8000 Apply pylint fixes in extmath to randomized_pca · scikit-learn/scikit-learn@f39254e · GitHub
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Apply pylint fixes in extmath to randomized_pca
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‎sklearn/utils/extmath.py

Lines changed: 13 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -440,7 +440,8 @@ def randomized_pca(A, n_components, n_oversamples=10, n_iter="auto",
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"""
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if n_iter == "auto":
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# Checks if the number of iterations is explicitly specified
443-
# Adjust n_iter. 7 was found a good compromise for PCA. See sklearn #5299
443+
# Adjust n_iter. 7 was found a good compromise for PCA.
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# See sklearn #5299
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n_iter = 7 if n_components < .1 * min(A.shape) else 4
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# Deal with "auto" mode
@@ -464,30 +465,36 @@ def randomized_pca(A, n_components, n_oversamples=10, n_iter="auto",
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# Normalized power iterations
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for _ in range(n_iter):
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Q = safe_sparse_dot(A.T, Q) - safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
468+
Q = safe_sparse_dot(A.T, Q) - \
469+
safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
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Q = _normalize_power_iteration(Q, power_iteration_normalizer)
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Q = safe_sparse_dot(A, Q) - safe_sparse_dot(c, Q)
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Q = _normalize_power_iteration(Q, power_iteration_normalizer)
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Q, _ = linalg.qr(Q, mode="economic")
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474-
QA = safe_sparse_dot(A.T, Q) - safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
476+
QA = safe_sparse_dot(A.T, Q) - \
477+
safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
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R, s, V = linalg.svd(QA.T, full_matrices=False)
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U = Q.dot(R)
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478481
else: # n_features > n_samples
479-
Q = random_state.normal(size=(n_samples, n_components + n_oversamples))
482+
Q = random_state.normal(
483+
size=(n_samples, n_components + n_oversamples)
484+
)
480485
if A.dtype.kind == "f":
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# Ensure f32 is preserved as f32
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Q = Q.astype(A.dtype, copy=False)
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484-
Q = safe_sparse_dot(A.T, Q) - safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
489+
Q = safe_sparse_dot(A.T, Q) - \
490+
safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
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# Normalized power iterations
487493
for _ in range(n_iter):
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Q = safe_sparse_dot(A, Q) - safe_sparse_dot(c, Q)
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Q = _normalize_power_iteration(Q, power_iteration_normalizer)
490-
Q = safe_sparse_dot(A.T, Q) - safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
496+
Q = safe_sparse_dot(A.T, Q) - \
497+
safe_sparse_dot(c.T, Q.sum(axis=0)[None, :])
491498
Q = _normalize_power_iteration(Q, power_iteration_normalizer)
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493500
Q, _ = linalg.qr(Q, mode="economic")

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