def create_dictionary_dl(lmbd, K=100, N=10000, dir_mnist='save_exp/mnist'):
import os.path as osp
fname = osp.join(dir_mnist, "D_mnist_K{}_lmbd{}.npy".format(K, lmbd))
if osp.exists(fname):
D = np.load(fname)
else:
from sklearn.decomposition import DictionaryLearning
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
im = mnist.train.next_batch(N)[0]
im = im.reshape(N, 28, 28)
im = [imresize(a, (17, 17), interp='bilinear', mode='L')-.5
for a in im]
X = np.array(im).reshape(N, -1)
print(X.shape)
dl = DictionaryLearning(K, alpha=lmbd*N, fit_algorithm='cd',
n_jobs=-1, verbose=1)
dl.fit(X)
D = dl.components_.reshape(K, -1)
np.save(fname, D)
return D
python类DictionaryLearning()的实例源码
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
def test_DictionaryLearning():
'''
test the DL method
:return: None
'''
X=[[1,2,3,4,5],
[6,7,8,9,10],
[10,9,8,7,6,],
[5,4,3,2,1] ]
print("before transform:",X)
dct=DictionaryLearning(n_components=3)
dct.fit(X)
print("components is :",dct.components_)
print("after transform:",dct.transform(X))
def test_dict_learning_shapes():
n_components = 5
dico = DictionaryLearning(n_components, random_state=0).fit(X)
assert_true(dico.components_.shape == (n_components, n_features))
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))
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.
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)
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)
code = dico.fit(X_read_only).transform(X_read_only)
assert_array_almost_equal(np.dot(code, dico.components_), X_read_only, decimal=2)
def test_dict_learning_unknown_fit_algorithm():
n_components = 5
dico = DictionaryLearning(n_components, fit_algorithm='<unknown>')
assert_raises(ValueError, dico.fit, X)
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)
