Quantifying pattern separation

Pattern separation will result when the similarity between the patterns produced by the network is larger than the orginal similarity between these patterns.



In [3]:

    
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

Input patterns



In [38]:

    
IN1 = np.zeros(100, dtype=int)
IN2 = np.zeros(100,dtype=int)
IN2[:50] = 1



In [40]:

    
np.corrcoef(IN1,IN2)









    



/home/jguzman/.local/lib/python2.7/site-packages/numpy-1.11.1-py2.7-linux-x86_64.egg/numpy/lib/function_base.py:2569: RuntimeWarning: invalid value encountered in true_divide
  c /= stddev[:, None]
/home/jguzman/.local/lib/python2.7/site-packages/numpy-1.11.1-py2.7-linux-x86_64.egg/numpy/lib/function_base.py:2570: RuntimeWarning: invalid value encountered in true_divide
  c /= stddev[None, :]






    Out[40]:





array([[ nan,  nan],
       [ nan,   1.]])



In [36]:

    
np.dot?



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