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analysis.py v 1.1.12.002, superscript.py

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v 0.0.0.003
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art
2020-02-18 20:29:22 -06:00
parent 260eea1fab
commit 75409cfe24
3 changed files with 45 additions and 20 deletions
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data analysis/analysis/__pycache__/analysis.cpython-37.pyc
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data analysis/analysis/analysis.py
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@@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.12.001"
__version__ = "1.1.12.002"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.12.002:
- removed team first time trueskill instantiation in favor of integration in superscript.py
1.1.12.001:
- improved readibility of regression outputs by stripping tensor data
- used map with lambda to acheive the improved readibility
@@ -394,35 +396,31 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
return regressions
@jit(nopython=True)
def elo(starting_score, opposing_scores, observed, N, K):
def elo(starting_score, opposing_score, observed, N, K):
expected = 1/(1+10**((np.array(opposing_scores) - starting_score)/N))
expected = 1/(1+10**((np.array(opposing_score) - 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):
def gliko2(starting_score, starting_rd, starting_vol, opposing_score, 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)
player.update_player([x for x in opposing_score], [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)]]
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)
player = Trueskill.Rating(player[0], player[1])
team_temp.append(player)
team_ratings.append(team_temp)
return Trueskill.rate(teams_data, observations)