mirror of
https://github.com/titanscouting/tra-analysis.git
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tra_analysis v 2.1.0-alpha.1
Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
Arthur Lu
parent
350e0f9ed3
commit
5aca65139e
@ -1,35 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"string = \"3+4+5\"\n",
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"re.sub(\"\\d+[+]{1}\\d+\", string, sum([int(i) for i in re.split(\"[+]{1}\", re.search(\"\\d+[+]{1}\\d+\", string).group())]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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648
analysis-master/tra_analysis/Analysis.py
Normal file
648
analysis-master/tra_analysis/Analysis.py
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# Titan Robotics Team 2022: Analysis Module
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# Written by Arthur Lu
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import Analysis'
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# this should be included in the local directory or environment variable
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# this module has been optimized for multhreaded computing
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "3.0.0"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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3.0.0:
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- exported several submodules to their own files while preserving backwards compatibility:
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- Array
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- ClassificationMetric
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- CorrelationTest
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- KNN
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- NaiveBayes
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- RandomForest
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- RegressionMetric
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- Sort
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- StatisticalTest
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- SVM
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- note: above listed submodules will not be supported in the future
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- future changes to all submodules will be held in their respective changelogs
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- future changes altering the parent package will be held in the __changelog__ of the parent package (in __init__.py)
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- changed reference to module name to Analysis
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2.3.1:
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- fixed bugs in Array class
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2.3.0:
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- overhauled Array class
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2.2.3:
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- fixed spelling of RandomForest
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- made n_neighbors required for KNN
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- made n_classifiers required for SVM
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2.2.2:
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- fixed 2.2.1 changelog entry
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- changed regression to return dictionary
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2.2.1:
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- changed all references to parent package analysis to tra_analysis
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2.2.0:
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- added Sort class
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- added several array sorting functions to Sort class including:
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- quick sort
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- merge sort
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- intro(spective) sort
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- heap sort
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- insertion sort
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- tim sort
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- selection sort
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- bubble sort
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- cycle sort
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- cocktail sort
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- tested all sorting algorithms with both lists and numpy arrays
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- depreciated sort function from Array class
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- added warnings as an import
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2.1.4:
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- added sort and search functions to Array class
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2.1.3:
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- changed output of basic_stats and histo_analysis to libraries
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- fixed __all__
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2.1.2:
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- renamed ArrayTest class to Array
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2.1.1:
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- added add, mul, neg, and inv functions to ArrayTest class
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- added normalize function to ArrayTest class
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- added dot and cross functions to ArrayTest class
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2.1.0:
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- added ArrayTest class
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- added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class
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- added elementwise_stats to ArrayTest which encapsulates elementwise statistics
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- appended to __all__ to reflect changes
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2.0.6:
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- renamed func functions in regression to lin, log, exp, and sig
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2.0.5:
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- moved random_forrest_regressor and random_forrest_classifier to RandomForrest class
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- renamed Metrics to Metric
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- renamed RegressionMetrics to RegressionMetric
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- renamed ClassificationMetrics to ClassificationMetric
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- renamed CorrelationTests to CorrelationTest
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- renamed StatisticalTests to StatisticalTest
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- reflected rafactoring to all mentions of above classes/functions
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2.0.4:
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- fixed __all__ to reflected the correct functions and classes
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- fixed CorrelationTests and StatisticalTests class functions to require self invocation
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- added missing math import
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- fixed KNN class functions to require self invocation
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- fixed Metrics class functions to require self invocation
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- various spelling fixes in CorrelationTests and StatisticalTests
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2.0.3:
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- bug fixes with CorrelationTests and StatisticalTests
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- moved glicko2 and trueskill to the metrics subpackage
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- moved elo to a new metrics subpackage
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2.0.2:
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- fixed docs
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2.0.1:
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- fixed docs
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2.0.0:
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- cleaned up wild card imports with scipy and sklearn
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- added CorrelationTests class
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- added StatisticalTests class
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- added several correlation tests to CorrelationTests
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- added several statistical tests to StatisticalTests
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1.13.9:
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- moved elo, glicko2, trueskill functions under class Metrics
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1.13.8:
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- moved Glicko2 to a seperate package
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1.13.7:
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- fixed bug with trueskill
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1.13.6:
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- cleaned up imports
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1.13.5:
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- cleaned up package
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1.13.4:
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- small fixes to regression to improve performance
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1.13.3:
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- filtered nans from regression
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1.13.2:
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- removed torch requirement, and moved Regression back to regression.py
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1.13.1:
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- bug fix with linear regression not returning a proper value
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- cleaned up regression
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- fixed bug with polynomial regressions
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1.13.0:
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- fixed all regressions to now properly work
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1.12.6:
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- fixed bg with a division by zero in histo_analysis
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1.12.5:
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- fixed numba issues by removing numba from elo, glicko2 and trueskill
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1.12.4:
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- renamed gliko to glicko
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1.12.3:
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- removed depreciated code
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1.12.2:
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- removed team first time trueskill instantiation in favor of integration in superscript.py
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1.12.1:
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- improved readibility of regression outputs by stripping tensor data
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- used map with lambda to acheive the improved readibility
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- lost numba jit support with regression, and generated_jit hangs at execution
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- TODO: reimplement correct numba integration in regression
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1.12.0:
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- temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
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1.11.010:
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- alphabeticaly ordered import lists
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1.11.9:
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- bug fixes
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1.11.8:
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- bug fixes
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1.11.7:
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- bug fixes
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1.11.6:
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- tested min and max
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- bug fixes
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1.11.5:
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- added min and max in basic_stats
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1.11.4:
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- bug fixes
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1.11.3:
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- bug fixes
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1.11.2:
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- consolidated metrics
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- fixed __all__
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1.11.1:
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- added test/train split to RandomForestClassifier and RandomForestRegressor
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1.11.0:
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- added RandomForestClassifier and RandomForestRegressor
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- note: untested
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1.10.0:
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- added numba.jit to remaining functions
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1.9.2:
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- kernelized PCA and KNN
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1.9.1:
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- fixed bugs with SVM and NaiveBayes
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1.9.0:
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- added SVM class, subclasses, and functions
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- note: untested
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1.8.0:
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- added NaiveBayes classification engine
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- note: untested
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1.7.0:
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- added knn()
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- added confusion matrix to decisiontree()
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1.6.2:
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- changed layout of __changelog to be vscode friendly
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1.6.1:
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- added additional hyperparameters to decisiontree()
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1.6.0:
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- fixed __version__
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- fixed __all__ order
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- added decisiontree()
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1.5.3:
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- added pca
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1.5.2:
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- reduced import list
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- added kmeans clustering engine
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1.5.1:
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- simplified regression by using .to(device)
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1.5.0:
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- added polynomial regression to regression(); untested
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1.4.0:
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- added trueskill()
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1.3.2:
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- renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
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1.3.1:
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- changed glicko2() to return tuple instead of array
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1.3.0:
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- added glicko2_engine class and glicko()
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- verified glicko2() accuracy
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1.2.3:
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- fixed elo()
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1.2.2:
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- added elo()
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- elo() has bugs to be fixed
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1.2.1:
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- readded regrression import
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1.2.0:
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- integrated regression.py as regression class
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- removed regression import
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- fixed metadata for regression class
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- fixed metadata for analysis class
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1.1.1:
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- regression_engine() bug fixes, now actaully regresses
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1.1.0:
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- added regression_engine()
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- added all regressions except polynomial
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1.0.7:
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- updated _init_device()
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1.0.6:
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- removed useless try statements
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1.0.5:
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- removed impossible outcomes
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1.0.4:
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- added performance metrics (r^2, mse, rms)
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1.0.3:
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- resolved nopython mode for mean, median, stdev, variance
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1.0.2:
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- snapped (removed) majority of uneeded imports
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- forced object mode (bad) on all jit
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- TODO: stop numba complaining about not being able to compile in nopython mode
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1.0.1:
|
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- removed from sklearn import * to resolve uneeded wildcard imports
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1.0.0:
|
||||
- removed c_entities,nc_entities,obstacles,objectives from __all__
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- applied numba.jit to all functions
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- depreciated and removed stdev_z_split
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- cleaned up histo_analysis to include numpy and numba.jit optimizations
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- depreciated and removed all regression functions in favor of future pytorch optimizer
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- depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
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- optimized z_normalize using sklearn.preprocessing.normalize
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- TODO: implement kernel/function based pytorch regression optimizer
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0.9.0:
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- refactored
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- numpyed everything
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- removed stats in favor of numpy functions
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0.8.5:
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- minor fixes
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0.8.4:
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- removed a few unused dependencies
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0.8.3:
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- added p_value function
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0.8.2:
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- updated __all__ correctly to contain changes made in v 0.8.0 and v 0.8.1
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0.8.1:
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- refactors
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- bugfixes
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0.8.0:
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- depreciated histo_analysis_old
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- depreciated debug
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- altered basic_analysis to take array data instead of filepath
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- refactor
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- optimization
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0.7.2:
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- bug fixes
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0.7.1:
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- bug fixes
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0.7.0:
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- added tanh_regression (logistical regression)
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- bug fixes
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0.6.5:
|
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- added z_normalize function to normalize dataset
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- bug fixes
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0.6.4:
|
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- bug fixes
|
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0.6.3:
|
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- bug fixes
|
||||
0.6.2:
|
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- bug fixes
|
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0.6.1:
|
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- corrected __all__ to contain all of the functions
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0.6.0:
|
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- added calc_overfit, which calculates two measures of overfit, error and performance
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- added calculating overfit to optimize_regression
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0.5.0:
|
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- added optimize_regression function, which is a sample function to find the optimal regressions
|
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- optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
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- planned addition: overfit detection in the optimize_regression function
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0.4.2:
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- added __changelog__
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- updated debug function with log and exponential regressions
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0.4.1:
|
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- added log regressions
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- added exponential regressions
|
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- added log_regression and exp_regression to __all__
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0.3.8:
|
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- added debug function to further consolidate functions
|
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0.3.7:
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- added builtin benchmark function
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- added builtin random (linear) data generation function
|
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- added device initialization (_init_device)
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0.3.6:
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- reorganized the imports list to be in alphabetical order
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- added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
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0.3.5:
|
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- major bug fixes
|
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- updated historical analysis
|
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- depreciated old historical analysis
|
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0.3.4:
|
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- added __version__, __author__, __all__
|
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- added polynomial regression
|
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- added root mean squared function
|
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- added r squared function
|
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0.3.3:
|
||||
- bug fixes
|
||||
- added c_entities
|
||||
0.3.2:
|
||||
- bug fixes
|
||||
- added nc_entities, obstacles, objectives
|
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- consolidated statistics.py to analysis.py
|
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0.3.1:
|
||||
- compiled 1d, column, and row basic stats into basic stats function
|
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0.3.0:
|
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- added historical analysis function
|
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0.2.x:
|
||||
- added z score test
|
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0.1.x:
|
||||
- major bug fixes
|
||||
0.0.x:
|
||||
- added loading csv
|
||||
- added 1d, column, row basic stats
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
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|
||||
__all__ = [
|
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'load_csv',
|
||||
'basic_stats',
|
||||
'z_score',
|
||||
'z_normalize',
|
||||
'histo_analysis',
|
||||
'regression',
|
||||
'Metric',
|
||||
'RegressionMetric',
|
||||
'ClassificationMetric',
|
||||
'kmeans',
|
||||
'pca',
|
||||
'decisiontree',
|
||||
'KNN',
|
||||
'NaiveBayes',
|
||||
'SVM',
|
||||
'RandomForrest',
|
||||
'CorrelationTest',
|
||||
'StatisticalTest',
|
||||
'Array',
|
||||
# all statistics functions left out due to integration in other functions
|
||||
]
|
||||
|
||||
# now back to your regularly scheduled programming:
|
||||
|
||||
# imports (now in alphabetical order! v 0.3.006):
|
||||
|
||||
import csv
|
||||
from tra_analysis.metrics import elo as Elo
|
||||
from tra_analysis.metrics import glicko2 as Glicko2
|
||||
import math
|
||||
import numba
|
||||
from numba import jit
|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy import optimize, stats
|
||||
import sklearn
|
||||
from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble
|
||||
from tra_analysis.metrics import trueskill as Trueskill
|
||||
import warnings
|
||||
|
||||
# import submodules
|
||||
|
||||
from .Array import Array
|
||||
from .ClassificationMetric import ClassificationMetric
|
||||
from .CorrelationTest_obj import CorrelationTest
|
||||
from .KNN_obj import KNN
|
||||
from .NaiveBayes_obj import NaiveBayes
|
||||
from .RandomForest_obj import RandomForest
|
||||
from .RegressionMetric import RegressionMetric
|
||||
from .Sort_obj import Sort
|
||||
from .StatisticalTest_obj import StatisticalTest
|
||||
from .SVM import SVM
|
||||
|
||||
class error(ValueError):
|
||||
pass
|
||||
|
||||
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)
|
||||
_min = npmin(data_t)
|
||||
_max = npmax(data_t)
|
||||
|
||||
return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
|
||||
|
||||
# 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 = sklearn.preprocessing.normalize(array, axis = arg)
|
||||
|
||||
return array
|
||||
|
||||
@jit(forceobj=True)
|
||||
# expects 2d array of [x,y]
|
||||
def histo_analysis(hist_data):
|
||||
|
||||
if len(hist_data[0]) > 2:
|
||||
|
||||
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 {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]}
|
||||
|
||||
else:
|
||||
|
||||
return None
|
||||
|
||||
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:
|
||||
|
||||
def lin(x, a, b):
|
||||
|
||||
return a * x + b
|
||||
|
||||
popt, pcov = scipy.optimize.curve_fit(lin, X, y)
|
||||
|
||||
coeffs = popt.flatten().tolist()
|
||||
regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1]))
|
||||
|
||||
except Exception as e:
|
||||
|
||||
pass
|
||||
|
||||
if 'log' in args: # formula: a log (b(x + c)) + d
|
||||
|
||||
try:
|
||||
|
||||
def log(x, a, b, c, d):
|
||||
|
||||
return a * np.log(b*(x + c)) + d
|
||||
|
||||
popt, pcov = scipy.optimize.curve_fit(log, X, y)
|
||||
|
||||
coeffs = popt.flatten().tolist()
|
||||
regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
|
||||
|
||||
except Exception as e:
|
||||
|
||||
pass
|
||||
|
||||
if 'exp' in args: # formula: a e ^ (b(x + c)) + d
|
||||
|
||||
try:
|
||||
|
||||
def exp(x, a, b, c, d):
|
||||
|
||||
return a * np.exp(b*(x + c)) + d
|
||||
|
||||
popt, pcov = scipy.optimize.curve_fit(exp, X, y)
|
||||
|
||||
coeffs = popt.flatten().tolist()
|
||||
regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
|
||||
|
||||
except Exception as e:
|
||||
|
||||
pass
|
||||
|
||||
if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
|
||||
|
||||
inputs = np.array([inputs])
|
||||
outputs = np.array([outputs])
|
||||
|
||||
plys = {}
|
||||
limit = len(outputs[0])
|
||||
|
||||
for i in range(2, limit):
|
||||
|
||||
model = sklearn.preprocessing.PolynomialFeatures(degree = i)
|
||||
model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression())
|
||||
model = model.fit(np.rot90(inputs), np.rot90(outputs))
|
||||
|
||||
params = model.steps[1][1].intercept_.tolist()
|
||||
params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
|
||||
params = params.flatten().tolist()
|
||||
|
||||
temp = ""
|
||||
counter = 0
|
||||
for param in params:
|
||||
temp += "(" + str(param) + "*x^" + str(counter) + ")"
|
||||
counter += 1
|
||||
plys["x^" + str(i)] = (temp)
|
||||
|
||||
regressions["ply"] = (plys)
|
||||
|
||||
if 'sig' in args: # formula: a tanh (b(x + c)) + d
|
||||
|
||||
try:
|
||||
|
||||
def sig(x, a, b, c, d):
|
||||
|
||||
return a * np.tanh(b*(x + c)) + d
|
||||
|
||||
popt, pcov = scipy.optimize.curve_fit(sig, X, y)
|
||||
|
||||
coeffs = popt.flatten().tolist()
|
||||
regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
|
||||
|
||||
except Exception as e:
|
||||
|
||||
pass
|
||||
|
||||
return regressions
|
||||
|
||||
class Metric:
|
||||
|
||||
def elo(self, starting_score, opposing_score, observed, N, K):
|
||||
|
||||
return Elo.calculate(starting_score, opposing_score, observed, N, K)
|
||||
|
||||
def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
|
||||
|
||||
player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
|
||||
|
||||
player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
|
||||
|
||||
return (player.rating, player.rd, player.vol)
|
||||
|
||||
def trueskill(self, 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:
|
||||
player = Trueskill.Rating(player[0], player[1])
|
||||
team_temp = team_temp + (player,)
|
||||
team_ratings.append(team_temp)
|
||||
|
||||
return Trueskill.rate(team_ratings, ranks=observations)
|
||||
|
||||
@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)
|
||||
|
||||
@jit(nopython=True)
|
||||
def npmin(data):
|
||||
|
||||
return np.amin(data)
|
||||
|
||||
@jit(nopython=True)
|
||||
def npmax(data):
|
||||
|
||||
return np.amax(data)
|
||||
|
||||
@jit(forceobj=True)
|
||||
def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
|
||||
|
||||
kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
|
||||
kernel.fit(data)
|
||||
predictions = kernel.predict(data)
|
||||
centers = kernel.cluster_centers_
|
||||
|
||||
return centers, predictions
|
||||
|
||||
@jit(forceobj=True)
|
||||
def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
|
||||
|
||||
kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
|
||||
|
||||
return kernel.fit_transform(data)
|
||||
|
||||
@jit(forceobj=True)
|
||||
def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
|
||||
model = model.fit(data_train,labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
metrics = ClassificationMetric(predictions, labels_test)
|
||||
|
||||
return model, metrics
|
||||
170
analysis-master/tra_analysis/Array.py
Normal file
170
analysis-master/tra_analysis/Array.py
Normal file
@ -0,0 +1,170 @@
|
||||
# Titan Robotics Team 2022: Array submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import Array'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.Array() here
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
import numpy as np
|
||||
|
||||
class Array(): # tests on nd arrays independent of basic_stats
|
||||
|
||||
def __init__(self, narray):
|
||||
|
||||
self.array = np.array(narray)
|
||||
|
||||
def __str__(self):
|
||||
|
||||
return str(self.array)
|
||||
|
||||
def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized
|
||||
if len(*args) == 0:
|
||||
return np.mean(self.array, axis = axis)
|
||||
else:
|
||||
return np.mean([*args], axis = axis)
|
||||
|
||||
def elementwise_median(self, *args, axis = 0):
|
||||
|
||||
if len(*args) == 0:
|
||||
return np.median(self.array, axis = axis)
|
||||
else:
|
||||
return np.median([*args], axis = axis)
|
||||
|
||||
def elementwise_stdev(self, *args, axis = 0):
|
||||
|
||||
if len(*args) == 0:
|
||||
return np.std(self.array, axis = axis)
|
||||
else:
|
||||
return np.std([*args], axis = axis)
|
||||
|
||||
def elementwise_variance(self, *args, axis = 0):
|
||||
|
||||
if len(*args) == 0:
|
||||
return np.var(self.array, axis = axis)
|
||||
else:
|
||||
return np.var([*args], axis = axis)
|
||||
|
||||
def elementwise_npmin(self, *args, axis = 0):
|
||||
|
||||
if len(*args) == 0:
|
||||
return np.amin(self.array, axis = axis)
|
||||
else:
|
||||
return np.amin([*args], axis = axis)
|
||||
|
||||
def elementwise_npmax(self, *args, axis = 0):
|
||||
|
||||
if len(*args) == 0:
|
||||
return np.amax(self.array, axis = axis)
|
||||
else:
|
||||
return np.amax([*args], axis = axis)
|
||||
|
||||
def elementwise_stats(self, *args, axis = 0):
|
||||
|
||||
_mean = self.elementwise_mean(*args, axis = axis)
|
||||
_median = self.elementwise_median(*args, axis = axis)
|
||||
_stdev = self.elementwise_stdev(*args, axis = axis)
|
||||
_variance = self.elementwise_variance(*args, axis = axis)
|
||||
_min = self.elementwise_npmin(*args, axis = axis)
|
||||
_max = self.elementwise_npmax(*args, axis = axis)
|
||||
|
||||
return _mean, _median, _stdev, _variance, _min, _max
|
||||
|
||||
def __getitem__(self, key):
|
||||
|
||||
return self.array[key]
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
|
||||
self.array[key] == value
|
||||
|
||||
def normalize(self, array):
|
||||
|
||||
a = np.atleast_1d(np.linalg.norm(array))
|
||||
a[a==0] = 1
|
||||
return array / np.expand_dims(a, -1)
|
||||
|
||||
def __add__(self, other):
|
||||
|
||||
return self.array + other.array
|
||||
|
||||
def __sub__(self, other):
|
||||
|
||||
return self.array - other.array
|
||||
|
||||
def __neg__(self):
|
||||
|
||||
return -self.array
|
||||
|
||||
def __abs__(self):
|
||||
|
||||
return abs(self.array)
|
||||
|
||||
def __invert__(self):
|
||||
|
||||
return 1/self.array
|
||||
|
||||
def __mul__(self, other):
|
||||
|
||||
return self.array.dot(other.array)
|
||||
|
||||
def __rmul__(self, other):
|
||||
|
||||
return self.array.dot(other.array)
|
||||
|
||||
def cross(self, other):
|
||||
|
||||
return np.cross(self.array, other.array)
|
||||
|
||||
def sort(self, array): # depreciated
|
||||
warnings.warn("Array.sort has been depreciated in favor of Sort")
|
||||
array_length = len(array)
|
||||
if array_length <= 1:
|
||||
return array
|
||||
middle_index = int(array_length / 2)
|
||||
left = array[0:middle_index]
|
||||
right = array[middle_index:]
|
||||
left = self.sort(left)
|
||||
right = self.sort(right)
|
||||
return self.__merge(left, right)
|
||||
|
||||
|
||||
def __merge(self, left, right):
|
||||
sorted_list = []
|
||||
left = left[:]
|
||||
right = right[:]
|
||||
while len(left) > 0 or len(right) > 0:
|
||||
if len(left) > 0 and len(right) > 0:
|
||||
if left[0] <= right[0]:
|
||||
sorted_list.append(left.pop(0))
|
||||
else:
|
||||
sorted_list.append(right.pop(0))
|
||||
elif len(left) > 0:
|
||||
sorted_list.append(left.pop(0))
|
||||
elif len(right) > 0:
|
||||
sorted_list.append(right.pop(0))
|
||||
return sorted_list
|
||||
|
||||
def search(self, arr, x):
|
||||
return self.__search(arr, 0, len(arr) - 1, x)
|
||||
|
||||
def __search(self, arr, low, high, x):
|
||||
if high >= low:
|
||||
mid = (high + low) // 2
|
||||
if arr[mid] == x:
|
||||
return mid
|
||||
elif arr[mid] > x:
|
||||
return binary_search(arr, low, mid - 1, x)
|
||||
else:
|
||||
return binary_search(arr, mid + 1, high, x)
|
||||
else:
|
||||
return -1
|
||||
36
analysis-master/tra_analysis/ClassificationMetric.py
Normal file
36
analysis-master/tra_analysis/ClassificationMetric.py
Normal file
@ -0,0 +1,36 @@
|
||||
# Titan Robotics Team 2022: ClassificationMetric submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import ClassificationMetric'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.ClassificationMetric() here
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import metrics
|
||||
|
||||
class ClassificationMetric():
|
||||
|
||||
def __new__(cls, predictions, targets):
|
||||
|
||||
return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
|
||||
|
||||
def cm(self, predictions, targets):
|
||||
|
||||
return sklearn.metrics.confusion_matrix(targets, predictions)
|
||||
|
||||
def cr(self, predictions, targets):
|
||||
|
||||
return sklearn.metrics.classification_report(targets, predictions)
|
||||
58
analysis-master/tra_analysis/CorrelationTest.py
Normal file
58
analysis-master/tra_analysis/CorrelationTest.py
Normal file
@ -0,0 +1,58 @@
|
||||
# Titan Robotics Team 2022: CorrelationTest submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.CorrelationTest() here
|
||||
- removed classness
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
def anova_oneway(self, *args): #expects arrays of samples
|
||||
|
||||
results = scipy.stats.f_oneway(*args)
|
||||
return {"f-value": results[0], "p-value": results[1]}
|
||||
|
||||
def pearson(self, x, y):
|
||||
|
||||
results = scipy.stats.pearsonr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def point_biserial(self, x,y):
|
||||
|
||||
results = scipy.stats.pointbiserialr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
|
||||
|
||||
results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
|
||||
|
||||
results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
|
||||
|
||||
results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
|
||||
return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
|
||||
41
analysis-master/tra_analysis/CorrelationTest_obj.py
Normal file
41
analysis-master/tra_analysis/CorrelationTest_obj.py
Normal file
@ -0,0 +1,41 @@
|
||||
# Only included for backwards compatibility! Do not update, CorrelationTest is preferred and supported.
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
class CorrelationTest:
|
||||
|
||||
def anova_oneway(self, *args): #expects arrays of samples
|
||||
|
||||
results = scipy.stats.f_oneway(*args)
|
||||
return {"f-value": results[0], "p-value": results[1]}
|
||||
|
||||
def pearson(self, x, y):
|
||||
|
||||
results = scipy.stats.pearsonr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def point_biserial(self, x,y):
|
||||
|
||||
results = scipy.stats.pointbiserialr(x, y)
|
||||
return {"r-value": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'):
|
||||
|
||||
results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True):
|
||||
|
||||
results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive)
|
||||
return {"tau": results[0], "p-value": results[1]}
|
||||
|
||||
def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None):
|
||||
|
||||
results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state)
|
||||
return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value
|
||||
@ -4,10 +4,12 @@
|
||||
# this module is cuda-optimized (as appropriate) and vectorized (except for one small part)
|
||||
# setup:
|
||||
|
||||
__version__ = "0.0.1"
|
||||
__version__ = "0.0.2"
|
||||
|
||||
# changelog should be viewed using print(analysis.fits.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.2:
|
||||
- renamed module to Fit
|
||||
0.0.1:
|
||||
- initial release, add circle fitting with LSC
|
||||
"""
|
||||
42
analysis-master/tra_analysis/KNN.py
Normal file
42
analysis-master/tra_analysis/KNN.py
Normal file
@ -0,0 +1,42 @@
|
||||
# Titan Robotics Team 2022: KNN submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import KNN'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.KNN() here
|
||||
- removed classness
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, neighbors
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.neighbors.KNeighborsClassifier()
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
|
||||
|
||||
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
|
||||
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
|
||||
model.fit(data_train, outputs_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, RegressionMetric(predictions, outputs_test)
|
||||
25
analysis-master/tra_analysis/KNN_obj.py
Normal file
25
analysis-master/tra_analysis/KNN_obj.py
Normal file
@ -0,0 +1,25 @@
|
||||
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, neighbors
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class KNN:
|
||||
|
||||
def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.neighbors.KNeighborsClassifier()
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
|
||||
|
||||
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
|
||||
model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
|
||||
model.fit(data_train, outputs_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, RegressionMetric(predictions, outputs_test)
|
||||
59
analysis-master/tra_analysis/NaiveBayes.py
Normal file
59
analysis-master/tra_analysis/NaiveBayes.py
Normal file
@ -0,0 +1,59 @@
|
||||
# Titan Robotics Team 2022: NaiveBayes submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import NaiveBayes'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.NaiveBayes() here
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, naive_bayes
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
def guassian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
43
analysis-master/tra_analysis/NaiveBayes_obj.py
Normal file
43
analysis-master/tra_analysis/NaiveBayes_obj.py
Normal file
@ -0,0 +1,43 @@
|
||||
# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import model_selection, naive_bayes
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class NaiveBayes:
|
||||
|
||||
def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
|
||||
model.fit(data_train, labels_train)
|
||||
predictions = model.predict(data_test)
|
||||
|
||||
return model, ClassificationMetric(predictions, labels_test)
|
||||
42
analysis-master/tra_analysis/RandomForest.py
Normal file
42
analysis-master/tra_analysis/RandomForest.py
Normal file
@ -0,0 +1,42 @@
|
||||
# Titan Robotics Team 2022: RandomForest submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import RandomForest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.RandomFores() here
|
||||
- removed classness
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import ensemble, model_selection
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
def random_forest_classifier(data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
|
||||
kernel.fit(data_train, labels_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
|
||||
|
||||
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
|
||||
kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
|
||||
kernel.fit(data_train, outputs_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, RegressionMetric(predictions, outputs_test)
|
||||
25
analysis-master/tra_analysis/RandomForest_obj.py
Normal file
25
analysis-master/tra_analysis/RandomForest_obj.py
Normal file
@ -0,0 +1,25 @@
|
||||
# Only included for backwards compatibility! Do not update, RandomForest is preferred and supported.
|
||||
|
||||
import sklearn
|
||||
from sklearn import ensemble, model_selection
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class RandomForest:
|
||||
|
||||
def random_forest_classifier(self, data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
|
||||
|
||||
data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
|
||||
kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
|
||||
kernel.fit(data_train, labels_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, ClassificationMetric(predictions, labels_test)
|
||||
|
||||
def random_forest_regressor(self, data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
|
||||
|
||||
data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
|
||||
kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
|
||||
kernel.fit(data_train, outputs_train)
|
||||
predictions = kernel.predict(data_test)
|
||||
|
||||
return kernel, RegressionMetric(predictions, outputs_test)
|
||||
40
analysis-master/tra_analysis/RegressionMetric.py
Normal file
40
analysis-master/tra_analysis/RegressionMetric.py
Normal file
@ -0,0 +1,40 @@
|
||||
# Titan Robotics Team 2022: RegressionMetric submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import RegressionMetric'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.RegressionMetric() here
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import metrics
|
||||
|
||||
class RegressionMetric():
|
||||
|
||||
def __new__(cls, predictions, targets):
|
||||
|
||||
return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
|
||||
|
||||
def r_squared(self, predictions, targets): # assumes equal size inputs
|
||||
|
||||
return sklearn.metrics.r2_score(targets, predictions)
|
||||
|
||||
def mse(self, predictions, targets):
|
||||
|
||||
return sklearn.metrics.mean_squared_error(targets, predictions)
|
||||
|
||||
def rms(self, predictions, targets):
|
||||
|
||||
return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
|
||||
79
analysis-master/tra_analysis/SVM.py
Normal file
79
analysis-master/tra_analysis/SVM.py
Normal file
@ -0,0 +1,79 @@
|
||||
# Titan Robotics Team 2022: SVM submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import SVM'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.SVM() here
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import sklearn
|
||||
from sklearn import svm
|
||||
from . import ClassificationMetric, RegressionMetric
|
||||
|
||||
class SVM:
|
||||
|
||||
class CustomKernel:
|
||||
|
||||
def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
|
||||
|
||||
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
|
||||
|
||||
class StandardKernel:
|
||||
|
||||
def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
|
||||
|
||||
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
|
||||
|
||||
class PrebuiltKernel:
|
||||
|
||||
class Linear:
|
||||
|
||||
def __new__(cls):
|
||||
|
||||
return sklearn.svm.SVC(kernel = 'linear')
|
||||
|
||||
class Polynomial:
|
||||
|
||||
def __new__(cls, power, r_bias):
|
||||
|
||||
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
|
||||
|
||||
class RBF:
|
||||
|
||||
def __new__(cls, gamma):
|
||||
|
||||
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
|
||||
|
||||
class Sigmoid:
|
||||
|
||||
def __new__(cls, r_bias):
|
||||
|
||||
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
|
||||
|
||||
def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
|
||||
|
||||
return kernel.fit(train_data, train_outputs)
|
||||
|
||||
def eval_classification(self, kernel, test_data, test_outputs):
|
||||
|
||||
predictions = kernel.predict(test_data)
|
||||
|
||||
return ClassificationMetric(predictions, test_outputs)
|
||||
|
||||
def eval_regression(self, kernel, test_data, test_outputs):
|
||||
|
||||
predictions = kernel.predict(test_data)
|
||||
|
||||
return RegressionMetric(predictions, test_outputs)
|
||||
408
analysis-master/tra_analysis/Sort.py
Normal file
408
analysis-master/tra_analysis/Sort.py
Normal file
@ -0,0 +1,408 @@
|
||||
# Titan Robotics Team 2022: Sort submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import Sort'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.Sort() here
|
||||
- removed classness
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
def quicksort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
less = []
|
||||
equal = []
|
||||
greater = []
|
||||
|
||||
if len(array) > 1:
|
||||
pivot = array[0]
|
||||
for x in array:
|
||||
if x < pivot:
|
||||
less.append(x)
|
||||
elif x == pivot:
|
||||
equal.append(x)
|
||||
elif x > pivot:
|
||||
greater.append(x)
|
||||
return sort(less)+equal+sort(greater)
|
||||
else:
|
||||
return array
|
||||
|
||||
return np.array(sort(a))
|
||||
|
||||
def mergesort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
if len(array) >1:
|
||||
middle = len(array) // 2
|
||||
L = array[:middle]
|
||||
R = array[middle:]
|
||||
|
||||
sort(L)
|
||||
sort(R)
|
||||
|
||||
i = j = k = 0
|
||||
|
||||
while i < len(L) and j < len(R):
|
||||
if L[i] < R[j]:
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
else:
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
while i < len(L):
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
k+= 1
|
||||
|
||||
while j < len(R):
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def introsort(a):
|
||||
|
||||
def sort(array, start, end, maxdepth):
|
||||
|
||||
array = array
|
||||
|
||||
if end - start <= 1:
|
||||
return
|
||||
elif maxdepth == 0:
|
||||
heapsort(array, start, end)
|
||||
else:
|
||||
p = partition(array, start, end)
|
||||
sort(array, start, p + 1, maxdepth - 1)
|
||||
sort(array, p + 1, end, maxdepth - 1)
|
||||
|
||||
return array
|
||||
|
||||
def partition(array, start, end):
|
||||
pivot = array[start]
|
||||
i = start - 1
|
||||
j = end
|
||||
|
||||
while True:
|
||||
i = i + 1
|
||||
while array[i] < pivot:
|
||||
i = i + 1
|
||||
j = j - 1
|
||||
while array[j] > pivot:
|
||||
j = j - 1
|
||||
|
||||
if i >= j:
|
||||
return j
|
||||
|
||||
swap(array, i, j)
|
||||
|
||||
def swap(array, i, j):
|
||||
array[i], array[j] = array[j], array[i]
|
||||
|
||||
def heapsort(array, start, end):
|
||||
build_max_heap(array, start, end)
|
||||
for i in range(end - 1, start, -1):
|
||||
swap(array, start, i)
|
||||
max_heapify(array, index=0, start=start, end=i)
|
||||
|
||||
def build_max_heap(array, start, end):
|
||||
def parent(i):
|
||||
return (i - 1)//2
|
||||
length = end - start
|
||||
index = parent(length - 1)
|
||||
while index >= 0:
|
||||
max_heapify(array, index, start, end)
|
||||
index = index - 1
|
||||
|
||||
def max_heapify(array, index, start, end):
|
||||
def left(i):
|
||||
return 2*i + 1
|
||||
def right(i):
|
||||
return 2*i + 2
|
||||
|
||||
size = end - start
|
||||
l = left(index)
|
||||
r = right(index)
|
||||
if (l < size and array[start + l] > array[start + index]):
|
||||
largest = l
|
||||
else:
|
||||
largest = index
|
||||
if (r < size and array[start + r] > array[start + largest]):
|
||||
largest = r
|
||||
if largest != index:
|
||||
swap(array, start + largest, start + index)
|
||||
max_heapify(array, largest, start, end)
|
||||
|
||||
maxdepth = (len(a).bit_length() - 1)*2
|
||||
|
||||
return sort(a, 0, len(a), maxdepth)
|
||||
|
||||
def heapsort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
|
||||
for i in range(n//2 - 1, -1, -1):
|
||||
heapify(array, n, i)
|
||||
|
||||
for i in range(n-1, 0, -1):
|
||||
array[i], array[0] = array[0], array[i]
|
||||
heapify(array, i, 0)
|
||||
|
||||
return array
|
||||
|
||||
def heapify(array, n, i):
|
||||
|
||||
array = array
|
||||
|
||||
largest = i
|
||||
l = 2 * i + 1
|
||||
r = 2 * i + 2
|
||||
|
||||
if l < n and array[i] < array[l]:
|
||||
largest = l
|
||||
|
||||
if r < n and array[largest] < array[r]:
|
||||
largest = r
|
||||
|
||||
if largest != i:
|
||||
array[i],array[largest] = array[largest],array[i]
|
||||
heapify(array, n, largest)
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def insertionsort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(1, len(array)):
|
||||
|
||||
key = array[i]
|
||||
|
||||
j = i-1
|
||||
while j >=0 and key < array[j] :
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
array[j+1] = key
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def timsort(a, block = 32):
|
||||
|
||||
BLOCK = block
|
||||
|
||||
def sort(array, n):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(0, n, BLOCK):
|
||||
insertionsort(array, i, min((i+31), (n-1)))
|
||||
|
||||
size = BLOCK
|
||||
while size < n:
|
||||
|
||||
for left in range(0, n, 2*size):
|
||||
|
||||
mid = left + size - 1
|
||||
right = min((left + 2*size - 1), (n-1))
|
||||
merge(array, left, mid, right)
|
||||
|
||||
size = 2*size
|
||||
|
||||
return array
|
||||
|
||||
def insertionsort(array, left, right):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(left + 1, right+1):
|
||||
|
||||
temp = array[i]
|
||||
j = i - 1
|
||||
while j >= left and array[j] > temp :
|
||||
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
|
||||
array[j+1] = temp
|
||||
|
||||
return array
|
||||
|
||||
|
||||
def merge(array, l, m, r):
|
||||
|
||||
len1, len2 = m - l + 1, r - m
|
||||
left, right = [], []
|
||||
for i in range(0, len1):
|
||||
left.append(array[l + i])
|
||||
for i in range(0, len2):
|
||||
right.append(array[m + 1 + i])
|
||||
|
||||
i, j, k = 0, 0, l
|
||||
|
||||
while i < len1 and j < len2:
|
||||
|
||||
if left[i] <= right[j]:
|
||||
array[k] = left[i]
|
||||
i += 1
|
||||
|
||||
else:
|
||||
array[k] = right[j]
|
||||
j += 1
|
||||
|
||||
k += 1
|
||||
|
||||
while i < len1:
|
||||
|
||||
array[k] = left[i]
|
||||
k += 1
|
||||
i += 1
|
||||
|
||||
while j < len2:
|
||||
array[k] = right[j]
|
||||
k += 1
|
||||
j += 1
|
||||
|
||||
return sort(a, len(a))
|
||||
|
||||
def selectionsort(a):
|
||||
array = a
|
||||
for i in range(len(array)):
|
||||
min_idx = i
|
||||
for j in range(i+1, len(array)):
|
||||
if array[min_idx] > array[j]:
|
||||
min_idx = j
|
||||
array[i], array[min_idx] = array[min_idx], array[i]
|
||||
return array
|
||||
|
||||
def shellsort(a):
|
||||
array = a
|
||||
n = len(array)
|
||||
gap = n//2
|
||||
|
||||
while gap > 0:
|
||||
|
||||
for i in range(gap,n):
|
||||
|
||||
temp = array[i]
|
||||
j = i
|
||||
while j >= gap and array[j-gap] >temp:
|
||||
array[j] = array[j-gap]
|
||||
j -= gap
|
||||
array[j] = temp
|
||||
gap //= 2
|
||||
|
||||
return array
|
||||
|
||||
def bubblesort(a):
|
||||
|
||||
def sort(array):
|
||||
for i, num in enumerate(array):
|
||||
try:
|
||||
if array[i+1] < num:
|
||||
array[i] = array[i+1]
|
||||
array[i+1] = num
|
||||
sort(array)
|
||||
except IndexError:
|
||||
pass
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cyclesort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
writes = 0
|
||||
|
||||
for cycleStart in range(0, len(array) - 1):
|
||||
item = array[cycleStart]
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
if pos == cycleStart:
|
||||
continue
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
while pos != cycleStart:
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cocktailsort(a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
swapped = True
|
||||
start = 0
|
||||
end = n-1
|
||||
while (swapped == True):
|
||||
swapped = False
|
||||
for i in range (start, end):
|
||||
if (array[i] > array[i + 1]) :
|
||||
array[i], array[i + 1]= array[i + 1], array[i]
|
||||
swapped = True
|
||||
if (swapped == False):
|
||||
break
|
||||
swapped = False
|
||||
end = end-1
|
||||
for i in range(end-1, start-1, -1):
|
||||
if (array[i] > array[i + 1]):
|
||||
array[i], array[i + 1] = array[i + 1], array[i]
|
||||
swapped = True
|
||||
start = start + 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
391
analysis-master/tra_analysis/Sort_obj.py
Normal file
391
analysis-master/tra_analysis/Sort_obj.py
Normal file
@ -0,0 +1,391 @@
|
||||
# Only included for backwards compatibility! Do not update, Sort is preferred and supported.
|
||||
|
||||
class Sort: # if you haven't used a sort, then you've never lived
|
||||
|
||||
def quicksort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
less = []
|
||||
equal = []
|
||||
greater = []
|
||||
|
||||
if len(array) > 1:
|
||||
pivot = array[0]
|
||||
for x in array:
|
||||
if x < pivot:
|
||||
less.append(x)
|
||||
elif x == pivot:
|
||||
equal.append(x)
|
||||
elif x > pivot:
|
||||
greater.append(x)
|
||||
return sort(less)+equal+sort(greater)
|
||||
else:
|
||||
return array
|
||||
|
||||
return np.array(sort(a))
|
||||
|
||||
def mergesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
if len(array) >1:
|
||||
middle = len(array) // 2
|
||||
L = array[:middle]
|
||||
R = array[middle:]
|
||||
|
||||
sort(L)
|
||||
sort(R)
|
||||
|
||||
i = j = k = 0
|
||||
|
||||
while i < len(L) and j < len(R):
|
||||
if L[i] < R[j]:
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
else:
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
while i < len(L):
|
||||
array[k] = L[i]
|
||||
i+= 1
|
||||
k+= 1
|
||||
|
||||
while j < len(R):
|
||||
array[k] = R[j]
|
||||
j+= 1
|
||||
k+= 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def introsort(self, a):
|
||||
|
||||
def sort(array, start, end, maxdepth):
|
||||
|
||||
array = array
|
||||
|
||||
if end - start <= 1:
|
||||
return
|
||||
elif maxdepth == 0:
|
||||
heapsort(array, start, end)
|
||||
else:
|
||||
p = partition(array, start, end)
|
||||
sort(array, start, p + 1, maxdepth - 1)
|
||||
sort(array, p + 1, end, maxdepth - 1)
|
||||
|
||||
return array
|
||||
|
||||
def partition(array, start, end):
|
||||
pivot = array[start]
|
||||
i = start - 1
|
||||
j = end
|
||||
|
||||
while True:
|
||||
i = i + 1
|
||||
while array[i] < pivot:
|
||||
i = i + 1
|
||||
j = j - 1
|
||||
while array[j] > pivot:
|
||||
j = j - 1
|
||||
|
||||
if i >= j:
|
||||
return j
|
||||
|
||||
swap(array, i, j)
|
||||
|
||||
def swap(array, i, j):
|
||||
array[i], array[j] = array[j], array[i]
|
||||
|
||||
def heapsort(array, start, end):
|
||||
build_max_heap(array, start, end)
|
||||
for i in range(end - 1, start, -1):
|
||||
swap(array, start, i)
|
||||
max_heapify(array, index=0, start=start, end=i)
|
||||
|
||||
def build_max_heap(array, start, end):
|
||||
def parent(i):
|
||||
return (i - 1)//2
|
||||
length = end - start
|
||||
index = parent(length - 1)
|
||||
while index >= 0:
|
||||
max_heapify(array, index, start, end)
|
||||
index = index - 1
|
||||
|
||||
def max_heapify(array, index, start, end):
|
||||
def left(i):
|
||||
return 2*i + 1
|
||||
def right(i):
|
||||
return 2*i + 2
|
||||
|
||||
size = end - start
|
||||
l = left(index)
|
||||
r = right(index)
|
||||
if (l < size and array[start + l] > array[start + index]):
|
||||
largest = l
|
||||
else:
|
||||
largest = index
|
||||
if (r < size and array[start + r] > array[start + largest]):
|
||||
largest = r
|
||||
if largest != index:
|
||||
swap(array, start + largest, start + index)
|
||||
max_heapify(array, largest, start, end)
|
||||
|
||||
maxdepth = (len(a).bit_length() - 1)*2
|
||||
|
||||
return sort(a, 0, len(a), maxdepth)
|
||||
|
||||
def heapsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
|
||||
for i in range(n//2 - 1, -1, -1):
|
||||
heapify(array, n, i)
|
||||
|
||||
for i in range(n-1, 0, -1):
|
||||
array[i], array[0] = array[0], array[i]
|
||||
heapify(array, i, 0)
|
||||
|
||||
return array
|
||||
|
||||
def heapify(array, n, i):
|
||||
|
||||
array = array
|
||||
|
||||
largest = i
|
||||
l = 2 * i + 1
|
||||
r = 2 * i + 2
|
||||
|
||||
if l < n and array[i] < array[l]:
|
||||
largest = l
|
||||
|
||||
if r < n and array[largest] < array[r]:
|
||||
largest = r
|
||||
|
||||
if largest != i:
|
||||
array[i],array[largest] = array[largest],array[i]
|
||||
heapify(array, n, largest)
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def insertionsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(1, len(array)):
|
||||
|
||||
key = array[i]
|
||||
|
||||
j = i-1
|
||||
while j >=0 and key < array[j] :
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
array[j+1] = key
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def timsort(self, a, block = 32):
|
||||
|
||||
BLOCK = block
|
||||
|
||||
def sort(array, n):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(0, n, BLOCK):
|
||||
insertionsort(array, i, min((i+31), (n-1)))
|
||||
|
||||
size = BLOCK
|
||||
while size < n:
|
||||
|
||||
for left in range(0, n, 2*size):
|
||||
|
||||
mid = left + size - 1
|
||||
right = min((left + 2*size - 1), (n-1))
|
||||
merge(array, left, mid, right)
|
||||
|
||||
size = 2*size
|
||||
|
||||
return array
|
||||
|
||||
def insertionsort(array, left, right):
|
||||
|
||||
array = array
|
||||
|
||||
for i in range(left + 1, right+1):
|
||||
|
||||
temp = array[i]
|
||||
j = i - 1
|
||||
while j >= left and array[j] > temp :
|
||||
|
||||
array[j+1] = array[j]
|
||||
j -= 1
|
||||
|
||||
array[j+1] = temp
|
||||
|
||||
return array
|
||||
|
||||
|
||||
def merge(array, l, m, r):
|
||||
|
||||
len1, len2 = m - l + 1, r - m
|
||||
left, right = [], []
|
||||
for i in range(0, len1):
|
||||
left.append(array[l + i])
|
||||
for i in range(0, len2):
|
||||
right.append(array[m + 1 + i])
|
||||
|
||||
i, j, k = 0, 0, l
|
||||
|
||||
while i < len1 and j < len2:
|
||||
|
||||
if left[i] <= right[j]:
|
||||
array[k] = left[i]
|
||||
i += 1
|
||||
|
||||
else:
|
||||
array[k] = right[j]
|
||||
j += 1
|
||||
|
||||
k += 1
|
||||
|
||||
while i < len1:
|
||||
|
||||
array[k] = left[i]
|
||||
k += 1
|
||||
i += 1
|
||||
|
||||
while j < len2:
|
||||
array[k] = right[j]
|
||||
k += 1
|
||||
j += 1
|
||||
|
||||
return sort(a, len(a))
|
||||
|
||||
def selectionsort(self, a):
|
||||
array = a
|
||||
for i in range(len(array)):
|
||||
min_idx = i
|
||||
for j in range(i+1, len(array)):
|
||||
if array[min_idx] > array[j]:
|
||||
min_idx = j
|
||||
array[i], array[min_idx] = array[min_idx], array[i]
|
||||
return array
|
||||
|
||||
def shellsort(self, a):
|
||||
array = a
|
||||
n = len(array)
|
||||
gap = n//2
|
||||
|
||||
while gap > 0:
|
||||
|
||||
for i in range(gap,n):
|
||||
|
||||
temp = array[i]
|
||||
j = i
|
||||
while j >= gap and array[j-gap] >temp:
|
||||
array[j] = array[j-gap]
|
||||
j -= gap
|
||||
array[j] = temp
|
||||
gap //= 2
|
||||
|
||||
return array
|
||||
|
||||
def bubblesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
for i, num in enumerate(array):
|
||||
try:
|
||||
if array[i+1] < num:
|
||||
array[i] = array[i+1]
|
||||
array[i+1] = num
|
||||
sort(array)
|
||||
except IndexError:
|
||||
pass
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cyclesort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
writes = 0
|
||||
|
||||
for cycleStart in range(0, len(array) - 1):
|
||||
item = array[cycleStart]
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
if pos == cycleStart:
|
||||
continue
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
while pos != cycleStart:
|
||||
|
||||
pos = cycleStart
|
||||
for i in range(cycleStart + 1, len(array)):
|
||||
if array[i] < item:
|
||||
pos += 1
|
||||
|
||||
while item == array[pos]:
|
||||
pos += 1
|
||||
array[pos], item = item, array[pos]
|
||||
writes += 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
|
||||
def cocktailsort(self, a):
|
||||
|
||||
def sort(array):
|
||||
|
||||
array = array
|
||||
|
||||
n = len(array)
|
||||
swapped = True
|
||||
start = 0
|
||||
end = n-1
|
||||
while (swapped == True):
|
||||
swapped = False
|
||||
for i in range (start, end):
|
||||
if (array[i] > array[i + 1]) :
|
||||
array[i], array[i + 1]= array[i + 1], array[i]
|
||||
swapped = True
|
||||
if (swapped == False):
|
||||
break
|
||||
swapped = False
|
||||
end = end-1
|
||||
for i in range(end-1, start-1, -1):
|
||||
if (array[i] > array[i + 1]):
|
||||
array[i], array[i + 1] = array[i + 1], array[i]
|
||||
swapped = True
|
||||
start = start + 1
|
||||
|
||||
return array
|
||||
|
||||
return sort(a)
|
||||
187
analysis-master/tra_analysis/StatisticalTest.py
Normal file
187
analysis-master/tra_analysis/StatisticalTest.py
Normal file
@ -0,0 +1,187 @@
|
||||
# Titan Robotics Team 2022: StatisticalTest submodule
|
||||
# Written by Arthur Lu
|
||||
# Notes:
|
||||
# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.0"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.0:
|
||||
- ported analysis.StatisticalTest() here
|
||||
- removed classness
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_independent(a, b, equal = True, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_statistic(o1, o2, equal = True):
|
||||
|
||||
results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_related(a, b, axis = 0, nan_policy='propagate'):
|
||||
|
||||
results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ks_fitness(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
|
||||
|
||||
results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
|
||||
return {"ks-value": results[0], "p-value": results[1]}
|
||||
|
||||
def chisquare(f_obs, f_exp = None, ddof = None, axis = 0):
|
||||
|
||||
results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
|
||||
return {"chisquared-value": results[0], "p-value": results[1]}
|
||||
|
||||
def powerdivergence(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
|
||||
|
||||
results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
|
||||
return {"powerdivergence-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ks_twosample(x, y, alternative = 'two_sided', mode = 'auto'):
|
||||
|
||||
results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
|
||||
return {"ks-value": results[0], "p-value": results[1]}
|
||||
|
||||
def es_twosample(x, y, t = (0.4, 0.8)):
|
||||
|
||||
results = scipy.stats.epps_singleton_2samp(x, y, t = t)
|
||||
return {"es-value": results[0], "p-value": results[1]}
|
||||
|
||||
def mw_rank(x, y, use_continuity = True, alternative = None):
|
||||
|
||||
results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
|
||||
return {"u-value": results[0], "p-value": results[1]}
|
||||
|
||||
def mw_tiecorrection(rank_values):
|
||||
|
||||
results = scipy.stats.tiecorrect(rank_values)
|
||||
return {"correction-factor": results}
|
||||
|
||||
def rankdata(a, method = 'average'):
|
||||
|
||||
results = scipy.stats.rankdata(a, method = method)
|
||||
return results
|
||||
|
||||
def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
|
||||
|
||||
results = scipy.stats.ranksums(a, b)
|
||||
return {"u-value": results[0], "p-value": results[1]}
|
||||
|
||||
def wilcoxon_signedrank(x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
|
||||
|
||||
results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def kw_htest(*args, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
|
||||
return {"h-value": results[0], "p-value": results[1]}
|
||||
|
||||
def friedman_chisquare(*args):
|
||||
|
||||
results = scipy.stats.friedmanchisquare(*args)
|
||||
return {"chisquared-value": results[0], "p-value": results[1]}
|
||||
|
||||
def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def combine_pvalues(pvalues, method = 'fisher', weights = None):
|
||||
|
||||
results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
|
||||
return {"combined-statistic": results[0], "p-value": results[1]}
|
||||
|
||||
def jb_fitness(x):
|
||||
|
||||
results = scipy.stats.jarque_bera(x)
|
||||
return {"jb-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ab_equality(x, y):
|
||||
|
||||
results = scipy.stats.ansari(x, y)
|
||||
return {"ab-value": results[0], "p-value": results[1]}
|
||||
|
||||
def bartlett_variance(*args):
|
||||
|
||||
results = scipy.stats.bartlett(*args)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def levene_variance(*args, center = 'median', proportiontocut = 0.05):
|
||||
|
||||
results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def sw_normality(x):
|
||||
|
||||
results = scipy.stats.shapiro(x)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def shapiro(x):
|
||||
|
||||
return "destroyed by facts and logic"
|
||||
|
||||
def ad_onesample(x, dist = 'norm'):
|
||||
|
||||
results = scipy.stats.anderson(x, dist = dist)
|
||||
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
||||
|
||||
def ad_ksample(samples, midrank = True):
|
||||
|
||||
results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
|
||||
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
||||
|
||||
def binomial(x, n = None, p = 0.5, alternative = 'two-sided'):
|
||||
|
||||
results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
|
||||
return {"p-value": results}
|
||||
|
||||
def fk_variance(*args, center = 'median', proportiontocut = 0.05):
|
||||
|
||||
results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
|
||||
return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
|
||||
|
||||
def mood_mediantest(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
|
||||
return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
|
||||
|
||||
def mood_equalscale(x, y, axis = 0):
|
||||
|
||||
results = scipy.stats.mood(x, y, axis = axis)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def skewtest(a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def kurtosistest(a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def normaltest(a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
170
analysis-master/tra_analysis/StatisticalTest_obj.py
Normal file
170
analysis-master/tra_analysis/StatisticalTest_obj.py
Normal file
@ -0,0 +1,170 @@
|
||||
# Only included for backwards compatibility! Do not update, StatisticalTest is preferred and supported.
|
||||
|
||||
import scipy
|
||||
from scipy import stats
|
||||
|
||||
class StatisticalTest:
|
||||
|
||||
def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_statistic(self, o1, o2, equal = True):
|
||||
|
||||
results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ttest_related(self, a, b, axis = 0, nan_policy='propagate'):
|
||||
|
||||
results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
|
||||
|
||||
results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
|
||||
return {"ks-value": results[0], "p-value": results[1]}
|
||||
|
||||
def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0):
|
||||
|
||||
results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
|
||||
return {"chisquared-value": results[0], "p-value": results[1]}
|
||||
|
||||
def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
|
||||
|
||||
results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
|
||||
return {"powerdivergence-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'):
|
||||
|
||||
results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
|
||||
return {"ks-value": results[0], "p-value": results[1]}
|
||||
|
||||
def es_twosample(self, x, y, t = (0.4, 0.8)):
|
||||
|
||||
results = scipy.stats.epps_singleton_2samp(x, y, t = t)
|
||||
return {"es-value": results[0], "p-value": results[1]}
|
||||
|
||||
def mw_rank(self, x, y, use_continuity = True, alternative = None):
|
||||
|
||||
results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
|
||||
return {"u-value": results[0], "p-value": results[1]}
|
||||
|
||||
def mw_tiecorrection(self, rank_values):
|
||||
|
||||
results = scipy.stats.tiecorrect(rank_values)
|
||||
return {"correction-factor": results}
|
||||
|
||||
def rankdata(self, a, method = 'average'):
|
||||
|
||||
results = scipy.stats.rankdata(a, method = method)
|
||||
return results
|
||||
|
||||
def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
|
||||
|
||||
results = scipy.stats.ranksums(a, b)
|
||||
return {"u-value": results[0], "p-value": results[1]}
|
||||
|
||||
def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
|
||||
|
||||
results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def kw_htest(self, *args, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
|
||||
return {"h-value": results[0], "p-value": results[1]}
|
||||
|
||||
def friedman_chisquare(self, *args):
|
||||
|
||||
results = scipy.stats.friedmanchisquare(*args)
|
||||
return {"chisquared-value": results[0], "p-value": results[1]}
|
||||
|
||||
def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def combine_pvalues(self, pvalues, method = 'fisher', weights = None):
|
||||
|
||||
results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
|
||||
return {"combined-statistic": results[0], "p-value": results[1]}
|
||||
|
||||
def jb_fitness(self, x):
|
||||
|
||||
results = scipy.stats.jarque_bera(x)
|
||||
return {"jb-value": results[0], "p-value": results[1]}
|
||||
|
||||
def ab_equality(self, x, y):
|
||||
|
||||
results = scipy.stats.ansari(x, y)
|
||||
return {"ab-value": results[0], "p-value": results[1]}
|
||||
|
||||
def bartlett_variance(self, *args):
|
||||
|
||||
results = scipy.stats.bartlett(*args)
|
||||
return {"t-value": results[0], "p-value": results[1]}
|
||||
|
||||
def levene_variance(self, *args, center = 'median', proportiontocut = 0.05):
|
||||
|
||||
results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def sw_normality(self, x):
|
||||
|
||||
results = scipy.stats.shapiro(x)
|
||||
return {"w-value": results[0], "p-value": results[1]}
|
||||
|
||||
def shapiro(self, x):
|
||||
|
||||
return "destroyed by facts and logic"
|
||||
|
||||
def ad_onesample(self, x, dist = 'norm'):
|
||||
|
||||
results = scipy.stats.anderson(x, dist = dist)
|
||||
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
||||
|
||||
def ad_ksample(self, samples, midrank = True):
|
||||
|
||||
results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
|
||||
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
||||
|
||||
def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'):
|
||||
|
||||
results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
|
||||
return {"p-value": results}
|
||||
|
||||
def fk_variance(self, *args, center = 'median', proportiontocut = 0.05):
|
||||
|
||||
results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
|
||||
return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
|
||||
|
||||
def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
|
||||
|
||||
results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
|
||||
return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
|
||||
|
||||
def mood_equalscale(self, x, y, axis = 0):
|
||||
|
||||
results = scipy.stats.mood(x, y, axis = axis)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def skewtest(self, a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
|
||||
def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
|
||||
|
||||
results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
|
||||
return {"z-score": results[0], "p-value": results[1]}
|
||||
@ -0,0 +1,39 @@
|
||||
# Titan Robotics Team 2022: tra_analysis package
|
||||
# Written by Arthur Lu, Jacob Levine, and Dev Singh
|
||||
# Notes:
|
||||
# this should be imported as a python package using 'import tra_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__ = "2.1.0-alpha.1"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
2.1.0-alpha.1:
|
||||
- moved multiple submodules under analysis to their own modules/files
|
||||
- added header, __version__, __changelog__, __author__, __all__ (unpopulated)
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
"Dev Singh <dev@devksingh.com>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
]
|
||||
|
||||
from . import Analysis
|
||||
from .Array import Array
|
||||
from .ClassificationMetric import ClassificationMetric
|
||||
from . import CorrelationTest
|
||||
from . import Fit
|
||||
from . import KNN
|
||||
from . import NaiveBayes
|
||||
from . import RandomForest
|
||||
from .RegressionMetric import RegressionMetric
|
||||
from . import Sort
|
||||
from . import StatisticalTest
|
||||
from .SVM import SVM
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,162 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"from decimal import Decimal\n",
|
||||
"from functools import reduce"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def add(string):\n",
|
||||
" while(len(re.findall(\"[+]{1}[-]?\", string)) != 0):\n",
|
||||
" string = re.sub(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split(\"[+]{1}\", re.search(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sub(string):\n",
|
||||
" while(len(re.findall(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string)) != 0):\n",
|
||||
" g = re.search(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string).group()\n",
|
||||
" if(re.search(\"[-]{1,2}\", g).group() == \"-\"):\n",
|
||||
" r = re.sub(\"[-]{1}\", \"+-\", g, 1)\n",
|
||||
" string = re.sub(g, r, string, 1)\n",
|
||||
" elif(re.search(\"[-]{1,2}\", g).group() == \"--\"):\n",
|
||||
" r = re.sub(\"[-]{2}\", \"+\", g, 1)\n",
|
||||
" string = re.sub(g, r, string, 1)\n",
|
||||
" else:\n",
|
||||
" pass\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def mul(string):\n",
|
||||
" while(len(re.findall(\"[*]{1}[-]?\", string)) != 0):\n",
|
||||
" string = re.sub(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split(\"[*]{1}\", re.search(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def div(string):\n",
|
||||
" while(len(re.findall(\"[/]{1}[-]?\", string)) != 0):\n",
|
||||
" string = re.sub(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split(\"[/]{1}\", re.search(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def exp(string):\n",
|
||||
" while(len(re.findall(\"[\\^]{1}[-]?\", string)) != 0):\n",
|
||||
" string = re.sub(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split(\"[\\^]{1}\", re.search(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def evaluate(string):\n",
|
||||
" string = exp(string)\n",
|
||||
" string = div(string)\n",
|
||||
" string = mul(string)\n",
|
||||
" string = sub(string)\n",
|
||||
" print(string)\n",
|
||||
" string = add(string)\n",
|
||||
" return string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "error",
|
||||
"ename": "SyntaxError",
|
||||
"evalue": "unexpected EOF while parsing (<ipython-input-13-f9fb4aededd9>, line 1)",
|
||||
"traceback": [
|
||||
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-13-f9fb4aededd9>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m def parentheses(string):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def parentheses(string):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "-158456325028528675187087900672.000000+0.8\n"
|
||||
},
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": "'-158456325028528675187087900672.000000'"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 22
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"string = \"8^32*4/-2+0.8\"\n",
|
||||
"evaluate(string)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.6-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user