analysis.py v 1.1.13.006

regression.py v 1.0.0.003
analysis pkg v 1.0.0.8
This commit is contained in:
ltcptgeneral 2020-03-08 16:48:19 -05:00
parent 3827bb7d84
commit 767a1197b3
12 changed files with 70 additions and 51 deletions

View File

@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: analysis
Version: 1.0.0.7
Version: 1.0.0.8
Summary: analysis package developed by Titan Scouting for The Red Alliance
Home-page: https://github.com/titanscout2022/tr2022-strategy
Author: The Titan Scouting Team

View File

@ -8,4 +8,5 @@ analysis/visualization.py
analysis.egg-info/PKG-INFO
analysis.egg-info/SOURCES.txt
analysis.egg-info/dependency_links.txt
analysis.egg-info/requires.txt
analysis.egg-info/top_level.txt

View File

@ -0,0 +1,6 @@
numba
numpy
scipy
scikit-learn
six
matplotlib

View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.13.005"
__version__ = "1.1.13.006"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.13.006:
- cleaned up imports
1.1.13.005:
- cleaned up package
1.1.13.004:
@ -283,10 +285,7 @@ import scipy
from scipy import *
import sklearn
from sklearn import *
try:
from analysis import trueskill as Trueskill
except:
import trueskill as Trueskill
from analysis import trueskill as Trueskill
class error(ValueError):
pass

View File

@ -5,17 +5,20 @@
# this module is cuda-optimized and vectorized (except for one small part)
# setup:
__version__ = "1.0.0.003"
__version__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.003:
1.0.0.004:
- bug fixes
1.0.0.002:
- fixed changelog
1.0.0.003:
- bug fixes
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:
1.0.0.001:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
"""
@ -40,6 +43,8 @@ __all__ = [
'CustomTrain'
]
import torch
global device
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"

View File

@ -7,10 +7,20 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.13.001"
__version__ = "1.1.13.006"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.13.006:
- cleaned up imports
1.1.13.005:
- cleaned up package
1.1.13.004:
- small fixes to regression to improve performance
1.1.13.003:
- filtered nans from regression
1.1.13.002:
- removed torch requirement, and moved Regression back to regression.py
1.1.13.001:
- bug fix with linear regression not returning a proper value
- cleaned up regression
@ -239,7 +249,6 @@ __author__ = (
)
__all__ = [
'_init_device',
'load_csv',
'basic_stats',
'z_score',
@ -260,7 +269,6 @@ __all__ = [
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Regression',
'Glicko2',
# all statistics functions left out due to integration in other functions
]
@ -273,15 +281,11 @@ import csv
import numba
from numba import jit
import numpy as np
import math
import scipy
from scipy import *
import sklearn
from sklearn import *
try:
from analysis import trueskill as Trueskill
except:
import trueskill as Trueskill
from analysis import trueskill as Trueskill
class error(ValueError):
pass
@ -344,15 +348,15 @@ def histo_analysis(hist_data):
def regression(inputs, outputs, args): # inputs, outputs expects N-D array
X = np.array(inputs)
y = np.array(outputs)
regressions = []
if 'lin' in args: # formula: ax + b
try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b):
return a * x + b
@ -369,9 +373,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d):
return a * np.log(b*(x + c)) + d
@ -388,9 +389,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d):
return a * np.exp(b*(x + c)) + d
@ -405,8 +403,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
inputs = [inputs]
outputs = [outputs]
inputs = np.array([inputs])
outputs = np.array([outputs])
plys = []
limit = len(outputs[0])
@ -430,9 +428,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
try:
X = np.array(inputs)
y = np.array(outputs)
def func(x, a, b, c, d):
return a * np.tanh(b*(x + c)) + d

View File

@ -5,17 +5,20 @@
# this module is cuda-optimized and vectorized (except for one small part)
# setup:
__version__ = "1.0.0.003"
__version__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.003:
1.0.0.004:
- bug fixes
1.0.0.002:
- fixed changelog
1.0.0.003:
- bug fixes
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:
1.0.0.001:
-initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-already vectorized (except for polynomial generation) and CUDA-optimized
"""
@ -40,6 +43,8 @@ __all__ = [
'CustomTrain'
]
import torch
global device
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"

Binary file not shown.

Binary file not shown.

Binary file not shown.

View File

@ -2,7 +2,7 @@ import setuptools
setuptools.setup(
name="analysis", # Replace with your own username
version="1.0.0.007",
version="1.0.0.008",
author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance",
@ -10,6 +10,14 @@ setuptools.setup(
long_description_content_type="text/markdown",
url="https://github.com/titanscout2022/tr2022-strategy",
packages=setuptools.find_packages(),
install_requires=[
"numba",
"numpy",
"scipy",
"scikit-learn",
"six",
"matplotlib"
],
license = "GNU General Public License v3.0",
classifiers=[
"Programming Language :: Python :: 3",