Add the polynomial kernel to the SVM code #12740
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require descriptive names
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| def __init__(self, X: List[List[float]], y: List[int]) -> None: |
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Please provide descriptive name for the parameter: X
Please provide descriptive name for the parameter: y
| self.y = np.array(y) | ||
| self.class_weights = {0: 1.0, 1: 1.0} # Example class weights, adjust as needed | ||
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| def get_Train_test_data(self) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]]: |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: get_Train_test_data
| return in_dim, out_dim | ||
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| @staticmethod | ||
| def one_hot_encode(labels, num_classes): |
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Please provide return type hint for the function: one_hot_encode. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: labels
Please provide type hint for the parameter: num_classes
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| """ | ||
| def __init__(self, dataloader, epoch: int, learning_rate: float, gamma=1, hidden_dim=2): |
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Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: dataloader
Please provide type hint for the parameter: gamma
Please provide type hint for the parameter: hidden_dim
| self.inter_variable = {} | ||
| self.weights1_list = [] | ||
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| def get_inout_dim(self): |
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Please provide return type hint for the function: get_inout_dim. If the function does not return a value, please provide the type hint as: def function() -> None:
| return learning_rate * self.gamma | ||
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| @staticmethod | ||
| def accuracy(label, y_hat): |
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Please provide return type hint for the function: accuracy. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: label
Please provide type hint for the parameter: y_hat
| return (y_hat.argmax(axis=1) == label.argmax(axis=1)).mean() | ||
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| @staticmethod | ||
| def loss(output, label): |
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Please provide return type hint for the function: loss. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide type hint for the parameter: output
Please provide type hint for the parameter: label
| """ | ||
| return np.sum((output - label) ** 2) / (2 * label.shape[0]) | ||
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| def get_acc_loss(self): |
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Please provide return type hint for the function: get_acc_loss. If the function does not return a value, please provide the type hint as: def function() -> None:
| """ | ||
| return self.test_accuracy, self.test_loss | ||
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| def train(self): |
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Please provide return type hint for the function: train. If the function does not return a value, please provide the type hint as: def function() -> None:
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| output = self.forward(x=batch_imgs, W1=W1, W2=W2, no_gradient=False) | ||
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| grad_W1, grad_W2 = self.back_prop(x=batch_imgs, y=batch_labels, W1=W1, W2=W2) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: grad_W1
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: grad_W2
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
Describe your change:
Add the polynomial kernel to the SVM code
Checklist: