def auto_run_bencher(bencher, time=5, ave=True, vis=True):
res = dict()
for b in builder.builder_list.values():
if bencher.rust and b.crate_version is None:
continue
if isinstance(b, builder.RustOnly) and not bencher.rust:
continue
print("running", bencher.__name__, "with", b.name)
single = auto_run_single(bencher, b, time, ave or vis)
res[b.name] = single
if vis:
visual.plot(bencher, res)
return res
predicted_stock_price = scaler.inverse_transform(dataset_test_total)
# count of Wrong predicted value after applying treshold
err_cnt = error_count(predicted_stock_price[:, 0],
predicted_stock_price[:, 1],
toler_treshold=5.0)
# Calc difference between real data price and predicted price
diff_rate = calc_diff(predicted_stock_price[:, 0], predicted_stock_price[:, 1])
# show the inputs and predicted outputs
for i in range(len(predicted_stock_price[:, 1])):
print("X=%s, Predicted=%s" %
(predicted_stock_price[i, 1], predicted_stock_price[i, 0]))
print("Error count: ", err_cnt, "\n diff rate: ", diff_rate, "\n")
## Visualising the results
plot(predicted=predicted_stock_price[:, 1])
plot(real=predicted_stock_price[:, 0])
plot(predicted=predicted_stock_price[:, 1], real=predicted_stock_price[:, 0])
# MSE
mse = mean_squared_error(predicted_stock_price[:, 0], predicted_stock_price[:,
1])
print("MSE: ", mse)
############ Visualizing the results ############
print("#############################################################")
# prin thn allagh, ola ta X_all kai y_all htan X kai y
inputs = np.array(X_all)
all_real_price = np.array(y_all)
print("all real price ", all_real_price, all_real_price.shape)
del regressor
regressor = load_model('1.h5')
real_stock_price = np.array(X_test)
inputs = real_stock_price
predicted_stock_price = regressor.predict(inputs)
dataset_test_total = pd.DataFrame()
dataset_test_total['real'] = real_stock_price
dataset_test_total['predicted'] = predicted_stock_price
predicted_stock_price = scaler.inverse_transform(dataset_test_total)
diff_rate = calc_diff(predicted_stock_price[:, 0], predicted_stock_price[:, 1])
inputs = np.array(X)
all_real_price = np.array(y)
all_predicted_price = regressor.predict(inputs)
dataset_pred_real = pd.DataFrame()
dataset_pred_real['real'] = all_real_price
dataset_pred_real['predicted'] = all_predicted_price
all_prices = scaler.inverse_transform(dataset_pred_real)
plot(predicted=all_prices[:, 0])
plot(real=all_prices[:, 1])
plot(predicted=all_prices[:, 0], real=all_prices[:, 1])
new_date = {'value': random.randint(0, 100)}
db.update(result[0], {"$set": new_date}) # 对查询到的第一条数据进行修改
print('成功将:{} 修改成: {}'.format(result[0], new_date))
except Exception as e:
print(e)
if __name__ == '__main__':
write()
print('成功写入10w条随机数据！\n')
insert()
print()
time.sleep(2)
delete()
print()
time.sleep(2)
result = search({'value': {'$gte': 0, '$lt': 10}}) # 查询条件 [0, 10)间的数据
print('0-10间的数据有:\n', list(result))
print()
time.sleep(2)
change()
print()
time.sleep(2)
plot()