machine_learning Namespace Reference

Machine learning algorithms. More...

Classes
class	adaline

Functions
int	save_u_matrix (const char *fname, const std::vector< std::vector< std::valarray< double >>> &W)
double	update_weights (const std::valarray< double > &X, std::vector< std::vector< std::valarray< double >>> *W, std::vector< std::valarray< double >> *D, double alpha, int R)
void	kohonen_som (const std::vector< std::valarray< double >> &X, std::vector< std::vector< std::valarray< double >>> *W, double alpha_min)
void	update_weights (const std::valarray< double > &x, std::vector< std::valarray< double >> *W, std::valarray< double > *D, double alpha, int R)
void	kohonen_som_tracer (const std::vector< std::valarray< double >> &X, std::vector< std::valarray< double >> *W, double alpha_min)

Detailed Description

Machine learning algorithms.

Function Documentation

kohonen_som()

void machine_learning::kohonen_som	(	const std::vector< std::valarray< double >> &	X,
		std::vector< std::vector< std::valarray< double >>> *	W,
		double	alpha_min
	)

Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.

Parameters

[in]	X	data set
[in,out]	W	weights matrix
[in]	alpha_min	terminal value of alpha

                                   {
    int num_samples = X.size();      // number of rows
    int num_features = X[0].size();  // number of columns
    int num_out = W->size();         // output matrix size
    int R = num_out >> 2, iter = 0;
    double alpha = 1.f;
 
    std::vector<std::valarray<double>> D(num_out);
    for (int i = 0; i < num_out; i++) D[i] = std::valarray<double>(num_out);
 
    double dmin = 1.f;        // average minimum distance of all samples
    double past_dmin = 1.f;   // average minimum distance of all samples
    double dmin_ratio = 1.f;  // change per step
 
    // Loop alpha from 1 to slpha_min
    for (; alpha > 0 && dmin_ratio > 1e-5; alpha -= 1e-4, iter++) {
        // Loop for each sample pattern in the data set
        for (int sample = 0; sample < num_samples; sample++) {
            // update weights for the current input pattern sample
            dmin += update_weights(X[sample], W, &D, alpha, R);
        }
 
        // every 100th iteration, reduce the neighborhood range
        if (iter % 300 == 0 && R > 1)
            R--;
 
        dmin /= num_samples;
 
        // termination condition variable -> % change in minimum distance
        dmin_ratio = (past_dmin - dmin) / past_dmin;
        if (dmin_ratio < 0)
            dmin_ratio = 1.f;
        past_dmin = dmin;
 
        std::cout << "iter: " << iter << "\t alpha: " << alpha << "\t R: " << R
                  << "\t d_min: " << dmin_ratio << "\r";
    }
 
    std::cout << "\n";
}

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kohonen_som_tracer()

void machine_learning::kohonen_som_tracer	(	const std::vector< std::valarray< double >> &	X,
		std::vector< std::valarray< double >> *	W,
		double	alpha_min
	)

Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.

Parameters

[in]	X	data set
[in,out]	W	weights matrix
[in]	alpha_min	terminal value of alpha

                                          {
    int num_samples = X.size();      // number of rows
    int num_features = X[0].size();  // number of columns
    int num_out = W->size();         // number of rows
    int R = num_out >> 2, iter = 0;
    double alpha = 1.f;
 
    std::valarray<double> D(num_out);
 
    // Loop alpha from 1 to slpha_min
    for (; alpha > alpha_min; alpha -= 0.01, iter++) {
        // Loop for each sample pattern in the data set
        for (int sample = 0; sample < num_samples; sample++) {
            // update weights for the current input pattern sample
            update_weights(X[sample], W, &D, alpha, R);
        }
 
        // every 10th iteration, reduce the neighborhood range
        if (iter % 10 == 0 && R > 1)
            R--;
    }
}

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save_u_matrix()

int machine_learning::save_u_matrix	(	const char *	fname,
		const std::vector< std::vector< std::valarray< double >>> &	W
	)

Create the distance matrix or U-matrix from the trained 3D weiths matrix and save to disk.

Parameters

[in]	fname	filename to save in (gets overwriten without confirmation)
[in]	W	model matrix to save

Returns

0 if all ok

-1 if file creation failed

                                                                      {
    std::ofstream fp(fname);
    if (!fp) {  // error with fopen
        char msg[120];
        std::snprintf(msg, sizeof(msg), "File error (%s): ", fname);
        std::perror(msg);
        return -1;
    }
 
    // neighborhood range
    unsigned int R = 1;
 
    for (int i = 0; i < W.size(); i++) {         // for each x
        for (int j = 0; j < W[0].size(); j++) {  // for each y
            double distance = 0.f;
 
            int from_x = std::max<int>(0, i - R);
            int to_x = std::min<int>(W.size(), i + R + 1);
            int from_y = std::max<int>(0, j - R);
            int to_y = std::min<int>(W[0].size(), j + R + 1);
            int l, m;
#ifdef _OPENMP
#pragma omp parallel for reduction(+ : distance)
#endif
            for (l = from_x; l < to_x; l++) {      // scan neighborhoor in x
                for (m = from_y; m < to_y; m++) {  // scan neighborhood in y
                    auto d = W[i][j] - W[l][m];
                    double d2 = std::pow(d, 2).sum();
                    distance += std::sqrt(d2);
                    // distance += d2;
                }
            }
 
            distance /= R * R;          // mean distance from neighbors
            fp << distance;             // print the mean separation
            if (j < W[0].size() - 1) {  // if not the last column
                fp << ',';              // suffix comma
            }
        }
        if (i < W.size() - 1)  // if not the last row
            fp << '\n';        // start a new line
    }
 
    fp.close();
    return 0;
}

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update_weights() [1/2]

void machine_learning::update_weights	(	const std::valarray< double > &	x,
		std::vector< std::valarray< double >> *	W,
		std::valarray< double > *	D,
		double	alpha,
		int	R
	)

Update weights of the SOM using Kohonen algorithm

Parameters

[in]	X	data point
[in,out]	W	weights matrix
[in,out]	D	temporary vector to store distances
[in]	alpha	learning rate \(0<\alpha\le1\)
[in]	R	neighborhood range

                                                                 {
    int j, k;
    int num_out = W->size();      // number of SOM output nodes
    int num_features = x.size();  // number of data features
 
#ifdef _OPENMP
#pragma omp for
#endif
    // step 1: for each output point
    for (j = 0; j < num_out; j++) {
        // compute Euclidian distance of each output
        // point from the current sample
        (*D)[j] = (((*W)[j] - x) * ((*W)[j] - x)).sum();
    }
 
    // step 2:  get closest node i.e., node with snallest Euclidian distance to
    // the current pattern
    auto result = std::min_element(std::begin(*D), std::end(*D));
    double d_min = *result;
    int d_min_idx = std::distance(std::begin(*D), result);
 
    // step 3a: get the neighborhood range
    int from_node = std::max(0, d_min_idx - R);
    int to_node = std::min(num_out, d_min_idx + R + 1);
 
    // step 3b: update the weights of nodes in the
    // neighborhood
#ifdef _OPENMP
#pragma omp for
#endif
    for (j = from_node; j < to_node; j++)
        // update weights of nodes in the neighborhood
        (*W)[j] += alpha * (x - (*W)[j]);
}

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update_weights() [2/2]

double machine_learning::update_weights	(	const std::valarray< double > &	X,
		std::vector< std::vector< std::valarray< double >>> *	W,
		std::vector< std::valarray< double >> *	D,
		double	alpha,
		int	R
	)

Update weights of the SOM using Kohonen algorithm

Parameters

[in]	X	data point - N features
[in,out]	W	weights matrix - PxQxN
[in,out]	D	temporary vector to store distances PxQ
[in]	alpha	learning rate \(0<\alpha\le1\)
[in]	R	neighborhood range

Returns

minimum distance of sample and trained weights

                             {
    int x, y;
    int num_out_x = static_cast<int>(W->size());       // output nodes - in X
    int num_out_y = static_cast<int>(W[0][0].size());  // output nodes - in Y
    int num_features = static_cast<int>(W[0][0][0].size());  //  features = in Z
    double d_min = 0.f;
 
#ifdef _OPENMP
#pragma omp for
#endif
    // step 1: for each output point
    for (x = 0; x < num_out_x; x++) {
        for (y = 0; y < num_out_y; y++) {
            (*D)[x][y] = 0.f;
            // compute Euclidian distance of each output
            // point from the current sample
            auto d = ((*W)[x][y] - X);
            (*D)[x][y] = (d * d).sum();
            (*D)[x][y] = std::sqrt((*D)[x][y]);
        }
    }
 
    // step 2:  get closest node i.e., node with snallest Euclidian distance
    // to the current pattern
    int d_min_x, d_min_y;
    get_min_2d(*D, &d_min, &d_min_x, &d_min_y);
 
    // step 3a: get the neighborhood range
    int from_x = std::max(0, d_min_x - R);
    int to_x = std::min(num_out_x, d_min_x + R + 1);
    int from_y = std::max(0, d_min_y - R);
    int to_y = std::min(num_out_y, d_min_y + R + 1);
 
    // step 3b: update the weights of nodes in the
    // neighborhood
#ifdef _OPENMP
#pragma omp for
#endif
    for (x = from_x; x < to_x; x++) {
        for (y = from_y; y < to_y; y++) {
            /* you can enable the following normalization if needed.
   personally, I found it detrimental to convergence */
            // const double s2pi = sqrt(2.f * M_PI);
            // double normalize = 1.f / (alpha * s2pi);
 
            /* apply scaling inversely proportional to distance from the
               current node */
            double d2 =
                (d_min_x - x) * (d_min_x - x) + (d_min_y - y) * (d_min_y - y);
            double scale_factor = std::exp(-d2 / (2.f * alpha * alpha));
 
            (*W)[x][y] += (X - (*W)[x][y]) * alpha * scale_factor;
        }
    }
    return d_min;
}

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