本文整理汇总了Python中sklearn.decomposition.fastica方法的典型用法代码示例。如果您正苦于以下问题:Python decomposition.fastica方法的具体用法?Python decomposition.fastica怎么用?Python decomposition.fastica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.fastica方法的4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_fastica_errors
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import fastica [as 别名]
def test_fastica_errors():
n_features = 3
n_samples = 10
rng = np.random.RandomState(0)
X = rng.random_sample((n_samples, n_features))
w_init = rng.randn(n_features + 1, n_features + 1)
assert_raises_regex(ValueError, 'max_iter should be greater than 1',
FastICA, max_iter=0)
assert_raises_regex(ValueError, r'alpha must be in \[1,2\]',
fastica, X, fun_args={'alpha': 0})
assert_raises_regex(ValueError, 'w_init has invalid shape.+'
r'should be \(3L?, 3L?\)',
fastica, X, w_init=w_init)
assert_raises_regex(ValueError,
'Invalid algorithm.+must be.+parallel.+or.+deflation',
fastica, X, algorithm='pizza')
示例2: test_non_square_fastica
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import fastica [as 别名]
def test_non_square_fastica(add_noise=False):
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(0)
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi * t))
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing matrix
mixing = rng.randn(6, 2)
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(6, n_samples)
center_and_norm(m)
k_, mixing_, s_ = fastica(m.T, n_components=2, random_state=rng)
s_ = s_.T
# Check that the mixing model described in the docstring holds:
assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if not add_noise:
assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=3)
assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=3)
示例3: test_fastica
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import fastica [as 别名]
def test_fastica(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.decomposition.fastica(random_state=self.random_state)
expected = decomposition.fastica(iris.data,
random_state=self.random_state)
self.assertEqual(len(result), 3)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.data.columns)
self.assert_numpy_array_almost_equal(result[0].values, expected[0])
self.assertIsInstance(result[1], pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result[1].values, expected[1])
self.assertIsInstance(result[2], pdml.ModelFrame)
tm.assert_index_equal(result[2].index, df.index)
self.assert_numpy_array_almost_equal(result[2].values, expected[2])
result = df.decomposition.fastica(return_X_mean=True,
random_state=self.random_state)
expected = decomposition.fastica(iris.data, return_X_mean=True,
random_state=self.random_state)
self.assertEqual(len(result), 4)
self.assertIsInstance(result[0], pdml.ModelFrame)
tm.assert_index_equal(result[0].index, df.data.columns)
self.assert_numpy_array_almost_equal(result[0].values, expected[0])
self.assertIsInstance(result[1], pdml.ModelFrame)
self.assert_numpy_array_almost_equal(result[1].values, expected[1])
self.assertIsInstance(result[2], pdml.ModelFrame)
tm.assert_index_equal(result[2].index, df.index)
self.assert_numpy_array_almost_equal(result[2].values, expected[2])
self.assert_numpy_array_almost_equal(result[3], expected[3])
示例4: _discover_structure
# 需要导入模块: from sklearn import decomposition [as 别名]
# 或者: from sklearn.decomposition import fastica [as 别名]
def _discover_structure(data):
# Add a random noise uniformly distributed to avoid singularity
# when performing the ICA
data += np.random.random_sample(data.shape)
# Create the ICA node to get the inverse of the mixing matrix
k, w, _ = decomposition.fastica(data)
w = np.dot(w, k)
n = w.shape[0]
best_nzd = float("inf")
best_slt = float("inf")
best_w_permuted = w
causality_matrix = None
causal_perm = None
if n < 9:
perm = LiNGAM._perms(n)
for i in range(perm.shape[1]):
perm_matrix = np.eye(n)
perm_matrix = perm_matrix[:, perm[:, i]]
w_permuted = perm_matrix.dot(w)
cost = LiNGAM._cost_non_zero_diag(w_permuted)
if cost < best_nzd:
best_nzd = cost
best_w_permuted = w_permuted
w_opt = best_w_permuted
w_opt = w_opt / np.diag(w_opt).reshape((n, 1))
b_matrix = np.eye(n) - w_opt
best_b_permuted = b_matrix
best_i = 0
for i in range(perm.shape[1]):
b_permuted = b_matrix[:, perm[:, i]][perm[:, i], :]
cost = LiNGAM._cost_strictly_lower_triangular(
b_permuted)
if cost < best_slt:
best_slt = cost
best_i = i
best_b_permuted = b_permuted
causal_perm = perm[:, best_i]
causality_matrix = b_matrix
percent_upper = best_slt / np.sum(best_b_permuted ** 2)
if percent_upper > 0.2:
# TODO(David): Change that code to raise an exception instead
logger.error("LiNGAM failed to run on the data set")
logger.error(
"--> B permuted matrix is at best {}% lower triangular"
.format(percent_upper))
return causality_matrix, causal_perm