def test_allows_to_set_constant_tau(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.0, 5.7]) * pq.s, t_stop=10.0 * pq.s)
tau = 0.5 * pq.s
expected = sp.array(
[[1.0, 0.40824829046386307],
[0.40824829046386307, 1.0]])
actual = stm.event_synchronization([a, b], tau)
assert_array_almost_equal(expected, actual)
def test_allows_use_of_different_kernel(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([1.0, 5.7]) * pq.s, t_stop=10.0 * pq.s)
tau = 1.0 * pq.s
kernel = sigproc.LaplacianKernel(1.0, normalize=False)
expected = sp.array(
[[1.0, 0.70480122722318095],
[0.70480122722318095, 1.0]])
actual = stm.event_synchronization([a, b], tau, kernel=kernel)
assert_array_almost_equal(expected, actual)
def test_returns_correct_event_synchronization(self):
a = neo.SpikeTrain(sp.array([1.0, 2.5, 6.5]) * pq.s, t_stop=7.0 * pq.s)
b = neo.SpikeTrain(sp.array([5.7, 1.0]) * pq.s, t_stop=10.0 * pq.s)
c = neo.SpikeTrain(sp.array([2.0, 2.1, 5.0]) * pq.s, t_stop=10.0 * pq.s)
expected = sp.array(
[[1.0, 0.81649658092772615, 0.0],
[0.81649658092772615, 1.0, 0.4082482904638631],
[0.0, 0.4082482904638631, 1.0]])
actual = stm.event_synchronization([a, b, c])
assert_array_almost_equal(expected, actual)
* 'hm': :func:`Hunter-Milton similarity measure
<.spike_train_metrics.hunter_milton_similarity>` subtracted from 1
* 'vp': :func:`Victor Purpura's distance
<.spike_train_metrics.victor_purpura_dist>` with :math:`q = 2/\\tau`
* 'vr': :func:`Van Rossum distance <.spike_train_metrics.van_rossum_dist>`
"""
import quantities as pq
import scipy as sp
import sklearn.base
import spykeutils.spike_train_metrics as stm
metric_defs = {
'es': ("Event synchronization",
lambda trains, tau: 1.0 - stm.event_synchronization(trains, tau)),
'hm': ("Hunter-Milton similarity measure",
lambda trains, tau: 1.0 - stm.hunter_milton_similarity(trains, tau)),
'vp': ("Victor-Purpura\'s distance",
lambda trains, tau: stm.victor_purpura_dist(trains, 2.0 / tau)),
'vr': ("Van Rossum distance",
lambda trains, tau: stm.van_rossum_dist(trains, tau))
}
class PrecomputedSpikeTrainMetricApplier(
sklearn.base.BaseEstimator, sklearn.base.TransformerMixin):
""" Precomputes a spike train metric on spike trains and retrieves the
corresponding Gram matrix (the matrix of all pairwise distances) for
requested sets of the initial data.
def calc_similarity(self, trains):
return stm.event_synchronization(trains)
import spykeutils.spike_train_generation as stg
import spykeutils.spike_train_metrics as stm
import sys
import timeit
tau = 5.0 * pq.ms
sampling_rate = 1000 * pq.Hz
metrics = {
"cs": (
r"$D_{\mathrm{CS}}$",
lambda trains: stm.cs_dist(trains, sigproc.CausalDecayingExpKernel(tau), sampling_rate),
),
"es": (r"$S_{\mathrm{ES}}$", lambda trains: stm.event_synchronization(trains, tau, sort=False)),
"hm": (r"$S_{\mathrm{HM}}$", lambda trains: stm.hunter_milton_similarity(trains, tau)),
"norm": (
r"$D_{\mathrm{ND}}$",
lambda trains: stm.norm_dist(trains, sigproc.CausalDecayingExpKernel(tau), sampling_rate),
),
"ss": (r"$S_{S}}$", lambda trains: stm.schreiber_similarity(trains, sigproc.LaplacianKernel(tau), sort=False)),
"vr": (r"$D_{\mathrm{R}}$", lambda trains: stm.van_rossum_dist(trains, tau, sort=False)),
"vp": (r"$D_{\mathrm{V}}$", lambda trains: stm.victor_purpura_dist(trains, 2.0 / tau)),
}
def print_available_metrics():
for key in metrics:
print "%s (%s)" % (key, metrics[key][0])
tau = 5.0 * pq.ms
sampling_rate = 1000 * pq.Hz
def trains_as_multiunits(trains):
half = len(trains) // 2
return {0: trains[:half], 1: trains[half:2 * half]}
metrics = {
'cs': ('Cauchy-Schwarz distance',
lambda trains: stm.cs_dist(
trains, sigproc.CausalDecayingExpKernel(tau), sampling_rate)),
'es': ('event synchronization',
lambda trains: stm.event_synchronization(trains, tau, sort=False)),
'hm': ('Hunter-Milton similarity measure',
lambda trains: stm.hunter_milton_similarity(trains, tau)),
'norm': ('norm distance',
lambda trains: stm.norm_dist(
trains, sigproc.CausalDecayingExpKernel(tau), sampling_rate)),
'ss': ('Schreiber et al. similarity measure',
lambda trains: stm.schreiber_similarity(
trains, sigproc.GaussianKernel(tau), sort=False)),
'vr': ('van Rossum distance',
lambda trains: stm.van_rossum_dist(trains, tau, sort=False)),
'vp': ('Victor-Purpura\'s distance',
lambda trains: stm.victor_purpura_dist(trains, 2.0 / tau)),
'vr_mu': ('van Rossum multi-unit distance',
lambda trains: stm.van_rossum_multiunit_dist(
trains_as_multiunits(trains), 0.5, tau)),
for j in xrange(i, len(trains)):
d[i, j] = d[j, i] = sp.sum(
sp.absolute(binned[0][i] - binned[0][j]) ** exponent)
return d
def normalized_vp_dist(trains, tau):
num_spikes = sp.atleast_2d(sp.asarray([st.size for st in trains]))
normalization = num_spikes + num_spikes.T
normalization[normalization == 0.0] = 1.0
return sp.asfarray(
stm.victor_purpura_dist(trains, 2.0 / tau, sort=False)) / normalization
metrics = {
'D_V': lambda trains, tau: stm.victor_purpura_dist(
trains, 2.0 / tau, sort=False),
'D_V^*': normalized_vp_dist,
'D_R': lambda trains, tau: stm.van_rossum_dist(
trains, tau, sort=False) ** 2 / 2.0,
'D_{R*}': lambda trains, tau: stm.van_rossum_dist(
trains, kernel=sigproc.TriangularKernel(
sp.sqrt(3) * tau, normalize=False),
sort=False),
'D_B': binning_distance,
'D_{B*}': lambda trains, tau: binning_distance(trains, tau, 1),
'D_{ES}': lambda trains, tau: stm.event_synchronization(
trains, tau, sort=False),
'D_{HM}': lambda trains, tau: 1.0 - stm.hunter_milton_similarity(trains, tau)
}
