Finite Markov distribution steps
StepMaranta is a Windows AI coding client for discussing finite probability transitions. Keep the matrix, initial state and iteration code together so model suggestions can be checked against the chosen stochastic convention.
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| Review before applying | Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes. |
|---|---|
| Connection | Base URL, credentials and a reachable server. |
| Project | Test zero steps and absorbing states, rejecting negative values and row sums differing from one by more than 1e-12. |
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 resultKeep probability conventions clear through a code change
StepMaranta gives a probability-modeling task a Windows AI coding workspace with project context and a connected-model conversation. Begin with the application's convention for states and transitions, then identify the routine and its consumer. Asking for an explanation before an edit helps distinguish a mismatch in interpretation from an error in the calculation.
A finite Markov-transition task can specify a starting distribution, a row-based transition matrix and a bounded number of steps. Bring the accepted probability range and row-sum tolerance into the request. Ask how the proposed code preserves that contract at zero steps and at an absorbing state, rather than accept output simply because the numbers appear plausible.
Review the resulting change with explicit expected distributions in your own project tests. Check malformed dimensions and negative values separately from valid transition behavior. Keep the original input alongside the output when reviewing mutation. This approach connects a numerical proposal to observable application requirements without presenting the workspace as a statistical validation service.
StepMaranta supports OpenAI-compatible or Anthropic endpoint connections and credentials when needed. You can choose an unrestricted AI model if your endpoint offers one. Restrictions, available tools and context limits depend on that model and server. Your definition of the transition convention and your verification results remain central to accepting the proposed code.
Questions for a transition-model patch
- Which matrix convention should I provide?
State whether each row describes transitions from a state and how the initial distribution is represented. Include a small expected result. This helps the model inspect the implementation without silently substituting a transposed convention that your callers do not use.
- Why check zero steps explicitly?
It makes the unchanged starting distribution an observable requirement and can reveal unintended mutation. State whether a separate result object is needed, then test the returned values and original input with your project's own checks before accepting the proposed implementation.
- How should row-sum tolerance enter the request?
Give the tolerance required by your application and explain what happens outside it. Review validation separately from transition calculation. A model can discuss alternatives, but it should not quietly choose a different acceptance threshold while implementing an unrelated requested change.
- Does a passing absorbing-state example validate the whole model?
No. It checks one useful behavior of the code. Include other relevant fixtures and assess whether the model assumptions fit your application. The workspace supports source review; it does not establish the scientific validity of the probabilities you supply.
StepMaranta · Updated 2026-10-25