void svmtrain (double *x, int *r, int *c,
double *y,
int *rowindex, int *colindex,
int *svm_type,
int *kernel_type,
int *degree,
double *gamma,
double *coef0,
double *cost,
double *nu,
int *weightlabels,
double *weights,
int *nweights,
double *cache,
double *tolerance,
double *epsilon,
int *shrinking,
int *cross,
int *sparse,
int *probability,
int *nclasses,
int *nr,
int *index,
int *labels,
int *nSV,
double *rho,
double *coefs,
double *sigma,
double *probA,
double *probB,
double *cresults,
double *ctotal1,
double *ctotal2,
char **error)
{
struct svm_parameter par;
struct svm_problem prob;
struct svm_model *model = NULL;
int i, ii;
const char* s;
/* set parameters */
par.svm_type = *svm_type;
par.kernel_type = *kernel_type;
par.degree = *degree;
par.gamma = *gamma;
par.coef0 = *coef0;
par.cache_size = *cache;
par.eps = *tolerance;
par.C = *cost;
par.nu = *nu;
par.nr_weight = *nweights;
if (par.nr_weight > 0) {
par.weight = (double *) malloc (sizeof(double) * par.nr_weight);
memcpy(par.weight, weights, par.nr_weight * sizeof(double));
par.weight_label = (int *) malloc (sizeof(int) * par.nr_weight);
memcpy(par.weight_label, weightlabels, par.nr_weight * sizeof(int));
}
par.p = *epsilon;
par.shrinking = *shrinking;
par.probability = *probability;
/* set problem */
prob.l = *r;
prob.y = y;
if (*sparse > 0)
prob.x = transsparse(x, *r, rowindex, colindex);
else
prob.x = sparsify(x, *r, *c);
/* check parameters & copy error message */
s = svm_check_parameter(&prob, &par);
if (s) {
strcpy(*error, s);
} else {
/* call svm_train */
model = svm_train(&prob, &par);
/* set up return values */
/* for (ii = 0; ii < model->l; ii++)
for (i = 0; i < *r; i++)
if (prob.x[i] == model->SV[ii]) index[ii] = i+1; */
svm_get_sv_indices(model, index);
*nr = model->l;
*nclasses = model->nr_class;
memcpy (rho, model->rho, *nclasses * (*nclasses - 1)/2 * sizeof(double));
if (*probability && par.svm_type != ONE_CLASS) {
if (par.svm_type == EPSILON_SVR || par.svm_type == NU_SVR)
*sigma = svm_get_svr_probability(model);
else {
memcpy(probA, model->probA,
*nclasses * (*nclasses - 1)/2 * sizeof(double));
memcpy(probB, model->probB,
*nclasses * (*nclasses - 1)/2 * sizeof(double));
}
}
for (i = 0; i < *nclasses-1; i++)
memcpy (coefs + i * *nr, model->sv_coef[i], *nr * sizeof (double));
if (*svm_type < 2) {
memcpy (labels, model->label, *nclasses * sizeof(int));
memcpy (nSV, model->nSV, *nclasses * sizeof(int));
}
/* Perform cross-validation, if requested */
if (*cross > 0)
do_cross_validation (&prob, &par, *cross, cresults,
ctotal1, ctotal2);
/* clean up memory */
svm_free_and_destroy_model(&model);
}
/* clean up memory */
if (par.nr_weight > 0) {
free(par.weight);
free(par.weight_label);
}
for (i = 0; i < *r; i++) free (prob.x[i]);
free (prob.x);
}
void predict(mxArray *plhs[], const mxArray *prhs[], struct svm_model *model, const int predict_probability)
{
int label_vector_row_num, label_vector_col_num;
int feature_number, testing_instance_number;
int instance_index;
double *ptr_instance, *ptr_label, *ptr_predict_label;
double *ptr_prob_estimates, *ptr_dec_values, *ptr;
struct svm_node *x;
mxArray *pplhs[1]; // transposed instance sparse matrix
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=NULL;
// prhs[1] = testing instance matrix
feature_number = (int)mxGetN(prhs[1]);
testing_instance_number = (int)mxGetM(prhs[1]);
label_vector_row_num = (int)mxGetM(prhs[0]);
label_vector_col_num = (int)mxGetN(prhs[0]);
if(label_vector_row_num!=testing_instance_number)
{
mexPrintf("Length of label vector does not match # of instances.\n");
fake_answer(plhs);
return;
}
if(label_vector_col_num!=1)
{
mexPrintf("label (1st argument) should be a vector (# of column is 1).\n");
fake_answer(plhs);
return;
}
ptr_instance = mxGetPr(prhs[1]);
ptr_label = mxGetPr(prhs[0]);
// transpose instance matrix
if(mxIsSparse(prhs[1]))
{
if(model->param.kernel_type == PRECOMPUTED)
{
// precomputed kernel requires dense matrix, so we make one
mxArray *rhs[1], *lhs[1];
rhs[0] = mxDuplicateArray(prhs[1]);
if(mexCallMATLAB(1, lhs, 1, rhs, "full"))
{
mexPrintf("Error: cannot full testing instance matrix\n");
fake_answer(plhs);
return;
}
ptr_instance = mxGetPr(lhs[0]);
mxDestroyArray(rhs[0]);
}
else
{
mxArray *pprhs[1];
pprhs[0] = mxDuplicateArray(prhs[1]);
if(mexCallMATLAB(1, pplhs, 1, pprhs, "transpose"))
{
mexPrintf("Error: cannot transpose testing instance matrix\n");
fake_answer(plhs);
return;
}
}
}
if(predict_probability)
{
if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
mexPrintf("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));
else
prob_estimates = (double *) malloc(nr_class*sizeof(double));
}
plhs[0] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
if(predict_probability)
{
// prob estimates are in plhs[2]
if(svm_type==C_SVC || svm_type==NU_SVC)
plhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class, mxREAL);
else
plhs[2] = mxCreateDoubleMatrix(0, 0, mxREAL);
}
else
{
// decision values are in plhs[2]
if(svm_type == ONE_CLASS ||
svm_type == EPSILON_SVR ||
svm_type == NU_SVR)
plhs[2] = mxCreateDoubleMatrix(testing_instance_number, 1, mxREAL);
else
plhs[2] = mxCreateDoubleMatrix(testing_instance_number, nr_class*(nr_class-1)/2, mxREAL);
}
ptr_predict_label = mxGetPr(plhs[0]);
ptr_prob_estimates = mxGetPr(plhs[2]);
ptr_dec_values = mxGetPr(plhs[2]);
x = (struct svm_node*)malloc((feature_number+1)*sizeof(struct svm_node) );
for(instance_index=0;instance_index<testing_instance_number;instance_index++)
{
int i;
double target_label, predict_label;
target_label = ptr_label[instance_index];
if(mxIsSparse(prhs[1]) && model->param.kernel_type != PRECOMPUTED) // prhs[1]^T is still sparse
read_sparse_instance(pplhs[0], instance_index, x);
else
{
for(i=0;i<feature_number;i++)
{
x[i].index = i+1;
x[i].value = ptr_instance[testing_instance_number*i+instance_index];
}
x[feature_number].index = -1;
}
if(predict_probability)
{
if(svm_type==C_SVC || svm_type==NU_SVC)
{
predict_label = svm_predict_probability(model, x, prob_estimates);
ptr_predict_label[instance_index] = predict_label;
for(i=0;i<nr_class;i++)
ptr_prob_estimates[instance_index + i * testing_instance_number] = prob_estimates[i];
} else {
predict_label = svm_predict(model,x);
ptr_predict_label[instance_index] = predict_label;
}
}
else
{
predict_label = svm_predict(model,x);
ptr_predict_label[instance_index] = predict_label;
if(svm_type == ONE_CLASS ||
svm_type == EPSILON_SVR ||
svm_type == NU_SVR)
{
double res;
svm_predict_values(model, x, &res);
ptr_dec_values[instance_index] = res;
}
else
{
double *dec_values = (double *) malloc(sizeof(double) * nr_class*(nr_class-1)/2);
svm_predict_values(model, x, dec_values);
for(i=0;i<(nr_class*(nr_class-1))/2;i++)
ptr_dec_values[instance_index + i * testing_instance_number] = dec_values[i];
free(dec_values);
}
}
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;
}
if(svm_type==NU_SVR || svm_type==EPSILON_SVR)
{
mexPrintf("Mean squared error = %g (regression)\n",error/total);
mexPrintf("Squared correlation coefficient = %g (regression)\n",
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
);
}
/* else */
/* mexPrintf("Accuracy = %g%% (%d/%d) (classification)\n", */
/* (double)correct/total*100,correct,total); */
// return accuracy, mean squared error, squared correlation coefficient
plhs[1] = mxCreateDoubleMatrix(3, 1, mxREAL);
ptr = mxGetPr(plhs[1]);
ptr[0] = (double)correct/total*100;
ptr[1] = error/total;
ptr[2] = ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt));
free(x);
if(prob_estimates != NULL)
free(prob_estimates);
}
/**
* @brief Predict with SVM for a given vector.
* @param type Type of feature vector: Type of svm-model (1, 2, ...)
* @param vec Feature vector for the SVM.
* @param prob Probability of correct prediction for each class.
* @return Returns the prediction label
*/
bool SVMPredictorSingle::process(int type, std::vector<double> &vec, std::vector<double> &prob)
{
int svm_type = svm_get_svm_type(model);
int nr_class = svm_get_nr_class(model);
double *prob_estimates = NULL;
int j;
if(predict_probability) {
if (svm_type == NU_SVR || svm_type == EPSILON_SVR)
printf("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));
else
prob_estimates = (double *) malloc(nr_class*sizeof(double));
}
// we copy now the feature vector
double predict_label;
for(unsigned idx = 0; idx < vec.size(); idx++) {
node[idx].index = idx+1;
node[idx].value = vec[idx];
node[idx+1].index = -1;
}
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC)) {
predict_label = svm_predict_probability(model, node, prob_estimates);
for(j=0;j<nr_class;j++)
prob.push_back(prob_estimates[j]);
}
else
predict_label = svm_predict(model, node);
if(predict_probability)
free(prob_estimates);
return predict_label;
}
void predict(FILE *input, FILE *output)
{
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=NULL;
int j;
if(predict_probability)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
printf("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));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
fprintf(output,"labels");
for(j=0;j<nr_class;j++)
fprintf(output," %d",labels[j]);
fprintf(output,"\n");
free(labels);
}
}
max_line_len = 1024;
line = (char *)malloc(max_line_len*sizeof(char));
while(readline(input) != NULL)
{
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 0
label = strtok(line," \t");
target_label = strtod(label,&endptr);
if(endptr == label)
exit_input_error(total+1);
while(1)
{
if(i>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
idx = strtok(NULL,":");
val = strtok(NULL," \t");
if(val == NULL)
break;
errno = 0;
x[i].index = (int) strtol(idx,&endptr,10);
if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
exit_input_error(total+1);
else
inst_max_index = x[i].index;
errno = 0;
x[i].value = strtod(val,&endptr);
if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
exit_input_error(total+1);
++i;
}
x[i].index = -1;
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
predict_label = svm_predict_probability(model,x,prob_estimates);
fprintf(output,"%g",predict_label);
for(j=0;j<nr_class;j++)
fprintf(output," %g",prob_estimates[j]);
fprintf(output,"\n");
}
else
{
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;
}
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
{
printf("Mean squared error = %g (regression)\n",error/total);
printf("Squared correlation coefficient = %g (regression)\n",
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
);
}
else
printf("Accuracy = %g%% (%d/%d) (classification)\n",
(double)correct/total*100,correct,total);
if(predict_probability)
free(prob_estimates);
}
int cLibsvmLiveSink::myFinaliseInstance()
{
int ap=0;
int ret = cDataSink::myFinaliseInstance();
if (ret==0) return 0;
// TODO: binary model files...
// load model
SMILE_MSG(2,"loading LibSVM model for instance '%s' ...",getInstName());
if((model=svm_load_model(modelfile))==0) {
COMP_ERR("can't open libSVM model file '%s'",modelfile);
}
nClasses = svm_get_nr_class(model);
svmType = svm_get_svm_type(model);
if(predictProbability) {
if ((svmType==NU_SVR) || (svmType==EPSILON_SVR)) {
nClasses = 0;
SMILE_MSG(2,"LibSVM prob. model (regression) for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g",svm_get_svr_probability(model));
} else {
labels=(int *) malloc(nClasses*sizeof(int));
svm_get_labels(model,labels);
SMILE_MSG(3,"LibSVM %i labels in model '%s':",nClasses,modelfile);
int j;
for(j=0;j<nClasses;j++)
SMILE_MSG(3," Label[%i] : '%d'",j,labels[j]);
}
}
//?? move this in front of above if() block ?
if ((predictProbability)&&(nClasses>0))
probEstimates = (double *) malloc(nClasses*sizeof(double));
// load scale
if((scale=svm_load_scale(scalefile))==0) {
COMP_ERR("can't open libSVM scale file '%s'",scalefile);
}
// load selection
loadSelection(fselection);
//TODO: check compatibility of getLevelN() (possibly after selection), number of features in model, and scale
if (nClasses>0) {
// load class mapping
loadClasses(classes);
} else {
if (classes != NULL) SMILE_IWRN(2,"not loading given class mapping file for regression SVR model (there are no classes...)!");
}
return ret;
}
int predict(double **values, int **indices, int rowNum, int *arrayNcol, int *labels, double **prob_estimates, int isProb)
{
int svm_type=svm_get_svm_type(model);
int nr_class=svm_get_nr_class(model);
int j;
LOGD("isProb:%d", isProb);
if(isProb)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
LOGD("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));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model,labels);
// fprintf(output,"labels");
// for(j=0;j<nr_class;j++)
// fprintf(output," %d",labels[j]);
// fprintf(output,"\n");
// free(labels);
}
}
// each record will receive
// a predicted label and a [nClass]-D probability estimate array
for (int i = 0; i < rowNum; i++)
{
int nCol = arrayNcol[i];
double target_label = 0;
int predict_label=0;
x = (struct svm_node *) realloc(x,(nCol+1)*sizeof(struct svm_node));
for (int j = 0; j < nCol; j++)
{
x[j].index = indices[i][j];
x[j].value = values[i][j];
}
x[nCol].index = -1;
// Probability prediction
if (isProb && (svm_type==C_SVC || svm_type==NU_SVC))
{
// prob_estimate[rowNum][nClass]
labels[i] = svm_predict_probability(model,x,prob_estimates[i]);
}
// without probability
else
{
labels[i] = svm_predict(model,x);
}
} // For
return 0;
}
JNIEXPORT jint JNICALL Java_LibSVMTest_doClassificationNative
(JNIEnv *env, jobject obj, jobjectArray valuesArr,
jobjectArray indicesArr, jint isProb, jintArray labelsArr, jdoubleArray probsArr) {
int *labels = env->GetIntArrayElements(labelsArr, NULL);
// initiate the arrays
int nRow = env->GetArrayLength(valuesArr);
double **probs = (double **) calloc(nRow, sizeof(double *));
jfloatArray vrows = (jfloatArray) env->GetObjectArrayElement(valuesArr, 0);
jintArray irows = (jintArray) env->GetObjectArrayElement(indicesArr, 0);
jfloat *velement = env->GetFloatArrayElements(vrows, NULL);
jint *ielement = env->GetIntArrayElements(irows, NULL);
int colNum = env->GetArrayLength(vrows);
probs[0] = env->GetDoubleArrayElements(probsArr, NULL);
if(isProb)
{
if(svm_check_probability_model(model_F)==0)
{
fprintf(stderr, "Model does not support probabiliy estimates\n");
exit(1);
}
}
else
{
if(svm_check_probability_model(model_F)!=0)
fprintf(stderr, "Model supports probability estimates, but disabled in prediction.\n");
}
if(isProb)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
fprintf(stderr, "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_F));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model_F,labels);
}
}
double predict_label=0;
struct svm_node *x = (struct svm_node *) malloc(64*sizeof(struct svm_node));
x = (struct svm_node *) realloc(x, (colNum+1)*sizeof(struct svm_node));
for (int j = 0; j < colNum; j++)
{
x[j].index = velement[j];
x[j].value = ielement[j];
}
x[colNum].index = -1;
// Probability prediction
if (isProb && (svm_type==C_SVC || svm_type==NU_SVC))
{
predict_label = svm_predict_probability(model_F,x,*probs);
labels[0]=predict_label;
}
else { labels[0] = svm_predict(model_F,x);}
env->ReleaseFloatArrayElements(vrows, velement, JNI_ABORT);
env->ReleaseIntArrayElements(irows, ielement, JNI_ABORT);
//added for fixing : JNI ERROR (app bug): local reference table overflow (max=512)
env->DeleteLocalRef(vrows);
env->DeleteLocalRef(irows);
// release the one-D arrays and strings
env->ReleaseIntArrayElements(labelsArr, labels, 0);
//added for fixing : JNI ERROR (app bug): local reference table overflow (max=512)
env->DeleteLocalRef(labelsArr);
free(x);
return 0;
}
void predict(FILE *input, FILE *output, FILE *output_w2, FILE *output_prob,int predict_probability, FILE *addr, int Ncol, int Nlig, long Npolar)
{
int svm_type=svm_get_svm_type(model);
int nr_class=svm_get_nr_class(model);
double *prob_estimates=NULL;
int i,j,k,l,num_block,p,n,count;
int Nband[Npolar];
long n_addr = 0,m=0, n_total_band, offset=0;
long max_num_pixel, num_rest_pixel, n_img_pixel = (long)Ncol * (long)Nlig;
float buff_float, zero = 0;
float **V_pol_rest, **V_pol_block, *v_pol_buff;
long int *tab_addr, buff_long_int;
float *output_label;
double *w2_predict;
float **w2_predict_out, *mean_dist, *max_prob, **prob_estimates_out;
max_num_pixel = (long)floor(memory_pixel /((long)4 * Npolar));
for(i=0; i< n_img_pixel; i++){// We initiaite the output classification file with '0' value
fwrite(&zero, sizeof(float), 1, output);
}
rewind(output);
/* for(i=0; i< n_img_pixel*(nr_class*(nr_class-1)/2 +1) ; i++){// We initiaite the output classification file with '0' value
fwrite(&zero, sizeof(float), 1, output_w2);
}
rewind(output_w2);
*/
if(predict_probability)
{
printf("Predict prob!!!!\n");
for(i=0; i< n_img_pixel*(nr_class + 1) ; i++){// We initiaite the output classification file with '0' value
fwrite(&zero, sizeof(float), 1, output_prob);
}
rewind(output_prob);
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
printf("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));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
//fprintf(output,"labels");
//for(j=0;j<nr_class;j++)
// printf(" %d\n",labels[j]);
//fprintf(output," %d",labels[j]);
//fprintf(output,"\n");
free(labels);
}
}
while(fread(&buff_long_int, sizeof(long int), 1, addr)!=0){ // We read the addr file to know the number of pixel to be classified
n_addr++;
}
n_addr = n_addr - Npolar;
rewind(addr);
tab_addr = (long int *) malloc((long int) (n_addr) * sizeof(long int));
for(i=0; i<Npolar;i++){
fread(&buff_long_int, sizeof(long int), 1, addr);
Nband[i] = (int)buff_long_int;
}
i=0;
while(fread(&tab_addr[i], sizeof(long int), 1, addr)!=0){ // We read the addr of each classified pixel
//hde
//printf("tab_addr[%d]:%d\n",i,tab_addr[i]);
i++;
}
//hde
int min,max;
min=tab_addr[0];
max=min;
for (i=0;i<n_addr;i++)
{
if (tab_addr[i]>max) {max=tab_addr[i];}
if (tab_addr[i]<min) {min=tab_addr[i];}
}
printf("min:%d; max:%d\n",min,max);
// fin HDE
// We compute the necessary number of block and remaining pixel
num_block = (int)floor(n_addr /max_num_pixel);
num_rest_pixel = n_addr - num_block * max_num_pixel;
printf("num_block: %i\n",num_block);
/////////////////////////////////////////////////Ajouter boucle de lecture des float input pour ensuite calculer le
//nb de bande total initial pour faire une lecutre par bloc de bande puis extraire celle qui sont interessante
while(fread(&buff_float, sizeof(float), 1, input)!=0){
m++;
}
rewind(input);
if(num_block > 0){//Loop on the block
V_pol_block = matrix_float((int)max_num_pixel,(int)Npolar);
output_label = vector_float((int)max_num_pixel);
// mean_dist = vector_float((int)max_num_pixel);
// w2_predict_out = matrix_float((int)max_num_pixel,nr_class*(nr_class-1)/2);
prob_estimates_out = matrix_float((int)max_num_pixel,nr_class);
max_prob = vector_float((int)max_num_pixel);
for (i=0; i<num_block; i++){ // blocs pour l'optimisation accès disque
for (j=0; j< max_num_pixel; j++){
for (k=0; k<Npolar; k++){
// pointe sur le pixel col,lig, k(bande), selon la bande
offset = (long)(sizeof(float)) * (Nband[k] * n_img_pixel + tab_addr[i*max_num_pixel + j]);
fseek(input, offset, SEEK_SET); //place le pointeur à l'offset calculé avant qui s'appelle offset !
fread(&V_pol_block[j][k], sizeof(float), 1, input);
}
}
for (j=0; j< max_num_pixel; j++){
for (k=0; k<Npolar; k++){
if(k>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
x[k].index = k+1;
x[k].value = scale(k, (double)V_pol_block[j][k]);
}
x[k].index = -1;
double predict_label;
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
//printf("Predict prob if block!!!!\n");
predict_label = svm_predict_probability(model,x,prob_estimates);
w2_predict = svm_w2(model,x);
output_label[j] = (float)predict_label;
// mean_dist[j] = 0.;
count = 0;
l=0;
max_prob[j] = 0.;
for(p=0;p<nr_class;p++){
for(n=p+1;n<nr_class;n++){
// w2_predict_out[j][l] =(float)w2_predict[l];
// printf("w2_predict[%i] : %g\n",w2_predict[l]);
if(p+1 == output_label[j] || n+1 == output_label[j]){
// mean_dist[j] = mean_dist[j] + w2_predict_out[j][l];
count = count + 1;
}
l++;
}
prob_estimates_out[j][p] = (float)prob_estimates[p];
if(max_prob[j] < prob_estimates_out[j][p] ){
max_prob[j] = prob_estimates_out[j][p];
}
}
// mean_dist[j] = mean_dist[j] / (float)count;
}
else
{
predict_label = svm_predict(model,x);
w2_predict = svm_w2(model,x);
output_label[j] = (float)predict_label;
// mean_dist[j] = 0.;
count = 0;
l=0;
for(p=0;p<nr_class;p++){
for(n=p+1;n<nr_class;n++){
// w2_predict_out[j][l] =(float)w2_predict[l];
if(p+1 == output_label[j] || n+1 == output_label[j]){
// mean_dist[j] = mean_dist[j] + w2_predict_out[j][l];
count = count + 1;
}
l++;
}
}
// mean_dist[j] = mean_dist[j] / (float)count;
}
}/* pixels block */
for (j=0; j< max_num_pixel; j++){ // On ecrit le fichier de classif
offset = (long)(sizeof(float)) * tab_addr[i*max_num_pixel + j];
fseek (output, offset, SEEK_SET);
fwrite(&output_label[j], sizeof(float), 1, output);
}
/* for (l=0; l< nr_class*(nr_class-1)/2; l++){ // On ecrit les cartes de distances
for (j=0; j< max_num_pixel; j++){
offset = (long)(sizeof(float)) * tab_addr[i*max_num_pixel + j];
fseek(output_w2, offset + n_img_pixel * sizeof(float) * l, SEEK_SET);
fwrite(&w2_predict_out[j][l], sizeof(float), 1, output_w2);
}
}
for (j=0; j< max_num_pixel; j++){ // On ecrit la distance moyenne
offset = (long)(sizeof(float)) * tab_addr[i*max_num_pixel + j];
fseek(output_w2, offset + n_img_pixel * sizeof(float) * (nr_class*(nr_class-1)/2), SEEK_SET);// on ecrit la distance moyenne parmi les classif bianaire renvoyant le label final
fwrite(&mean_dist[j], sizeof(float), 1, output_w2);
}
*/
if(predict_probability){
//printf("Predict prob fin if block ecriture!!!!\n");
for (l=0; l< nr_class; l++){ // On ecrit les cartes de probabilité
for (j=0; j< max_num_pixel; j++){
offset = (long)(sizeof(float)) * tab_addr[i*max_num_pixel + j];
fseek(output_prob, offset + n_img_pixel * sizeof(float) * l, SEEK_SET);
fwrite(&prob_estimates_out[j][l], sizeof(float), 1, output_prob);
}
}
for (j=0; j< max_num_pixel; j++){
offset = (long)(sizeof(float)) * tab_addr[i*max_num_pixel + j];
fseek(output_prob, offset + n_img_pixel * sizeof(float) * nr_class, SEEK_SET);
fwrite(&max_prob[j], sizeof(float), 1, output_prob);
}
}
}/* Loop over the blocks*/
free_matrix_float(V_pol_block, max_num_pixel);
// free_matrix_float(w2_predict_out, max_num_pixel);
free_matrix_float(prob_estimates_out, max_num_pixel);
free_vector_float(output_label);
// free_vector_float(mean_dist);
free(w2_predict);
free_vector_float(max_prob);
printf("FREE block: %i\n",num_block);
}else{
printf("PAS DE BLOCK\n");
}
if(num_rest_pixel > 0){// Loop on the remaining pixel
V_pol_rest = matrix_float((int)num_rest_pixel,(int)Npolar);/* Vector of polarimetrics indicators for the remaining pixel */
output_label = vector_float((int)num_rest_pixel);
// w2_predict_out = matrix_float((int)num_rest_pixel,nr_class*(nr_class-1)/2);
// mean_dist = vector_float((int)num_rest_pixel);
prob_estimates_out = matrix_float((int)num_rest_pixel,nr_class);
max_prob = vector_float((int)max_num_pixel);
for (j=0; j< num_rest_pixel;j++){
for (k=0; k<Npolar; k++){
offset = (long)(sizeof(float)) * (Nband[k] * n_img_pixel + tab_addr[num_block*max_num_pixel + j]);
fseek (input, offset , SEEK_SET);
fread(&V_pol_rest[j][k], sizeof(float), 1, input);
//hde
//printf("valeur pixel a adresse %d et pour bande %d: %d\n", tab_addr[num_block*max_num_pixel + j],k,V_pol_rest[j][k]);
}
}
for (j=0; j< num_rest_pixel;j++){
for (k=0; k<Npolar; k++){
if(k>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
x[k].index = k+1;
x[k].value = scale(k, (double)V_pol_rest[j][k]);
}
x[k].index = -1;
double predict_label;
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
//printf("Predict prob if fin apres block!!!!\n");
predict_label = svm_predict_probability(model,x,prob_estimates);
w2_predict = svm_w2(model,x);
output_label[j] = (float)predict_label;
// mean_dist[j] = 0.;
count = 0;
l=0;
max_prob[j] = 0.;
for(p=0;p<nr_class;p++){
for(n=p+1;n<nr_class;n++){
// w2_predict_out[j][l] =(float)w2_predict[l];
// printf("w2_predict[%i] : %g\n",l,w2_predict[l]);
// printf("w2_predict_out[%i][%i] : %g\n",j,l,w2_predict_out[j][l]);
if(p+1 == output_label[j] || n+1 == output_label[j]){
// mean_dist[j] = mean_dist[j] + w2_predict_out[j][l];
count = count + 1;
}
l++;
}
prob_estimates_out[j][p] = (float)prob_estimates[p];
if(max_prob[j] < prob_estimates_out[j][p] ){
max_prob[j] = prob_estimates_out[j][p];
}
}
// mean_dist[j] = mean_dist[j] / (float)count;
//printf("mean_dist[%i],%f\n",j,mean_dist[j]);
}
else
{
predict_label = svm_predict(model,x);
w2_predict = svm_w2(model,x);
output_label[j] = (float)predict_label;
// mean_dist[j] = 0.;
count = 0;
l=0;
for(p=0;p<nr_class;p++){
for(n=p+1;n<nr_class;n++){
// w2_predict_out[j][l] =(float)w2_predict[l];
if(p+1 == output_label[j] || n+1 == output_label[j]){
// mean_dist[j] = mean_dist[j] + w2_predict_out[j][l];
count = count + 1;
}
l++;
}
}
// mean_dist[j] = mean_dist[j] / (float)count;
}
}
for (j=0; j< num_rest_pixel; j++){
offset = (long)(sizeof(float)) * (tab_addr[num_block*max_num_pixel + j]);
fseek (output, offset, SEEK_SET);
fwrite(&output_label[j], sizeof(float), 1, output);
}
/* for (l=0; l< nr_class*(nr_class-1)/2; l++){
for (j=0; j< num_rest_pixel; j++){
offset = (long)(sizeof(float)) * (tab_addr[num_block*max_num_pixel + j]);
fseek(output_w2, offset + n_img_pixel * sizeof(float) * l, SEEK_SET);
fwrite(&w2_predict_out[j][l], sizeof(float), 1, output_w2);
}
}
for (j=0; j< num_rest_pixel; j++){
offset = (long)(sizeof(float)) * (tab_addr[num_block*max_num_pixel + j]);
fseek(output_w2, offset + n_img_pixel * sizeof(float) * (nr_class*(nr_class-1)/2), SEEK_SET);// on ecrit la distance moyenne parmi les classif bianaire renvoyant le label final
fwrite(&mean_dist[j], sizeof(float), 1, output_w2);
}
*/
if(predict_probability){
//printf("Predict prob if fin apres blockeCRITUR!!!!!!!\n");
for (l=0; l< nr_class; l++){ // On ecrit les cartes de probabilité
for (j=0; j< num_rest_pixel; j++){
offset = (long)(sizeof(float)) * (tab_addr[num_block*max_num_pixel + j]);
fseek(output_prob, offset + n_img_pixel * sizeof(float) * l, SEEK_SET);
fwrite(&prob_estimates_out[j][l], sizeof(float), 1, output_prob);
//printf("prob_estimates_out[%i][%i] : %f\n", j, l, prob_estimates_out[j][l]);
}
}
for (j=0; j< num_rest_pixel; j++){
offset = (long)(sizeof(float)) * (tab_addr[num_block*max_num_pixel + j]);
fseek(output_prob, offset + n_img_pixel * sizeof(float) * nr_class, SEEK_SET);
fwrite(&max_prob[j], sizeof(float), 1, output_prob);
}
}
free_matrix_float(V_pol_rest, num_rest_pixel);
// free_matrix_float(w2_predict_out, num_rest_pixel);
free_matrix_float(prob_estimates_out, num_rest_pixel);
free_vector_float(output_label);
free(w2_predict);
// free_vector_float(mean_dist);
free_vector_float(max_prob);
}/* remaning pixel */
free(tab_addr);
if(predict_probability)
free(prob_estimates);
}
void predict(FILE *input, FILE *output)
{
int correct = 0;
int total = 0;
double error = 0, brier_score = 0, log_likelihood = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
int svm_type=svm_get_svm_type(model);
int nr_class=svm_get_nr_class(model);
int *labels=(int *) malloc(nr_class*sizeof(int));
double *prob_estimates=NULL;
int j;
if(predict_probability)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
printf("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));
else
{
svm_get_labels(model,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
fprintf(output,"labels");
for(j=0;j<nr_class;j++)
fprintf(output," %d",labels[j]);
fprintf(output,"\n");
}
}
while(1)
{
int i = 0;
int c;
double target,v;
if (fscanf(input,"%lf",&target)==EOF)
break;
while(1)
{
if(i>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
do {
c = getc(input);
if(c=='\n' || c==EOF) goto out2;
} while(isspace(c));
ungetc(c,input);
fscanf(input,"%d:%lf",&x[i].index,&x[i].value);
++i;
}
out2:
x[i++].index = -1;
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
v = svm_predict_probability(model,x,prob_estimates, predict_probability);
fprintf(output,"%g ",v);
for(j=0;j<nr_class;j++)
fprintf(output,"%g ",prob_estimates[j]);
fprintf(output,"\n");
}
else
{
v = svm_predict(model,x);
fprintf(output,"%g\n",v);
}
if(v == target)
++correct;
error += (v-target)*(v-target);
sumv += v;
sumy += target;
sumvv += v*v;
sumyy += target*target;
sumvy += v*target;
++total;
//Brier Score and log likelihood
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
int j;
double bscore=0;
for( j=0; j<nr_class && target != labels[j]; j++)
;
if(j < nr_class)
{
for(int i=0; i<nr_class; i++)
bscore += prob_estimates[i]*prob_estimates[i];
bscore = (bscore - 2 * prob_estimates[j] + 1)/nr_class;
brier_score += bscore;
log_likelihood += log(prob_estimates[j]);
}
}
}
printf("Accuracy = %g%% (%d/%d) (classification)\n",
(double)correct/total*100,correct,total);
printf("Mean squared error = %g (regression)\n",error/total);
printf("Squared correlation coefficient = %g (regression)\n",
((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/
((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))
);
if(predict_probability)
{
printf("Brier score = %g\n", brier_score/total);
printf("Normalized log likelihood = %g\n", log_likelihood/total);
free(prob_estimates);
free(labels);
}
}
int SVM::test(char* data_filename, char* filename_model, double &dTestError)
{
int correct = 0;
int total = 0;
double error = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
struct svm_model* svmModel;
int svm_type;
int nr_class;
double *prob_estimates=NULL;
FILE *fInputTestData = NULL;
int max_line_len = 0;
char *line = NULL;
int max_nr_attr = 64;
struct svm_node *x = NULL;
fInputTestData = fopen(data_filename,"r");
if(fInputTestData == NULL)
{
fprintf(stderr,"can't open input file %s\n",data_filename);
return -1;
}
if((svmModel=svm_load_model(filename_model))==0)
{
fprintf(stderr,"can't open model file %s\n",filename_model);
return -3;
}
svm_type=svm_get_svm_type(svmModel);
nr_class=svm_get_nr_class(svmModel);
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
printf("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(svmModel));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(svmModel,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
//printf("labels");
//for(j=0;j<nr_class;j++)
//printf(" %d",labels[j]);
//printf("\n");
free(labels);
}
max_line_len = 1024;
while((line = this->read_line(fInputTestData, max_line_len)) != NULL)
{
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 0
label = strtok(line," \t");
target_label = strtod(label,&endptr);
if(endptr == label)
{
fprintf(stderr,"Wrong input format at line %d\n", total+1);
return -2;
}
x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));
while(1)
{
if(i>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
idx = strtok(NULL,":");
val = strtok(NULL," \t");
if(val == NULL)
break;
errno = 0;
x[i].index = (int) strtol(idx,&endptr,10);
if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
{
fprintf(stderr,"Wrong input format at line %d\n", total+1);
return -2;
}
else
inst_max_index = x[i].index;
errno = 0;
x[i].value = strtod(val,&endptr);
if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
{
fprintf(stderr,"Wrong input format at line %d\n", total+1);
return -2;
}
++i;
}
x[i].index = -1;
if (svm_type==C_SVC || svm_type==NU_SVC)
{
predict_label = svm_predict_probability(svmModel,x,prob_estimates);
//printf("%g",predict_label);
//for(j=0;j<nr_class;j++)
// printf(" %g",prob_estimates[j]);
//printf("\n");
}
else
{
predict_label = svm_predict(svmModel,x);
// printf("%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;
free(line);
free(x);
}
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
{
printf("Mean squared error = %g (regression)\n",error/total);
printf("Squared correlation coefficient = %g (regression)\n",
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
);
}
else
{
printf("Classification Accuracy = %g%% (%d/%d)\n",
(double)correct/total*100,correct,total);
}
dTestError = 1-((double)correct/(double)total);
fclose(fInputTestData);
free(prob_estimates);
svm_destroy_model(svmModel);
return 0;
}
int predict(float **values, int **indices, int rowNum, int colNum, int *labels, double *prob_estimates, int isProb)
{
int svm_type=svm_get_svm_type(model);
int nr_class=svm_get_nr_class(model);
int j;
if(isProb)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
LOGD("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));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model,labels);
// fprintf(output,"labels");
// for(j=0;j<nr_class;j++)
// fprintf(output," %d",labels[j]);
// fprintf(output,"\n");
// free(labels);
}
}
for (int i = 0; i < rowNum; i++)
{
double target_label, predict_label=0;
x = (struct svm_node *) realloc(x,(colNum+1)*sizeof(struct svm_node));
for (int j = 0; j < colNum; j++)
{
x[j].index = indices[i][j];
x[j].value = values[i][j];
}
x[colNum].index = -1;
// Probability prediction
if (isProb && (svm_type==C_SVC || svm_type==NU_SVC))
{
predict_label = svm_predict_probability(model,x,prob_estimates);
labels[0]=predict_label;
}
else { labels[i] = svm_predict(model,x); }
} // For
return 0;
}
//predict the expected value using the trained svm and the input
double ML2::predictML( double velocity[])
{
double error = 0;
//get info about svm using the model
int svm_type=svm_get_svm_type(model);
int nr_class=svm_get_nr_class(model);
double *prob_estimates=NULL;
int j;
int predict_probability = param.probability;
cout<<"svm type : "<<svm_type<<endl<<flush;
cout<<"nr class : "<<nr_class<<endl<<flush;
cout<<"predict probability : "<<predict_probability<<endl<<flush;
if(predict_probability)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
cout<<"Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="<<svm_get_svr_probability(model)<<endl<<flush;
}
int i = 0;
double target_label, predict_label;
//allocate space for x
int max_nr_attr = datacols + 1;
x = Malloc(struct svm_node,max_nr_attr);
//store each of the velocity parameter
for( i = 0 ; i < datacols; i++){
x[i].index = i+1;
x[i].value = velocity[i];
}
//end of the x
x[i].index = -1;
//predict the value
predict_label = svm_predict(model,x);
//free x
free(x);
cout<<"prediction "<<predict_label <<endl<<flush;
if(predict_probability)
free(prob_estimates);
return predict_label;
}
void predict(FILE *input, FILE *output)
{
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=NULL;
int j;
// This block by Jianxin Wu, for average accuracy computation
int ii,label_index;
// number of correct predictions in each category
int* correct_sub = (int *)malloc(nr_class*sizeof(int));
for(ii=0;ii<nr_class;ii++) correct_sub[ii] = 0;
// number of testing examples in each category
int* total_sub = (int *)malloc(nr_class*sizeof(int));
for(ii=0;ii<nr_class;ii++) total_sub[ii] = 0;
int* labels_avg = (int*)malloc(nr_class*sizeof(int));
svm_get_labels(model,labels_avg);
if(predict_probability)
{
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
printf("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));
else
{
int *labels=(int *) malloc(nr_class*sizeof(int));
svm_get_labels(model,labels);
prob_estimates = (double *) malloc(nr_class*sizeof(double));
fprintf(output,"labels");
for(j=0;j<nr_class;j++)
fprintf(output," %d",labels[j]);
fprintf(output,"\n");
free(labels);
}
}
max_line_len = 1024;
line = (char *)malloc(max_line_len*sizeof(char));
while(readline(input) != NULL)
{
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 0
label = strtok(line," \t");
target_label = strtod(label,&endptr);
if(endptr == label)
exit_input_error(total+1);
while(1)
{
if(i>=max_nr_attr-1) // need one more for index = -1
{
max_nr_attr *= 2;
x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
}
idx = strtok(NULL,":");
val = strtok(NULL," \t");
if(val == NULL)
break;
errno = 0;
x[i].index = (int) strtol(idx,&endptr,10);
if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
exit_input_error(total+1);
else
inst_max_index = x[i].index;
errno = 0;
x[i].value = strtod(val,&endptr);
if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
exit_input_error(total+1);
++i;
}
x[i].index = -1;
if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
{
predict_label = svm_predict_probability(model,x,prob_estimates);
fprintf(output,"%g",predict_label);
for(j=0;j<nr_class;j++)
fprintf(output," %g",prob_estimates[j]);
fprintf(output,"\n");
}
else
{
predict_label = svm_predict(model,x);
fprintf(output,"%g\n",predict_label);
}
// This block by Jianxin Wu, for average accuracy
label_index = FindLabel((int)target_label,labels_avg);
total_sub[label_index]++;
if(predict_label == target_label) correct_sub[label_index]++;
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;
}
if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
{
printf("Mean squared error = %g (regression)\n",error/total);
printf("Squared correlation coefficient = %g (regression)\n",
((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
);
}
else
printf("Accuracy = %g%% (%d/%d) (classification)\n",
(double)correct/total*100,correct,total);
if(predict_probability)
free(prob_estimates);
// This block (till endo of function) by Jianxin WU
// Print per-category accuracy and average accuracy of categories
double sub_score = 0;
int nonempty_category = 0;
for(ii=0;ii<nr_class;ii++)
{
if(total_sub[ii]>0)
{
sub_score += (correct_sub[ii]*1.0/total_sub[ii]);
nonempty_category++;
}
}
printf("-----------\n");
for(ii=0;ii<nr_class;ii++)
{
printf("%d / %d (Category %d)\n",correct_sub[ii],total_sub[ii],labels_avg[ii]);
}
printf("-----------\n");
printf("Mean Accuray across classes = %g%%\n",sub_score*100.0/nonempty_category);
free(correct_sub);
free(total_sub);
free(labels_avg);
}
