def test_mcanode_v1():
line_x = numx.zeros((1000, 2), "d")
line_y = numx.zeros((1000, 2), "d")
line_x[:, 0] = numx.linspace(-1, 1, num=1000, endpoint=1)
line_y[:, 1] = numx.linspace(-0.2, 0.2, num=1000, endpoint=1)
mat = numx.concatenate((line_x, line_y))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
mca = MCANode()
for i in xrange(5):
mca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = mca.get_projmatrix()
bv = bpca.get_projmatrix()[:, ::-1]
dcosines = numx.zeros(v.shape[1])
for dim in xrange(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def doPCA(in_pts, D, tol=.8):
pts=copy(in_pts)
pts.shape = (len(in_pts),D)
p=PCANode(output_dim=tol)
p.train(pts)
p.stop_training()
return p
def test_ccipcanode_v1():
line_x = numx.zeros((1000, 2), "d")
line_y = numx.zeros((1000, 2), "d")
line_x[:, 0] = numx.linspace(-1, 1, num=1000, endpoint=1)
line_y[:, 1] = numx.linspace(-0.2, 0.2, num=1000, endpoint=1)
mat = numx.concatenate((line_x, line_y))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
pca = CCIPCANode()
for i in range(5):
pca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = pca.get_projmatrix()
bv = bpca.get_projmatrix()
dcosines = numx.zeros(v.shape[1])
for dim in range(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def applyPCA(features, components):
"""
Dimension reduction of a matrix using pca
"""
features = np.array(features)
pcanode = PCANode(output_dim=components)
pcanode.train(features)
pcanode.stop_training()
# print "Explained variance: %s" % pcanode.explained_variance
return pcanode(features)
def bench_mdp(X, y, T, valid):
#
# .. MDP ..
#
from mdp.nodes import PCANode
start = datetime.now()
clf = PCANode(output_dim=n_components)
clf.train(X)
clf.stop_training()
delta = datetime.now() - start
ev = explained_variance(X, clf.v.T).sum()
return ev, delta
def bench_mdp(X, y, T, valid):
#
# .. MDP ..
#
from mdp.nodes import PCANode
start = datetime.now()
clf = PCANode(output_dim=n_components)
clf.train(X)
clf.stop_training()
delta = datetime.now() - start
ev = explained_variance(X, clf.v.T).sum()
return ev, delta
def pca(data, output_dim=2, standardize=True):
""" This package is using the PCA module form
url : http://folk.uio.no/henninri/pca_module/
"""
"""
T, P, explained_var = PCA_svd(data, standardize=True)
return T,P,explained_var
"""
""" ALTERNATE MPLEMENTATION FROM MDP """
pca_node = PCANode(output_dim=output_dim)
pca_node.train(data)
pca_node.stop_training()
return pca_node
class PCAWrapperNode(SpatialFilteringNode): #, PCANode):
""" Reuse the implementation of the Principal Component Analysis of mdp
This node implements the unsupervised principal component
analysis algorithm for spatial filtering.
**Parameters**
:retained_channels: Determines how many of the PCA pseudo channels
are retained. Default is None which means "all channels".
:load_path: An absolute path from which the PCA eigenmatrix
is loaded.
If not specified, this matrix is learned from the training data.
(*optional, default: None*)
**Exemplary Call**
.. code-block:: yaml
-
node : PCA
parameters:
retained_channels : 42
"""
def __init__(self, retained_channels=None, load_path=None, **kwargs):
# Must be set before constructor of superclass is set
self.trainable = (load_path == None)
super(PCAWrapperNode, self).__init__(**kwargs)
self.output_dim = retained_channels
# Load patterns from file if requested
if load_path != None:
filter_file = open(load_path, 'r')
avg, v = cPickle.load(filter_file)
self.set_permanent_attributes(avg=avg, v=v, trainable=False, filters=v)
self.set_permanent_attributes(# The number of channels that will be retained
retained_channels = retained_channels,
output_dim = retained_channels,
channel_names = None,
new_channels = None,
wrapped_node=None
)
def is_trainable(self):
""" Returns whether this node is trainable. """
return self.trainable
def is_supervised(self):
""" Returns whether this node requires supervised training. """
return False
def _train(self, data, label = None):
""" Updates the estimated covariance matrix based on *data*. """
# We simply ignore the class label since we
# are doing unsupervised learning
if self.wrapped_node is None:
self.wrapped_node = PCANode()
x=data.view(numpy.ndarray)
self.wrapped_node.train(x)
if self.channel_names is None:
self.channel_names = data.channel_names
def _stop_training(self, debug=False):
""" Stops training by forwarding to super class. """
self.wrapped_node.stop_training()
#super(PCAWrapperNode, self)._stop_training(debug)
self.v = self.wrapped_node.v
self.avg = self.wrapped_node.avg
self.filters = self.v
def _execute(self, data, n = None):
""" Execute learned transformation on *data*.
Projects the given data to the axis of the most significant
eigenvectors and returns the data in this lower-dimensional subspace.
"""
# 'INITIALIZATION'
if self.retained_channels==None:
self.retained_channels = data.shape[1]
if n is None:
n = self.retained_channels
if self.channel_names is None:
self.channel_names = data.channel_names
if len(self.channel_names)<self.retained_channels:
self.retained_channels = len(self.channel_names)
self._log("To many channels chosen for the retained channels! Replaced by maximum number.",level=logging.CRITICAL)
if not(self.output_dim==self.retained_channels):
# overwrite internal output_dim variable, since it is set wrong
self._output_dim = self.retained_channels
# 'Real' Processing
#projected_data = super(PCANodeWrapper, self)._execute(data, n)
x = data.view(numpy.ndarray)
projected_data = mult(x-self.avg, self.v[:, :self.retained_channels])
if self.new_channels is None:
self.new_channel_names = ["pca%03d" % i
for i in range(projected_data.shape[1])]
return TimeSeries(projected_data, self.new_channel_names,
data.sampling_frequency, data.start_time,
data.end_time, data.name, data.marker_name)
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir*. """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned eigenvector and eigenvalues
name = "%s_sp%s.pickle" % ("eigenmatrix", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(cPickle.dumps((self.avg, self.v), protocol=2))
result_file.close()
class PCAWrapperNode(SpatialFilteringNode): #, PCANode):
""" Reuse the implementation of the Principal Component Analysis of mdp
For a theoretical description of how PCA works, the following tutorial
is extremely useful.
======= =========================================================
Title A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation, Discussion and Singular Value Decomposition
Author Jon Shlens
Link http://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf
======= =========================================================
This node implements the unsupervised principal component
analysis algorithm for spatial filtering.
.. note:: The original PCANode can execute the Principal Component Analysis
in 2 ways. The first method(which is also the default) involves the
computation of the eigenvalues of a symmetric matrix. This is obviously
a rather fast approach. Nonetheless, this approach sometimes fails and
negative eigenvalues are obtained from the computation. The problem can
be solved by using the Singular Value Decomposition method in the PCA.
This is easily done by setting ``svd=True`` when initializing the
:mod:`~pySPACE.missions.nodes.spatial_filtering.pca`. The SVD approach
is more robust but also less cost-effective when it comes to computation
time.
**Parameters**
:retained_channels: Determines how many of the PCA pseudo channels
are retained. Default is None which means "all channels".
:load_path: An absolute path from which the PCA eigenmatrix
is loaded.
If not specified, this matrix is learned from the training data.
(*optional, default: None*)
**mdp parameters**
:svd: if True use Singular Value Decomposition instead of the
standard eigenvalue problem solver. Use it when PCANode
complains about singular covariance matrices
(*optional, default: False*)
:reduce: Keep only those principal components which have a variance
larger than 'var_abs' and a variance relative to the
first principal component larger than 'var_rel' and a
variance relative to total variance larger than 'var_part'
(set var_part to None or 0 for no filtering).
Note: when the 'reduce' switch is enabled, the actual number
of principal components (self.output_dim) may be different
from that set when creating the instance.
(*optional, default: False*)
**Exemplary Call**
.. code-block:: yaml
-
node : PCA
parameters:
retained_channels : 42
"""
def __init__(self,
retained_channels=None,
load_path=None,
svd=False,
reduce=False,
**kwargs):
# Must be set before constructor of superclass is set
self.trainable = (load_path == None)
super(PCAWrapperNode, self).__init__(**kwargs)
self.output_dim = retained_channels
# Load patterns from file if requested
if load_path != None:
filter_file = open(load_path, 'r')
avg, v = cPickle.load(filter_file)
self.set_permanent_attributes(avg=avg,
v=v,
trainable=False,
filters=v)
self.set_permanent_attributes( # The number of channels that will be retained
retained_channels=retained_channels,
output_dim=retained_channels,
channel_names=None,
new_channels=None,
wrapped_node=None,
svd=svd, # choose the method to use when computing the PCA
reduce=reduce)
def is_trainable(self):
""" Returns whether this node is trainable. """
return self.trainable
def is_supervised(self):
""" Returns whether this node requires supervised training. """
return False
def _train(self, data, label=None):
""" Updates the estimated covariance matrix based on *data*. """
# We simply ignore the class label since we
# are doing unsupervised learning
if self.wrapped_node is None:
self.wrapped_node = PCANode(svd=self.svd, reduce=self.reduce)
x = 1.0 * data.view(type=numpy.ndarray)
self.wrapped_node.train(x)
if self.channel_names is None:
self.channel_names = data.channel_names
def _stop_training(self, debug=False):
""" Stops training by forwarding to super class. """
self.wrapped_node.stop_training()
#super(PCAWrapperNode, self)._stop_training(debug)
self.v = self.wrapped_node.v
self.avg = self.wrapped_node.avg
self.filters = self.v
def _execute(self, data, n=None):
""" Execute learned transformation on *data*.
Projects the given data to the axis of the most significant
eigenvectors and returns the data in this lower-dimensional subspace.
"""
# 'INITIALIZATION'
if self.retained_channels == None:
self.retained_channels = data.shape[1]
if n is None:
n = self.retained_channels
if self.channel_names is None:
self.channel_names = data.channel_names
if len(self.channel_names) < self.retained_channels:
self.retained_channels = len(self.channel_names)
self._log(
"To many channels chosen for the retained channels! Replaced by maximum number.",
level=logging.CRITICAL)
if not (self.output_dim == self.retained_channels):
# overwrite internal output_dim variable, since it is set wrong
self._output_dim = self.retained_channels
# 'Real' Processing
#projected_data = super(PCANodeWrapper, self)._execute(data, n)
x = data.view(numpy.ndarray)
projected_data = mult(x - self.avg, self.v[:, :self.retained_channels])
if self.new_channels is None:
self.new_channel_names = [
"pca%03d" % i for i in range(projected_data.shape[1])
]
return TimeSeries(projected_data, self.new_channel_names,
data.sampling_frequency, data.start_time,
data.end_time, data.name, data.marker_name)
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir*. """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned eigenvector and eigenvalues
name = "%s_sp%s.pickle" % ("eigenmatrix", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(cPickle.dumps((self.avg, self.v), protocol=2))
result_file.close()
class PCAWrapperNode(SpatialFilteringNode): #, PCANode):
""" Reuse the implementation of the Principal Component Analysis of mdp
This node implements the unsupervised principal component
analysis algorithm for spatial filtering.
**Parameters**
:retained_channels: Determines how many of the PCA pseudo channels
are retained. Default is None which means "all channels".
:load_path: An absolute path from which the PCA eigenmatrix
is loaded.
If not specified, this matrix is learned from the training data.
(*optional, default: None*)
**Exemplary Call**
.. code-block:: yaml
-
node : PCA
parameters:
retained_channels : 42
"""
def __init__(self, retained_channels=None, load_path=None, **kwargs):
# Must be set before constructor of superclass is set
self.trainable = (load_path == None)
super(PCAWrapperNode, self).__init__(**kwargs)
self.output_dim = retained_channels
# Load patterns from file if requested
if load_path != None:
filter_file = open(load_path, 'r')
avg, v = cPickle.load(filter_file)
self.set_permanent_attributes(avg=avg,
v=v,
trainable=False,
filters=v)
self.set_permanent_attributes( # The number of channels that will be retained
retained_channels=retained_channels,
output_dim=retained_channels,
channel_names=None,
new_channels=None,
wrapped_node=None)
def is_trainable(self):
""" Returns whether this node is trainable. """
return self.trainable
def is_supervised(self):
""" Returns whether this node requires supervised training. """
return False
def _train(self, data, label=None):
""" Updates the estimated covariance matrix based on *data*. """
# We simply ignore the class label since we
# are doing unsupervised learning
if self.wrapped_node is None:
self.wrapped_node = PCANode()
x = data.view(numpy.ndarray)
self.wrapped_node.train(x)
if self.channel_names is None:
self.channel_names = data.channel_names
def _stop_training(self, debug=False):
""" Stops training by forwarding to super class. """
self.wrapped_node.stop_training()
#super(PCAWrapperNode, self)._stop_training(debug)
self.v = self.wrapped_node.v
self.avg = self.wrapped_node.avg
self.filters = self.v
def _execute(self, data, n=None):
""" Execute learned transformation on *data*.
Projects the given data to the axis of the most significant
eigenvectors and returns the data in this lower-dimensional subspace.
"""
# 'INITIALIZATION'
if self.retained_channels == None:
self.retained_channels = data.shape[1]
if n is None:
n = self.retained_channels
if self.channel_names is None:
self.channel_names = data.channel_names
if len(self.channel_names) < self.retained_channels:
self.retained_channels = len(self.channel_names)
self._log(
"To many channels chosen for the retained channels! Replaced by maximum number.",
level=logging.CRITICAL)
if not (self.output_dim == self.retained_channels):
# overwrite internal output_dim variable, since it is set wrong
self._output_dim = self.retained_channels
# 'Real' Processing
#projected_data = super(PCANodeWrapper, self)._execute(data, n)
x = data.view(numpy.ndarray)
projected_data = mult(x - self.avg, self.v[:, :self.retained_channels])
if self.new_channels is None:
self.new_channel_names = [
"pca%03d" % i for i in range(projected_data.shape[1])
]
return TimeSeries(projected_data, self.new_channel_names,
data.sampling_frequency, data.start_time,
data.end_time, data.name, data.marker_name)
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir*. """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned eigenvector and eigenvalues
name = "%s_sp%s.pickle" % ("eigenmatrix", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(cPickle.dumps((self.avg, self.v), protocol=2))
result_file.close()
class PCAWrapperNode(SpatialFilteringNode): #, PCANode):
""" Reuse the implementation of the Principal Component Analysis of mdp
For a theoretical description of how PCA works, the following tutorial
is extremely useful.
======= =========================================================
Title A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation, Discussion and Singular Value Decomposition
Author Jon Shlens
Link http://www.cs.princeton.edu/picasso/mats/PCA-Tutorial-Intuition_jp.pdf
======= =========================================================
This node implements the unsupervised principal component
analysis algorithm for spatial filtering.
.. note:: The original PCANode can execute the Principal Component Analysis
in 2 ways. The first method(which is also the default) involves the
computation of the eigenvalues of a symmetric matrix. This is obviously
a rather fast approach. Nonetheless, this approach sometimes fails and
negative eigenvalues are obtained from the computation. The problem can
be solved by using the Singular Value Decomposition method in the PCA.
This is easily done by setting ``svd=True`` when initializing the
:mod:`~pySPACE.missions.nodes.spatial_filtering.pca`. The SVD approach
is more robust but also less cost-effective when it comes to computation
time.
**Parameters**
:retained_channels: Determines how many of the PCA pseudo channels
are retained. Default is None which means "all channels".
:load_path: An absolute path from which the PCA eigenmatrix
is loaded.
If not specified, this matrix is learned from the training data.
(*optional, default: None*)
**mdp parameters**
:svd: if True use Singular Value Decomposition instead of the
standard eigenvalue problem solver. Use it when PCANode
complains about singular covariance matrices
(*optional, default: False*)
:reduce: Keep only those principal components which have a variance
larger than 'var_abs' and a variance relative to the
first principal component larger than 'var_rel' and a
variance relative to total variance larger than 'var_part'
(set var_part to None or 0 for no filtering).
Note: when the 'reduce' switch is enabled, the actual number
of principal components (self.output_dim) may be different
from that set when creating the instance.
(*optional, default: False*)
**Exemplary Call**
.. code-block:: yaml
-
node : PCA
parameters:
retained_channels : 42
"""
def __init__(self, retained_channels=None, load_path=None,
svd=False, reduce=False, **kwargs):
# Must be set before constructor of superclass is set
self.trainable = (load_path == None)
super(PCAWrapperNode, self).__init__(**kwargs)
self.output_dim = retained_channels
# Load patterns from file if requested
if load_path != None:
filter_file = open(load_path, 'r')
avg, v = cPickle.load(filter_file)
self.set_permanent_attributes(avg=avg, v=v, trainable=False, filters=v)
self.set_permanent_attributes( # The number of channels that will be retained
retained_channels=retained_channels,
output_dim=retained_channels,
channel_names=None,
new_channels=None,
wrapped_node=None,
svd=svd, # choose the method to use when computing the PCA
reduce=reduce
)
def is_trainable(self):
""" Returns whether this node is trainable. """
return self.trainable
def is_supervised(self):
""" Returns whether this node requires supervised training. """
return False
def _train(self, data, label = None):
""" Updates the estimated covariance matrix based on *data*. """
# We simply ignore the class label since we
# are doing unsupervised learning
if self.wrapped_node is None:
self.wrapped_node = PCANode(svd=self.svd, reduce=self.reduce)
x = 1.0 * data.view(type=numpy.ndarray)
self.wrapped_node.train(x)
if self.channel_names is None:
self.channel_names = data.channel_names
def _stop_training(self, debug=False):
""" Stops training by forwarding to super class. """
self.wrapped_node.stop_training()
#super(PCAWrapperNode, self)._stop_training(debug)
self.v = self.wrapped_node.v
self.avg = self.wrapped_node.avg
self.filters = self.v
def _execute(self, data, n = None):
""" Execute learned transformation on *data*.
Projects the given data to the axis of the most significant
eigenvectors and returns the data in this lower-dimensional subspace.
"""
# 'INITIALIZATION'
if self.retained_channels==None:
self.retained_channels = data.shape[1]
if n is None:
n = self.retained_channels
if self.channel_names is None:
self.channel_names = data.channel_names
if len(self.channel_names)<self.retained_channels:
self.retained_channels = len(self.channel_names)
self._log("To many channels chosen for the retained channels! Replaced by maximum number.",level=logging.CRITICAL)
if not(self.output_dim==self.retained_channels):
# overwrite internal output_dim variable, since it is set wrong
self._output_dim = self.retained_channels
# 'Real' Processing
#projected_data = super(PCANodeWrapper, self)._execute(data, n)
x = data.view(numpy.ndarray)
projected_data = mult(x-self.avg, self.v[:, :self.retained_channels])
if self.new_channels is None:
self.new_channel_names = ["pca%03d" % i
for i in range(projected_data.shape[1])]
return TimeSeries(projected_data, self.new_channel_names,
data.sampling_frequency, data.start_time,
data.end_time, data.name, data.marker_name)
def store_state(self, result_dir, index=None):
""" Stores this node in the given directory *result_dir*. """
if self.store:
node_dir = os.path.join(result_dir, self.__class__.__name__)
create_directory(node_dir)
# This node only stores the learned eigenvector and eigenvalues
name = "%s_sp%s.pickle" % ("eigenmatrix", self.current_split)
result_file = open(os.path.join(node_dir, name), "wb")
result_file.write(cPickle.dumps((self.avg, self.v), protocol=2))
result_file.close()
