don't need these either
This commit is contained in:
Cowwan
parent
2bcd52a8da
commit
d76d5ae884
4 changed files with 0 additions and 4122 deletions
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@ -1,211 +0,0 @@
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// SVMWrapper.cpp : Defines the exported functions for the DLL application.
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//
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#include "stdafx.h"
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#include <cmath>
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#include "svm.h"
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using namespace std;
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extern "C" void __cdecl print_log(const char *str) {
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return;
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}
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struct SVMWrapper {
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struct svm_model *model;
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int cols;
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int classes;
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double *means;
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double *sdevs;
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struct svm_parameter param;
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struct svm_problem *problem;
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};
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typedef struct svm_node svmnode;
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svmnode **make_svmnode_array(float features[], int rows, int cols, double *means, double *sdevs, int rescale) {
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svmnode **x = new svmnode*[rows];
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svmnode *nodes = new svmnode[rows * (cols + 1)];
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double row_factor = 1.0 / (double)rows;
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if (rescale) {
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for (int c = 0; c < cols; c++) {
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means[c] = 0;
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sdevs[c] = 0;
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}
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for (int r = 0; r < rows; r++) {
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for (int c = 0; c < cols; c++) {
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means[c] += (double)features[r * cols + c];
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}
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}
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for (int c = 0; c < cols; c++) {
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means[c] *= row_factor;
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}
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for (int r = 0; r < rows; r++) {
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for (int c = 0; c < cols; c++) {
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double sq_diff = ((double)features[r * cols + c] - means[c]);
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sq_diff *= sq_diff;
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sdevs[c] += sq_diff;
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}
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}
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for (int c = 0; c < cols; c++) {
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sdevs[c] *= row_factor;
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sdevs[c] = sqrt(sdevs[c]);
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sdevs[c] = 1.0 / sdevs[c];
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}
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}
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for (int r = 0; r < rows; r++) {
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x[r] = &(nodes[r * (cols + 1)]);
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int idx = 0;
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for (int c = 0; c < cols; c++) {
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x[r][c].index = c + 1;
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x[r][c].value = ((double)features[r * cols + c] - means[c]) * sdevs[c];
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}
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x[r][cols].index = -1;
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}
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return x;
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}
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void make_problem(struct svm_problem *problem, float features[], float labels[], int rows, int cols, double *means, double *sdevs) {
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problem->l = rows;
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problem->y = new double[rows];
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for (int i = 0; i < rows; i++)
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problem->y[i] = (double)labels[i];
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problem->x = make_svmnode_array(features, rows, cols, means, sdevs, 1);
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}
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void destroy_svmnode_array(svmnode **x) {
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delete x[0];
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delete x;
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}
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void destroy_problem(struct svm_problem *problem) {
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delete problem->y;
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destroy_svmnode_array(problem->x);
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}
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extern "C" {
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__declspec(dllexport) void *__cdecl trainModel(float features[], float labels[], float weights[], int rows, int cols, int classes, int probability, float C) {
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if (!(rows > 0 && cols > 0))
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return NULL;
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svm_set_print_string_function(print_log);
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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sw->param.svm_type = C_SVC;
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sw->param.kernel_type = RBF;
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sw->param.degree = 3;
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sw->param.gamma = 1.0 / (double)cols;
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sw->param.coef0 = 0;
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sw->param.nu = 0.5;
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sw->param.cache_size = 100;
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sw->param.C = C;
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sw->param.eps = 0.001;
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sw->param.p = 0.1;
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sw->param.shrinking = 1;
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sw->param.probability = probability;
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sw->param.nr_weight = 0;
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sw->param.weight_label = NULL;
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sw->param.weight = NULL;
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if (weights != NULL) {
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sw->param.nr_weight = classes;
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sw->param.weight_label = new int[classes];
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sw->param.weight = new double[classes];
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for (int i = 0; i < classes; i++) {
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sw->param.weight_label[i] = i;
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sw->param.weight[i] = (double)weights[i];
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}
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}
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sw->problem = new struct svm_problem();
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make_problem(sw->problem, features, labels, rows, cols, sw->means, sw->sdevs);
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sw->model = svm_train(sw->problem, &sw->param);
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if (weights != NULL) {
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delete sw->param.weight_label;
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delete sw->param.weight;
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}
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return sw;
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}
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__declspec(dllexport) void __cdecl predict(void *ptr, float features[], float predictions[], double probabilities[], int rows) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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int cols = sw->cols;
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svmnode **x = make_svmnode_array(features, rows, cols, sw->means, sw->sdevs, 0);
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for (int r = 0; r < rows; r++)
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predictions[r] = (float)svm_predict_probability(sw->model, x[r], &(probabilities[r * sw->classes]));
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destroy_svmnode_array(x);
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return;
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}
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__declspec(dllexport) void *__cdecl loadModel(char *filename, int cols, int classes, double means[], double sdevs[]) {
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->model = svm_load_model(filename);
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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for (int c = 0; c < cols; c++) {
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sw->means[c] = means[c];
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sw->sdevs[c] = sdevs[c];
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}
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return sw;
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}
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__declspec(dllexport) void *__cdecl loadModelString(char *modelString, int cols, int classes, double means[], double sdevs[]) {
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->model = svm_load_model_string(modelString);
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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for (int c = 0; c < cols; c++) {
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sw->means[c] = means[c];
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sw->sdevs[c] = sdevs[c];
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}
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sw->problem = NULL;
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return sw;
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}
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__declspec(dllexport) void __cdecl getScales(void *ptr, double means[], double sdevs[]) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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int cols = sw->cols;
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for (int c = 0; c < cols; c++) {
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means[c] = sw->means[c];
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sdevs[c] = sw->sdevs[c];
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}
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}
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__declspec(dllexport) char *__cdecl saveModelString (void *ptr) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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return svm_save_model_string(sw->model);
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}
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__declspec(dllexport) int __cdecl saveModel(void *ptr, char *filename) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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return svm_save_model(filename, sw->model);
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}
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__declspec(dllexport) void __cdecl destroyModel(void *ptr) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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delete sw->means;
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delete sw->sdevs;
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svm_free_and_destroy_model(&sw->model);
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if (sw->problem != NULL) {
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destroy_problem(sw->problem);
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delete sw->problem;
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}
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delete sw;
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}
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}
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// ThunderSVMWrapper.cpp : Defines the exported functions for the DLL.
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//
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#include "pch.h"
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#include "framework.h"
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#include "ThunderSVMWrapper.h"
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#include "thundersvm/thundersvm.h"
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#include "thundersvm/model/svmmodel.h"
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#include "thundersvm/model/svc.h"
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#include <cmath>
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using namespace std;
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extern "C" void __cdecl print_log(const char *str) {
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return;
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}
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struct problem {
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int r;
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int c;
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DataSet::node2d x;
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vector<double> y;
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DataSet dataset;
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};
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struct SVMWrapper {
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SVC *model;
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int cols;
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int classes;
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double *means;
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double *sdevs;
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SvmParam param;
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struct problem problem;
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};
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typedef struct svm_node svmnode;
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void make_svmnode_array(DataSet::node2d &dataset, float features[], int rows, int cols, double *means, double *sdevs, int rescale) {
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double row_factor = 1.0 / (double)rows;
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if (rescale) {
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for (int c = 0; c < cols; c++) {
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means[c] = 0;
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sdevs[c] = 0;
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}
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for (int r = 0; r < rows; r++) {
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for (int c = 0; c < cols; c++) {
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means[c] += (double)features[r * cols + c];
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}
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}
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for (int c = 0; c < cols; c++) {
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means[c] *= row_factor;
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}
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for (int r = 0; r < rows; r++) {
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for (int c = 0; c < cols; c++) {
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double sq_diff = ((double)features[r * cols + c] - means[c]);
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sq_diff *= sq_diff;
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sdevs[c] += sq_diff;
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}
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}
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for (int c = 0; c < cols; c++) {
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sdevs[c] *= row_factor;
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sdevs[c] = sqrt(sdevs[c]);
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sdevs[c] = 1.0 / sdevs[c];
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}
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}
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for (int r = 0; r < rows; r++) {
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dataset[r] = vector<DataSet::node>(cols);
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int idx = 0;
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for (int c = 0; c < cols; c++) {
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dataset[r][c].index = c + 1;
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dataset[r][c].value = (float)((features[r * cols + c] - means[c]) * sdevs[c]);
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}
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}
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}
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void make_problem(struct problem &problem, float features[], float labels[], int rows, int cols, double *means, double *sdevs) {
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problem.r = rows;
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problem.c = cols;
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problem.y = vector<double>(rows);
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for (int i = 0; i < rows; i++)
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problem.y[i] = (double)labels[i];
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problem.x = DataSet::node2d(rows);
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make_svmnode_array(problem.x, features, rows, cols, means, sdevs, 1);
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problem.dataset = DataSet(problem.x, cols, problem.y);
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}
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void destroy_problem(struct problem &problem) {
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problem.y.clear();
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problem.x.clear();
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}
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extern "C" {
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__declspec(dllexport) void *__cdecl trainModel(float features[], float labels[], float weights[], int rows, int cols, int classes, int probability, float C) {
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if (!(rows > 0 && cols > 0))
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return NULL;
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//svm_set_print_string_function(print_log);
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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sw->param.svm_type = SvmParam::C_SVC;
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sw->param.kernel_type = SvmParam::RBF;
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sw->param.degree = 3;
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sw->param.gamma = 1.0 / (double)cols;
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sw->param.coef0 = 0;
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sw->param.nu = 0.5;
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sw->param.C = C;
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sw->param.epsilon = 0.001;
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sw->param.p = 0.1;
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sw->param.probability = probability;
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sw->param.nr_weight = 0;
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sw->param.weight_label = NULL;
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sw->param.weight = NULL;
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if (weights != NULL) {
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sw->param.nr_weight = classes;
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sw->param.weight_label = new int[classes];
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sw->param.weight = new double[classes];
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for (int i = 0; i < classes; i++) {
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sw->param.weight_label[i] = i;
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sw->param.weight[i] = (double)weights[i];
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}
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}
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make_problem(sw->problem, features, labels, rows, cols, sw->means, sw->sdevs);
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sw->model = new SVC();//svm_train(sw->problem, &sw->param);
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sw->model->train(sw->problem.dataset, sw->param);
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if (weights != NULL) {
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delete sw->param.weight_label;
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delete sw->param.weight;
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}
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return sw;
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}
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__declspec(dllexport) void __cdecl predict(void *ptr, float features[], float predictions[], double probabilities[], int rows) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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int cols = sw->cols;
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DataSet::node2d instances = DataSet::node2d(rows);
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make_svmnode_array(instances, features, rows, sw->cols, sw->means, sw->sdevs, 0);
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vector<double> predicted = sw->model->predict(instances, -1);
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instances.clear();
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for (int r = 0; r < rows; r++)
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predictions[r] = (float)predicted[r];
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if (probabilities != NULL && sw->param.probability) {
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vector<float> probs = sw->model->get_prob_predict();
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for (int r = 0; r < rows; r++)
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for (int c = 0; c < sw->classes; c++)
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probabilities[c + r * sw->classes] = probs[c + r * sw->classes];
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}
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predicted.clear();
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return;
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}
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__declspec(dllexport) void *__cdecl loadModel(char *filename, int cols, int classes, double means[], double sdevs[]) {
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->model = new SVC();
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sw->model->load_from_file(filename);
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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for (int c = 0; c < cols; c++) {
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sw->means[c] = means[c];
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sw->sdevs[c] = sdevs[c];
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}
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return sw;
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}
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__declspec(dllexport) void *__cdecl loadModelString(char *modelString, int cols, int classes, double means[], double sdevs[]) {
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struct SVMWrapper *sw = new SVMWrapper();
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sw->cols = cols;
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sw->model = new SVC();
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sw->model->load_from_string(modelString);
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sw->classes = classes;
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sw->means = new double[cols];
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sw->sdevs = new double[cols];
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for (int c = 0; c < cols; c++) {
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sw->means[c] = means[c];
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sw->sdevs[c] = sdevs[c];
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}
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return sw;
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}
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__declspec(dllexport) void __cdecl getScales(void *ptr, double means[], double sdevs[]) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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int cols = sw->cols;
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for (int c = 0; c < cols; c++) {
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means[c] = sw->means[c];
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sdevs[c] = sw->sdevs[c];
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}
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}
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__declspec(dllexport) const char *__cdecl saveModelString(void *ptr) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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string model = sw->model->save_to_string();
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return _strdup(model.c_str());
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}
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__declspec(dllexport) int __cdecl saveModel(void *ptr, char *filename) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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sw->model->save_to_file(string(filename));
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return 1;
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}
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__declspec(dllexport) void __cdecl destroyModel(void *ptr) {
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struct SVMWrapper *sw = (struct SVMWrapper *)ptr;
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delete sw->means;
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delete sw->sdevs;
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delete sw->model;
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destroy_problem(sw->problem);
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delete sw;
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}
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}
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3583
Source/svm.cpp
3583
Source/svm.cpp
File diff suppressed because it is too large
Load diff
106
Source/svm.h
106
Source/svm.h
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@ -1,106 +0,0 @@
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#ifndef _LIBSVM_H
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#define _LIBSVM_H
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#define LIBSVM_VERSION 324
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#ifdef __cplusplus
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extern "C" {
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#endif
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extern int libsvm_version;
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struct svm_node
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{
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int index;
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double value;
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};
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struct svm_problem
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{
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int l;
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double *y;
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struct svm_node **x;
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};
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enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; /* svm_type */
|
||||
enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */
|
||||
|
||||
struct svm_parameter
|
||||
{
|
||||
int svm_type;
|
||||
int kernel_type;
|
||||
int degree; /* for poly */
|
||||
double gamma; /* for poly/rbf/sigmoid */
|
||||
double coef0; /* for poly/sigmoid */
|
||||
|
||||
/* these are for training only */
|
||||
double cache_size; /* in MB */
|
||||
double eps; /* stopping criteria */
|
||||
double C; /* for C_SVC, EPSILON_SVR and NU_SVR */
|
||||
int nr_weight; /* for C_SVC */
|
||||
int *weight_label; /* for C_SVC */
|
||||
double* weight; /* for C_SVC */
|
||||
double nu; /* for NU_SVC, ONE_CLASS, and NU_SVR */
|
||||
double p; /* for EPSILON_SVR */
|
||||
int shrinking; /* use the shrinking heuristics */
|
||||
int probability; /* do probability estimates */
|
||||
};
|
||||
|
||||
//
|
||||
// svm_model
|
||||
//
|
||||
struct svm_model
|
||||
{
|
||||
struct svm_parameter param; /* parameter */
|
||||
int nr_class; /* number of classes, = 2 in regression/one class svm */
|
||||
int l; /* total #SV */
|
||||
struct svm_node **SV; /* SVs (SV[l]) */
|
||||
double **sv_coef; /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
|
||||
double *rho; /* constants in decision functions (rho[k*(k-1)/2]) */
|
||||
double *probA; /* pariwise probability information */
|
||||
double *probB;
|
||||
int *sv_indices; /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
|
||||
|
||||
/* for classification only */
|
||||
|
||||
int *label; /* label of each class (label[k]) */
|
||||
int *nSV; /* number of SVs for each class (nSV[k]) */
|
||||
/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
|
||||
/* XXX */
|
||||
int free_sv; /* 1 if svm_model is created by svm_load_model*/
|
||||
/* 0 if svm_model is created by svm_train */
|
||||
};
|
||||
|
||||
struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
|
||||
void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);
|
||||
|
||||
int svm_save_model(const char *model_file_name, const struct svm_model *model);
|
||||
char *svm_save_model_string(const svm_model *model);
|
||||
struct svm_model *svm_load_model(const char *model_file_name);
|
||||
struct svm_model *svm_load_model_string(char *model_string);
|
||||
|
||||
int svm_get_svm_type(const struct svm_model *model);
|
||||
int svm_get_nr_class(const struct svm_model *model);
|
||||
void svm_get_labels(const struct svm_model *model, int *label);
|
||||
void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
|
||||
int svm_get_nr_sv(const struct svm_model *model);
|
||||
double svm_get_svr_probability(const struct svm_model *model);
|
||||
|
||||
double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
|
||||
double svm_predict(const struct svm_model *model, const struct svm_node *x);
|
||||
double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);
|
||||
|
||||
void svm_free_model_content(struct svm_model *model_ptr);
|
||||
void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
|
||||
void svm_destroy_param(struct svm_parameter *param);
|
||||
|
||||
const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
|
||||
int svm_check_probability_model(const struct svm_model *model);
|
||||
|
||||
void svm_set_print_string_function(void (*print_func)(const char *));
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
#endif /* _LIBSVM_H */
|
||||
Loading…
Add table
Add a link
Reference in a new issue