using the minimum CV error criterion. Then, the fitted model using the "best" settings is used to classify the entire dataset. The classification errors are shown.This example uses stratified cross-validation to select parameter settings for C
and using the minimum CV error criterion. Then, the fitted model using the "best" settings is used to classify the entire dataset. The classification errors are shown.
import com.imsl.datamining.*;
import com.imsl.datamining.supportvectormachine.*;
import com.imsl.datamining.neural.*;
import com.imsl.stat.*;
public class SupportVectorMachineEx2 {
public static void main(String[] args) throws Exception {
SVClassification.VariableType[] irisVarType = {
SVClassification.VariableType.CATEGORICAL,
SVClassification.VariableType.QUANTITATIVE_CONTINUOUS,
SVClassification.VariableType.QUANTITATIVE_CONTINUOUS,
SVClassification.VariableType.QUANTITATIVE_CONTINUOUS,
SVClassification.VariableType.QUANTITATIVE_CONTINUOUS
};
String dashes
= "--------------------------------------------------------------";
double[][] irisFisherData = {
{1.0, 5.1, 3.5, 1.4, .2}, {1.0, 4.9, 3.0, 1.4, .2},
{1.0, 4.7, 3.2, 1.3, .2}, {1.0, 4.6, 3.1, 1.5, .2},
{1.0, 5.0, 3.6, 1.4, .2}, {1.0, 5.4, 3.9, 1.7, .4},
{1.0, 4.6, 3.4, 1.4, .3}, {1.0, 5.0, 3.4, 1.5, .2},
{1.0, 4.4, 2.9, 1.4, .2}, {1.0, 4.9, 3.1, 1.5, .1},
{1.0, 5.4, 3.7, 1.5, .2}, {1.0, 4.8, 3.4, 1.6, .2},
{1.0, 4.8, 3.0, 1.4, .1}, {1.0, 4.3, 3.0, 1.1, .1},
{1.0, 5.8, 4.0, 1.2, .2}, {1.0, 5.7, 4.4, 1.5, .4},
{1.0, 5.4, 3.9, 1.3, .4}, {1.0, 5.1, 3.5, 1.4, .3},
{1.0, 5.7, 3.8, 1.7, .3}, {1.0, 5.1, 3.8, 1.5, .3},
{1.0, 5.4, 3.4, 1.7, .2}, {1.0, 5.1, 3.7, 1.5, .4},
{1.0, 4.6, 3.6, 1.0, .2}, {1.0, 5.1, 3.3, 1.7, .5},
{1.0, 4.8, 3.4, 1.9, .2}, {1.0, 5.0, 3.0, 1.6, .2},
{1.0, 5.0, 3.4, 1.6, .4}, {1.0, 5.2, 3.5, 1.5, .2},
{1.0, 5.2, 3.4, 1.4, .2}, {1.0, 4.7, 3.2, 1.6, .2},
{1.0, 4.8, 3.1, 1.6, .2}, {1.0, 5.4, 3.4, 1.5, .4},
{1.0, 5.2, 4.1, 1.5, .1}, {1.0, 5.5, 4.2, 1.4, .2},
{1.0, 4.9, 3.1, 1.5, .1}, {1.0, 5.0, 3.2, 1.2, .2},
{1.0, 5.5, 3.5, 1.3, .2}, {1.0, 4.9, 3.1, 1.5, .1},
{1.0, 4.4, 3.0, 1.3, .2}, {1.0, 5.1, 3.4, 1.5, .2},
{1.0, 5.0, 3.5, 1.3, .3}, {1.0, 4.5, 2.3, 1.3, .3},
{1.0, 4.4, 3.2, 1.3, .2}, {1.0, 5.0, 3.5, 1.6, .6},
{1.0, 5.1, 3.8, 1.9, .4}, {1.0, 4.8, 3.0, 1.4, .3},
{1.0, 5.1, 3.8, 1.6, .2}, {1.0, 4.6, 3.2, 1.4, .2},
{1.0, 5.3, 3.7, 1.5, .2}, {1.0, 5.0, 3.3, 1.4, .2},
{2.0, 7.0, 3.2, 4.7, 1.4}, {2.0, 6.4, 3.2, 4.5, 1.5},
{2.0, 6.9, 3.1, 4.9, 1.5}, {2.0, 5.5, 2.3, 4.0, 1.3},
{2.0, 6.5, 2.8, 4.6, 1.5}, {2.0, 5.7, 2.8, 4.5, 1.3},
{2.0, 6.3, 3.3, 4.7, 1.6}, {2.0, 4.9, 2.4, 3.3, 1.0},
{2.0, 6.6, 2.9, 4.6, 1.3}, {2.0, 5.2, 2.7, 3.9, 1.4},
{2.0, 5.0, 2.0, 3.5, 1.0}, {2.0, 5.9, 3.0, 4.2, 1.5},
{2.0, 6.0, 2.2, 4.0, 1.0}, {2.0, 6.1, 2.9, 4.7, 1.4},
{2.0, 5.6, 2.9, 3.6, 1.3}, {2.0, 6.7, 3.1, 4.4, 1.4},
{2.0, 5.6, 3.0, 4.5, 1.5}, {2.0, 5.8, 2.7, 4.1, 1.0},
{2.0, 6.2, 2.2, 4.5, 1.5}, {2.0, 5.6, 2.5, 3.9, 1.1},
{2.0, 5.9, 3.2, 4.8, 1.8}, {2.0, 6.1, 2.8, 4.0, 1.3},
{2.0, 6.3, 2.5, 4.9, 1.5}, {2.0, 6.1, 2.8, 4.7, 1.2},
{2.0, 6.4, 2.9, 4.3, 1.3}, {2.0, 6.6, 3.0, 4.4, 1.4},
{2.0, 6.8, 2.8, 4.8, 1.4}, {2.0, 6.7, 3.0, 5.0, 1.7},
{2.0, 6.0, 2.9, 4.5, 1.5}, {2.0, 5.7, 2.6, 3.5, 1.0},
{2.0, 5.5, 2.4, 3.8, 1.1}, {2.0, 5.5, 2.4, 3.7, 1.0},
{2.0, 5.8, 2.7, 3.9, 1.2}, {2.0, 6.0, 2.7, 5.1, 1.6},
{2.0, 5.4, 3.0, 4.5, 1.5}, {2.0, 6.0, 3.4, 4.5, 1.6},
{2.0, 6.7, 3.1, 4.7, 1.5}, {2.0, 6.3, 2.3, 4.4, 1.3},
{2.0, 5.6, 3.0, 4.1, 1.3}, {2.0, 5.5, 2.5, 4.0, 1.3},
{2.0, 5.5, 2.6, 4.4, 1.2}, {2.0, 6.1, 3.0, 4.6, 1.4},
{2.0, 5.8, 2.6, 4.0, 1.2}, {2.0, 5.0, 2.3, 3.3, 1.0},
{2.0, 5.6, 2.7, 4.2, 1.3}, {2.0, 5.7, 3.0, 4.2, 1.2},
{2.0, 5.7, 2.9, 4.2, 1.3}, {2.0, 6.2, 2.9, 4.3, 1.3},
{2.0, 5.1, 2.5, 3.0, 1.1}, {2.0, 5.7, 2.8, 4.1, 1.3},
{3.0, 6.3, 3.3, 6.0, 2.5}, {3.0, 5.8, 2.7, 5.1, 1.9},
{3.0, 7.1, 3.0, 5.9, 2.1}, {3.0, 6.3, 2.9, 5.6, 1.8},
{3.0, 6.5, 3.0, 5.8, 2.2}, {3.0, 7.6, 3.0, 6.6, 2.1},
{3.0, 4.9, 2.5, 4.5, 1.7}, {3.0, 7.3, 2.9, 6.3, 1.8},
{3.0, 6.7, 2.5, 5.8, 1.8}, {3.0, 7.2, 3.6, 6.1, 2.5},
{3.0, 6.5, 3.2, 5.1, 2.0}, {3.0, 6.4, 2.7, 5.3, 1.9},
{3.0, 6.8, 3.0, 5.5, 2.1}, {3.0, 5.7, 2.5, 5.0, 2.0},
{3.0, 5.8, 2.8, 5.1, 2.4}, {3.0, 6.4, 3.2, 5.3, 2.3},
{3.0, 6.5, 3.0, 5.5, 1.8}, {3.0, 7.7, 3.8, 6.7, 2.2},
{3.0, 7.7, 2.6, 6.9, 2.3}, {3.0, 6.0, 2.2, 5.0, 1.5},
{3.0, 6.9, 3.2, 5.7, 2.3}, {3.0, 5.6, 2.8, 4.9, 2.0},
{3.0, 7.7, 2.8, 6.7, 2.0}, {3.0, 6.3, 2.7, 4.9, 1.8},
{3.0, 6.7, 3.3, 5.7, 2.1}, {3.0, 7.2, 3.2, 6.0, 1.8},
{3.0, 6.2, 2.8, 4.8, 1.8}, {3.0, 6.1, 3.0, 4.9, 1.8},
{3.0, 6.4, 2.8, 5.6, 2.1}, {3.0, 7.2, 3.0, 5.8, 1.6},
{3.0, 7.4, 2.8, 6.1, 1.9}, {3.0, 7.9, 3.8, 6.4, 2.0},
{3.0, 6.4, 2.8, 5.6, 2.2}, {3.0, 6.3, 2.8, 5.1, 1.5},
{3.0, 6.1, 2.6, 5.6, 1.4}, {3.0, 7.7, 3.0, 6.1, 2.3},
{3.0, 6.3, 3.4, 5.6, 2.4}, {3.0, 6.4, 3.1, 5.5, 1.8},
{3.0, 6.0, 3.0, 4.8, 1.8}, {3.0, 6.9, 3.1, 5.4, 2.1},
{3.0, 6.7, 3.1, 5.6, 2.4}, {3.0, 6.9, 3.1, 5.1, 2.3},
{3.0, 5.8, 2.7, 5.1, 1.9}, {3.0, 6.8, 3.2, 5.9, 2.3},
{3.0, 6.7, 3.3, 5.7, 2.5}, {3.0, 6.7, 3.0, 5.2, 2.3},
{3.0, 6.3, 2.5, 5.0, 1.9}, {3.0, 6.5, 3.0, 5.2, 2.0},
{3.0, 6.2, 3.4, 5.4, 2.3}, {3.0, 5.9, 3.0, 5.1, 1.8}
};
// Create a scaled version of the Iris attribute data.
double[][] x = new double[150][4];
double[][] xx = new double[150][4];
// Get the data.
for (int i = 0; i < 150; i++) {
for (int j = 0; j < 4; j++) {
x[i][j] = irisFisherData[i][j + 1];
}
}
// Scale the data.
double realMin = 0.0, realMax = 10.0, targetMin = 0.0, targetMax = 1.0;
ScaleFilter scaleFilter = new ScaleFilter(ScaleFilter.BOUNDED_SCALING);
scaleFilter.setBounds(realMin, realMax, targetMin, targetMax);
for (int i = 0; i < 150; i++) {
xx[i] = scaleFilter.encode(x[i]);
}
// Build a training data set.
int nTrain = 30;
double[][] xy = new double[nTrain][5];
int ii = 0;
// The response variable (Iris Species) is encoded starting in "1".
// Here, subtract 1 from the response because the class assumes
// 0 based categorical response variable.
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 10; j++) {
xy[ii][0] = irisFisherData[(i * 50) + j][0] - 1;
System.arraycopy(xx[(i * 50 + j)], 0, xy[ii], 1, 4);
ii++;
}
}
// Construct a Support Vector Machine.
SVClassification svm = new SVClassification(xy, 0, irisVarType);
double[] gamma = {0.1};
double C = 2.0;
double result;
double minResult = 10000.0;
double bestGamma = 0.0;
double bestC = 0.0;
CrossValidation svmCV = new CrossValidation(svm);
svmCV.setNumberOfSampleFolds(5);
svmCV.setRandomObject(new Random(123457));
svmCV.setStratifiedCrossValidation(true);
for (int i = 0; i < 10; i++) {
for (int j = 0; j < 5; j++) {
svm.setRegularizationParameter(C);
svm.setKernelParameters(gamma);
svmCV.crossValidate();
result = svmCV.getCrossValidatedError();
if (result < minResult) {
minResult = result;
bestGamma = gamma[0];
bestC = C;
}
gamma[0] = gamma[0] * 2.0;
}
gamma[0] = 0.1;
C = C * 2.0;
}
System.out.printf("Best C: %5.0f \n", bestC);
System.out.printf("Best gamma: %5.3f \n", bestGamma);
System.out.printf("Minimum CV error: %5.3f \n", minResult);
svm.setRegularizationParameter(bestC);
gamma[0] = bestGamma;
svm.setKernelParameters(gamma);
// Train the model on the training sample (30 observations).
svm.fitModel();
// Classify the entire data set with the fitted model
// using the "best" C and gamma parameter values.
xy = new double[150][5];
double[] knownClass = new double[150];
for (int i = 0; i < 150; i++) {
xy[i][0] = irisFisherData[i][0] - 1;
knownClass[i] = xy[i][0];
System.arraycopy(xx[i], 0, xy[i], 1, 4);
}
double[] predictedClass = svm.predict(xy);
int[][] classErrors = svm.getClassErrors(knownClass, predictedClass);
System.out.println("\n Iris Classification Error Rates");
System.out.println("\n" + dashes);
System.out.println(" Setosa Versicolour Virginica | TOTAL");
System.out.println(" " + classErrors[0][0] + "/" + classErrors[0][1]
+ " " + classErrors[1][0] + "/" + classErrors[1][1]
+ " " + classErrors[2][0] + "/" + classErrors[2][1]
+ " " + classErrors[3][0] + "/" + classErrors[3][1]);
System.out.println(dashes);
}
}
Best C: 64 Best gamma: 1.600 Minimum CV error: 0.000 Iris Classification Error Rates -------------------------------------------------------------- Setosa Versicolour Virginica | TOTAL 0/50 1/50 3/50 4/150 --------------------------------------------------------------Link to Java source.