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Cosmetic changes in covariance.

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commit f3f7b47e988c7f213c7bf5af90ec7fb1c476e0ab 1 parent 762d270
@fabianp fabianp authored
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2  doc/modules/classes.rst
@@ -291,6 +291,8 @@ Covariance Estimators
:template: function.rst
covariance.ledoit_wolf
+ covariance.shrunk_covariance
+ covariance.oas
Signal Decomposition
View
2  scikits/learn/covariance/empirical_covariance_.py
@@ -137,7 +137,7 @@ def fit(self, X, assume_centered=False, **params):
return self
def score(self, X_test, assume_centered=False):
- """Computes the likelihood of a gaussian data set with
+ """Computes the log-likelihood of a gaussian data set with
`self.covariance_` as an estimator of its covariance matrix.
Parameters
View
34 scikits/learn/covariance/shrunk_covariance_.py
@@ -14,6 +14,7 @@
# avoid division truncation
from __future__ import division
+
import numpy as np
from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance
@@ -26,16 +27,17 @@ def shrunk_covariance(emp_cov, shrinkage=0.1):
Params
------
- emp_cov: 2D ndarray, shape (n_features, n_features)
+ emp_cov: array-like, shape (n_features, n_features)
Covariance matrix to be shrunk
+
shrinkage: float, 0 <= shrinkage <= 1
coefficient in the convex combination used for the computation
of the shrunk estimate.
Returns
-------
- shrunk_cov: 2D ndarray
- Shrunk covariance
+ shrunk_cov: array-like
+ shrunk covariance
Notes
-----
@@ -70,10 +72,10 @@ class ShrunkCovariance(EmpiricalCovariance):
Attributes
----------
- `covariance_` : 2D ndarray, shape (n_features, n_features)
+ `covariance_` : array-like, shape (n_features, n_features)
Estimated covariance matrix
- `precision_` : 2D ndarray, shape (n_features, n_features)
+ `precision_` : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
(stored only if store_precision is True)
@@ -133,7 +135,7 @@ def ledoit_wolf(X, assume_centered=False):
Parameters
----------
- X: 2D ndarray, shape (n_samples, n_features)
+ X: array-like, shape (n_samples, n_features)
Data from which to compute the covariance estimate
assume_centered: Boolean
@@ -144,7 +146,7 @@ def ledoit_wolf(X, assume_centered=False):
Returns
-------
- shrunk_cov: 2D ndarray, shape (n_features, n_features)
+ shrunk_cov: array-like, shape (n_features, n_features)
Shrunk covariance
shrinkage: float
@@ -206,10 +208,10 @@ class LedoitWolf(EmpiricalCovariance):
Attributes
----------
- `covariance_` : 2D ndarray, shape (n_features, n_features)
+ `covariance_` : array-like, shape (n_features, n_features)
Estimated covariance matrix
- `precision_` : 2D ndarray, shape (n_features, n_features)
+ `precision_` : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
(stored only if store_precision is True)
@@ -266,14 +268,14 @@ def fit(self, X, assume_centered=False):
# OAS estimator
def oas(X, assume_centered=False):
- """Estimates the covariance matrix with the Oracle Approximating Shrinkage.
+ """Estimate covariance with the Oracle Approximating Shrinkage algorithm.
Parameters
----------
- X: 2D ndarray, shape (n_samples, n_features)
+ X: array-like, shape (n_samples, n_features)
Data from which to compute the covariance estimate
- assume_centered: Boolean
+ assume_centered: boolean
If True, data are not centered before computation.
Usefull to work with data whose mean is significantly equal to
zero but is not exactly zero.
@@ -281,7 +283,7 @@ def oas(X, assume_centered=False):
Returns
-------
- shrunk_cov: 2D ndarray, shape (n_features, n_features)
+ shrunk_cov: array-like, shape (n_features, n_features)
Shrunk covariance
shrinkage: float
@@ -340,10 +342,10 @@ class OAS(EmpiricalCovariance):
Attributes
----------
- `covariance_` : 2D ndarray, shape (n_features, n_features)
+ `covariance_` : array-like, shape (n_features, n_features)
Estimated covariance matrix
- `precision_` : 2D ndarray, shape (n_features, n_features)
+ `precision_` : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
(stored only if store_precision is True)
@@ -377,7 +379,7 @@ def fit(self, X, assume_centered=False):
Training data, where n_samples is the number of samples
and n_features is the number of features.
- assume_centered: Boolean
+ assume_centered: boolean
If True, data are not centered before computation.
Usefull to work with data whose mean is significantly equal to
zero but is not exactly zero.
View
2  scikits/learn/covariance/tests/test_covariance.py
@@ -73,7 +73,7 @@ def test_shrunk_covariance():
assert(cov.precision_ is None)
-def test_lw():
+def test_ledoit_wolf():
"""Tests LedoitWolf module on a simple dataset.
"""
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