使用python的numpy模块实现逻辑回归模型的代码，供大家参考，具体内容如下
使用了numpy模块，pandas模块，matplotlib模块
1.初始化参数
def initial_para(nums_feature):
"""initial the weights and bias which is zero"""
#nums_feature是输入数据的属性数目，因此权重w是[1, nums_feature]维
#且w和b均初始化为0
w = np.zeros((1, nums_feature))
b = 0
return w, b
2.逻辑回归方程
def activation(x, w , b):
"""a linear function and then sigmoid activation function:
x_ = w*x +b,y = 1/(1+exp(-x_))"""
#线性方程，输入的x是[batch, 2]维，输出是[1, batch]维，batch是模型优化迭代一次输入数据的数目
#[1, 2] * [2, batch] = [1, batch], 所以是w * x.T（x的转置）
#np.dot是矩阵乘法
x_ = np.dot(w, x.T) + b
#np.exp是实现e的x次幂
sigmoid = 1 / (1 + np.exp(-x_))
return sigmoid
3.梯度下降
def gradient_descent_batch(x, w, b, label, learning_rate):
#获取输入数据的数目，即batch大小
n = len(label)
#进行逻辑回归预测
sigmoid = activation(x, w, b)
#损失函数，np.sum是将矩阵求和
cost = -np.sum(label.T * np.log(sigmoid) + (1-label).T * np.log(1-sigmoid)) / n
#求对w和b的偏导(即梯度值)
g_w = np.dot(x.T, (sigmoid - label.T).T) / n
g_b = np.sum((sigmoid - label.T)) / n
#根据梯度更新参数
w = w - learning_rate * g_w.T
b = b - learning_rate * g_b
return w, b, cost
4.模型优化
def optimal_model_batch(x, label, nums_feature, step=10000, batch_size=1):
"""train the model with batch"""
length = len(x)
w, b = initial_para(nums_feature)
for i in range(step):
#随机获取一个batch数目的数据
num = randint(0, length - 1 - batch_size)
x_batch = x[num:(num+batch_size), :]
label_batch = label[num:num+batch_size]
#进行一次梯度更新(优化)
w, b, cost = gradient_descent_batch(x_batch, w, b, label_batch, 0.0001)
#每1000次打印一下损失值
if i%1000 == 0:
print('step is : ', i, ', cost is: ', cost)
return w, b
5.读取数据，数据预处理，训练模型，评估精度
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from random import randint
from sklearn.preprocessing import StandardScaler
def _main():
#读取csv格式的数据data_path是数据的路径
data = pd.read_csv('data_path')
#获取样本属性和标签
x = data.iloc[:, 2:4].values
y = data.iloc[:, 4].values
#将数据集分为测试集和训练集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)
#数据预处理，去均值化
standardscaler = StandardScaler()
x_train = standardscaler.fit_transform(x_train)
x_test = standardscaler.transform(x_test)
#w, b = optimal_model(x_train, y_train, 2, 50000)
#训练模型
w, b = optimal_model_batch(x_train, y_train, 2, 50000, 64)
print('trian is over')
#对测试集进行预测，并计算精度
predict = activation(x_test, w, b).T
n = 0
for i, p in enumerate(predict):
if p >=0.5:
if y_test[i] == 1:
n += 1
else:
if y_test[i] == 0:
n += 1
print('accuracy is : ', n / len(y_test))
6.结果可视化
predict = np.reshape(np.int32(predict), [len(predict)])
#将预测结果以散点图的形式可视化
for i, j in enumerate(np.unique(predict)):
plt.scatter(x_test[predict == j, 0], x_test[predict == j, 1],
c = ListedColormap(('red', 'blue'))(i), label=j)
plt.show()
