vhasu

static vector<double>
calculateGradient(vector<FeatureVector> sorted_feature_vectors, vector<double> new_parameters) {

    vector<double> gradient(sorted_feature_vectors[0].get_fcount()-1,0);//need to be optimized,how to get the length of the features

    int list_length = sorted_feature_vectors.size();

    vector<double> exponents;
    double expsum = 0.0;

    for(int i = 0; i < list_length; i++){
        double temp = exp(calculateInnerProduct(new_parameters,sorted_feature_vectors[i].get_fvals()));
        exponents.push_back(temp);
        expsum += temp;
    }

    for(int i = 0; i < list_length; i++){//not convenient, but in order to get the feature, I have to run the ranklist loop first. need to see whether the fvals could be a vector
        map<int,double> feature_sets = sorted_feature_vectors[i].get_fvals();

        //for each feature in special feature vector
        for (map<int,double>::iterator iter = feature_sets.begin(); iter != feature_sets.end(); ++iter){

            gradient[iter->first-1] += iter->second * exponents[i]/ expsum;

        }

    }

    map<int,double> first_place_in_ground_truth_feature_sets = sorted_feature_vectors[0].get_fvals();

    for (map<int,double>::iterator iter = first_place_in_ground_truth_feature_sets.begin(); iter != first_place_in_ground_truth_feature_sets.end(); ++iter){

            gradient[iter->first-1] -= iter->second;

        }

    return gradient;
}