From 031e45ac19ffa04503cb855fa1ce86d7133b6cce Mon Sep 17 00:00:00 2001 From: art Date: Sat, 4 Jan 2020 10:13:25 -0600 Subject: [PATCH] analysis.py v 1.1.11.007 --- .gitignore | 2 ++ data analysis/analysis/analysis.py | 24 +++++++++++++----------- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/.gitignore b/.gitignore index b44c3081..e5e132ff 100644 --- a/.gitignore +++ b/.gitignore @@ -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 diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py index 3a3804c3..23e18282 100644 --- a/data analysis/analysis/analysis.py +++ b/data analysis/analysis/analysis.py @@ -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)