def svd_pca(data, fast=False, output_dimension=None, centre=None,
auto_transpose=True):
"""Perform PCA using SVD.
Parameters
----------
data : numpy array
MxN array of input data (M variables, N trials)
fast : bool
Wheter to use randomized svd estimation to estimate a limited number of
componentes given by output_dimension
output_dimension : int
Number of components to estimate when fast is True
centre : None | 'variables' | 'trials'
If None no centring is applied. If 'variable' the centring will be
performed in the variable axis. If 'trials', the centring will be
performed in the 'trials' axis.
auto_transpose : bool
If True, automatically transposes the data to boost performance
Returns
-------
factors : numpy array
loadings : numpy array
explained_variance : numpy array
mean : numpy array or None (if center is None)
"""
N, M = data.shape
if centre is not None:
if centre == 'variables':
mean = data.mean(1)[:, np.newaxis]
elif centre == 'trials':
mean = data.mean(0)[np.newaxis, :]
else:
raise AttributeError(
'centre must be one of: None, variables, trials')
data -= mean
else:
mean = None
if auto_transpose is True:
if N < M:
_logger.info("Auto transposing the data")
data = data.T
else:
auto_transpose = False
if fast is True and sklearn_installed is True:
if output_dimension is None:
raise ValueError('When using fast_svd it is necessary to '
'define the output_dimension')
U, S, V = fast_svd(data, output_dimension)
else:
U, S, V = scipy.linalg.svd(data, full_matrices=False)
if auto_transpose is False:
factors = V.T
explained_variance = S ** 2 / N
loadings = U * S
else:
loadings = V.T
explained_variance = S ** 2 / N
factors = U * S
return factors, loadings, explained_variance, mean
def svd(X):
return fast_svd(X, p)
def svd(X):
return fast_svd(X, rank)
def svd(X):
return fast_svd(X, rank)
def svd_pca(data,
fast=False,
output_dimension=None,
centre=None,
auto_transpose=True):
"""Perform PCA using SVD.
Parameters
----------
data : numpy array
MxN array of input data (M variables, N trials)
fast : bool
Wheter to use randomized svd estimation to estimate a limited number of
componentes given by output_dimension
output_dimension : int
Number of components to estimate when fast is True
centre : None | 'variables' | 'trials'
If None no centring is applied. If 'variable' the centring will be
performed in the variable axis. If 'trials', the centring will be
performed in the 'trials' axis.
auto_transpose : bool
If True, automatically transposes the data to boost performance
Returns
-------
factors : numpy array
loadings : numpy array
explained_variance : numpy array
mean : numpy array or None (if center is None)
"""
N, M = data.shape
if centre is not None:
if centre == 'variables':
mean = data.mean(1)[:, np.newaxis]
elif centre == 'trials':
mean = data.mean(0)[np.newaxis, :]
else:
raise AttributeError(
'centre must be one of: None, variables, trials')
data -= mean
else:
mean = None
if auto_transpose is True:
if N < M:
_logger.info("Auto transposing the data")
data = data.T
else:
auto_transpose = False
if fast is True and sklearn_installed is True:
if output_dimension is None:
raise ValueError('When using fast_svd it is necessary to '
'define the output_dimension')
U, S, V = fast_svd(data, output_dimension)
else:
U, S, V = scipy.linalg.svd(data, full_matrices=False)
if auto_transpose is False:
factors = V.T
explained_variance = S**2 / N
loadings = U * S
else:
loadings = V.T
explained_variance = S**2 / N
factors = U * S
return factors, loadings, explained_variance, mean
def svd(X):
return fast_svd(X, p)
