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Fixed gradient decent

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This commit is contained in:
Ziver Koc 2018-10-01 17:06:53 +02:00
parent cf94310598
commit 98f2219366
2 changed files with 44 additions and 22 deletions
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27
src/zutil/ml/LinearRegression.java
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@ -40,17 +40,8 @@ public class LinearRegression {
Matrix.Elemental.pow(normalized,2));
}
/**
* Calculates the gradiant of the current provided theta.
*/
protected static double calculateGradiant(double[][] x, double[] y, double[] theta){
int m = y.length; // number of training examples
double[] hypothesis = calculateHypothesis(x, theta);
double[] normalized = Matrix.subtract(hypothesis, y);
return 1/m * Matrix.sum(
Matrix.Elemental.multiply(Matrix.transpose(x), normalized));
private static double calculateDiff(double[] vector1, double[] vector2){
return Math.abs(Matrix.sum(vector1) - Matrix.sum(vector2));
}
/**
@ -58,12 +49,16 @@ public class LinearRegression {
*/
public static double[] gradientDescent(double[][] x, double[] y, double[] theta, double alpha){
double[] newTheta = theta.clone();
double gradient;
double[] prevTheta = new double[newTheta.length];
double thetaDiff = 0;
int i = 0;
for (int i=0; (gradient = calculateGradiant(x, y, newTheta)) != 0; i++) {
logger.fine("Gradient Descent iteration " + i + ", gradiant: " + gradient);
do {
logger.fine("Gradient Descent iteration " + i + ", diff to previous iteration: " + thetaDiff);
System.arraycopy(newTheta, 0, prevTheta, 0, newTheta.length);
newTheta = gradientDescentIteration(x, y, newTheta, alpha);
}
++i;
} while ((thetaDiff=calculateDiff(prevTheta, newTheta)) > 0.0001);
return newTheta;
}
@ -84,7 +79,7 @@ public class LinearRegression {
double[] normalized = Matrix.subtract(hypothesis, y);
for (int j= 0; j < theta.length; j++) {
newTheta[j] = theta[j] - alpha * (1.0/m) * Matrix.sum(
newTheta[j] = theta[j] - (alpha/m) * Matrix.sum(
Matrix.Elemental.multiply(normalized, Matrix.getColumn(x, j)));
}

39
test/zutil/ml/LinearRegressionTest.java
View file

@ -1,6 +1,7 @@
package zutil.ml;
import org.junit.Test;
import zutil.io.MultiPrintStream;
import zutil.log.LogUtil;
import java.util.logging.Level;
@ -36,8 +37,8 @@ public class LinearRegressionTest {
}
// Does not work
//@Test
public void gradientAscent() {
@Test
public void gradientDescent() {
double[][] x = {
{1.0, 0.1, 0.6, 1.1},
{1.0, 0.2, 0.7, 1.2},
@ -59,14 +60,40 @@ public class LinearRegressionTest {
2
};
double[] resultTheta = LinearRegression.gradientDescent(x, y, theta, 0);
// Alpha zero
assertEquals(0.73482, LinearRegression.calculateCost(x, y, resultTheta), 0.000001);
double[] resultTheta = LinearRegression.gradientDescent(x, y, theta, 0);
System.out.println("Result Theta (alpha = 0):");
System.out.println(MultiPrintStream.dumpToString(resultTheta));
assertArrayEquals(theta, resultTheta, 0.000001);
// Alpha +
resultTheta = LinearRegression.gradientDescent(x, y, theta, 0.1);
System.out.println("Result Theta (alpha = 0.1):");
System.out.println(MultiPrintStream.dumpToString(resultTheta));
assertArrayEquals(
new double[]{-1.31221, -1.98259, 0.36131, 1.70520},
resultTheta, 0.001);
}
@Test
public void gradientAscentIteration() {
double[] theta = LinearRegression.gradientDescentIteration( // one iteration
public void gradientDescentIteration() {
// Zero iterations
double[] theta = LinearRegression.gradientDescentIteration(
/* x */ new double[][]{{1, 5},{1, 2},{1, 4},{1, 5}},
/* y */ new double[]{1, 6, 4, 2},
/* theta */ new double[]{0, 0},
/* alpha */0.0);
assertArrayEquals(new double[]{0.0, 0.0}, theta, 0.000001);
// One iteration
theta = LinearRegression.gradientDescentIteration(
/* x */ new double[][]{{1, 5},{1, 2},{1, 4},{1, 5}},
/* y */ new double[]{1, 6, 4, 2},
/* theta */ new double[]{0, 0},