@Test
public void test() {
int i, j, k, M = 1000, N = 2;
double a, b, lambda, omega = 1.3, test, yy;
double[] x = new double[2];
double[] y = new double[M];
double[][] data = new double[M][N];
boolean localflag, globalflag = false;
// Test Svm
System.out.println("Testing Svm");
// Create two disjoint sets of points
Ran myran = new Ran(17);
for (i = 0; i < M / 2; i++) {
y[i] = 1.0;
a = myran.doub();
b = 2.0 * myran.doub() - 1.0;
data[i][0] = 1.0 + (a - b);
data[i][1] = 1.0 + (a + b);
}
for (i = M / 2; i < M; i++) {
y[i] = -1.0;
a = myran.doub();
b = 2.0 * myran.doub() - 1.0;
data[i][0] = -1.0 - (a - b);
data[i][1] = -1.0 - (a + b);
}
// Linear kernel
Svmlinkernel linkernel = new Svmlinkernel(data, y);
Svm linsvm = new Svm(linkernel);
lambda = 10;
k = 0;
do {
test = linsvm.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
int nerror = 0;
for (i = 0; i < M; i++) {
// if (i%10 == 0) System.out.printf((y[i]==1.0) << " %f\n", (linsvm.predict(i) >= 1.0));
// if ((y[i] == 1.0) != (linsvm.predict(i) >= 1.0))
// System.out.printf(data[i][0] << " %f\n", data[i][1] << " %f\n", y[i] << " %f\n",
// linsvm.predict(i));
nerror += ((y[i] == 1.0) != (linsvm.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("Errors: %d\n", nerror);
// Polynomial kernel
Svmpolykernel polykernel = new Svmpolykernel(data, y, 1.0, 1.0, 2.0);
Svm polysvm = new Svm(polykernel);
lambda = 10;
k = 0;
do {
test = polysvm.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
nerror = 0;
for (i = 0; i < M; i++) {
nerror += ((y[i] == 1.0) != (polysvm.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("Errors: %d\n", nerror);
// Gaussian kernel
Svmgausskernel gausskernel = new Svmgausskernel(data, y, 1.0);
Svm gausssvm = new Svm(gausskernel);
lambda = 10;
k = 0;
do {
test = gausssvm.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
nerror = 0;
for (i = 0; i < M; i++) {
nerror += ((y[i] == 1.0) != (gausssvm.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("Errors: %d\n", nerror);
// Need to add tests for harder test case and resolve issue that the two
// support vectors give an erroneous indication for two of the kernels above
// Example similar to the book
Normaldev ndev = new Normaldev(0.0, 0.5, 17);
for (j = 0; j < 4; j++) { // Four quadrants
for (i = 0; i < M / 4; i++) {
k = (M / 4) * j + i;
if (j == 0) {
y[k] = 1.0;
data[k][0] = 1.0 + ndev.dev();
data[k][1] = 1.0 + ndev.dev();
} else if (j == 1) {
y[k] = -1.0;
data[k][0] = -1.0 + ndev.dev();
data[k][1] = 1.0 + ndev.dev();
} else if (j == 2) {
y[k] = 1.0;
data[k][0] = -1.0 + ndev.dev();
data[k][1] = -1.0 + ndev.dev();
} else {
y[k] = -1.0;
data[k][0] = 1.0 + ndev.dev();
data[k][1] = -1.0 + ndev.dev();
}
}
}
// Linear kernel
Svmlinkernel linkernel2 = new Svmlinkernel(data, y);
Svm linsvm2 = new Svm(linkernel2);
System.out.printf("Errors: ");
for (lambda = 0.001; lambda < 10000; lambda *= 10) {
k = 0;
do {
test = linsvm2.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
nerror = 0;
for (i = 0; i < M; i++) {
nerror += ((y[i] == 1.0) != (linsvm2.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("%d ", nerror);
// Test new data
nerror = 0;
for (j = 0; j < 4; j++) { // Four quadrants
for (i = 0; i < M / 4; i++) {
if (j == 0) {
yy = 1.0;
x[0] = 1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 1) {
yy = -1.0;
x[0] = -1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 2) {
yy = 1.0;
x[0] = -1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
} else {
yy = -1.0;
x[0] = 1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
}
nerror += ((yy == 1.0) != (linsvm2.predict(x) >= 0.0) ? 1 : 0);
}
}
System.out.printf("%d ", nerror);
}
System.out.println();
// Polynomial kernel
Svmpolykernel polykernel2 = new Svmpolykernel(data, y, 1.0, 1.0, 4.0);
Svm polysvm2 = new Svm(polykernel2);
System.out.printf("Errors: ");
for (lambda = 0.001; lambda < 10000; lambda *= 10) {
k = 0;
do {
test = polysvm2.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
// Test training set
nerror = 0;
for (i = 0; i < M; i++) {
nerror += ((y[i] == 1.0) != (polysvm2.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("%d ", nerror);
// Test new data
nerror = 0;
for (j = 0; j < 4; j++) { // Four quadrants
for (i = 0; i < M / 4; i++) {
if (j == 0) {
yy = 1.0;
x[0] = 1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 1) {
yy = -1.0;
x[0] = -1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 2) {
yy = 1.0;
x[0] = -1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
} else {
yy = -1.0;
x[0] = 1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
}
nerror += ((yy == 1.0) != (polysvm2.predict(x) >= 0.0) ? 1 : 0);
}
}
System.out.printf("%d ", nerror);
}
System.out.println();
// Gaussian kernel
Svmgausskernel gausskernel2 = new Svmgausskernel(data, y, 1.0);
Svm gausssvm2 = new Svm(gausskernel2);
System.out.printf("Errors: ");
for (lambda = 0.001; lambda < 10000; lambda *= 10) {
k = 0;
do {
test = gausssvm2.relax(lambda, omega);
// System.out.printf(test);
k++;
} while (test > 1.e-3 && k < 100);
nerror = 0;
for (i = 0; i < M; i++) {
nerror += ((y[i] == 1.0) != (gausssvm2.predict(i) >= 0.0) ? 1 : 0);
}
System.out.printf("%d ", nerror);
// Test new data
nerror = 0;
for (j = 0; j < 4; j++) { // Four quadrants
for (i = 0; i < M / 4; i++) {
if (j == 0) {
yy = 1.0;
x[0] = 1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 1) {
yy = -1.0;
x[0] = -1.0 + ndev.dev();
x[1] = 1.0 + ndev.dev();
} else if (j == 2) {
yy = 1.0;
x[0] = -1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
} else {
yy = -1.0;
x[0] = 1.0 + ndev.dev();
x[1] = -1.0 + ndev.dev();
}
nerror += ((yy == 1.0) != (gausssvm2.predict(x) >= 0.0) ? 1 : 0);
}
}
System.out.printf("%d ", nerror);
}
System.out.println();
// Test the algorithm on test data after learning
// Do a scan over lambda to find best value
localflag = false;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Svm: *************************");
}
if (globalflag) System.out.println("Failed\n");
else System.out.println("Passed\n");
}
@Test
public void test() {
int i, j, N = 100, M = 10;
double pi = acos(-1.0), sumx2, sa = 0, sb = 0, sbeps;
double[] x = new double[N], y = new double[N], yy = new double[N], sig = new double[N];
boolean localflag, globalflag = false;
// Test Fitab
System.out.println("Testing Fitab");
Ran myran = new Ran(17);
sumx2 = 0;
for (i = 0; i < N; i++) {
x[i] = 10.0 * myran.doub();
y[i] = sqrt(2.0) + pi * x[i];
sig[i] = 1.0;
sumx2 += SQR(x[i]);
}
Fitab fit1 = new Fitab(x, y, sig); // Perfect fit, no noise
sbeps = 1.e-12;
localflag = abs(fit1.a - sqrt(2.0)) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab: Fitted constant term a has incorrect value");
}
localflag = abs(fit1.b - pi) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab: Fitted slope b has incorrect value");
}
localflag = fit1.chi2 > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab: Chi^2 not zero for perfect linear data");
}
localflag = abs(fit1.q - 1.0) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab: Probability not 1.0 for perfect linear data");
}
localflag = abs(fit1.siga / fit1.sigb - sqrt(sumx2 / N)) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab: Ratio of siga/sigb incorrect for special case");
}
// Test 2
for (j = 0; j < M; j++) {
for (i = 0; i < N; i++) {
sig[i] = 0.1 * (j + 1); // 0.1*(j+1)*sqrt(y[i]);
}
Fitab fit = new Fitab(x, y, sig);
localflag = abs(fit.a - sqrt(2.0)) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: Fitted constant term a has incorrect value");
}
localflag = abs(fit.b - pi) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: Fitted slope b has incorrect value");
}
localflag = fit.chi2 > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: Chi^2 not zero for perfect linear data");
}
localflag = abs(fit.q - 1.0) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: Probability not 1.0 for perfect linear data");
}
if (j == 0) {
sa = fit.siga;
sb = fit.sigb;
} else {
localflag = (fit.siga / sa - (j + 1)) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: siga did not scale properly with data errors");
}
localflag = (fit.sigb / sb - (j + 1)) > sbeps;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test2: sigb did not scale properly with data errors");
}
}
}
// Test 3
Normaldev ndev = new Normaldev(0.0, 1.0, 17);
for (j = 0; j < M; j++) {
for (i = 0; i < N; i++) {
yy[i] = y[i] + ndev.dev();
sig[i] = 1.0;
}
Fitab fit3 = new Fitab(x, yy, sig);
// System.out.printf(fit3.a << " %f\n", fit3.b);
// System.out.printf(fit3.siga << " %f\n", fit3.sigb);
// System.out.printf(fit3.chi2 << " %f\n", fit3.q << endl);
localflag = abs(fit3.a - sqrt(2.0)) > 3.0 * fit3.siga;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test3: Fitted constant term a, or error siga, may be incorrect");
}
localflag = abs(fit3.b - pi) > 3.0 * fit3.sigb;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test3: Fitted slope b, or error sigb, may be incorrect");
}
localflag = fit3.chi2 > 1.3 * N;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test3: Chi^2 is unexpectedly high");
}
localflag = abs(fit3.q) < 0.1;
globalflag = globalflag || localflag;
if (localflag) {
fail("*** Fitab,Test3: Probability q suggests a possibly bad fit");
}
}
if (globalflag) System.out.println("Failed\n");
else System.out.println("Passed\n");
}
