Piece of code for calculation of likehood gradient in multinomial logistic regression for one training sample

mlt_gradient_for_sample.cpp

void MultinomialLogRelModel::gradientForSample(const valarray<double>& sample_features, int label, valarray<double>& gradient) const
{
	double sum_exp = 0.0;
	valarray<double> features(0.0, (num_sample_features + 1) * num_classes);
	valarray<double> gradient_add(0.0, (num_sample_features + 1) * num_classes);

	for (int k = 0; k < num_classes; ++k)
	{
		featurize(sample_features, k, features);
		double wf = inner_product(begin(model_weights), end(model_weights), begin(features), 0.0);
		double exponent = exp(wf);
		gradient_add -= features * exponent;
		sum_exp += exponent;
	}
	gradient_add /= sum_exp;
	featurize(sample_features, label, features);
	gradient_add += features;		
	gradient += gradient_add;
}