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ErrorFunctions.cpp 2.69 KiB
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  • //
    // Created by martin on 7/15/18.
    //
    
    
    #include <vector>
    
    
    #include "ErrorFunctions.h"
    
    
    size_t ErrorFunction::get_dimension() {
        return this->dimension;
    }
    
    MSE::MSE(NeuralNetwork *net, DataSet *ds) {
        this->net = net;
        this->ds = ds;
    
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        this->dimension = net->get_n_weights() + net->get_n_biases();
    
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    double MSE::eval(std::vector<double> *weights) {
    
        unsigned int dim_out = this->ds->get_output_dim();
    
        size_t n_elements = this->ds->get_n_elements();
        double error = 0.0, val;
    
        std::vector<std::pair<std::vector<double>, std::vector<double>>>* data = this->ds->get_data();
    
    
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    //    //TODO instead use something smarter
    //    this->net->copy_weights(weights);
    
    
        std::vector<double> output( dim_out );
    
        for(unsigned int i = 0; i < n_elements; ++i){  // Iterate through every element in the test set
    
    
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            this->net->eval_single(std::get<0>(data->at(i)), output, weights);  // Compute the net output and store it into 'output' variable
    
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    //        printf("errors: ");
    
            for(unsigned int j = 0; j < dim_out; ++j) {  // Compute difference for every element of the output vector
    
                val = output[j] - std::get<1>(data->at(i))[j];
                error += val * val;
    
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    //            printf("%f, ", val * val);
    
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    //        printf("\n");
    
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    //    printf("n_elements: %d\n", n_elements);
    
        return error/n_elements;
    
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    ErrorSum::ErrorSum() {
    
        this->summand_coefficient = nullptr;
    
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        this->dimension = 0;
    
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    ErrorSum::~ErrorSum(){
    
        if( this->summand ){
            delete this->summand;
        }
    
        if( this->summand_coefficient ){
            delete this->summand_coefficient;
        }
    
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    double ErrorSum::eval(std::vector<double> *weights) {
    
        for( unsigned int i = 0; i < this->summand->size(); ++i ){
    
            output += this->summand->at( i )->eval( weights ) * this->summand_coefficient->at( i );
    
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    void ErrorSum::add_error_function(ErrorFunction *F, double alpha) {
    
        if(!this->summand){
            this->summand = new std::vector<ErrorFunction*>(0);
        }
    
        this->summand->push_back( F );
    
        if(!this->summand_coefficient){
            this->summand_coefficient = new std::vector<double>(0);
        }
        this->summand_coefficient->push_back( alpha );
    
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        if(F->get_dimension() > this->dimension){
            this->dimension = F->get_dimension();
        }
    
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    size_t ErrorSum::get_dimension() {
    
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    //    if(!this->dimension) {
    //        size_t max = 0;
    //        for(auto e : *this->summand) {
    //            if(e->get_dimension() > max) {
    //                max = e->get_dimension();
    //            }
    //        };
    //
    //        this->dimension = max;
    //    }
    
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        return this->dimension;