def compute_pca(time_series):
"""
# type: (TimeSeries) -> PrincipalComponents
Compute the temporal covariance between nodes in the time_series.
Parameters
__________
time_series : TimeSeries
The timeseries to which the PCA is to be applied.
"""
ts_shape = time_series.data.shape
# Need more measurements than variables
if ts_shape[0] < ts_shape[2]:
msg = "PCA requires a longer timeseries (tpts > number of nodes)."
log.error(msg)
raise Exception(msg)
# (nodes, nodes, state-variables, modes)
weights_shape = (ts_shape[2], ts_shape[2], ts_shape[1], ts_shape[3])
log.info("weights shape will be: %s" % str(weights_shape))
fractions_shape = (ts_shape[2], ts_shape[1], ts_shape[3])
log.info("fractions shape will be: %s" % str(fractions_shape))
weights = numpy.zeros(weights_shape)
fractions = numpy.zeros(fractions_shape)
# One inter-node temporal covariance matrix for each state-var & mode.
for mode in range(ts_shape[3]):
for var in range(ts_shape[1]):
data = time_series.data[:, var, :, mode]
fracts, w = _compute_weights_and_fractions(data)
fractions[:, var, mode] = fracts
weights[:, :, var, mode] = w
log.debug("fractions")
log.debug(narray_describe(fractions))
log.debug("weights")
log.debug(narray_describe(weights))
pca_result = mode_decompositions.PrincipalComponents(
source=time_series,
fractions=fractions,
weights=weights,
norm_source=numpy.array([]),
component_time_series=numpy.array([]),
normalised_component_time_series=numpy.array([]))
return pca_result
def evaluate(self):
"""
Compute the temporal covariance between nodes in the time_series.
"""
cls_attr_name = self.__class__.__name__ + ".time_series"
# self.time_series.trait["data"].log_debug(owner = cls_attr_name)
ts_shape = self.time_series.data.shape
# Need more measurements than variables
if ts_shape[0] < ts_shape[2]:
msg = "PCA requires a longer timeseries (tpts > number of nodes)."
self.log.error(msg)
raise Exception(msg)
# (nodes, nodes, state-variables, modes)
weights_shape = (ts_shape[2], ts_shape[2], ts_shape[1], ts_shape[3])
self.log.info("weights shape will be: %s" % str(weights_shape))
fractions_shape = (ts_shape[2], ts_shape[1], ts_shape[3])
self.log.info("fractions shape will be: %s" % str(fractions_shape))
weights = numpy.zeros(weights_shape)
fractions = numpy.zeros(fractions_shape)
# One inter-node temporal covariance matrix for each state-var & mode.
for mode in range(ts_shape[3]):
for var in range(ts_shape[1]):
data = self.time_series.data[:, var, :, mode]
data_pca = PCA_mlab(data)
fractions[:, var, mode] = data_pca.fracs
weights[:, :, var, mode] = data_pca.Wt
self.log.debug("fractions")
self.log.debug(narray_describe(fractions))
self.log.debug("weights")
self.log.debug(narray_describe(weights))
pca_result = mode_decompositions.PrincipalComponents(
source=self.time_series,
fractions=fractions,
weights=weights,
norm_source=numpy.array([]),
component_time_series=numpy.array([]),
normalised_component_time_series=numpy.array([]))
return pca_result
def test_principalcomponents(self):
data = numpy.random.random((10, 10, 10, 10))
ts = time_series.TimeSeries(data=data)
dt = mode_decompositions.PrincipalComponents(source = ts,
fractions = numpy.random.random((10, 10, 10)),
weights = data)
dt.configure()
dt.compute_norm_source()
dt.compute_component_time_series()
dt.compute_normalised_component_time_series()
summary = dt.summary_info
assert summary['Mode decomposition type'] == 'PrincipalComponents'
assert dt.source is not None
assert dt.weights.shape == (10, 10, 10, 10)
assert dt.fractions.shape == (10, 10, 10)
assert dt.norm_source.shape == (10, 10, 10, 10)
assert dt.component_time_series.shape == (10, 10, 10, 10)
assert dt.normalised_component_time_series.shape == (10, 10, 10, 10)
def evaluate(self):
"""
Compute the temporal covariance between nodes in the time_series.
"""
cls_attr_name = self.__class__.__name__+".time_series"
self.time_series.trait["data"].log_debug(owner = cls_attr_name)
ts_shape = self.time_series.data.shape
#Need more measurements than variables
if ts_shape[0] < ts_shape[2]:
msg = "PCA requires a longer timeseries (tpts > number of nodes)."
LOG.error(msg)
raise Exception, msg
#(nodes, nodes, state-variables, modes)
weights_shape = (ts_shape[2], ts_shape[2], ts_shape[1], ts_shape[3])
LOG.info("weights shape will be: %s" % str(weights_shape))
fractions_shape = (ts_shape[2], ts_shape[1], ts_shape[3])
LOG.info("fractions shape will be: %s" % str(fractions_shape))
weights = numpy.zeros(weights_shape)
fractions = numpy.zeros(fractions_shape)
#One inter-node temporal covariance matrix for each state-var & mode.
for mode in range(ts_shape[3]):
for var in range(ts_shape[1]):
data = self.time_series.data[:, var, :, mode]
data_pca = mlab.PCA(data)
fractions[:, var, mode ] = data_pca.fracs
weights[:, :, var, mode] = data_pca.Wt
util.log_debug_array(LOG, fractions, "fractions")
util.log_debug_array(LOG, weights, "weights")
pca_result = mode_decompositions.PrincipalComponents(
source = self.time_series,
fractions = fractions,
weights = weights,
use_storage = False)
return pca_result