8000 DOC Ensures that OrthogonalMatchingPursuit passes numpydoc validation by mani2106 · Pull Request #21296 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that OrthogonalMatchingPursuit passes numpydoc validation #21296

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Oct 20, 2021
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DOC Ensures that OrthogonalMatchingPursuit passes numpydoc validation #21296

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2151847
Remove Orthmatchpursuit from exceptions
mani2106 Oct 10, 2021
b905064
ensure doc validations pass
mani2106 Oct 10, 2021
e5a8e1b
shorten line length
mani2106 Oct 10, 2021
6fa76d1
remove trailing ws
mani2106 Oct 10, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -18,7 +18,6 @@
"MultiTaskElasticNetCV",
"MultiTaskLasso",
"MultiTaskLassoCV",
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
"PassiveAggressiveClassifier",
"PassiveAggressiveRegressor",
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44 changes: 23 additions & 21 deletions sklearn/linear_model/_omp.py
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8000
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Expand Up @@ -604,7 +604,7 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

fit_intercept : bool, default=True
whether to calculate the intercept for this model. If set
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be centered).

Expand Down Expand Up @@ -653,16 +653,18 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):

.. versionadded:: 1.0

Examples
See Also
--------
>>> from sklearn.linear_model import OrthogonalMatchingPursuit
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = OrthogonalMatchingPursuit(normalize=False).fit(X, y)
>>> reg.score(X, y)
0.9991...
>>> reg.predict(X[:1,])
array([-78.3854...])
orthogonal_mp : Solves n_targets Orthogonal Matching Pursuit problems.
orthogonal_mp_gram : Solves n_targets Orthogonal Matching Pursuit
problems using only the Gram matrix X.T * X and the product X.T * y.
lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
Lars : Least Angle Regression model a.k.a. LAR.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
sklearn.decomposition.sparse_encode : Generic sparse coding.
Each column of the result is the solution to a Lasso problem.
OrthogonalMatchingPursuitCV : Cross-validated
Orthogonal Matching Pursuit model (OMP).

Notes
-----
Expand All @@ -676,15 +678,16 @@ class OrthogonalMatchingPursuit(MultiOutputMixin, RegressorMixin, LinearModel):
Matching Pursuit Technical Report - CS Technion, April 2008.
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

See Also
Examples
--------
orthogonal_mp
orthogonal_mp_gram
lars_path
Lars
LassoLars
sklearn.decomposition.sparse_encode
OrthogonalMatchingPursuitCV
>>> from sklearn.linear_model import OrthogonalMatchingPursuit
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = OrthogonalMatchingPursuit(normalize=False).fit(X, y)
>>> reg.score(X, y)
0.9991...
>>> reg.predict(X[:1,])
array([-78.3854...])
"""

def __init__(
Expand All @@ -711,13 +714,12 @@ def fit(self, X, y):
Training data.

y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X's dtype if necessary

Target values. Will be cast to X's dtype if necessary.

Returns
-------
self : object
returns an instance of self.
Returns an instance of self.
"""
_normalize = _deprecate_normalize(
self.normalize, default=True, estimator_name=self.__class__.__name__
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0