01.数据集ex2data1.txt，前两列是两个特征(第一列成绩1,第二列成绩2),最后一列是类别（0代表不被录取,1代表被录取）,
利用线性逻辑回归算法训练数据集，画出决策边界.
注:逻辑回归算法也可以直接调用sklearn里的逻辑回归算法。

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn import linear_model
data=np.genfromtxt("ex2data1.txt",delimiter=",")

x_data=data[:,:-1]
y_data=data[:,-1]
def plot ():
    x0=[]
    x1=[]
    y0=[]
    y1=[]
    for i in range(len(x_data)):
        if y_data[i]==0:
            x0.append(x_data[i,0])
            y0.append(x_data[i,1])
        else:
            x1.append(x_data[i,0])
            y1.append(x_data[i,1])
    plt.scatter(x0,y0,marker="o",color="b",label="label0",s=40)
    plt.scatter(x1,y1,marker="*",color="y",label="label1",s=60)
    plt.legend()
plot()
plt.show()

logistic=linear_model.LogisticRegression()
logistic.fit(x_data,y_data)
print('截距',logistic.intercept_)
print('系数',logistic.coef_)
print(logistic.coef_[0])
print(logistic.coef_[0][0])

#画出决策边界
plot()
x_test=np.array([[30],[100]])
y_test=(-logistic.intercept_-x_test*logistic.coef_[0][0])/logistic.coef_[0][1]
plt.plot(x_test,y_test,'r')
plt.show