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Stochastic Gradient Descent, but this time in Java.
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package com.nikhilgopal.spark; | |
/** | |
* Created by nikhilgopal on 3/24/17. | |
*/ | |
public class SGDLinReg { | |
public static void main(String[] args) { | |
double[] coefficients = {0.4, 0.8}; | |
double[][] dataset = { | |
{1.0, 1.0}, | |
{2.0, 3.0}, | |
{4.0, 3.0}, | |
{3.0, 2.0}, | |
{5.0, 5.0} | |
}; | |
double[] newcoef = coef_sgd(dataset, 0.001, 500); | |
System.out.println(newcoef[0] + " " + newcoef[1]); | |
} | |
private static double[] coef_sgd(double[][] train, double l_rate, double n_epoch) { | |
double[] coefficients = {0.0, 0.0}; | |
for (int e = 0; e < n_epoch; e++) { | |
double sum_error = 0.0; | |
for (int w = 0; w < train.length; w++) { | |
double yhat = predict(train[w], coefficients); | |
double error = yhat - train[w][1]; | |
sum_error += error*error; | |
coefficients[0] = coefficients[0] = l_rate*error; | |
for (int k = 0; k < (train[w].length-1); k++) { | |
coefficients[k+1] = coefficients[k+1] - l_rate * error * train[w][k]; | |
} | |
} | |
System.out.println("EPOCH " + e + " LRATE " + l_rate + " ERR " + sum_error); | |
} | |
return coefficients; | |
} | |
private static double predict(double[] row, double[] coef) { | |
double yhat = coef[0]; | |
for (int i = 0; i < (coef.length-1); i++) { | |
yhat += coef[i+1] * row[i]; | |
} | |
return yhat; | |
} | |
} |
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