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Evolution.py
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from DatasetProcessing import *
from Visualization import *
from GradientDescent import *
import numpy as np
from tabulate import tabulate
"""
"""
class Evolution(object):
"""Initialization variables"""
def __init__(self):
pass
@staticmethod
def startEvolution(numberOfIteration, writeToOutputTxt, mutationProbabilityVar):
"""Метод запуска эволюции в цикле.
Args:
numberOfIteration: число, количество шагов эволюции.
writeToOutputTxt: флаг записи в таблицы в файл outputEvolution.txt: 1 - включен.
mutationProbabilityVar: вероятность мутации особи.
Returns:
MSE_histTop0_NP.tolist(): лист, первая лучшая ошибка.
MSE_histTop1_NP.tolist(): лист, вторая лучшая ошибка.
MSE_histTop2_NP.tolist(): лист, третья лучшая ошибка.
MSE_hist0_NP.tolist(): лист, изменение ошибки особи 0.
MSE_hist1_NP.tolist(): лист, изменение ошибки особи 1.
MSE_hist2_NP.tolist(): лист, изменение ошибки особи 2.
MSE_hist3_NP.tolist(): лист, изменение ошибки особи 3.
MSE_hist4_NP.tolist(): лист, изменение ошибки особи 4.
MSE_hist5_NP.tolist(): лист, изменение ошибки особи 5.
MSE_hist6_NP.tolist(): лист, изменение ошибки особи 6.
wTop_NP.tolist(): лист, содержащий наилучшие веса w0 для x0, w1 для x1, w2 для x2.
"""
weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5, weight_NP6 = \
Evolution.weightStartIntialization()
MSE_histTop0_NP = np.zeros(numberOfIteration)
MSE_histTop1_NP = np.zeros(numberOfIteration)
MSE_histTop2_NP = np.zeros(numberOfIteration)
MSE_hist0_NP = np.zeros(numberOfIteration)
MSE_hist1_NP = np.zeros(numberOfIteration)
MSE_hist2_NP = np.zeros(numberOfIteration)
MSE_hist3_NP = np.zeros(numberOfIteration)
MSE_hist4_NP = np.zeros(numberOfIteration)
MSE_hist5_NP = np.zeros(numberOfIteration)
MSE_hist6_NP = np.zeros(numberOfIteration)
weight_hist01_NP = np.zeros(numberOfIteration)
weight_hist02_NP = np.zeros(numberOfIteration)
weight_hist03_NP = np.zeros(numberOfIteration)
for i in range(numberOfIteration):
MSE_top, MSE_toOutput, weight_NP0new, weight_NP1new, weight_NP2new, weight_NP3new, weight_NP4new, \
weight_NP5new, weight_NP6new = \
Evolution.testEvo(i, writeToOutputTxt, weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4,
weight_NP5, weight_NP6, mutationProbabilityVar)
MSE_histTop0_NP[i] = MSE_top[0]
MSE_histTop1_NP[i] = MSE_top[1]
MSE_histTop2_NP[i] = MSE_top[2]
MSE_hist0_NP[i] = MSE_toOutput[0]
MSE_hist1_NP[i] = MSE_toOutput[1]
MSE_hist2_NP[i] = MSE_toOutput[2]
MSE_hist3_NP[i] = MSE_toOutput[3]
MSE_hist4_NP[i] = MSE_toOutput[4]
MSE_hist5_NP[i] = MSE_toOutput[5]
MSE_hist6_NP[i] = MSE_toOutput[6]
weight_hist01_NP[i] = weight_NP0[0]
weight_hist02_NP[i] = weight_NP0[1]
weight_hist03_NP[i] = weight_NP0[2]
weight_NP0 = weight_NP0new
weight_NP1 = weight_NP1new
weight_NP2 = weight_NP2new
weight_NP3 = weight_NP3new
weight_NP4 = weight_NP4new
weight_NP5 = weight_NP5new
weight_NP6 = weight_NP6new
print("i(MSE_min)=", np.argmin(MSE_hist0_NP), "MSE_min=", min(MSE_hist0_NP),
"weight0_min=", weight_hist01_NP[np.argmin(MSE_hist0_NP)],
"weight1_min=", weight_hist02_NP[np.argmin(MSE_hist0_NP)],
"weight2_min=", weight_hist03_NP[np.argmin(MSE_hist0_NP)])
# wLast
wTop_NP = []
wTop_NP = np.vstack((float(weight_hist01_NP[np.argmin(MSE_hist0_NP)]),
float(weight_hist02_NP[np.argmin(MSE_hist0_NP)]),
float(weight_hist03_NP[np.argmin(MSE_hist0_NP)])))
return MSE_histTop0_NP.tolist(), MSE_histTop1_NP.tolist(), MSE_histTop2_NP.tolist(), MSE_hist0_NP.tolist(), \
MSE_hist1_NP.tolist(), MSE_hist2_NP.tolist(), MSE_hist3_NP.tolist(), MSE_hist4_NP.tolist(), \
MSE_hist5_NP.tolist(), MSE_hist6_NP.tolist(), wTop_NP.tolist()
#TODO: area of visibility
# @staticmethod
# def calculateNewPrice(wTop_NP):
# """Метод подсчета новых цен на основе результатов работы алгоритма.
#
# Args:
# wTop_NP: массив numpy, содержащий наилучшие веса w0 для x0, w1 для x1, w2 для x2.
#
# Returns:
# YNew_NP: массив numpy, содержащий рассчитанные цены.
# """
# data = DatasetProcessing.getDataset('dataset.txt')
# normalizeData = DatasetProcessing.getNormalizeDataset(data)
# area, rooms, Y = DatasetProcessing.getSeparetedData(normalizeData)
# x0 = np.ones(len(area))
# XTranspose_NP = np.vstack((x0, area, rooms)) # двумерный массив [,.....,],[,.....,]
# X_NP = np.transpose(XTranspose_NP)
# YNew_NP = X_NP.dot(wTop_NP)
# return YNew_NP
@staticmethod
def weightStartIntialization():
"""Метод инициализации начальной популяции.
Args:
Returns:
weight_NP0: массив numpy, особь 0.
weight_NP1: массив numpy, особь 1.
weight_NP2: массив numpy, особь 2.
weight_NP3: массив numpy, особь 3.
weight_NP4: массив numpy, особь 4.
weight_NP5: массив numpy, особь 5.
weight_NP6: массив numpy, особь 6.
"""
# print(wLast) = print(np.random.rand(3, 1).tolist())
weight_NP0 = np.random.randn(3, 1)
weight_NP1 = np.random.randn(3, 1)
weight_NP2 = np.random.randn(3, 1)
weight_NP3 = np.random.randn(3, 1)
weight_NP4 = np.random.randn(3, 1)
weight_NP5 = np.random.randn(3, 1)
weight_NP6 = np.random.randn(3, 1)
return weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5, weight_NP6
@staticmethod
def calculateMSE(weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5, weight_NP6):
"""Метод подсчета среднеквадратичной ошибки.
Args:
weight_NP0: массив numpy, особь 0.
weight_NP1: массив numpy, особь 1.
weight_NP2: массив numpy, особь 2.
weight_NP3: массив numpy, особь 3.
weight_NP4: массив numpy, особь 4.
weight_NP5: массив numpy, особь 5.
weight_NP6: массив numpy, особь 6.
Returns:
MSE_toProcess: массив numpy, ошибки всех особей на данной иттерации, для дальнейшей обработки.
MSE_toOutput: массив numpy, ошибки всех особей на данной иттерации, для конечного вывода.
"""
data = DatasetProcessing.getDataset('dataset.txt')
normalizeData = DatasetProcessing.getNormalizeDataset(data)
area, rooms, Y = DatasetProcessing.getSeparetedData(normalizeData)
x0 = np.ones(len(area))
XTranspose_NP = np.vstack((x0, area, rooms)) # двумерный массив [,.....,],[,.....,]
X_NP = np.transpose(XTranspose_NP)
YBad_NP = np.asarray(Y)
YBad_NP = YBad_NP.reshape((1, -1)) # transpose feature
Y_NP = np.transpose(YBad_NP)
m, n = np.shape(X_NP)
N = Y_NP.shape[0]
# stepsNumber = 100000 ##
# i = 0
YNew_NP0 = X_NP.dot(weight_NP0)
YNew_NP1 = X_NP.dot(weight_NP1)
YNew_NP2 = X_NP.dot(weight_NP2)
YNew_NP3 = X_NP.dot(weight_NP3)
YNew_NP4 = X_NP.dot(weight_NP4)
YNew_NP5 = X_NP.dot(weight_NP5)
YNew_NP6 = X_NP.dot(weight_NP6)
MSE0 = np.sum((YNew_NP0 - Y_NP) ** 2) / (2 * N)
MSE1 = np.sum((YNew_NP1 - Y_NP) ** 2) / (2 * N)
MSE2 = np.sum((YNew_NP2 - Y_NP) ** 2) / (2 * N)
MSE3 = np.sum((YNew_NP3 - Y_NP) ** 2) / (2 * N)
MSE4 = np.sum((YNew_NP4 - Y_NP) ** 2) / (2 * N)
MSE5 = np.sum((YNew_NP5 - Y_NP) ** 2) / (2 * N)
MSE6 = np.sum((YNew_NP6 - Y_NP) ** 2) / (2 * N)
MSE_toProcess = []
MSE_toProcess.append(MSE0)
MSE_toProcess.append(MSE1)
MSE_toProcess.append(MSE2)
MSE_toProcess.append(MSE3)
MSE_toProcess.append(MSE4)
MSE_toProcess.append(MSE5)
MSE_toProcess.append(MSE6)
MSE_toOutput = []
MSE_toOutput.append(MSE0)
MSE_toOutput.append(MSE1)
MSE_toOutput.append(MSE2)
MSE_toOutput.append(MSE3)
MSE_toOutput.append(MSE4)
MSE_toOutput.append(MSE5)
MSE_toOutput.append(MSE6)
return MSE_toProcess, MSE_toOutput
@staticmethod
def mutationProbability(mutationProbabilityVar):
"""Метод подсчета вероятности мутации особи и её хромосом.
Args:
mutationProbabilityVar: вероятность мутации особи.
Returns:
mutationIndividual: число, вероятность мутации особи.
mutationChromosomes: число, вероятность 33% мутации одной из трех хромосом.
"""
mutationIndividual = random.randint(1, 100) # mutationProbabilityVar %
if (mutationIndividual <= mutationProbabilityVar):
mutationChromosomes = random.randint(1, 99) # 33%
else:
mutationChromosomes = 0
return mutationIndividual, mutationChromosomes
@staticmethod
def selectionIndividuals(parentFirst, parentSecond, weight, mutationProbabilityVar):
"""Метод проведения селекции особей.
Args:
parentFirst: число, первый родитель.
parentSecond: число, второй родитель.
weight: массив numpy, хромосомы родителей.
Returns:
w_NPnew: массив numpy, новая особь.
"""
weight_NP1_1 = np.asarray(weight[parentFirst])
weight_NP1_2 = np.asarray(weight[parentSecond])
weight_NP1_1List = weight_NP1_1.tolist()
weight_NP1_2List = weight_NP1_2.tolist()
string10 = str(float(weight_NP1_1List[0][0]))
string20 = str(float(weight_NP1_2List[0][0]))
string11 = str(float(weight_NP1_1List[1][0]))
string21 = str(float(weight_NP1_2List[1][0]))
string12 = str(float(weight_NP1_1List[2][0]))
string22 = str(float(weight_NP1_2List[2][0]))
# mutation
mutationIndividual, mutationChromosomes = Evolution.mutationProbability(mutationProbabilityVar)
if (mutationIndividual < 5):
weight_mutation = np.random.randn(3, 1)
if (mutationChromosomes < 33):
string10 = str(float(weight_mutation[0][0]))
elif (33 <= mutationChromosomes < 66):
string11 = str(float(weight_mutation[1][0]))
elif (99 <= mutationChromosomes):
string12 = str(float(weight_mutation[2][0]))
# w[0]
string0 = string10[:5] + string20[5] + string10[6] + string20[7] + string10[8] + string20[9] + \
string10[10] + string20[11] + string10[12:]
# w[1]
string1 = string11[:5] + string21[5] + string11[6] + string21[7] + string11[8] + string21[9] + \
string11[10] + string21[11] + string11[12:]
# w[2]
string2 = string12[:5] + string22[5] + string12[6] + string22[7] + string12[8] + string22[9] + \
string12[10] + string22[11] + string12[12:]
w_NPnew = np.vstack((float(string0), float(string1), float(string2)))
return w_NPnew
@staticmethod
def testEvo(evolutionIteration, writeToOutputTxt, weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4,
weight_NP5, weight_NP6, mutationProbabilityVar):
"""Метод проведения эволюции.
Args:
evolutionIteration: число, текущая итерация эволюции.
writeToOutputTxt: флаг записи в таблицы в файл outputEvolution.txt: 1 - включен.
weight_NP0: массив numpy, особь 0.
weight_NP1: массив numpy, особь 1.
weight_NP2: массив numpy, особь 2.
weight_NP3: массив numpy, особь 3.
weight_NP4: массив numpy, особь 4.
weight_NP5: массив numpy, особь 5.
weight_NP6: массив numpy, особь 6.
mutationProbabilityVar: вероятность мутации особи.
Returns:
MSE_toз: массив numpy, ошибки всех особей на данной иттерации, для дальнейшей обработки.
MSE_toOutput: массив numpy, ошибки всех особей на данной иттерации, для конечного вывода.
weight_NP0new: массив numpy, обновленный, особь 0.
weight_NP1new: массив numpy, обновленный, особь 1.
weight_NP2new: массив numpy, обновленный, особь 2.
weight_NP3new: массив numpy, обновленный, особь 3.
weight_NP4new: массив numpy, обновленный, особь 4.
weight_NP5new: массив numpy, обновленный, особь 5.
weight_NP6new: массив numpy, обновленный, особь 6.
"""
MSE_toProcess, MSE_toOutput = \
Evolution.calculateMSE(weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5, weight_NP6)
MSE_top = []
MSE_place = []
# отбор: поиск лучшего MSE
for k in range(3):
for j in range(len(MSE_toProcess)):
if MSE_toProcess[j] == min(MSE_toProcess):
# print(j, " - ", MSE_total[j])
MSE_place.append(j)
MSE_top.append(MSE_toProcess[j])
MSE_toProcess[j] = 10000.0
break
# print(MSE_top)
# print(MSE_place)
# отбор: вывод лучших весов по лучшим MSE
weight_top = []
for numberMSE_place in range(3):
# for numberWeight in range(7):
if MSE_place[numberMSE_place] == 0:
# print("weight_NP0", weight_NP0)
weight_top.append(weight_NP0.tolist())
if MSE_place[numberMSE_place] == 1:
# print("weight_NP1", weight_NP1)
weight_top.append(weight_NP1.tolist())
if MSE_place[numberMSE_place] == 2:
# print("weight_NP2", weight_NP2)
weight_top.append(weight_NP2.tolist())
if MSE_place[numberMSE_place] == 3:
# print("weight_NP3", weight_NP3)
weight_top.append(weight_NP3.tolist())
if MSE_place[numberMSE_place] == 4:
# print("weight_NP4", weight_NP4)
weight_top.append(weight_NP4.tolist())
if MSE_place[numberMSE_place] == 5:
# print("weight_NP5", weight_NP5)
weight_top.append(weight_NP5.tolist())
if MSE_place[numberMSE_place] == 6:
# print("weight_NP6", weight_NP6)
weight_top.append(weight_NP6.tolist())
weight_NP0 = np.asarray(weight_top[0])
weight_NP0List = weight_NP0.tolist()
if (writeToOutputTxt == 1):
my_file = open('outputEvolution.txt', 'a')
table = [[evolutionIteration, MSE_top[0], float(weight_NP0List[0][0]), float(weight_NP0List[1][0]),
float(weight_NP0List[2][0]), MSE_top[1], MSE_top[2], MSE_toOutput[0], MSE_toOutput[1],
MSE_toOutput[2], MSE_toOutput[3], MSE_toOutput[4], MSE_toOutput[5], MSE_toOutput[6]]]
if (evolutionIteration == 0):
my_file.write(tabulate(table,
headers=["i", "MSE_top[0]", "w0", "w1", "w2", "MSE_top[1]",
"MSE_top[2]", "MSE_total[0]", "MSE_total[1]", "MSE_total[2]",
"MSE_total[3]", "MSE_total[4]", "MSE_total[5]", "MSE_total[6]"],
tablefmt='orgtbl'))
else:
my_file.write(tabulate(table,
# headers=["i", "MSE_top[0]", "w0", "w1", "w2", "MSE_top[1]",
# "MSE_top[2]", "MSE_total[0]", "MSE_total[1]", "MSE_total[2]",
# "MSE_total[3]", "MSE_total[4]", "MSE_total[5]", "MSE_total[6]"],
tablefmt='orgtbl'))
my_file.write("\n")
my_file.close()
if (evolutionIteration == 0):
print(tabulate(table,
headers=["i", "MSE_top[0]", "w0", "w1", "w2", "MSE_top[1]",
"MSE_top[2]", "MSE_total[0]", "MSE_total[1]", "MSE_total[2]",
"MSE_total[3]", "MSE_total[4]", "MSE_total[5]", "MSE_total[6]"],
tablefmt='orgtbl'))
else:
print(tabulate(table,
# headers=["i", "MSE_top[0]", "w0", "w1", "w2", "MSE_top[1]",
# "MSE_top[2]", "MSE_total[0]", "MSE_total[1]", "MSE_total[2]",
# "MSE_total[3]", "MSE_total[4]", "MSE_total[5]", "MSE_total[6]"],
tablefmt='orgtbl'))
# new weight calculate
# w0
weight_NP0new = np.asarray(weight_top[0])
# w1
weight_NP1new = Evolution.selectionIndividuals(0, 1, weight_top, mutationProbabilityVar)
# w2
weight_NP2new = Evolution.selectionIndividuals(1, 0, weight_top, mutationProbabilityVar)
# w3
weight_NP3new = Evolution.selectionIndividuals(0, 2, weight_top, mutationProbabilityVar)
# w4
weight_NP4new = Evolution.selectionIndividuals(2, 0, weight_top, mutationProbabilityVar)
# w5
weight_NP5new = Evolution.selectionIndividuals(2, 1, weight_top, mutationProbabilityVar)
# w6
weight_NP6new = Evolution.selectionIndividuals(1, 2, weight_top, mutationProbabilityVar)
return MSE_top, MSE_toOutput, weight_NP0new, weight_NP1new, weight_NP2new, \
weight_NP3new, weight_NP4new, weight_NP5new, weight_NP6new
# @staticmethod
# def selection(iteration, MSE_total, weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5,
# weight_NP6):
# MSE_top = []
# MSE_place = []
# # отбор: поиск лучшего MSE
#
# for k in range(3):
# # print(MSE_total)
# # print(range(len(MSE_total)))
#
# for j in range(len(MSE_total)):
# if MSE_total[j] == min(MSE_total):
# # print(j, " - ", MSE_total[j])
# MSE_place.append(j)
# MSE_top.append(MSE_total[j])
# MSE_total[j] = 10000.0
# break
# # print(MSE_top)
# # print(MSE_place)
#
#
# # отбор: вывод лучших весов по лучшим MSE
# weight_top = []
# for numberMSE_place in range(3):
# # for numberWeight in range(7):
# if MSE_place[numberMSE_place] == 0:
# # print("weight_NP0", weight_NP0)
# weight_top.append(weight_NP0.tolist())
# if MSE_place[numberMSE_place] == 1:
# # print("weight_NP1", weight_NP1)
# weight_top.append(weight_NP1.tolist())
# if MSE_place[numberMSE_place] == 2:
# # print("weight_NP2", weight_NP2)
# weight_top.append(weight_NP2.tolist())
# if MSE_place[numberMSE_place] == 3:
# # print("weight_NP3", weight_NP3)
# weight_top.append(weight_NP3.tolist())
# if MSE_place[numberMSE_place] == 4:
# # print("weight_NP4", weight_NP4)
# weight_top.append(weight_NP4.tolist())
# if MSE_place[numberMSE_place] == 5:
# # print("weight_NP5", weight_NP5)
# weight_top.append(weight_NP5.tolist())
# if MSE_place[numberMSE_place] == 6:
# # print("weight_NP6", weight_NP6)
# weight_top.append(weight_NP6.tolist())
# # print(weight_top, "\n")
# # print(weight_top[0])
# # print("------------------------------")
# table = [[iteration, MSE_top[0], MSE_top[1], MSE_top[2], MSE_total[0], MSE_total[1], MSE_total[2], MSE_total[3],
# MSE_total[4], MSE_total[5], MSE_total[6]]]
# print(tabulate(table,
# # headers=["MSE_top[0]", "MSE_top[1]",
# # "MSE_top[2]", "kLearningRate",
# # "current alphaLearningRate",
# # "epsilonLimitation", "errorAvg(MSE)",
# # "MSE[i]", "MSE[i-1]", "MSE[0]", "abs(MSE[i] - MSE[i-1])",
# # "weight_NP[1]", "weight_NP[2]", "abs(weight_NP[1] - weight_NP[2]"],
# tablefmt='orgtbl'))
# return weight_top
#
# @staticmethod
# def crossing(weight_top):
# weight_NP0 = np.asarray(weight_top[0])
# # print("weight_NP0", weight_NP0)
# # w1 [0]
# weight_NP1_1 = np.asarray(weight_top[0])
# weight_NP1_2 = np.asarray(weight_top[1])
# weight_NP1_1List = weight_NP1_1.tolist()
# weight_NP1_2List = weight_NP1_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP1_1List[0][0]))
# string1 = str(float(weight_NP1_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w1 [1]
# string0 = str(float(weight_NP1_1List[1][0]))
# string1 = str(float(weight_NP1_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w1 [2]
# string0 = str(float(weight_NP1_1List[2][0]))
# string1 = str(float(weight_NP1_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP1 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP1", weight_NP1)
#
# # w2 [0]
# weight_NP2_1 = np.asarray(weight_top[1])
# weight_NP2_2 = np.asarray(weight_top[0])
# weight_NP2_1List = weight_NP2_1.tolist()
# weight_NP2_2List = weight_NP2_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP2_1List[0][0]))
# string1 = str(float(weight_NP2_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w2 [1]
# string0 = str(float(weight_NP2_1List[1][0]))
# string1 = str(float(weight_NP2_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w2 [2]
# string0 = str(float(weight_NP2_1List[2][0]))
# string1 = str(float(weight_NP2_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10] \
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP2 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP2", weight_NP2)
#
# # w3 [0]
# weight_NP3_1 = np.asarray(weight_top[0])
# weight_NP3_2 = np.asarray(weight_top[2])
# weight_NP3_1List = weight_NP3_1.tolist()
# weight_NP3_2List = weight_NP3_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP3_1List[0][0]))
# string1 = str(float(weight_NP3_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w3 [1]
# string0 = str(float(weight_NP3_1List[1][0]))
# string1 = str(float(weight_NP3_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w3 [2]
# string0 = str(float(weight_NP3_1List[2][0]))
# string1 = str(float(weight_NP3_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP3 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP3", weight_NP3)
#
# # w4 [0]
# weight_NP4_1 = np.asarray(weight_top[2])
# weight_NP4_2 = np.asarray(weight_top[0])
# weight_NP4_1List = weight_NP4_1.tolist()
# weight_NP4_2List = weight_NP4_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP4_1List[0][0]))
# string1 = str(float(weight_NP4_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w4 [1]
# string0 = str(float(weight_NP4_1List[1][0]))
# string1 = str(float(weight_NP4_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w4 [2]
# string0 = str(float(weight_NP4_1List[2][0]))
# string1 = str(float(weight_NP4_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP4 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP4", weight_NP4)
#
# # w5 [0]
# weight_NP5_1 = np.asarray(weight_top[2])
# weight_NP5_2 = np.asarray(weight_top[1])
# weight_NP5_1List = weight_NP5_1.tolist()
# weight_NP5_2List = weight_NP5_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP5_1List[0][0]))
# string1 = str(float(weight_NP5_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w5 [1]
# string0 = str(float(weight_NP5_1List[1][0]))
# string1 = str(float(weight_NP5_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w5 [2]
# string0 = str(float(weight_NP5_1List[2][0]))
# string1 = str(float(weight_NP5_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP5 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP5", weight_NP5)
#
# # w6 [0]
# weight_NP6_1 = np.asarray(weight_top[1])
# weight_NP6_2 = np.asarray(weight_top[2])
# weight_NP6_1List = weight_NP6_1.tolist()
# weight_NP6_2List = weight_NP6_2.tolist()
# # print("weight_NP0List", weight_NP1List)
# # print("weight_NP0List[0]", weight_NP1List[0])
# # print("weight_NP0List[0][0]", weight_NP1List[0][0])
# # print("type(weight_NP0List[0][0])", type(weight_NP1List[0][0]))
# # print("str(float(weight_NP0List[0][0]))", str(float(weight_NP1List[0][0])))
# # print("type(str(float(weight_NP0List[0][0])))", type(str(float(weight_NP1List[0][0]))))
# string0 = str(float(weight_NP6_1List[0][0]))
# string1 = str(float(weight_NP6_2List[0][0]))
# string00 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w6 [1]
# string0 = str(float(weight_NP6_1List[1][0]))
# string1 = str(float(weight_NP6_2List[1][0]))
# string10 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# # w6 [2]
# string0 = str(float(weight_NP6_1List[2][0]))
# string1 = str(float(weight_NP6_2List[2][0]))
# string20 = string0[:5] + string1[5] + string0[6] + string1[7] + string0[8] + string1[9] + string0[10]\
# + string1[11] + string0[12] + string1[13] + string0[14] + string1[15]
# weight_NP6 = np.vstack((float(string00), float(string10), float(string20)))
# # print("weight_NP6", weight_NP6)
# return weight_NP0, weight_NP1, weight_NP2, weight_NP3, weight_NP4, weight_NP5, weight_NP6