From 993d24a2b4eef2a62a49729f74054a2892d3da92 Mon Sep 17 00:00:00 2001
From: Frae19 <515745323@qq.com>
Date: Tue, 5 Nov 2024 15:22:19 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 96 ++---------------------------------------------------
 1 file changed, 2 insertions(+), 94 deletions(-)

diff --git a/function.py b/function.py
index 289d16f..ea8690f 100644
--- a/function.py
+++ b/function.py
@@ -28,92 +28,7 @@ name = "Tanjunfeng"
 
 # 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。
-
-    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): 迭代次数，默认为 1000。
-
-    Returns:
-    - numpy array: 训练得到的权重。
-    """
-    m, n = X.shape
-    weights = np.zeros(n)
-    bias = 0
-
-    for _ in range(iterations):
-        z = np.dot(X, weights) + bias
-        h = sigmoid(z)
-        dw = (1 / m) * np.dot(X.T, (h - y))
-        db = (1 / m) * np.sum(h - y)
-        weights -= learning_rate * dw
-        bias -= learning_rate * db
-
-    return weights
-
-def predict_logreg(inX, weights):
-    """
-    对率回归预测函数。
-
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
-    z = np.dot(inX, weights)
-    h = sigmoid(z)
-    # 调整分类阈值
-    return 1 if h > 0.6 else 0
-
-def load_data(filepath):
-    """
-    从文件中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
-
-    Args:
-    - directory (str): 数据文件路径。
-
-    Returns:
-    - np.array: 特征数据。
-    - np.array: 标签数据。
-    """
-    data = []
-    with open(filepath, 'r') as f:
-        for line in f:
-            row = []
-            values = line.strip().split()
-            for value in values:
-                try:
-                    row.append(float(value))
-                except ValueError:
-                    row.append(-1)
-            data.append(row)
-    np_data = np.array(data)
-    X = np_data[:, :-1]
-    y = np_data[:, -1]
-    return X, y
-
-if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
-    # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
-
-    # 加载测试集
-    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=5000)
-
-    # 测试单个数据
-    correct_count = 0import numpy as np
+import numpy as np
 
 
 def sigmoid(z):
@@ -194,11 +109,4 @@ if __name__ == "__main__":
 
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
-    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
-    total_count = len(y_test)
-    for i in range(total_count):
-        y_pred = predict_logreg(X_test[i], weights)
-        if y_pred == y_test[i]:
-            correct_count += 1
-    accuracy = correct_count / total_count
-    print(f"准确率: {accuracy}")
\ No newline at end of file
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
\ No newline at end of file