New version 0.0.3

RELEASE.md

@@ -1,3 +1,3 @@
-# Next Release 
+# v0.0.3
 
 - Fixed multiplayer evidence

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="trueskillthroughtime",
-    version="0.0.2",
+    version="0.0.3",
     author="Gustavo Landfried",
     author_email="[email protected]",
     description="The temporal learning estimator: Individual learning curves with reliable initial estimates and guaranteed comparability between distant estimates.",
@@ -20,7 +20,7 @@
     ],
     packages=['trueskillthroughtime'],
     #python_requires=">=3.6",
-    download_url = 'https://github.com/glandfried/TrueSkillThroughTime.py/archive/refs/tags/v0.0.2.tar.gz',
+    download_url = 'https://github.com/glandfried/TrueSkillThroughTime.py/archive/refs/tags/v0.0.3.tar.gz',
     install_requires=[
           'numba',
     ]

test/runtest.py

@@ -1,7 +1,7 @@
 import unittest
 import sys
 sys.path.append('..')
-import trueskillthroughtim as ttt
+import trueskillthroughtime as ttt
 #import old
 from importlib import reload  # Python 3.4+ only.
 reload(ttt)
@@ -169,17 +169,6 @@ def test_NvsN_Draw(self):
         self.assertAlmostEqual(c.mu,14.907,3)
         self.assertAlmostEqual(c.sigma,0.996,3)
 
-        # TODO:
-        #   1. Pasar este test a julia
-        #   2. Reportar ISSUE 
-        # ISSUE:
-        # import trueskill as ts
-        # env = ts.TrueSkill(draw_probability=0.25, beta=25/6, tau=0.0)
-        # _ta = [env.Player(15,1), env.Player(15,1)]
-        # _tb = [env.Player(30,2)]
-        # [r1, r2], [r3] = env.rate([_ta,_tb], [0,0])
-        # trueskill.Player(mu=15.000, sigma=1.143)
-
     def test_NvsNvsN_mixt(self):
         ta = [ttt.Player(ttt.Gaussian(12.,3.),25.0/6,25.0/300)
              ,ttt.Player(ttt.Gaussian(18.,3.),25.0/6,25.0/300)]