generated from LufsX/Experiment-LogReg
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import numpy as np
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from sklearn.preprocessing import StandardScaler
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# 填写学号和姓名
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id = "20221042323"
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name = "李顺潮"
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def train_logreg(X, y, other_params="你自己设置一些其它参数", learning_rate=0.01, num_iterations=1000):
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"""
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对率回归训练函数。
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Args:
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- X (numpy array): 特征数据。
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- y (numpy array): 标签数据。
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- other_params (str): 其它参数，这里暂未使用。
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- learning_rate (float): 学习率。
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- num_iterations (int): 迭代次数。
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Returns:
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- numpy array: 训练得到的权重。
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"""
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# 添加偏置项
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X_b = np.c_[np.ones((X.shape[0], 1)), X] # 添加一列1作为偏置项
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m, n = X_b.shape
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weights = np.zeros(n)
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# 梯度下降
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for iteration in range(num_iterations):
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y_pred_prob = 1 / (1 + np.exp(-np.dot(X_b, weights)))
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dw = (1 / m) * np.dot(X_b.T, (y_pred_prob - y))
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weights -= learning_rate * dw
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return weights
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def predict_logreg(inX, weights):
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"""
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对率回归预测函数。
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Args:
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- inX (numpy array): 输入待分类的样本。
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- weights (numpy array): 训练得到的权重。
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Returns:
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- int: 预测的分类标签。
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"""
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# 添加偏置项
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inX_b = np.c_[1, inX]
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y_pred_prob = 1 / (1 + np.exp(-np.dot(inX_b, weights)))
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return 1 if y_pred_prob > 0.5 else 0
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def load_data(filepath):
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"""
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从文件中加载数据。
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Args:
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- filepath (str): 数据文件路径。
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Returns:
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- np.array: 特征数据。
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- np.array: 标签数据。
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"""
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data = np.loadtxt(filepath, delimiter=",")
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X = data[:, :-1] # 特征
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y = data[:, -1] # 标签，转换为0和1
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y = np.where(y == 2, 1, 0) # 将标签2转换为1，其他（1和3，这里只有1）转换为0
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return X, y
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if __name__ == "__main__":
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# 加载训练集
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X_train, y_train = load_data("./data/horse-colic.data")
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# 数据标准化
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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# 训练模型
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weights = train_logreg(X_train_scaled, y_train)
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# 测试预测（这里示例性地选择一个样本进行预测）
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sample_index = 0 # 选择第一个样本进行预测
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y_pred = predict_logreg(X_train_scaled[sample_index], weights)
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print(f"预测结果: {y_pred}, 实际结果: {y_train[sample_index]}") |