analysis.py v 1.2.1.000

This commit is contained in:
ltcptgeneral 2020-05-04 14:50:36 -05:00
parent 4498387ac5
commit 64ae1b7026

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@ -7,10 +7,15 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.2.0.006" __version__ = "1.2.1.000"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.2.1.000:
- added ArrayTest class
- added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class
- added elementwise_stats to ArrayTest which encapsulates elementwise statistics
- appended to __all__ to reflect changes
1.2.0.006: 1.2.0.006:
- renamed func functions in regression to lin, log, exp, and sig - renamed func functions in regression to lin, log, exp, and sig
1.2.0.005: 1.2.0.005:
@ -304,6 +309,7 @@ __all__ = [
'RandomForrest', 'RandomForrest',
'CorrelationTest', 'CorrelationTest',
'StatisticalTest', 'StatisticalTest',
'ArrayTest',
# all statistics functions left out due to integration in other functions # all statistics functions left out due to integration in other functions
] ]
@ -931,4 +937,41 @@ class StatisticalTest:
def normaltest(self, a, axis = 0, nan_policy = 'propogate'): def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
return {"z-score": results[0], "p-value": results[1]} return {"z-score": results[0], "p-value": results[1]}
class ArrayTest(): # tests on nd arrays independent of basic_stats
def elementwise_mean(self, *args): # expects arrays that are size normalized
return np.mean([*args], axis = 0)
def elementwise_median(self, *args):
return np.median([*args], axis = 0)
def elementwise_stdev(self, *args):
return np.std([*args], axis = 0)
def elementwise_variance(self, *args):
return np.var([*args], axis = 0)
def elementwise_npmin(self, *args):
return np.amin([*args], axis = 0)
def elementwise_npmax(self, *args):
return np.amax([*args], axis = 0)
def elementwise_stats(self, *args):
_mean = self.elementwise_mean(*args)
_median = self.elementwise_median(*args)
_stdev = self.elementwise_stdev(*args)
_variance = self.elementwise_variance(*args)
_min = self.elementwise_npmin(*args)
_max = self.elementwise_npmax(*args)
return _mean, _median, _stdev, _variance, _min, _max