alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-27 01:59:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py v 1.1.12.006

Browse Source 
analysis pkg v 1.0.0.003
This commit is contained in:
ltcptgeneral 2020-03-04 21:20:00 -06:00
parent 0b1dd04216
commit db99028661
6 changed files with 32 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
analysis-master/analysis.egg-info/PKG-INFO
View File

@ -1,6 +1,6 @@
Metadata-Version: 2.1 Metadata-Version: 2.1
Name: analysis Name: analysis
Version: 1.0.0.2 Version: 1.0.0.3
Summary: analysis package developed by Titan Scouting for The Red Alliance Summary: analysis package developed by Titan Scouting for The Red Alliance
Home-page: https://github.com/titanscout2022/tr2022-strategy Home-page: https://github.com/titanscout2022/tr2022-strategy
Author: The Titan Scouting Team Author: The Titan Scouting Team

10
analysis-master/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.12.005" __version__ = "1.1.12.006"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.12.006:
- fixed bg with a division by zero in histo_analysis
1.1.12.005: 1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill - fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004: 1.1.12.004:
@ -323,6 +325,8 @@ def z_normalize(array, *args):
# expects 2d array of [x,y] # expects 2d array of [x,y]
def histo_analysis(hist_data): def histo_analysis(hist_data):
if(len(hist_data[0]) > 2):
hist_data = np.array(hist_data) hist_data = np.array(hist_data)
derivative = np.array(len(hist_data) - 1, dtype = float) derivative = np.array(len(hist_data) - 1, dtype = float)
t = np.diff(hist_data) t = np.diff(hist_data)
@ -331,6 +335,10 @@ def histo_analysis(hist_data):
return basic_stats(derivative)[0], basic_stats(derivative)[3] return basic_stats(derivative)[0], basic_stats(derivative)[3]
else:
return None
def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array
regressions = [] regressions = []

10
analysis-master/build/lib/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.12.005" __version__ = "1.1.12.006"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.12.006:
- fixed bg with a division by zero in histo_analysis
1.1.12.005: 1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill - fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004: 1.1.12.004:
@ -323,6 +325,8 @@ def z_normalize(array, *args):
# expects 2d array of [x,y] # expects 2d array of [x,y]
def histo_analysis(hist_data): def histo_analysis(hist_data):
if(len(hist_data[0]) > 2):
hist_data = np.array(hist_data) hist_data = np.array(hist_data)
derivative = np.array(len(hist_data) - 1, dtype = float) derivative = np.array(len(hist_data) - 1, dtype = float)
t = np.diff(hist_data) t = np.diff(hist_data)
@ -331,6 +335,10 @@ def histo_analysis(hist_data):
return basic_stats(derivative)[0], basic_stats(derivative)[3] return basic_stats(derivative)[0], basic_stats(derivative)[3]
else:
return None
def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array
regressions = [] regressions = []

BIN
analysis-master/dist/analysis-1.0.0.3-py3-none-any.whl vendored Normal file
View File

Binary file not shown.

BIN
analysis-master/dist/analysis-1.0.0.3.tar.gz vendored Normal file
View File

Binary file not shown.

2
analysis-master/setup.py
View File

@ -2,7 +2,7 @@ import setuptools
setuptools.setup( setuptools.setup(
name="analysis", # Replace with your own username name="analysis", # Replace with your own username
version="1.0.0.002", version="1.0.0.003",
author="The Titan Scouting Team", author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com", author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance", description="analysis package developed by Titan Scouting for The Red Alliance",