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analysis.py v 1.1.11.007

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art 2020-01-04 10:13:25 -06:00
parent e5650b1fe4
commit a20c155cfc
2 changed files with 15 additions and 11 deletions
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2
.gitignore vendored
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@ -12,3 +12,5 @@ data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.
data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj
data analysis/test.ipynb
data analysis/.ipynb_checkpoints/test-checkpoint.ipynb
.vscode/settings.json
.vscode

24
data analysis/analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.11.006"
__version__ = "1.1.11.007"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.11.007:
- bug fixes
1.1.11.006:
- tested min and max
- bug fixes
@ -389,9 +391,9 @@ def trueskill(teams_data, observations):#teams_data is array of array of tuples
class RegressionMetrics():
def __new__(self, predictions, targets):
def __new__(cls, predictions, targets):
return self.r_squared(self, predictions, targets), self.mse(self, predictions, targets), self.rms(self, predictions, targets)
return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
def r_squared(self, predictions, targets): # assumes equal size inputs
@ -407,9 +409,9 @@ class RegressionMetrics():
class ClassificationMetrics():
def __new__(self, predictions, targets):
def __new__(cls, predictions, targets):
return self.cm(self, predictions, targets), self.cr(self, predictions, targets)
return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
def cm(self, predictions, targets):
@ -538,13 +540,13 @@ class SVM:
class CustomKernel:
def __new__(self, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class StandardKernel:
def __new__(self, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
@ -552,25 +554,25 @@ class SVM:
class Linear:
def __new__(self):
def __new__(cls):
return sklearn.svm.SVC(kernel = 'linear')
class Polynomial:
def __new__(self, power, r_bias):
def __new__(cls, power, r_bias):
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
class RBF:
def __new__(self, gamma):
def __new__(cls, gamma):
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
class Sigmoid:
def __new__(self, r_bias):
def __new__(cls, r_bias):
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)