New expression detection code
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Emiliana
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83e400a1d1
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3a83d96827
16 changed files with 4734 additions and 2 deletions
192
Source/SVMWrapper.cpp
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192
Source/SVMWrapper.cpp
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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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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[], int rows, int cols) {
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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->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 = 4;
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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 = 0;
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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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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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return sw;
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}
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__declspec(dllexport) void __cdecl predict(void *ptr, float features[], float predictions[], 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(sw->model, x[r]);
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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, 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->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, 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->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 getModel(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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3583
Source/svm.cpp
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3583
Source/svm.cpp
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File diff suppressed because it is too large
Load diff
106
Source/svm.h
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106
Source/svm.h
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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 */
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enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */
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struct svm_parameter
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{
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int svm_type;
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int kernel_type;
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int degree; /* for poly */
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double gamma; /* for poly/rbf/sigmoid */
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double coef0; /* for poly/sigmoid */
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/* these are for training only */
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double cache_size; /* in MB */
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double eps; /* stopping criteria */
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double C; /* for C_SVC, EPSILON_SVR and NU_SVR */
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int nr_weight; /* for C_SVC */
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int *weight_label; /* for C_SVC */
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double* weight; /* for C_SVC */
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double nu; /* for NU_SVC, ONE_CLASS, and NU_SVR */
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double p; /* for EPSILON_SVR */
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int shrinking; /* use the shrinking heuristics */
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int probability; /* do probability estimates */
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};
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//
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// svm_model
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//
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struct svm_model
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{
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struct svm_parameter param; /* parameter */
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int nr_class; /* number of classes, = 2 in regression/one class svm */
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int l; /* total #SV */
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struct svm_node **SV; /* SVs (SV[l]) */
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double **sv_coef; /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */
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double *rho; /* constants in decision functions (rho[k*(k-1)/2]) */
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double *probA; /* pariwise probability information */
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double *probB;
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int *sv_indices; /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */
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/* for classification only */
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int *label; /* label of each class (label[k]) */
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int *nSV; /* number of SVs for each class (nSV[k]) */
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/* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
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/* XXX */
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int free_sv; /* 1 if svm_model is created by svm_load_model*/
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/* 0 if svm_model is created by svm_train */
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};
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struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param);
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void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target);
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int svm_save_model(const char *model_file_name, const struct svm_model *model);
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char *svm_save_model_string(const svm_model *model);
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struct svm_model *svm_load_model(const char *model_file_name);
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struct svm_model *svm_load_model_string(char *model_string);
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int svm_get_svm_type(const struct svm_model *model);
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int svm_get_nr_class(const struct svm_model *model);
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void svm_get_labels(const struct svm_model *model, int *label);
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void svm_get_sv_indices(const struct svm_model *model, int *sv_indices);
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int svm_get_nr_sv(const struct svm_model *model);
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double svm_get_svr_probability(const struct svm_model *model);
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double svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values);
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double svm_predict(const struct svm_model *model, const struct svm_node *x);
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double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates);
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void svm_free_model_content(struct svm_model *model_ptr);
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void svm_free_and_destroy_model(struct svm_model **model_ptr_ptr);
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void svm_destroy_param(struct svm_parameter *param);
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const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param);
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int svm_check_probability_model(const struct svm_model *model);
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void svm_set_print_string_function(void (*print_func)(const char *));
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#ifdef __cplusplus
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}
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#endif
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#endif /* _LIBSVM_H */
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