darknet-sys 0.3.5

#include "matrix.h"
#include "utils.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>

void free_matrix(matrix m)
{
    int i;
    for(i = 0; i < m.rows; ++i) free(m.vals[i]);
    free(m.vals);
}

float matrix_topk_accuracy(matrix truth, matrix guess, int k)
{
    int* indexes = (int*)xcalloc(k, sizeof(int));
    int n = truth.cols;
    int i,j;
    int correct = 0;
    for(i = 0; i < truth.rows; ++i){
        top_k(guess.vals[i], n, k, indexes);
        for(j = 0; j < k; ++j){
            int class_id = indexes[j];
            if(truth.vals[i][class_id]){
                ++correct;
                break;
            }
        }
    }
    free(indexes);
    return (float)correct/truth.rows;
}

void scale_matrix(matrix m, float scale)
{
    int i,j;
    for(i = 0; i < m.rows; ++i){
        for(j = 0; j < m.cols; ++j){
            m.vals[i][j] *= scale;
        }
    }
}

matrix resize_matrix(matrix m, int size)
{
    int i;
    if (m.rows == size) return m;
    if (m.rows < size) {
        m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
        for (i = m.rows; i < size; ++i) {
            m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
        }
    } else if (m.rows > size) {
        for (i = size; i < m.rows; ++i) {
            free(m.vals[i]);
        }
        m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
    }
    m.rows = size;
    return m;
}

void matrix_add_matrix(matrix from, matrix to)
{
    assert(from.rows == to.rows && from.cols == to.cols);
    int i,j;
    for(i = 0; i < from.rows; ++i){
        for(j = 0; j < from.cols; ++j){
            to.vals[i][j] += from.vals[i][j];
        }
    }
}

matrix make_matrix(int rows, int cols)
{
    int i;
    matrix m;
    m.rows = rows;
    m.cols = cols;
    m.vals = (float**)xcalloc(m.rows, sizeof(float*));
    for(i = 0; i < m.rows; ++i){
        m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
    }
    return m;
}

matrix hold_out_matrix(matrix *m, int n)
{
    int i;
    matrix h;
    h.rows = n;
    h.cols = m->cols;
    h.vals = (float**)xcalloc(h.rows, sizeof(float*));
    for(i = 0; i < n; ++i){
        int index = rand()%m->rows;
        h.vals[i] = m->vals[index];
        m->vals[index] = m->vals[--(m->rows)];
    }
    return h;
}

float *pop_column(matrix *m, int c)
{
    float* col = (float*)xcalloc(m->rows, sizeof(float));
    int i, j;
    for(i = 0; i < m->rows; ++i){
        col[i] = m->vals[i][c];
        for(j = c; j < m->cols-1; ++j){
            m->vals[i][j] = m->vals[i][j+1];
        }
    }
    --m->cols;
    return col;
}

matrix csv_to_matrix(char *filename)
{
    FILE *fp = fopen(filename, "r");
    if(!fp) file_error(filename);

    matrix m;
    m.cols = -1;

    char *line;

    int n = 0;
    int size = 1024;
    m.vals = (float**)xcalloc(size, sizeof(float*));
    while((line = fgetl(fp))){
        if(m.cols == -1) m.cols = count_fields(line);
        if(n == size){
            size *= 2;
            m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
        }
        m.vals[n] = parse_fields(line, m.cols);
        free(line);
        ++n;
    }
    m.vals = (float**)xrealloc(m.vals, n * sizeof(float*));
    m.rows = n;
    return m;
}

void matrix_to_csv(matrix m)
{
    int i, j;

    for(i = 0; i < m.rows; ++i){
        for(j = 0; j < m.cols; ++j){
            if(j > 0) printf(",");
            printf("%.17g", m.vals[i][j]);
        }
        printf("\n");
    }
}

void print_matrix(matrix m)
{
    int i, j;
    printf("%d X %d Matrix:\n",m.rows, m.cols);
    printf(" __");
    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
    printf("__ \n");

    printf("|  ");
    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
    printf("  |\n");

    for(i = 0; i < m.rows; ++i){
        printf("|  ");
        for(j = 0; j < m.cols; ++j){
            printf("%15.7f ", m.vals[i][j]);
        }
        printf(" |\n");
    }
    printf("|__");
    for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
    printf("__|\n");
}


matrix make_matrix(int rows, int cols);

void copy(float *x, float *y, int n);
float dist(float *x, float *y, int n);
int *sample(int n);

int closest_center(float *datum, matrix centers)
{
    int j;
    int best = 0;
    float best_dist = dist(datum, centers.vals[best], centers.cols);
    for (j = 0; j < centers.rows; ++j) {
        float new_dist = dist(datum, centers.vals[j], centers.cols);
        if (new_dist < best_dist) {
            best_dist = new_dist;
            best = j;
        }
    }
    return best;
}

float dist_to_closest_center(float *datum, matrix centers)
{
    int ci = closest_center(datum, centers);
    return dist(datum, centers.vals[ci], centers.cols);
}

int kmeans_expectation(matrix data, int *assignments, matrix centers)
{
    int i;
    int converged = 1;
    for (i = 0; i < data.rows; ++i) {
        int closest = closest_center(data.vals[i], centers);
        if (closest != assignments[i]) converged = 0;
        assignments[i] = closest;
    }
    return converged;
}

void kmeans_maximization(matrix data, int *assignments, matrix centers)
{
    matrix old_centers = make_matrix(centers.rows, centers.cols);

    int i, j;
    int *counts = (int*)xcalloc(centers.rows, sizeof(int));
    for (i = 0; i < centers.rows; ++i) {
        for (j = 0; j < centers.cols; ++j) {
            old_centers.vals[i][j] = centers.vals[i][j];
            centers.vals[i][j] = 0;
        }
    }
    for (i = 0; i < data.rows; ++i) {
        ++counts[assignments[i]];
        for (j = 0; j < data.cols; ++j) {
            centers.vals[assignments[i]][j] += data.vals[i][j];
        }
    }
    for (i = 0; i < centers.rows; ++i) {
        if (counts[i]) {
            for (j = 0; j < centers.cols; ++j) {
                centers.vals[i][j] /= counts[i];
            }
        }
    }

    for (i = 0; i < centers.rows; ++i) {
        for (j = 0; j < centers.cols; ++j) {
            if(centers.vals[i][j] == 0) centers.vals[i][j] = old_centers.vals[i][j];
        }
    }
    free(counts);
    free_matrix(old_centers);
}



void random_centers(matrix data, matrix centers) {
    int i;
    int *s = sample(data.rows);
    for (i = 0; i < centers.rows; ++i) {
        copy(data.vals[s[i]], centers.vals[i], data.cols);
    }
    free(s);
}

int *sample(int n)
{
    int i;
    int* s = (int*)xcalloc(n, sizeof(int));
    for (i = 0; i < n; ++i) s[i] = i;
    for (i = n - 1; i >= 0; --i) {
        int swap = s[i];
        int index = rand() % (i + 1);
        s[i] = s[index];
        s[index] = swap;
    }
    return s;
}

float dist(float *x, float *y, int n)
{
    //printf(" x0 = %f, x1 = %f, y0 = %f, y1 = %f \n", x[0], x[1], y[0], y[1]);
    float mw = (x[0] < y[0]) ? x[0] : y[0];
    float mh = (x[1] < y[1]) ? x[1] : y[1];
    float inter = mw*mh;
    float sum = x[0] * x[1] + y[0] * y[1];
    float un = sum - inter;
    float iou = inter / un;
    return 1 - iou;
}

void copy(float *x, float *y, int n)
{
    int i;
    for (i = 0; i < n; ++i) y[i] = x[i];
}

model do_kmeans(matrix data, int k)
{
    matrix centers = make_matrix(k, data.cols);
    int* assignments = (int*)xcalloc(data.rows, sizeof(int));
    //smart_centers(data, centers);
    random_centers(data, centers);  // IoU = 67.31% after kmeans

    /*
    // IoU = 63.29%, anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
    centers.vals[0][0] = 10; centers.vals[0][1] = 13;
    centers.vals[1][0] = 16; centers.vals[1][1] = 30;
    centers.vals[2][0] = 33; centers.vals[2][1] = 23;
    centers.vals[3][0] = 30; centers.vals[3][1] = 61;
    centers.vals[4][0] = 62; centers.vals[4][1] = 45;
    centers.vals[5][0] = 59; centers.vals[5][1] = 119;
    centers.vals[6][0] = 116; centers.vals[6][1] = 90;
    centers.vals[7][0] = 156; centers.vals[7][1] = 198;
    centers.vals[8][0] = 373; centers.vals[8][1] = 326;
    */

    // range centers [min - max] using exp graph or Pyth example
    if (k == 1) kmeans_maximization(data, assignments, centers);
    int i;
    for(i = 0; i < 1000 && !kmeans_expectation(data, assignments, centers); ++i) {
        kmeans_maximization(data, assignments, centers);
    }
    printf("\n iterations = %d \n", i);
    model m;
    m.assignments = assignments;
    m.centers = centers;
    return m;
}