tpm
¶
Functions for manipulating transition probability matrices.

pyphi.tpm.
tpm_indices
(tpm)¶ Indices of nodes in the TPM.

pyphi.tpm.
is_state_by_state
(tpm)¶ Return
True
iftpm
is in statebystate form, otherwiseFalse
.

pyphi.tpm.
condition_tpm
(tpm, fixed_nodes, state)¶ Return a TPM conditioned on the given fixed node indices, whose states are fixed according to the given statetuple.
The dimensions of the new TPM that correspond to the fixed nodes are collapsed onto their state, making those dimensions singletons suitable for broadcasting. The number of dimensions of the conditioned TPM will be the same as the unconditioned TPM.

pyphi.tpm.
expand_tpm
(tpm)¶ Broadcast a statebynode TPM so that singleton dimensions are expanded over the full network.

pyphi.tpm.
marginalize_out
(indices, tpm)¶ Marginalize out a node from a TPM.
Parameters:  indices (list[int]) – The indices of nodes to be marginalized out.
 tpm (np.ndarray) – The TPM to marginalize the node out of.
Returns: A TPM with the same number of dimensions, with the nodes marginalized out.
Return type: np.ndarray

pyphi.tpm.
infer_edge
(tpm, a, b, contexts)¶ Infer the presence or absence of an edge from node A to node B.
Let S be the set of all nodes in a network. Let A’ = S  {A}. We call the state of A’ the context C of A. There is an edge from A to B if there exists any context C(A) such that p(B  C(A), A=0) =/= p(B  C(A), A=1).
Parameters:  tpm (np.ndarray) – The TPM in statebynode, ndimensional form.
 a (int) – The index of the putative source node.
 b (int) – The index of the putative sink node.
Returns: True if the edge A>B exists, False otherwise.
Return type: bool

pyphi.tpm.
infer_cm
(tpm)¶ Infer the connectivity matrix associated with a statebynode TPM in ndimensional form.