本文整理匯總了Python中sklearn.decomposition.MiniBatchSparsePCA方法的典型用法代碼示例。如果您正苦於以下問題：Python decomposition.MiniBatchSparsePCA方法的具體用法？Python decomposition.MiniBatchSparsePCA怎麽用？Python decomposition.MiniBatchSparsePCA使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sklearn.decomposition的用法示例。
在下文中一共展示了decomposition.MiniBatchSparsePCA方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_mini_batch_correct_shapes
# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import MiniBatchSparsePCA [as 別名]
def test_mini_batch_correct_shapes(norm_comp):
rng = np.random.RandomState(0)
X = rng.randn(12, 10)
pca = MiniBatchSparsePCA(n_components=8, random_state=rng,
normalize_components=norm_comp)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (8, 10))
assert_equal(U.shape, (12, 8))
# test overcomplete decomposition
pca = MiniBatchSparsePCA(n_components=13, random_state=rng,
normalize_components=norm_comp)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (13, 10))
assert_equal(U.shape, (12, 13))
# XXX: test always skipped 示例2: test_objectmapper
# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import MiniBatchSparsePCA [as 別名]
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) 示例3: test_mini_batch_fit_transform
# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import MiniBatchSparsePCA [as 別名]
def test_mini_batch_fit_transform(norm_comp):
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = MiniBatchSparsePCA(n_components=3, random_state=0,
alpha=alpha,
normalize_components=norm_comp).fit(Y)
U1 = spca_lars.transform(Y)
# Test multiple CPUs
if sys.platform == 'win32': # fake parallelism for win32
import sklearn.utils._joblib.parallel as joblib_par
_mp = joblib_par.multiprocessing
joblib_par.multiprocessing = None
try:
spca = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0,
normalize_components=norm_comp)
U2 = spca.fit(Y).transform(Y)
finally:
joblib_par.multiprocessing = _mp
else: # we can efficiently use parallelism
spca = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0,
normalize_components=norm_comp)
U2 = spca.fit(Y).transform(Y)
assert not np.all(spca_lars.components_ == 0)
assert_array_almost_equal(U1, U2)
# Test that CD gives similar results
spca_lasso = MiniBatchSparsePCA(n_components=3, method='cd', alpha=alpha,
random_state=0,
normalize_components=norm_comp).fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_) 示例4: test_mini_batch_correct_shapes
# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import MiniBatchSparsePCA [as 別名]
def test_mini_batch_correct_shapes():
rng = np.random.RandomState(0)
X = rng.randn(12, 10)
pca = MiniBatchSparsePCA(n_components=8, random_state=rng)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (8, 10))
assert_equal(U.shape, (12, 8))
# test overcomplete decomposition
pca = MiniBatchSparsePCA(n_components=13, random_state=rng)
U = pca.fit_transform(X)
assert_equal(pca.components_.shape, (13, 10))
assert_equal(U.shape, (12, 13)) 示例5: test_mini_batch_fit_transform
# 需要導入模塊: from sklearn import decomposition [as 別名]
# 或者: from sklearn.decomposition import MiniBatchSparsePCA [as 別名]
def test_mini_batch_fit_transform():
raise SkipTest("skipping mini_batch_fit_transform.")
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = MiniBatchSparsePCA(n_components=3, random_state=0,
alpha=alpha).fit(Y)
U1 = spca_lars.transform(Y)
# Test multiple CPUs
if sys.platform == 'win32': # fake parallelism for win32
import sklearn.externals.joblib.parallel as joblib_par
_mp = joblib_par.multiprocessing
joblib_par.multiprocessing = None
try:
U2 = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0).fit(Y).transform(Y)
finally:
joblib_par.multiprocessing = _mp
else: # we can efficiently use parallelism
U2 = MiniBatchSparsePCA(n_components=3, n_jobs=2, alpha=alpha,
random_state=0).fit(Y).transform(Y)
assert_true(not np.all(spca_lars.components_ == 0))
assert_array_almost_equal(U1, U2)
# Test that CD gives similar results
spca_lasso = MiniBatchSparsePCA(n_components=3, method='cd', alpha=alpha,
random_state=0).fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)