def get_stim_cw_neighbors(c, o, n_i, rf, r2=2, cw_r2=None):
_, s, trs = generate_cc_types((n_i,)*c, (rf,)*c, order=o, excl=True)
ind = np.argmin(np.sum(np.abs(s - (n_i/2,)*c), axis=1))
r = np.sqrt(r2)
ds = u.euclidean_distance(s, s[ind])
close_stim_mask = ds <= r
all_cw = trs(s)
ds_cw = u.euclidean_distance(all_cw, all_cw[ind])
if cw_r2 is None:
ds_nextmin = ds_cw[ds_cw > 0]
md = np.min(ds_nextmin)
else:
md = np.sqrt(cw_r2)
md_mask = ds_cw == md
n_md_neigh_close = np.sum(np.logical_and(close_stim_mask, md_mask))
n_md_neigh = np.sum(md_mask)
# blah
ana_c_cw = volume_nball(r, o) - 1
# if o == 1 or (rf > 1 and o < c):
if o < c:
ana_c_cw = 2*r*c
ana_all_cw = _close_words(c, o, n_i, rf=rf, excl=True)
print('close and nn', ana_c_cw, '||', n_md_neigh_close)
print(' nn', ana_all_cw, '||', n_md_neigh)
print('min dist', md)
print('close/all', n_md_neigh_close/n_md_neigh)
return md, n_md_neigh_close, n_md_neigh
def obtain_stretchfactor_cent(cent, rad, trans, filt, n_pts=1000):
pts = generate_points_on_sphere(n_pts, len(cent), cent=cent, rad=rad)
rep_pts, _, _ = simulate_pop_resp(pts, trans, filt, noise_var=0)
rep_cent, _, _ = simulate_pop_resp(cent.reshape((1, -1)), trans, filt,
noise_var=0)
stretches = gu.euclidean_distance(rep_pts, rep_cent)/rad
return stretches
def find_distances(trans_types):
ind_pairs = list(it.combinations(range(len(trans_types)), 2))
dists = np.zeros(len(ind_pairs))
for i, p in enumerate(ind_pairs):
t1 = trans_types[p[0]]
t2 = trans_types[p[1]]
dists[i] = u.euclidean_distance(t1, t2)
return dists
def integ_lattice_in_ball(r, d, eps=.000001):
int_size = int(np.ceil(2 * r + 1))
int_size_ind = np.floor(int_size / 2).astype(int)
midpt = range(int(int_size))[int(int_size_ind / 2)]
pts = np.array(list(it.product(range(int_size), repeat=d)))
pts_est = volume_nball(r, d)
ds = gu.euclidean_distance(pts, (midpt, ) * d)
pts_rea = np.sum(ds <= r + eps)
return pts_est, pts_rea
def _ident_close_words(c, o, n_i, rf, cent=True):
_, s, trs = generate_cc_types((n_i,)*c, (rf,)*c, order=o, excl=True)
ind = np.argmin(np.sum(np.abs(s - (n_i/2,)*c), axis=1))
all_cw = trs(s)
ds_cw = u.euclidean_distance(all_cw, all_cw[ind])
ds_nextmin = ds_cw[ds_cw > 0]
md = np.min(ds_nextmin)
md_mask = ds_cw == md
return s[md_mask], s[ind]
def compute_num_neighbors(c, o, n_i, rf, one_feat=False):
_, s, trs = generate_cc_types((n_i,)*c, (rf,)*c, order=o, excl=True)
ind = np.argmin(np.sum(np.abs(s - (n_i/2,)*c), axis=1))
if one_feat:
fv = s[0, 0]
mask = s[:, 0] != fv
ind = 0
print((n_i-1)*n_i**(c - 1))
else:
mask = np.ones(len(s))
cw = trs(s)
ds = u.euclidean_distance(cw[mask], cw[ind])
ds = ds[ds > 0]
md = np.min(ds)
n_neigh = np.sum(ds == md)
return md, n_neigh
def get_all_distances(self):
dists_all = np.zeros((len(self.rep), len(self.rep)))
for i, stim_i in enumerate(self.rep):
dists_all[i] = u.euclidean_distance(stim_i, self.rep)
return dists_all
def decode_word(word, types):
dists = u.euclidean_distance(word, types)
ind = np.argmin(dists)
return types[ind], ind