def process(search_code): data = fetch_data(search_code) matched_sessions = filter(data) if matched_sessions: playsound("src/Rooster.mp3") else: print("No Match found")
def process(district_code): data = fetch_data(district_code) matched_sessions = filter(data) if matched_sessions: pass # filename = '/tmp/sessions.csv' # write_data(filename, matched_sessions) # send_email(filename) # send_data(matched_sessions) else: print("No Match found")
def post_rec(): if request.args.get('max_price', '') != '': max_price = float(re.sub("[\$]", "", request.args.get('max_price', ''))) else: max_price = None if request.args.get('min_price', '') != '': min_price = float(re.sub("[\$]", "", request.args.get('min_price', ''))) else: min_price = None if request.args.get('movein', '') != '': movein = datetime.strptime(request.args.get('movein', ''), '%Y-%m-%d') else: movein = None if request.args.get('work', '') != '': work = str(request.args.get('work', '')) else: work = None # print str(request.args.get('local', '')), len(str(request.args.get('local', ''))) if request.args.get('local', '') in types: local = str(request.args.get('local', '')) else: local = None neighborhoods = request.args.getlist('neighborhood[]') attributes = request.args.getlist('attributes[]') description = re.sub(r'\\[ntr]', ' ', request.args.get('description', '')) description = re.sub('\\s+', ' ', description) input = [ max_price, min_price, movein, neighborhoods, attributes, description, work, local ] keep = filter(listings, input) filtered_listings = listings.ix[keep] match = cluster(filtered_listings, description, keep) df_match = listings.ix[match] df_match = df_match[[ 'title', 'price', 'neighborhood', 'movein', 'attributes', 'description', 'latitude', 'longitude' ]] # print input[6], input[7] if bool(input[6] and input[7]): final_listings = add_gmaps_cols(df_match, input[6], input[7]) json = final_listings.to_json() return recpage(json) else: json = df_match.to_json() return recpage(json)
def main(): [X_train, y_train, X_test, y_test] = example0() valid_col = filter(X_train) print len(valid_col) valid_col = dimension_reduce(X_train, valid_col) print len(valid_col) X_train = format(X_train, valid_col) X_test = format(X_test, valid_col) N, sl = X_train.shape num_classes = len(np.unique(y_train)) """Hyperparamaters""" batch_size = 128 max_iterations = 1000 dropout = 0.8 config = { 'num_layers': 3, #number of layers of stacked RNN's 'hidden_size': 128, #memory cells in a layer 'max_grad_norm': 5, #maximum gradient norm during training 'batch_size': batch_size, 'learning_rate': .003, 'sl': sl, 'num_classes': num_classes } epochs = np.floor(batch_size * max_iterations / N) print('Train %.0f samples in approximately %d epochs' % (N, epochs)) model = Model(config) """Session time""" sess = tf.Session() sess.run(model.init_op) cost_train_ma = -np.log( 1 / float(num_classes) + 1e-9) #Moving average training cost acc_train_ma = 0.0 for i in range(max_iterations): X_batch, y_batch = sample_batch(X_train, y_train, batch_size) cost_train, acc_train, _ = sess.run( [model.cost, model.accuracy, model.train_op], feed_dict={ model.input: X_batch, model.labels: y_batch, model.keep_prob: dropout }) cost_train_ma = cost_train_ma * 0.99 + cost_train * 0.01 acc_train_ma = acc_train_ma * 0.99 + acc_train * 0.01 #Evaluate validation performance if i % 20 == 0: X_batch, y_batch = sample_batch(X_test, y_test, batch_size) cost_val, summ, acc_val = sess.run( [model.cost, model.merged, model.accuracy], feed_dict={ model.input: X_batch, model.labels: y_batch, model.keep_prob: 1.0 }) print( 'At %5.0f/%5.0f: COST %5.3f/%5.3f(%5.3f) -- Acc %5.3f/%5.3f(%5.3f)' % (i, max_iterations, cost_train, cost_val, cost_train_ma, acc_train, acc_val, acc_train_ma)) plt.plot(i, acc_train, 'r*') plt.plot(i, cost_train, 'kd') cost_val, summ, acc_val = sess.run( [model.cost, model.merged, model.accuracy], feed_dict={ model.input: X_test, model.labels: y_test, model.keep_prob: 1.0 }) epoch = float(i) * batch_size / N print('Trained %.1f epochs, accuracy is %5.3f and cost is %5.3f' % (epoch, acc_val, cost_val)) plt.xlabel('Iteration') plt.ylabel('Accuracy & Cost') plt.show()