andrewjkuo

# initialise teams
t1 = Team(
    name='Sydney Sixers', bat_order=t1_pids, bat_lookup=bat_lookup, bwl_lookup=bwl_lookup,
    mtc_ply_df=mtc_ply_df
)
t2 = Team(
    name='Brisbane Heat', bat_order=t2_pids, bat_lookup=bat_lookup, bwl_lookup=bwl_lookup,
    mtc_ply_df=mtc_ply_df
)

andrewjkuo

class MatchSimulator:
    def __init__(self, t1, t2, ground, model, ave_score=160, verbose=False, pid2pname=None):
        self.t_bat = t1 # batting team
        self.t_bwl = t2 # bowling team

        self.model = model # ball prediction model
        self.verbose = verbose # flag to run simulation in verbose mode
        self.pid2pname = pid2pname # dictionary mapping player ids to player names (for running in verbose mode)

        self.res_opts = ['res_0', 'res_1', 'res_2', 'res_3', 'res_4', 'res_6', 'res_W', 'res_WD'] # list of ball result options

andrewjkuo

class Team:
    def __init__(self, name, bat_order, bat_lookup, bwl_lookup, mtc_ply_df):
        self.name = name # team name
        self.bat_lookup = bat_lookup # batter stats dictionary
        self.bwl_lookup = bwl_lookup # bowler stats dictionary
        self.mtc_ply_df = mtc_ply_df # historical player statistic (pandas dataframe)
        self.order = {i+1:x for i, x in enumerate(bat_order)} # batting order
        self.p_header = ['W','0','1','2','3','4','6','WD']

        # batting innings state

andrewjkuo

# initial population
pop = [Robot() for x in range(pop_size)]
results = []

# run evolution
for i in tqdm(range(num_gen)):
    scores = np.zeros(pop_size)
    
    # iterate through all robots
    for idx, rob in enumerate(pop):

andrewjkuo

class Robot:
    """
    Class for representing a rubbish-collecting robot
    """
    def __init__(self, p1_dna=None, p2_dna=None, m_rate=mutation_rate, w_pen=wall_penalty, nr_pen=no_rub_penalty, r_score=rubbish_score):
        self.m_rate = m_rate # mutation rate
        self.wall_penalty = w_pen # penalty for crashing into a wall
        self.no_rub_penalty = nr_pen # penalty for picking up rubbish in empty cell
        self.rubbish_score = r_score # reward for picking up rubbish
        self.p1_dna = p1_dna # parent 1 DNA

andrewjkuo

class Environment:
    """
    Class for representing a grid environment full of rubbish. Each cell can be:
    'o': empty
    'x': rubbish
    'w': wall
    """
    def __init__(self, p=rubbish_prob, g_size=grid_size):
        self.p = p # probability of a cell being rubbish
        self.g_size = g_size # excluding walls

andrewjkuo

"""
Import Packages
"""
import numpy as np
from tqdm.notebook import tqdm

"""
Set Parameters
"""
# Simulation Settings