { 1.1, 0.9 }
};
// Let's create an analysis with centering (covariance method)
// but no standardization (correlation method) and whitening:
var pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Center,
Whiten = true
};
// Now we can learn the linear projection from the data
MultivariateLinearRegression transform = pca.Learn(data);
// Finally, we can project all the data
double[][] output1 = pca.Transform(data);
// Or just its first components by setting
// NumberOfOutputs to the desired components:
pca.NumberOfOutputs = 1;
// And then calling transform again:
double[][] output2 = pca.Transform(data);
// We can also limit to 80% of explained variance:
pca.ExplainedVariance = 0.8;
// And then call transform again:
double[][] output3 = pca.Transform(data);
#endregion
double[] eigenvalues = { 1.28402771, 0.0490833989 };
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
double[,] eigenvectors =
{
{ 0.19940687993951403, -1.1061252858739095 },
{ 0.21626410214440508, 1.0199057073792104 }
};
// Everything is alright (up to the 9 decimal places shown in the tutorial)
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, rtol: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, rtol: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, rtol: 1e-5));
pca.ExplainedVariance = 1.0;
double[][] actual = pca.Transform(data);
double[][] expected =
{
new double[] { 0.243560157209023, -0.263472650637184 },
new double[] { -0.522902576315494, 0.214938218565977 },
new double[] { 0.291870144299372, 0.578317788814594 },
new double[] { 0.0806632088164338, 0.19622137941132 },
new double[] { 0.492962746459375, -0.315204397734004 },
new double[] { 0.268558011864442, 0.263724118751361 },
new double[] { -0.0291545644762578, -0.526334573603598 },
new double[] { -0.336693495487974, 0.0698378585807067 },
new double[] { -0.128858004446015, 0.0267280693333571 },
new double[] { -0.360005627922904, -0.244755811482527 }
};
// var str = actual.ToString(CSharpJaggedMatrixFormatProvider.InvariantCulture);
// Everything is correct (up to 8 decimal places)
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
Assert.IsTrue(expected.IsEqual(output1, atol: 1e-8));
Assert.IsTrue(expected.Get(null, 0, 1).IsEqual(output2, atol: 1e-8));
Assert.IsTrue(expected.Get(null, 0, 1).IsEqual(output3, atol: 1e-8));
actual = transform.Transform(data);
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
}
public void PC()
{
Random rng = new Random(1);
double s = 1.0 / Math.Sqrt(2.0);
MultivariateSample MS = new MultivariateSample(2);
RectangularMatrix R = new RectangularMatrix(1000, 2);
for (int i = 0; i < 1000; i++)
{
double r1 = 2.0 * rng.NextDouble() - 1.0;
double r2 = 2.0 * rng.NextDouble() - 1.0;
double x = r1 * 4.0 * s - r2 * 9.0 * s;
double y = r1 * 4.0 * s + r2 * 9.0 * s;
R[i, 0] = x; R[i, 1] = y;
MS.Add(x, y);
}
Console.WriteLine("x {0} {1}", MS.Column(0).Mean, MS.Column(0).Variance);
Console.WriteLine("y {0} {1}", MS.Column(1).Mean, MS.Column(1).Variance);
Console.WriteLine("SVD");
SingularValueDecomposition SVD = R.SingularValueDecomposition();
for (int i = 0; i < SVD.Dimension; i++)
{
Console.WriteLine("{0} {1}", i, SVD.SingularValue(i));
ColumnVector v = SVD.RightSingularVector(i);
Console.WriteLine(" {0} {1}", v[0], v[1]);
}
Console.WriteLine("PCA");
PrincipalComponentAnalysis PCA = MS.PrincipalComponentAnalysis();
Console.WriteLine("Dimension = {0} Count = {1}", PCA.Dimension, PCA.Count);
for (int i = 0; i < PCA.Dimension; i++)
{
PrincipalComponent PC = PCA.Component(i);
Console.WriteLine(" {0} {1} {2} {3}", PC.Index, PC.Weight, PC.VarianceFraction, PC.CumulativeVarianceFraction);
RowVector v = PC.NormalizedVector();
Console.WriteLine(" {0} {1}", v[0], v[1]);
}
// reconstruct
SquareMatrix U = SVD.LeftTransformMatrix();
SquareMatrix V = SVD.RightTransformMatrix();
double x1 = U[0, 0] * SVD.SingularValue(0) * V[0, 0] + U[0, 1] * SVD.SingularValue(1) * V[0, 1];
Console.WriteLine("x1 = {0} {1}", x1, R[0, 0]);
double y1 = U[0, 0] * SVD.SingularValue(0) * V[1, 0] + U[0, 1] * SVD.SingularValue(1) * V[1, 1];
Console.WriteLine("y1 = {0} {1}", y1, R[0, 1]);
double x100 = U[100, 0] * SVD.SingularValue(0) * V[0, 0] + U[100, 1] * SVD.SingularValue(1) * V[0, 1];
Console.WriteLine("x100 = {0} {1}", x100, R[100, 0]);
double y100 = U[100, 0] * SVD.SingularValue(0) * V[1, 0] + U[100, 1] * SVD.SingularValue(1) * V[1, 1];
Console.WriteLine("y100 = {0} {1}", y100, R[100, 1]);
ColumnVector d1 = U[0, 0] * SVD.SingularValue(0) * SVD.RightSingularVector(0) +
U[0, 1] * SVD.SingularValue(1) * SVD.RightSingularVector(1);
Console.WriteLine("d1 = ({0} {1})", d1[0], d1[1]);
ColumnVector d100 = U[100, 0] * SVD.SingularValue(0) * SVD.RightSingularVector(0) +
U[100, 1] * SVD.SingularValue(1) * SVD.RightSingularVector(1);
Console.WriteLine("d100 = ({0} {1})", d100[0], d100[1]);
Console.WriteLine("compare");
MultivariateSample RS = PCA.TransformedSample();
IEnumerator <double[]> RSE = RS.GetEnumerator();
RSE.MoveNext();
double[] dv1 = RSE.Current;
Console.WriteLine("{0} {1}", dv1[0], dv1[1]);
Console.WriteLine("{0} {1}", U[0, 0], U[0, 1]);
RSE.Dispose();
}
public void PrincipalComponentAnalysis()
{
int D = 3;
int N = 10;
// construct a sample
Random rng = new Random(1);
MultivariateSample sample = new MultivariateSample(D);
for (int i = 0; i < N; i++)
{
double x = 1.0 * rng.NextDouble() - 1.0;
double y = 4.0 * rng.NextDouble() - 2.0;
double z = 9.0 * rng.NextDouble() - 3.0;
sample.Add(x, y, z);
}
// get its column means
RowVector mu = new RowVector(D);
for (int i = 0; i < D; i++)
{
mu[i] = sample.Column(i).Mean;
}
// get total variance
double tVariance = GetTotalVariance(sample);
Console.WriteLine(tVariance);
// do a principal component analysis
PrincipalComponentAnalysis pca = sample.PrincipalComponentAnalysis();
Assert.IsTrue(pca.Dimension == sample.Dimension);
Assert.IsTrue(pca.Count == sample.Count);
// check that the PCs behave as expected
Assert.IsTrue(pca.Components.Count == pca.Dimension);
for (int i = 0; i < pca.Dimension; i++)
{
PrincipalComponent pc = pca.Components[i];
Assert.IsTrue(pc.Index == i);
Assert.IsTrue(pc.Analysis == pca);
Assert.IsTrue(TestUtilities.IsNearlyEqual(pc.Weight * pc.NormalizedVector, pc.ScaledVector()));
Assert.IsTrue(pca.MinimumDimension(pc.CumulativeVarianceFraction) == i + 1);
}
// Check enumerator, and verify that variance fractions behave as expected.
int count = 0;
double cumulative = 0.0;
double previous = Double.PositiveInfinity;
foreach (PrincipalComponent pc in pca.Components)
{
Assert.IsTrue(pc.Index == count);
count++;
Assert.IsTrue((0.0 <= pc.VarianceFraction) && (pc.VarianceFraction <= 1.0));
Assert.IsTrue(pc.VarianceFraction <= previous);
previous = pc.VarianceFraction;
cumulative += pc.VarianceFraction;
Assert.IsTrue(TestUtilities.IsNearlyEqual(cumulative, pc.CumulativeVarianceFraction));
}
Assert.IsTrue(count == pca.Components.Count);
// express the sample in terms of principal components
MultivariateSample csample = pca.TransformedSample();
// check that the explained variances are as claimed
for (int rD = 1; rD <= D; rD++)
{
MultivariateSample rSample = new MultivariateSample(D);
foreach (double[] cEntry in csample)
{
RowVector x = mu.Copy();
for (int i = 0; i < rD; i++)
{
PrincipalComponent pc = pca.Components[i];
x += (cEntry[i] * pc.Weight) * pc.NormalizedVector;
}
rSample.Add(x);
}
double rVariance = GetTotalVariance(rSample);
Console.WriteLine("{0} {1}", rD, rVariance);
Assert.IsTrue(TestUtilities.IsNearlyEqual(rVariance / tVariance, pca.Components[rD - 1].CumulativeVarianceFraction));
}
}
public void ExceptionTest()
{
double[,] data =
{
{ 1, 2 },
{ 5, 2 },
{ 2, 2 },
{ 4, 2 },
};
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis(data, AnalysisMethod.Standardize);
bool thrown = false;
try { pca.Compute(); }
catch (ArithmeticException ex)
{
ex.ToString();
thrown = true;
}
// Assert that an appropriate exception has been
// thrown in the case of a constant variable.
Assert.IsTrue(thrown);
}
private double[][][] getProjectedSequences(double[][][] sequences, PrincipalComponentAnalysis pca)
{
int nseqs = sequences.GetLength(0);
double[][][] projSeqs = new double[nseqs][][];
for (int i = 0; i < nseqs; i++)
{
projSeqs[i] = getProjectedSequence(sequences[i], pca);
}
return projSeqs;
}
public void covariance_new_interface()
{
double[] mean = Measures.Mean(data, dimension: 0);
double[][] cov = Measures.Covariance(data.ToJagged());
#region doc_learn_3
// Create the Principal Component Analysis
// specifying the CovarianceMatrix method:
var pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.CovarianceMatrix,
Means = mean // pass the original data mean vectors
};
// Learn the PCA projection using passing the cov matrix
MultivariateLinearRegression transform = pca.Learn(cov);
// Now, we can transform data as usual
double[,] actual = pca.Transform(data);
#endregion
double[,] expected = new double[,]
{
{ 0.827970186, -0.175115307 },
{ -1.77758033, 0.142857227 },
{ 0.992197494, 0.384374989 },
{ 0.274210416, 0.130417207 },
{ 1.67580142, -0.209498461 },
{ 0.912949103, 0.175282444 },
{ -0.099109437, -0.349824698 },
{ -1.14457216, 0.046417258 },
{ -0.438046137, 0.017764629 },
{ -1.22382056, -0.162675287 },
};
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actual, expected, 0.01));
// Transform
double[,] image = pca.Transform(data);
// Reverse
double[,] reverse = pca.Revert(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(reverse, data, 1e-5));
actual = transform.Transform(data.ToJagged()).ToMatrix();
Assert.IsTrue(Matrix.IsEqual(actual, expected, 1e-5));
}
public void TransformTest2()
{
// Lindsay's tutorial data
double[,] datat = data.Transpose();
PrincipalComponentAnalysis target = new PrincipalComponentAnalysis(datat);
// Compute
target.Compute();
// Transform
double[,] actual = target.Transform(datat);
// Assert the scores equals the transformation of the input
double[,] result = target.Result;
Assert.IsTrue(Matrix.IsEqual(result, actual, 0.01));
}
internal void PCCompute(List<SpikeEvent> spikes)
{
// Matrix dimensions
int numObs = spikes.Count;
int wavelength = spikes[0].Waveform.Length;
// Create waveform matrix
double[,] waveforms = new double[numObs, wavelength];
for (int i = 0; i < numObs; ++i)
{
for (int j = 0; j < wavelength; ++j)
{
waveforms[i, j] = spikes[i].Waveform[j];
}
}
// Make PCA object
pca = new PrincipalComponentAnalysis(waveforms, AnalysisMethod.Standardize);
// PC Decomp.
pca.Compute();
// Project
currentProjection = new double[numObs][];
double[,] tmp = pca.Transform(waveforms);
for (int i = 0; i < tmp.GetLength(0); ++i)
{
currentProjection[i] = new double[projectionDimension];
for (int j = 0; j < projectionDimension; ++j)
currentProjection[i][j] = tmp[i, j];
}
//// Create projection matrix
//double maxPC = double.MinValue;
//currentProjection = new double[numObs][];
//for (int i = 0; i < numObs; ++i)
//{
// currentProjection[i] = new double[projectionDimension];
// for (int j = 0; j < projectionDimension; ++j)
// {
// currentProjection[i][j] = pca.ComponentMatrix[i, j];
// if (currentProjection[i][j] > maxPC)
// {
// maxPC = currentProjection[i][j];
// }
// }
//}
//// Normalize projection
//for (int i = 0; i < numObs; ++i)
//{
// for (int j = 0; j < projectionDimension; ++j)
// {
// currentProjection[i][j] = 10000 * (currentProjection[i][j] / maxPC);
// }
//}
}
public ChannelModel(SerializationInfo info, StreamingContext ctxt)
{
this.kVals = (int[])info.GetValue("kVals", typeof(int[]));
this.logLike = (double[])info.GetValue("logLike", typeof(double[]));
this.rissanen = (double[])info.GetValue("rissanen", typeof(double[]));
this.mdl = (double[])info.GetValue("mdl", typeof(double[]));
this.channelNumber = (int)info.GetValue("channelNumber", typeof(int));
this.K = (int)info.GetValue("K", typeof(int));
this.projectionDimension = (int)info.GetValue("projectionDimension", typeof(int));
this.currentProjection = (double[][])info.GetValue("currentProjection", typeof(double[][]));
this.maxK = (int)info.GetValue("maxK", typeof(int));
this.gmm = (GaussianMixtureModel)info.GetValue("gmm", typeof(GaussianMixtureModel));
this.pca = (PrincipalComponentAnalysis)info.GetValue("pca", typeof(PrincipalComponentAnalysis));
this.unitStartIndex = (int)info.GetValue("unitStartIndex", typeof(int));
this.pValue = (double)info.GetValue("pValue",typeof(double));
}
static void Main(string[] args)
{
//for correct symbol of float point
System.Globalization.CultureInfo customCulture = (System.Globalization.CultureInfo)System.Threading.Thread.CurrentThread.CurrentCulture.Clone();
customCulture.NumberFormat.NumberDecimalSeparator = ".";
System.Threading.Thread.CurrentThread.CurrentCulture = customCulture;
//This is a program for demonstrating machine
//learning and classifying the spectrum of light sources using .net
//read data (If you use linux do not forget to correct the path to the files)
string trainCsvFilePath = @"data\train.csv";
string testCsvFilePath = @"data\test.csv";
DataTable trainTable = new CsvReader(trainCsvFilePath, true).ToTable();
DataTable testTable = new CsvReader(testCsvFilePath, true).ToTable();
// Convert the DataTable to input and output vectors (train and test)
int[] trainOutputs = trainTable.Columns["label"].ToArray <int>();
trainTable.Columns.Remove("label");
double[][] trainInputs = trainTable.ToJagged <double>();
int[] testOutputs = testTable.Columns["label"].ToArray <int>();
testTable.Columns.Remove("label");
double[][] testInputs = testTable.ToJagged <double>();
// training model SVM classifier
var teacher = new MulticlassSupportVectorLearning <Gaussian>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization <Gaussian>()
{
// Estimate a suitable guess for the Gaussian kernel's parameters.
// This estimate can serve as a starting point for a grid search.
UseKernelEstimation = true
}
};
// Learn a machine
var machine = teacher.Learn(trainInputs, trainOutputs);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(testInputs);
// print result
int i = 0;
Console.WriteLine("results - (predict ,real labels)");
foreach (int pred in predicted)
{
Console.Write("({0},{1} )", pred, testOutputs[i]);
i++;
}
//calculate the accuracy
double error = new ZeroOneLoss(testOutputs).Loss(predicted);
Console.WriteLine("\n accuracy: {0}", 1 - error);
// consider the decrease in the dimension of features using PCA
var pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Center,
Whiten = true
};
pca.NumberOfOutputs = 2;
MultivariateLinearRegression transform = pca.Learn(trainInputs);
double[][] outputPCA = pca.Transform(trainInputs);
// print it on the scatter plot
ScatterplotBox.Show(outputPCA, trainOutputs).Hold();
Console.ReadLine();
}
internal void PCCompute(List <SpikeEvent> spikes)
{
// Matrix dimensions
int numObs = spikes.Count;
int wavelength = spikes[0].Waveform.Length;
// Create waveform matrix
double[,] waveforms = new double[numObs, wavelength];
for (int i = 0; i < numObs; ++i)
{
for (int j = 0; j < wavelength; ++j)
{
waveforms[i, j] = spikes[i].Waveform[j];
}
}
// Make PCA object
pca = new PrincipalComponentAnalysis(waveforms, AnalysisMethod.Standardize);
// PC Decomp.
pca.Compute();
// Project
currentProjection = new double[numObs][];
double[,] tmp = pca.Transform(waveforms);
for (int i = 0; i < tmp.GetLength(0); ++i)
{
currentProjection[i] = new double[projectionDimension];
for (int j = 0; j < projectionDimension; ++j)
{
currentProjection[i][j] = tmp[i, j];
}
}
//// Create projection matrix
//double maxPC = double.MinValue;
//currentProjection = new double[numObs][];
//for (int i = 0; i < numObs; ++i)
//{
// currentProjection[i] = new double[projectionDimension];
// for (int j = 0; j < projectionDimension; ++j)
// {
// currentProjection[i][j] = pca.ComponentMatrix[i, j];
// if (currentProjection[i][j] > maxPC)
// {
// maxPC = currentProjection[i][j];
// }
// }
//}
//// Normalize projection
//for (int i = 0; i < numObs; ++i)
//{
// for (int j = 0; j < projectionDimension; ++j)
// {
// currentProjection[i][j] = 10000 * (currentProjection[i][j] / maxPC);
// }
//}
}
//Deserialization constructor
public FeatureExtractionPCA(SerializationInfo info, StreamingContext context)
: base(info, context)
{
pca = (PrincipalComponentAnalysis)info.GetValue("pca", typeof(PrincipalComponentAnalysis));
numDimensions = (int?)info.GetValue("numDimensions", typeof(int?));
}
public void ExceptionTest()
{
double[,] data =
{
{ 1, 2 },
{ 5, 2 },
{ 2, 2 },
{ 4, 2 },
};
var pca = new PrincipalComponentAnalysis(data, AnalysisMethod.Standardize);
bool thrown = false;
try { pca.Compute(); }
catch (ArithmeticException ex)
{
ex.ToString();
thrown = true;
}
// Default behavior changed: now an exception is not thrown anymore.
// Instead, a small constant is added when computing standard deviations.
Assert.IsFalse(thrown);
var str1 = pca.SingularValues.ToCSharp();
var str2 = pca.ComponentVectors.ToCSharp();
Assert.IsTrue(pca.SingularValues.IsEqual(new double[] { 1.73205080756888, 0 }, 1e-7));
Assert.IsTrue(pca.ComponentVectors.IsEqual(new double[][] {
new double[] { 1, 0 },
new double[] { 0, -1 }
}, 1e-7));
}
static void Main(string[] args)
{
Console.SetWindowSize(100, 60);
// Read in the Credit Card Fraud dataset
// TODO: change the path to point to your data directory
string dataDirPath = @"\\Mac\Home\Documents\c-sharp-machine-learning\ch.10\input-data";
// Load the data into a data frame
string dataPath = Path.Combine(dataDirPath, "creditcard.csv");
Console.WriteLine("Loading {0}\n\n", dataPath);
var df = Frame.ReadCsv(
dataPath,
hasHeaders: true,
inferTypes: true
);
Console.WriteLine("* Shape: {0}, {1}\n\n", df.RowCount, df.ColumnCount);
string[] featureCols = df.ColumnKeys.Where(
x => !x.Equals("Time") && !x.Equals("Class")
).ToArray();
var noFraudData = df.Rows[
df["Class"].Where(x => x.Value == 0.0).Keys
].Columns[featureCols];
double[][] data = BuildJaggedArray(
noFraudData.ToArray2D <double>(), noFraudData.RowCount, featureCols.Length
);
double[][] wholeData = BuildJaggedArray(
df.Columns[featureCols].ToArray2D <double>(), df.RowCount, featureCols.Length
);
int[] labels = df.GetColumn <int>("Class").ValuesAll.ToArray();
var pca = new PrincipalComponentAnalysis(
PrincipalComponentMethod.Standardize
);
pca.Learn(data);
double[][] transformed = pca.Transform(wholeData);
double[][] first2Components = transformed.Select(x => x.Where((y, i) => i < 2).ToArray()).ToArray();
ScatterplotBox.Show("Component #1 vs. Component #2", first2Components, labels);
double[][] next2Components = transformed.Select(
x => x.Where((y, i) => i >= 1 && i <= 2).ToArray()
).ToArray();
ScatterplotBox.Show("Component #2 vs. Component #3", next2Components, labels);
next2Components = transformed.Select(
x => x.Where((y, i) => i >= 2 && i <= 3).ToArray()
).ToArray();
ScatterplotBox.Show("Component #3 vs. Component #4", next2Components, labels);
next2Components = transformed.Select(
x => x.Where((y, i) => i >= 3 && i <= 4).ToArray()
).ToArray();
ScatterplotBox.Show("Component #4 vs. Component #5", next2Components, labels);
DataSeriesBox.Show(
pca.Components.Select((x, i) => (double)i),
pca.Components.Select(x => x.CumulativeProportion)
).SetTitle("Explained Variance");
System.IO.File.WriteAllLines(
Path.Combine(dataDirPath, "explained-variance.csv"),
pca.Components.Select((x, i) => String.Format("{0},{1:0.0000}", i + 1, x.CumulativeProportion))
);
Console.WriteLine("exporting train set...");
System.IO.File.WriteAllLines(
Path.Combine(dataDirPath, "pca-features.csv"),
transformed.Select((x, i) => String.Format("{0},{1}", String.Join(",", x), labels[i]))
);
Console.WriteLine("\n\n\n\n\nDONE!!!");
Console.ReadKey();
}
public void FromCorrelationConstructorTest()
{
double[] mean = Accord.Statistics.Tools.Mean(data);
double[] stdDev = Accord.Statistics.Tools.StandardDeviation(data);
double[,] cov = Accord.Statistics.Tools.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));
}
//initialize item
void init()
{
pca = new PrincipalComponentAnalysis();
totalData = Directory.GetDirectories(savedDirectoryName).Length;
Console.WriteLine($"Total Data : {totalData}");
}
public void TransformTest1()
{
PrincipalComponentAnalysis target = new PrincipalComponentAnalysis(data);
// Compute
target.Compute();
// Transform
double[][] actual = target.Transform(data.ToArray());
// first inversed.. ?
double[][] expected = new double[][]
{
new double[] { 0.827970186, -0.175115307 },
new double[] { -1.77758033, 0.142857227 },
new double[] { 0.992197494, 0.384374989 },
new double[] { 0.274210416, 0.130417207 },
new double[] { 1.67580142, -0.209498461 },
new double[] { 0.912949103, 0.175282444 },
new double[] { -0.099109437, -0.349824698 },
new double[] { -1.14457216, 0.046417258 },
new double[] { -0.438046137, 0.017764629 },
new double[] { -1.22382056, -0.162675287 },
};
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actual, expected, 0.01));
}
public void LoadSamples(int percentDataTraining)
{
var samples = File.ReadAllLines(pathFile)
.Select(x => x.Split(' ')
.ToList())
.OrderBy(x => Guid.NewGuid())
.ToList();
var position = samples.Count() * percentDataTraining / 100;
Console.WriteLine($"position: {position}");
TestSamples = samples.Skip(position).ToList();
File.WriteAllLines("Datas/test.txt", TestSamples
.Select(x => x
.Aggregate((y, z) => y + z))
.ToArray());
Samples = samples.Take(position).ToList();
File.WriteAllLines("Datas/train.txt", Samples
.Select(x => x
.Aggregate((y, z) => y + z))
.ToArray());
Labels = samples.Select(x => x.Last()).ToList();
Attributes = Enumerable.Range(0, samples.First().Count() - 1).Select(x => x.ToString()).ToList();
var data = Samples.Select(x => x.Select(y => double.Parse(y)).ToArray()).ToArray();
var principalComponentAnalysis = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Center,
Whiten = true
};
var transform = principalComponentAnalysis.Learn(data);
var newdata = principalComponentAnalysis.Transform(data);
newdata.ToList().ForEach(x =>
{
x.ToList().ForEach(y => Console.Write(y + " "));
Console.WriteLine();
});
principalComponentAnalysis.NumberOfOutputs = 1;
newdata = principalComponentAnalysis.Transform(data);
newdata.ToList().ForEach(x =>
{
x.ToList().ForEach(y => Console.Write(y + " "));
Console.WriteLine();
});
principalComponentAnalysis.ExplainedVariance = 0.8;
newdata = principalComponentAnalysis.Transform(data);
newdata.ToList().ForEach(x =>
{
x.ToList().ForEach(y => Console.Write(y + " "));
Console.WriteLine();
});
var q = 1;
}
// testHold.WaitOne(5000);
//IEndPointClient logger;
List <PCAData2D> runPCA(List <NeatGenome> bestGenome, bool firstBehavior = true, int xBins = 0, int yBins = 0)
{
var totalStopWatch = System.Diagnostics.Stopwatch.StartNew();
// Create new stopwatch
var stopwatch = System.Diagnostics.Stopwatch.StartNew();
List <long> uIDs = bestGenome.Select(x => x.GenomeId).ToList();
//make sure we have the right fitness!
//TODO: Check multi-objective code to see what value has absolute fitness
List <double> absoluteFitness = bestGenome.Select(x => x.RealFitness).ToList();
if (bestGenome.Count == 0)
{
return(null);
}
//we know topBody > 0 by above check
int componentCount = Math.Min(80, (firstBehavior ? bestGenome[0].Behavior.behaviorList.Count : bestGenome[0].SecondBehavior.behaviorList.Count));
//double componentCount = (double)fn.Json.Args[1];
//create our double array that's going to be condensed
double[,] collectedData = new double[bestGenome.Count, componentCount];
int xyIndex = 0;
foreach (IGenome genome in bestGenome)
{
//need to grab the behavior objects from the genome, and enter them as data
var behaviorList = (firstBehavior ? genome.Behavior.behaviorList : genome.SecondBehavior.behaviorList);
for (var ix = 0; ix < componentCount; ix++)
{
collectedData[xyIndex, ix] = (double)behaviorList[ix];
}
xyIndex++;
}
try
{
stopwatch.Stop();
Console.WriteLine("Time before kernel: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
//higher gaussian seemed better at spreading out behavior
//might try polynomial of 3rd or 4th degree, constant = 0 by default
// IKernel kernel = new Polynomial(3, 0);//new Gaussian(1.9);//new Polynomial((int)numDegree.Value, (double)numConstant.Value);
// KernelPrincipalComponentAnalysis kpca = new KernelPrincipalComponentAnalysis(collectedData, kernel,
//(PrincipalComponentAnalysis.AnalysisMethod.Correlation));
PrincipalComponentAnalysis kpca = new PrincipalComponentAnalysis(collectedData,
(PrincipalComponentAnalysis.AnalysisMethod.Correlation));
try
{
kpca.Compute();
}
catch (Exception e)
{
Console.WriteLine(e.Message);
return(null);
}
stopwatch.Stop();
Console.WriteLine("Time During PCA: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
double[,] transform = kpca.Transform(collectedData, 2);
stopwatch.Stop();
Console.WriteLine("Time During Transform: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
List <PCAData2D> uidAndPoints = binAllPoints(transform, uIDs, absoluteFitness, xBins, yBins);
stopwatch.Stop();
Console.WriteLine("Time During Binning: " + stopwatch.ElapsedMilliseconds);
//List<PCAData2D> uidAndPoints = new List<PCAData2D>();
//for (int ix = 0; ix < bestGenome.Count; ix++)
//{
// uidAndPoints.Add(new PCAData2D() { uid = uIDs[ix], x = mappedResults[ix, 0], y = mappedResults[ix, 1] });
//}
totalStopWatch.Stop();
Console.WriteLine("Total Time For PCA: " + totalStopWatch.ElapsedMilliseconds);
return(uidAndPoints);
}
catch (Exception e)
{
totalStopWatch.Stop();
Console.WriteLine("Total Time For (failed) PCA: " + totalStopWatch.ElapsedMilliseconds);
Console.WriteLine("Failed to run PCA");
return(null);
}
}
static void Main(string[] args)
{
Console.SetWindowSize(100, 60);
// Read in the Cyber Attack dataset
// TODO: change the path to point to your data directory
string dataDirPath = @"\\Mac\Home\Documents\c-sharp-machine-learning\ch.9\input-data";
// Load the data into a data frame
string dataPath = Path.Combine(dataDirPath, "data.csv");
Console.WriteLine("Loading {0}\n\n", dataPath);
var rawDF = Frame.ReadCsv(
dataPath,
hasHeaders: true,
inferTypes: true
);
// Encode Categorical Variables
string[] categoricalVars =
{
"protocol_type", "service", "flag", "land"
};
// Encode Target Variables
IDictionary <string, int> targetVarEncoding = new Dictionary <string, int>
{
{ "normal", 0 },
{ "dos", 1 },
{ "probe", 2 },
{ "r2l", 3 },
{ "u2r", 4 }
};
var featuresDF = Frame.CreateEmpty <int, string>();
foreach (string col in rawDF.ColumnKeys)
{
if (col.Equals("attack_type"))
{
continue;
}
else if (col.Equals("attack_category"))
{
featuresDF.AddColumn(
col,
rawDF.GetColumn <string>(col).Select(x => targetVarEncoding[x.Value])
);
}
else if (categoricalVars.Contains(col))
{
var categoryDF = EncodeOneHot(rawDF.GetColumn <string>(col), col);
foreach (string newCol in categoryDF.ColumnKeys)
{
featuresDF.AddColumn(newCol, categoryDF.GetColumn <int>(newCol));
}
}
else
{
featuresDF.AddColumn(
col,
rawDF[col].Select((x, i) => double.IsNaN(x.Value) ? 0.0 : x.Value)
);
}
}
Console.WriteLine("* Shape: {0}, {1}\n\n", featuresDF.RowCount, featuresDF.ColumnCount);
Console.WriteLine("* Exporting feature set...");
featuresDF.SaveCsv(Path.Combine(dataDirPath, "features.csv"));
// Build PCA with only normal data
var rnd = new Random();
int[] normalIdx = featuresDF["attack_category"]
.Where(x => x.Value == 0)
.Keys
.OrderBy(x => rnd.Next())
.Take(90000).ToArray();
int[] attackIdx = featuresDF["attack_category"]
.Where(x => x.Value > 0)
.Keys
.OrderBy(x => rnd.Next())
.Take(10000).ToArray();
int[] totalIdx = normalIdx.Concat(attackIdx).ToArray();
var normalSet = featuresDF.Rows[normalIdx];
string[] nonZeroValueCols = normalSet.ColumnKeys.Where(
x => !x.Equals("attack_category") && normalSet[x].Max() != normalSet[x].Min()
).ToArray();
double[][] normalData = BuildJaggedArray(
normalSet.Columns[nonZeroValueCols].ToArray2D <double>(),
normalSet.RowCount,
nonZeroValueCols.Length
);
double[][] wholeData = BuildJaggedArray(
featuresDF.Rows[totalIdx].Columns[nonZeroValueCols].ToArray2D <double>(),
totalIdx.Length,
nonZeroValueCols.Length
);
int[] labels = featuresDF
.Rows[totalIdx]
.GetColumn <int>("attack_category")
.ValuesAll.ToArray();
var pca = new PrincipalComponentAnalysis(
PrincipalComponentMethod.Standardize
);
pca.Learn(normalData);
double[][] transformed = pca.Transform(wholeData);
double[][] first2Components = transformed.Select(
x => x.Where((y, i) => i < 2).ToArray()
).ToArray();
ScatterplotBox.Show("Component #1 vs. Component #2", first2Components, labels);
double[][] next2Components = transformed.Select(
x => x.Where((y, i) => i < 3 && i >= 1).ToArray()
).ToArray();
ScatterplotBox.Show("Component #2 vs. Component #3", next2Components, labels);
next2Components = transformed.Select(
x => x.Where((y, i) => i < 4 && i >= 2).ToArray()
).ToArray();
ScatterplotBox.Show("Component #3 vs. Component #4", next2Components, labels);
next2Components = transformed.Select(
x => x.Where((y, i) => i < 5 && i >= 3).ToArray()
).ToArray();
ScatterplotBox.Show("Component #4 vs. Component #5", next2Components, labels);
next2Components = transformed.Select(
x => x.Where((y, i) => i < 6 && i >= 4).ToArray()
).ToArray();
ScatterplotBox.Show("Component #5 vs. Component #6", next2Components, labels);
double[] explainedVariance = pca.Components
.Select(x => x.CumulativeProportion)
.Where(x => x < 1)
.ToArray();
DataSeriesBox.Show(
explainedVariance.Select((x, i) => (double)i),
explainedVariance
).SetTitle("Explained Variance");
System.IO.File.WriteAllLines(
Path.Combine(dataDirPath, "explained-variance.csv"),
explainedVariance.Select((x, i) => String.Format("{0},{1:0.0000}", i, x))
);
Console.WriteLine("* Exporting pca-transformed feature set...");
System.IO.File.WriteAllLines(
Path.Combine(
dataDirPath,
"pca-transformed-features.csv"
),
transformed.Select(x => String.Join(",", x))
);
System.IO.File.WriteAllLines(
Path.Combine(
dataDirPath,
"pca-transformed-labels.csv"
),
labels.Select(x => x.ToString())
);
Console.WriteLine("\n\n\n\n\nDONE!!!");
Console.ReadKey();
}
public static Tuple <Dictionary <string, Dictionary <int, List <Room> > >, Dictionary <string, double[, ]> > Factorize(List <Room> rooms, int dimensionReduction)
{
Dictionary <string, double[, ]> matrices = new Dictionary <string, double[, ]>();
int column = 0;
int size = 0;
foreach (var room in rooms)
{
foreach (var layer in room.objects)
{
if (!matrices.ContainsKey(layer.Key))
{
size = layer.Value.GetLength(0) * layer.Value.GetLength(1);
matrices[layer.Key] = new double[rooms.Count, layer.Value.GetLength(0) * layer.Value.GetLength(1)];
}
matrices[layer.Key].FillColumn(column, layer.Value);
}
column++;
}
Dictionary <string, double[, ]> Ws = new Dictionary <string, double[, ]>();
Dictionary <string, double[, ]> Hs = new Dictionary <string, double[, ]>();
Dictionary <string, double[, ]> components = new Dictionary <string, double[, ]>();
foreach (var mat in matrices)
{
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis(mat.Value);
pca.Compute();
for (int ii = 0; ii < dimensionReduction; ii++)
{
pca.ComponentMatrix.rowToMatrix(ii, 12, 10).matToBitmap(0, 0).Save("pca" + mat.Key + ii + ".png");
}
components[mat.Key] = pca.ComponentMatrix;
for (int jj = 0; jj < rooms.Count; jj++)
{
rooms[jj].setCoefficients(mat.Key, pca.Result, jj, dimensionReduction);
}
/*
* NMF nmf = new NMF(mat.Value, dimensionReduction, 2000);
* Ws[mat.Key] = nmf.LeftNonnegativeFactors;
* Hs[mat.Key] = nmf.RightNonnegativeFactors;
* for (int ii = 0; ii < rooms.Count; ii++) {
* rooms[ii].setCoefficients(mat.Key,nmf.RightNonnegativeFactors, ii);
* }
* string str = "";
* for (int xx = 0; xx < nmf.RightNonnegativeFactors.GetLength(1); xx++) {
* for (int jj = 0; jj < nmf.RightNonnegativeFactors.GetLength(0); jj++) {
* str += nmf.RightNonnegativeFactors[jj, xx] + ",";
* }
* str += "\n";
* }
* System.IO.File.WriteAllText(mat.Key + "W.txt", str);
* str = "";
* for (int xx = 0; xx < nmf.LeftNonnegativeFactors.GetLength(1); xx++) {
* for (int jj = 0; jj < nmf.LeftNonnegativeFactors.GetLength(0); jj++) {
* str += nmf.LeftNonnegativeFactors[jj, xx] + ",";
* }
* str += "\n";
* }
* System.IO.File.WriteAllText(mat.Key + "H.txt", str);
* for (int ii = 0; ii < nmf.LeftNonnegativeFactors.GetLength(1); ii++) {
* double[,] W = nmf.LeftNonnegativeFactors.rowToMatrix(ii, 12, 10);
* W.matToBitmap(0, 25).Save(mat.Key + ii + "W.png");
* }
* if (mat.Key == "blocks") {
* double[,] reconstructed = new double[12, 10];
* for (int ii = 0; ii < rooms[0].coefficients["blocks"].Length; ii++) {
* double w = rooms[0].coefficients["blocks"][ii];
* int counter = 0;
* for (int xx = 0; xx < 12; xx++) {
* for (int yy = 0; yy < 10; yy++) {
* reconstructed[xx, yy] += w * Ws["blocks"][counter, ii];
* counter++;
* }
* }
* }
* reconstructed.matToBitmap(0, 1).Save("room0Reconstructed.png");
* }
* */
}
int counter = 0;
// double[,] xy = new double[rooms.Count,2];
double[][] clusterData = new double[rooms.Count][];
foreach (var room in rooms)
{
int compCounter = 0;
double[] coeffs = new double[room.coefficients.Count * dimensionReduction];
foreach (var comp in room.coefficients)
{
foreach (var coef in comp.Value)
{
coeffs[compCounter] = coef;
compCounter++;
}
}
clusterData[counter] = coeffs;
Room reconstructed = room.reconstruct(components, 1);
reconstructed.toBitmap().Save("room" + counter + "Reconstructed.png");
counter++;
}
int numberofClusters = 25;
KMeans kmeans = new KMeans(numberofClusters);
kmeans.Tolerance = 0.5;
int[] clusters = kmeans.Compute(clusterData);
Dictionary <string, SortedSet <int> > clusteredRooms = new Dictionary <string, SortedSet <int> >();
Dictionary <int, SortedSet <string> > roomClusters = new Dictionary <int, SortedSet <string> >();
Dictionary <string, Dictionary <int, List <Room> > > output = new Dictionary <string, Dictionary <int, List <Room> > >();
int[] clusterCounts = new int[numberofClusters];
for (int ii = 0; ii < rooms.Count; ii++)
{
rooms[ii].setType();
if (!clusteredRooms.ContainsKey(rooms[ii].roomType))
{
output[rooms[ii].roomType] = new Dictionary <int, List <Room> >();
clusteredRooms[rooms[ii].roomType] = new SortedSet <int>();
}
if (!output[rooms[ii].roomType].ContainsKey(clusters[ii]))
{
output[rooms[ii].roomType][clusters[ii]] = new List <Room>();
}
if (!roomClusters.ContainsKey(clusters[ii]))
{
roomClusters[clusters[ii]] = new SortedSet <string>();
}
output[rooms[ii].roomType][clusters[ii]].Add(rooms[ii]);
roomClusters[clusters[ii]].Add(rooms[ii].roomType);
clusterCounts[clusters[ii]]++;
clusteredRooms[rooms[ii].roomType].Add(clusters[ii]);
// Console.WriteLine(ii + " " + clusters[ii]);
}
for (int ii = 0; ii < clusterCounts.Length; ii++)
{
string str = "";
foreach (var roomtype in roomClusters[ii])
{
str += roomtype + " ";
}
// Console.WriteLine("Cluster "+ ii + " = " +clusterCounts[ii] + " : " + str);
}
foreach (var roomType in clusteredRooms)
{
string str = "";
foreach (var cluster in roomType.Value)
{
str += cluster + " ";
}
// Console.WriteLine(roomType.Key + " " + str);
}
return(new Tuple <Dictionary <string, Dictionary <int, List <Room> > >, Dictionary <string, double[, ]> >(output, components));
}
public void PrincipalComponentAnalysis()
{
int D = 3;
int N = 10;
// construct a sample
Random rng = new Random(1);
MultivariateSample sample = new MultivariateSample(D);
for (int i = 0; i < N; i++)
{
double x = 1.0 * rng.NextDouble() - 1.0;
double y = 4.0 * rng.NextDouble() - 2.0;
double z = 9.0 * rng.NextDouble() - 3.0;
sample.Add(x, y, z);
}
// get its column means
RowVector mu = new RowVector(D);
for (int i = 0; i < D; i++)
{
mu[i] = sample.Column(i).Mean;
}
// get total variance
double tVariance = GetTotalVariance(sample);
Console.WriteLine(tVariance);
// do a principal component analysis
PrincipalComponentAnalysis pca = sample.PrincipalComponentAnalysis();
Assert.IsTrue(pca.Dimension == sample.Dimension);
Assert.IsTrue(pca.Count == sample.Count);
// check that the PCs behave as expected
for (int i = 0; i < pca.Dimension; i++)
{
PrincipalComponent pc = pca.Component(i);
Assert.IsTrue(pc.Index == i);
Assert.IsTrue(TestUtilities.IsNearlyEqual(pc.Weight * pc.NormalizedVector(), pc.ScaledVector()));
Assert.IsTrue((0.0 <= pc.VarianceFraction) && (pc.VarianceFraction <= 1.0));
if (i == 0)
{
Assert.IsTrue(pc.VarianceFraction == pc.CumulativeVarianceFraction);
}
else
{
PrincipalComponent ppc = pca.Component(i - 1);
Assert.IsTrue(pc.VarianceFraction <= ppc.VarianceFraction);
Assert.IsTrue(TestUtilities.IsNearlyEqual(ppc.CumulativeVarianceFraction + pc.VarianceFraction, pc.CumulativeVarianceFraction));
}
}
// express the sample in terms of principal components
MultivariateSample csample = pca.TransformedSample();
// check that the explained variances are as claimed
for (int rD = 1; rD <= D; rD++)
{
MultivariateSample rSample = new MultivariateSample(D);
foreach (double[] cEntry in csample)
{
RowVector x = mu.Copy();
for (int i = 0; i < rD; i++)
{
PrincipalComponent pc = pca.Component(i);
x += (cEntry[i] * pc.Weight) * pc.NormalizedVector();
}
rSample.Add(x);
}
double rVariance = GetTotalVariance(rSample);
Console.WriteLine("{0} {1}", rD, rVariance);
Assert.IsTrue(TestUtilities.IsNearlyEqual(rVariance / tVariance, pca.Component(rD - 1).CumulativeVarianceFraction));
}
}
static void Main(string[] args)
{
// Specify which files to use.
var projectDir = Directory.GetParent(Directory.GetCurrentDirectory()).Parent.Parent.FullName;
var pathFiles = Directory.EnumerateFiles(projectDir + @"\DocumentClusteringExample\Samples").ToList();
// Hyper parameters.
// This option prevent overfitting on missing words.
var replaceMissingValueWithRandomValue = false;
var usePCA = false;
var numberOfOutputPCA = 100;
var distanceFunction = new PearsonCorrelation();
var strategy = ValueStrategy.Freq;
var minVectorElements = 2;
var freqMin = 2;
var minWordCount = 1;
var maxWordCount = 3;
var minGroupOfWordsLength = 3;
var minWordLength = 1;
var firstWordMinLength = 1;
var lastWordMinLength = 1;
var maxComposition = int.MaxValue;
var badWords = File.ReadLines(projectDir + @"\DocumentClusteringExample\stop-words-english.txt")
.Where(m => !string.IsNullOrWhiteSpace(m))
.ToArray();
var badPatternList = new string[]
{
};
// Files -> List of expressions (Our dictionary based on files)
var expressions = ExtractExpressionFromTextFiles.ExtractExpressions(
pathFiles,
new ExtractExpressionFromTextFilesOption
{
BadPatternList = badPatternList,
BadWords = badWords,
FirstWordMinLength = firstWordMinLength,
LastWordMinLength = lastWordMinLength,
MaxExpressionComposition = maxComposition,
MaxWordCount = maxWordCount,
MinGroupOfWordsLength = minGroupOfWordsLength,
MinWordCount = minWordCount,
MinWordFrequency = freqMin,
MinWordLength = minWordLength
});
Console.WriteLine("Expressions: " + expressions.Count);
// Files -> Vectors
var expressionVectorOption = new TextFileToExpressionVectorOption
{
MinVectorElements = minVectorElements,
BadPatternList = badPatternList,
MaxWordCount = maxWordCount,
MinWordCount = minWordCount,
Strategy = strategy,
ReplaceMissingValueWithRandomValue = replaceMissingValueWithRandomValue
};
List <Tuple <string, double[]> > filesToVector = new List <Tuple <string, double[]> >();
foreach (var pathFile in pathFiles)
{
filesToVector.Add(
new Tuple <string, double[]>(
pathFile,
TextFileToExpressionVector.GenerateExpressionVector(
expressions,
pathFile,
expressionVectorOption)
)
);
}
var vectors = filesToVector
.Select(m => m.Item2)
.ToList();
Console.WriteLine("vectors count: " + vectors.Count);
// Remove non-representative vectors
for (int i = 0; i < vectors.Count; i++)
{
var vector = vectors[i];
if (vector.Sum() < minVectorElements)
{
vectors.RemoveAt(i);
pathFiles.RemoveAt(i);
i--;
}
}
Console.WriteLine("vectors count (after removing non-representative vectors): " + vectors.Count);
// Reduce the vector size with PCA.
if (usePCA)
{
Console.WriteLine("Reducing vector size with PCA");
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
pca.NumberOfOutputs = numberOfOutputPCA;
var trainingVector = vectors.ToArray();
Shuffle(trainingVector);
trainingVector = trainingVector.Take(600).ToArray();
var pcaResult = pca.Learn(trainingVector);
var reducedVectorsWithPCA = pcaResult.Transform(vectors.ToArray());
stopwatch.Stop();
Console.WriteLine("PCA duration: " + stopwatch.Elapsed.ToString());
vectors = reducedVectorsWithPCA.ToList();
}
// Run HDBSCAN algo.
Console.WriteLine("HDBSCAN starting...");
var contraintsList = new List <HdbscanConstraint>();
if (usePCA)
{
for (int i = 1; i < numberOfOutputPCA; i++)
{
contraintsList.Add(new HdbscanConstraint(i - 1, i, HdbscanConstraintType.CannotLink));
}
}
var watch = Stopwatch.StartNew();
var result = HdbscanRunner.Run(new HdbscanParameters
{
DataSet = vectors.ToArray(),
MinPoints = 5,
MinClusterSize = 5,
DistanceFunction = distanceFunction,
Constraints = contraintsList,
UseMultipleThread = true
});
watch.Stop();
Console.WriteLine("HDBSCAN done " + watch.Elapsed);
// Read results.
var labels = result.Labels;
int n = labels.Max();
Console.WriteLine("\n\n");
int clusterId = 0;
for (int iCluster = 1; iCluster <= n; iCluster++)
{
Dictionary <string, int> categories = new Dictionary <string, int>();
bool anyFound = false;
for (int i = 0; i < labels.Length; i++)
{
if (labels[i] == iCluster)
{
var fileName = Path.GetFileNameWithoutExtension(pathFiles[i]);
var category = fileName.Split('-')[0].Trim();
if (categories.ContainsKey(category))
{
var count = categories[category];
categories.Remove(category);
categories.Add(category, count + 1);
}
else
{
categories.Add(category, 1);
}
anyFound = true;
}
}
if (anyFound)
{
clusterId++;
Console.WriteLine("Cluster #" + clusterId);
Console.WriteLine();
foreach (var category in categories)
{
Console.WriteLine(category.Key + ": " + category.Value);
}
Console.ReadLine();
}
}
Console.WriteLine("Press any key to continue...");
Console.ReadLine();
}
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();
}
public void ConstructorTest()
{
// Reproducing Lindsay Smith's "Tutorial on Principal Component Analysis"
// using the framework's default method. The tutorial can be found online
// at http://www.sccg.sk/~haladova/principal_components.pdf
// Step 1. Get some data
// ---------------------
double[,] data =
{
{ 2.5, 2.4 },
{ 0.5, 0.7 },
{ 2.2, 2.9 },
{ 1.9, 2.2 },
{ 3.1, 3.0 },
{ 2.3, 2.7 },
{ 2.0, 1.6 },
{ 1.0, 1.1 },
{ 1.5, 1.6 },
{ 1.1, 0.9 }
};
// Step 2. Subtract the mean
// -------------------------
// Note: The framework does this automatically. By default, the framework
// uses the "Center" method, which only subtracts the mean. However, it is
// also possible to remove the mean *and* divide by the standard deviation
// (thus performing the correlation method) by specifying "Standardize"
// instead of "Center" as the AnalysisMethod.
AnalysisMethod method = AnalysisMethod.Center; // AnalysisMethod.Standardize
// Step 3. Compute the covariance matrix
// -------------------------------------
// Note: Accord.NET does not need to compute the covariance
// matrix in order to compute PCA. The framework uses the SVD
// method which is more numerically stable, but may require
// more processing or memory. In order to replicate the tutorial
// using covariance matrices, please see the next unit test.
// Create the analysis using the selected method
var pca = new PrincipalComponentAnalysis(data, method);
// Compute it
pca.Compute();
// Step 4. Compute the eigenvectors and eigenvalues of the covariance matrix
// -------------------------------------------------------------------------
// Note: Since Accord.NET uses the SVD method rather than the Eigendecomposition
// method, the Eigenvalues are computed from the singular values. However, it is
// not the Eigenvalues themselves which are important, but rather their proportion:
// Those are the expected eigenvalues, in descending order:
double[] eigenvalues = { 1.28402771, 0.0490833989 };
// And this will be their proportion:
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
// Those are the expected eigenvectors,
// in descending order of eigenvalues:
double[,] eigenvectors =
{
{ -0.677873399, -0.735178656 },
{ -0.735178656, 0.677873399 }
};
// Now, here is the place most users get confused. The fact is that
// the Eigenvalue decomposition (EVD) is not unique, and both the SVD
// and EVD routines used by the framework produces results which are
// numerically different from packages such as STATA or MATLAB, but
// those are correct.
// If v is an eigenvector, a multiple of this eigenvector (such as a*v, with
// a being a scalar) will also be an eigenvector. In the Lindsay case, the
// framework produces a first eigenvector with inverted signs. This is the same
// as considering a=-1 and taking a*v. The result is still correct.
// Retrieve the first expected eigenvector
double[] v = eigenvectors.GetColumn(0);
// Multiply by a scalar and store it back
eigenvectors.SetColumn(0, v.Multiply(-1));
// Everything is alright (up to the 9 decimal places shown in the tutorial)
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, threshold: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, threshold: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, threshold: 1e-5));
// Step 5. Deriving the new data set
// ---------------------------------
double[,] actual = pca.Transform(data);
// transformedData shown in pg. 18
double[,] expected = new double[, ]
{
{ 0.827970186, -0.175115307 },
{ -1.77758033, 0.142857227 },
{ 0.992197494, 0.384374989 },
{ 0.274210416, 0.130417207 },
{ 1.67580142, -0.209498461 },
{ 0.912949103, 0.175282444 },
{ -0.099109437, -0.349824698 },
{ -1.14457216, 0.046417258 },
{ -0.438046137, 0.017764629 },
{ -1.22382056, -0.162675287 },
};
// Everything is correct (up to 8 decimal places)
Assert.IsTrue(expected.IsEqual(actual, threshold: 1e-8));
}
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 transform_more_columns_than_samples()
{
// Lindsay's tutorial data
double[,] datat = data.Transpose();
var target = new PrincipalComponentAnalysis(datat);
// Compute
target.Compute();
// Transform
double[,] actual = target.Transform(datat);
// Assert the scores equals the transformation of the input
double[,] result = target.Result;
Assert.IsTrue(Matrix.IsEqual(result, actual, 0.01));
Assert.AreEqual(2, result.Rows());
Assert.AreEqual(2, result.Columns());
Assert.IsTrue(result.IsSquare());
}
public void learn_weights()
{
double[][] raw =
{
new[] { 2.5, 2.4, 1 },
new[] { 0.5, 0.7, 1 },
new[] { 2.2, 2.9, 0.5 },
new[] { 2.2, 2.9, 0.5 },
new[] { 1.9, 2.2, 1 },
new[] { 3.1, 3.0, 1 },
new[] { 2.3, 2.7, 1 },
new[] { 2.0, 1.6, 1 },
new[] { 1.0, 1.1, 0.25 },
new[] { 1.0, 1.1, 0.25 },
new[] { 1.0, 1.1, 0.25 },
new[] { 1.0, 1.1, 0.25 },
new[] { 1.5, 1.6, 1 },
new[] { 42.5, 7.6, 0 },
new[] { 743.5, 5.6, 0 },
new[] { 1.5, 16, 0 },
new[] { 1.1, 0.9, 1 }
};
double[][] data = raw.GetColumns(0, 1);
double[] weights = raw.GetColumn(2);
var method = PrincipalComponentMethod.Center;
var pca = new PrincipalComponentAnalysis(method);
pca.Learn(data, weights);
double[] eigenvalues = { 1.28402771, 0.0490833989 };
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
double[,] eigenvectors =
{
{ -0.677873399, -0.735178656 },
{ -0.735178656, 0.677873399 }
};
double[] v = eigenvectors.GetColumn(0);
eigenvectors.SetColumn(0, v.Multiply(-1));
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, rtol: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, rtol: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, rtol: 0.1));
double[][] actual = pca.Transform(data);
string a = actual.ToCSharp();
/*
* double[,] expected = new double[,]
* {
* { 0.827970186, -0.175115307 },
* { -1.77758033, 0.142857227 },
* { 0.992197494, 0.384374989 },
* { 0.274210416, 0.130417207 },
* { 1.67580142, -0.209498461 },
* { 0.912949103, 0.175282444 },
* { -0.099109437, -0.349824698 },
* { -1.14457216, 0.046417258 },
* { -0.438046137, 0.017764629 },
* { -1.22382056, -0.162675287 },
* };
*/
double[][] expected =
{
new double[] { 0.827970186201088, -0.175115307046916 },
new double[] { -1.77758032528043, 0.142857226544281 },
new double[] { 0.992197494414889, 0.384374988880413 }, // weight is 0.5
new double[] { 0.992197494414889, 0.384374988880413 }, // weight is 0.5
new double[] { 0.2742104159754, 0.130417206574127 },
new double[] { 1.67580141864454, -0.209498461256753 },
new double[] { 0.912949103158809, 0.17528244362037 },
new double[] { -0.0991094374984439, -0.349824698097121 },
new double[] { -1.14457216379866, 0.0464172581832816 }, // weight is 0.25
new double[] { -1.14457216379866, 0.0464172581832816 }, // weight is 0.25
new double[] { -1.14457216379866, 0.0464172581832816 }, // weight is 0.25
new double[] { -1.14457216379866, 0.0464172581832816 }, // weight is 0.25
new double[] { -0.43804613676245, 0.0177646296750834 },
new double[] { 31.7658351361525, -26.0573198564776 }, // weight is 0
new double[] { 505.4847301932, -542.773304190164 }, // weight is 0
new double[] { 10.148526503077, 9.77914156847845 }, // weight is 0
new double[] { -1.22382055505474, -0.162675287076762 }
};
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
}
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 learn_whiten_success()
{
#region doc_learn_1
// Below is the same data used on the excellent paper "Tutorial
// On Principal Component Analysis", by Lindsay Smith (2002).
double[][] data =
{
new double[] { 2.5, 2.4 },
new double[] { 0.5, 0.7 },
new double[] { 2.2, 2.9 },
new double[] { 1.9, 2.2 },
new double[] { 3.1, 3.0 },
new double[] { 2.3, 2.7 },
new double[] { 2.0, 1.6 },
new double[] { 1.0, 1.1 },
new double[] { 1.5, 1.6 },
new double[] { 1.1, 0.9 }
};
// Let's create an analysis with centering (covariance method)
// but no standardization (correlation method) and whitening:
var pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Center,
Whiten = true
};
// Now we can learn the linear projection from the data
MultivariateLinearRegression transform = pca.Learn(data);
// Finally, we can project all the data
double[][] output1 = pca.Transform(data);
// Or just its first components by setting
// NumberOfOutputs to the desired components:
pca.NumberOfOutputs = 1;
// And then calling transform again:
double[][] output2 = pca.Transform(data);
// We can also limit to 80% of explained variance:
pca.ExplainedVariance = 0.8;
// And then call transform again:
double[][] output3 = pca.Transform(data);
#endregion
double[] eigenvalues = { 1.28402771, 0.0490833989 };
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
double[,] eigenvectors =
{
{ 0.19940687993951403, -1.1061252858739095 },
{ 0.21626410214440508, 1.0199057073792104 }
};
// Everything is alright (up to the 9 decimal places shown in the tutorial)
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, rtol: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, rtol: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, rtol: 1e-5));
pca.ExplainedVariance = 1.0;
double[][] actual = pca.Transform(data);
double[][] expected =
{
new double[] { 0.243560157209023, -0.263472650637184 },
new double[] { -0.522902576315494, 0.214938218565977 },
new double[] { 0.291870144299372, 0.578317788814594 },
new double[] { 0.0806632088164338, 0.19622137941132 },
new double[] { 0.492962746459375, -0.315204397734004 },
new double[] { 0.268558011864442, 0.263724118751361 },
new double[] { -0.0291545644762578, -0.526334573603598 },
new double[] { -0.336693495487974, 0.0698378585807067 },
new double[] { -0.128858004446015, 0.0267280693333571 },
new double[] { -0.360005627922904, -0.244755811482527 }
};
// var str = actual.ToString(CSharpJaggedMatrixFormatProvider.InvariantCulture);
// Everything is correct (up to 8 decimal places)
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
Assert.IsTrue(expected.IsEqual(output1, atol: 1e-8));
Assert.IsTrue(expected.Get(null, 0, 1).IsEqual(output2, atol: 1e-8));
Assert.IsTrue(expected.Get(null, 0, 1).IsEqual(output3, atol: 1e-8));
actual = transform.Transform(data);
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
}
public void Revert_new_method()
{
var target = new PrincipalComponentAnalysis();
// Compute
var transform = target.Learn(data.ToJagged());
// Transform
double[][] image = target.Transform(data.ToJagged());
// Reverse
double[][] actual = target.Revert(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actual, data, 0.01));
// Reverse
double[][] actual2 = transform.Inverse().Transform(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actual2, data, 0.01));
Assert.IsTrue(Matrix.IsEqual(actual2, actual, 1e-5));
}
public void learn_standardize()
{
double[][] data =
{
new double[] { 2.5, 2.4 },
new double[] { 0.5, 0.7 },
new double[] { 2.2, 2.9 },
new double[] { 1.9, 2.2 },
new double[] { 3.1, 3.0 },
new double[] { 2.3, 2.7 },
new double[] { 2.0, 1.6 },
new double[] { 1.0, 1.1 },
new double[] { 1.5, 1.6 },
new double[] { 1.1, 0.9 }
};
var pca = new PrincipalComponentAnalysis()
{
Method = PrincipalComponentMethod.Standardize,
Whiten = false
};
MultivariateLinearRegression transform = pca.Learn(data);
double[][] output1 = pca.Transform(data);
double[] eigenvalues = { 1.925929272692245, 0.074070727307754519 };
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
double[,] eigenvectors =
{
{ 0.70710678118654791, -0.70710678118654791 },
{ 0.70710678118654791, 0.70710678118654791 }
};
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, rtol: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, rtol: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, rtol: 1e-5));
pca.ExplainedVariance = 1.0;
double[][] actual = pca.Transform(data);
// var str = actual.ToCSharp();
double[][] expected =
{
new double[] { 1.03068028963519, -0.212053139513466 },
new double[] { -2.19045015647317, 0.168942295968493 },
new double[] { 1.17818776184333, 0.47577321493322 },
new double[] { 0.323294642065681, 0.161198977394117 },
new double[] { 2.07219946786664, -0.251171725759119 },
new double[] { 1.10117414355213, 0.218653302562498 },
new double[] { -0.0878525068874546, -0.430054465638535 },
new double[] { -1.40605089061245, 0.0528100914316325 },
new double[] { -0.538118242086245, 0.0202112695602547 },
new double[] { -1.48306450890365, -0.204309820939091 }
};
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
Assert.IsTrue(expected.IsEqual(output1, atol: 1e-8));
actual = transform.Transform(data);
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-8));
}
/// <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;
}
// Create a matrix from the source data table
double[][] sourceMatrix = (dgvAnalysisSource.DataSource as DataTable).ToArray(out columnNames);
// Create and compute a new Simple Descriptive Analysis
sda = new DescriptiveAnalysis(columnNames).Learn(sourceMatrix);
// Show the descriptive analysis on the screen
dgvDistributionMeasures.DataSource = sda.Measures;
// Populates statistics overview tab with analysis data
dgvStatisticCenter.DataSource = new ArrayDataView(sda.DeviationScores, columnNames);
dgvStatisticStandard.DataSource = new ArrayDataView(sda.StandardScores, columnNames);
dgvStatisticCovariance.DataSource = new ArrayDataView(sda.CovarianceMatrix, columnNames);
dgvStatisticCorrelation.DataSource = new ArrayDataView(sda.CorrelationMatrix, columnNames);
var method = (PrincipalComponentMethod)cbMethod.SelectedValue;
// Create the Principal Component Analysis of the data
pca = new PrincipalComponentAnalysis(method);
pca.Learn(sourceMatrix); // Finally, compute the analysis!
// Populate components overview with analysis data
dgvFeatureVectors.DataSource = new ArrayDataView(pca.ComponentVectors);
dgvPrincipalComponents.DataSource = pca.Components;
dgvProjectionComponents.DataSource = pca.Components;
distributionView.DataSource = pca.Components;
cumulativeView.DataSource = pca.Components;
numComponents.Maximum = pca.Components.Count;
numComponents.Value = 1;
numThreshold.Value = (decimal)pca.Components[0].CumulativeProportion * 100;
}
public void ComputeRanking(List <ICoordinate> points, bool[] pointLabels, List <string> identities = null, PrincipalComponentAnalysis pca = null)
{
var mapping = new Dictionary <ICoordinate, Tuple <int, bool, double, string> >(); //original idx, 1/0 label, distance, string name (for debugging)
ICoordinate remappedCenter;
if (pca != null)
{
var reverted = pca.Revert(new[] { new[] { CenterOfMass.X, CenterOfMass.Y } });
remappedCenter = new Coordinate3D(reverted[0][0], reverted[0][1], reverted[0][2]);
}
else
{
remappedCenter = CenterOfMass;
}
for (var i = 0; i < points.Count; i++)
{
mapping.Add(points[i],
new Tuple <int, bool, double, string>(i, pointLabels[i], points[i].EuclideanDistance(remappedCenter),
identities != null ? identities[i] : ""));
}
var rankedMap = mapping.OrderBy(pt => pt.Value.Item3).ToList();
PointRanks = rankedMap
.Select((pt, idx) => new { id = pt.Value.Item1, rank = idx })
.OrderBy(t => t.id).Select(t => t.rank)
.ToArray();
var namedLabelVector = rankedMap.Select(pt => pt.Value.Item4).ToArray();
InducedLabledVector = rankedMap.Select(pt => pt.Value.Item2).ToArray();
}
public void TransformTest3()
{
PrincipalComponentAnalysis target = new PrincipalComponentAnalysis(data);
// Compute
target.Compute();
bool thrown = false;
try
{
double[,] actual = target.Transform(data, 3);
}
catch { thrown = true; }
Assert.IsTrue(thrown);
}
// testHold.WaitOne(5000);
//IEndPointClient logger;
List<PCAData2D> runPCA(List<NeatGenome> bestGenome, bool firstBehavior = true, int xBins =0 , int yBins =0)
{
var totalStopWatch = System.Diagnostics.Stopwatch.StartNew();
// Create new stopwatch
var stopwatch = System.Diagnostics.Stopwatch.StartNew();
List<long> uIDs = bestGenome.Select(x => x.GenomeId).ToList();
//make sure we have the right fitness!
//TODO: Check multi-objective code to see what value has absolute fitness
List<double> absoluteFitness = bestGenome.Select(x => x.RealFitness).ToList();
if(bestGenome.Count == 0)
return null;
//we know topBody > 0 by above check
int componentCount = Math.Min(80, (firstBehavior ? bestGenome[0].Behavior.behaviorList.Count : bestGenome[0].SecondBehavior.behaviorList.Count));
//double componentCount = (double)fn.Json.Args[1];
//create our double array that's going to be condensed
double[,] collectedData = new double[bestGenome.Count, componentCount];
int xyIndex = 0;
foreach (IGenome genome in bestGenome)
{
//need to grab the behavior objects from the genome, and enter them as data
var behaviorList = (firstBehavior ? genome.Behavior.behaviorList : genome.SecondBehavior.behaviorList);
for (var ix = 0; ix < componentCount; ix++)
{
collectedData[xyIndex, ix] = (double)behaviorList[ix];
}
xyIndex++;
}
try
{
stopwatch.Stop();
Console.WriteLine("Time before kernel: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
//higher gaussian seemed better at spreading out behavior
//might try polynomial of 3rd or 4th degree, constant = 0 by default
// IKernel kernel = new Polynomial(3, 0);//new Gaussian(1.9);//new Polynomial((int)numDegree.Value, (double)numConstant.Value);
// KernelPrincipalComponentAnalysis kpca = new KernelPrincipalComponentAnalysis(collectedData, kernel,
//(PrincipalComponentAnalysis.AnalysisMethod.Correlation));
PrincipalComponentAnalysis kpca = new PrincipalComponentAnalysis(collectedData,
(PrincipalComponentAnalysis.AnalysisMethod.Correlation));
try
{
kpca.Compute();
}
catch (Exception e)
{
Console.WriteLine(e.Message);
return null;
}
stopwatch.Stop();
Console.WriteLine("Time During PCA: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
double[,] transform = kpca.Transform(collectedData, 2);
stopwatch.Stop();
Console.WriteLine("Time During Transform: " + stopwatch.ElapsedMilliseconds);
stopwatch = System.Diagnostics.Stopwatch.StartNew();
List<PCAData2D> uidAndPoints = binAllPoints(transform, uIDs, absoluteFitness, xBins, yBins);
stopwatch.Stop();
Console.WriteLine("Time During Binning: " + stopwatch.ElapsedMilliseconds);
//List<PCAData2D> uidAndPoints = new List<PCAData2D>();
//for (int ix = 0; ix < bestGenome.Count; ix++)
//{
// uidAndPoints.Add(new PCAData2D() { uid = uIDs[ix], x = mappedResults[ix, 0], y = mappedResults[ix, 1] });
//}
totalStopWatch.Stop();
Console.WriteLine("Total Time For PCA: " + totalStopWatch.ElapsedMilliseconds);
return uidAndPoints;
}
catch (Exception e)
{
totalStopWatch.Stop();
Console.WriteLine("Total Time For (failed) PCA: " + totalStopWatch.ElapsedMilliseconds);
Console.WriteLine("Failed to run PCA");
return null;
}
}
public void Revert()
{
PrincipalComponentAnalysis target = new PrincipalComponentAnalysis(data);
// Compute
target.Compute();
// Transform
double[,] image = target.Transform(data);
// Reverse
double[,] actual = target.Revert(image);
// Verify both are equal with 0.01 tolerance value
Assert.IsTrue(Matrix.IsEqual(actual, data, 0.01));
}
private void btnRunAnalysis_Click(object sender, EventArgs e)
{
if (dgvAnalysisSource.DataSource == null)
{
MessageBox.Show("Please load some data first.");
return;
}
// Finishes and save any pending changes to the given data
dgvAnalysisSource.EndEdit();
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvAnalysisSource.DataSource as DataTable).ToMatrix(out sourceColumnNames);
// Creates the Simple Descriptive Analysis of the given source
sda = new DescriptiveAnalysis(sourceMatrix, sourceColumnNames);
sda.Compute();
// Populates statistics overview tab with analysis data
dgvStatisticCenter.DataSource = new ArrayDataView(sda.DeviationScores, sourceColumnNames);
dgvStatisticStandard.DataSource = new ArrayDataView(sda.StandardScores, sourceColumnNames);
dgvStatisticCovariance.DataSource = new ArrayDataView(sda.CovarianceMatrix, sourceColumnNames);
dgvStatisticCorrelation.DataSource = new ArrayDataView(sda.CorrelationMatrix, sourceColumnNames);
dgvDistributionMeasures.DataSource = sda.Measures;
// Creates the Principal Component Analysis of the given source
pca = new PrincipalComponentAnalysis(sda.Source,
(AnalysisMethod)cbMethod.SelectedValue);
// Compute the Principal Component Analysis
pca.Compute();
// Populates components overview with analysis data
dgvFeatureVectors.DataSource = new ArrayDataView(pca.ComponentMatrix);
dgvPrincipalComponents.DataSource = pca.Components;
dgvProjectionComponents.DataSource = pca.Components;
numComponents.Maximum = pca.Components.Count;
numComponents.Value = 1;
numThreshold.Value = (decimal)pca.Components[0].CumulativeProportion * 100;
CreateComponentCumulativeDistributionGraph(graphCurve);
CreateComponentDistributionGraph(graphShare);
}
public void adjustTest()
{
double[,] data = (double[,])PrincipalComponentAnalysisTest.data.Clone();
PrincipalComponentAnalysis target = new PrincipalComponentAnalysis(data, AnalysisMethod.Standardize);
double[,] expected =
{
{ 0.87874523495823, 0.578856809114491 },
{ -1.66834240260186, -1.42942191638476 },
{ 0.496682089324217, 1.16952702249663 },
{ 0.114618943690204, 0.342588723761638 },
{ 1.64287152622626, 1.28766106517305 },
{ 0.624036471202221, 0.933258937143772 },
{ 0.241973325568208, -0.366215532296923 },
{ -1.03157049321184, -0.956885745679056 },
{ -0.394798583821814, -0.366215532296923 },
{ -0.904216111333831, -1.19315383103191 }
};
double[,] actual = target.Adjust(data, false);
Assert.IsTrue(expected.IsEqual(actual, 0.00001));
Assert.AreNotEqual(data, actual);
actual = target.Adjust(data, true);
Assert.IsTrue(expected.IsEqual(actual, 0.00001));
Assert.AreEqual(data, actual);
}
public static Output Whitening(double[,] matrix)
{
if (matrix == null)
{
throw new ArgumentNullException(nameof(matrix));
}
// Step 1: convert matrix to a jagged array
double[][] jaggedArray = matrix.ToJagged();
// Step 2: do PCA whitening
var pca = new PrincipalComponentAnalysis()
{
// the "Center" method only subtracts the mean.
Method = PrincipalComponentMethod.Center,
Whiten = true,
};
pca.Learn(jaggedArray);
pca.Transform(jaggedArray);
pca.ExplainedVariance = 0.95;
double[][] transformedData = pca.Transform(jaggedArray);
double[,] projectedData = transformedData.ToMatrix();
double[,] eigenVectors = pca.ComponentVectors.ToMatrix();
int components = pca.Components.Count;
// double[] eigneValues = pca.Eigenvalues; //sorted
// int rows = projectedData.GetLength(0);
int columns = projectedData.GetLength(1); //this is actually the number of output vectors before reversion
// Step 3: revert a set of projected data into its original space
// the output of the "Revert(Double[][])" method in Accord did not make sense.
// however, we use its API to do so.
double[,] reversion = Revert(projectedData, eigenVectors, components);
// Build Projection Matrix
// To do so, we need eigenVectors, and the number of columns of the projected data
double[,] projectionMatrix = GetProjectionMatrix(eigenVectors, columns);
// write the projection matrix to disk
/*
* // FIRST STEP: sort the eigenvectors based on the eigenvalue
* var eigPairs = new List<Tuple<double, double[]>>();
*
* for (int i = 0; i < eigneValues.GetLength(0); i++)
* {
* eigPairs.Add(Tuple.Create(Math.Abs(eigneValues[i]), GetColumn(eigenVectors, i)));
* }
*
* // sort in descending order based on the eigenvalues
* eigPairs.Sort((x, y) => y.Item1.CompareTo(x.Item1));
*/
var output = new Output()
{
ProjectionMatrix = projectionMatrix,
Reversion = reversion,
EigenVectors = eigenVectors,
Components = components,
};
return(output);
}
public void ConstructorTest()
{
// Reproducing Lindsay Smith's "Tutorial on Principal Component Analysis"
// using the framework's default method. The tutorial can be found online
// at http://www.sccg.sk/~haladova/principal_components.pdf
// Step 1. Get some data
// ---------------------
double[,] data =
{
{ 2.5, 2.4 },
{ 0.5, 0.7 },
{ 2.2, 2.9 },
{ 1.9, 2.2 },
{ 3.1, 3.0 },
{ 2.3, 2.7 },
{ 2.0, 1.6 },
{ 1.0, 1.1 },
{ 1.5, 1.6 },
{ 1.1, 0.9 }
};
// Step 2. Subtract the mean
// -------------------------
// Note: The framework does this automatically. By default, the framework
// uses the "Center" method, which only subtracts the mean. However, it is
// also possible to remove the mean *and* divide by the standard deviation
// (thus performing the correlation method) by specifying "Standardize"
// instead of "Center" as the AnalysisMethod.
AnalysisMethod method = AnalysisMethod.Center; // AnalysisMethod.Standardize
// Step 3. Compute the covariance matrix
// -------------------------------------
// Note: Accord.NET does not need to compute the covariance
// matrix in order to compute PCA. The framework uses the SVD
// method which is more numerically stable, but may require
// more processing or memory. In order to replicate the tutorial
// using covariance matrices, please see the next unit test.
// Create the analysis using the selected method
var pca = new PrincipalComponentAnalysis(data, method);
// Compute it
pca.Compute();
// Step 4. Compute the eigenvectors and eigenvalues of the covariance matrix
// -------------------------------------------------------------------------
// Note: Since Accord.NET uses the SVD method rather than the Eigendecomposition
// method, the Eigenvalues are computed from the singular values. However, it is
// not the Eigenvalues themselves which are important, but rather their proportion:
// Those are the expected eigenvalues, in descending order:
double[] eigenvalues = { 1.28402771, 0.0490833989 };
// And this will be their proportion:
double[] proportion = eigenvalues.Divide(eigenvalues.Sum());
// Those are the expected eigenvectors,
// in descending order of eigenvalues:
double[,] eigenvectors =
{
{ -0.677873399, -0.735178656 },
{ -0.735178656, 0.677873399 }
};
// Now, here is the place most users get confused. The fact is that
// the Eigenvalue decomposition (EVD) is not unique, and both the SVD
// and EVD routines used by the framework produces results which are
// numerically different from packages such as STATA or MATLAB, but
// those are correct.
// If v is an eigenvector, a multiple of this eigenvector (such as a*v, with
// a being a scalar) will also be an eigenvector. In the Lindsay case, the
// framework produces a first eigenvector with inverted signs. This is the same
// as considering a=-1 and taking a*v. The result is still correct.
// Retrieve the first expected eigenvector
double[] v = eigenvectors.GetColumn(0);
// Multiply by a scalar and store it back
eigenvectors.SetColumn(0, v.Multiply(-1));
// Everything is alright (up to the 9 decimal places shown in the tutorial)
Assert.IsTrue(eigenvectors.IsEqual(pca.ComponentMatrix, threshold: 1e-9));
Assert.IsTrue(proportion.IsEqual(pca.ComponentProportions, threshold: 1e-9));
Assert.IsTrue(eigenvalues.IsEqual(pca.Eigenvalues, threshold: 1e-5));
// Step 5. Deriving the new data set
// ---------------------------------
double[,] actual = pca.Transform(data);
// transformedData shown in pg. 18
double[,] expected = new double[,]
{
{ 0.827970186, -0.175115307 },
{ -1.77758033, 0.142857227 },
{ 0.992197494, 0.384374989 },
{ 0.274210416, 0.130417207 },
{ 1.67580142, -0.209498461 },
{ 0.912949103, 0.175282444 },
{ -0.099109437, -0.349824698 },
{ -1.14457216, 0.046417258 },
{ -0.438046137, 0.017764629 },
{ -1.22382056, -0.162675287 },
};
// Everything is correct (up to 8 decimal places)
Assert.IsTrue(expected.IsEqual(actual, threshold: 1e-8));
}
private double[][] getProjectedSequence(double[][] sequence, PrincipalComponentAnalysis pca)
{
if (pca == null)
return sequence;
int numComponents = pca.GetNumberOfComponents(1.0f);
double[,] data = jaggedToMulti(sequence);
string fn = System.IO.Path.GetRandomFileName();
using (StreamWriter sr = new StreamWriter("Z:/WindowsFolders/Desktop/" + fn))
{
for (int i = 0; i < data.GetLength(0); i++)
{
for (int j = 0; j < data.GetLength(1); j++)
{
sr.Write(data[i, j] + " ");
}
sr.WriteLine();
}
}
double[,] projectedData = pca.Transform(data, numComponents);
double[][] projTrainSeq = multiToJagged(projectedData);
return projTrainSeq;
}
