本文整理汇总了Python中sklearn.decomposition.IncrementalPCA方法的典型用法代码示例。如果您正苦于以下问题:Python decomposition.IncrementalPCA方法的具体用法?Python decomposition.IncrementalPCA怎么用?Python decomposition.IncrementalPCA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
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在下文中一共展示了decomposition.IncrementalPCA方法的15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: low_mem_pca
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def low_mem_pca(data):
"""
Run Singular Value Decomposition (SVD) on input data.
Parameters
----------
data : (S [*E] x T) array_like
Optimally combined (S x T) or full multi-echo (S*E x T) data.
Returns
-------
u : (S [*E] x C) array_like
Component weight map for each component.
s : (C,) array_like
Variance explained for each component.
v : (C x T) array_like
Component timeseries.
"""
from sklearn.decomposition import IncrementalPCA
ppca = IncrementalPCA(n_components=(data.shape[-1] - 1))
ppca.fit(data)
v = ppca.components_.T
s = ppca.explained_variance_
u = np.dot(np.dot(data, v), np.diag(1. / s))
return u, s, v
示例2: test_incremental_pca
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca():
# Incremental PCA on dense arrays.
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2))
assert_almost_equal(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(), 1)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]))
示例3: test_incremental_pca_check_projection
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_check_projection():
# Test that the projection of data is correct.
rng = np.random.RandomState(1999)
n, p = 100, 3
X = rng.randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
# Get the reconstruction of the generated data X
# Note that Xt has the same "components" as X, just separated
# This is what we want to ensure is recreated correctly
Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
# Normalize
Yt /= np.sqrt((Yt ** 2).sum())
# Make sure that the first element of Yt is ~1, this means
# the reconstruction worked as expected
assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
示例4: test_incremental_pca_validation
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_validation():
# Test that n_components is >=1 and <= n_features.
X = np.array([[0, 1, 0], [1, 0, 0]])
n_samples, n_features = X.shape
for n_components in [-1, 0, .99, 4]:
assert_raises_regex(ValueError,
"n_components={} invalid for n_features={}, need"
" more rows than columns for IncrementalPCA "
"processing".format(n_components, n_features),
IncrementalPCA(n_components, batch_size=10).fit, X)
# Tests that n_components is also <= n_samples.
n_components = 3
assert_raises_regex(ValueError,
"n_components={} must be less or equal to "
"the batch number of samples {}".format(
n_components, n_samples),
IncrementalPCA(
n_components=n_components).partial_fit, X)
示例5: test_n_components_none
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_n_components_none():
# Ensures that n_components == None is handled correctly
rng = np.random.RandomState(1999)
for n_samples, n_features in [(50, 10), (10, 50)]:
X = rng.rand(n_samples, n_features)
ipca = IncrementalPCA(n_components=None)
# First partial_fit call, ipca.n_components_ is inferred from
# min(X.shape)
ipca.partial_fit(X)
assert ipca.n_components_ == min(X.shape)
# Second partial_fit call, ipca.n_components_ is inferred from
# ipca.components_ computed from the first partial_fit call
ipca.partial_fit(X)
assert ipca.n_components_ == ipca.components_.shape[0]
示例6: test_incremental_pca_partial_fit
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_partial_fit():
# Test that fit and partial_fit get equivalent results.
rng = np.random.RandomState(1999)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= .00001 # make middle component relatively small
X += [5, 4, 3] # make a large mean
# same check that we can find the original data from the transformed
# signal (since the data is almost of rank n_components)
batch_size = 10
ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X)
pipca = IncrementalPCA(n_components=2, batch_size=batch_size)
# Add one to make sure endpoint is included
batch_itr = np.arange(0, n + 1, batch_size)
for i, j in zip(batch_itr[:-1], batch_itr[1:]):
pipca.partial_fit(X[i:j, :])
assert_almost_equal(ipca.components_, pipca.components_, decimal=3)
示例7: test_whitening
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_whitening():
# Test that PCA and IncrementalPCA transforms match to sign flip.
X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0.,
effective_rank=2, random_state=1999)
prec = 3
n_samples, n_features = X.shape
for nc in [None, 9]:
pca = PCA(whiten=True, n_components=nc).fit(X)
ipca = IncrementalPCA(whiten=True, n_components=nc,
batch_size=250).fit(X)
Xt_pca = pca.transform(X)
Xt_ipca = ipca.transform(X)
assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec)
Xinv_ipca = ipca.inverse_transform(Xt_ipca)
Xinv_pca = pca.inverse_transform(Xt_pca)
assert_almost_equal(X, Xinv_ipca, decimal=prec)
assert_almost_equal(X, Xinv_pca, decimal=prec)
assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)
示例8: test_incremental_pca_partial_fit_float_division
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_partial_fit_float_division():
# Test to ensure float division is used in all versions of Python
# (non-regression test for issue #9489)
rng = np.random.RandomState(0)
A = rng.randn(5, 3) + 2
B = rng.randn(7, 3) + 5
pca = IncrementalPCA(n_components=2)
pca.partial_fit(A)
# Set n_samples_seen_ to be a floating point number instead of an int
pca.n_samples_seen_ = float(pca.n_samples_seen_)
pca.partial_fit(B)
singular_vals_float_samples_seen = pca.singular_values_
pca2 = IncrementalPCA(n_components=2)
pca2.partial_fit(A)
pca2.partial_fit(B)
singular_vals_int_samples_seen = pca2.singular_values_
np.testing.assert_allclose(singular_vals_float_samples_seen,
singular_vals_int_samples_seen)
示例9: fit
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def fit(self,X):
"""Fit detector.
Parameters
----------
X : dataframe of shape (n_samples, n_features)
The input samples.
"""
# a=str(ts[:,0])
X=X.to_numpy()
timestamp = np.asarray(X[:,0].astype(np.datetime64))
pca = IncrementalPCA(n_components=1)
value=np.reshape(pca.fit_transform(X[:,1:]),-1)
X = pd.Series(value, index=timestamp)
X.index = X.index.map(lambda d: to_epoch(str(d)))
lts = TimeSeries(X.to_dict())
self.ts=timestamp
self.ts_value=value
self.detector = anomaly_detector.AnomalyDetector(lts)
return self
示例10: test_incremental_pca_check_projection
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_check_projection():
# Test that the projection of data is correct.
rng = np.random.RandomState(1999)
n, p = 100, 3
X = rng.randn(n, p) * 0.1
X[:10] += np.array([3, 4, 5])
Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
X = da.from_array(X, chunks=(3, -1))
Xt = da.from_array(Xt, chunks=(4, 3))
# Get the reconstruction of the generated data X
# Note that Xt has the same "components" as X, just separated
# This is what we want to ensure is recreated correctly
Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
assert isinstance(Yt, da.Array)
# Normalize
Yt /= np.sqrt((Yt ** 2).sum())
# Make sure that the first element of Yt is ~1, this means
# the reconstruction worked as expected
assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)
示例11: test_incremental_pca_validation
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_validation():
# Test that n_components is >=1 and <= n_features.
X = np.array([[0, 1, 0], [1, 0, 0]])
X = da.from_array(X, chunks=[4, -1])
n_samples, n_features = X.shape
for n_components in [-1, 0, 0.99, 4]:
with pytest.raises(
ValueError,
match="n_components={} invalid"
" for n_features={}, need more rows than"
" columns for IncrementalPCA"
" processing".format(n_components, n_features),
):
IncrementalPCA(n_components, batch_size=10).fit(X)
# Tests that n_components is also <= n_samples.
n_components = 3
with pytest.raises(
ValueError,
match="n_components={} must be"
" less or equal to the batch number of"
" samples {}".format(n_components, n_samples),
):
IncrementalPCA(n_components=n_components).partial_fit(X)
示例12: test_incremental_pca_set_params
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_incremental_pca_set_params():
# Test that components_ sign is stable over batch sizes.
rng = np.random.RandomState(1999)
n_samples = 100
n_features = 20
X = rng.randn(n_samples, n_features)
X2 = rng.randn(n_samples, n_features)
X3 = rng.randn(n_samples, n_features)
X = da.from_array(X, chunks=[4, -1])
X2 = da.from_array(X2, chunks=[4, -1])
X3 = da.from_array(X3, chunks=[4, -1])
ipca = IncrementalPCA(n_components=20)
ipca.fit(X)
# Decreasing number of components
ipca.set_params(n_components=10)
with pytest.raises(ValueError):
ipca.partial_fit(X2)
# Increasing number of components
ipca.set_params(n_components=15)
with pytest.raises(ValueError):
ipca.partial_fit(X3)
# Returning to original setting
ipca.set_params(n_components=20)
ipca.partial_fit(X)
示例13: test_explained_variances
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_explained_variances(svd_solver):
# Test that PCA and IncrementalPCA calculations match
X = datasets.make_low_rank_matrix(
1000, 100, tail_strength=0.0, effective_rank=10, random_state=1999
)
X = da.from_array(X, chunks=[400, -1])
prec = 3
n_samples, n_features = X.shape
for nc in [None, 99]:
pca = PCA(n_components=nc, svd_solver=svd_solver).fit(X)
ipca = IncrementalPCA(
n_components=nc, batch_size=100, svd_solver=svd_solver
).fit(X)
assert_almost_equal(
pca.explained_variance_, ipca.explained_variance_, decimal=prec
)
assert_almost_equal(
pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec
)
assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec)
示例14: test_whitening
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [as 别名]
def test_whitening(svd_solver):
# Test that PCA and IncrementalPCA transforms match to sign flip.
X = datasets.make_low_rank_matrix(
1000, 10, tail_strength=0.0, effective_rank=2, random_state=1999
)
X = da.from_array(X, chunks=[200, -1])
prec = 3
n_samples, n_features = X.shape
for nc in [None, 9]:
pca = PCA(whiten=True, n_components=nc, svd_solver=svd_solver).fit(X)
ipca = IncrementalPCA(
whiten=True, n_components=nc, batch_size=250, svd_solver=svd_solver
).fit(X)
Xt_pca = pca.transform(X)
Xt_ipca = ipca.transform(X)
assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec)
Xinv_ipca = ipca.inverse_transform(Xt_ipca)
Xinv_pca = pca.inverse_transform(Xt_pca)
assert_almost_equal(X.compute(), Xinv_ipca, decimal=prec)
assert_almost_equal(X.compute(), Xinv_pca, decimal=prec)
assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)
示例15: test_objectmapper
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import IncrementalPCA [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)