print('----------')
print('Neurono svoriniai koeficientai:')
print("w1 = {}".format(syn0[0]))
print("w2 = {}".format(syn0[1]))
print("b = {}".format(bias))
# print(syn0)
# print("Output After Training:")
Tsu = np.dot(data, syn0) + bias
# print(val)
TsuRes = calcDirivAndE(Tsu)
# year[2:202]
TsuDeNormalized = deNormalization(TsuRes,
min(answerForSunActivity)[0],
max(answerForSunActivity)[0])
draw.DrawDiff(year[2:202], dataForTrainingAnswer, TsuDeNormalized)
Ts = np.dot(dataNormalized, syn0) + bias
TsRes = calcDirivAndE(Ts)
TsDeNormalized = deNormalization(TsRes,
min(answerForSunActivity)[0],
max(answerForSunActivity)[0])
print(len(TsDeNormalized))
print(len(answerForSunActivity))
eVector = list()
for real, predicted in zip(answerForSunActivity, TsDeNormalized):
e = real[0] - predicted[0]
eVector.append(e)
draw.DrowPlot(year[2:], eVector)
print("w1 = {}".format(syn0[0]))
print("w2 = {}".format(syn0[1]))
print("b = {}".format(bias))
#
# # print(syn0)
# # print("Output After Training:")
Tsu = np.dot(data, syn0) + bias
# # print(val)
TsuRes = calcDirivAndE(Tsu)
# # year[2:202]
TsuDeNormalized = deNormalization(TsuRes, min(dataWithEmpty['duration']),
max(dataWithEmpty['duration']))
TsuGraphicData = []
for el in TsuRes:
TsuGraphicData.append(el[0])
draw.DrawDiff(iDataNormalize[0:300], resDataNormalized[0:300], TsuGraphicData)
# print(resDataNormalized[0:300])
# Ts = np.dot(dataNormalized, syn0) + bias
# TsRes = calcDirivAndE(Ts)
# TsDeNormalized = deNormalization(TsRes, min(answerForSunActivity)[0], max(answerForSunActivity)[0])
# print(len(TsDeNormalized))
# print(len(answerForSunActivity))
#
# eVector = list()
# for real, predicted in zip(answerForSunActivity, TsDeNormalized):
# e = real[0] - predicted[0]
# eVector.append(e)
#
# draw.DrowPlot(year[2:], eVector)
# draw.DrowHist(eVector)
#
# [0],
# [1],
# [1],
# [1]
# ]
#
net = linear_model.LinearRegression()
net.fit(np.array(dataForTraining), np.array(dataForTrainingAnswer))
w1 = net.coef_[0][0]
w2 = net.coef_[0][1]
b = net.intercept_
Tsu = net.predict(dataForTraining)
draw.DrawDiff(year[2:202], dataForTrainingAnswer, Tsu)
Ts = net.predict(sunSpotActivityDataUsage)
draw.DrawDiff(year[2:], sunSpotActivity[2:], Ts)
eVector = list()
for real, predicted in zip(answerForSunActivity, Ts):
e = real - predicted
eVector.append(e)
draw.DrowPlot(year[2:], eVector)
draw.DrowHist(eVector)
predictionMSE = mse(eVector)
predictionMAD = mad(eVector)
print('MSE = {}'.format(predictionMSE))