competitive transfer function produces incorrect output about neurolab HOT 12 CLOSED

See nl.trans.Competitive.__doc__:
:Returns:
        y : ndarray
            may take the following values: 0, 1
            '1' if is a MINIMAL element of x, else '0'
    :Example:
        >>> f = Competitive()
        >>> f([-5, -0.1, 0, 0.1, 100])
        array([ 1.,  0.,  0.,  0.,  0.])

The MATLAB compet function returns '1' in the position of the maximal 
element of x.

Regards,

Chris de Villiers
Consulting Electronics Engineer

Trusc Technologies (Pty) Ltd.
PO Box 902
Vredendal
8160

Tel: +27 (0) 27 213 3878
Fax: +27 (0) 86 695 5578
Mobile: +27 (0) 82 895 4699

Thanks for you interested of neurolab

I find neurolab very useful. It was a trivial matter to change the code of the 
Competitive function so that it now gives the same output as the MATLAB compet 
function.

Hello Zuev

Please could you give me some idea of how to use neurolab to duplicate 
the pattern recognition demo in MATLAB, where the network is created as
net = newff(alphabet,targets,10,{'logsig','logsig'}), where alphabet is 
a 35 x 26 array, and targets is a 26 x 26 array. How would you set this 
up in neurolab?


Regards,

Chris de Villiers
Electronics R&D Engineer

Trusc Technologies (Pty) Ltd.
PO Box 902
Vredendal
8160

Tel: +27 (0) 27 213 3878
Fax: +27 (0) 86 695 5578
Mobile: +27 (0) 82 895 4699

Hello Chris

I used older version NNT (4.0.2 (R13)), may now  API has changed.

If I understand you correctly, you need something it:

import neurolab as nl
import numpy as np

# example patterns: 26 letters with 35 points on each letter
i = np.random.rand(26, 35)
t = np.random.rand(26, 26)
# create network wits 2 layers
# with 35 inputs (5*7) and 26 outputs
net = nl.net.newff([[0,1]]*35, [10, 26], [nl.trans.LogSig()]*2)
net.trainf = nl.train.train_bfgs

net.train(i, t, show=10, epochs=100)

Thanks for the feedback, I'll try it.

Hello Zuev

I got it to work, but I can't simulate with only one letter. It would 
appear that I need to simulate with the entire alphabet. I get an error 
when I try to input one letter (35x1 vector). I expect output to be 26x1 
vector with a 1 in the letter position and 0 everywhere else. Am I doing 
something wrong? This is possible in MATLAB.

letter = np.empty(35)

print net.sim([letter])
# or
print net.step(letter)

Thanks. I didn't realize I had to give sim([x]) instead of sim(x).

Any idea why I get an exp overflow warning with large epoch (e.g. epoch 
= 200)?

I dont know. For help you I need a running code.

I create group: http://groups.google.com/group/py-neurolab
Please ask you questions there