def test_fmri_to_cosmo():
skip_if_no_external('nibabel')
from mvpa2.datasets.mri import fmri_dataset
# test exporting an fMRI dataset to CoSMoMVPA
pymvpa_ds = fmri_dataset(
samples=pathjoin(pymvpa_dataroot, 'example4d.nii.gz'),
targets=[1, 2], sprefix='voxel')
cosmomvpa_struct = cosmo.map2cosmo(pymvpa_ds)
_assert_set_equal(cosmomvpa_struct.keys(), ['a', 'fa', 'sa', 'samples'])
a_dict = dict(_obj2tup(cosmomvpa_struct['a']))
mri_keys = ['imgaffine', 'voxel_eldim', 'voxel_dim']
_assert_subset(mri_keys, a_dict.keys())
for k in mri_keys:
c_value = a_dict[k]
p_value = pymvpa_ds.a[k].value
if isinstance(p_value, tuple):
c_value = c_value.ravel()
p_value = np.asarray(p_value).ravel()
assert_array_almost_equal(c_value, p_value)
fa_dict = dict(_obj2tup(cosmomvpa_struct['fa']))
fa_keys = ['voxel_indices']
_assert_set_equal(fa_dict.keys(), fa_keys)
for k in fa_keys:
assert_array_almost_equal(fa_dict[k].T, pymvpa_ds.fa[k].value)
def test_mapper_vs_zscore():
"""Test by comparing to results of elderly z-score function
"""
# data: 40 sample feature line in 20d space (40x20; samples x features)
dss = [
dataset_wizard(np.concatenate(
[np.arange(40) for i in range(20)]).reshape(20,-1).T,
targets=1, chunks=1),
] + datasets.values()
for ds in dss:
ds1 = deepcopy(ds)
ds2 = deepcopy(ds)
zsm = ZScoreMapper(chunks_attr=None)
assert_raises(RuntimeError, zsm.forward, ds1.samples)
idhashes = (idhash(ds1), idhash(ds1.samples))
zsm.train(ds1)
idhashes_train = (idhash(ds1), idhash(ds1.samples))
assert_equal(idhashes, idhashes_train)
# forward dataset
ds1z_ds = zsm.forward(ds1)
idhashes_forwardds = (idhash(ds1), idhash(ds1.samples))
# must not modify samples in place!
assert_equal(idhashes, idhashes_forwardds)
# forward samples explicitly
ds1z = zsm.forward(ds1.samples)
idhashes_forward = (idhash(ds1), idhash(ds1.samples))
assert_equal(idhashes, idhashes_forward)
zscore(ds2, chunks_attr=None)
assert_array_almost_equal(ds1z, ds2.samples)
assert_array_equal(ds1.samples, ds.samples)
def test_searchlight_errors_per_trial():
# To make sure that searchlight can return error/accuracy per trial
from mvpa2.clfs.gnb import GNB
from mvpa2.generators.partition import OddEvenPartitioner
from mvpa2.measures.base import CrossValidation
from mvpa2.measures.searchlight import sphere_searchlight
from mvpa2.measures.gnbsearchlight import sphere_gnbsearchlight
from mvpa2.testing.datasets import datasets
from mvpa2.misc.errorfx import prediction_target_matches
dataset = datasets['3dsmall'].copy()
# randomly permute samples so we break any random correspondence
# to strengthen tests below
sample_idx = np.arange(len(dataset))
dataset = dataset[np.random.permutation(sample_idx)]
dataset.sa.targets = ['L%d' % l for l in dataset.sa.targets]
dataset.fa['voxel_indices'] = dataset.fa.myspace
sample_clf = GNB() # fast and deterministic
part = OddEvenPartitioner()
# only do partial to save time
cv = CrossValidation(sample_clf, part, errorfx=None) #prediction_target_matches)
# Just to compare error
cv_error = CrossValidation(sample_clf, part)
# Large searchlight radius so we get entire ROI, 2 centers just to make sure
# that all stacking works correctly
sl = sphere_searchlight(cv, radius=10, center_ids=[0, 1])
results = sl(dataset)
sl_gnb = sphere_gnbsearchlight(sample_clf, part, radius=10, errorfx=None,
center_ids=[0, 1])
results_gnbsl = sl_gnb(dataset)
# inspect both results
# verify that partitioning was done correctly
partitions = list(part.generate(dataset))
for res in (results, results_gnbsl):
assert('targets' in res.sa.keys()) # should carry targets
assert('cvfolds' in res.sa.keys()) # should carry cvfolds
for ipart in xrange(len(partitions)):
assert_array_equal(dataset[partitions[ipart].sa.partitions == 2].targets,
res.sa.targets[res.sa.cvfolds == ipart])
assert_datasets_equal(results, results_gnbsl)
# one "accuracy" per each trial
assert_equal(results.shape, (len(dataset), 2))
# with accuracies the same in both searchlights since the same
# features were to be selected in both cases due too large radii
errors_dataset = cv(dataset)
assert_array_equal(errors_dataset.samples[:, 0], results.samples[:, 0])
assert_array_equal(errors_dataset.samples[:, 0], results.samples[:, 1])
# and error matching (up to precision) the one if we run with default error function
assert_array_almost_equal(np.mean(results.targets[:, None] != results.samples, axis=0)[0],
np.mean(cv_error(dataset)))
def test_eep_load():
eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin'))
ds = [ eep_dataset(source, targets=[1, 2]) for source in
(eb, os.path.join(pymvpa_dataroot, 'eep.bin')) ]
for d in ds:
assert_equal(d.nsamples, 2)
assert_equal(d.nfeatures, 128)
assert_equal(np.unique(d.fa.channels[4*23:4*23+4]), 'Pz')
assert_array_almost_equal([np.arange(-0.002, 0.005, 0.002)] * 32,
d.a.mapper.reverse1(d.fa.timepoints))
def test_eep_load():
eb = EEPBin(pathjoin(pymvpa_dataroot, 'eep.bin'))
ds = [ eep_dataset(source, targets=[1, 2]) for source in
(eb, pathjoin(pymvpa_dataroot, 'eep.bin')) ]
for d in ds:
assert_equal(d.nsamples, 2)
assert_equal(d.nfeatures, 128)
assert_equal(np.unique(d.fa.channels[4*23:4*23+4]), 'Pz')
assert_array_almost_equal([np.arange(-0.002, 0.005, 0.002)] * 32,
d.a.mapper.reverse1(d.fa.timepoints))
def test_streamline_equal_mapper(self):
self.build_streamline_things()
self.prototypes_equal = self.dataset.samples
self.pm = PrototypeMapper(similarities=self.similarities,
prototypes=self.prototypes_equal,
demean=False)
self.pm.train(self.dataset.samples)
## debug("MAP","projected data: "+str(self.pm.proj))
# check size:
assert_array_equal(self.pm.proj.shape, (len(self.dataset.samples), len(self.prototypes_equal)*len(self.similarities)))
# test symmetry
assert_array_almost_equal(self.pm.proj, self.pm.proj.T)
def test_streamline_equal_mapper(self):
self.build_streamline_things()
self.prototypes_equal = self.dataset.samples
self.pm = PrototypeMapper(similarities=self.similarities,
prototypes=self.prototypes_equal,
demean=False)
self.pm.train(self.dataset.samples)
## debug("MAP","projected data: "+str(self.pm.proj))
# check size:
assert_array_equal(self.pm.proj.shape, (len(self.dataset.samples), len(self.prototypes_equal)*len(self.similarities)))
# test symmetry
assert_array_almost_equal(self.pm.proj, self.pm.proj.T)
def test_array_collectable():
c = ArrayCollectable()
# empty by default
assert_equal(c.name, None)
assert_equal(c.value, None)
# late assignment
c.name = 'somename'
assert_raises(ValueError, c._set, 12345)
assert_equal(c.value, None)
c.value = np.arange(5)
assert_equal(c.name, 'somename')
assert_array_equal(c.value, np.arange(5))
# immediate content
data = np.random.random(size=(3,10))
c = ArrayCollectable(data.copy(), 'myname',
"This is a test", length=3)
assert_equal(c.name, 'myname')
assert_array_equal(c.value, data)
assert_equal(c.__doc__, "This is a test")
assert_equal(str(c), 'myname')
# repr
from numpy import array
e = eval(repr(c))
assert_equal(e.name, 'myname')
assert_array_almost_equal(e.value, data)
assert_equal(e.__doc__, "This is a test")
# cannot assign array of wrong length
assert_raises(ValueError, c._set, np.arange(5))
assert_equal(len(c), 3)
# shallow copy DOES create a view of value array
c.value = np.arange(3)
d = copy.copy(c)
assert_true(d.value.base is c.value)
# names starting with _ are not allowed
assert_raises(ValueError, c._set_name, "_underscore")
def test_mapper_vs_zscore():
"""Test by comparing to results of elderly z-score function
"""
# data: 40 sample feature line in 20d space (40x20; samples x features)
dss = [
dataset_wizard(np.concatenate([np.arange(40)
for i in range(20)]).reshape(20, -1).T,
targets=1,
chunks=1),
] + datasets.values()
for ds in dss:
ds1 = deepcopy(ds)
ds2 = deepcopy(ds)
zsm = ZScoreMapper(chunks_attr=None)
assert_raises(RuntimeError, zsm.forward, ds1.samples)
idhashes = (idhash(ds1), idhash(ds1.samples))
zsm.train(ds1)
idhashes_train = (idhash(ds1), idhash(ds1.samples))
assert_equal(idhashes, idhashes_train)
# forward dataset
ds1z_ds = zsm.forward(ds1)
idhashes_forwardds = (idhash(ds1), idhash(ds1.samples))
# must not modify samples in place!
assert_equal(idhashes, idhashes_forwardds)
# forward samples explicitly
ds1z = zsm.forward(ds1.samples)
idhashes_forward = (idhash(ds1), idhash(ds1.samples))
assert_equal(idhashes, idhashes_forward)
zscore(ds2, chunks_attr=None)
assert_array_almost_equal(ds1z, ds2.samples)
assert_array_equal(ds1.samples, ds.samples)
def test_symmetry(self):
self.build_vector_based_pm()
assert_array_almost_equal(self.pm.proj[:,self.samples.shape[0]],
self.pm.proj.T[self.samples.shape[0],:])
assert_array_equal(self.pm.proj[:,self.samples.shape[0]],
self.pm.proj.T[self.samples.shape[0],:])
def test_zscore():
"""Test z-scoring transformation
"""
# dataset: mean=2, std=1
samples = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)).\
reshape((16, 1))
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
assert_equal(data.samples.mean(), 2.0)
assert_equal(data.samples.std(), 1.0)
data_samples = data.samples.copy()
zscore(data, chunks_attr='chunks')
# copy should stay intact
assert_equal(data_samples.mean(), 2.0)
assert_equal(data_samples.std(), 1.0)
# we should be able to operate on ndarrays
# But we can't change type inplace for an array, can't we?
assert_raises(TypeError, zscore, data_samples, chunks_attr=None)
# so lets do manually
data_samples = data_samples.astype(float)
zscore(data_samples, chunks_attr=None)
assert_array_equal(data.samples, data_samples)
# check z-scoring
check = np.array([-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0],
dtype='float64').reshape(16, 1)
assert_array_equal(data.samples, check)
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
zscore(data, chunks_attr=None)
assert_array_equal(data.samples, check)
# check z-scoring taking set of labels as a baseline
data = dataset_wizard(samples.copy(),
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples + 1.0)
# check that zscore modifies in-place; only guaranteed if no upcasting is
# necessary
samples = samples.astype('float')
data = dataset_wizard(samples,
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples)
# verify that if param_est is set but chunks_attr is None
# performs zscoring across entire dataset correctly
data = data.copy()
data_01 = data.select({'targets': [0, 1]})
zscore(data_01, chunks_attr=None)
zscore(data, chunks_attr=None, param_est=('targets', [0, 1]))
assert_array_equal(data_01.samples, data.select({'targets': [0, 1]}))
# these might be duplicating code above -- but twice is better than nothing
# dataset: mean=2, std=1
raw = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2))
# dataset: mean=12, std=1
raw2 = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)) + 10
# zscore target
check = [-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0]
ds = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
pristine = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
zm = ZScoreMapper()
# should do global zscore by default
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check]))
# should not modify the source
assert_array_equal(pristine, ds)
# if we tell it a different mean it should obey the order
zm = ZScoreMapper(params=(3,1))
zm.train(ds)
assert_array_almost_equal(zm.forward(ds), np.transpose([check]) - 1 )
assert_array_equal(pristine, ds)
# let's look at chunk-wise z-scoring
ds = dataset_wizard(np.hstack((raw.copy(), raw2.copy())),
targets=range(32),
chunks=[0] * 16 + [1] * 16)
# by default chunk-wise
zm = ZScoreMapper()
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# we should be able to do that same manually
zm = ZScoreMapper(params={0: (2,1), 1: (12,1)})
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# And just a smoke test for warnings reporting whenever # of
# samples per chunk is low.
# on 1 sample per chunk
zds1 = ZScoreMapper(chunks_attr='chunks', auto_train=True)(
ds[[0, -1]])
ok_(np.all(zds1.samples == 0)) # they all should be 0
# on 2 samples per chunk
zds2 = ZScoreMapper(chunks_attr='chunks', auto_train=True)(
ds[[0, 1, -10, -1]])
assert_array_equal(np.unique(zds2.samples), [-1., 1]) # they all should be -1 or 1
# on 3 samples per chunk -- different warning
ZScoreMapper(chunks_attr='chunks', auto_train=True)(
ds[[0, 1, 2, -3, -2, -1]])
# test if std provided as a list not as an array is handled
# properly -- should zscore all features (not just first/none
# as it was before)
ds = dataset_wizard(np.arange(32).reshape((8,-1)),
targets=range(8), chunks=[0] * 8)
means = [0, 1, -10, 10]
std0 = np.std(ds[:, 0]) # std deviation of first one
stds = [std0, 10, .1, 1]
zm = ZScoreMapper(params=(means, stds),
auto_train=True)
dsz = zm(ds)
assert_array_almost_equal((np.mean(ds, axis=0) - np.asanyarray(means))/np.array(stds),
np.mean(dsz, axis=0))
assert_array_almost_equal(np.std(ds, axis=0)/np.array(stds),
np.std(dsz, axis=0))
def test_zscore():
"""Test z-scoring transformation
"""
# dataset: mean=2, std=1
samples = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)).\
reshape((16, 1))
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
assert_equal(data.samples.mean(), 2.0)
assert_equal(data.samples.std(), 1.0)
data_samples = data.samples.copy()
zscore(data, chunks_attr='chunks')
# copy should stay intact
assert_equal(data_samples.mean(), 2.0)
assert_equal(data_samples.std(), 1.0)
# we should be able to operate on ndarrays
# But we can't change type inplace for an array, can't we?
assert_raises(TypeError, zscore, data_samples, chunks_attr=None)
# so lets do manually
data_samples = data_samples.astype(float)
zscore(data_samples, chunks_attr=None)
assert_array_equal(data.samples, data_samples)
# check z-scoring
check = np.array([-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0],
dtype='float64').reshape(16, 1)
assert_array_equal(data.samples, check)
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
zscore(data, chunks_attr=None)
assert_array_equal(data.samples, check)
# check z-scoring taking set of labels as a baseline
data = dataset_wizard(samples.copy(),
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples + 1.0)
# check that zscore modifies in-place; only guaranteed if no upcasting is
# necessary
samples = samples.astype('float')
data = dataset_wizard(samples,
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples)
# these might be duplicating code above -- but twice is better than nothing
# dataset: mean=2, std=1
raw = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2))
# dataset: mean=12, std=1
raw2 = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)) + 10
# zscore target
check = [-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0]
ds = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
pristine = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
zm = ZScoreMapper()
# should do global zscore by default
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check]))
# should not modify the source
assert_array_equal(pristine, ds)
# if we tell it a different mean it should obey the order
zm = ZScoreMapper(params=(3,1))
zm.train(ds)
assert_array_almost_equal(zm.forward(ds), np.transpose([check]) - 1 )
assert_array_equal(pristine, ds)
# let's look at chunk-wise z-scoring
ds = dataset_wizard(np.hstack((raw.copy(), raw2.copy())),
targets=range(32),
chunks=[0] * 16 + [1] * 16)
# by default chunk-wise
zm = ZScoreMapper()
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# we should be able to do that same manually
zm = ZScoreMapper(params={0: (2,1), 1: (12,1)})
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
def test_zscore():
"""Test z-scoring transformation
"""
# dataset: mean=2, std=1
samples = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)).\
reshape((16, 1))
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
assert_equal(data.samples.mean(), 2.0)
assert_equal(data.samples.std(), 1.0)
data_samples = data.samples.copy()
zscore(data, chunks_attr='chunks')
# copy should stay intact
assert_equal(data_samples.mean(), 2.0)
assert_equal(data_samples.std(), 1.0)
# we should be able to operate on ndarrays
# But we can't change type inplace for an array, can't we?
assert_raises(TypeError, zscore, data_samples, chunks_attr=None)
# so lets do manually
data_samples = data_samples.astype(float)
zscore(data_samples, chunks_attr=None)
assert_array_equal(data.samples, data_samples)
# check z-scoring
check = np.array([-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0],
dtype='float64').reshape(16, 1)
assert_array_equal(data.samples, check)
data = dataset_wizard(samples.copy(), targets=range(16), chunks=[0] * 16)
zscore(data, chunks_attr=None)
assert_array_equal(data.samples, check)
# check z-scoring taking set of labels as a baseline
data = dataset_wizard(samples.copy(),
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples + 1.0)
# check that zscore modifies in-place; only guaranteed if no upcasting is
# necessary
samples = samples.astype('float')
data = dataset_wizard(samples,
targets=[0, 2, 2, 2, 1] + [2] * 11,
chunks=[0] * 16)
zscore(data, param_est=('targets', [0, 1]))
assert_array_equal(samples, data.samples)
# these might be duplicating code above -- but twice is better than nothing
# dataset: mean=2, std=1
raw = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2))
# dataset: mean=12, std=1
raw2 = np.array((0, 1, 3, 4, 2, 2, 3, 1, 1, 3, 3, 1, 2, 2, 2, 2)) + 10
# zscore target
check = [-2, -1, 1, 2, 0, 0, 1, -1, -1, 1, 1, -1, 0, 0, 0, 0]
ds = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
pristine = dataset_wizard(raw.copy(), targets=range(16), chunks=[0] * 16)
zm = ZScoreMapper()
# should do global zscore by default
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check]))
# should not modify the source
assert_array_equal(pristine, ds)
# if we tell it a different mean it should obey the order
zm = ZScoreMapper(params=(3, 1))
zm.train(ds)
assert_array_almost_equal(zm.forward(ds), np.transpose([check]) - 1)
assert_array_equal(pristine, ds)
# let's look at chunk-wise z-scoring
ds = dataset_wizard(np.hstack((raw.copy(), raw2.copy())),
targets=range(32),
chunks=[0] * 16 + [1] * 16)
# by default chunk-wise
zm = ZScoreMapper()
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# we should be able to do that same manually
zm = ZScoreMapper(params={0: (2, 1), 1: (12, 1)})
zm.train(ds) # train
assert_array_almost_equal(zm.forward(ds), np.transpose([check + check]))
# And just a smoke test for warnings reporting whenever # of
# samples per chunk is low.
# on 1 sample per chunk
zds1 = ZScoreMapper(chunks_attr='chunks', auto_train=True)(ds[[0, -1]])
ok_(np.all(zds1.samples == 0)) # they all should be 0
# on 2 samples per chunk
zds2 = ZScoreMapper(chunks_attr='chunks',
auto_train=True)(ds[[0, 1, -10, -1]])
assert_array_equal(np.unique(zds2.samples),
[-1., 1]) # they all should be -1 or 1
# on 3 samples per chunk -- different warning
ZScoreMapper(chunks_attr='chunks',
auto_train=True)(ds[[0, 1, 2, -3, -2, -1]])
# test if std provided as a list not as an array is handled
# properly -- should zscore all features (not just first/none
# as it was before)
ds = dataset_wizard(np.arange(32).reshape((8, -1)),
targets=range(8),
chunks=[0] * 8)
means = [0, 1, -10, 10]
std0 = np.std(ds[:, 0]) # std deviation of first one
stds = [std0, 10, .1, 1]
zm = ZScoreMapper(params=(means, stds), auto_train=True)
dsz = zm(ds)
assert_array_almost_equal(
(np.mean(ds, axis=0) - np.asanyarray(means)) / np.array(stds),
np.mean(dsz, axis=0))
assert_array_almost_equal(
np.std(ds, axis=0) / np.array(stds), np.std(dsz, axis=0))
def test_symmetry(self):
self.build_vector_based_pm()
assert_array_almost_equal(self.pm.proj[:,self.samples.shape[0]],
self.pm.proj.T[self.samples.shape[0],:])
assert_array_equal(self.pm.proj[:,self.samples.shape[0]],
self.pm.proj.T[self.samples.shape[0],:])
