Python机器学习库sklearn——逻辑回归

# -*- coding: UTF-8 -*-

import numpy as np # 快速操作结构数组的工具
import pandas as pd # 数据分析处理工具

# 样本数据集，第一列为x1，第二列为x2，第三列为分类（二种类别）
data=[
[-0.017612,14.053064,0],
[-1.395634,4.662541,1],
[-0.752157,6.538620,0],
[-1.322371,7.152853,0],
[0.423363,11.054677,0],
[0.406704,7.067335,1],
[0.667394,12.741452,0],
[-2.460150,6.866805,1],
[0.569411,9.548755,0],
[-0.026632,10.427743,0],
[0.850433,6.920334,1],
[1.347183,13.175500,0],
[1.176813,3.167020,1],
[-1.781871,9.097953,0],
[-0.566606,5.749003,1],
[0.931635,1.589505,1],
[-0.024205,6.151823,1],
[-0.036453,2.690988,1],
[-0.196949,0.444165,1],
[1.014459,5.754399,1]
]

#生成X和y矩阵
dataMat = np.mat(data)
y = dataMat[:,2]   # 类别变量
b = np.ones(y.shape)  # 添加全1列向量代表b偏量
X = np.column_stack((b, dataMat[:,0:2]))  # 特征属性集和b偏量组成x
X = np.mat(X)

# 特征数据归一化
# import sklearn.preprocessing as preprocessing   #sk的去均值和归一化
# scaler=preprocessing.StandardScaler()
# X = scaler.fit_transform(X)   # 对特征数据集去均值和归一化，可以加快机器性能
# X = np.mat(X)
# # print(X)
# ========逻辑回归========

from sklearn import metrics
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X, y)
print('逻辑回归模型:\n',model)
# 使用模型预测
predicted = model.predict(X)   #预测分类
answer = model.predict_proba(X)  #预测分类概率
print(answer)