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)); }
/// <summary> /// 学習ボタンクリック /// </summary> /// <param name="sender"></param> /// <param name="e"></param> private void btnLearn_Click(object sender, EventArgs e) { int cntRows = dgvHistory.Rows.Count; double[][] input = Jagged.Zeros(cntRows, 32 * 32); int[] output = new int[cntRows]; string tmpCharDigit; // グリッドのデータを1行ずつ学習データとして格納 for (int i = 0; i < cntRows; i++) { input.SetRow(i, (double[])dgvHistory.Rows[i].Cells["features"].Value); tmpCharDigit = dgvHistory.Rows[i].Cells["answer"].Value.ToString(); output[i] = int.Parse(tmpCharDigit); } IKernel kernel; kernel = new Polynomial(2, 0.0000); kda = new KernelDiscriminantAnalysis(kernel) { Threshold = 0.0005, Regularization = 0.0001 }; Application.DoEvents(); kda.Learn(input, output); btnQuestion.Enabled = true; }
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 SerializeTest() { double[][] actual, expected = new double[][] { new double[] { -109.160894622401, -127.729010764102 }, new double[] { -109.194678442625, -114.24653758324 }, new double[] { -109.238116380388, -112.905892408598 }, new double[] { -109.209886124532, -132.26101651421 }, new double[] { -109.174352521775, -143.574080034334 }, new double[] { -109.204229997471, -972.320404618979 }, new double[] { 291.003271433059, 81.2380025750026 }, new double[] { 290.982068268582, -259.413571936544 }, new double[] { 290.973346814048, -161.838508509099 }, new double[] { 290.998656827956, -728.677216732875 } }; int[] output = { 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 }; var target = new KernelDiscriminantAnalysis() { Kernel = new Polynomial(4) }; double[][] inputs = LinearDiscriminantAnalysisTest.inputs.ToJagged(); int[] outputs = LinearDiscriminantAnalysisTest.output; target.Learn(inputs, output); actual = target.Transform(inputs); var str = actual.ToCSharp(); Assert.IsTrue(Matrix.IsEqual(actual, expected, 0.01)); var copy = Serializer.DeepClone(target); actual = copy.Transform(inputs); Assert.IsTrue(Matrix.IsEqual(actual, expected, 0.01)); Assert.IsTrue(target.Kernel.Equals(copy.Kernel)); Assert.IsTrue(target.ScatterBetweenClass.IsEqual(copy.ScatterBetweenClass)); Assert.IsTrue(target.ScatterMatrix.IsEqual(copy.ScatterMatrix)); Assert.IsTrue(target.ScatterWithinClass.IsEqual(copy.ScatterWithinClass)); Assert.IsTrue(target.StandardDeviations.IsEqual(copy.StandardDeviations)); Assert.IsTrue(target.Classifications.IsEqual(copy.Classifications)); Assert.IsTrue(target.Classifier.NumberOfInputs.IsEqual(copy.Classifier.NumberOfInputs)); Assert.IsTrue(target.Classifier.NumberOfOutputs.IsEqual(copy.Classifier.NumberOfOutputs)); Assert.IsTrue(target.Classifier.First.Weights.IsEqual(copy.Classifier.First.Weights)); Assert.IsTrue(target.Classifier.Second.Function.Equals(copy.Classifier.Second.Function)); Assert.IsTrue(target.Classifier.Second.Means.IsEqual(copy.Classifier.Second.Means)); Assert.IsTrue(target.NumberOfClasses.IsEqual(copy.NumberOfClasses)); Assert.IsTrue(target.NumberOfInputs.Equals(copy.NumberOfInputs)); Assert.IsTrue(target.NumberOfOutputs.Equals(copy.NumberOfOutputs)); }
public void learn_test() { #region doc_learn // 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 }; // We'll create a KDA using a Linear kernel var kda = new KernelDiscriminantAnalysis() { Kernel = new Linear() // We can choose any kernel function }; // Compute the analysis and create a classifier var classifier = kda.Learn(inputs, output); // Now we can project the data into KDA space: double[][] projection = kda.Transform(inputs); // Or perform classification using: int[] results = kda.Classify(inputs); #endregion double[][] classifierProjection = kda.Classifier.First.Transform(inputs); Assert.IsTrue(projection.IsEqual(classifierProjection)); 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.Get(null, 0, 2).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); } }
/// <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).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; // Create the kernel function IKernel kernel = createKernel(); // Get the input values (the two first columns) this.inputs = sourceMatrix.GetColumns(0, 1); // Get only the associated labels (last column) this.outputs = sourceMatrix.GetColumn(2).ToMulticlass(); // Creates the Kernel Discriminant Analysis of the given data kda = new KernelDiscriminantAnalysis(kernel) { // Keep only the important components Threshold = (double)numThreshold.Value, NumberOfOutputs = 2 // use two components }; // Use the analysis to create a classifier var classifier = kda.Learn(inputs, outputs); if (kda.Discriminants.Count < 2) { MessageBox.Show("Could not gather enough components to create" + " a 2D plot. Please try a smaller threshold value."); return; } // Perform the transformation of the data double[][] result = kda.Transform(inputs); // 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.DiscriminantVectors.Transpose()); 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."; }
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; }