def str_to_irrep_np(self, cube_str):
'''
cube_str: string representation of 2-cube state
Returns: numpy matrix
'''
otup, gtup = get_wreath(cube_str)
return self.tup_to_irrep_np(otup, gtup)
def time_irreps(cube, n):
c2 = init_2cube()
cubes = [scramble(c2, 100) for c in range(n)]
wreath_tups = [get_wreath(c) for c in cubes]
npstart = time.time()
for ot, pt in wreath_tups:
cube.tup_to_irrep_np(ot, pt)
npend = time.time()
print('Elapsed: {:4.3f}'.format(npend - npstart))
def str_to_irrep_sp(self, cube_str):
otup, gtup = get_wreath(cube_str)
re = self.yor_dict[gtup]
rem = re.mul(
torch.sparse.FloatTensor(re.indices(), self.cyclic_irreps_re[otup],
re.size()))
imm = re.mul(
torch.sparse.FloatTensor(re.indices(), self.cyclic_irreps_im[otup],
re.size()))
return rem, imm
def eval_cube(c, mat, model_np, model_sp, env_s, env_np, pkls):
idx_to_nbrs, idx_to_cube, idx_to_dist, cube_to_idx = pkls
otup, ptup = get_wreath(c)
cidx = cube_to_idx[c]
np_mat = env_np.tup_irrep_inv_np(otup, ptup)
sp_r, sp_i = env_s.irrep_inv(c)
# numpy model actually computes the transpose of the matrix
npr, npi = model_np.forward(np_mat.T)
spr, spi = model_sp.forward(sp_r, sp_i)
true_val = mat[cidx]
log.info('True: {:.4f} | NP: {:.4f} | SP: {:.4f}'.format(true_val, npr, spr.item()))
def str_to_irrep_sp_inv(self, cube_str):
otup, gtup = get_wreath(cube_str)
wreath_el = WreathCycSn.from_tup(otup, gtup, 3)
oinv, ginv = wreath_el.inv_tup_rep()
re = self.yor_dict[ginv]
rem = re.mul(
torch.sparse.FloatTensor(re.indices(), self.cyclic_irreps_re[oinv],
re.size()))
imm = re.mul(
torch.sparse.FloatTensor(re.indices(), self.cyclic_irreps_im[oinv],
re.size()))
return rem, imm
def str_to_irrep_th(self, cube_str):
otup, gtup = get_wreath(cube_str)
return self.tup_to_irrep_th(otup, gtup)
def _str_irrep_test(self, cube_str):
otup, ptup = get_wreath(cube_str)
return self._irrep_test(otup, ptup)
def str_irrep(self, cube_str):
otup, ptup = get_wreath(cube_str)
return self.irrep(otup, ptup)