本文整理汇总了Python中sklearn.decomposition.MiniBatchDictionaryLearning方法的典型用法代码示例。如果您正苦于以下问题:Python decomposition.MiniBatchDictionaryLearning方法的具体用法?Python decomposition.MiniBatchDictionaryLearning怎么用?Python decomposition.MiniBatchDictionaryLearning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类sklearn.decomposition
的用法示例。
在下文中一共展示了decomposition.MiniBatchDictionaryLearning方法的13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_dict_learning_online_verbosity
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_verbosity():
n_components = 5
# test verbosity
from io import StringIO
import sys
old_stdout = sys.stdout
try:
sys.stdout = StringIO()
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=1,
random_state=0)
dico.fit(X)
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=2,
random_state=0)
dico.fit(X)
dict_learning_online(X, n_components=n_components, alpha=1, verbose=1,
random_state=0)
dict_learning_online(X, n_components=n_components, alpha=1, verbose=2,
random_state=0)
finally:
sys.stdout = old_stdout
assert dico.components_.shape == (n_components, n_features)
示例2: test_dict_learning_online_partial_fit
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_partial_fit():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
dict1 = MiniBatchDictionaryLearning(n_components, n_iter=10 * len(X),
batch_size=1,
alpha=1, shuffle=False, dict_init=V,
random_state=0).fit(X)
dict2 = MiniBatchDictionaryLearning(n_components, alpha=1,
n_iter=1, dict_init=V,
random_state=0)
for i in range(10):
for sample in X:
dict2.partial_fit(sample[np.newaxis, :])
assert not np.all(sparse_encode(X, dict1.components_, alpha=1) == 0)
assert_array_almost_equal(dict1.components_, dict2.components_,
decimal=2)
示例3: learn_dictionary_mini
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def learn_dictionary_mini(patches, n_c=512, a=1, n_i=800, n_j=3, b_s=3, es=5, fit_algorithm='lars'):
"""
patches - patches to learn on (should be normalized before)
n_c - number of components (atoms) e.g. 512
a - alpha sparsity controlling parameter
n_i - total number of iterations to perform
b_s - batch size: number of samples in each mini-batch
fit_algorithm - {‘lars’, ‘cd’}
n_j - number of parallel jobs to run (number of threads)
e_s - size of each element in the dictionary
"""
dic = MiniBatchDictionaryLearning(n_components=n_c, alpha=a, n_iter=n_i,
n_jobs=n_j, batch_size=b_s, fit_algorithm=fit_algorithm)
print ("Start learning dictionary_mini: n_c: "+str(n_c)+", alpha: "+str(a)+", n_i: " +
str(n_i)+", n_j: "+str(n_j)+", es: "+str(es)+", b_s: "+str(b_s))
v1 = dic.fit(patches).components_
d1 = v1.reshape(n_c, es, es, es) # e.g. 512x5x5x5
return d1
示例4: test_objectmapper
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [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)
示例5: test_dict_learning_online_verbosity
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_verbosity():
n_components = 5
# test verbosity
from sklearn.externals.six.moves import cStringIO as StringIO
import sys
old_stdout = sys.stdout
try:
sys.stdout = StringIO()
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=1,
random_state=0)
dico.fit(X)
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, verbose=2,
random_state=0)
dico.fit(X)
dict_learning_online(X, n_components=n_components, alpha=1, verbose=1,
random_state=0)
dict_learning_online(X, n_components=n_components, alpha=1, verbose=2,
random_state=0)
finally:
sys.stdout = old_stdout
assert_true(dico.components_.shape == (n_components, n_features))
示例6: test_dict_learning_online_partial_fit
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_partial_fit():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features) # random init
V /= np.sum(V ** 2, axis=1)[:, np.newaxis]
dict1 = MiniBatchDictionaryLearning(n_components, n_iter=10 * len(X),
batch_size=1,
alpha=1, shuffle=False, dict_init=V,
random_state=0).fit(X)
dict2 = MiniBatchDictionaryLearning(n_components, alpha=1,
n_iter=1, dict_init=V,
random_state=0)
for i in range(10):
for sample in X:
dict2.partial_fit(sample[np.newaxis, :])
assert_true(not np.all(sparse_encode(X, dict1.components_, alpha=1) ==
0))
assert_array_almost_equal(dict1.components_, dict2.components_,
decimal=2)
示例7: test_dict_learning_online_positivity
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_positivity(transform_algorithm,
positive_code,
positive_dict):
rng = np.random.RandomState(0)
n_components = 8
dico = MiniBatchDictionaryLearning(
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()
code, dictionary = dict_learning_online(X, n_components=n_components,
alpha=1, random_state=rng,
positive_dict=positive_dict,
positive_code=positive_code)
if positive_dict:
assert (dictionary >= 0).all()
else:
assert (dictionary < 0).any()
if positive_code:
assert (code >= 0).all()
else:
assert (code < 0).any()
示例8: test_dict_learning_online_estimator_shapes
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_estimator_shapes():
n_components = 5
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, random_state=0)
dico.fit(X)
assert dico.components_.shape == (n_components, n_features)
示例9: test_dict_learning_online_overcomplete
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_overcomplete():
n_components = 12
dico = MiniBatchDictionaryLearning(n_components, n_iter=20,
random_state=0).fit(X)
assert dico.components_.shape == (n_components, n_features)
示例10: test_dict_learning_online_initialization
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_initialization():
n_components = 12
rng = np.random.RandomState(0)
V = rng.randn(n_components, n_features)
dico = MiniBatchDictionaryLearning(n_components, n_iter=0,
dict_init=V, random_state=0).fit(X)
assert_array_equal(dico.components_, V)
示例11: learn_dictionary
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def learn_dictionary(X, n_filters, filter_size, n_sample=1000,
n_sample_patches=0, **kwargs):
"""
learn a dictionary of n_filters atoms from n_sample images from X
"""
n_channels = X.shape[1]
# subsample n_sample images randomly
rand_idx = np.random.choice(len(X), n_sample, replace=False)
# extract patches
patch_size = (filter_size, filter_size)
patches = PatchExtractor(patch_size).transform(
X[rand_idx, ...].reshape(n_sample, X.shape[2], X.shape[3], X.shape[1]))
patches = patches.reshape(patches.shape[0], -1)
patches -= np.mean(patches, axis=0)
patches /= np.std(patches, axis=0)
if n_sample_patches > 0 and (n_sample_patches < len(patches)):
np.random.shuffle(patches)
patches = patches[:n_sample_patches, ...]
# learn dictionary
print('Learning dictionary for weight initialization...')
dico = MiniBatchDictionaryLearning(n_components=n_filters, alpha=1, n_iter=1000, batch_size=10, shuffle=True,
verbose=True, **kwargs)
W = dico.fit(patches).components_
W = W.reshape(n_filters, n_channels, filter_size, filter_size)
print('Dictionary learned.')
return W.astype(np.float32)
示例12: test_dict_learning_online_estimator_shapes
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_estimator_shapes():
n_components = 5
dico = MiniBatchDictionaryLearning(n_components, n_iter=20, random_state=0)
dico.fit(X)
assert_true(dico.components_.shape == (n_components, n_features))
示例13: test_dict_learning_online_overcomplete
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import MiniBatchDictionaryLearning [as 别名]
def test_dict_learning_online_overcomplete():
n_components = 12
dico = MiniBatchDictionaryLearning(n_components, n_iter=20,
random_state=0).fit(X)
assert_true(dico.components_.shape == (n_components, n_features))