public void learn_test() { #region doc_learn Accord.Math.Random.Generator.Seed = 0; // Example regression problem. Suppose we are trying // to model the following equation: f(x, y) = 2x + y double[][] inputs = // (x, y) { new double[] { 0, 1 }, // 2*0 + 1 = 1 new double[] { 4, 3 }, // 2*4 + 3 = 11 new double[] { 8, -8 }, // 2*8 - 8 = 8 new double[] { 2, 2 }, // 2*2 + 2 = 6 new double[] { 6, 1 }, // 2*6 + 1 = 13 new double[] { 5, 4 }, // 2*5 + 4 = 14 new double[] { 9, 1 }, // 2*9 + 1 = 19 new double[] { 1, 6 }, // 2*1 + 6 = 8 }; double[] outputs = // f(x, y) { 1, 11, 8, 6, 13, 14, 19, 8 }; // Create the sequential minimal optimization teacher var learn = new SequentialMinimalOptimizationRegression<Polynomial>() { Kernel = new Polynomial(2), // Polynomial Kernel of 2nd degree Complexity = 100 }; // Run the learning algorithm SupportVectorMachine<Polynomial> svm = learn.Learn(inputs, outputs); // Compute the predicted scores double[] predicted = svm.Score(inputs); // Compute the error between the expected and predicted double error = new SquareLoss(outputs).Loss(predicted); // Compute the answer for one particular example double fxy = svm.Score(inputs[0]); // 1.0003849827673186 #endregion Assert.AreEqual(1.0, fxy, 1e-2); for (int i = 0; i < outputs.Length; i++) Assert.AreEqual(outputs[i], predicted[i], 1e-2); }
private void btnCreate_Click(object sender, EventArgs e) { if (dgvLearningSource.DataSource == null) { MessageBox.Show("Please load some data first."); return; } // Finishes and save any pending changes to the given data dgvLearningSource.EndEdit(); // Creates a matrix from the entire source data table double[,] table = (dgvLearningSource.DataSource as DataTable).ToMatrix(out columnNames); // Get only the input vector values (first two columns) double[][] inputs = table.GetColumns(0).ToArray(); // Get only the outputs (last column) double[] outputs = table.GetColumn(1); // Create the specified Kernel IKernel kernel = createKernel(); // Creates a new SMO for regression learning algorithm var smo = new SequentialMinimalOptimizationRegression() { // Set learning parameters Complexity = (double)numC.Value, Tolerance = (double)numT.Value, Epsilon = (double)numEpsilon.Value }; try { // Run svm = smo.Learn(inputs, outputs); lbStatus.Text = "Training complete!"; } catch (ConvergenceException) { lbStatus.Text = "Convergence could not be attained. " + "The learned machine might still be usable."; } // Check if we got support vectors if (svm.SupportVectors.Length == 0) { dgvSupportVectors.DataSource = null; graphSupportVectors.GraphPane.CurveList.Clear(); return; } // Show support vectors on the Support Vectors tab page double[][] supportVectorsWeights = svm.SupportVectors.InsertColumn(svm.Weights); string[] supportVectorNames = columnNames.RemoveAt(columnNames.Length - 1).Concatenate("Weight"); dgvSupportVectors.DataSource = new ArrayDataView(supportVectorsWeights, supportVectorNames); // Show the support vector labels on the scatter plot double[] supportVectorLabels = new double[svm.SupportVectors.Length]; for (int i = 0; i < supportVectorLabels.Length; i++) { int j = inputs.Find(sv => sv == svm.SupportVectors[i])[0]; supportVectorLabels[i] = outputs[j]; } double[][] graph = svm.SupportVectors.InsertColumn(supportVectorLabels); CreateScatterplot(graphSupportVectors, graph.ToMatrix()); // Get the ranges for each variable (X and Y) DoubleRange range = table.GetColumn(0).GetRange(); double[][] map = Vector.Interval(range, 0.05).ToArray(); // Classify each point in the Cartesian coordinate system double[] result = map.Apply(svm.Compute); double[,] surface = map.ToMatrix().InsertColumn(result); CreateScatterplot(zedGraphControl2, surface); }