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
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
Thanks for sharing such a helpful, and understandable blog. I really enjoyed reading it.
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