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Python decomposition.DictionaryLearning方法代码示例

本文整理汇总了Python中sklearn.decomposition.DictionaryLearning方法的典型用法代码示例。如果您正苦于以下问题:Python decomposition.DictionaryLearning方法的具体用法?Python decomposition.DictionaryLearning怎么用?Python decomposition.DictionaryLearning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.decomposition的用法示例。


在下文中一共展示了decomposition.DictionaryLearning方法的15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_size

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_size():
    np.random.seed(0)
    N = 50
    L = 12
    n_features = 16
    D = np.random.randn(L, n_features)
    B = np.array(sp.sparse.random(N, L, density=0.5).todense())
    X = np.dot(B, D)
    dico1 = ApproximateKSVD(n_components=L, transform_n_nonzero_coefs=L)
    dico1.fit(X)
    gamma1 = dico1.transform(X)
    e1 = norm(X - gamma1.dot(dico1.components_))

    dico2 = DictionaryLearning(n_components=L, transform_n_nonzero_coefs=L)
    dico2.fit(X)
    gamma2 = dico2.transform(X)
    e2 = norm(X - gamma2.dot(dico2.components_))

    assert dico1.components_.shape == dico2.components_.shape
    assert gamma1.shape == gamma2.shape
    assert e1 < e2 
开发者ID:nel215,项目名称:ksvd,代码行数:23,代码来源:test_ksvd.py

示例2: test_dict_learning_positivity

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_positivity(transform_algorithm,
                                  positive_code,
                                  positive_dict):
    n_components = 5
    dico = DictionaryLearning(
        n_components, transform_algorithm=transform_algorithm, random_state=0,
        positive_code=positive_code, positive_dict=positive_dict).fit(X)
    code = dico.transform(X)
    if positive_dict:
        assert (dico.components_ >= 0).all()
    else:
        assert (dico.components_ < 0).any()
    if positive_code:
        assert (code >= 0).all()
    else:
        assert (code < 0).any() 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:18,代码来源:test_dict_learning.py

示例3: test_objectmapper

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.decomposition.PCA, decomposition.PCA)
        self.assertIs(df.decomposition.IncrementalPCA,
                      decomposition.IncrementalPCA)
        self.assertIs(df.decomposition.KernelPCA, decomposition.KernelPCA)
        self.assertIs(df.decomposition.FactorAnalysis,
                      decomposition.FactorAnalysis)
        self.assertIs(df.decomposition.FastICA, decomposition.FastICA)
        self.assertIs(df.decomposition.TruncatedSVD, decomposition.TruncatedSVD)
        self.assertIs(df.decomposition.NMF, decomposition.NMF)
        self.assertIs(df.decomposition.SparsePCA, decomposition.SparsePCA)
        self.assertIs(df.decomposition.MiniBatchSparsePCA,
                      decomposition.MiniBatchSparsePCA)
        self.assertIs(df.decomposition.SparseCoder, decomposition.SparseCoder)
        self.assertIs(df.decomposition.DictionaryLearning,
                      decomposition.DictionaryLearning)
        self.assertIs(df.decomposition.MiniBatchDictionaryLearning,
                      decomposition.MiniBatchDictionaryLearning)

        self.assertIs(df.decomposition.LatentDirichletAllocation,
                      decomposition.LatentDirichletAllocation) 
开发者ID:pandas-ml,项目名称:pandas-ml,代码行数:24,代码来源:test_decomposition.py

示例4: test_dict_learning_shapes

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_shapes():
    n_components = 5
    dico = DictionaryLearning(n_components, random_state=0).fit(X)
    assert_equal(dico.components_.shape, (n_components, n_features))

    n_components = 1
    dico = DictionaryLearning(n_components, random_state=0).fit(X)
    assert_equal(dico.components_.shape, (n_components, n_features))
    assert_equal(dico.transform(X).shape, (X.shape[0], n_components)) 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:11,代码来源:test_dict_learning.py

示例5: test_dict_learning_overcomplete

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_overcomplete():
    n_components = 12
    dico = DictionaryLearning(n_components, random_state=0).fit(X)
    assert dico.components_.shape == (n_components, n_features)


# positive lars deprecated 0.22 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:9,代码来源:test_dict_learning.py

示例6: test_dict_learning_reconstruction

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_reconstruction():
    n_components = 12
    dico = DictionaryLearning(n_components, transform_algorithm='omp',
                              transform_alpha=0.001, random_state=0)
    code = dico.fit(X).transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X)

    dico.set_params(transform_algorithm='lasso_lars')
    code = dico.transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2)

    # used to test lars here too, but there's no guarantee the number of
    # nonzero atoms is right. 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:15,代码来源:test_dict_learning.py

示例7: test_dict_learning_reconstruction_parallel

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_reconstruction_parallel():
    # regression test that parallel reconstruction works with n_jobs>1
    n_components = 12
    dico = DictionaryLearning(n_components, transform_algorithm='omp',
                              transform_alpha=0.001, random_state=0, n_jobs=4)
    code = dico.fit(X).transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X)

    dico.set_params(transform_algorithm='lasso_lars')
    code = dico.transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2) 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:13,代码来源:test_dict_learning.py

示例8: test_dict_learning_nonzero_coefs

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_nonzero_coefs():
    n_components = 4
    dico = DictionaryLearning(n_components, transform_algorithm='lars',
                              transform_n_nonzero_coefs=3, random_state=0)
    code = dico.fit(X).transform(X[np.newaxis, 1])
    assert len(np.flatnonzero(code)) == 3

    dico.set_params(transform_algorithm='omp')
    code = dico.transform(X[np.newaxis, 1])
    assert_equal(len(np.flatnonzero(code)), 3) 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:12,代码来源:test_dict_learning.py

示例9: test_dict_learning_unknown_fit_algorithm

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_unknown_fit_algorithm():
    n_components = 5
    dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
    assert_raises(ValueError, dico.fit, X) 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:6,代码来源:test_dict_learning.py

示例10: test_dict_learning_split

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_split():
    n_components = 5
    dico = DictionaryLearning(n_components, transform_algorithm='threshold',
                              random_state=0)
    code = dico.fit(X).transform(X)
    dico.split_sign = True
    split_code = dico.transform(X)

    assert_array_almost_equal(split_code[:, :n_components] -
                              split_code[:, n_components:], code) 
开发者ID:PacktPublishing,项目名称:Mastering-Elasticsearch-7.0,代码行数:12,代码来源:test_dict_learning.py

示例11: learn_dictionary

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def learn_dictionary(patches, n_c=512, a=1, n_i=100, n_j=3, es=5, fit_algorithm='lars'):
    dic = DictionaryLearning(n_components=n_c, alpha=a, max_iter=n_i,
                             n_jobs=n_j, fit_algorithm=fit_algorithm)
    print ("Start learning dictionary: n_c: "+str(n_c)+", alpha: "+str(a)+", n_i: " +
           str(n_i)+", es: "+str(es)+", n_j: "+str(n_j))
    v2 = dic.fit(patches).components_
    d2 = v2.reshape(n_c, es, es, es)  # e.g. 512x5x5x5
    return d2 
开发者ID:konopczynski,项目名称:Vessel3DDL,代码行数:10,代码来源:LearnDictionary.py

示例12: test_dict_learning_overcomplete

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_overcomplete():
    n_components = 12
    dico = DictionaryLearning(n_components, random_state=0).fit(X)
    assert_true(dico.components_.shape == (n_components, n_features)) 
开发者ID:alvarobartt,项目名称:twitter-stock-recommendation,代码行数:6,代码来源:test_dict_learning.py

示例13: test_dict_learning_reconstruction_parallel

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_reconstruction_parallel():
    # regression test that parallel reconstruction works with n_jobs=-1
    n_components = 12
    dico = DictionaryLearning(n_components, transform_algorithm='omp',
                              transform_alpha=0.001, random_state=0, n_jobs=-1)
    code = dico.fit(X).transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X)

    dico.set_params(transform_algorithm='lasso_lars')
    code = dico.transform(X)
    assert_array_almost_equal(np.dot(code, dico.components_), X, decimal=2) 
开发者ID:alvarobartt,项目名称:twitter-stock-recommendation,代码行数:13,代码来源:test_dict_learning.py

示例14: test_dict_learning_lassocd_readonly_data

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_lassocd_readonly_data():
    n_components = 12
    with TempMemmap(X) as X_read_only:
        dico = DictionaryLearning(n_components, transform_algorithm='lasso_cd',
                                  transform_alpha=0.001, random_state=0,
                                  n_jobs=-1)
        with ignore_warnings(category=ConvergenceWarning):
            code = dico.fit(X_read_only).transform(X_read_only)
        assert_array_almost_equal(np.dot(code, dico.components_), X_read_only,
                                  decimal=2) 
开发者ID:alvarobartt,项目名称:twitter-stock-recommendation,代码行数:12,代码来源:test_dict_learning.py

示例15: test_dict_learning_split

# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import DictionaryLearning [as 别名]
def test_dict_learning_split():
    n_components = 5
    dico = DictionaryLearning(n_components, transform_algorithm='threshold',
                              random_state=0)
    code = dico.fit(X).transform(X)
    dico.split_sign = True
    split_code = dico.transform(X)

    assert_array_equal(split_code[:, :n_components] -
                       split_code[:, n_components:], code) 
开发者ID:alvarobartt,项目名称:twitter-stock-recommendation,代码行数:12,代码来源:test_dict_learning.py


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