for j in np.unique(which_neuron[session_st==i])] for k in stim_on[these_trials]]
pseudopop.append(act)
binned = np.concatenate(pseudopop, axis=1).transpose((1, 0, 2))
print('Binned the data')
#%%
nT = binned.shape[2]
nTri = binned.shape[1]
labels = (np.arange(nTri) > (nTri // 2)).astype(int)
# labels = np.random.permutation(np.arange(nTri)>(nTri//2)).astype(int)
times = (np.arange(nT)[None, :] * np.ones((nTri, 1))).astype(int)
clf = assistants.LinearDecoder(binned.shape[0], 1, svm.LinearSVC)
clf.fit(binned[:, :70, :].transpose(1, 2, 0),
labels[:70, None, None] * np.ones((1, nT, 1)),
t_=(np.arange(nT)[None, :] * np.ones((70, 1))).astype(int))
perf = clf.test(binned[:, 70:, :].transpose(1, 2, 0),
labels[70:, None, None] * np.ones((1, nT, 1)),
t_=(np.arange(nT)[None, :] * np.ones(
(nTri - 70, 1))).astype(int))
#%%
data = gio.Dataset.from_readfunc(swa.load_buschman_data,
BASE_DIR,
max_files=np.inf,
seconds=True)
else:
task_dics.append(list(np.setdiff1d(range(num_cond),d)))
# if num_cond>8:
Q = len(task_dics)
D_fake = assistants.Dichotomies(num_cond, task_dics, extra=50)
mi = np.array([fake_task.information(p) for p in D_fake])
midx = np.append(range(Q),np.flip(np.argsort(mi[Q:]))+Q)
# these_dics = args['dichotomies'] + [D_fake.combs[i] for i in midx]
D = assistants.Dichotomies(num_cond, [D_fake.combs[i] for i in midx], extra=0)
# else:
# Q = len(args['dichotomies'])
# D = assistants.Dichotomies(num_cond)
# mi = np.array([fake_task.information(p) for p in D])
# midx = np.arange(D.ntot, dtype=int)
clf = assistants.LinearDecoder(z.shape[1], 1, assistants.MeanClassifier)
gclf = assistants.LinearDecoder(z.shape[1], 1, svm.LinearSVC)
dclf = assistants.LinearDecoder(z.shape[1], D.ntot, svm.LinearSVC)
# clf = LinearDecoder(this_exp.dim_input, 1, MeanClassifier)
# gclf = LinearDecoder(this_exp.dim_input, 1, svm.LinearSVC)
# dclf = LinearDecoder(this_exp.dim_input, D.ntot, svm.LinearSVC)
# K = int(num_cond/2) - 1 # use all but one pairing
K = int(num_cond/4) # use half the pairings
PS = np.zeros(D.ntot)
CCGP = [] #np.zeros((D.ntot, 100))
out_corr = []
spec = []
d = np.zeros((n_compute, D.ntot))
pos_conds = []
# z = linreg.predict(this_exp.train_data[0])@W1.T
n_compute = np.min([5000, z.shape[0]])
idx = np.random.choice(z.shape[0], n_compute, replace=False)
# idx_tst = idx[::4] # save 1/4 for test set
# idx_trn = np.setdiff1d(idx, idx_tst)
cond = this_exp.train_conditions[idx]
# cond = util.decimal(this_exp.train_data[1][idx,...])
num_cond = len(np.unique(cond))
xor = np.where(~(np.isin(range(8), args['dichotomies'][0])
^ np.isin(range(8), args['dichotomies'][1])))[0]
# Loop over dichotomies
D = assistants.Dichotomies(num_cond, args['dichotomies'] + [xor], extra=50)
clf = assistants.LinearDecoder(N, 1, assistants.MeanClassifier)
gclf = assistants.LinearDecoder(N, 1, svm.LinearSVC)
dclf = assistants.LinearDecoder(N, D.ntot, svm.LinearSVC)
# clf = LinearDecoder(this_exp.dim_input, 1, MeanClassifier)
# gclf = LinearDecoder(this_exp.dim_input, 1, svm.LinearSVC)
# dclf = LinearDecoder(this_exp.dim_input, D.ntot, svm.LinearSVC)
# K = int(num_cond/2) - 1 # use all but one pairing
K = int(num_cond / 4) # use half the pairings
PS = np.zeros(D.ntot)
CCGP = np.zeros(D.ntot)
d = np.zeros((n_compute, D.ntot))
pos_conds = []
for i, pos in enumerate(D):
pos_conds.append(pos)
z = emb(this_exp.train_data[1], newf=f)
centroids = np.stack([z[cond==i,:].mean(0) for i in np.unique(cond)])
_, S, _ = la.svd(centroids-centroids.mean(0)[None,:],full_matrices=False)
pr_.append(((S**2).sum()**2)/(S**4).sum())
# # U = [email protected]()@V.T
# U = (z[:ndat,:]+eps1)@V.T
# U = [email protected]
# pcs.append(U)
eps1 = np.random.randn(ndat, dim)*noise
eps2 = np.random.randn(ndat, dim)*noise
clf = assistants.LinearDecoder(dim, 1, assistants.MeanClassifier)
gclf = assistants.LinearDecoder(dim, 1, svm.LinearSVC)
# rclf = svm.LinearSVC()
rclf = linear_model.LogisticRegression()
D_fake = assistants.Dichotomies(len(np.unique(this_exp.train_conditions)), this_exp.task.positives, extra=50)
Q = len(this_exp.task.positives)
mi = np.array([this_exp.task.information(p) for p in D_fake])
midx = np.append(range(Q),np.flip(np.argsort(mi[Q:]))+Q)
# these_dics = args['dichotomies'] + [D_fake.combs[i] for i in midx]
D = assistants.Dichotomies(len(np.unique(cond)), [D_fake.combs[i] for i in midx], extra=0)
PS = []
DCorr_res = []
DCorr_proj = []
DCorr_marg = []