mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-28 02:09:08 +00:00
695 lines
23 KiB
Python
695 lines
23 KiB
Python
# Titan Robotics Team 2022: Data Analysis Module
|
|
# Written by Arthur Lu & Jacob Levine
|
|
# Notes:
|
|
# this should be imported as a python module using 'import analysis'
|
|
# this should be included in the local directory or environment variable
|
|
# this module has been optimized for multhreaded computing
|
|
# current benchmark of optimization: 1.33 times faster
|
|
# setup:
|
|
|
|
__version__ = "1.1.5.001"
|
|
|
|
# changelog should be viewed using print(analysis.__changelog__)
|
|
__changelog__ = """changelog:
|
|
1.1.5.002:
|
|
- reduced import list
|
|
- added kmeans clustering engine
|
|
1.1.5.001:
|
|
- simplified regression by using .to(device)
|
|
1.1.5.000:
|
|
- added polynomial regression to regression(); untested
|
|
1.1.4.000:
|
|
- added trueskill()
|
|
1.1.3.002:
|
|
- renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
|
|
1.1.3.001:
|
|
- changed glicko2() to return tuple instead of array
|
|
1.1.3.000:
|
|
- added glicko2_engine class and glicko()
|
|
- verified glicko2() accuracy
|
|
1.1.2.003:
|
|
- fixed elo()
|
|
1.1.2.002:
|
|
- added elo()
|
|
- elo() has bugs to be fixed
|
|
1.1.2.001:
|
|
- readded regrression import
|
|
1.1.2.000:
|
|
- integrated regression.py as regression class
|
|
- removed regression import
|
|
- fixed metadata for regression class
|
|
- fixed metadata for analysis class
|
|
1.1.1.001:
|
|
- regression_engine() bug fixes, now actaully regresses
|
|
1.1.1.000:
|
|
- added regression_engine()
|
|
- added all regressions except polynomial
|
|
1.1.0.007:
|
|
- updated _init_device()
|
|
1.1.0.006:
|
|
- removed useless try statements
|
|
1.1.0.005:
|
|
- removed impossible outcomes
|
|
1.1.0.004:
|
|
- added performance metrics (r^2, mse, rms)
|
|
1.1.0.003:
|
|
- resolved nopython mode for mean, median, stdev, variance
|
|
1.1.0.002:
|
|
- snapped (removed) majority of uneeded imports
|
|
- forced object mode (bad) on all jit
|
|
- TODO: stop numba complaining about not being able to compile in nopython mode
|
|
1.1.0.001:
|
|
- removed from sklearn import * to resolve uneeded wildcard imports
|
|
1.1.0.000:
|
|
- removed c_entities,nc_entities,obstacles,objectives from __all__
|
|
- applied numba.jit to all functions
|
|
- depreciated and removed stdev_z_split
|
|
- cleaned up histo_analysis to include numpy and numba.jit optimizations
|
|
- depreciated and removed all regression functions in favor of future pytorch optimizer
|
|
- depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
|
|
- optimized z_normalize using sklearn.preprocessing.normalize
|
|
- TODO: implement kernel/function based pytorch regression optimizer
|
|
1.0.9.000:
|
|
- refactored
|
|
- numpyed everything
|
|
- removed stats in favor of numpy functions
|
|
1.0.8.005:
|
|
- minor fixes
|
|
1.0.8.004:
|
|
- removed a few unused dependencies
|
|
1.0.8.003:
|
|
- added p_value function
|
|
1.0.8.002:
|
|
- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
|
|
1.0.8.001:
|
|
- refactors
|
|
- bugfixes
|
|
1.0.8.000:
|
|
- depreciated histo_analysis_old
|
|
- depreciated debug
|
|
- altered basic_analysis to take array data instead of filepath
|
|
- refactor
|
|
- optimization
|
|
1.0.7.002:
|
|
- bug fixes
|
|
1.0.7.001:
|
|
- bug fixes
|
|
1.0.7.000:
|
|
- added tanh_regression (logistical regression)
|
|
- bug fixes
|
|
1.0.6.005:
|
|
- added z_normalize function to normalize dataset
|
|
- bug fixes
|
|
1.0.6.004:
|
|
- bug fixes
|
|
1.0.6.003:
|
|
- bug fixes
|
|
1.0.6.002:
|
|
- bug fixes
|
|
1.0.6.001:
|
|
- corrected __all__ to contain all of the functions
|
|
1.0.6.000:
|
|
- added calc_overfit, which calculates two measures of overfit, error and performance
|
|
- added calculating overfit to optimize_regression
|
|
1.0.5.000:
|
|
- added optimize_regression function, which is a sample function to find the optimal regressions
|
|
- optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
|
|
- planned addition: overfit detection in the optimize_regression function
|
|
1.0.4.002:
|
|
- added __changelog__
|
|
- updated debug function with log and exponential regressions
|
|
1.0.4.001:
|
|
- added log regressions
|
|
- added exponential regressions
|
|
- added log_regression and exp_regression to __all__
|
|
1.0.3.008:
|
|
- added debug function to further consolidate functions
|
|
1.0.3.007:
|
|
- added builtin benchmark function
|
|
- added builtin random (linear) data generation function
|
|
- added device initialization (_init_device)
|
|
1.0.3.006:
|
|
- reorganized the imports list to be in alphabetical order
|
|
- added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
|
|
1.0.3.005:
|
|
- major bug fixes
|
|
- updated historical analysis
|
|
- depreciated old historical analysis
|
|
1.0.3.004:
|
|
- added __version__, __author__, __all__
|
|
- added polynomial regression
|
|
- added root mean squared function
|
|
- added r squared function
|
|
1.0.3.003:
|
|
- bug fixes
|
|
- added c_entities
|
|
1.0.3.002:
|
|
- bug fixes
|
|
- added nc_entities, obstacles, objectives
|
|
- consolidated statistics.py to analysis.py
|
|
1.0.3.001:
|
|
- compiled 1d, column, and row basic stats into basic stats function
|
|
1.0.3.000:
|
|
- added historical analysis function
|
|
1.0.2.xxx:
|
|
- added z score test
|
|
1.0.1.xxx:
|
|
- major bug fixes
|
|
1.0.0.xxx:
|
|
- added loading csv
|
|
- added 1d, column, row basic stats
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
"Jacob Levine <jlevine@imsa.edu>",
|
|
)
|
|
|
|
__all__ = [
|
|
'_init_device',
|
|
'load_csv',
|
|
'basic_stats',
|
|
'z_score',
|
|
'z_normalize',
|
|
'histo_analysis',
|
|
'regression',
|
|
'elo',
|
|
'gliko2',
|
|
'trueskill',
|
|
'r_squared',
|
|
'mse',
|
|
'rms',
|
|
'Regression',
|
|
'Gliko2'
|
|
# all statistics functions left out due to integration in other functions
|
|
]
|
|
|
|
# now back to your regularly scheduled programming:
|
|
|
|
# imports (now in alphabetical order! v 1.0.3.006):
|
|
|
|
import csv
|
|
import numba
|
|
from numba import jit
|
|
import numpy as np
|
|
import math
|
|
try:
|
|
from analysis import trueskill as Trueskill
|
|
except:
|
|
import trueskill as Trueskill
|
|
import sklearn
|
|
from sklearn import *
|
|
import torch
|
|
|
|
class error(ValueError):
|
|
pass
|
|
|
|
def _init_device(): # initiates computation device for ANNs
|
|
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
|
|
return device
|
|
|
|
@jit(forceobj=True)
|
|
def load_csv(filepath):
|
|
with open(filepath, newline='') as csvfile:
|
|
file_array = np.array(list(csv.reader(csvfile)))
|
|
csvfile.close()
|
|
return file_array
|
|
|
|
# expects 1d array
|
|
@jit(forceobj=True)
|
|
def basic_stats(data):
|
|
|
|
data_t = np.array(data).astype(float)
|
|
|
|
_mean = mean(data_t)
|
|
_median = median(data_t)
|
|
_stdev = stdev(data_t)
|
|
_variance = variance(data_t)
|
|
|
|
return _mean, _median, _stdev, _variance
|
|
|
|
# returns z score with inputs of point, mean and standard deviation of spread
|
|
@jit(forceobj=True)
|
|
def z_score(point, mean, stdev):
|
|
score = (point - mean) / stdev
|
|
|
|
return score
|
|
|
|
# expects 2d array, normalizes across all axes
|
|
@jit(forceobj=True)
|
|
def z_normalize(array, *args):
|
|
|
|
array = np.array(array)
|
|
for arg in args:
|
|
array = sklearnpreprocessing.normalize(array, axis = arg)
|
|
|
|
return array
|
|
|
|
@jit(forceobj=True)
|
|
# expects 2d array of [x,y]
|
|
def histo_analysis(hist_data):
|
|
|
|
hist_data = np.array(hist_data)
|
|
derivative = np.array(len(hist_data) - 1, dtype = float)
|
|
t = np.diff(hist_data)
|
|
derivative = t[1] / t[0]
|
|
np.sort(derivative)
|
|
|
|
return basic_stats(derivative)[0], basic_stats(derivative)[3]
|
|
|
|
@jit(forceobj=True)
|
|
def regression(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01, power_limit = None): # inputs, outputs expects N-D array
|
|
|
|
if power_limit == None:
|
|
power_limit = len(outputs)
|
|
else:
|
|
power_limit += 1
|
|
|
|
regressions = []
|
|
Regression.set_device(device)
|
|
|
|
if 'lin' in args:
|
|
|
|
model = Regression.SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
|
|
regressions.append((model[0].parameters, model[1][::-1][0]))
|
|
|
|
if 'log' in args:
|
|
|
|
model = Regression.SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
|
|
regressions.append((model[0].parameters, model[1][::-1][0]))
|
|
|
|
if 'exp' in args:
|
|
|
|
model = Regression.SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
|
|
regressions.append((model[0].parameters, model[1][::-1][0]))
|
|
|
|
if 'ply' in args:
|
|
|
|
plys = []
|
|
|
|
for i in range(2, power_limit):
|
|
|
|
model = Regression.SGDTrain(Regression.PolyRegKernel(len(inputs),i), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations_ply * 10 ** i, learning_rate=lr_ply * 10 ** -i, return_losses=True)
|
|
plys.append((model[0].parameters, model[1][::-1][0]))
|
|
|
|
regressions.append(plys)
|
|
|
|
if 'sig' in args:
|
|
|
|
model = Regression.SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
|
|
regressions.append((model[0].parameters, model[1][::-1][0]))
|
|
|
|
return regressions
|
|
|
|
@jit(nopython=True)
|
|
def elo(starting_score, opposing_scores, observed, N, K):
|
|
|
|
expected = 1/(1+10**((np.array(opposing_scores) - starting_score)/N))
|
|
|
|
return starting_score + K*(np.sum(observed) - np.sum(expected))
|
|
|
|
@jit(forceobj=True)
|
|
def gliko2(starting_score, starting_rd, starting_vol, opposing_scores, opposing_rd, observations):
|
|
|
|
player = Gliko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
|
|
|
player.update_player([x for x in opposing_scores], [x for x in opposing_rd], observations)
|
|
|
|
return (player.rating, player.rd, player.vol)
|
|
|
|
@jit(forceobj=True)
|
|
def trueskill(teams_data, observations):#teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
|
|
|
|
team_ratings = []
|
|
|
|
for team in teams_data:
|
|
team_temp = []
|
|
for player in team:
|
|
if player != None:
|
|
player = Trueskill.Rating(player[0], player[1])
|
|
team_temp.append(player)
|
|
else:
|
|
player = Trueskill.Rating()
|
|
team_temp.append(player)
|
|
team_ratings.append(team_temp)
|
|
|
|
return Trueskill.rate(teams_data, observations)
|
|
|
|
@jit(forceobj=True)
|
|
def r_squared(predictions, targets): # assumes equal size inputs
|
|
|
|
return sklearn.metrics.r2_score(np.array(targets), np.array(predictions))
|
|
|
|
@jit(forceobj=True)
|
|
def mse(predictions, targets):
|
|
|
|
return sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions))
|
|
|
|
@jit(forceobj=True)
|
|
def rms(predictions, targets):
|
|
|
|
return math.sqrt(sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions)))
|
|
|
|
@jit(nopython=True)
|
|
def mean(data):
|
|
|
|
return np.mean(data)
|
|
|
|
@jit(nopython=True)
|
|
def median(data):
|
|
|
|
return np.median(data)
|
|
|
|
@jit(nopython=True)
|
|
def stdev(data):
|
|
|
|
return np.std(data)
|
|
|
|
@jit(nopython=True)
|
|
def variance(data):
|
|
|
|
return np.var(data)
|
|
|
|
def kmeans(data, kernel=sklearn.cluster.KMeans()):
|
|
|
|
kernel.fit(data)
|
|
predictions = kernel.predict(data)
|
|
centers = kernel.cluster_centers_
|
|
|
|
return centers, predictions
|
|
|
|
class Regression:
|
|
|
|
# Titan Robotics Team 2022: CUDA-based Regressions Module
|
|
# Written by Arthur Lu & Jacob Levine
|
|
# Notes:
|
|
# this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
|
|
# this module is cuda-optimized and vectorized (except for one small part)
|
|
# setup:
|
|
|
|
__version__ = "1.0.0.002"
|
|
|
|
# changelog should be viewed using print(analysis.regression.__changelog__)
|
|
__changelog__ = """
|
|
1.0.0.002:
|
|
-Added more parameters to log, exponential, polynomial
|
|
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
|
|
to train the scaling and shifting of sigmoids
|
|
|
|
1.0.0.001:
|
|
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
|
|
-already vectorized (except for polynomial generation) and CUDA-optimized
|
|
"""
|
|
|
|
__author__ = (
|
|
"Jacob Levine <jlevine@imsa.edu>",
|
|
"Arthur Lu <learthurgo@gmail.com>"
|
|
)
|
|
|
|
__all__ = [
|
|
'factorial',
|
|
'take_all_pwrs',
|
|
'num_poly_terms',
|
|
'set_device',
|
|
'LinearRegKernel',
|
|
'SigmoidalRegKernel',
|
|
'LogRegKernel',
|
|
'PolyRegKernel',
|
|
'ExpRegKernel',
|
|
'SigmoidalRegKernelArthur',
|
|
'SGDTrain',
|
|
'CustomTrain'
|
|
]
|
|
|
|
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
|
|
|
|
#todo: document completely
|
|
|
|
def set_device(new_device):
|
|
global device
|
|
device=new_device
|
|
|
|
class LinearRegKernel():
|
|
parameters= []
|
|
weights=None
|
|
bias=None
|
|
def __init__(self, num_vars):
|
|
self.weights=torch.rand(num_vars, requires_grad=True, device=device)
|
|
self.bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.bias]
|
|
def forward(self,mtx):
|
|
long_bias=self.bias.repeat([1,mtx.size()[1]])
|
|
return torch.matmul(self.weights,mtx)+long_bias
|
|
|
|
class SigmoidalRegKernel():
|
|
parameters= []
|
|
weights=None
|
|
bias=None
|
|
sigmoid=torch.nn.Sigmoid()
|
|
def __init__(self, num_vars):
|
|
self.weights=torch.rand(num_vars, requires_grad=True, device=device)
|
|
self.bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.bias]
|
|
def forward(self,mtx):
|
|
long_bias=self.bias.repeat([1,mtx.size()[1]])
|
|
return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
|
|
|
|
class SigmoidalRegKernelArthur():
|
|
parameters= []
|
|
weights=None
|
|
in_bias=None
|
|
scal_mult=None
|
|
out_bias=None
|
|
sigmoid=torch.nn.Sigmoid()
|
|
def __init__(self, num_vars):
|
|
self.weights=torch.rand(num_vars, requires_grad=True, device=device)
|
|
self.in_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.scal_mult=torch.rand(1, requires_grad=True, device=device)
|
|
self.out_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
|
|
def forward(self,mtx):
|
|
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
|
|
long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
|
|
return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
|
|
|
|
class LogRegKernel():
|
|
parameters= []
|
|
weights=None
|
|
in_bias=None
|
|
scal_mult=None
|
|
out_bias=None
|
|
def __init__(self, num_vars):
|
|
self.weights=torch.rand(num_vars, requires_grad=True, device=device)
|
|
self.in_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.scal_mult=torch.rand(1, requires_grad=True, device=device)
|
|
self.out_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
|
|
def forward(self,mtx):
|
|
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
|
|
long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
|
|
return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
|
|
|
|
class ExpRegKernel():
|
|
parameters= []
|
|
weights=None
|
|
in_bias=None
|
|
scal_mult=None
|
|
out_bias=None
|
|
def __init__(self, num_vars):
|
|
self.weights=torch.rand(num_vars, requires_grad=True, device=device)
|
|
self.in_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.scal_mult=torch.rand(1, requires_grad=True, device=device)
|
|
self.out_bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
|
|
def forward(self,mtx):
|
|
long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
|
|
long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
|
|
return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
|
|
|
|
class PolyRegKernel():
|
|
parameters= []
|
|
weights=None
|
|
bias=None
|
|
power=None
|
|
def __init__(self, num_vars, power):
|
|
self.power=power
|
|
num_terms=self.num_poly_terms(num_vars, power)
|
|
self.weights=torch.rand(num_terms, requires_grad=True, device=device)
|
|
self.bias=torch.rand(1, requires_grad=True, device=device)
|
|
self.parameters=[self.weights,self.bias]
|
|
def num_poly_terms(self,num_vars, power):
|
|
if power == 0:
|
|
return 0
|
|
return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
|
|
def factorial(self,n):
|
|
if n==0:
|
|
return 1
|
|
else:
|
|
return n*self.factorial(n-1)
|
|
def take_all_pwrs(self, vec, pwr):
|
|
#todo: vectorize (kinda)
|
|
combins=torch.combinations(vec, r=pwr, with_replacement=True)
|
|
out=torch.ones(combins.size()[0]).to(device).to(torch.float)
|
|
for i in torch.t(combins).to(device).to(torch.float):
|
|
out *= i
|
|
if pwr == 1:
|
|
return out
|
|
else:
|
|
return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
|
|
def forward(self,mtx):
|
|
#TODO: Vectorize the last part
|
|
cols=[]
|
|
for i in torch.t(mtx):
|
|
cols.append(self.take_all_pwrs(i,self.power))
|
|
new_mtx=torch.t(torch.stack(cols))
|
|
long_bias=self.bias.repeat([1,mtx.size()[1]])
|
|
return torch.matmul(self.weights,new_mtx)+long_bias
|
|
|
|
def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
|
|
optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
|
|
data_cuda=data.to(device)
|
|
ground_cuda=ground.to(device)
|
|
if (return_losses):
|
|
losses=[]
|
|
for i in range(iterations):
|
|
with torch.set_grad_enabled(True):
|
|
optim.zero_grad()
|
|
pred=kernel.forward(data_cuda)
|
|
ls=loss(pred,ground_cuda)
|
|
losses.append(ls.item())
|
|
ls.backward()
|
|
optim.step()
|
|
return [kernel,losses]
|
|
else:
|
|
for i in range(iterations):
|
|
with torch.set_grad_enabled(True):
|
|
optim.zero_grad()
|
|
pred=kernel.forward(data_cuda)
|
|
ls=loss(pred,ground_cuda)
|
|
ls.backward()
|
|
optim.step()
|
|
return kernel
|
|
|
|
def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
|
|
data_cuda=data.to(device)
|
|
ground_cuda=ground.to(device)
|
|
if (return_losses):
|
|
losses=[]
|
|
for i in range(iterations):
|
|
with torch.set_grad_enabled(True):
|
|
optim.zero_grad()
|
|
pred=kernel.forward(data)
|
|
ls=loss(pred,ground)
|
|
losses.append(ls.item())
|
|
ls.backward()
|
|
optim.step()
|
|
return [kernel,losses]
|
|
else:
|
|
for i in range(iterations):
|
|
with torch.set_grad_enabled(True):
|
|
optim.zero_grad()
|
|
pred=kernel.forward(data_cuda)
|
|
ls=loss(pred,ground_cuda)
|
|
ls.backward()
|
|
optim.step()
|
|
return kernel
|
|
|
|
class Gliko2:
|
|
|
|
_tau = 0.5
|
|
|
|
def getRating(self):
|
|
return (self.__rating * 173.7178) + 1500
|
|
|
|
def setRating(self, rating):
|
|
self.__rating = (rating - 1500) / 173.7178
|
|
|
|
rating = property(getRating, setRating)
|
|
|
|
def getRd(self):
|
|
return self.__rd * 173.7178
|
|
|
|
def setRd(self, rd):
|
|
self.__rd = rd / 173.7178
|
|
|
|
rd = property(getRd, setRd)
|
|
|
|
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
|
|
|
|
self.setRating(rating)
|
|
self.setRd(rd)
|
|
self.vol = vol
|
|
|
|
def _preRatingRD(self):
|
|
|
|
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
|
|
|
|
def update_player(self, rating_list, RD_list, outcome_list):
|
|
|
|
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
|
|
RD_list = [x / 173.7178 for x in RD_list]
|
|
|
|
v = self._v(rating_list, RD_list)
|
|
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
|
|
self._preRatingRD()
|
|
|
|
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
|
|
|
|
tempSum = 0
|
|
for i in range(len(rating_list)):
|
|
tempSum += self._g(RD_list[i]) * \
|
|
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
|
self.__rating += math.pow(self.__rd, 2) * tempSum
|
|
|
|
|
|
def _newVol(self, rating_list, RD_list, outcome_list, v):
|
|
|
|
i = 0
|
|
delta = self._delta(rating_list, RD_list, outcome_list, v)
|
|
a = math.log(math.pow(self.vol, 2))
|
|
tau = self._tau
|
|
x0 = a
|
|
x1 = 0
|
|
|
|
while x0 != x1:
|
|
# New iteration, so x(i) becomes x(i-1)
|
|
x0 = x1
|
|
d = math.pow(self.__rating, 2) + v + math.exp(x0)
|
|
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
|
|
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
|
|
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
|
|
(math.pow(self.__rating, 2) + v) \
|
|
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
|
|
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
|
|
x1 = x0 - (h1 / h2)
|
|
|
|
return math.exp(x1 / 2)
|
|
|
|
def _delta(self, rating_list, RD_list, outcome_list, v):
|
|
|
|
tempSum = 0
|
|
for i in range(len(rating_list)):
|
|
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
|
|
return v * tempSum
|
|
|
|
def _v(self, rating_list, RD_list):
|
|
|
|
tempSum = 0
|
|
for i in range(len(rating_list)):
|
|
tempE = self._E(rating_list[i], RD_list[i])
|
|
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
|
|
return 1 / tempSum
|
|
|
|
def _E(self, p2rating, p2RD):
|
|
|
|
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
|
|
(self.__rating - p2rating)))
|
|
|
|
def _g(self, RD):
|
|
|
|
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
|
|
|
|
def did_not_compete(self):
|
|
|
|
self._preRatingRD()
|