Thanks Lars for your answer. However, the code is working fine and that was my
mistake where I tried to get the output.
On Saturday, March 22, 2014 5:10 AM, Lars Buitinck <[email protected]> wrote:
2014-03-21 21:15 GMT+01:00 Arezou Moussavi <[email protected]>:
> Another issue is when I print the BernoulliRBM's parameters (by printing the
> variable assigned to BernoulliRBM ())I see completely different parameters
> that I have assigned in the program. for example I used rbm.n_components =
> 1200 but 250 is shown in the parameters, the same thing happens for learning
> rate and the number of iterations.
That sounds like a bug. Do you have a minimal code snippet [1] that
reproduces the behavior? E.g.
import numpy as np
from sklearn.neural_network import BernoulliRBM
rbm = BernoulliRBM(n_components=25)
print(rbm.n_components == 25)
X = np.random.randn(200, 100)
rbm.fit(X)
print(rbm.components_.shape)
produces exactly the numbers that it ought to with the current
bleeding-edge version.
[1] http://www.sscce.org/
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