Week 3: Generic Conceptor Framework for Speaker Recognition

Based on the description in "Section 3.12 Example: Dynamical Pattern Recognition" of the tech report a generic frame work for pattern recognition is implemented and added to the Python module.

Edit: 

All conceptor-based functions related to recognition tasks are now put into conceptor.recognition of the Python module.

A usage example:
   
     import conceptor.recognition as recog

     new_recogniser = recog.Recognizer()

     new_recogniser.train(training_data)

     results = new_recognizer.predict(test_data)

, where training_data is a list of feature_size * sample_size dimension numpy arrays with each array corresponding to a training dataset from one class; test_data is a feature_size * sample_size dimension numpy array to be recognized; results is a sample_size dimension vector with each element an integer as a class index.

This framework repeats the results shown in the tech report:
http://nbviewer.ipython.org/github/littleowen/Conceptor/blob/master/ClassifyTest.ipynb