/* Returns the sum of each weight * value of each feature. */
public double predictedValue(RegressionState state, double[] weight) {
double sum = weight[0] * 1;
for (int i = 1; i < TOTAL_FEATURES; i++) {
sum += weight[i] * state.getFeature(i);
}
return sum;
}
/* calculate the gradient descent. */
public void gradientDescent(double stepsize) {
copyArr(weight, copy);
int i = 0;
while (i < TOTAL_FEATURES) {
double sumAll = 0;
double sumTotal = 0;
Iterator<RegressionState> it = gameHistory.iterator();
while (it.hasNext()) {
RegressionState aState = it.next();
sumAll = predictedValue(aState, copy);
sumAll -= reward;
sumTotal += sumAll * aState.getFeature(i);
} // end inner while
weight[i] = copy[i] - stepsize * ((double) 1 / gameHistory.size()) * sumTotal;
i++;
} // end while
}