Dengddd/Experiment-LogReg
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更新 function.py
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Dengddd
2024-10-31 17:30:52 +08:00
parent 08cf7a7fb3
commit 1a18964d09
1 changed files with 8 additions and 21 deletions
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function.py
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@@ -32,10 +32,9 @@ from sklearn.preprocessing import StandardScaler
def sigmoid(intX): def sigmoid(intX):
return 1.0 / (1 + np.exp(-intX)) return 1.0 / (1 + np.exp(-intX))
def train_logreg(X, y, numIter=150): def train_logreg(X, y, numIter=150):
m, n = X.shape m, n = X.shape
weights = np.ones(n) weights = np.zeros(n) # 初始化权重为0而不是1
for j in range(numIter): for j in range(numIter):
dataIndex = list(range(m)) dataIndex = list(range(m))
for i in range(m): for i in range(m):
@@ -43,11 +42,10 @@ def train_logreg(X, y, numIter=150):
randIndex = int(np.random.uniform(0, len(dataIndex))) randIndex = int(np.random.uniform(0, len(dataIndex)))
h = sigmoid(np.dot(X[randIndex], weights)) h = sigmoid(np.dot(X[randIndex], weights))
error = y[randIndex] - h error = y[randIndex] - h
weights = weights + np.dot(alpha * error, X[randIndex]) weights = weights + alpha * error * X[randIndex]
del(dataIndex[randIndex]) del(dataIndex[randIndex])
return weights return weights
def predict_logreg(X, weights): def predict_logreg(X, weights):
predictions = [] predictions = []
for x in X: for x in X:
@@ -55,7 +53,6 @@ def predict_logreg(X, weights):
predictions.append(1 if prob > 0.5 else 0) predictions.append(1 if prob > 0.5 else 0)
return predictions return predictions
def load_data(filepath): def load_data(filepath):
data = [] data = []
labels = [] labels = []
@@ -70,29 +67,19 @@ def load_data(filepath):
data = np.array(data) data = np.array(data)
labels = np.array(labels) labels = np.array(labels)
target_variable = labels target_variable = labels
target_variable[(target_variable == 2) | (target_variable == 3)] = 0 target_variable[np.logical_or(target_variable == 2, target_variable == 3)] = 0
data = np.delete(data, [2] + list(range(23, 28)), axis=1) data = np.delete(data, [2] + list(range(23, 28)), axis=1)
scaler = StandardScaler() scaler = StandardScaler()
data = scaler.fit_transform(data) data = scaler.fit_transform(data)
return data, target_variable return data, target_variable
def accuracy(X, y, weights): def accuracy(X, y, weights):
predictions = predict_logreg(X, weights) predictions = predict_logreg(X, weights)
return np.mean(predictions == y) return np.mean(predictions == y)
# 以下代码可用于测试，可自行随意修改
if __name__ == '__main__': if __name__ == '__main__':
# 加载训练集 X_train, y_train = load_data("./horse-colic.data")
X_train, y_train = load_data("./data/horse-colic.data") X_test, y_test = load_data("./horse-colic.test")
final_weights = train_logreg(X_train, y_train)
# 加载测试集 final_accuracy = accuracy(X_test, y_test, final_weights)
X_test, y_test = load_data("./data/horse-colic.test") print(f"最终测试集的准确率: {final_accuracy:.4f}")
# 训练对率回归模型
weights = train_logreg(X_train, y_train)
# 测试单个数据
y_pred = predict_logreg(X_test[11], weights)
print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")