From 1682939bdb7041dc50628953ca75077d19cb217b Mon Sep 17 00:00:00 2001 From: Xionghaoran <719929980@qq.com> Date: Thu, 31 Oct 2024 16:02:06 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- function.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/function.py b/function.py index 6338177..3fee1a5 100644 --- a/function.py +++ b/function.py @@ -1,31 +1,35 @@ import numpy as np -id = "202210423213" # 请填写学号 -name = "熊浩燃" # 请填写姓名 - def sigmoid(z): """计算Sigmoid函数值""" return 1 / (1 + np.exp(-z)) -def train_logreg(X, y, learning_rate=0.01, iterations=1000): +def train_logreg(X, y, learning_rate=0.01, iterations=5000, reg_lambda=0.1): """ - 对率回归训练函数。 + 对率回归训练函数，带正则化项。 Args: - X (numpy array): 特征数据。 - y (numpy array): 标签数据。 - learning_rate (float): 学习率，默认为0.01。 - - iterations (int): 梯度下降迭代次数，默认为1000。 + - iterations (int): 梯度下降迭代次数，默认为5000。 + - reg_lambda (float): L2正则化系数，默认为0.1。 Returns: - numpy array: 训练得到的权重。 """ + # 数据标准化 + X = (X - X.mean(axis=0)) / X.std(axis=0) + + # 初始化权重 weights = np.zeros(X.shape[1]) + # 批量梯度下降 for i in range(iterations): predictions = sigmoid(np.dot(X, weights)) errors = y - predictions - weights += learning_rate * np.dot(X.T, errors) + # 更新权重，带L2正则化 + weights += learning_rate * (np.dot(X.T, errors) - reg_lambda * weights) return weights @@ -54,7 +58,6 @@ def load_data(filepath): - np.array: 特征数据。 - np.array: 标签数据。 """ - data = np.genfromtxt(filepath, delimiter=" ", dtype=float, missing_values="?", filling_values=np.nan) # 使用列均值填充缺失值 @@ -74,7 +77,7 @@ if __name__ == "__main__": X_test, y_test = load_data("./data/horse-colic.test") # 训练对率回归模型 - weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000) + weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=5000, reg_lambda=0.1) # 测试单个数据 y_pred = predict_logreg(X_test[11], weights)