def get_match_probabilities(match_fixtures):
feature_vectors = []
net = NeuralNet()
for fixture in tqdm(match_fixtures, desc='Getting match probabilities...'):
home_team, away_team = fixture['home team'], fixture['away team']
feature_vectors.append(
np.hstack((PREDICTED_LINEUPS[home_team],
PREDICTED_LINEUPS[away_team])).reshape((1, 36)))
predictions = net.predict(np.vstack((x for x in feature_vectors)))
match_probabilities = [x for x in predictions]
return match_probabilities
global is_drawing
if event == cv2.EVENT_LBUTTONDOWN:
if is_drawing:
start_point = (x, y)
elif event == cv2.EVENT_MOUSEMOVE:
if is_drawing:
end_point = (x, y)
draw_line(canvas, start_point, end_point)
start_point = end_point
elif event == cv2.EVENT_LBUTTONUP:
is_drawing = False
cv2.namedWindow("Test Canvas")
cv2.setMouseCallback("Test Canvas", on_mouse_events)
while (True):
cv2.imshow("Test Canvas", canvas)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('s'):
is_drawing = True
elif key == ord('c'):
canvas[100:500, 100:500] = 0
elif key == ord('p'):
image = canvas[100:500, 100:500]
result = net.predict(image)
print("PREDICTION : ", result)
cv2.destroyAllWindows()
def main():
bet_tracker = BetTracker()
match_data = read_match_data(season='2017-2018')
player_data = read_player_data(season='2017-2018')
net = NeuralNet()
bank = [100]
all_odds = []
for match in match_data:
print(match['info']['date'], match['info']['home team'],
match['info']['away team'])
home_players_matched = match_lineups_to_fifa_players(
match['info']['home lineup names'],
match['info']['home lineup numbers'],
match['info']['home lineup nationalities'],
constants.LINEUP_TO_PLAYER_TEAM_MAPPINGS['ALL']
[match['info']['home team']], match['info']['season'], player_data)
away_players_matched = match_lineups_to_fifa_players(
match['info']['away lineup names'],
match['info']['away lineup numbers'],
match['info']['away lineup nationalities'],
constants.LINEUP_TO_PLAYER_TEAM_MAPPINGS['ALL']
[match['info']['away team']], match['info']['season'], player_data)
home_feature_vector = create_feature_vector_from_players(
home_players_matched)
away_feature_vector = create_feature_vector_from_players(
away_players_matched)
feature_vector = np.array(home_feature_vector +
away_feature_vector).reshape(-1, 36)
feature_vector = normalise_features(feature_vector)
probabilities = net.predict(feature_vector)
pred_home_odds, pred_draw_odds, pred_away_odds = [
1 / x for x in probabilities[0]
]
home_odds, draw_odds, away_odds = match['info']['home odds'], match[
'info']['draw odds'], match['info']['away odds']
all_odds.append((pred_home_odds, home_odds))
all_odds.append((pred_away_odds, away_odds))
if pred_home_odds < home_odds < 3.2 and 0.02 <= probabilities[0][
0] - 1 / home_odds:
stake = calculate_stake(home_odds,
probability=1 / pred_home_odds,
method='kelly') * bet_tracker.bankroll
profit = stake * home_odds - stake
bet = Bet(true_odds=home_odds,
predicted_odds=pred_home_odds,
stake=stake,
profit=profit,
match=match,
type='home')
bet_tracker.make_bet(bet)
if match['info']['home goals'] > match['info']['away goals']:
bet_tracker.bet_won()
else:
bet_tracker.bet_lost()
bank.append(bet_tracker.bankroll)
elif pred_away_odds < away_odds < 3.2 and 0.02 <= probabilities[0][
2] - 1 / away_odds:
stake = calculate_stake(away_odds,
probability=1 / pred_away_odds,
method='kelly') * bet_tracker.bankroll
profit = stake * away_odds - stake
bet = Bet(true_odds=away_odds,
predicted_odds=pred_away_odds,
stake=stake,
profit=profit,
match=match,
type='away')
bet_tracker.make_bet(bet)
if match['info']['home goals'] < match['info']['away goals']:
bet_tracker.bet_won()
else:
bet_tracker.bet_lost()
bank.append(bet_tracker.bankroll)
return bet_tracker, bank, all_odds
elif event == cv2.EVENT_LBUTTONUP:
is_drawing = False
cv2.namedWindow("Test Canvas")
cv2.setMouseCallback("Test Canvas", on_mouse_events)
counter = 0
while (True):
cv2.imshow("Test Canvas", canvas)
key = cv2.waitKey(1) & 0xFF
if counter == 0:
print(
"Press <q> to exit, <p> to predict, <c> to clear, <mouse left> to draw"
)
counter = 1
if key == ord('q'):
break
elif key == ord('c'):
canvas[100:500, 100:500] = 0
elif key == ord('p'):
image = canvas[100:500, 100:500]
result_cnn = net.predict(image)
result_rnn = net.predict_rnn(image)
print("CNN PREDICTION : ", result_cnn)
print("RNN PREDICTION : ", result_rnn)
cv2.destroyAllWindows()
with our neural network
"""
import numpy as np
from model import NeuralNet
from generate_data import inputs, labels
# Min is 1 and max is 1024
first = 101
last = 1024
X = inputs(first, last)
y = labels(first, last)
model = NeuralNet(input_shape=(10, ))
model.compile(lr=0.001)
model.train(X, y, batch_size=32, epochs=1000)
first_test = 1
last_test = 100
X_test = inputs(first_test, last_test)
y_pred = model.predict(X_test)
iter = range(first_test, last_test + 1)
for i in range(last_test - first_test + 1):
if y_pred[i] == 0:
pred = i + 1
elif y_pred[i] == 1:
pred = "fizz"
elif y_pred[i] == 2:
pred = "buzz"
else:
pred = "fizzbuzz"
print("{}: {}".format(i + 1, pred))
