如何为libsvm创建训练数据(作为svm_node结构)

Question

我正在尝试使用libsvm以编程方式为一个简单的xor问题训练一个svm来理解库的工作原理.问题(我认为)似乎是我错误地构造了svm_node; 也许我无法理解指针的整个指针.有人可以帮忙吗？我首先为xor问题构造一个矩阵,然后尝试将矩阵中的值赋给svm_node(我在这里使用2个步骤,因为我的实际数据将采用矩阵格式).

测试模型时,我得到的值不正确(总是-1).

在上一个问题中,我得到了参数C和gamma的帮助; 这些应该没问题,因为我使用其他代码得到了xor问题的正确分类.再次感谢Pedrom!

我在几个地方搜索了答案,例如自述文件和SvmToy示例; 然而,没有运气.

这是输出错误分类的代码......

提前致谢!

//Parameters---------------------------------------------------------------------
svm_parameter param;
param.svm_type = C_SVC;
param.kernel_type = RBF;
param.degree = 3;
param.gamma = 0.5;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 1;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;


//Problem definition-------------------------------------------------------------
svm_problem prob;

//Length, 4 examples
prob.l = 4;

//x values matrix of xor values
QVector< QVector<double> >matrix;
QVector<double>row(2);

row[0] = 1;row[1] = 1;
matrix.push_back(row);
row[0] = 1;row[1] = 0;
matrix.push_back(row);
row[0] = 0;row[1] = 1;
matrix.push_back(row);
row[0] = 0;row[1] = 0;
matrix.push_back(row);

//This part i have trouble understanding
svm_node* x_space = new svm_node[3];
svm_node** x = new svm_node *[prob.l];

//Trying to assign from matrix to svm_node training examples
for (int row = 0;row < matrix.size(); row++){
    for (int col = 0;col < 2;col++){
        x_space[col].index = col;
        x_space[col].value = matrix[row][col];
    }
    x_space[2].index = -1;      //Each row of properties should be terminated with a -1 according to the readme
    x[row] = x_space;
}

prob.x = x;

//yvalues
prob.y = new double[prob.l];
prob.y[0] = -1;
prob.y[1] = 1;
prob.y[2] = 1;
prob.y[3] = -1;

//Train model---------------------------------------------------------------------
svm_model *model = svm_train(&prob,&param);


//Test model----------------------------------------------------------------------
svm_node* testnode = new svm_node[3];
testnode[0].index = 0;
testnode[0].value = 1;
testnode[1].index = 1;
testnode[1].value = 0;
testnode[2].index = -1;

//Should return 1 but returns -1
double retval = svm_predict(model,testnode);
qDebug()<<retval;

Answer 1

看来你一直试图让这个例子工作数周.我遵循了libsvm附带的svm-train.c中的样式.我用你的C和gamma值.这是工作.我尝试了XOR示例中的所有点,它给出了正确的结果.

您遇到的问题的摘要是您没有为您训练的4个数据点分配空间,因此您只需覆盖数据.这是C中指针的一个典型错误.它可能会帮助你刷新C/C++中的指针.

这是代码:

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

struct svm_parameter param;     // set by parse_command_line
struct svm_problem prob;        // set by read_problem
struct svm_model *model;
struct svm_node *x_space;

int main(int argc, char **argv)
{
    char input_file_name[1024];
    char model_file_name[1024];
    const char *error_msg;

    param.svm_type = C_SVC;
    param.kernel_type = RBF;
    param.degree = 3;
    param.gamma = 0.5;
    param.coef0 = 0;
    param.nu = 0.5;
    param.cache_size = 100;
    param.C = 1;
    param.eps = 1e-3;
    param.p = 0.1;
    param.shrinking = 1;
    param.probability = 0;
    param.nr_weight = 0;
    param.weight_label = NULL;
    param.weight = NULL;


    //Problem definition-------------------------------------------------------------
    prob.l = 4;

    //x values matrix of xor values
    double matrix[prob.l][2];
    matrix[0][0] = 1;
    matrix[0][1] = 1;

    matrix[1][0] = 1;
    matrix[1][1] = 0;

    matrix[2][0] = 0;
    matrix[2][1] = 1;

    matrix[3][0] = 0;
    matrix[3][1] = 0;


    //This part i have trouble understanding
    svm_node** x = Malloc(svm_node*,prob.l);

    //Trying to assign from matrix to svm_node training examples
    for (int row = 0;row <prob.l; row++){
        svm_node* x_space = Malloc(svm_node,3);
        for (int col = 0;col < 2;col++){
            x_space[col].index = col;
            x_space[col].value = matrix[row][col];
        }
        x_space[2].index = -1;      //Each row of properties should be terminated with a -1 according to the readme
        x[row] = x_space;
    }

    prob.x = x;

    //yvalues
    prob.y = Malloc(double,prob.l);
    prob.y[0] = -1;
    prob.y[1] = 1;
    prob.y[2] = 1;
    prob.y[3] = -1;

    //Train model---------------------------------------------------------------------
    svm_model *model = svm_train(&prob,&param);


    //Test model----------------------------------------------------------------------
    svm_node* testnode = Malloc(svm_node,3);
    testnode[0].index = 0;
    testnode[0].value = 1;
    testnode[1].index = 1;
    testnode[1].value = 0;
    testnode[2].index = -1;

    //This works correctly:
    double retval = svm_predict(model,testnode);
    printf("retval: %f\n",retval);


    svm_destroy_param(&param);
    free(prob.y);
    free(prob.x);
    free(x_space);

    return 0;
}