#!/usr/bin/python# -*- coding:UTF-8 -*-#導(dǎo)入需要的包import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport operatorfrom sklearn import datasets,linear_modelfrom sklearn.linear_model import LogisticRegression#讀取文件df = pd.read_table('newdata.txt',header=None,sep=',')#讀取日期tdate = sorted(df.loc[:,0])#將以列項(xiàng)為數(shù)據(jù)，將球號(hào)碼取出，寫(xiě)入到csv文件中，并取50行數(shù)據(jù)# Function to red number to csv filedef 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()#調(diào)用取號(hào)碼函數(shù)# create fileRedToCsv('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')#獲取數(shù)據(jù)，X_parameter為numid數(shù)據(jù),Y_parameter為number數(shù)據(jù)# Function to get datadef 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#訓(xùn)練線性模型# Function for Fitting our data to Linear modeldef 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#獲取預(yù)測(cè)結(jié)果函數(shù)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']#調(diào)用函數(shù)分別預(yù)測(cè)紅球、藍(lán)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)# 獲取X,Y數(shù)據(jù)預(yù)測(cè)結(jié)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 modeldef 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()#顯示模型圖像，如果需要畫(huà)圖，將“獲取X,Y數(shù)據(jù)預(yù)測(cè)結(jié)果”這塊注釋去掉，“調(diào)用函數(shù)分別預(yù)測(cè)紅球、藍(lán)球”這塊代碼注釋下# show_linear_line(X,Y)

#取5個(gè)數(shù)，計(jì)算的結(jié)果num 1 Intercept value 5.66666666667num 1 coefficient [-0.6]num 1 Predicted value: [ 2.06666667]num 2 Intercept value 7.33333333333num 2 coefficient [ 0.2]num 2 Predicted value: [ 8.53333333]num 3 Intercept value 14.619047619num 3 coefficient [-0.51428571]num 3 Predicted value: [ 11.53333333]num 4 Intercept value 17.7619047619num 4 coefficient [-0.37142857]num 4 Predicted value: [ 15.53333333]num 5 Intercept value 21.7142857143num 5 coefficient [ 1.11428571]num 5 Predicted value: [ 28.4]num 6 Intercept value 28.5238095238num 6 coefficient [ 0.65714286]num 6 Predicted value: [ 32.46666667]num 1 Intercept value 9.57142857143num 1 coefficient [-0.82857143]num 1 Predicted value: [ 4.6]

#取12個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：3 7 12 15 24 30 7#取15個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：4 7 13 15 25 31 7#取18個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：4 8 13 16 23 31 8#取20個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：4 7 12 22 24 27 10#取25個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：7 8 13 17 24 30 6#取50個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：4 10 14 18 23 29 8#取100個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：5 11 15 19 24 29 8#取500個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：5 10 15 20 24 29 9#取1000個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：5 10 14 19 24 29 9#取1939個(gè)數(shù)，計(jì)算的結(jié)果四舍五入：5 10 14 19 24 29 9