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analysis.py - v 1.0.5.000
changelog: - added optimize_regression function, which is a sample function to find the optimal regressions - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) - planned addition: overfit detection in the optimize_regression function
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@ -8,9 +8,13 @@
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#setup:
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__version__ = "1.0.4.001"
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__version__ = "1.0.5.000"
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__changelog__ = """changelog:
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1.0.5.000:
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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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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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@ -678,7 +682,7 @@ def strip_data(data, mode):
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raise error("mode error")
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def optimize_regression(x, y, _range, resolution):#_range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
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#usage not: for demonstration purpose only, performance is shit
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if type(resolution) != int:
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raise error("resolution must be int")
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@ -719,7 +723,27 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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pass
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return [eqs, rmss, r2s]
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for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
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if r2s[i] == 1:
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eqs[i] = ""
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rmss[i] = ""
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r2s[i] = ""
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while True: #removes all equations marked for removal
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try:
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eqs.remove('')
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rmss.remove('')
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r2s.remove('')
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except:
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break
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return eqs, rmss, r2s
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def basic_analysis(filepath): #assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
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