def mainpca():
unknowndata = UnknownData() # 未知数据产生聚类中心
unknowndata = fill(unknowndata, 1778, 336, 254)
unknowndata = normalization(unknowndata, 1778, 336)
U1, S1, V1 = pca(np.array(unknowndata, dtype='float'), 2)
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.savefig('F:\\导出的图片.png')
# plt.show()
problemdata = ProblemData() # 问题数据
problemdata = fill(problemdata, 2093, 336, 299)
problemdata = normalization(problemdata, 2093, 336)
U2, S2, V2 = pca(np.array(problemdata, dtype='float'), 2)
# plt.scatter(U2[:, 0], U2[:, 1])
testdata = TestData() # 测试正确率的数据
testdata = fill(testdata, 476, 336, 68)
testdata = normalization(testdata, 476, 336)
U3, S3, V3 = pca(np.array(testdata, dtype='float'), 2)
# plt.scatter(U3[:, 0], U3[:, 1],c='orange')
# plt.show()
# plt.subplot2grid((2,2),(0,0))
# plt.scatter(U2[:, 0], U2[:, 1])
# plt.subplot2grid((2,2),(0,1))
# plt.scatter(U3[:, 0], U3[:, 1],c='orange')
# # plt.savefig('F:\\导出的图片1.png')
# plt.subplot2grid((2,2),(1,0))
# plotData(U1, centroids_all, idx)
# # plt.savefig('F:\\导出的图片3.png')
# plt.show()
# a = np.random.randint(0, 84)
data_7, day, ECI, time, name = PredictData() # 做感知差识别的数据
data_7 = fill(data_7, 7, 336, 1)
data_7 = normalization(data_7, 7, 336)
U4, S4, V4 = pca(np.array(data_7, dtype='float'), 2)
data_arg = U4[day]
print("降维后的感知差识别数据：", data_arg[0], data_arg[1])
print("ECI:", ECI)
print("time:", time)
print("name:", name)
T = U2 # 预测
P = U3
num1 = num2 = 0
num3 = num4 = 0
string1 = '该日数据存在感知差问题'
string2 = '该日数据感知正常'
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), '%')
dis1 = euler_distance(data_arg, centroids[0])
dis2 = euler_distance(data_arg, centroids[1])
if dis1 < dis2:
string = string1
print(string)
else:
string = string2
print(string)
else:
print("感知正常中心点为:", 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), '%')
dis1 = euler_distance(data_arg, centroids[0])
dis2 = euler_distance(data_arg, centroids[1])
if dis1 > dis2:
string = string1
print(string)
else:
string = string2
print(string)
return string, ECI, time, name
# model_file = 'net/net-5-2-78.15%/encode.ckpt'
model_file = 'net/net-5-3-72.69%/encode.ckpt'
# model_file = 'net/net-5-4-80.46%/encode.ckpt'
# model_file = 'net/net-5-5-86.76%/encode.ckpt'
saver.restore(sess, model_file)
# print(sess.run(weights['encoder_h1']))
encoder_result = sess.run(encoder_op, feed_dict={X: data})
# # plt.scatter(encoder_result[:, 0], encoder_result[:, 1])
return encoder_result
if __name__ == '__main__':
unknowndata = UnknownData()
unknowndata = fill(unknowndata, 1778, 336, 254)
unknowndata = normalization(unknowndata, 1778, 336)
result = encode(unknowndata)
# tf.reset_default_graph()
# problemdata = ProblemData()
# problemdata = fill(problemdata, 2093, 336, 299)
# problemdata = normalization(problemdata, 2093, 336)
# result_ = encode(problemdata)
tf.reset_default_graph()
testdata = TestData()
testdata = fill(testdata, 476, 336, 68)
testdata = normalization(testdata, 476, 336)
result__ = encode(testdata)
# init_centroids = np.array(kpp_centers(result, 2))
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)
# _, c = sess.run([optimizer, cost], feed_dict={X: data})
# if epoch % 100 == 0:
# print("cost={:.9f}".format(c))
# encoder_result = sess.run(encoder_op, feed_dict={X: data})
# plt.scatter(encoder_result[:, 0], encoder_result[:, 1])
# saver.save(sess, 'net/net-4-/encode.ckpt')
model_file = 'net/net-4-80%/encode.ckpt'
saver.restore(sess, model_file)
encoder_result = sess.run(encoder_op, feed_dict={X: data})
plt.scatter(encoder_result[:, 0], encoder_result[:, 1])
return encoder_result
unknowndata = UnknownData()
unknowndata = fill(unknowndata, 1778, 336, 254)
unknowndata = normalization(unknowndata, 1778, 336)
result = encode(unknowndata)
# # plt.show()
# tf.reset_default_graph()
# problemdata = ProblemData()
# problemdata = fill(problemdata, 2093, 336, 299)
# problemdata = normalization(problemdata, 2093, 336)
# result_ = encode(problemdata)
init_centroids = np.array(kpp_centers(result, 2))
idx, centroids_all = runKmeans(result, init_centroids, 100)
centroids = centroids_all[-1]
plotData(result, centroids_all, idx)
plt.show()