From 23cee6d56a3566e1b806ebd663dbc6880b1f3375 Mon Sep 17 00:00:00 2001 From: leiwu <1929652729@qq.com> Date: Sun, 3 Nov 2024 19:57:29 +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 | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/function.py b/function.py index 606ca90..346980b 100644 --- a/function.py +++ b/function.py @@ -1,4 +1,3 @@ -# function.py import numpy as np """ @@ -28,6 +27,7 @@ name = "雷武" # 这里写自己的代码实现 def sigmoid(z): return 1 / (1 + np.exp(-z)) + def train_logreg(X, y, learning_rate=0.01, iterations=1000): """ 对率回归训练函数。 @@ -43,7 +43,7 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000): m, n = X.shape weights = np.zeros(n) bias = 0 - for i in range(iterations): + for i in range(iterations): linear_model = np.dot(X, weights) + bias y_pred = sigmoid(linear_model) @@ -96,8 +96,8 @@ 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, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000) # 测试单个数据 - y_pred = predict_logreg(X_test[11], weights) + y_pred = predict_logreg(X_test[11], weights, bias) print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")