specifying Python version as 2.7 now

CHANGES.rst

@@ -4,7 +4,7 @@ Changelog
 0.3.7 (????-??-??)
 ------------------
 
-- ...
+- upgraded Weka to 3.9.0
 
 
 0.3.6 (2016-04-02)

README.md

@@ -6,14 +6,14 @@ using the [javabridge](https://pypi.python.org/pypi/javabridge) library.
 Requirements:
 
 * Python 2.7 (does not work with Python 3)
- * javabridge (>= 1.0.11)
+ * javabridge (1.0.11)
  * matplotlib (optional)
  * pygraphviz (optional)
  * PIL (optional)
-* Oracle JDK 1.6+
+* Oracle JDK 1.7+
 
 Uses:
-* Weka (3.7.14-SNAPSHOT rev 12410)
+* Weka (3.9.0)
 
 ## Installation
 
@@ -28,7 +28,7 @@ You can post questions, patches or enhancement requests in the following Google
 
 https://groups.google.com/forum/#!forum/python-weka-wrapper
 
-## Code examples
+## Examples
 See [python-weka-wrapper-examples](https://github.com/fracpete/python-weka-wrapper-examples)
 repository for example code on the various APIs. Also, check out the sphinx
 documentation in the **doc** directory. You can generate HTML documentation
@@ -41,79 +41,3 @@ Available online documentation:
  * [API](http://pythonhosted.org/python-weka-wrapper/api.html)
  * [Examples](http://pythonhosted.org/python-weka-wrapper/examples.html)
 
-## Command-line examples
-
-Below are some examples of command-line use of the library. You can find these also
-on [PyPi](http://pythonhosted.org/python-weka-wrapper/commandline.html).
-
-### Data generators
-
-Artifical data can be generated using one of Weka's data generators, e.g., the `Agrawal` classification generator:
-
-<pre>
-python weka/datagenerators.py \
-    weka.datagenerators.classifiers.classification.Agrawal \
-    -o /tmp/out.arff
-</pre>
-
-### Filters
-
-Filtering a single ARFF dataset, removing the last attribute using the `Remove` filter:
-
-<pre>
-python weka/filters.py \
-    -i /my/datasets/iris.arff \
-    -o /tmp/out.arff \
-    -c last \
-    weka.filters.unsupervised.attribute.Remove \
-    -R last
-</pre>
-
-### Classifiers
-
-Example on how to cross-validate a `J48` classifier (with confidence factor 0.3) on the iris UCI dataset:
-
-<pre>
-python weka/classifiers.py \
-    -t /my/datasets/iris.arff \
-    -c last \
-    weka.classifiers.trees.J48
-    -C 0.3
-</pre>
-
-### Clusterers
-
-Example on how to perform classes-to-clusters evaluation for `SimpleKMeans` (with 3 clusters) using the iris UCI dataset:
-
-<pre>
-python weka/clusterers.py \
-    -t /my/datasets/iris.arff \
-    -c last \
-    weka.clusterers.SimpleKMeans
-    -N 3
-</pre>
-
-### Attribute selection
-
-You can perform attribute selection using `BestFirst` as search algorithm and `CfsSubsetEval` as evaluator as follows:
-
-<pre>
-python weka/attribute_selection.py \
-    -i /my/datasets/iris.arff \
-    -x 5 \
-    -n 42 \
-    -s "weka.attributeSelection.BestFirst -D 1 -N 5"
-    weka.attributeSelection.CfsSubsetEval \
-    -P 1 \
-    -E 1
-</pre>
-
-### Associator
-
-Associators, like `Apriori`, can be run like this:
-
-<pre>
-python weka/associators.py \
-    -t /my/datasets/lung-cancer.arff \
-    weka.associations.Apriori -N 9 -I
-</pre>

setup.py

@@ -49,9 +49,9 @@ def download_file(url, outfile):
 
 def download_weka():
     """
-    Downloads the monolithic Weka jar from sourceforget.net if nececssary.
+    Downloads the Weka jar from github associated with this release if nececssary.
     """
-    url = "https://github.com/fracpete/python-weka-wrapper/blob/e4518862a42f234c2f75da46965d4051b07898b4/python/weka/lib/weka.jar?raw=true"
+    url = "https://github.com/fracpete/python-weka-wrapper/raw/8fc11c3f728fbde705fb817953cb7844d6987ae6/python/weka/lib/weka.jar"
     outfile = os.path.join(os.path.dirname(__file__), "python", "weka", "lib", "weka.jar")
     if not os.path.exists(outfile):
         if not download_file(url, outfile):

tests/wekatests/coretests/version.py

@@ -26,7 +26,7 @@ def test_version(self):
         """
         Tests the version.weka_version() method
         """
-        self.assertEqual("3.7.14-SNAPSHOT", version.weka_version())
+        self.assertEqual("3.9.0", version.weka_version())
 
 
 def suite():