Python sklearn.decomposition.DictionaryLearning() Examples

The following are 18 code examples for showing how to use sklearn.decomposition.DictionaryLearning(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: ksvd   Author: nel215   File: test_ksvd.py    License: Apache License 2.0 6 votes vote down vote up
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 
Example 2
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 6 votes vote down vote up
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() 
Example 3
Project: pandas-ml   Author: pandas-ml   File: test_decomposition.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
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) 
Example 4
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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)) 
Example 5
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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 
Example 6
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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. 
Example 7
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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) 
Example 8
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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) 
Example 9
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
def test_dict_learning_unknown_fit_algorithm():
    n_components = 5
    dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
    assert_raises(ValueError, dico.fit, X) 
Example 10
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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) 
Example 11
Project: Vessel3DDL   Author: konopczynski   File: LearnDictionary.py    License: MIT License 5 votes vote down vote up
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 
Example 12
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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)) 
Example 13
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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)) 
Example 14
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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. 
Example 15
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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) 
Example 16
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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) 
Example 17
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
def test_dict_learning_unknown_fit_algorithm():
    n_components = 5
    dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
    assert_raises(ValueError, dico.fit, X) 
Example 18
Project: twitter-stock-recommendation   Author: alvarobartt   File: test_dict_learning.py    License: MIT License 5 votes vote down vote up
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)