机器学习笔记-正则化线型回归与逻辑回归

机器学习笔记-损失函数与正规方程的代码表示

机器学习笔记-逻辑回归(Logistic Regression)损失函数代码表示

# 正则化模型
import numpy as np

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

正则化线型回归

# 正则化线性回归
def linearcostReg(theta, X, y, learningRate):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    first = np.log(sigmoid(X*theta.T))
    second = y
    reg = learningRate * np.power(theta[:, 1, theta.shape[0]], 2)
    return np.sum(np.power((first - second), 2), reg) / (2 * len(X))

正则化逻辑回归

# 正则化逻辑回归模型
def costReg(theta, X, y, learningRate):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    first = np.multiply(-y, np.log(sigmoid(X * theta.T)))
    second = np.multiply((1 - y), np.log(1-sigmoid(X * theta.T)))
    reg = (learningRate/(2 * len(X)) * np.sum(np.power(theta[:, 1, theta.shape[1]], 2)))
    return np.sum(first - second) / (len(X)) + reg