def make_resp(filt,
model,
quenched,
K,
mgs,
tag,
COR,
ncor,
n_trial=1,
NOISE=True):
P = COR[0]
CNS = COR[1]
C1 = COR[2]
C2 = COR[3]
print(P.shape)
print(CNS.shape)
print(C1.shape)
print(C2.shape)
GVnn = MGSM.MGSM_gnn(filt, CNS, C1, C2, P, model)
return GVnn
def make_att_resp(filt1,
filt2,
model,
quenched,
K,
mgs,
tag,
COR,
ncor,
qcor,
fcor,
n_trial=1):
P = COR[0]
CNS = COR[1]
C1 = COR[2]
C2 = COR[3]
NCNS = ncor[0]
NC1 = ncor[1]
NC2 = ncor[2]
QCNS = qcor[0]
QC1 = qcor[1]
QC2 = qcor[2]
FCNS = fcor[0]
FC1 = fcor[1]
FC2 = fcor[2]
fullcor = ncor[3]
fullqcor = qcor[3]
print(CNS.shape)
print(C1.shape)
#make sure everything is the right type
assert model in ("ours", "coen_cagli")
assert quenched in (True, False)
assert type(ncor) == list
GV = []
GVnn = []
#run it for each value of k
SP = []
SPnn = []
for k in K:
print(k)
GV.append(
MGSM.MGSM_att_g(filt1, filt2, [CNS, C1, C2],
[NCNS * (k * k), NC1 * (k * k), NC2 * (k * k)],
[QCNS, QC1, QC2], [FCNS, FC1, FC2], P))
GVnn.append(MGSM.MGSM_gnn(filt2, [CNS, C1, C2], P))
SP.append(
MGSM.get_att_CC_seg_weight(filt1, filt2, CNS, C1, C2,
NCNS * (k * k), NC1 * (k * k),
NC2 * (k * k), QCNS, QC1, QC2, FCNS,
FC1, FC2, P))
SPnn.append(MGSM.get_CC_seg_weight(filt2, CNS, C1, C2, P))
GV = np.concatenate(GV)
GVnn = np.concatenate(GVnn)
SP = np.concatenate(SP)
SPnn = np.concatenate(SPnn)
np.savetxt(tag + "_noisy.csv", GV)
np.savetxt(tag + "_clean.csv", GVnn)
np.savetxt(tag + "_SP_noisy.csv", SP)
np.savetxt(tag + "_SP_clean.csv", SPnn)
return GV, GVnn
def make_resp(filt, model, quenched, K, mgs, tag, COR, ncor, n_trial=1):
P = COR[0]
CNS = COR[1]
C1 = COR[2]
C2 = COR[3]
NCNS = ncor[0]
NC1 = ncor[1]
NC2 = ncor[2]
fullcor = ncor[3]
print(CNS.shape)
print(C1.shape)
#make sure everything is the right type
assert model in ("ours", "coen_cagli")
assert quenched in (True, False)
assert type(ncor) == list
GV = []
GVnn = []
#generate samples for each filter set
if quenched:
nftemp = np.tile(filt, [n_trial, 1, 1])
noise = np.reshape(
np.random.multivariate_normal(np.zeros(np.prod(nftemp.shape[1:])),
fullcor, n_trial * filt.shape[0]),
tuple([n_trial * filt.shape[0]]) + filt.shape[1:])
nftemp += noise
else:
nftemp = filt
#run it for each value of k
SP = []
SPnn = []
for k in K:
print(k)
print(nftemp.shape)
GV.append(
MGSM.MGSM_g(nftemp, [CNS, C1, C2],
[NCNS * (k * k), NC1 * (k * k), NC2 * (k * k)], P))
GVnn.append(MGSM.MGSM_gnn(filt, [CNS, C1, C2], P))
if model == "ours":
SP.append(
MGSM.get_noisy_seg_weight(filt, CNS, C1, C2, NCNS * (k * k),
NC1 * (k * k), NC2 * (k * k), P))
SPnn.append(MGSM.get_seg_weight(filt, CNS, C1, C2, P))
elif model == "coen_cagli":
SP.append(
MGSM.get_noisy_CC_seg_weight(filt, CNS, C1, C2, NCNS * (k * k),
NC1 * (k * k), NC2 * (k * k), P))
SPnn.append(MGSM.get_CC_seg_weight(filt, CNS, C1, C2, P))
GV = np.concatenate(GV)
GVnn = np.concatenate(GVnn)
SP = np.concatenate(SP)
SPnn = np.concatenate(SPnn)
np.savetxt(tag + "_noisy.csv", GV)
np.savetxt(tag + "_clean.csv", GVnn)
np.savetxt(tag + "_SP_noisy.csv", SP)
np.savetxt(tag + "_SP_clean.csv", SPnn)
return GV, GVnn