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Merge pull request #60 from titanscouting/fix-test-analysis
Fixed test_analysis.py
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commit
f111c1c865
@ -1,6 +1,15 @@
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from tra_analysis import analysis as an
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from tra_analysis import Analysis as an
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from tra_analysis import metrics
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from tra_analysis import Array
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from tra_analysis import fits
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from tra_analysis import ClassificationMetric
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from tra_analysis import CorrelationTest
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from tra_analysis import Fit
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from tra_analysis import KNN
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from tra_analysis import NaiveBayes
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from tra_analysis import RandomForest
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from tra_analysis import RegressionMetric
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from tra_analysis import Sort
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from tra_analysis import StatisticalTest
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from tra_analysis import SVM
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def test_():
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def test_():
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test_data_linear = [1, 3, 6, 7, 9]
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test_data_linear = [1, 3, 6, 7, 9]
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@ -21,15 +30,15 @@ def test_():
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assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
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assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
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assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
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assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
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#assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
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#assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
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assert all(a == b for a, b in zip(an.Sort().quicksort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().mergesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().introsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().heapsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.introsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().insertionsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.insertionsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().timsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.timsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().selectionsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.selectionsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().shellsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.shellsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().bubblesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().cyclesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(an.Sort().cocktailsort(test_data_scrambled), test_data_sorted))
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assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted))
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assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
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assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)
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@ -1,5 +1,5 @@
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# Titan Robotics Team 2022: Sort submodule
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# Titan Robotics Team 2022: Sort submodule
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# Written by Arthur Lu
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# Written by Arthur Lu and James Pan
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# Notes:
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import Sort'
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# this should be imported as a python module using 'from tra_analysis import Sort'
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# setup:
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# setup:
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@ -14,11 +14,14 @@ __changelog__ = """changelog:
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__author__ = (
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"Arthur Lu <learthurgo@gmail.com>",
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"James Pan <zpan@imsa.edu>"
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)
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)
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__all__ = [
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__all__ = [
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]
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]
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import numpy as np
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def quicksort(a):
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def quicksort(a):
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def sort(array):
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def sort(array):
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@ -1,5 +1,5 @@
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# Titan Robotics Team 2022: tra_analysis package
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# Titan Robotics Team 2022: tra_analysis package
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# Written by Arthur Lu, Jacob Levine, and Dev Singh
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# Written by Arthur Lu, Jacob Levine, Dev Singh, and James Pan
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# Notes:
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# Notes:
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# this should be imported as a python package using 'import tra_analysis'
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# this should be imported as a python package using 'import tra_analysis'
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# this should be included in the local directory or environment variable
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# this should be included in the local directory or environment variable
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@ -20,6 +20,7 @@ __author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"Arthur Lu <learthurgo@gmail.com>",
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"Jacob Levine <jlevine@imsa.edu>",
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"Jacob Levine <jlevine@imsa.edu>",
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"Dev Singh <dev@devksingh.com>",
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"Dev Singh <dev@devksingh.com>",
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"James Pan <zpan@imsa.edu>"
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)
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)
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__all__ = [
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__all__ = [
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