def get_sig(self, trajec):
max_sigdim = getattr(self, "max_sigdim")
if max_sigdim is None:
raise ValueError("hyperparameter needed: 'max_sigdim'")
return pathsig.stream2logsig(trajec, self.max_sigdim)
def Logsigastensor(array, degree):
logsig = ts.stream2logsig(array, degree)
dimension = len(array[0])
M = changeofbasis(dimension, degree)
result = np.dot(logsig, M)
return result
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 run_esig(A,n,m,deg,islog):
m = int(m) # m is the dimension
n = int(n)
islog = int(islog)
deg = int(deg)
B = np.reshape(A,(n,m),order='F')
siglength = siglen(m,deg,islog);
sigmat = np.zeros((1,siglength))
if islog:
sigmat[0,:] = ts.stream2logsig(B,deg)
else:
sigmat[0,:] = ts.stream2sig(B,deg)
return(sigmat)
def embed(self, data):
return ts.stream2sig(data.concat_drawing(), self.degree) if not self.log else ts.stream2logsig(data.concat_drawing(), self.degree)
def get_sig(self, path, map):
if isinstance(path, np.ndarray):
return pathsig.stream2logsig(path, self.max_sigdim)
elif path == []:
return []
def _logsig(self, path):
return tosig.stream2logsig(path, self.order)
def logsignature(self, order):
return tosig.stream2logsig(self.path, order)
def compute_log_signature(self, stream, depth):
return tosig.stream2logsig(stream, depth)
def signature(self, degree, log=False):
return ts.stream2sig(self.concat_drawing(),
degree) if not log else ts.stream2logsig(
self.concat_drawing(), degree)