def setLearningRateIndex(self, index): """ This updates the pointers in the learning rates :param index: the index :return: """ if index >= len(self.learning_rates): index = len(self.learning_rates) -1 logging.warning('Trying to set the learning rate on an index greater than learnign rates available') self.current_learning_rate_index = index self.total_epochs = self.learning_rates[self.current_learning_rate_index][EPOCHS_INDEX] self.current_learning_rate = self.learning_rates[self.current_learning_rate_index][LEARNING_RATE_INDEX]
def predicted_values(self, as_list=False, add_header=False): """ Get an array of dictionaries (unless as_list=True) for predicted values :return: predicted_values """ ret = [] # foreach row in the result extract only the predicted values for row in self.data_array: # prepare the result, either a dict or a list if as_list: ret_row = [] else: ret_row = {} # append predicted values for col in self.predicted_columns: col_index = self.columns.index(col) if as_list: ret_row += [row[col_index]] else: ret_row[self._getOrigColum(col)] = row[col_index] # append confidence col_index = self.columns.index(KEY_CONFIDENCE) if as_list: # add confidence if its a dictionary ret_row += row[col_index] else: if len(row) < col_index: logging.warning( 'Output is smaller than expected, see transaction_output_data.py' ) ret_row[KEY_CONFIDENCE] = 0 else: try: ret_row[KEY_CONFIDENCE] = row[col_index] except: ret_row[KEY_CONFIDENCE] = 0 # append row to result ret += [ret_row] # if add_header and as_list True, add the header to the result if as_list and add_header: header = [ self._getOrigColum(col) for col in self.predicted_columns ] + [KEY_CONFIDENCE] ret = [header] + ret return ret