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163 lines
3.9 KiB
Python
163 lines
3.9 KiB
Python
# Titan Robotics Team 2022: Superscript Script
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# setup:
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__version__ = "0.0.0.005"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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0.0.0.005:
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- imported pickle
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- created custom database object
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0.0.0.004:
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- fixed simpleloop to actually return a vector
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0.0.0.003:
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- added metricsloop which is unfinished
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0.0.0.002:
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- added simpleloop which is untested until data is provided
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0.0.0.001:
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- created script
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- added analysis, numba, numpy imports
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"Jacob Levine <jlevine@imsa.edu>",
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)
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__all__ = [
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]
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# imports:
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from analysis import analysis as an
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from numba import jit
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import numpy as np
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import pickle
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import tba_match_result_request as tba
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try:
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from analysis import trueskill as Trueskill
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except:
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import trueskill as Trueskill
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def main():
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pass
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def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
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return_vector = []
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for team in data:
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team_vector = []
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for variable in team:
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variable_vector = []
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for test in tests:
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if(test == "basic" or test == "basic_stats" or test == 0):
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variable_vector.append(an.basic_stats(variable))
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if(test == "histo" or test == "histo_analysis" or test == 1):
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variable_vector.append(an.histo_analysis(variable))
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if(test == "r.lin" or test == "regression.lin" or test == 2):
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variable_vector.append(an.regression("cpu", range(0, len(variable) - 1), variable, ["lin"]))
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if(test == "r.log" or test == "regression.log" or test == 3):
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variable_vector.append(an.regression("cpu", range(0, len(variable) - 1), variable, ["log"]))
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if(test == "r.exp" or test == "regression.exp" or test == 4):
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variable_vector.append(an.regression("cpu", range(0, len(variable) - 1), variable, ["exp"]))
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if(test == "r.ply" or test == "regression.ply" or test == 5):
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variable_vector.append(an.regression("cpu", range(0, len(variable) - 1), variable, ["ply"]))
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if(test == "r.sig" or test == "regression.sig" or test == 6):
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variable_vector.append(an.regression("cpu", range(0, len(variable) - 1), variable, ["sig"]))
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team_vector.append(variable_vector)
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return_vector.append(team_vector)
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return return_vector
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class database:
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data = {}
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elo_starting_score = 1500
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N = 1500
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K = 32
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gl2_starting_score = 1500
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gl2_starting_rd = 350
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gl2_starting_vol = 0.06
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def __init__(self, team_lookup):
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super().__init__()
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for team in team_lookup:
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elo = elo_starting_score
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gl2 = {"score": gl2_starting_score, "rd": gl2_starting_rd, "vol": gl2_starting_vol}
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ts = Trueskill.Rating()
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data[str(team)] = {"elo": elo, "gl2": gl2, "ts": ts}
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def get_team(self, team):
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return data[team]
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def get_elo(self, team):
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return data[team]["elo"]
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def get_gl2(self, team):
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return data[team]["gl2"]
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def get_ts(self, team):
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return data[team]["ts"]
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def set_team(self, team, ndata):
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data[team] = ndata
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def set_elo(self, team, nelo):
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data[team]["elo"] = nelo
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def set_gl2(self, team, ngl2):
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data[team]["gl2"] = ngl2
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def set_ts(self, team, nts):
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data[team]["ts"] = nts
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def save_database(self, location):
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pickle.dump(data, open(location, "wb"))
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def load_database(self, location):
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data = pickle.load(open(location, "rb"))
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def metricsloop(group_data, observations, database, tests): # listener based metrics update
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pass
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main() |