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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)));
}