schedule_data['winner'] = schedule_data.apply(get_winner, axis=1)
logging.info('Saving Game Data to Database')
saveFrameToTable(dataFrame=schedule_data, tableName='schedule_data', sqldbName='NBA_data', dbFolder=data_folder, e_option='replace')
######
# Create population parameters
upper_bounds = np.repeat(1, player_char_num + team_char_num + schedule_char_num)
lower_bounds = np.repeat(-1, player_char_num + team_char_num + schedule_char_num)
######
# Create initial population
logging.info('Initializing Population')
population = GA_Funs.create_population(lower_bounds, upper_bounds, n=N_pop, type_fill='random')
######
# Assess initial fitness
fitness = np.repeat(0.0, N_pop)
for n in range(N_pop):
fitness[n] = GA_Funs.get_fitness(population[n,], schedule_data,
team_data1, team_data2, team_cols1, team_cols2,
player_data1, player_data2, player_cols1, player_cols2, health_data)
######
# Start Genetic Algorithm
dbFolder=data_folder,
e_option='replace')
######
# Create population parameters
upper_bounds = np.repeat(1,
player_char_num + team_char_num + schedule_char_num)
lower_bounds = np.repeat(-1,
player_char_num + team_char_num + schedule_char_num)
######
# Create initial population
logging.info('Initializing Population')
population = GA_Funs.create_population(lower_bounds,
upper_bounds,
n=N_pop,
type_fill='random')
######
# Assess initial fitness
fitness = np.repeat(0.0, N_pop)
for n in range(N_pop):
fitness[n] = GA_Funs.get_fitness(population[n, ], schedule_data,
team_data1, team_data2, team_cols1,
team_cols2, player_data1, player_data2,
player_cols1, player_cols2, health_data)
######
# Start Genetic Algorithm
