public void RevertTest3()
{
string path = @"Resources\examples.xls";
// Create a new reader, opening a given path
ExcelReader reader = new ExcelReader(path);
// Afterwards, we can query the file for all
// worksheets within the specified workbook:
string[] sheets = reader.GetWorksheetList();
// Finally, we can request an specific sheet:
DataTable table = reader.GetWorksheet("Wikipedia");
// Now, we have loaded the Excel file into a DataTable. We
// can go further and transform it into a matrix to start
// running other algorithms on it:
double[,] matrix = table.ToMatrix();
IKernel kernel = new Polynomial(2);
// Create analysis
KernelPrincipalComponentAnalysis target = new KernelPrincipalComponentAnalysis(matrix,
kernel, AnalysisMethod.Center, centerInFeatureSpace: true);
target.Compute();
double[,] forward = target.Result;
double[,] reversion = target.Revert(forward);
Assert.IsTrue(!reversion.HasNaN());
}
private void btnReversion_Click(object sender, EventArgs e)
{
double[][] reversionSource = (double[][])(dgvReversionSource.DataSource as ArrayDataView).ArrayData;
double[][] reversionResult = kpca.Revert(reversionSource, (int)numNeighbor.Value);
dgvReversionResult.DataSource = new ArrayDataView(reversionResult);
// Creates a matrix from the source data table
double[][] sourceMatrix = (dgvProjectionSource.DataSource as DataTable).ToJagged();
// Create a new plot with the original Z column
double[][] graph = reversionResult.InsertColumn(sourceMatrix.GetColumn(2));
reversionScatterplot.DataSource = graph;
}
public void RevertTest()
{
// Using a linear kernel should be equivalent to standard PCA
IKernel kernel = new Linear();
// Create analysis
KernelPrincipalComponentAnalysis target = new KernelPrincipalComponentAnalysis(data, kernel, AnalysisMethod.Center);
// Compute
target.Compute();
// Compute image
double[,] image = target.Transform(data, 2);
// Compute pre-image
double[,] preimage = target.Revert(image);
// Check if pre-image equals the original data
Assert.IsTrue(Matrix.IsEqual(data, preimage, 0.0001));
}
private void btnReversion_Click(object sender, EventArgs e)
{
double[,] reversionSource = (double[, ])(dgvReversionSource.DataSource as ArrayDataView).ArrayData;
double[,] m = kpca.Revert(reversionSource, (int)numNeighbor.Value);
dgvReversionResult.DataSource = new ArrayDataView(m);
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvProjectionSource.DataSource as DataTable).ToMatrix();
// Create a new plot with the original Z column
double[,] graph = new double[sourceMatrix.GetLength(0), 3];
for (int i = 0; i < graph.GetLength(0); i++)
{
graph[i, 0] = m[i, 0];
graph[i, 1] = m[i, 1];
graph[i, 2] = sourceMatrix[i, 2];
}
reversionScatterplot.DataSource = graph;
}
public void RevertTest2_new_method()
{
string path = @"Resources\examples.xls";
// Create a new reader, opening a given path
ExcelReader reader = new ExcelReader(path);
// Afterwards, we can query the file for all
// worksheets within the specified workbook:
string[] sheets = reader.GetWorksheetList();
// Finally, we can request an specific sheet:
DataTable table = reader.GetWorksheet("Wikipedia");
// Now, we have loaded the Excel file into a DataTable. We
// can go further and transform it into a matrix to start
// running other algorithms on it:
double[][] matrix = table.ToArray();
IKernel kernel = new Gaussian(5);
// Create analysis
var target = new KernelPrincipalComponentAnalysis(kernel)
{
Method = PrincipalComponentMethod.Center,
Center = true // Center in feature space
};
var regression = target.Learn(matrix);
double[][] forward = regression.Transform(matrix);
double[][] reversion = target.Revert(forward);
Assert.IsTrue(!reversion.HasNaN());
}
