Connection
Base URL, credentials and a reachable server.
Product
Product features · Finite Markov distribution steps
01
Keep the initial vector and transition rows together while examining how a proposed iteration preserves probability mass.
Selection
A model available through the configured endpoint.
Capabilities
Context limits and tool support are determined by the model and server.
Finite Markov distribution steps
● ● ●StepMaranta●
A concrete starting point · Finite Markov distribution steps ×
from math import fsum, isfinite
def markov_steps(initial, matrix, steps):
if type(initial) is not list or not 1 <= len(initial) <= 8:
raise ValueError("state count")
n = len(initial)
if type(matrix) is not list or len(matrix) != n or any(
type(row) is not list or len(row) != n for row in matrix):
raise ValueError("square matrix")
rows = [initial] + matrix
if any(type(x) not in (int, float) or not 0 <= x <= 1 or
not isfinite(x) for row in rows for x in row):
raise ValueError("probability values")
if any(abs(fsum(row)-1) > 1e-12 for row in rows):
raise ValueError("row sums")
if type(steps) is not int or not 0 <= steps <= 100:
raise ValueError("step count")
result = initial[:]
for _ in range(steps):
result = [fsum(result[i] * matrix[i][j] for i in range(n))
for j in range(n)]
return result✳Advance a probability vector through up to 100 transitions of a Markov matrix with at most eight states.
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