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main_encoder_7.py
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import math
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from fill_normalization import fill, normalization
from kmeansByAndrew import runKmeans, plotData
from K_means改 import kpp_centers
from problem_data import ProblemData
from predict_data import PredictData
from unknown_data import UnknownData
from test_data_new import TestData
# from en_coder_4 import encode
from en_coder_5 import encode
def euler_distance(point1, point2):
# 计算两点之间的欧拉距离，支持多维
distance = 0.0
for a, b in zip(point1, point2):
distance += math.pow(a - b, 2)
return math.sqrt(distance)
unknowndata = UnknownData() #未知数据产生聚类中心
unknowndata = fill(unknowndata, 1778, 336, 254)
unknowndata = normalization(unknowndata, 1778, 336)
U1 = encode(unknowndata)
init_centroids = np.array(kpp_centers(U1, 2))
idx, centroids_all = runKmeans(U1, init_centroids, 100)
centroids = centroids_all[-1]
print("感知不明数据产生的聚类中心点：\n", centroids[0], centroids[1])
# plotData(U1, centroids_all, idx)
# plt.scatter(U1[:, 0], U1[:, 1])
# plt.savefig('F:\\导出的图片.png')
# plt.show()
tf.reset_default_graph()
problemdata = ProblemData() #问题数据
problemdata = fill(problemdata, 2093, 336, 299)
problemdata = normalization(problemdata, 2093, 336)
U2 = encode(problemdata)
# plt.scatter(U2[:, 0], U2[:, 1],c='red')
tf.reset_default_graph()
testdata = TestData() #测试准确度的数据
testdata = fill(testdata, 476, 336, 68)
testdata = normalization(testdata, 476, 336)
U3 = encode(testdata)
# plt.scatter(U3[:, 0], U3[:, 1],c='blue')
# plt.show()
# plt.subplot2grid((2,2),(0,0))
# plt.scatter(U2[:, 0], U2[:, 1],c='red')
# plt.subplot2grid((2,2),(0,1))
# plt.scatter(U3[:, 0], U3[:, 1],c='blue')
# # plt.savefig('F:\\导出的图片1.png')
# plt.subplot2grid((2,2),(1,0))
# # plt.scatter(U1[:, 0], U1[:, 1],c='gold')
# plotData(U1, centroids_all, idx)
# # plt.savefig('F:\\导出的图片3.png')
# plt.show()
tf.reset_default_graph()
a = np.random.randint(0, 12)
predictdata = PredictData() # 做感知差识别的数据
predictdata = fill(predictdata, 84, 336, 12)
predictdata = normalization(predictdata, 84, 336)
U4 = encode(predictdata)
data_arg = U4[a*7: (a+1)*7]
for i in range(7):
print("降维后的第{}天数据".format(i), data_arg[i])
# plt.show()
T = U2
P = U3
num1 = num2 = 0
num3 = num4 = 0
for i in range(2093):
if euler_distance(T[i], centroids[0]) <= euler_distance(T[i], centroids[1]):
num1 += 1
else:
num2 += 1
if num1 >= num2:
print("感知差数据聚类中心点为：", centroids[0][0], centroids[0][1])
# centroids0为问题小区中心点
for i in range(476):
if euler_distance(P[i], centroids[0]) <= euler_distance(P[i], centroids[1]):
num3 += 1
print('预测准确度为：', '%.2f' % (100 * num3 / 476), '%')
for i in range(7):
dis1 = euler_distance(data_arg[i], centroids[0])
dis2 = euler_distance(data_arg[i], centroids[1])
if dis1 < dis2:
print("第{}天数据存在感知差问题".format(i))
else:
print("第{}天数据感知正常".format(i))
else:
print("感知正常聚类中心点为:", centroids[1][0], centroids[1][1])
# centroids1为问题小区中心点
for i in range(476):
if euler_distance(P[i], centroids[0]) >= euler_distance(P[i], centroids[1]):
num4 += 1
print('预测准确度为：', '%.2f' % (100 * num4 / 476), '%')
for i in range(7):
dis1 = euler_distance(data_arg[i], centroids[0])
dis2 = euler_distance(data_arg[i], centroids[1])
if dis1 > dis2:
print("第{}天数据存在感知差问题".format(i))
else:
print("第{}天数据感知正常".format(i))