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* Add doctests to radix_sort()
* fixup! Format Python code with psf/black push
* Update radix_sort.py
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83 lines
2.0 KiB
Python
83 lines
2.0 KiB
Python
from collections import Counter
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from random import random
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from typing import Dict, List, Tuple
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class MarkovChainGraphUndirectedUnweighted:
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"""
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Undirected Unweighted Graph for running Markov Chain Algorithm
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"""
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def __init__(self):
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self.connections = {}
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def add_node(self, node: str) -> None:
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self.connections[node] = {}
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def add_transition_probability(
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self, node1: str, node2: str, probability: float
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) -> None:
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if node1 not in self.connections:
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self.add_node(node1)
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if node2 not in self.connections:
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self.add_node(node2)
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self.connections[node1][node2] = probability
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def get_nodes(self) -> List[str]:
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return list(self.connections)
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def transition(self, node: str) -> str:
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current_probability = 0
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random_value = random()
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for dest in self.connections[node]:
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current_probability += self.connections[node][dest]
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if current_probability > random_value:
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return dest
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def get_transitions(
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start: str, transitions: List[Tuple[str, str, float]], steps: int
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) -> Dict[str, int]:
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"""
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Running Markov Chain algorithm and calculating the number of times each node is
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visited
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>>> transitions = [
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... ('a', 'a', 0.9),
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... ('a', 'b', 0.075),
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... ('a', 'c', 0.025),
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... ('b', 'a', 0.15),
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... ('b', 'b', 0.8),
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... ('b', 'c', 0.05),
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... ('c', 'a', 0.25),
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... ('c', 'b', 0.25),
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... ('c', 'c', 0.5)
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... ]
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>>> result = get_transitions('a', transitions, 5000)
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>>> result['a'] > result['b'] > result['c']
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True
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"""
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graph = MarkovChainGraphUndirectedUnweighted()
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for node1, node2, probability in transitions:
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graph.add_transition_probability(node1, node2, probability)
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visited = Counter(graph.get_nodes())
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node = start
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for _ in range(steps):
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node = graph.transition(node)
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visited[node] += 1
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return visited
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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