def changeofbasis(dimension, degree):
liebasis = ts.logsigkeys(dimension, degree)
tensorbasis = ts.sigkeys(dimension, degree)
n = ts.logsigdim(dimension, degree)
m = ts.sigdim(dimension, degree)
matrix = np.zeros((n, m))
pos = tensorbasis.split()
columnindex = []
pos[0] = '(0)'
for row in pos:
row = row.strip('(')
row = row.strip(')')
row = row.split(',')
row = [int(i) for i in row]
columnindex.append(row)
#print(columnindex)
rowindex = liebasis.split()
expand = []
for i in range(n):
B = Lietotensor(rowindex[i])
#print(B)
for j in B:
for k in range(m):
#print(type(j[1]))
#print(type(k))
if j[1] == columnindex[k]:
matrix[i][k] = matrix[i][k] + j[0]
return matrix
def siglen(dim,deg,islog):
dim = int(dim)
deg = int(deg)
islog = int(islog)
if (islog):
return(ts.logsigdim(dim,deg))
else:
return(ts.sigdim(dim,deg))
end
def categorical_path_sig(node_index, mol, adjacent_matrix, index_map, L,
cat_dim, sig_deg, flag):
"""function will output the signatures/logsignatures of categorical path of a node up to radius L
input: node_index
adjacent matrix
mol:molecule information dictionary
index_map: a dictionary assign atom labels to each index of categorical path
L: radius around the node
cat_dim: number of atom types
sig_deg:degree of signature
flag: 0 return signature, 1 return log-signature
output: signatures/logsignatures of the all categorical paths of the node up to length L
has shape (m,n), m depends on the number of paths at each L, n depends on the cat_dim and sig_deg
"""
sig_dim = ts.sigdim(cat_dim, sig_deg)
logsig_dim = ts.logsigdim(cat_dim, sig_deg)
signatures = None
log_signatures = None
if (flag == 0):
for i in range(L):
paths = get_paths(node_index, adjacent_matrix, i + 1)
num_path = len(paths)
paths_sig = np.zeros([num_path, sig_dim], dtype=float)
k = 0
for path in paths:
mat = categorical_path(path, mol, cat_dim, index_map, i)
paths_sig[k, ] = ts.stream2sig(np.transpose(mat), sig_deg)
k = k + 1
if signatures is None:
signatures = paths_sig
else:
signatures = np.concatenate(([signatures, paths_sig]), axis=0)
return (signatures)
elif (flag == 1):
for i in range(L):
paths = get_paths(node_index, adjacent_matrix, i + 1)
num_path = len(paths)
paths_logsig = np.zeros([num_path, logsig_dim], dtype=float)
k = 0
for path in paths:
mat = categorical_path(path, mol, cat_dim, index_map, i)
paths_logsig[k, ] = ts.stream2logsig(np.transpose(mat),
sig_deg)
k = k + 1
if log_signatures is None:
log_signatures = paths_logsig
else:
log_signatures = np.concatenate(
([log_signatures, paths_logsig]), axis=0)
return log_signatures
def coordinate_path_sig(node_index, mol, adjacent_matrix, L, sig_deg, flag):
"""
function will output the signatures/logsignatures of coordinate path of a node up to radius L
Input: node_index: starting node
adjacent_matrix: the matrix shows the connection between atoms
mol:molecule information dictionary
L: length of path
sig_deg: degree of signature
flag: 0 return signature, 1 return log_signature
output: signatures/logsignatures of the all coordinate paths of the node up to length L
has shape (m,n), m depends on the number of paths at each L, n= sig_dim/logsig_dim
"""
sig_dim = ts.sigdim(3, sig_deg)
logsig_dim = ts.logsigdim(3, sig_deg)
signatures = None
log_signatures = None
if (flag == 0):
for i in range(L):
paths = get_paths(node_index, adjacent_matrix, i + 1)
num_path = len(paths)
paths_sig = np.zeros([num_path, sig_dim])
#paths_logsig = np.zeros([num_path, logsig_dim])
k = 0
for path in paths:
temp = np.zeros([len(path), 3])
for j in range(len(path)):
temp[j, :] = mol['atoms'][path[j]].features
paths_sig[k, ] = ts.stream2sig(temp, sig_deg)
#paths_logsig[k,] = ts.stream2logsig(temp, sig_deg)
k = k + 1
if signatures is None:
signatures = paths_sig
else:
signatures = np.concatenate(([signatures, paths_sig]), axis=0)
return (signatures)
if (flag == 1):
for i in range(L):
paths = get_paths(node_index, adjacent_matrix, i + 1)
num_path = len(paths)
paths_logsig = np.zeros([num_path, logsig_dim])
k = 0
for path in paths:
temp = np.zeros([len(path), 3])
for j in range(len(path)):
temp[j, :] = mol['atoms'][path[j]].features
paths_logsig[k, ] = ts.stream2logsig(temp, sig_deg)
k = k + 1
if log_signatures is None:
log_signatures = paths_logsig
else:
log_signatures = np.concatenate(
([log_signatures, paths_logsig]), axis=0)
return log_signatures
def log_sig_dim(self, dimension, depth):
"""
Get the number of elements in the log signature
"""
return tosig.logsigdim(dimension, depth)