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analysis.py v 2.3.0

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Signed-off-by: Arthur Lu <learthurgo@gmail.com>
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Arthur Lu 2020-09-19 22:04:24 +00:00
parent f1982eb93d
commit cd869c0a8e
1 changed files with 75 additions and 42 deletions
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analysis-master/tra_analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "2.2.3" __version__ = "2.3.0"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
2.3.0:
- overhauled Array class
2.2.3: 2.2.3:
- fixed spelling of RandomForest - fixed spelling of RandomForest
- made n_neighbors required for KNN - made n_neighbors required for KNN
@ -786,7 +788,7 @@ class CorrelationTest:
def anova_oneway(self, *args): #expects arrays of samples def anova_oneway(self, *args): #expects arrays of samples
results = scipy.stats.f_oneway(*args) results = scipy.stats.f_oneway(*args)
return {"F-value": results[0], "p-value": results[1]} return {"f-value": results[0], "p-value": results[1]}
def pearson(self, x, y): def pearson(self, x, y):
@ -985,81 +987,112 @@ class StatisticalTest:
return {"z-score": results[0], "p-value": results[1]} return {"z-score": results[0], "p-value": results[1]}
class Array(): # tests on nd arrays independent of basic_stats 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): # expects arrays that are size normalized 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)
return np.mean([*args], axis = 0) def elementwise_median(self, *args, axis = 0):
def elementwise_median(self, *args): if len(*args) == 0:
return np.median(self.array, axis = axis)
else:
return np.median([*args], axis = axis)
return np.median([*args], axis = 0) def elementwise_stdev(self, *args, axis = 0):
def elementwise_stdev(self, *args): if len(*args) == 0:
return np.std(self.array, axis = axis)
else:
return np.std([*args], axis = axis)
return np.std([*args], axis = 0) def elementwise_variance(self, *args, axis = 0):
def elementwise_variance(self, *args): if len(*args) == 0:
return np.var(self.array, axis = axis)
else:
return np.var([*args], axis = axis)
return np.var([*args], axis = 0) def elementwise_npmin(self, *args, axis = 0):
def elementwise_npmin(self, *args): if len(*args) == 0:
return np.amin(self.array, axis = axis)
else:
return np.amin([*args], axis = axis)
return np.amin([*args], axis = 0) def elementwise_npmax(self, *args, axis = 0):
def elementwise_npmax(self, *args): if len(*args) == 0:
return np.amax(self.array, axis = axis)
else:
return np.amax([*args], axis = axis)
return np.amax([*args], axis = 0) def elementwise_stats(self, *args, axis = 0):
def elementwise_stats(self, *args): _mean = self.elementwise_mean(*args, axis = axis)
_median = self.elementwise_median(*args, axis = axis)
_mean = self.elementwise_mean(*args) _stdev = self.elementwise_stdev(*args, axis = axis)
_median = self.elementwise_median(*args) _variance = self.elementwise_variance(*args, axis = axis)
_stdev = self.elementwise_stdev(*args) _min = self.elementwise_npmin(*args, axis = axis)
_variance = self.elementwise_variance(*args) _max = self.elementwise_npmax(*args, axis = axis)
_min = self.elementwise_npmin(*args)
_max = self.elementwise_npmax(*args)
return _mean, _median, _stdev, _variance, _min, _max 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): def normalize(self, array):
a = np.atleast_1d(np.linalg.norm(array)) a = np.atleast_1d(np.linalg.norm(array))
a[a==0] = 1 a[a==0] = 1
return array / np.expand_dims(a, -1) return array / np.expand_dims(a, -1)
def add(self, *args): def __add__(self, other):
temp = np.array([]) return self.array + other
for a in args: def __sub__(self, other):
temp += a
return self.array - other
def __neg__(self):
return temp return -self.array
def mul(self, *args): def __abs__(self):
temp = np.array([]) return abs(self.array)
for a in args: def __invert__(self):
temp *= a
return temp
def neg(self, array): return 1/self.array
return -array
def inv(self, array): def __mul__(self, other):
return 1/array return self.array.dot(other)
def dot(self, a, b): def __rmul__(self, other):
return np.dot(a, b) return self.array.dot(other)
def cross(self, a, b): def cross(self, other):
return np.cross(a, b) return np.cross(self.array, b)
def sort(self, array): # depreciated def sort(self, array): # depreciated
warnings.warn("Array.sort has been depreciated in favor of Sort") warnings.warn("Array.sort has been depreciated in favor of Sort")