def gram(self, X, Y=None, diag=False, logsp=False):
assert (len(np.shape(X)) == 2)
if diag:
assert ()
else:
dists = eucldist(X, Y, power=2.)
assert (not logsp)
return np.where(dists <= self.threshold_sq_eucl_distance, self.spike,
self.non_spike)
def test_eucldist():
a, b = randn(100, 3), randn(200, 3)
variants = ("simple", "extension")
truth_est = []
for (name, pow) in [("sqeuclidean", 2), ("euclidean", 1)]:
for dist_args in [(a, b), (a, a)]:
ground_truth = cdist(*dist_args, name)
for v in variants:
d = eucldist(*dist_args, power=pow, variant=v)
assert (allclose(ground_truth, d, atol=1e-05))
def gram(self, X, Y=None, diag=False, logsp=False):
assert (len(np.shape(X)) == 2)
# if X=Y, use more efficient pdist call which exploits symmetry
if diag:
assert ()
else:
dists = eucldist(X, Y, power=1.)
assert (not logsp)
return exp(-2 * np.sin(np.pi * dists / self.period)**2 / self.ls**2)
def test_eucldist():
a, b = randn(100, 3), randn(200, 3)
ab_cd = cdist(a, b, "sqeuclidean")
ab_v1, ab_v2 = eucldist(a, b, power=2,
variant="simple"), eucldist(a,
b,
power=2,
variant="extension")
aa_cd = cdist(a, a, "sqeuclidean")
aa_v1, aa_v2 = eucldist(a, power=2,
variant="simple"), eucldist(a,
power=2,
variant="extension")
for (ground_truth, est) in [(ab_cd, [ab_v1, ab_v2]),
(aa_cd, [aa_v1, aa_v2])]:
for variant in est:
assert (allclose(ground_truth, variant, atol=1e-05))
def gram(self, X, Y=None, diag=False, logsp=False):
assert (len(np.shape(X)) == 2)
X = X / self.period
if Y is not None:
Y = Y / self.period
if diag:
assert ()
else:
dists = eucldist(X, Y, power=1.)
assert (not logsp)
return exp(-2 * np.sin(np.pi * dists)**2 / self.ls**2)
def gram(self, X, Y=None, diag=False, logsp=False):
assert (len(np.shape(X)) == 2)
inp_dim = np.shape(X)[1]
if diag:
if Y is None:
sq_dists = np.zeros(X.shape[0])
else:
assert (X.shape == Y.shape)
sq_dists = np.sum((X - Y)**2, 1)
else:
sq_dists = eucldist(X, Y, power=2.)
return self.gram_sqdist(sq_dists, inp_dim, logsp=logsp)
def gram(self, X, Y=None, diag=False, logsp=False):
assert (len(np.shape(X)) == 2)
# if X=Y, use more efficient pdist call which exploits symmetry
if diag:
if Y is None:
dists = np.zeros(X.shape[0])
else:
assert (X.shape == Y.shape)
dists = np.sum((X - Y)**2, 1)
else:
dists = sq_dists = eucldist(X, Y, power=1.)
rval = self._const_factor * dists - self._log_norm * np.shape(X)[1]
if not logsp:
return exp(rval)
return rval