import Network as net

import ActivationFunction as af

import Evaluation as ev

import numpy as np

import os

import math

import time

_DataSetPath = os.path.join(os.path.dirname(os.path.abspath(

__file__)), 'DataSet') # Get the directory for StringFunctions

_curentdatapath = os.path.join(_DataSetPath, "Bike Sharing-hour.data")

data = np.loadtxt(_curentdatapath, dtype=float, delimiter=",")

np.random.shuffle(data)

for i in range(len(data[0,:])):

_max = np.max(data[:,i])

_min = np.min(data[:,i])

data[:,i] = (data[:,i]-_min)/(_max - _min)

trainPortion = int(len(data)*66/100)

trainData = data[:trainPortion]

testData = data[trainPortion:]

test_dataset = testData[:, :-1].tolist()

test_target = testData[:, -1:].tolist()

dataset = trainData[:, :-1].tolist()

target = trainData[:, -1:].tolist()

myNet5 = net.Dense()

myNet5.AddLayer(13, isInput=True)

myNet5.AddLayer(5, activationFunction=af.Sigmoid)

myNet5.AddLayer(1, activationFunction=af.Sigmoid)

myNet20 = net.Dense()

myNet20.AddLayer(13, isInput=True)

myNet20.AddLayer(20, activationFunction=af.Sigmoid)

myNet20.AddLayer(1, activationFunction=af.Sigmoid)

start_time = time.time()

myNet5.Train(dataset, target, iterationCount= 20, learningRateStart=.6, learningRateEnd=.0, regulationRate=.02)

train5_Time = time.time() - start_time

start_time = time.time()

myNet20.Train(dataset, target, iterationCount= 20, learningRateStart=.6, learningRateEnd=.0, regulationRate=.02)

train20_Time = time.time() - start_time

# **************************************** Print Metrics *************************************************************

from sklearn.metrics import mean_squared_error

t5, o5= ev.GetPredictions(myNet5, test_dataset, test_target)

t20, o20= ev.GetPredictions(myNet20, test_dataset, test_target)

print(" ")

print("---------------------------------------------------------------------------------")

print("------------------------------ Bike Sharing Data Set ----------------------------")

print(">>>>>>>>>>>>> 5 hidden units")

print("mean_squared_error : {}".format(round(mean_squared_error(t5, o5),6)))

print("Root_MSE : {}".format(round(math.sqrt(mean_squared_error(t5, o5)),6)))

print("Training Time : {} Seconds".format(round(train5_Time,3)))

print("- - - - - - - - - - - - - - - - - - - - - - - - - - -")

print(">>>>>>>>>>>>> 20 hidden units")

print("mean_squared_error : {}".format(round(mean_squared_error(t20, o20),6)))

print("Root_MSE : {}".format(round(math.sqrt(mean_squared_error(t20, o20)),6)))

print("Training Time : {} Seconds".format(round(train20_Time,3)))

print("---------------------------------------------------------------------------------")

print(" ")