def _stop_training(self, debug=False):
##### request the covariance matrices and clean up
self.cov_mtx, self.avg, self.tlen = self._cov_mtx.fix()
del self._cov_mtx
# do not center around the mean:
# we want the second moment matrix (centered about 0) and
# not the second central moment matrix (centered about the mean), i.e.
# the covariance matrix
self.dcov_mtx, self.davg, self.dtlen = self._dcov_mtx.fix(center=False)
del self._dcov_mtx
rng = self._set_range()
#### solve the generalized eigenvalue problem
# the eigenvalues are already ordered in ascending order
try:
self.d, self.sf = self._symeig(self.dcov_mtx,
self.cov_mtx,
range=rng,
overwrite=(not debug))
d = self.d
# check that we get only *positive* eigenvalues
if d.min() < 0:
err_msg = (
"Got negative eigenvalues: %s."
" You may either set output_dim to be smaller,"
" or prepend the SFANode with a PCANode(reduce=True)"
" or PCANode(svd=True)" % str(d))
raise NodeException(err_msg)
except SymeigException, exception:
errstr = str(exception) + "\n Covariance matrices may be singular."
raise NodeException(errstr)
def set_filters(self, filters):
if not isinstance(filters, numx.ndarray):
raise NodeException("'filters' argument must be a numpy array")
if filters.ndim != 3:
raise NodeException('Filters must be specified in a 3-dim array, with each '+
'filter on a different row')
self._filters = filters
def _pre_execution_checks(self, x):
"""This method contains all pre-execution checks.
It can be used when a subclass defines multiple execution methods.
In this case, the output dimension depends on the type of
convolution we use (padding, full, ...). Also, we want to
to be able to accept 3D arrays.
"""
# check input rank
if not x.ndim in [2, 3]:
error_str = "x has rank %d, should be 2 or 3" % (x.ndim)
raise NodeException(error_str)
# set 2D shape if necessary
if self._input_shape is None:
if x.ndim == 2:
error_str = "Cannot infer 2D shape from 1D data points. " + \
"Data must have rank 3, or shape argument given."
raise NodeException(error_str)
else:
self._input_shape = x.shape[1:]
# set the input dimension if necessary
if self.input_dim is None:
self.input_dim = numx.prod(self._input_shape)
# set the dtype if necessary
if self.dtype is None:
self.dtype = x.dtype
# check the input dimension
if not numx.prod(x.shape[1:]) == self.input_dim:
error_str = "x has dimension %d, should be %d" % (x.shape[1],
self.input_dim)
raise NodeException(error_str)
# set output_dim if necessary
if self.output_dim is None:
input_shape = self.input_shape
filters_shape = self.filters.shape
if self.mode == 'same':
self._output_shape = input_shape
elif self.mode == 'full':
self._output_shape = (input_shape[0] + filters_shape[1] - 1,
input_shape[1] + filters_shape[2] - 1)
else: # mode == 'valid'
self._output_shape = (input_shape[0] - filters_shape[1] + 1,
input_shape[1] - filters_shape[2] + 1)
self.output_dim = self.filters.shape[0] * numx.prod(
self._output_shape)
if x.shape[0] == 0:
error_str = "x must have at least one observation (zero given)"
raise NodeException(error_str)
def _stop_training(self, debug=False):
##### request the covariance matrices and clean up
if hasattr(self, '_dcov_mtx'):
self.cov_mtx, self.avg, self.tlen = self._cov_mtx.fix()
del self._cov_mtx
# do not center around the mean:
# we want the second moment matrix (centered about 0) and
# not the second central moment matrix (centered about the mean), i.e.
# the covariance matrix
if hasattr(self, '_dcov_mtx'):
self.dcov_mtx, self.davg, self.dtlen = self._dcov_mtx.fix(center=False)
del self._dcov_mtx
rng = self._set_range()
#### solve the generalized eigenvalue problem
# the eigenvalues are already ordered in ascending order
try:
try:
# We first try to fulfill the extended signature described
# in mdp.utils.symeig_semidefinite
self.d, self.sf = self._symeig(
self.dcov_mtx, self.cov_mtx, True, "on", rng,
overwrite=(not debug),
rank_threshold=self.rank_threshold, dfc_out=self)
except TypeError:
self.d, self.sf = self._symeig(
self.dcov_mtx, self.cov_mtx, True, "on", rng,
overwrite=(not debug))
d = self.d
# check that we get only *positive* eigenvalues
if d.min() < 0:
err_msg = ("Got negative eigenvalues: %s.\n"
"You may either set output_dim to be smaller,\n"
"or prepend the SFANode with a PCANode(reduce=True)\n"
"or PCANode(svd=True)\n"
"or set a rank deficit method, e.g.\n"
"create the SFA node with rank_deficit_method='auto'\n"
"and try higher values for rank_threshold, e.g. try\n"
"your_node.rank_threshold = 1e-10, 1e-8, 1e-6, ..."%str(d))
raise NodeException(err_msg)
except SymeigException as exception:
errstr = (str(exception)+"\n Covariance matrices may be singular.\n"
+SINGULAR_VALUE_MSG)
raise NodeException(errstr)
if not debug:
# delete covariance matrix if no exception occurred
del self.cov_mtx
del self.dcov_mtx
# store bias
self._bias = mult(self.avg, self.sf)
def _stop_training(self):
try:
inv_xTx = utils.inv(self._xTx + self.ridge_param *
mdp.numx.eye(self._input_dim + 1))
except numx_linalg.LinAlgError, exception:
errstr = (str(exception) +
"\n Input data may be redundant (i.e., some of the " +
"variables may be linearly dependent).")
raise NodeException(errstr)
def _stop_training(self):
try:
if self.use_pinv:
invfun = utils.pinv
else:
invfun = utils.inv
inv_xTx = invfun(self._xTx)
except numx_linalg.LinAlgError, exception:
errstr = (str(exception) +
"\n Input data may be redundant (i.e., some of the " +
"variables may be linearly dependent).")
raise NodeException(errstr)
def pseudo_inverse(self, y):
"""This function returns a pseudo-inverse of the execute frame.
y == execute(x) is only ``True`` if y belongs to the domain of execute
and has been computed with a sufficently large x.
If gap > 1 some of the last rows will be filled with zeros.
:param y: The execute frame.
:type y: numpy.ndarray
:return: A pseudo-inverse of the given frame.
:rtype: numpy.ndarray
"""
self._if_training_stop_training()
# set the output dimension if necessary
if not self.output_dim:
# if the input_dim is not defined, raise an exception
if not self.input_dim:
errstr = ("Number of input dimensions undefined. Inversion"
"not possible.")
raise NodeException(errstr)
self.outputdim = self.input_dim
# control the dimension of y
self._check_output(y)
# cast
y = self._refcast(y)
gap = self.gap
exp_length = y.shape[0]
cols = self.input_dim
rest = (self.time_frames - 1) * gap
rows = exp_length + rest
x = numx.zeros((rows, cols), dtype=self.dtype)
x[:exp_length, :] = y[:, :cols]
count = 1
# Note that if gap > 1 some of the last rows will be filled with zeros!
block_sz = min(gap, exp_length)
for row in range(max(exp_length, gap), rows, gap):
x[row:row + block_sz, :] = y[-block_sz:,
count * cols:(count + 1) * cols]
count += 1
return x
def _pre_inversion_checks(self, y):
"""This method contains all pre-inversion checks.
It can be used when a subclass defines multiple inversion methods.
"""
if not self.is_invertible():
raise IsNotInvertibleException("This node is not invertible.")
# set the output dimension if necessary
if self.output_dim is None:
# if the input_dim is not defined, raise an exception
if self.input_dim is None:
errstr = ("Number of input dimensions undefined. Inversion"
"not possible.")
raise NodeException(errstr)
self.output_dim = self.input_dim
# control the dimension of y
self._check_output(y)
def __init__(self,
noise_func=mdp.numx_rand.normal,
noise_args=(0, 1),
noise_type='additive',
input_dim=None,
output_dim=None,
dtype=None):
"""Initializes an object of type 'NoiseNode'.
:param noise_func: A function that generates noise. It must
take a ``size`` keyword argument and return
a random array of that size. Default is normal noise.
:type noise_func: function
:param noise_args: Tuple of additional arguments passed to `noise_func`.
Default is (0,1) for (mean, standard deviation)
of the normal distribution.
:type noise_args: tuple
:param noise_type: Either ``'additive'`` or ``'multiplicative'``.
:type noise_type: str
:param input_dim: The input dimensionality.
:type input_dim: int
:param output_dim: The output dimensionality.
:type output_dim: int
:param dtype: The datatype.
:type dtype: numpy.dtype or str
"""
super(NoiseNode, self).__init__(input_dim=input_dim,
output_dim=output_dim,
dtype=dtype)
self.noise_func = noise_func
self.noise_args = noise_args
valid_noise_types = ['additive', 'multiplicative']
if noise_type not in valid_noise_types:
err_str = '%s is not a valid noise type' % str(noise_type)
raise NodeException(err_str)
else:
self.noise_type = noise_type
def __init__(self,
noise_func=mdp.numx_rand.normal,
noise_args=(0, 1),
noise_type='additive',
input_dim=None,
output_dim=None,
dtype=None):
"""
Add noise to input signals.
:Arguments:
noise_func
A function that generates noise. It must
take a ``size`` keyword argument and return
a random array of that size. Default is normal noise.
noise_args
Tuple of additional arguments passed to `noise_func`.
Default is (0,1) for (mean, standard deviation)
of the normal distribution.
noise_type
Either ``'additive'`` or ``'multiplicative'``.
'additive'
returns ``x + noise``.
'multiplicative'
returns ``x * (1 + noise)``
Default is ``'additive'``.
"""
super(NoiseNode, self).__init__(input_dim=input_dim,
output_dim=output_dim,
dtype=dtype)
self.noise_func = noise_func
self.noise_args = noise_args
valid_noise_types = ['additive', 'multiplicative']
if noise_type not in valid_noise_types:
err_str = '%s is not a valid noise type' % str(noise_type)
raise NodeException(err_str)
else:
self.noise_type = noise_type
def generate_input(self, len_or_y=1, noise=False):
"""Generate data from the prior distribution.
If the training phase has not been completed yet, call stop_training.
:param len_or_y: If integer, it specified the number of observation
to generate. If array, it is used as a set of samples
of the latent variables
:param noise: If true, generation includes the estimated noise
:type noise: bool
:return: The generated data.
:rtype: numpy.ndarray
"""
self._if_training_stop_training()
# set the output dimension if necessary
if self.output_dim is None:
# if the input_dim is not defined, raise an exception
if self.input_dim is None:
errstr = ("Number of input dimensions undefined. Inversion "
"not possible.")
raise NodeException(errstr)
self.output_dim = self.input_dim
if isinstance(len_or_y, int):
size = (len_or_y, self.output_dim)
y = self._refcast(mdp.numx_rand.normal(size=size))
else:
y = self._refcast(len_or_y)
self._check_output(y)
res = mult(y, self.A.T) + self.mu
if noise:
ns = mdp.numx_rand.normal(size=(y.shape[0], self.input_dim))
ns *= numx.sqrt(self.sigma)
res += self._refcast(ns)
return res
def set_rank_deficit_method(self, rank_deficit_method):
if rank_deficit_method == 'pca':
self._symeig = symeig_semidefinite_pca
elif rank_deficit_method == 'reg':
self._symeig = symeig_semidefinite_reg
elif rank_deficit_method == 'svd':
self._symeig = symeig_semidefinite_svd
elif rank_deficit_method == 'ldl':
try:
from scipy.linalg.lapack import dsytrf
except ImportError:
err_msg = ("ldl method for solving SFA with rank deficit covariance "
"requires at least SciPy 1.0.")
raise NodeException(err_msg)
self._symeig = symeig_semidefinite_ldl
elif rank_deficit_method == 'auto':
self._symeig = symeig_semidefinite_pca
elif rank_deficit_method == 'none':
self._symeig = symeig
else:
raise ValueError("Invalid value for rank_deficit_method: %s" \
%str(rank_deficit_method))
def __init__(self,
filters,
input_shape=None,
approach='fft',
mode='full',
boundary='fill',
fillvalue=0,
output_2d=True,
input_dim=None,
dtype=None):
"""
Input arguments:
input_shape -- Is a tuple (h,w) that corresponds to the height and
width of the input 2D data. If the input data is given
in a flattened format, it is first reshaped before
convolution
approach -- 'approach' is one of ['linear', 'fft']
'linear': convolution is done by linear filtering;
'fft': convoltion is done using the Fourier Transform
If 'approach' is 'fft', the 'boundary' and 'fillvalue' arguments
are ignored, and are assumed to be 'fill' and 0, respectively.
(*Default* = 'fft')
mode -- Convolution mode, as defined in scipy.signal.convolve2d
'mode' is one of ['valid', 'same', 'full']
(*Default* = 'full')
boundary -- Boundary condition, as defined in scipy.signal.convolve2d
'boundary' is one of ['fill', 'wrap', 'symm']
(*Default* = 'fill')
fillvalue -- Value to fill pad input arrays with
(*Default* = 0)
output_2d -- If True, the output array is 2D; the first index
corresponds to data points; every output data point
is the result of flattened convolution results, with
the output of each filter concatenated together.
If False, the output array is 4D; the format is
data[idx,filter_nr,x,y], with
filter_nr: index of convolution filter
idx: data point index
x, y: 2D coordinates
"""
super(Convolution2DNode, self).__init__(input_dim=input_dim,
dtype=dtype)
self.filters = filters
self._input_shape = input_shape
if approach not in ['linear', 'fft']:
raise NodeException(
"'approach' argument must be one of ['linear', 'fft']")
self._approach = approach
if mode not in ['valid', 'same', 'full']:
raise NodeException(
"'mode' argument must be one of ['valid', 'same', 'full']")
self._mode = mode
self.boundary = boundary
self.fillvalue = fillvalue
self.output_2d = output_2d
self._output_shape = None
def __init__(self, lags=1, sfa_ica_coeff=(1., 1.), icaweights=None,
sfaweights=None, whitened=False, white_comp = None,
white_parm = None, eps_contrast=1e-6, max_iter=10000,
RP=None, verbose=False, input_dim=None, output_dim=None,
dtype=None):
"""
Perform Independent Slow Feature Analysis.
The notation is the same used in the paper by Blaschke et al. Please
refer to the paper for more information.
:Parameters:
lags
list of time-lags to generate the time-delayed covariance
matrices (in the paper this is the set of \tau). If
lags is an integer, time-lags 1,2,...,'lags' are used.
Note that time-lag == 0 (instantaneous correlation) is
always implicitly used.
sfa_ica_coeff
a list of float with two entries, which defines the
weights of the SFA and ICA part of the objective
function. They are called b_{SFA} and b_{ICA} in the
paper.
sfaweights
weighting factors for the covariance matrices relative
to the SFA part of the objective function (called
\kappa_{SFA}^{\tau} in the paper). Default is
[1., 0., ..., 0.]
For possible values see the description of icaweights.
icaweights
weighting factors for the cov matrices relative
to the ICA part of the objective function (called
\kappa_{ICA}^{\tau} in the paper). Default is 1.
Possible values are:
- an integer ``n``: all matrices are weighted the same
(note that it does not make sense to have ``n != 1``)
- a list or array of floats of ``len == len(lags)``:
each element of the list is used for weighting the
corresponding matrix
- ``None``: use the default values.
whitened
``True`` if input data is already white, ``False``
otherwise (the data will be whitened internally).
white_comp
If whitened is false, you can set ``white_comp`` to the
number of whitened components to keep during the
calculation (i.e., the input dimensions are reduced to
``white_comp`` by keeping the components of largest variance).
white_parm
a dictionary with additional parameters for whitening.
It is passed directly to the WhiteningNode constructor.
Ex: white_parm = { 'svd' : True }
eps_contrast
Convergence is achieved when the relative
improvement in the contrast is below this threshold.
Values in the range [1E-4, 1E-10] are usually
reasonable.
max_iter
If the algorithms does not achieve convergence within
max_iter iterations raise an Exception. Should be
larger than 100.
RP
Starting rotation-permutation matrix. It is an
input_dim x input_dim matrix used to initially rotate the
input components. If not set, the identity matrix is used.
In the paper this is used to start the algorithm at the
SFA solution (which is often quite near to the optimum).
verbose
print progress information during convergence. This can
slow down the algorithm, but it's the only way to see
the rate of improvement and immediately spot if something
is going wrong.
output_dim
sets the number of independent components that have to
be extracted. Note that if this is not smaller than
input_dim, the problem is solved linearly and SFA
would give the same solution only much faster.
"""
# check that the "lags" argument has some meaningful value
if isinstance(lags, (int, int)):
lags = list(range(1, lags+1))
elif isinstance(lags, (list, tuple)):
lags = numx.array(lags, "i")
elif isinstance(lags, numx.ndarray):
if not (lags.dtype.char in ['i', 'l']):
err_str = "lags must be integer!"
raise NodeException(err_str)
else:
pass
else:
err_str = ("Lags must be int, list or array. Found "
"%s!" % (type(lags).__name__))
raise NodeException(err_str)
self.lags = lags
# sanity checks for weights
if icaweights is None:
self.icaweights = 1.
else:
if (len(icaweights) != len(lags)):
err = ("icaweights vector length is %d, "
"should be %d" % (str(len(icaweights)), str(len(lags))))
raise NodeException(err)
self.icaweights = icaweights
if sfaweights is None:
self.sfaweights = [0]*len(lags)
self.sfaweights[0] = 1.
else:
if (len(sfaweights) != len(lags)):
err = ("sfaweights vector length is %d, "
"should be %d" % (str(len(sfaweights)), str(len(lags))))
raise NodeException(err)
self.sfaweights = sfaweights
# store attributes
self.sfa_ica_coeff = sfa_ica_coeff
self.max_iter = max_iter
self.verbose = verbose
self.eps_contrast = eps_contrast
# if input is not white, insert a WhiteningNode
self.whitened = whitened
if not whitened:
if white_parm is None:
white_parm = {}
if output_dim is not None:
white_comp = output_dim
elif white_comp is not None:
output_dim = white_comp
self.white = WhiteningNode(input_dim=input_dim,
output_dim=white_comp,
dtype=dtype, **white_parm)
# initialize covariance matrices
self.covs = [ DelayCovarianceMatrix(dt, dtype=dtype) for dt in lags ]
# initialize the global rotation-permutation matrix
# if not set that we'll eventually be an identity matrix
self.RP = RP
# initialize verbose structure to print nice and useful progress info
if verbose:
info = { 'sweep' : max(len(str(self.max_iter)), 5),
'perturbe': max(len(str(self.max_iter)), 5),
'float' : 5+8,
'fmt' : "%.5e",
'sep' : " | "}
f1 = "Sweep".center(info['sweep'])
f1_2 = "Pertb". center(info['perturbe'])
f2 = "SFA part".center(info['float'])
f3 = "ICA part".center(info['float'])
f4 = "Contrast".center(info['float'])
header = info['sep'].join([f1, f1_2, f2, f3, f4])
info['header'] = header+'\n'
info['line'] = len(header)*"-"
self._info = info
# finally call base class constructor
super(ISFANode, self).__init__(input_dim, output_dim, dtype)
def _optimize(self):
# optimize contrast function
# save initial contrast
sfa, ica = self._get_contrast(self.covs)
self.initial_contrast = {'SFA': sfa,
'ICA': ica,
'TOT': sfa + ica}
# info headers
if self.verbose:
print(self._info['header']+self._info['line'])
# initialize control variables
# contrast
contrast = sfa+ica
# local rotation matrix
Q = self._get_eye()
# local copy of correlation matrices
covs = self.covs.copy()
# maximum improvement in the contrast function
max_increase = self.eps_contrast
# Number of sweeps
sweep = 0
# flag for stopping sweeping
sweeping = True
# flag to check if we already perturbed the outer space
# - negative means that we exit from this routine
# because we hit numerical precision or because
# there's no outer space to be perturbed (input_dim == outpu_dim)
# - positive means the number of perturbations done
# before finding no further improvement
perturbed = 0
# size of the perturbation matrix
psize = self._effective_input_dim-self.output_dim
# if there is no outer space don't perturbe
if self._effective_input_dim == self.output_dim:
perturbed = -1
# local eye matrix
eye = self._get_eye()
# main loop
# we'll keep on sweeping until the contrast has improved less
# then self.eps_contrast
part_sweep = 0
while sweeping:
# update number of sweeps
sweep += 1
# perform a single sweep
max_increase, covs, Q, contrast = self._do_sweep(covs, Q, contrast)
if max_increase < 0 or contrast == 0:
# we hit numerical precision, exit!
sweeping = False
if perturbed == 0:
perturbed = -1
else:
perturbed = -perturbed
if (max_increase < self.eps_contrast) and (max_increase) >= 0 :
# rate of change is small for all pairs in a sweep
if perturbed == 0:
# perturbe the outer space one time with a random rotation
perturbed = 1
elif perturbed >= 1 and part_sweep == sweep-1:
# after the last pertubation no useful step has
# been done. exit!
sweeping = False
elif perturbed < 0:
# we can't perturbe anymore
sweeping = False
# keep track of the last sweep we perturbed
part_sweep = sweep
# perform perturbation if needed
if perturbed >= 1 and sweeping is True:
# generate a random rotation matrix for the external subspace
PRT = eye.copy()
rot = self._get_rnd_rotation(psize)
# generate a random permutation matrix for the ext. subspace
perm = self._get_rnd_permutation(psize)
# combine rotation and permutation
rot_perm = mult(rot, perm)
# apply rotation+permutation
PRT[self.output_dim:, self.output_dim:] = rot_perm
covs.transform(PRT)
Q = mult(Q, PRT)
# increment perturbation counter
perturbed += 1
# verbose progress information
if self.verbose:
table_entry = self._fmt_prog_info(sweep,
perturbed,
contrast)
_sys.stdout.write(table_entry+len(table_entry)*'\b')
_sys.stdout.flush()
# if we made too many sweeps exit with error!
if sweep == self.max_iter:
err_str = ("Failed to converge, maximum increase= "
"%.5e" % (max_increase))
raise NodeException(err_str)
# if we land here, we have converged!
# calculate output contrast
sfa, ica = self._get_contrast(covs)
contrast = sfa+ica
# print final information
if self.verbose:
print(self._fmt_prog_info(sweep, perturbed,
contrast, sfa, ica))
print(self._info['line'])
self.final_contrast = {'SFA': sfa,
'ICA': ica,
'TOT': sfa + ica}
# finally return optimal rotation matrix
return Q
def _stop_training(self, debug=False):
# debug argument is ignored but needed by the base class
super(NIPALSNode, self)._stop_training()
self._adjust_output_dim()
if self.desired_variance is not None:
des_var = True
else:
des_var = False
X = self.data
conv = self.conv
dtype = self.dtype
mean = X.mean(axis=0)
self.avg = mean
max_it = self.max_it
tlen = self.tlen
# remove mean
X -= mean
var = X.var(axis=0).sum()
self.total_variance = var
exp_var = 0
eigenv = numx.zeros((self.input_dim, self.input_dim), dtype=dtype)
d = numx.zeros((self.input_dim,), dtype = dtype)
for i in range(self.input_dim):
it = 0
# first score vector t is initialized to first column in X
t = X[:, 0]
# initialize difference
diff = conv + 1
while diff > conv:
# increase iteration counter
it += 1
# Project X onto t to find corresponding loading p
# and normalize loading vector p to length 1
p = old_div(mult(X.T, t),mult(t, t))
p /= sqrt(mult(p, p))
# project X onto p to find corresponding score vector t_new
t_new = mult(X, p)
# difference between new and old score vector
tdiff = t_new - t
diff = (tdiff*tdiff).sum()
t = t_new
if it > max_it:
msg = ('PC#%d: no convergence after'
' %d iterations.'% (i, max_it))
raise NodeException(msg)
# store ith eigenvector in result matrix
eigenv[i, :] = p
# remove the estimated principal component from X
D = numx.outer(t, p)
X -= D
D = mult(D, p)
d[i] = old_div((D*D).sum(),(tlen-1))
exp_var += old_div(d[i],var)
if des_var and (exp_var >= self.desired_variance):
self.output_dim = i + 1
break
self.d = d[:self.output_dim]
self.v = eigenv[:self.output_dim, :].T
self.explained_variance = exp_var
def set_boundary(self, boundary):
if boundary not in ['fill', 'wrap', 'symm']:
raise NodeException(
"'boundary' argument must be one of ['fill', 'wrap', 'symm']")
self._boundary = boundary
def _set_output_dim(self, n):
msg = 'Output dim can not be explicitly set!'
raise NodeException(msg)
def __init__(self, filters, input_shape = None,
approach = 'fft',
mode = 'full', boundary = 'fill', fillvalue = 0,
output_2d = True,
input_dim = None, dtype = None):
"""Initializes an object of type 'Convolution2DNode'.
:param filters: Specifies a set of 2D filters that are
convolved with the input data during execution.
:type filters: numpy.ndarray
:param input_shape: Is a tuple (h,w) that corresponds to the height and
width of the input 2D data. If the input data is given
in a flattened format, it is first reshaped before
convolution
:type input_shape: tuple
:param approach: 'approach' is one of ['linear', 'fft']
- 'linear': convolution is done by linear filtering;
- 'fft': convoltion is done using the Fourier Transform
If 'approach' is 'fft', the 'boundary' and 'fillvalue' arguments
are ignored, and are assumed to be 'fill' and 0, respectively.
(*Default* = 'fft')
:type approach: str
:param mode: Convolution mode, as defined in scipy.signal.convolve2d
'mode' is one of ['valid', 'same', 'full']
(*Default* = 'full')
:type mode: str
:param boundary: Boundary condition, as defined in scipy.signal.convolve2d
'boundary' is one of ['fill', 'wrap', 'symm']
(*Default* = 'fill')
:type boundary: str
:param fillvalue: Value to fill pad input arrays with
(*Default* = 0)
:type fillvalue: numeric
:param output_2d: If True, the output array is 2D; the first index
corresponds to data points; every output data point
is the result of flattened convolution results, with
the output of each filter concatenated together.
If False, the output array is 4D; the format is
data[idx,filter_nr,x,y], with
- filter_nr: index of convolution filter
- idx: data point index
- x, y: 2D coordinates
:type output_2d: bool
:param input_dim: The input dimensionality.
:type input_dim: int
:param dtype: The datatype.
:type dtype: numpy.dtype or str
"""
super(Convolution2DNode, self).__init__(input_dim=input_dim,
dtype=dtype)
self.filters = filters
self._input_shape = input_shape
if approach not in ['linear', 'fft']:
raise NodeException("'approach' argument must be one of ['linear', 'fft']")
self._approach = approach
if mode not in ['valid', 'same', 'full']:
raise NodeException("'mode' argument must be one of ['valid', 'same', 'full']")
self._mode = mode
self.boundary = boundary
self.fillvalue = fillvalue
self.output_2d = output_2d
self._output_shape = None
def _stop_training(self):
#### some definitions
verbose = self.verbose
typ = self.dtype
tol = self.tol
d = self.input_dim
# if the number of latent variables is not specified,
# set it equal to the number of input components
if not self.output_dim:
self.output_dim = d
k = self.output_dim
# indices of the diagonal elements of a dxd or kxk matrix
idx_diag_d = [i * (d + 1) for i in range(d)]
idx_diag_k = [i * (k + 1) for i in range(k)]
# constant term in front of the log-likelihood
const = -d / 2. * numx.log(2. * numx.pi)
##### request the covariance matrix and clean up
cov_mtx, mu, tlen = self._cov_mtx.fix()
del self._cov_mtx
cov_diag = cov_mtx.diagonal()
##### initialize the parameters
# noise variances
sigma = cov_diag
# loading factors
# Zoubin uses the determinant of cov_mtx^1/d as scale but it's
# too slow for large matrices. Is the product of the diagonal a good
# approximation?
if d <= 300:
scale = det(cov_mtx)**(1. / d)
else:
scale = numx.product(sigma)**(1. / d)
if scale <= 0.:
err = ("The covariance matrix of the data is singular. "
"Redundant dimensions need to be removed.")
raise NodeException(err)
A = normal(0., sqrt(scale / k), size=(d, k)).astype(typ)
##### EM-cycle
lhood_curve = []
base_lhood = None
old_lhood = -numx.inf
for t in xrange(self.max_cycles):
## compute B = (A A^T + Sigma)^-1
B = mult(A, A.T)
# B += diag(sigma), avoid computing diag(sigma) which is dxd
B.ravel().put(idx_diag_d, B.ravel().take(idx_diag_d) + sigma)
# this quantity is used later for the log-likelihood
# abs is there to avoid numerical errors when det < 0
log_det_B = numx.log(abs(det(B)))
# end the computation of B
B = inv(B)
## other useful quantities
trA_B = mult(A.T, B)
trA_B_cov_mtx = mult(trA_B, cov_mtx)
##### E-step
## E_yyT = E(y_n y_n^T | x_n)
E_yyT = -mult(trA_B, A) + mult(trA_B_cov_mtx, trA_B.T)
# E_yyT += numx.eye(k)
E_yyT.ravel().put(idx_diag_k, E_yyT.ravel().take(idx_diag_k) + 1.)
##### M-step
A = mult(trA_B_cov_mtx.T, inv(E_yyT))
sigma = cov_diag - (mult(A, trA_B_cov_mtx)).diagonal()
##### log-likelihood
trace_B_cov = (B * cov_mtx.T).sum()
# this is actually likelihood/tlen.
lhood = const - 0.5 * log_det_B - 0.5 * trace_B_cov
if verbose:
print 'cycle', t, 'log-lhood:', lhood
##### convergence criterion
if base_lhood is None:
base_lhood = lhood
else:
# convergence criterion
if (lhood -
base_lhood) < (1. + tol) * (old_lhood - base_lhood):
break
if lhood < old_lhood:
# this should never happen
# it sometimes does, e.g. if the noise is extremely low,
# because of numerical rounding effects
warnings.warn(_LHOOD_WARNING, mdp.MDPWarning)
old_lhood = lhood
lhood_curve.append(lhood)
self.tlen = tlen
self.A = A
self.mu = mu.reshape(1, d)
self.sigma = sigma
## MAP matrix
# compute B = (A A^T + Sigma)^-1
B = mult(A, A.T).copy()
B.ravel().put(idx_diag_d, B.ravel().take(idx_diag_d) + sigma)
B = inv(B)
self.E_y_mtx = mult(B.T, A)
self.lhood = lhood_curve