pyotc.otc_backend.graph package¶
Submodules¶
pyotc.otc_backend.graph.utils module¶
- pyotc.otc_backend.graph.utils.adj_to_trans(A)[source]¶
Converts an adjacency matrix into a row-stochastic transition matrix.
- Parameters:
A (np.ndarray) – Adjacency matrix of shape (n, n).
- Returns:
Transition matrix of shape (n, n), where each row sums to 1.
- Return type:
np.ndarray
- pyotc.otc_backend.graph.utils.get_01_cost(V1, V2)[source]¶
Computes a binary cost matrix between node features of two graphs based on inequality.
Given two vectors representing features of nodes from two graphs, this function returns a binary cost matrix where each entry is 1 if the corresponding features differ, and 0 otherwise.
- Parameters:
V1 (np.ndarray) – Feature vector for nodes in graph 1, of shape (n1,).
V2 (np.ndarray) – Feature vector for nodes in graph 2, of shape (n2,).
- Returns:
- Binary cost matrix of shape (n1, n2), where entry (i, j) is 1
if V1[i] != V2[j], else 0.
- Return type:
np.ndarray
- pyotc.otc_backend.graph.utils.get_degree_cost(A1, A2)[source]¶
Computes a cost matrix based on squared degree differences between nodes.
- Parameters:
A1 (np.ndarray) – First adjacency matrix of shape (n1, n1).
A2 (np.ndarray) – Second adjacency matrix of shape (n2, n2).
- Returns:
Cost matrix of shape (n1, n2) with squared degree differences.
- Return type:
cost_mat (np.ndarray)
- pyotc.otc_backend.graph.utils.get_sq_cost(V1, V2)[source]¶
Computes a cost matrix based on squared differences between node features of two graphs.
Given two vectors representing node features from two graphs, this function computes a cost matrix where each entry (i, j) is the squared difference between the i-th feature in graph 1 and the j-th feature in graph 2.
- Parameters:
V1 (np.ndarray) – Feature vector for nodes in graph 1, of shape (n1,).
V2 (np.ndarray) – Feature vector for nodes in graph 2, of shape (n2,).
- Returns:
Cost matrix of shape (n1, n2), where entry (i, j) = (V1[i] - V2[j]) ** 2.
- Return type:
np.ndarray
Module contents¶
Routines producing stationary processes on graphs