def survivor_dp():
teams_playing_in_week = get_elo_probabilities()
current_states = set([()])
best_log_prob = {(): 0.0}
best_path = {(): []}
for week in range(6):
next_states = set()
next_log_prob_dict = {}
for current_state in current_states:
for team, log_prob in teams_playing_in_week[week]:
if team in current_state:
continue
next_state = tuple(sorted(list(current_state) + [team]))
next_log_prob = best_log_prob[current_state] + log_prob
if next_log_prob > next_log_prob_dict.get(next_state, -1e100):
next_log_prob_dict[next_state] = next_log_prob
best_path[next_state] = best_path[current_state] + [team]
next_states.add(next_state)
# compute best log prob, best path
best_state, best_prob = max(next_log_prob_dict.iteritems(), key=operator.itemgetter(1))
path = best_path[best_state]
print "Best after week ", week, np.exp(best_prob), path
best_log_prob.update(next_log_prob_dict)
current_states = next_states
return current_states, best_log_prob, best_path
def survivor_dp():
teams_playing_in_week = get_elo_probabilities()
current_states = set([()])
best_log_prob = {(): 0.0}
best_path = {(): []}
for week in range(6):
next_states = set()
next_log_prob_dict = {}
for current_state in current_states:
for team, log_prob in teams_playing_in_week[week]:
if team in current_state:
continue
next_state = tuple(sorted(list(current_state) + [team]))
next_log_prob = best_log_prob[current_state] + log_prob
if next_log_prob > next_log_prob_dict.get(next_state, -1e100):
next_log_prob_dict[next_state] = next_log_prob
best_path[next_state] = best_path[current_state] + [team]
next_states.add(next_state)
# compute best log prob, best path
best_state, best_prob = max(next_log_prob_dict.iteritems(),
key=operator.itemgetter(1))
path = best_path[best_state]
print "Best after week ", week, np.exp(best_prob), path
best_log_prob.update(next_log_prob_dict)
current_states = next_states
return current_states, best_log_prob, best_path
import random
from collections import defaultdict
from pprint import pprint as pp
import numpy as np
from scipy import stats
from util import do_cprofile, get_elo_probabilities, get_opponents_by_week
NWEEKS = 17
teams_playing_in_week = get_elo_probabilities()
probability_by_week = []
for team_prob_tuples in teams_playing_in_week:
probability_by_week.append({t: np.exp(prob) for t, prob in team_prob_tuples})
opponents_by_week = get_opponents_by_week(teams_playing_in_week)
# convert team probs into a dict of teams and normalized probabilities
team_sample_probs_by_week = []
for team_prob_tuples in teams_playing_in_week:
t_p_tuples = [(t, np.exp(p) ** 1.5) for t, p in team_prob_tuples]
teams, probs = zip(*t_p_tuples)
normalized_probs = np.array(probs) / sum(probs)
team_sample_probs_by_week.append((teams, normalized_probs))
def repeat_until(pred, fn):
x = fn()
while not pred(x):
from util import get_elo_probabilities, TEAMS
# monte carlo simulation suggests 12.5 for 10 ppl at 0.75, 10.5 for 10 ppl at .7
# lets go for week 12
GAMES = 12
POPULATION = 1000
PRUNE = 0.1
GENERATIONS = 100
#STARTING_PICKS = ['MIA', 'PIT']
STARTING_PICKS = ['DAL', 'NO', 'SEA']
NUM_STARTING_PICKS = len(STARTING_PICKS)
# TODO write out and read in data from other sources (lines?)
teams_playing_in_week = get_elo_probabilities()
week_dicts = []
for team_prob_tuples in teams_playing_in_week:
week_dicts.append({t: prob for t, prob in team_prob_tuples})
def is_valid(genome):
return all(t in week_dicts[i] for i, t in enumerate(genome))
def repeat_until(pred, fn):
x = fn()
while not pred(x):
x = fn()
return x
