def load_svm(self, filename=None):
if not filename:
svm_file = config.get_classifier_filename(self, self.cls, self.train_dataset)
else:
svm_file = filename
if not os.path.exists(svm_file):
print("Svm %s is not trained"%svm_file)
return None
else:
model = load_svm(svm_file)
return model
def train(self, pos, neg, kernel, C): y = [1]*pos.shape[0] + [-1]*neg.shape[0] x = np.concatenate((pos,neg)) model = train_svm(x, y, kernel, C) self.svm = model print 'model.score(C=%d): %f'%(C, model.score(x,y)) table_t = svm_proba(x, model) y2 = np.array(y) y2 = (y2+1)/2 # switch to 0/1 ap,_,_ = Evaluation.compute_cls_pr(table_t[:,1], y2) print 'ap on train set: %f'%ap filename = config.get_classifier_filename(self, self.cls, self.train_dataset) self.svm = model self.save_svm(model, filename) return model
