alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-27 01:59:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py v 1.1.11.004

Browse Source
This commit is contained in:
ltcptgeneral 2019-11-16 16:21:06 -06:00
parent 1717cc17a1
commit 0ba3a56ea7
1 changed files with 3 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
data analysis/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.11.003" __version__ = "1.1.11.004"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.11.004:
- bug fixes
1.1.11.003: 1.1.11.003:
- bug fixes - bug fixes
1.1.11.002: 1.1.11.002:
@ -378,7 +380,6 @@ def trueskill(teams_data, observations):#teams_data is array of array of tuples
return Trueskill.rate(teams_data, observations) return Trueskill.rate(teams_data, observations)
@jit(forceobj=True)
class RegressionMetrics(): class RegressionMetrics():
def __new__(self, predictions, targets): def __new__(self, predictions, targets):
@ -397,7 +398,6 @@ class RegressionMetrics():
return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
@jit(forceobj=True)
class ClassificationMetrics(): class ClassificationMetrics():
def __new__(self, predictions, targets): def __new__(self, predictions, targets):
@ -479,8 +479,6 @@ def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform"
return model, RegressionMetrics(predictions, outputs_test) return model, RegressionMetrics(predictions, outputs_test)
@jit(forceobj=True)
class NaiveBayes: class NaiveBayes:
def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
@ -519,7 +517,6 @@ class NaiveBayes:
return model, ClassificationMetrics(predictions, labels_test) return model, ClassificationMetrics(predictions, labels_test)
@jit(forceobj=True)
class SVM: class SVM:
class CustomKernel: class CustomKernel: