from com.test.msbd5001.methods import one_hot_encoder, k_fold, get_train_and_valid_set
from keras.layers import Dense, LeakyReLU, ReLU, ELU
from keras import Sequential
import keras
from sklearn.metrics import mean_squared_error
csv_file = pd.read_csv('train.csv')
standard_features = preprocess_data(csv_file)
x_axis = preprocess_data(train_features=csv_file)
x_axis = normalize_data(train_features=x_axis,
standard_features=standard_features)
x_axis = one_hot_encoder(x_axis)
column_count = len(x_axis[0])
y_axis = csv_file['time'].values.tolist()
# x_axis = np.array(x_axis)
# y_axis = np.array(y_axis)
# print(x_axis, y_axis)
x_axis, y_axis, x_valid, y_valid = get_train_and_valid_set(x_axis, y_axis, 0)
x_valid = np.array(x_valid)
y_valid = np.array(y_valid)
k = 5
import os
import csv
import tensorflow as tf
from com.test.msbd5001.train_model_02 import preprocess_data, normalize_data
from com.test.msbd5001.methods import one_hot_encoder, get_train_and_valid_set, k_fold
MAX_ITER = 10001
LEARNING_RATE = 0.05
test_csv_file = pd.read_csv('test.csv')
test_x_axis = preprocess_data(test_csv_file)
test_x_axis = one_hot_encoder(test_x_axis)
# print(len(test_x_axis))
csv_file = pd.read_csv('train.csv')
standard_features = preprocess_data(csv_file)
x_axis = preprocess_data(train_features=csv_file)
x_axis = normalize_data(x_axis, standard_features)
# print(x_axis['time'])
x_axis = one_hot_encoder(x_axis)
# x_axis = [x_axis]
column_count = len(x_axis[0])
# print(column_count)