def set_sparse_signals(self): """Set random sparse signals""" self.params_dSs = [self.mu_dSs, self.sigma_dSs] self.params_Ss0 = [self.mu_Ss0, self.sigma_Ss0] self.dSs, self.idxs = sparse_vector([self.Nn, self.Kk], self.params_dSs, seed=self.seed_dSs) # Ss0 is the ideal (learned) background stimulus without noise self.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk], self.idxs, self.params_Ss0, seed=self.seed_Ss0) # The true signal, including background noise self.Ss = self.dSs + self.Ss0_noisy
def set_manual_signals(self):
"""
Set manually-selected sparse signals.
"""
params_dSs = [self.mu_dSs, self.sigma_dSs]
params_Ss0 = [self.mu_Ss0, self.sigma_Ss0]
self.idxs = self.manual_dSs_idxs
self.dSs = manual_sparse_vector(self.Nn,
self.manual_dSs_idxs,
params_dSs,
seed=self.seed_dSs)
# Ss0 is the ideal (learned) background stimulus without noise
self.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk],
self.manual_dSs_idxs,
params_Ss0,
seed=self.seed_Ss0)
# The true signal, including background noise
self.Ss = self.dSs + self.Ss0_noisy
def set_sparse_signals(self):
"""
Set random sparse signals
"""
params_dSs = [self.mu_dSs, self.sigma_dSs]
params_Ss0 = [self.mu_Ss0, self.sigma_Ss0]
self.dSs, self.idxs = sparse_vector([self.Nn, self.Kk],
params_dSs,
seed=self.seed_dSs)
# Replace components with conflicting background odor
if self.Kk_split is not None and self.Kk_split != 0:
assert 0 <= self.Kk_split <= self.Kk, \
"Splitting sparse signal into two levels requires Kk_split" \
" to be non-negative and less than or equal to Kk."
assert self.mu_dSs_2 is not None \
and self.sigma_dSs_2 is not None, \
"Splitting sparse signal into two levels requires that" \
" mu_dSs_2 and sigma_dSs_2 are set."
sp.random.seed(self.seed_dSs)
self.idxs_2 = sp.random.choice(self.idxs[0],
self.Kk_split,
replace=False)
for idx_2 in self.idxs_2:
self.dSs[idx_2] = sp.random.normal(self.mu_dSs_2,
self.sigma_dSs_2)
else:
self.idxs_2 = []
self.Kk_split = 0
# Ss0 is the ideal (learned) background stimulus without noise
self.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk],
self.idxs,
params_Ss0,
seed=self.seed_Ss0)
self.Ss = self.dSs + self.Ss0_noisy
def set_sparse_signals(self):
"""
Set random sparse signals
"""
# Possibly override manual indices
if self.manual_dSs_idxs is not None:
self.set_manual_signals()
return
# Can override Kk_split with this; manually set compelxity of signal 2
if (self.Kk_1 is not None) and (self.Kk_2 is not None):
self.Kk = self.Kk_1 + self.Kk_2
self.Kk_split = self.Kk_2
params_dSs = [self.mu_dSs, self.sigma_dSs]
params_Ss0 = [self.mu_Ss0, self.sigma_Ss0]
self.dSs, self.idxs = sparse_vector([self.Nn, self.Kk],
params_dSs,
seed=self.seed_dSs)
# Replace components with conflicting background odor
if (self.Kk_split is not None) and (self.Kk_split != 0):
assert 0 <= self.Kk_split <= self.Kk, \
"Splitting sparse signal into two levels requires Kk_split" \
" to be non-negative and less than or equal to Kk."
assert self.mu_dSs_2 is not None \
and self.sigma_dSs_2 is not None, \
"Splitting sparse signal into two levels requires that" \
" mu_dSs_2 and sigma_dSs_2 are set."
if self.seed_dSs_2 is None:
# Want both odor 1 and odor 2 to be determined by seed_dSs
sp.random.seed(self.seed_dSs)
self.idxs_2 = sp.random.choice(self.idxs[0],
self.Kk_split,
replace=False)
else:
# Want odor 1 and 2 to be determined by respective seeds.
self.dSs, self.idxs = sparse_vector([self.Nn, self.Kk_1],
params_dSs,
seed=self.seed_dSs)
_, self.idxs_2 = sparse_vector([self.Nn, self.Kk_split],
params_dSs,
seed=self.seed_dSs_2)
# Re-define the full idxs and the odor 2 idxs subset
self.idxs = ((list(self.idxs[0]) + list(self.idxs_2[0])), )
self.idxs_2 = self.idxs_2[0]
for idx_2 in self.idxs_2:
self.dSs[idx_2] = random_matrix(
1, params=[self.mu_dSs_2, self.sigma_dSs_2])
else:
self.idxs_2 = []
self.Kk_split = 0
# Ss0 is the ideal (learned) background stimulus without noise
self.Ss0, self.Ss0_noisy = sparse_vector_bkgrnd([self.Nn, self.Kk],
self.idxs,
params_Ss0,
seed=self.seed_Ss0)
self.Ss = self.dSs + self.Ss0_noisy