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Python OAS.score方法代码示例

本文整理汇总了Python中sklearn.covariance.OAS.score方法的典型用法代码示例。如果您正苦于以下问题:Python OAS.score方法的具体用法?Python OAS.score怎么用?Python OAS.score使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在sklearn.covariance.OAS的用法示例。


在下文中一共展示了OAS.score方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_oas

# 需要导入模块: from sklearn.covariance import OAS [as 别名]
# 或者: from sklearn.covariance.OAS import score [as 别名]
def test_oas():
    """Tests OAS module on a simple dataset.

    """
    # test shrinkage coeff on a simple data set
    oa = OAS()
    oa.fit(X, assume_centered=True)
    assert_almost_equal(oa.shrinkage_, 0.018740, 4)
    assert_almost_equal(oa.score(X, assume_centered=True), -5.03605, 4)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X, assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
    scov.fit(X, assume_centered=True)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    oa = OAS()
    oa.fit(X_1d, assume_centered=True)
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d, assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    assert_array_almost_equal((X_1d ** 2).sum() / n_samples, oa.covariance_, 4)

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False)
    oa.fit(X, assume_centered=True)
    assert_almost_equal(oa.score(X, assume_centered=True), -5.03605, 4)
    assert(oa.precision_ is None)

    ### Same tests without assuming centered data
    # test shrinkage coeff on a simple data set
    oa = OAS()
    oa.fit(X)
    assert_almost_equal(oa.shrinkage_, 0.020236, 4)
    assert_almost_equal(oa.score(X), 2.079025, 4)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
    scov.fit(X)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    oa = OAS()
    oa.fit(X_1d)
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False)
    oa.fit(X)
    assert_almost_equal(oa.score(X), 2.079025, 4)
    assert(oa.precision_ is None)
开发者ID:bvtrach,项目名称:scikit-learn,代码行数:62,代码来源:test_covariance.py

示例2: test_oas

# 需要导入模块: from sklearn.covariance import OAS [as 别名]
# 或者: from sklearn.covariance.OAS import score [as 别名]
def test_oas():
    """Tests OAS module on a simple dataset.

    """
    # test shrinkage coeff on a simple data set
    X_centered = X - X.mean(axis=0)
    oa = OAS(assume_centered=True)
    oa.fit(X_centered)
    shrinkage_ = oa.shrinkage_
    score_ = oa.score(X_centered)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_centered,
                                                 assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_, assume_centered=True)
    scov.fit(X_centered)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    X_1d = X[:, 0].reshape((-1, 1))
    oa = OAS(assume_centered=True)
    oa.fit(X_1d)
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d, assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    assert_array_almost_equal((X_1d ** 2).sum() / n_samples, oa.covariance_, 4)

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False, assume_centered=True)
    oa.fit(X_centered)
    assert_almost_equal(oa.score(X_centered), score_, 4)
    assert(oa.precision_ is None)

    ### Same tests without assuming centered data
    # test shrinkage coeff on a simple data set
    oa = OAS()
    oa.fit(X)
    assert_almost_equal(oa.shrinkage_, shrinkage_, 4)
    assert_almost_equal(oa.score(X), score_, 4)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
    scov.fit(X)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    X_1d = X[:, 0].reshape((-1, 1))
    oa = OAS()
    oa.fit(X_1d)
    oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
    assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)

    # test with one sample
    X_1sample = np.arange(5)
    oa = OAS()
    with warnings.catch_warnings(record=True):
        oa.fit(X_1sample)

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False)
    oa.fit(X)
    assert_almost_equal(oa.score(X), score_, 4)
    assert(oa.precision_ is None)
开发者ID:GbalsaC,项目名称:bitnamiP,代码行数:72,代码来源:test_covariance.py

示例3: test_oas

# 需要导入模块: from sklearn.covariance import OAS [as 别名]
# 或者: from sklearn.covariance.OAS import score [as 别名]
def test_oas():
    # Tests OAS module on a simple dataset.
    # test shrinkage coeff on a simple data set
    X_centered = X - X.mean(axis=0)
    oa = OAS(assume_centered=True)
    oa.fit(X_centered)
    shrinkage_ = oa.shrinkage_
    score_ = oa.score(X_centered)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_centered,
                                                 assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_, assume_centered=True)
    scov.fit(X_centered)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    X_1d = X[:, 0:1]
    oa = OAS(assume_centered=True)
    oa.fit(X_1d)
    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d, assume_centered=True)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
    assert_array_almost_equal((X_1d ** 2).sum() / n_samples, oa.covariance_, 4)

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False, assume_centered=True)
    oa.fit(X_centered)
    assert_almost_equal(oa.score(X_centered), score_, 4)
    assert(oa.precision_ is None)

    # Same tests without assuming centered data--------------------------------
    # test shrinkage coeff on a simple data set
    oa = OAS()
    oa.fit(X)
    assert_almost_equal(oa.shrinkage_, shrinkage_, 4)
    assert_almost_equal(oa.score(X), score_, 4)
    # compare shrunk covariance obtained from data and from MLE estimate
    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
    # compare estimates given by OAS and ShrunkCovariance
    scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
    scov.fit(X)
    assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)

    # test with n_features = 1
    X_1d = X[:, 0].reshape((-1, 1))
    oa = OAS()
    oa.fit(X_1d)
    oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d)
    assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
    assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_)
    assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)

    # test with one sample
    # warning should be raised when using only 1 sample
    X_1sample = np.arange(5).reshape(1, 5)
    oa = OAS()
    assert_warns(UserWarning, oa.fit, X_1sample)
    assert_array_almost_equal(oa.covariance_,
                              np.zeros(shape=(5, 5), dtype=np.float64))

    # test shrinkage coeff on a simple data set (without saving precision)
    oa = OAS(store_precision=False)
    oa.fit(X)
    assert_almost_equal(oa.score(X), score_, 4)
    assert(oa.precision_ is None)
开发者ID:AlexisMignon,项目名称:scikit-learn,代码行数:72,代码来源:test_covariance.py


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