generated from LufsX/Experiment-LogReg
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@@ -55,7 +55,7 @@ def train_logreg(X, y, other_params=None):
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z = np.dot(X, weights)
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z = np.dot(X, weights)
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predictions = sigmoid(z)
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predictions = sigmoid(z)
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# 计算梯度
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# 计算梯度
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gradient = np.dot(X.T, (predictions - y)) / m
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gradient = np.dot(X.T, (predictions.astype(float) - y.astype(float))) / m
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# 更新权重
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# 更新权重
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weights -= learning_rate * gradient
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weights -= learning_rate * gradient
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