# -*- coding: utf-8 -*-
"""
Created on Mon Dec 2 14:49:59 2018 @author: zhen
""" import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime def normal(a): #最大值最小值归一化
return (a - np.min(a)) / (np.max(a) - np.min(a)+0.000001) def normalization(x): # np.std:计算矩阵的标准差（方差的算术平方根）
return (x - np.mean(x)) / np.std(x) def corrcoef(a,b):
corrc = np.corrcoef(a,b) # 计算皮尔逊相关系数，用于度量两个变量之间的相关性，其值介于-1到1之间
corrc = corrc[0,1]
return (16 * ((1 - corrc) / (1 + corrc)) ** 1) # ** 表示乘方 startTimeStamp = datetime.now() # 获取当前时间
# 加载数据
filename = 'C:/Users/zhen/.spyder-py3/sh000300_2017.csv'
# 获取第一，二列的数据
all_date = pd.read_csv(filename,usecols=[0, 1, 3], dtype = 'str')
all_date = np.array(all_date)
data = all_date[:, 0]
times = all_date[:, 1] data_points = pd.read_csv(filename,usecols=[3])
data_points = np.array(data_points)
data_points = data_points[:,0] #数据 topk = 10 #只显示top-10
baselen = 100
basebegin = 361
basedata = data[basebegin]+' '+times[basebegin]+'~'+data[basebegin+baselen-1]+' '+times[basebegin+baselen-1]
base = data_points[basebegin:basebegin+baselen]#一天的数据是240个点
length = len(data_points) #数据长度 # 分割片段
subseries = []
dateseries = []
for j in range(0,length):
if (j < (basebegin - baselen) or j > (basebegin + baselen - 1)) and j <length - baselen:
subseries.append(data_points[j:j+baselen])
dateseries.append(j) #开始位置 # 片段搜索
listdistance = []
for i in range(0, len(subseries)):
tt = np.array(subseries[i])
distance = corrcoef(base, tt)
listdistance.append(distance) # 排序
index = np.argsort(listdistance,kind='quicksort') #排序，返回排序后的索引序列 # 显示，要匹配的数据
plt.figure(0)
plt.plot((base),label = basedata, linewidth='')
plt.legend(loc='upper left')
plt.title('Base data') # 原始数据
plt.figure(1)
num = index[0]
length = len(subseries[num])
begin = data[dateseries[num]]+' '+times[dateseries[num]]
end = data[dateseries[num]+length-1]+' '+times[dateseries[num]+length-1]
label = begin+'~'+end
plt.plot((subseries[num]), label=label, linewidth='')
plt.legend(loc='upper left')
plt.title('Similarity data') # 结果集对比
plt.figure(2)
plt.plot(normalization(base),label= basedata,linewidth='')
length = len(subseries[num])
begin = data[dateseries[num]] + ' ' + times[dateseries[num]]
end = data[dateseries[num] + length - 1] + ' ' + times[dateseries[num] + length - 1]
label = begin + '~' + end
plt.plot(normalization(subseries[num]), label=label, linewidth='')
plt.legend(loc='lower right')
plt.title('normal similarity search')
plt.show() endTimeStamp=datetime.now()
print('run time', (endTimeStamp-startTimeStamp).seconds, "s")
结果：
