Python双色球线性回归算法预测下期中奖结果 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示例

#!/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)

#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]

#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