import numpy as np
# Returns indexes of active & terminal states
def detect_states(matrix):
active, terminal = [], []
for rowN, row in enumerate(matrix):
(active if sum(row) else terminal).append(rowN)
return(active,terminal)
# Convert elements of array in simplest form
def simplest_form(B):
B = B.round().astype(int).A1 # np.matrix --> np.array
gcd = np.gcd.reduce(B)
B = np.append(B, B.sum()) # append the common denom
return (B / gcd).astype(int)
# Finds solution by calculating Absorbing probabilities
def solution(m):
active, terminal = detect_states(m)
if 0 in terminal: # special case when s0 is terminal
return [1] + [0]*len(terminal[1:]) + [1]
m = np.matrix(m, dtype=float)[active, :] # list --> np.matrix (active states only)
comm_denom = np.prod(m.sum(1)) # product of sum of all active rows (used later)
P = m / m.sum(1) # divide by sum of row to convert to probability matrix
Q, R = P[:, active], P[:, terminal] # separate Q & R
I = np.identity(len(Q))
N = (I - Q) ** (-1) # calc fundamental matrix
B = N[0] * R * comm_denom / np.linalg.det(N) # get absorbing probs & get them close to some integer
return simplest_form(B)