def predict(self, input_data, n_times=None):
self.discrete_validation(input_data)
input_data = format_data(bin2sign(input_data), row1d=True)
if self.mode == 'async':
if n_times is None:
n_times = self.n_times
_, n_features = input_data.shape
output_data = input_data
for _ in range(n_times):
position = random.randint(0, n_features - 1)
raw_new_value = output_data.dot(self.weight[:, position])
output_data[:, position] = sign(raw_new_value)
else:
output_data = input_data.dot(self.weight)
return step(output_data).astype(int)
def predict(self, input_data, n_times=None):
self.discrete_validation(input_data)
input_data = format_data(bin2sign(input_data), row1d=True)
if self.mode == 'async':
if n_times is None:
n_times = self.n_times
_, n_features = input_data.shape
output_data = input_data
for _ in range(n_times):
position = random.randint(0, n_features - 1)
raw_new_value = output_data.dot(self.weight[:, position])
output_data[:, position] = sign(raw_new_value)
else:
output_data = input_data.dot(self.weight)
return step(output_data).astype(int)
def format_predict(self, predicted_result):
return step(predicted_result).astype(int)
