public void Minimize(IHypothesis hypothesis, Vector[] inputs, double[] outputs)
{
if (iterationCount < 1) return;
// first iteration
var prevGrad = hypothesis.BatchGradient(inputs, outputs);
hypothesis.Weights = hypothesis.Weights - learningRate * prevGrad;
// other iterations
for (var dummy = 1; dummy < iterationCount; ++dummy)
{
// find gradient of cost(loss) function
var grad = hypothesis.BatchGradient(inputs, outputs);
// find Euclidean norm of delta of last two gradients
var dGrad = (grad - prevGrad);
var dGradNorm = dGrad.EuclideanNorm;
// adopt adaptive learning rate
var rate = learningRate * dGradNorm;
// do gradient step
hypothesis.Weights = hypothesis.Weights - (learningRate * dGradNorm) * grad;
// store gradient thus we can easily find delta of gradient
prevGrad = grad;
}
}
public void Minimize(IHypothesis hypothesis, Vector[] inputs, double[] outputs)
{
if (inputs.Length == 0)
return;
var inputCount = inputs.Length;
var inputSize = inputs[0].Size;
// calculate invert avg multiplier
var contribution = 1.0d / (double)inputSize;
// prevent to passing small inputs away without weigths have been changed
var currentRefreshRate = System.Math.Min(refreshRate, inputCount);
for (var dummy = 0; dummy < iterationCount; ++dummy)
{
var gradAcc = new Vector(inputSize);
for (var i = 0; i < inputCount; ++i)
{
// find gradient of cost(loss) function
gradAcc += hypothesis.Gradient(inputs[i], outputs[i]);
if ((i % currentRefreshRate) == 0)
{
// do gradient step
hypothesis.Weights = hypothesis.Weights - learningRate * contribution * gradAcc;
// reset accumulator
gradAcc.Transform(x => 0.0d);
}
}
hypothesis.Weights = hypothesis.Weights - learningRate * contribution * gradAcc;
}
}