Template
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
import numpy as np
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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(-np.clip(z, -500, 500)))
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def train_logreg(X, y, numIter=1000, learning_rate=0.01, 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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for i in range(m):
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z = np.dot(X[i], weights)
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h = sigmoid(z)
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error = y[i] - h
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gradient = X[i] * error
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reg_term = lambda_reg * weights
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gradient += reg_term
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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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z = np.dot(inX, weights)
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prob = sigmoid(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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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[222])
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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[222]
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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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return X, 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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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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X_train, mean, std = standardize(X_train)
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X_test = (X_test - mean) / std
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# 尝试不同的学习率和正则化参数
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learning_rates = [0.01, 0.001, 0.0001]
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lambda_regs = [0.1, 0.01, 0.001]
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best_accuracy = 0
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best_weights = None
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for lr in learning_rates:
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for reg in lambda_regs:
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weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=lr, lambda_reg=reg)
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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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if accuracy_test > best_accuracy:
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best_accuracy = accuracy_test
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best_weights = weights
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print(f"Best Test Accuracy: {best_accuracy:.2f}")
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if __name__ == "__main__":
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main() |