analysis.py v 1.1.13.006

regression.py v 1.0.0.003
analysis pkg v 1.0.0.8

analysis-master/analysis/analysis.py

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.005"
+__version__ = "1.1.13.006"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.006:
+        - cleaned up imports
     1.1.13.005:
         - cleaned up package
     1.1.13.004:
@@ -283,10 +285,7 @@ import scipy
 from scipy import *
 import sklearn
 from sklearn import *
-try:
-    from analysis import trueskill as Trueskill
-except:
-    import trueskill as Trueskill
+from analysis import trueskill as Trueskill
 
 class error(ValueError):
     pass

analysis-master/analysis/regression.py

@@ -5,19 +5,22 @@
 #   this module is cuda-optimized and vectorized (except for one small part)
 # setup:
 
-__version__ = "1.0.0.003"
+__version__ = "1.0.0.004"
 
 # changelog should be viewed using print(analysis.regression.__changelog__)
 __changelog__ = """
-1.0.0.003:
-    - bug fixes
-1.0.0.002:
-    -Added more parameters to log, exponential, polynomial
-    -Added SigmoidalRegKernelArthur, because Arthur apparently needs
-    to train the scaling and shifting of sigmoids
-1.0.0.001:
-    -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
-    -already vectorized (except for polynomial generation) and CUDA-optimized
+    1.0.0.004:
+        - bug fixes
+        - fixed changelog
+    1.0.0.003:
+        - bug fixes
+    1.0.0.002:
+        -Added more parameters to log, exponential, polynomial
+        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
+        to train the scaling and shifting of sigmoids
+    1.0.0.001:
+        -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -already vectorized (except for polynomial generation) and CUDA-optimized
 """
 
 __author__ = (
@@ -40,6 +43,8 @@ __all__ = [
     'CustomTrain'
 ]
 
+import torch
+
 global device
 
 device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"