Yould is a generator for pronounceable random words. It uses Markov chains trained on a large body of prose.

It looks like you can also install it as a Python library.

Here are a few I came up with:
Knotive
Destrus
Hathis
Conce

They might make good names for future products somday. Can you find any good ones?

Here is the blog post where Yould’s author talks about how he came up with the idea and a bit about how it works.

Posted by Greg Pinero (Primary Searcher) as Python, Other, Machine Learning at 9:01 PM MST

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This was a frusterating 30 minutes of searching to find out how to get Weka to do something that really should have been obvious.

In this data mining competition I was trying out, they give you a final test set without the actual answers and your job is to predict that the actual values are, and well, send them in. I did manage to get it figured out after a lot of searching. So I’m back on my path to winning

So here’s the easiest way to use a model to simply get its predictions without training it again:

If you right-click on the result list you’ll get a popup
with options including “Load model” which allows you to load
some saved model; and there is another option “Re-evaluate
model on the current test-set”; obviously before you can
do that, you will have to load some test-set: tick “Supplied
test set” and then the “Set” button and select your file …

I found that answer here, and later in the thread they also mention a way to do it with the command line interface (CLI).

If that quoted answer above still isn’t clear, allow me to summarize:

1. You open Weka and get that little window with the four buttons.
2. Click on Explorer.
3. load any old bogus data set you’re not going to need just so it allows you to get to the classifier tab.
4. Then under test options you choose “supplied test set” and point it to the data you want it to make predictions on. (Note: this 5. must have all of the same attributes, in the same order that you trained your model on.).
6. Finally right click in the result list area and select load model.
7. Point it to your saved model.
8. Next right click to model you just loaded and tell it to re-evaluate on current test set.

Here’s what your output might look like:

You know, all frusteration aside, Weka really is an incredibly useful, free program, and perhaps I should be glad the developers are spending time on the machine learning side of development instead of my personal needs…

My search terms (see the frusteration build):

1. weka run a model on test data
2. weka use a model
3. weka + “use a model”
4. weka + (get OR output) + predictions + without + training
5. weka + “without training”
6. weka don’t have actual
7. weka cli
8. weka + sucks
9. “weka sucks”

Here is more information about saving models in Weka.

Tags: Weka, machine learning, data mining, predictions

Posted by Greg Pinero (Primary Searcher) as Machine Learning at 11:43 PM MST

2 Comments »

Here is an exhaustive list of the 4 free Artificial Neural Network Libraries for use with your Python code. In the coming weeks I’m going to try out a few of them and update this post. I’d love to hear anyone’s experiences with any of these. If you’ve used them please leave us a comment.

Here they are listed in the order of my perception of their overall quality:

1. First we have the Fast Artificial Neural Network Library (FANN). It is written in C but says it has Python bindings. The bindings for Python look hard to set up but on the plus side it looks well maintained and claims to be extremely fast.

2. Next there is the bpnn.py neural network code by Neil Schemenauer. It’s just a lone .py file sitting out in the middle of the internet. What looks good about this is that it’s written in pure Python, and it’s really short. This makes me think it will be easy to understand and verify. It’s also public domain which is nice. I’m also encouraged by the fact that this fancy IBM type article used this module. Perhaps that article will serve as a de facto documentation for the module?

3. The Pyro Python Robotics project seems to have some neural network stuff but on a cursory glance it appears to be more a tutorial than something you can download and use.

4. Finally, NEURObjects is a set of C++ library classes for neural networks development. I only include it here because I always hear that it’s easy to make a Python wrapper for C++ code. Of course I’ve never been able to figure out how myself, but it might work as a last resort. (Python experts, please give us a tutorial on this wrapper stuff!)

I’m surpsised I didn’t find more free Python neural network libraries. If you know of any I’ve left out, please leave a comment. I would especially think that something would have been done with numpy since that would make for a fast running neural network.

And just in case you want to use a neural network but can’t think of a good excuse, here are a couple of good (financial) ideas to get you started.

Tags: ANN, MLP, Artificial Neural Network, Multilayer Perceptron, Backpropagation, weights, Python Neural network, Python Neural networks, neural network, neural networks, machine learning, data mining

Posted by Greg Pinero (Primary Searcher) as Python, Machine Learning at 9:07 PM MST

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So you’ve downloaded and installed Weka to your Linux system but when you click on Explorer or Experimenter you get this error:
Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException ...

Just follow these steps to fix it:

1. Create a “LookAndFeel.props” file in your home directory:
   Type:touch ~/LookAndFeel.props into system command line.
2. Type this into the first line of the newly created LookAndFeel.props file:
   Theme=javax.swing.plaf.metal.MetalLookAndFeel
3. Save the file and exit.
4. Start WEKA and everything should now work.

Here’s a detailed version of the answer and explains a little bit of the why of this problem.

Here’s the full text of the original error:

chiefinnovator@MAIN1:~$ cd /home/chiefinnovator/weka/weka-3-4-8a/
chiefinnovator@MAIN1:~/weka/weka-3-4-8a$ java -jar weka.jar
 /usr/share/themes/Simple/gtk-2.0/gtkrc:46: Engine "thinice" is unsupported, ignoring
/usr/share/themes/Simple/gtk-2.0/gtkrc:53: Engine "redmond95" is unsupported, ignoring
/usr/share/themes/Simple/gtk-2.0/gtkrc:57: Engine "redmond95" is unsupported, ignoring
---Registering Weka Editors---
Exception in thread "AWT-EventQueue-0" java.lang.NullPointerException
        at weka.gui.explorer.PreprocessPanel.addPropertyChangeListener(Unknown Source)
        at javax.swing.plaf.synth.SynthPanelUI.installListeners(SynthPanelUI.java:49)
        at javax.swing.plaf.synth.SynthPanelUI.installUI(SynthPanelUI.java:38)
        at javax.swing.JComponent.setUI(JComponent.java:652)
        at javax.swing.JPanel.setUI(JPanel.java:131)
        at javax.swing.JPanel.updateUI(JPanel.java:104)
        at javax.swing.JPanel.(JPanel.java:64)
        at javax.swing.JPanel.(JPanel.java:87)
        at javax.swing.JPanel.(JPanel.java:95)
        at weka.gui.explorer.PreprocessPanel.(Unknown Source)
        at weka.gui.explorer.Explorer.(Unknown Source)
        at weka.gui.GUIChooser$3.actionPerformed(Unknown Source)
        at java.awt.Button.processActionEvent(Button.java:388)
        at java.awt.Button.processEvent(Button.java:356)
        at java.awt.Component.dispatchEventImpl(Component.java:3955)
        at java.awt.Component.dispatchEvent(Component.java:3803)
        at java.awt.EventQueue.dispatchEvent(EventQueue.java:463)
        at java.awt.EventDispatchThread.pumpOneEventForHierarchy(EventDispatchThread.java:242)
        at java.awt.EventDispatchThread.pumpEventsForHierarchy(EventDispatchThread.java:163)
        at java.awt.EventDispatchThread.pumpEvents(EventDispatchThread.java:157)        at java.awt.EventDispatchThread.pumpEvents(EventDispatchThread.java:149)        at java.awt.EventDispatchThread.run(EventDispatchThread.java:110)

Tags: weka, ubuntu, java, weka Exception, AWT-EventQueue-0, java.lang.NullPointerException

Posted by Greg Pinero (Primary Searcher) as Machine Learning, Ubuntu at 10:35 PM MST

2 Comments »

A competitive learning network (CLN) is an unsupervised learning technique that is used to find similarities in examples in a dataset and therefore suggest groupings of the examples. This is known as a clustering algorithm since it “clusters” the data examples together into similar groups. It is called unsupervised because we do not tell the algorithm how to group the examples together. It is left to its own devices to figure out any similarities in the data.
A CLN is made up of two layers of nodes; an input layer and an output layer. Each node in the input layer connects to every node in the output layer. Each connection has a weight assigned to it. The values assigned to these weights are where knowledge is stored in this network. The each node in the input layer is associated with a feature in the dataset. Each node in the output layer corresponds to a potential grouping of the data.
Read the rest of this entry »

Posted by Greg Pinero (Primary Searcher) as Machine Learning at 12:58 PM MST

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