generated from LufsX/Experiment-LogReg
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# function.py
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import accuracy_score
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# 学号和姓名
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id = "202210423223"
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name = "李顺潮"
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def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.1):
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m, n = X.shape
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weights = np.zeros(n)
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bias = 0
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for _ in range(iterations):
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linear_output = np.dot(X, weights) + bias
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y_predicted = 1 / (1 + np.exp(-linear_output))
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# 计算梯度（包括正则化项）
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dw = (1 / m) * np.dot(X.T, (y_predicted - y)) + (lambda_reg / m) * weights
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db = (1 / m) * np.sum(y_predicted - y)
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# 更新权重和偏置
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weights -= learning_rate * dw
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bias -= learning_rate * db
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return weights, bias
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def predict_logreg(inX, weights, bias):
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linear_output = np.dot(inX, weights) + bias
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y_predicted = 1 if linear_output >= 0 else 0
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return y_predicted
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def load_data(filepath):
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data = []
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with open(filepath, 'r') as file:
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for line in file:
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parts = line.strip().split()
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features = [float(part) if part != '?' else np.nan for part in parts[:-1]]
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features = np.nan_to_num(features, nan=0.0)
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label = int(parts[-1])
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data.append(features + [label])
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data_array = np.array(data)
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X = data_array[:, :-1]
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y = data_array[:, -1]
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# 特征缩放
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scaler = StandardScaler()
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X_scaled = scaler.fit_transform(X)
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return X_scaled, y
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if __name__ == "__main__":
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# 加载训练集和测试集
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X_train, y_train = load_data("./data/horse-colic.data")
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X_test, y_test = load_data("./data/horse-colic.test")
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# 训练模型
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weights, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, lambda_reg=0.1)
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# 测试模型并评估性能
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y_pred = [predict_logreg(x, weights, bias) for x in X_test]
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accuracy = accuracy_score(y_test, y_pred)
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print(f"模型准确率: {accuracy}")
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# 如果测试样本有效
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if len(y_test) > 11:
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sample_index = 11
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print(f"预测结果: {y_pred[sample_index]}, 实际结果: {y_test[sample_index]}") |