libsvm是基于支持向量機(support vector machine, SVM)實現的開源庫，由臺灣大學林智仁(Chih-Jen Lin)教授等開發，它主要用于分類(支持二分類和多分類)和回歸。它的License是BSD-3-Clause，最新發布版本是v322。libsvm具有操作簡單、易于使用、快速有效、且對SVM所涉及的參數調節相對較少的特點。Code地址： https://github.com/cjlin1/libsvm?

libsvm直接支持的開發語言包括：C++、java、matlab、python。C++主要包括兩個文件，一個是頭文件svm.h，一個是實現文件svm.cpp，將這兩個文件加入到工程中就可以使用libsvm了。

在windows平臺下，可以直接使用libsvm/windows目錄下已經編譯好的執行文件。

關于支持向量機的基礎介紹可以參考： http://blog.csdn.net/fengbingchun/article/details/78326704?

libsvm中支持的svm類型包括：C-SVC(multi-class classification)、nu-SVC(multi-class classification)、one-class SVM、epsilon-SVR(regression)、nu-SVR(regression)；支持的kernel類型包括：linear(u’*v)、polynomial((gamma*u’*v+coef0)^degree)、radial basis function(徑向基, exp(-gamma*|u-v|^2))、sigmoid(tanh(gamma*u’*v+coef0))、precomputed kernel(kernel values in training_set_file)。libsvm中需要的參數雖然較多，但是絕大多數都有其默認值。

libsvm中對數據集進行縮放的目的在于：避免一些特征值范圍過大而另一些特征值范圍過小；避免在訓練時為了計算核函數而計算內積的時候引起數值計算的困難。通常將數據縮放到[-1, 1]或[0, 1]之間。

libsvm使用一般步驟：

(1)、按照libsvm所要求的格式準備數據集；

(2)、對數據進行簡單的縮放操作；

(3)、考慮選用RBF(radial basis function)核參數；

(4)、如果選用RBF，通過libsvm/tools/grid.py采用交叉驗證獲取最佳參數C與gamma；

(5)、采用最佳參數C與g對整個訓練集進行訓練獲取支持向量機模型；

(6)、利用獲取的模型進行測試與預測。

測試代碼執行步驟：

(1)、訓練和測試數據采用UCI/Liver-disorders( https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#liver-disorders?)：二分類、訓練樣本為145、測試樣本為200、特征長度為5，分別保存為liver-disorders_train.txt、liver-disorders_predict.txt；

(2)、通過libsvm/tools/checkdata.py來驗證liver-disorders數據集是否滿足libsvm所要求的數據格式(注：在windows下，此兩個.txt文件編碼格式要由utf-8改為ansi，否則執行checkdata.py會有errror)，執行結果如下：

(3)、調用test_libsvm_scale函數對liver-disorders_train.txt進行縮放操作，并保存scale后的數據liver-disorders_train_scale.txt，并通過checkdata.py驗證數據格式；

(4)、通過liver-disorders_train_scale.txt文件調用libsvm/tools/grid.py生成最優參數C和gamma，執行結果如下：

(5)、train，生成svm.model文件，執行結果如下，與libsvm/windows/svm-train.exe執行結果完全一致：

(6)、predict，執行結果如下，與libsvm/windows/svm-predict.exe執行結果完全一致：

以下二分類測試代碼是參考libsvm中的svm-scale.c、svm-train.c、svm-predict.c：

#include "libsvm.hpp"
#include <iostream>
#include <limits>
#include <algorithm>
#include <fstream>
#include "svm.h"
#include "common.hpp"#define Malloc(type,n) (type *)malloc((n)*sizeof(type))static char* readline(FILE* input, int& max_line_len, char** line)
{if (fgets(*line, max_line_len, input) == nullptr) return nullptr;int len{ 0 };while (strrchr(*line, '\n') == nullptr) {max_line_len *= 2;*line = (char *)realloc(*line, max_line_len);len = (int)strlen(*line);if (fgets(*line + len, max_line_len - len, input) == nullptr) break;}return *line;
}static void output(const double* feature_max, const double* feature_min, double lower, double upper, long int& new_num_nonzeros, int index, double value, std::ofstream& out_file)
{/* skip single-valued attribute */if (feature_max[index] == feature_min[index]) return;if (value == feature_min[index]) value = lower;else if (value == feature_max[index]) value = upper;else value = lower + (upper - lower) * (value - feature_min[index]) / (feature_max[index] - feature_min[index]);if (value != 0) {//fprintf(stdout, "%d:%g ", index, value);out_file << index << ":" << value<<" ";new_num_nonzeros++;}
}int test_libsvm_scale(const char* input_file_name, const char* output_file_name)
{// reference: libsvm/svm-scale.cconst double lower{ -1. }, upper{ 1. }; // lower: x scaling lower limit(default -1); upper: x scaling upper limit(default +1)const double y_lower{ 0. }, y_upper{ 1. }, y_scaling{ 0. }; // y scaling limits (default: no y scaling)double y_max{ std::numeric_limits<double>::max() };double y_min{ std::numeric_limits<double>::lowest() };char* line{ nullptr };int max_line_len{ 1024 };double* feature_max{ nullptr };double* feature_min{ nullptr };int max_index{ 0 }, min_index{ 0 }, index{ 0 };long int num_nonzeros{ 0 }, new_num_nonzeros{ 0 };FILE* fp = fopen(input_file_name, "r");CHECK(nullptr != fp);line = (char *)malloc(max_line_len * sizeof(char));#define SKIP_TARGET \while (isspace(*p)) ++p; \while (!isspace(*p)) ++p;#define SKIP_ELEMENT \while (*p != ':') ++p; \++p; \while (isspace(*p)) ++p; \while (*p && !isspace(*p)) ++p;// pass 1: find out max index of attributesmax_index = 0;min_index = 1;while (readline(fp, max_line_len, &line) != nullptr) {char* p = line;SKIP_TARGETwhile (sscanf(p, "%d:%*f", &index) == 1) {max_index = std::max(max_index, index);min_index = std::min(min_index, index);SKIP_ELEMENTnum_nonzeros++;}}CHECK(min_index >= 1);rewind(fp);feature_max = (double *)malloc((max_index + 1)* sizeof(double));feature_min = (double *)malloc((max_index + 1)* sizeof(double));CHECK(feature_max != nullptr && feature_min != nullptr);for (int i = 0; i <= max_index; ++i) {feature_max[i] = std::numeric_limits<double>::lowest();feature_min[i] = std::numeric_limits<double>::max();}// pass 2: find out min/max valuewhile (readline(fp, max_line_len, &line) != nullptr) {char *p = line;int next_index = 1;double target;double value;CHECK(sscanf(p, "%lf", &target) == 1);y_max = std::max(y_max, target);y_min = std::min(y_min, target);SKIP_TARGETwhile (sscanf(p, "%d:%lf", &index, &value) == 2) {for (int i = next_index; i<index; ++i) {feature_max[i] = std::max(feature_max[i], 0.);feature_min[i] = std::min(feature_min[i], 0.);}feature_max[index] = std::max(feature_max[index], value);feature_min[index] = std::min(feature_min[index], value);SKIP_ELEMENTnext_index = index + 1;}for (int i = next_index; i <= max_index; ++i) {feature_max[i] = std::max(feature_max[i], 0.);feature_min[i] = std::min(feature_min[i], 0.);}}rewind(fp);std::ofstream out_file(output_file_name);CHECK(out_file);// pass 3: scalewhile (readline(fp, max_line_len, &line) != nullptr) {char *p = line;int next_index = 1;double target;double value;CHECK(sscanf(p, "%lf", &target) == 1);//fprintf(stdout, "%g ", target);out_file << target << " ";SKIP_TARGETwhile (sscanf(p, "%d:%lf", &index, &value) == 2) {for (int i = next_index; i<index; ++i)output(feature_max, feature_min, lower, upper, new_num_nonzeros, i, 0, out_file);output(feature_max, feature_min, lower, upper, new_num_nonzeros, index, value, out_file);SKIP_ELEMENTnext_index = index + 1;}for (int i = next_index; i <= max_index; ++i)output(feature_max, feature_min, lower, upper, new_num_nonzeros, i, 0, out_file);//fprintf(stdout, "\n");out_file << std::endl;}CHECK(new_num_nonzeros <= num_nonzeros);free(line);free(feature_max);free(feature_min);fclose(fp);out_file.close();return 0;
}static int read_problem(const char* input_file_name, svm_problem& prob, int& max_line_len, svm_parameter& param, char** line, svm_node** x_space)
{int max_index, inst_max_index;size_t elements, j;char* endptr;char *idx, *val, *label;FILE* fp = fopen(input_file_name, "r");CHECK(fp != nullptr);prob.l = 0;elements = 0;max_line_len = 1024;*line = Malloc(char, max_line_len);while (readline(fp, max_line_len, line) != nullptr) {char *p = strtok(*line, " \t"); // label// featureswhile (1) {p = strtok(nullptr, " \t");if (p == nullptr || *p == '\n') // check '\n' as ' ' may be after the last featurebreak;++elements;}++elements;++prob.l;}rewind(fp);prob.y = Malloc(double, prob.l);prob.x = Malloc(struct svm_node *, prob.l);*x_space = Malloc(struct svm_node, elements);max_index = 0;j = 0;for (int i = 0; i<prob.l; i++) {inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0readline(fp, max_line_len, line);prob.x[i] = &(*x_space)[j];label = strtok(*line, " \t\n");CHECK(label != nullptr); // empty lineprob.y[i] = strtod(label, &endptr);CHECK(!(endptr == label || *endptr != '\0'));while (1) {idx = strtok(nullptr, ":");val = strtok(nullptr, " \t");if (val == nullptr) break;errno = 0;(*x_space)[j].index = (int)strtol(idx, &endptr, 10);if (endptr == idx || errno != 0 || *endptr != '\0' || (*x_space)[j].index <= inst_max_index) {CHECK(0);} else {inst_max_index = (*x_space)[j].index;}errno = 0;(*x_space)[j].value = strtod(val, &endptr);if (endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr))) CHECK(0);++j;}if (inst_max_index > max_index) max_index = inst_max_index;(*x_space)[j++].index = -1;}if (param.gamma == 0 && max_index > 0) param.gamma = 1.0 / max_index;if (param.kernel_type == PRECOMPUTED) {for (int i = 0; i<prob.l; i++) {CHECK(prob.x[i][0].index == 0);if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index) {CHECK(0);}}}fclose(fp);return 0;
}int test_libsvm_two_classification_train()
{// reference: libsvm/svm-train.cconst std::string input_file_name{ "E:/GitCode/NN_Test/data/database/liver-disorders/liver-disorders_train.txt" },output_file_name{ "E:/GitCode/NN_Test/data/database/liver-disorders/liver-disorders_train_scale.txt" },svm_model{"E:/GitCode/NN_Test/data/svm.model"};CHECK(0 == test_libsvm_scale(input_file_name.c_str(), output_file_name.c_str())); // data scalestruct svm_parameter param = {};struct svm_problem prob = {};struct svm_model* model = nullptr;struct svm_node* x_space = nullptr;int cross_validation{ 0 };int nr_fold{0};char* line = nullptr;int max_line_len{0};param = { C_SVC, RBF, 3, 0.0078125, 0., 100, 1e-3, 512., 0, nullptr, nullptr, 0.5, 0.1, 1, 0 };CHECK(read_problem(output_file_name.c_str(), prob, max_line_len, param, &line, &x_space) == 0);CHECK(svm_check_parameter(&prob, ?m) == nullptr);model = svm_train(&prob, ?m);CHECK(svm_save_model(svm_model.c_str(), model) == 0);svm_free_and_destroy_model(&model);svm_destroy_param(?m);free(prob.y);free(prob.x);free(x_space);free(line);return 0;
}static int predict(FILE* input, FILE* output, const svm_model* model, int& max_line_len, char** line, int& max_nr_attr, svm_node** x)
{int correct = 0;int total = 0;double error = 0;double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;int svm_type = svm_get_svm_type(model);int nr_class = svm_get_nr_class(model);double *prob_estimates = nullptr;int j;max_line_len = 1024;*line = (char *)malloc(max_line_len*sizeof(char));while (readline(input, max_line_len, line) != nullptr) {int i = 0;double target_label, predict_label;char *idx, *val, *label, *endptr;int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0label = strtok(*line, " \t\n");CHECK(label != nullptr); // empty linetarget_label = strtod(label, &endptr);CHECK(!(endptr == label || *endptr != '\0'));while (1) {if (i >= max_nr_attr - 1) { // need one more for index = -1max_nr_attr *= 2;*x = (struct svm_node *) realloc(*x, max_nr_attr*sizeof(struct svm_node));}idx = strtok(nullptr, ":");val = strtok(nullptr, " \t");if (val == nullptr) break;errno = 0;(*x)[i].index = (int)strtol(idx, &endptr, 10);CHECK(!(endptr == idx || errno != 0 || *endptr != '\0' || (*x)[i].index <= inst_max_index));inst_max_index = (*x)[i].index;errno = 0;(*x)[i].value = strtod(val, &endptr);CHECK(!(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr))));++i;}(*x)[i].index = -1;predict_label = svm_predict(model, *x);fprintf(output, "%g\n", predict_label);if (predict_label == target_label) ++correct;error += (predict_label - target_label)*(predict_label - target_label);sump += predict_label;sumt += target_label;sumpp += predict_label*predict_label;sumtt += target_label*target_label;sumpt += predict_label*target_label;++total;}fprintf(stdout, "Accuracy = %g%% (%d/%d) (classification)\n", (double)correct / total * 100, correct, total);return 0;
}int test_libsvm_two_classification_predict()
{// reference: libsvm/svm-predict.cconst std::string input_file_name{ "E:/GitCode/NN_Test/data/database/liver-disorders/liver-disorders_predict.txt" },scale_file_name{ "E:/GitCode/NN_Test/data/database/liver-disorders/liver-disorders_predict_scale.txt" },svm_model{ "E:/GitCode/NN_Test/data/svm.model" },predict_result_file_name{ "E:/GitCode/NN_Test/data/svm_predict_result.txt" };CHECK(0 == test_libsvm_scale(input_file_name.c_str(), scale_file_name.c_str())); // data scalestruct svm_node* x = nullptr;int max_nr_attr = 64;struct svm_model* model = nullptr;int predict_probability = 0;char* line = nullptr;int max_line_len = 0;FILE* input = fopen(scale_file_name.c_str(), "r");CHECK(input != nullptr);FILE* output = fopen(predict_result_file_name.c_str(), "w");CHECK(output != nullptr);CHECK((model = svm_load_model(svm_model.c_str())) != nullptr);x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));CHECK(svm_check_probability_model(model) == 0);predict(input, output, model, max_line_len, &line, max_nr_attr, &x);svm_free_and_destroy_model(&model);free(x);free(line);fclose(input);fclose(output);return 0;
}

GitHub： https://github.com/fengbingchun/NN_Test?

總結

以上是生活随笔為你收集整理的libsvm库简介及使用的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。