Issue 12322 #12323
Issue 12322 #12323
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algorithms-keeper
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added
require descriptive names
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| Radial Basis Function Network: https://en.wikipedia.org/wiki/Radial_basis_function_network | ||
| """ | ||
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| def __init__(self, n_centers: int, sigma: float): |
There was a problem hiding this comment.
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:
| self.centers: np.ndarray | None = None # To be initialized during training | ||
| self.weights: np.ndarray | None = None # To be initialized during training | ||
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| def _gaussian(self, x: np.ndarray, center: np.ndarray) -> float: |
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Please provide descriptive name for the parameter: x
| """ | ||
| return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * self.sigma**2)) | ||
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| def _compute_rbf(self, x: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function _compute_rbf
Please provide descriptive name for the parameter: x
| rbf_outputs[j, i] = self._gaussian(x[j], center) | ||
| return rbf_outputs | ||
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| def fit(self, x: np.ndarray, y: np.ndarray): |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function fit
Please provide return type hint for the function: fit. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
| # Calculate weights using the pseudo-inverse | ||
| self.weights = np.linalg.pinv(rbf_outputs).dot(y) | ||
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| def predict(self, x: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function predict
Please provide descriptive name for the parameter: x
There was a problem hiding this comment.
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Repository:
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Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| Radial Basis Function Network: https://en.wikipedia.org/wiki/Radial_basis_function_network | ||
| """ | ||
|
|
||
| def __init__(self, n_centers: int, sigma: float): |
There was a problem hiding this comment.
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:
| self.centers: np.ndarray | None = None # To be initialized during training | ||
| self.weights: np.ndarray | None = None # To be initialized during training | ||
|
|
||
| def _gaussian(self, x: np.ndarray, center: np.ndarray) -> float: |
There was a problem hiding this comment.
Please provide descriptive name for the parameter: x
| """ | ||
| return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * self.sigma**2)) | ||
|
|
||
| def _compute_rbf(self, x: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function _compute_rbf
Please provide descriptive name for the parameter: x
| rbf_outputs[j, i] = self._gaussian(x[j], center) | ||
| return rbf_outputs | ||
|
|
||
| def fit(self, x: np.ndarray, y: np.ndarray): |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function fit
Please provide return type hint for the function: fit. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide descriptive name for the parameter: x
Please provide descriptive name for the parameter: y
| # Calculate weights using the pseudo-inverse | ||
| self.weights = np.linalg.pinv(rbf_outputs).dot(y) | ||
|
|
||
| def predict(self, x: np.ndarray) -> np.ndarray: |
There was a problem hiding this comment.
As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function predict
Please provide descriptive name for the parameter: x
algorithms-keeper
bot
removed
require descriptive names
There was a problem hiding this comment.
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Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| weights (np.ndarray): Weights for the output layer. | ||
| """ | ||
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|
||
| def __init__(self, num_centers: int, spread: float): |
There was a problem hiding this comment.
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:
| rbf_outputs[j, i] = self._gaussian_rbf(input_data[j], center) | ||
| return rbf_outputs | ||
|
|
||
| def fit(self, input_data: np.ndarray, target_values: np.ndarray): |
There was a problem hiding this comment.
Please provide return type hint for the function: fit. If the function does not return a value, please provide the type hint as: def function() -> None:
There was a problem hiding this comment.
Click here to look at the relevant links ⬇️
🔗 Relevant Links
Repository:
Python:
Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| weights (np.ndarray): Weights for the output layer. | ||
| """ | ||
|
|
||
| def __init__(self, num_centers: int, spread: float): |
There was a problem hiding this comment.
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:
| rbf_outputs[j, i] = self._gaussian_rbf(input_data[j], center) | ||
| return rbf_outputs | ||
|
|
||
| def fit(self, input_data: np.ndarray, target_values: np.ndarray): |
There was a problem hiding this comment.
Please provide return type hint for the function: fit. If the function does not return a value, please provide the type hint as: def function() -> None:
algorithms-keeper
bot
removed
the
require type hints
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Closing tests_are_failing PRs to prepare for Hacktoberfest |
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