/* call-seq:
* model.predict_probability(example)
*
* Classify an example and return both the label (or regression
* value), as well as the array of probability found for each class.
*
* The first element of the returned array contains the label. The
* second element is another array which contains the probability
* value found for each model class.
*
* Library function
* svm_predict_probability[https://github.com/cjlin1/libsvm/blob/master/README#L591].
*
* @return [Array<Float, Array<Float>>] predicted label and
* probability per class
*/
static VALUE cModel_predict_probability(VALUE obj,VALUE example) {
struct svm_node *x;
struct svm_model *model;
double class;
double *c_estimates;
VALUE estimates;
VALUE target;
int i;
x = example_to_internal(example);
Data_Get_Struct(obj, struct svm_model, model);
c_estimates = calloc(model->nr_class, sizeof(double));
if(c_estimates == 0) {
rb_raise(rb_eNoMemError, "on predict probability estimates allocation" " %s:%i", __FILE__,__LINE__);
}
class = svm_predict_probability(model, x, c_estimates);
estimates = rb_ary_new();
for (i = 0; i < model->nr_class; i++)
rb_ary_push(estimates, rx_from_double(c_estimates[i]));
free(c_estimates);
free(x);
target = rb_ary_new();
rb_ary_push(target, rb_float_new(class));
rb_ary_push(target, estimates);
return target;
}
static VALUE cModel_predict(VALUE obj,VALUE example) {
struct svm_node *x;
struct svm_model *model;
double class;
x = example_to_internal(example);
Data_Get_Struct(obj, struct svm_model, model);
class = svm_predict(model, x);
return rb_float_new(class);
}
