Template
115 lines
3.0 KiB
Python
115 lines
3.0 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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"""
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Sigmoid 激活函数。
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"""
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return 1 / (1 + np.exp(-z))
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def train_logreg(X, y, numIter=1000, learning_rate=0.06, batch_size=128, lambda_reg=0.01):
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m, n = X.shape
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weights = np.zeros(n)
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for j in range(numIter):
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dataIndex = np.random.permutation(m)
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for i in range(0, m, batch_size):
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end = min(i + batch_size, m)
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batch_X = X[dataIndex[i:end]]
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batch_y = y[dataIndex[i:end]]
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z = np.dot(batch_X, weights)
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h = 1 / (1 + np.exp(-z))
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error = batch_y - h
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gradient = np.dot(batch_X.T, error) / len(batch_y)
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reg_term = 2 * lambda_reg * weights
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gradient -= reg_term / len(batch_y)
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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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"""
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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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z = np.dot(inX, weights)
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prob = 1 / (1 + np.exp(-z))
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return 1 if 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，若不自行正确实现，则无法通过评测，不记录成绩。
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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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- X (np.array): 特征数据。
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- y (np.array): 标签数据。
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"""
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file = open(filepath)
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fr = file.readlines()
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data_list = []
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y = []
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for data in fr:
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data = data.strip()
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data = data.split(' ')
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data_25 = data[24]
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y.append(data[22])
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del data[-1]
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del data[26]
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del data[25]
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del data[24]
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del data[22]
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del data[2]
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for i in data_25:
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data.append(i)
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data = [0 if data_elem == '?' else float(data_elem) for data_elem in data]
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data_list.append(data)
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data_number = len(y)
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y_train = np.array([1 if y[j] == '2' or y[j] == '3' else 0 for j in range(data_number)])
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X = np.array(data_list)
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X_train = np.insert(X, 0, 1, axis=1) # 添加偏置项
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return X_train, y_train
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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, numIter=1000, learning_rate=0.01, lambda_reg=0.1)
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# 测试单个数据
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y_pred_test = [predict_logreg(x, weights) for x in X_test]
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accuracy_test = np.mean(y_pred_test == y_test)
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print(f"Test Accuracy: {accuracy_test:.2f}")
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if __name__ == "__main__":
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main() |