371 lines
10 KiB
C
371 lines
10 KiB
C
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include "../svm.h"
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#include "mex.h"
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#include "svm_model_matlab.h"
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#ifdef MX_API_VER
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#if MX_API_VER < 0x07030000
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typedef int mwIndex;
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#endif
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#endif
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#define CMD_LEN 2048
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int print_null(const char *s,...) {}
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int (*info)(const char *fmt,...) = &mexPrintf;
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void read_sparse_instance(const mxArray *prhs, int index, struct svm_node *x)
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{
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int i, j, low, high;
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mwIndex *ir, *jc;
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double *samples;
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ir = mxGetIr(prhs);
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jc = mxGetJc(prhs);
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samples = mxGetPr(prhs);
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// each column is one instance
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j = 0;
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low = (int)jc[index], high = (int)jc[index+1];
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for(i=low;i<high;i++)
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{
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x[j].index = (int)ir[i] + 1;
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x[j].value = samples[i];
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j++;
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}
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x[j].index = -1;
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}
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static void fake_answer(int nlhs, mxArray *plhs[])
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{
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int i;
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for(i=0;i<nlhs;i++)
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plhs[i] = mxCreateDoubleMatrix(0, 0, mxREAL);
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}
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void predict(int nlhs, mxArray *plhs[], const mxArray *prhs[], struct svm_model *model, const int predict_probability)
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{
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int label_vector_row_num, label_vector_col_num;
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int feature_number, testing_instance_number;
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int instance_index;
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double *ptr_instance, *ptr_label, *ptr_predict_label;
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double *ptr_prob_estimates, *ptr_dec_values, *ptr;
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struct svm_node *x;
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mxArray *pplhs[1]; // transposed instance sparse matrix
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mxArray *tplhs[3]; // temporary storage for plhs[]
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int correct = 0;
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int total = 0;
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double error = 0;
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double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
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int svm_type=svm_get_svm_type(model);
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int nr_class=svm_get_nr_class(model);
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double *prob_estimates=NULL;
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// prhs[1] = testing instance matrix
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feature_number = (int)mxGetN(prhs[1]);
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testing_instance_number = (int)mxGetM(prhs[1]);
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label_vector_row_num = (int)mxGetM(prhs[0]);
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label_vector_col_num = (int)mxGetN(prhs[0]);
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if(label_vector_row_num!=testing_instance_number)
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{
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mexPrintf("Length of label vector does not match # of instances.\n");
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fake_answer(nlhs, plhs);
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return;
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}
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if(label_vector_col_num!=1)
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{
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mexPrintf("label (1st argument) should be a vector (# of column is 1).\n");
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fake_answer(nlhs, plhs);
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return;
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}
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ptr_instance = mxGetPr(prhs[1]);
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ptr_label = mxGetPr(prhs[0]);
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// transpose instance matrix
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if(mxIsSparse(prhs[1]))
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{
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if(model->param.kernel_type == PRECOMPUTED)
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{
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// precomputed kernel requires dense matrix, so we make one
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mxArray *rhs[1], *lhs[1];
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rhs[0] = mxDuplicateArray(prhs[1]);
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if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
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{
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mexPrintf("Error: cannot full testing instance matrix\n");
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fake_answer(nlhs, plhs);
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return;
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}
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ptr_instance = mxGetPr(lhs[0]);
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mxDestroyArray(rhs[0]);
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}
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else
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{
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mxArray *pprhs[1];
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pprhs[0] = mxDuplicateArray(prhs[1]);
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if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
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{
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mexPrintf("Error: cannot transpose testing instance matrix\n");
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fake_answer(nlhs, plhs);
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return;
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}
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}
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}
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if(predict_probability)
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{
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if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
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info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
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else
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prob_estimates = (double *) malloc(nr_class*sizeof(double));
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}
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tplhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
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if(predict_probability)
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{
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// prob estimates are in plhs[2]
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if(svm_type==C_SVC || svm_type==NU_SVC)
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tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL);
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else
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tplhs[2] = mxCreateDoubleMatrix(0, 0, mxREAL);
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}
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else
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{
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// decision values are in plhs[2]
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if(svm_type == ONE_CLASS ||
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svm_type == EPSILON_SVR ||
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svm_type == NU_SVR ||
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nr_class == 1) // if only one class in training data, decision values are still returned.
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tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
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else
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tplhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class*(nr_class-1)/2, mxREAL);
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}
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ptr_predict_label = mxGetPr(tplhs[0]);
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ptr_prob_estimates = mxGetPr(tplhs[2]);
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ptr_dec_values = mxGetPr(tplhs[2]);
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x = (struct svm_node*)malloc((feature_number+1)*sizeof(struct svm_node) );
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for(instance_index=0;instance_index<testing_instance_number;instance_index++)
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{
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int i;
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double target_label, predict_label;
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target_label = ptr_label[instance_index];
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if(mxIsSparse(prhs[1]) && model->param.kernel_type != PRECOMPUTED) // prhs[1]^T is still sparse
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read_sparse_instance(pplhs[0], instance_index, x);
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else
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{
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for(i=0;i<feature_number;i++)
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{
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x[i].index = i+1;
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x[i].value = ptr_instance[testing_instance_number*i+instance_index];
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}
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x[feature_number].index = -1;
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}
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if(predict_probability)
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{
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if(svm_type==C_SVC || svm_type==NU_SVC)
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{
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predict_label = svm_predict_probability(model, x, prob_estimates);
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ptr_predict_label[instance_index] = predict_label;
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for(i=0;i<nr_class;i++)
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ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i];
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} else {
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predict_label = svm_predict(model,x);
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ptr_predict_label[instance_index] = predict_label;
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}
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}
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else
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{
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if(svm_type == ONE_CLASS ||
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svm_type == EPSILON_SVR ||
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svm_type == NU_SVR)
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{
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double res;
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predict_label = svm_predict_values(model, x, &res);
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ptr_dec_values[instance_index] = res;
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}
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else
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{
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double *dec_values = (double *) malloc(sizeof(double) * nr_class*(nr_class-1)/2);
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predict_label = svm_predict_values(model, x, dec_values);
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if(nr_class == 1)
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ptr_dec_values[instance_index] = 1;
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else
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for(i=0;i<(nr_class*(nr_class-1))/2;i++)
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ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i];
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free(dec_values);
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}
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ptr_predict_label[instance_index] = predict_label;
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}
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if(predict_label == target_label)
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++correct;
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error += (predict_label-target_label)*(predict_label-target_label);
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sump += predict_label;
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sumt += target_label;
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sumpp += predict_label*predict_label;
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sumtt += target_label*target_label;
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sumpt += predict_label*target_label;
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++total;
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}
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if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
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{
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info("Mean squared error = %g (regression)\n",error/total);
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info("Squared correlation coefficient = %g (regression)\n",
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((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
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((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
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);
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}
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else
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info("Accuracy = %g%% (%d/%d) (classification)\n",
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(double)correct/total*100,correct,total);
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// return accuracy, mean squared error, squared correlation coefficient
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tplhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL);
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ptr = mxGetPr(tplhs[1]);
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ptr[0] = (double)correct/total*100;
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ptr[1] = error/total;
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ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
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((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt));
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free(x);
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if(prob_estimates != NULL)
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free(prob_estimates);
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switch(nlhs)
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{
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case 3:
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plhs[2] = tplhs[2];
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plhs[1] = tplhs[1];
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case 1:
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case 0:
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plhs[0] = tplhs[0];
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}
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}
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void exit_with_help()
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{
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mexPrintf(
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"Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')\n"
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" [predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')\n"
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"Parameters:\n"
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" model: SVM model structure from svmtrain.\n"
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" libsvm_options:\n"
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" -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n"
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" -q : quiet mode (no outputs)\n"
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"Returns:\n"
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" predicted_label: SVM prediction output vector.\n"
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" accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.\n"
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" prob_estimates: If selected, probability estimate vector.\n"
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);
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}
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void mexFunction( int nlhs, mxArray *plhs[],
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int nrhs, const mxArray *prhs[] )
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{
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int prob_estimate_flag = 0;
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struct svm_model *model;
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info = &mexPrintf;
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if(nlhs == 2 || nlhs > 3 || nrhs > 4 || nrhs < 3)
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{
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exit_with_help();
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fake_answer(nlhs, plhs);
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return;
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}
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if(!mxIsDouble(prhs[0]) || !mxIsDouble(prhs[1])) {
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mexPrintf("Error: label vector and instance matrix must be double\n");
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fake_answer(nlhs, plhs);
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return;
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}
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if(mxIsStruct(prhs[2]))
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{
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const char *error_msg;
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// parse options
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if(nrhs==4)
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{
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int i, argc = 1;
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char cmd[CMD_LEN], *argv[CMD_LEN/2];
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// put options in argv[]
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mxGetString(prhs[3], cmd, mxGetN(prhs[3]) + 1);
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if((argv[argc] = strtok(cmd, " ")) != NULL)
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while((argv[++argc] = strtok(NULL, " ")) != NULL)
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;
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for(i=1;i<argc;i++)
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{
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if(argv[i][0] != '-') break;
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if((++i>=argc) && argv[i-1][1] != 'q')
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{
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exit_with_help();
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fake_answer(nlhs, plhs);
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return;
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}
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switch(argv[i-1][1])
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{
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case 'b':
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prob_estimate_flag = atoi(argv[i]);
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break;
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case 'q':
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i--;
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info = &print_null;
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break;
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default:
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mexPrintf("Unknown option: -%c\n", argv[i-1][1]);
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exit_with_help();
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fake_answer(nlhs, plhs);
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return;
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}
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}
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}
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model = matlab_matrix_to_model(prhs[2], &error_msg);
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if (model == NULL)
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{
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mexPrintf("Error: can't read model: %s\n", error_msg);
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fake_answer(nlhs, plhs);
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return;
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}
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if(prob_estimate_flag)
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{
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if(svm_check_probability_model(model)==0)
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{
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mexPrintf("Model does not support probabiliy estimates\n");
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fake_answer(nlhs, plhs);
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svm_free_and_destroy_model(&model);
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return;
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}
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}
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else
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{
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if(svm_check_probability_model(model)!=0)
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info("Model supports probability estimates, but disabled in predicton.\n");
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}
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predict(nlhs, plhs, prhs, model, prob_estimate_flag);
|
||
|
|
// destroy model
|
||
|
|
svm_free_and_destroy_model(&model);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
mexPrintf("model file should be a struct array\n");
|
||
|
|
fake_answer(nlhs, plhs);
|
||
|
|
}
|
||
|
|
|
||
|
|
return;
|
||
|
|
}
|