[Add Randomized SVD in PCA] #298
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tpppppub
reviewed
Aug 11, 2023
sml/pca/simple_pca_emul.py
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| @@ -59,17 +61,17 @@ def proc_reconstruct(X): | |||
| ) | |||
| emulator.up() | |||
| # Create a simple dataset | |||
| X = random.normal(random.PRNGKey(0), (15, 100)) | |||
| X = random.normal(random.PRNGKey(0), (1000, 10)) | |||
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请 emulator run 之前请先 seal 要处理的数据
deadlywing
reviewed
Aug 11, 2023
sml/pca/simple_pca.py
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| class SimplePCA: | ||
| def __init__( | ||
| self, | ||
| method: str, | ||
| n_components: int, | ||
| max_iter: int = 100, | ||
| n_iter: int = 4, |
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这里有两个iter数的超参，感觉需要从语意上明确一下，如：
max_iter -> power_iter
n_iter -> projection_iter
sml/pca/simple_pca.py
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|
|
||
| class SimplePCA: | ||
| def __init__( | ||
| self, | ||
| method: str, | ||
| n_components: int, | ||
| max_iter: int = 100, | ||
| n_iter: int = 4, | ||
| random_state: int = 0, |
sml/pca/simple_pca.py
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| """ | ||
| assert len(X.shape) == 2, f"Expected X to be 2 dimensional array, got {X.shape}" |
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这个shape check应该是都需要的吧，没必要移到power method里面
sml/pca/simple_pca.py
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| # Remove the component from the covariance matrix | ||
| cov_matrix -= eigval * jnp.outer(vec, vec) | ||
| elif self._method == Method.PCA_rsvd: | ||
| random_state = np.random.RandomState(self._random_state) |
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随机矩阵不能在函数里生成，可以考虑在fit里增加一个入参
sml/pca/simple_pca.py
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| @@ -56,71 +72,105 @@ def __init__( | |||
| self._mean = None | |||
| self._components = None | |||
| self._variances = None | |||
| self._n_iter = n_iter # used in rsvd | |||
| self._random_state = random_state # used in rsvd | |||
| self._scale = scale # used in rsvd | |||
| self._method = Method(method) | |||
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| def fit(self, X): | |||
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另外，有几个点麻烦您注意一下哈：
感谢～ |
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What problem does this PR solve?
使用 SPU 优化 PCA 算法
Issue Number: Fixed #259
Possible side effects?