Gradient descent in Java

private double getResult(double[][] trainingData, boolean enableFactor){
double result = 0;
for (int i = 0; i < trainingData.length; i++) {
    result = (getHypothesisResult(trainingData[i][0]) - trainingData[i][1]);
    if (enableFactor) result = result*trainingData[i][0]; 
}
return result;

In this func. result variable overwritten each iteration, the old value being lost. When inputing the values only the last item on array is calculating. Rest of them dont matter.

To solve this issue, it's necessary to normalize the data with this formular: (Xi-mu)/s. Xi is the current training set value, mu the average of values in the current column and s the maximum value minus the minimum value of the current column. This formula will get the training data approximately into a range between -1 and 1 which allowes to choose higher learning rates and gradient descent to converge faster. But it's afterwards necessary to denormalize the predicted result.