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问题四.py
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问题四.py
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import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from scipy.misc import imresize
from sklearn.neighbors import KNeighborsClassifier
# 1、建立模型对数据进行训练
#n_clusetrs 这个是设置你要分为多少类
def kmes(http):
a = os.listdir(http)
n = len(a)
y_t = [None]*n
b_q = ['YD1.xlsx', 'YD10.xlsx', 'YD11.xlsx', 'YD2.xlsx', 'YD3.xlsx', 'YD4.xlsx',
'YD5.xlsx', 'YD6.xlsx', 'YD7.xlsx', 'YD8.xlsx', 'YD9.xlsx']
for i in range(n):
x_1 = pd.read_excel(http+'/'+a[i])
x_1 = np.array(x_1)
b_q.append(a[i])
x_1 = x_1[::,[1,3]]
# 训练
if a[i] == b_q[1]:
kms = KMeans(n_clusters=4)
kms.fit(x_1) # 这个是无监督学习没有预测训练值
y_t[i] = kms.predict(x_1)
plt.scatter(x_1[::, 1], x_1[::, 0], s=2, c=y_t[i]) # 0,len(x_1[::,0]))
elif a[i] == b_q[2]:
kms = KMeans(n_clusters=2)
kms.fit(x_1) # 这个是无监督学习没有预测训练值
y_t[i] = kms.predict(x_1)
elif a[i] == b_q[8]:
kms = KMeans(n_clusters=9)
kms.fit(x_1) # 这个是无监督学习没有预测训练值
y_t[i] = kms.predict(x_1)
# plt.scatter(x_1[::,1],x_1[::,0],s=2,c=y_t[i]) # 0,len(x_1[::,0]))
else:
kms = KMeans()
kms.fit(x_1) # 这个是无监督学习没有预测训练值
y_t[i] = kms.predict(x_1)
return y_t,b_q
# Y_t,b_1 = kmes('./代码附件/附件一/电流电压')
# print(type(Y_t))
# plt.show()
# 2、对分解出来的单一态操作记录数据做模式识别。
def Analysis(http_htp):
The_label = os.listdir(http_htp)
n = len(The_label)
for i in range(n):
data = pd.read_excel(http_htp+'/'+The_label[i])
data = np.array(data)
plt.plot(data[::,1])
plt.title(The_label[i])
plt.xlabel('时间/(s)')
plt.ylabel('IC/(A)')
plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置字体为SimHei显示中文
plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
plt.show()
# Analysis('./代码附件/附件一/电流电压')
Analysis('./代码附件/附件四/设备数据')
def kms_identify(http_http):
a = os.listdir(http_http)
n = len(a)
y_ = [None]*n
b_q = ['YD1.xlsx', 'YD10.xlsx', 'YD11.xlsx', 'YD2.xlsx', 'YD3.xlsx', 'YD4.xlsx',
'YD5.xlsx', 'YD6.xlsx', 'YD7.xlsx', 'YD8.xlsx', 'YD9.xlsx']
knn = KNeighborsClassifier() # 构造分类器
for i in range(n):
data_1 = pd.read_excel(http_http + '/' + a[i])
data_1 = np.array(data_1)
data_x_train = data_1[::, [9,10]]
data_1 = data_1[::, [1, 3, 4]]
data_x = imresize(data_1, [len(data_1[::, 0]), 3], 'bilinear')
if a[i] == b_q[0]:
data_text = imresize(data_x, [394, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
elif a[i] == b_q[4]:
data_text = imresize(data_x, [394, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
elif a[i] == b_q[3]: #and b_q[6] and b_q[2]):
data_text = imresize(data_x, [1283, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
elif a[i]==b_q[9]:
data_text = imresize(data_x, [1283, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
elif a[i]==b_q[2]:
data_text = imresize(data_x, [1283, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
else:
data_text = imresize(data_x, [1524, 3], 'bilinear')
knn.fit(data_x, data_x_train)
y_[i] = knn.predict(data_text) # 进行预测的结果
return y_
y_pred = kms_identify('./代码附件/单一态分解数据')
# 3、数据保存到txt文件夹中：
# file=open('YD1.txt','w')
# file.write(str(y_pred[0][0]))
# file.close()
import csv
for i in range(len(y_pred)):
data = pd.DataFrame(y_pred[i])
data.to_csv('./保存file4/data%d.csv'%(i), index=False, sep=',', quoting=csv.QUOTE_NONNUMERIC)
# 4、电量估计分析
import 泰迪杯.electric as ele
def ele_dl(http_1):
a = os.listdir(http_1)
n = len(a)
x = [];y = []
d_ele = [None]*2
b_q = ['YD1.xlsx', 'YD10.xlsx', 'YD11.xlsx', 'YD2.xlsx', 'YD3.xlsx', 'YD4.xlsx',
'YD5.xlsx', 'YD6.xlsx', 'YD7.xlsx', 'YD8.xlsx', 'YD9.xlsx']
for i in range(n):
data_sq = pd.read_excel(http_1+'/'+a[i])
data_sq = np.array(data_sq)[::,1:-1]
x_2d = data_sq
x_8d = data_sq
if a[i] == b_q[0]:
x_2d[::,-1] = ele.electric_quantity(data_sq)
x_2ele = imresize(x_2d,[893,len(x_2d[0,::])])
x = x_2ele[::, -1]
elif a[i] == b_q[4]:
x_8d[::,-1] = ele.electric_quantity(data_sq)
x_8ele = imresize(x_2d, [893, len(x_8d[0, ::])])
y = x_8ele[::, -1]
return x,y
x,y = ele_dl('./代码附件/附件一/电流电压')
plt.plot(x/1000)
plt.title('YD1电量')
plt.xlabel('时间/(s)')
plt.ylabel('实时电量/(kwh)')
plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置字体为SimHei显示中文
plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
plt.show()
plt.plot(y/1000)
plt.title('YD3电量')
plt.xlabel('时间/(s)')
plt.ylabel('实时电量/(kwh)')
plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置字体为SimHei显示中文
plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号
plt.show()