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