Template
69 lines
1.6 KiB
Python
69 lines
1.6 KiB
Python
import numpy as np
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"""
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请在这里填写学号和姓名，不填写即成绩无效。
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"""
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id = "202210423224"
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name = "吴杨洋"
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def sigmoid(z):
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, learning_rate=0.01, iterations=1000):
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m, n = X.shape
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weights = np.zeros(n)
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for i in range(iterations):
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linear_model = np.dot(X, weights)
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y_predicted = sigmoid(linear_model)
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gradient = np.dot(X.T, (y_predicted - y)) / m
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weights -= learning_rate * gradient
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return weights
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def predict_logreg(inX, weights):
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linear_model = np.dot(inX, weights)
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y_predicted = sigmoid(linear_model)
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return 1 if y_predicted > 0.5 else 0
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def load_data(filepath):
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data = np.genfromtxt(filepath, delimiter=' ', dtype=float)
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X = data[:, :-1]
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y = data[:, -1]
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return X, y
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def standardize(X):
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mean = X.mean(axis=0)
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std = X.std(axis=0)
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return (X - mean) / std, mean, std
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def 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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# 加载测试集
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X_test, y_test = load_data("./data/horse-colic.test")
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# 标准化特征数据
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X_train, mean, std = standardize(X_train)
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X_test = (X_test - mean) / std
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# 训练对率回归模型
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weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
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# 测试单个数据
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correct_predictions = 0
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for i in range(X_test.shape[0]):
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y_pred = predict_logreg(X_test[i], weights)
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if y_pred == y_test[i]:
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correct_predictions += 1
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accuracy = correct_predictions / X_test.shape[0]
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print(f"Accuracy: {accuracy:.2f}")
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if __name__ == "__main__":
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main() |