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analysis.py v 1.0.4.001
changelog: - added __changelog__ - updated debug function with log and exponential regressions
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analysis.py
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analysis.py
@ -10,6 +10,51 @@
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__version__ = "1.0.4.001"
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__changelog__ = """changelog:
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1.0.4.002:
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- added __changelog__
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- updated debug function with log and exponential regressions
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1.0.4.001:
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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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1.0.3.008:
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- added debug function to further consolidate functions
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1.0.3.007:
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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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1.0.3.006:
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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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1.0.3.005:
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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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1.0.3.004:
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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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1.0.3.003:
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- bug fixes
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- added c_entities
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1.0.3.002:
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- bug fixes
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- added nc_entities, obstacles, objectives
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- consolidated statistics.py to analysis.py
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1.0.3.001:
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- compiled 1d, column, and row basic stats into basic stats function
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1.0.3.000:
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- added historical analysis function
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1.0.2.xxx:
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- added z score test
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1.0.1.xxx:
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- major bug fixes
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1.0.0.xxx:
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- added loading csv
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- added 1d, column, row basic stats""" #changelog should be viewed using print(analysis.__changelog__)
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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@ -564,7 +609,7 @@ def exp_regression(x, y, base):
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for i in range(len(y)):
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y_fit.append(np.log(y[i]) / np.log(base))
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y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
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reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
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@ -714,6 +759,10 @@ def debug():
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print(poly_regression([1, 2, 3, 4, 5], [1, 2, 4, 8, 16], 2))
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print(log_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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print(exp_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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#statistics def below------------------------------------------------------------------------------------------------------------------------------------------------------
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class StatisticsError(ValueError):
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