def act(self, action, record=True, format=True): assert (self.context.strip() + action.strip()) assert (settings.getint('top-keks') is not None) result = self.generator.generate( self.get_story() + action, self.context + ' '.join(self.memory), temperature=settings.getfloat('temp'), top_p=settings.getfloat('top-p'), top_k=settings.getint('top-keks'), repetition_penalty=settings.getfloat('rep-pen')) if record: self.actions.append(format_input(action)) self.results.append(format_input(result)) return format_result(result) if format else result
def input_fn(serialized_input_data, content_type='text/csv'): print('Deserializing the input data.') if content_type == 'text/csv': try: data = serialized_input_data.decode('utf-8') except: data = serialized_input_data print(data) print(type(data)) #calling lookuptable vector_table = get_lookup_table() # process input data and turn to numpy array using lookup table formatted_input_data = format_input(data) print(formatted_input_data) print(type(formatted_input_data)) vectorised_input = lookup_table(vector_table, formatted_input_data) return vectorised_input raise Exception('Requested unsupported ContentType in content_type: ' + content_type)
def predict_fn(input_data, model): print('Determining nearest cluster.') #calling lookuptable lookup_table = get_lookup_table() # process input data and turn to numpy array using lookup table formatted_input_data = format_input(input_data) vectorised_input = lookup_table(search_table=lookup_table, formatted_input_data) output = sagemaker_model.predict(vectorised_input) return result
def new_chat(update: Update, context: CallbackContext) -> int: interest = format_input(update.message.text) if interest in conversation_json: for answer in conversation_json[interest]['respostas']: answer_user(update, answer) time.sleep(0.8) if 'conversa' in conversation_json[interest]: context.user_data['conversation_json'] = conversation_json[ interest]['conversa'] return KEEP_CHATING else: update.message.reply_text( f'Não ouvi falar sobre {interest}, me conta mais!') return NEW_CHAT
def __init__(self, crib): self.crib = utils.format_input(crib) self.ciphertext = "" self.logger = logging.getLogger()
stimuli_arr, actions_arr, stim_sides_arr, session_uuids = [], [], [], [] # select particular mice mouse_name = 'KS016' for i in range(len(sess_id)): if mice_names[i] == mouse_name: # take only sessions of first mice data = utils.load_session(sess_id[i]) if data['choice'] is not None and data['probabilityLeft'][0] == 0.5: stim_side, stimuli, actions, pLeft_oracle = utils.format_data(data) stimuli_arr.append(stimuli) actions_arr.append(actions) stim_sides_arr.append(stim_side) session_uuids.append(sess_id[i]) # format data stimuli, actions, stim_side = utils.format_input(stimuli_arr, actions_arr, stim_sides_arr) session_uuids = np.array(session_uuids) # import models from models.expSmoothing_stimside import expSmoothing_stimside as exp_stimside from models.expSmoothing_prevAction import expSmoothing_prevAction as exp_prevAction from models.optimalBayesian import optimal_Bayesian as optBay from models.biasedApproxBayesian import biased_ApproxBayesian as baisedApproxBay from models.biasedBayesian import biased_Bayesian ''' If you are interested in fitting (and the prior) of the mice behavior ''' model = exp_prevAction('./results/inference/', session_uuids, mouse_name, actions, stimuli, stim_side) model.load_or_train(remove_old=False) param = model.get_parameters() # if you want the parameters