本文整理汇总了Python中sklearn.decomposition.SparsePCA.transform方法的典型用法代码示例。如果您正苦于以下问题：Python SparsePCA.transform方法的具体用法？Python SparsePCA.transform怎么用？Python SparsePCA.transform使用的例子？那么恭喜您, 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.SparsePCA的用法示例。
在下文中一共展示了SparsePCA.transform方法的6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_fit_transform
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def test_fit_transform():
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
random_state=0)
spca_lars.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:
spca = SparsePCA(n_components=3, n_jobs=2, random_state=0,
alpha=alpha).fit(Y)
U2 = spca.transform(Y)
finally:
joblib_par.multiprocessing = _mp
else: # we can efficiently use parallelism
spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
random_state=0).fit(Y)
U2 = spca.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 = SparsePCA(n_components=3, method='cd', random_state=0,
alpha=alpha)
spca_lasso.fit(Y)
assert_array_almost_equal(spca_lasso.components_, spca_lars.components_)示例2: test_fit_transform_parallel
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def test_fit_transform_parallel():
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng) # wide array
spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
random_state=0)
spca_lars.fit(Y)
U1 = spca_lars.transform(Y)
# Test multiple CPUs
spca = SparsePCA(n_components=3, n_jobs=2, method='lars', alpha=alpha,
random_state=0).fit(Y)
U2 = spca.transform(Y)
assert_true(not np.all(spca_lars.components_ == 0))
assert_array_almost_equal(U1, U2)示例3: sparse_pca
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def sparse_pca(self):
"""
Runs PCA on view and returns projected view, the principle components,
and explained variance.
"""
model = SparsePCA(n_components=param['components'], alpha=param['sparse_pca_alpha'])
model.fit(self.view)
return model.transform(self.view), model.components_示例4: test_scaling_fit_transform
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def test_scaling_fit_transform():
alpha = 1
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
spca_lars = SparsePCA(n_components=3, method='lars', alpha=alpha,
random_state=rng, normalize_components=True)
results_train = spca_lars.fit_transform(Y)
results_test = spca_lars.transform(Y[:10])
assert_allclose(results_train[0], results_test[0])示例5: test_pca_vs_spca
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def test_pca_vs_spca():
rng = np.random.RandomState(0)
Y, _, _ = generate_toy_data(3, 1000, (8, 8), random_state=rng)
Z, _, _ = generate_toy_data(3, 10, (8, 8), random_state=rng)
spca = SparsePCA(alpha=0, ridge_alpha=0, n_components=2,
normalize_components=True)
pca = PCA(n_components=2)
pca.fit(Y)
spca.fit(Y)
results_test_pca = pca.transform(Z)
results_test_spca = spca.transform(Z)
assert_allclose(np.abs(spca.components_.dot(pca.components_.T)),
np.eye(2), atol=1e-5)
results_test_pca *= np.sign(results_test_pca[0, :])
results_test_spca *= np.sign(results_test_spca[0, :])
assert_allclose(results_test_pca, results_test_spca)示例6: transform
# 需要导入模块: from sklearn.decomposition import SparsePCA [as 别名]
# 或者: from sklearn.decomposition.SparsePCA import transform [as 别名]
def transform(xTrain,yTrain,xTest):
pca = SparsePCA(n_components=2);
newXTrain = pca.fit_transform(xTrain,yTrain)
newXTest = pca.transform(xTest)
return newXTrain,newXTest