def test_one_on_one_uneven(self):
player1 = analyze.new_player('p1', 0.5)
player2 = analyze.new_player('p2', 0.75)
player3 = analyze.new_player('p3', 0.5)
player4 = analyze.new_player('p4', 0.75)
players = [player1, player2, player3, player4]
chance_of_win = self.markov_analyzer.eval_unordered(players, 0, False)
self.assertGreater(0.5, chance_of_win)
def test_two_players_uneven(self):
player1 = analyze.new_player('p1', 0.5)
player2 = analyze.new_player('p2', 0.75)
players = [player1, player2]
chain = self.markov_analyzer.build_chain(players)
p1_state = chain.get_state( (0,0,0) )
p1_wins = chain.get_state( (0, 'win', 0, 0) )
p2_state = chain.get_state( (1,0,0) )
p2_wins = chain.get_state( (1, 'win', 0, 0) )
self.assertAlmostEqual(0.5, chain.get_transition(p1_state, p1_wins))
self.assertAlmostEqual(0.75, chain.get_transition(p2_state, p2_wins))
self.assertAlmostEqual(0.5, chain.get_transition(p1_state, p2_state))
self.assertAlmostEqual(0.25, chain.get_transition(p2_state, p1_state))
def test_absorb_prob_matches_steady_state(self):
player1 = analyze.new_player('p1', 0.5)
player2 = analyze.new_player('p2', 0.75)
players = [player1, player2]
chain = self.markov_analyzer.build_chain(players)
p1_state = chain.get_state( (0,7,7) )
p1_wins = chain.get_state( (0, 'win', 0, 0) )
p2_state = chain.get_state( (1,0,0) )
p2_wins = chain.get_state( (1, 'win', 0, 0) )
steady = chain.steady_state(p1_state)
probs = chain.get_absorbing_probabilities()
p1_wins_val = steady[p1_wins]
p2_wins_val = steady[p2_wins]
self.assertAlmostEqual(p1_wins_val, probs[p1_state][p1_wins])
self.assertAlmostEqual(p2_wins_val, probs[p1_state][p2_wins])
def test_symbolic_matches_numeric(self):
balls=3
symbolic_player1 = {}
symbolic_player1['sink'] = sympy.Symbol('p1_sink');
symbolic_player1['foul_end'] = sympy.Symbol('p1_foul_end');
symbolic_player2 = {}
symbolic_player2['sink'] = sympy.Symbol('p2_sink');
symbolic_player2['foul_end'] = sympy.Symbol('p2_foul_end');
symbolic_players = [symbolic_player1, symbolic_player2]
symbolic_chain = self.symbolic_analyzer.build_chain(symbolic_players,
ballsPerTeam=balls)
player1 = analyze.new_player('p1', 0.5, 0.1)
player2 = analyze.new_player('p2', 0.75, 0.2)
players = [player1, player2]
chain = self.analyzer.build_chain(players,
ballsPerTeam=balls)
probs = chain.get_absorbing_probabilities()
p1_state = chain.get_state( (0, balls-1, balls-1) )
sym_probs = symbolic_chain.get_absorbing_probabilities()
sym_p1_state = symbolic_chain.get_state( (0, balls-1, balls-1) )
steady = chain.steady_state(p1_state)
symbolic_probs = symbolic_chain.get_absorbing_probabilities()
sym_steady = symbolic_probs[sym_p1_state]
vals = {
symbolic_player1['sink']: 0.5,
symbolic_player1['foul_end']: 0.1,
symbolic_player2['sink']: 0.75,
symbolic_player2['foul_end']: 0.2
}
for end_state in chain.get_end_states():
label = end_state.label
sym_end_state = symbolic_chain.get_state(label)
self.assertAlmostEqual(steady[end_state],
sym_steady[sym_end_state].subs(vals))
def json_to_matches(matches_json):
players_for_each_match = map(players_from_match, matches_json)
all_player_names = set(itertools.chain(*players_for_each_match))
player_lookup = {}
for name in all_player_names:
player_lookup[name] = analyze.new_player(name)
matches = []
for match in matches_json:
newMatch = json_to_match(player_lookup, match)
matches.append(newMatch)
return matches
