这里用来显示调用scikit-learn实现逻辑回归

In [1]:

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np

In [2]:

data = np.loadtxt("../data/2-logistic_regression/data1.txt", delimiter=",",dtype= np.float64)
X = data[:, 0:-1]
y = data[:, -1]

# 划分为训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 归一化
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)

# 逻辑回归
model = LogisticRegression()
model.fit(x_train, y_train)

# 预测
predict = model.predict(x_test)
right = sum(predict == y_test)

predict = np.hstack((predict.reshape(-1, 1), y_test.reshape(-1, 1)))  # 将预测值和真实值放在一块，好观察
print(predict)
print('测试集准确率：%f%%' % (right * 100.0 / predict.shape[0]))  # 计算在测试集上的准确度

[[ 0.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 0.  0.]
 [ 1.  1.]
 [ 1.  1.]
 [ 0.  0.]
 [ 1.  1.]
 [ 1.  1.]
 [ 0.  0.]
 [ 0.  0.]
 [ 0.  1.]
 [ 0.  0.]
 [ 0.  0.]
 [ 1.  1.]
 [ 1.  1.]
 [ 0.  0.]]
测试集准确率：90.000000%