def predict_whole(self, mfccs):
num_item = np.shape(mfccs, 0)
result = np.zeros(num_item, self.class_num)
feature = get_feature_npy(mfccs)
for label in self.labels:
result[:, self.label_to_idx[label]] = self.discriminators[
label].predict(feature)
return result
def regress_one(self, mfcc):
mfcc = mfcc[np.newaxis, :]
result = np.zeros(self.class_num)
feature = get_feature_npy(mfcc)
for label in self.labels:
result[self.label_to_idx[label]] = self.regressors[label].predict(
feature)
return result
def predict_meta(self, mfcc):
mfcc = mfcc[np.newaxis, :]
feature = get_feature_npy(mfcc)
result = np.zeros(self.meta_class_num)
if not len(self.meta_discriminators):
return None
for i in range(self.meta_class_num):
result[i] = self.meta_discriminators[i].predict(feature)
return result
def predict_one(self, mfcc, meta_label=None):
labels = []
if not len(self.meta_discriminators):
labels = self.labels
else:
for i in range(self.meta_class_num):
labels += self.meta_labels[i] if meta_label[i] else []
mfcc = mfcc[np.newaxis, :]
result = np.zeros(self.class_num)
feature = get_feature_npy(mfcc) # feature: 1*40
for label in labels:
result[self.label_to_idx[label]] = self.discriminators[
label].predict(feature)
return result