本文实例讲述了Python数据分析之双色球基于线性回归算法预测下期中奖结果。分享给大家供大家参考,具体如下:
前面讲述了关于双色球的各种算法,这里将进行下期双色球号码的预测,想想有些小激动啊。
代码中使用了线性回归算法,这个场景使用这个算法,预测效果一般,各位可以考虑使用其他算法尝试结果。
发现之前有很多代码都是重复的工作,为了让代码看的更优雅,定义了函数,去调用,顿时高大上了
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#!/usr/bin/python
# -*- coding:UTF-8 -*-
#导入需要的包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import operator
from sklearn import datasets,linear_model
from sklearn.linear_model import LogisticRegression
#读取文件
df = pd.read_table( 'newdata.txt' ,header = None ,sep = ',' )
#读取日期
tdate = sorted (df.loc[:, 0 ])
#将以列项为数据,将球号码取出,写入到csv文件中,并取50行数据
# Function to red number to csv file
def RedToCsv(h_num,num,csv_name):
h_num = df.loc[:,num:num].values
h_num = h_num[ 50 :: - 1 ]
renum2 = pd.DataFrame(h_num)
renum2.to_csv(csv_name,header = None )
fp = file (csv_name)
s = fp.read()
fp.close()
a = s.split( '\n' )
a.insert( 0 , 'numid,number' )
s = '\n' .join(a)
fp = file (csv_name, 'w' )
fp.write(s)
fp.close()
#调用取号码函数
# create file
RedToCsv( 'red1' , 1 , 'rednum1data.csv' )
RedToCsv( 'red2' , 2 , 'rednum2data.csv' )
RedToCsv( 'red3' , 3 , 'rednum3data.csv' )
RedToCsv( 'red4' , 4 , 'rednum4data.csv' )
RedToCsv( 'red5' , 5 , 'rednum5data.csv' )
RedToCsv( 'red6' , 6 , 'rednum6data.csv' )
RedToCsv( 'blue1' , 7 , 'bluenumdata.csv' )
#获取数据,X_parameter为numid数据,Y_parameter为number数据
# Function to get data
def get_data(file_name):
data = pd.read_csv(file_name)
X_parameter = []
Y_parameter = []
for single_square_feet ,single_price_value in zip (data[ 'numid' ],data[ 'number' ]):
X_parameter.append([ float (single_square_feet)])
Y_parameter.append( float (single_price_value))
return X_parameter,Y_parameter
#训练线性模型
# Function for Fitting our data to Linear model
def linear_model_main(X_parameters,Y_parameters,predict_value):
# Create linear regression object
regr = linear_model.LinearRegression()
#regr = LogisticRegression()
regr.fit(X_parameters, Y_parameters)
predict_outcome = regr.predict(predict_value)
predictions = {}
predictions[ 'intercept' ] = regr.intercept_
predictions[ 'coefficient' ] = regr.coef_
predictions[ 'predicted_value' ] = predict_outcome
return predictions
#获取预测结果函数
def get_predicted_num(inputfile,num):
X,Y = get_data(inputfile)
predictvalue = 51
result = linear_model_main(X,Y,predictvalue)
print "num " + str (num) + " Intercept value " , result[ 'intercept' ]
print "num " + str (num) + " coefficient" , result[ 'coefficient' ]
print "num " + str (num) + " Predicted value: " ,result[ 'predicted_value' ]
#调用函数分别预测红球、蓝球
get_predicted_num( 'rednum1data.csv' , 1 )
get_predicted_num( 'rednum2data.csv' , 2 )
get_predicted_num( 'rednum3data.csv' , 3 )
get_predicted_num( 'rednum4data.csv' , 4 )
get_predicted_num( 'rednum5data.csv' , 5 )
get_predicted_num( 'rednum6data.csv' , 6 )
get_predicted_num( 'bluenumdata.csv' , 1 )
# 获取X,Y数据预测结果
# X,Y = get_data('rednum1data.csv')
# predictvalue = 21
# result = linear_model_main(X,Y,predictvalue)
# print "red num 1 Intercept value " , result['intercept']
# print "red num 1 coefficient" , result['coefficient']
# print "red num 1 Predicted value: ",result['predicted_value']
# Function to show the resutls of linear fit model
def show_linear_line(X_parameters,Y_parameters):
# Create linear regression object
regr = linear_model.LinearRegression()
#regr = LogisticRegression()
regr.fit(X_parameters, Y_parameters)
plt.figure(figsize = ( 12 , 6 ),dpi = 80 )
plt.legend(loc = 'best' )
plt.scatter(X_parameters,Y_parameters,color = 'blue' )
plt.plot(X_parameters,regr.predict(X_parameters),color = 'red' ,linewidth = 4 )
plt.xticks(())
plt.yticks(())
plt.show()
#显示模型图像,如果需要画图,将“获取X,Y数据预测结果”这块注释去掉,“调用函数分别预测红球、蓝球”这块代码注释下
# show_linear_line(X,Y)
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画图结果:
预测2016-05-15开奖结果:
实际开奖结果:05 06 10 16 22 26 11
以下为预测值:
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#取5个数,计算的结果
num 1 Intercept value 5.66666666667
num 1 coefficient [ - 0.6 ]
num 1 Predicted value: [ 2.06666667 ]
num 2 Intercept value 7.33333333333
num 2 coefficient [ 0.2 ]
num 2 Predicted value: [ 8.53333333 ]
num 3 Intercept value 14.619047619
num 3 coefficient [ - 0.51428571 ]
num 3 Predicted value: [ 11.53333333 ]
num 4 Intercept value 17.7619047619
num 4 coefficient [ - 0.37142857 ]
num 4 Predicted value: [ 15.53333333 ]
num 5 Intercept value 21.7142857143
num 5 coefficient [ 1.11428571 ]
num 5 Predicted value: [ 28.4 ]
num 6 Intercept value 28.5238095238
num 6 coefficient [ 0.65714286 ]
num 6 Predicted value: [ 32.46666667 ]
num 1 Intercept value 9.57142857143
num 1 coefficient [ - 0.82857143 ]
num 1 Predicted value: [ 4.6 ]
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四舍五入结果:
2 9 12 16 28 33 5
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#取12个数,计算的结果四舍五入:
3 7 12 15 24 30 7
#取15个数,计算的结果四舍五入:
4 7 13 15 25 31 7
#取18个数,计算的结果四舍五入:
4 8 13 16 23 31 8
#取20个数,计算的结果四舍五入:
4 7 12 22 24 27 10
#取25个数,计算的结果四舍五入:
7 8 13 17 24 30 6
#取50个数,计算的结果四舍五入:
4 10 14 18 23 29 8
#取100个数,计算的结果四舍五入:
5 11 15 19 24 29 8
#取500个数,计算的结果四舍五入:
5 10 15 20 24 29 9
#取1000个数,计算的结果四舍五入:
5 10 14 19 24 29 9
#取1939个数,计算的结果四舍五入:
5 10 14 19 24 29 9
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看来预测中奖真是有些难度,随机性太高,双色球预测案例,只是为了让入门数据分析的朋友有些思路,要想中大奖还是有难度的,多做好事善事多积德行善吧。
希望本文所述对大家Python程序设计有所帮助。
原文链接:http://blog.csdn.net/levy_cui/article/details/51497709