#n_pivots = int(n_samples / 2)
n_pivots = 200
# <codecell>
# Theano expressions
# parameters
ps = breze.util.ParameterSet(P=(n_hidden, n_pivots),
W=(n_targets, n_pivots),
V=(n_hidden, n_features),
l=(1,1))
# expressions
srbf = StackedRBF(ps.P, ps.W, ps.V, ps.l)
RL = srbf.regression_objective(RX, RZ)
# functions
f_RL = function(inputs=[ps.flat], outputs=RL)
f_VL = function(inputs=[ps.flat], outputs=srbf.regression_objective(VX, VZ))
f_TL = function(inputs=[ps.flat], outputs=srbf.regression_objective(TX, TZ))
f_dRL = function(inputs=[ps.flat], outputs=T.grad(RL, ps.flat))
# <codecell>
# initialize parameters
ps.data[:] = np.random.random(ps.data.shape) - 0.5
ps['l'] = 2;
# test Theano expressions
print "Training loss: %f" % gp.as_numpy_array(f_RL(ps.data))
