def sample_markov_chains(self,beta,step):
cp.prod(self.h,self.w,self.v,'t','n')
cp.matrix_plus_col(self.h,self.bias_hi)
cp.apply_scalar_functor(self.h,cp.scalar_functor.MULT,beta)
cp.apply_scalar_functor(self.h,cp.scalar_functor.SIGM)
cp.rnd_binarize(self.h)
cp.prod(self.v,self.w,self.h,'n','n')
cp.matrix_plus_col(self.v,self.bias_lo)
cp.apply_scalar_functor(self.v,cp.scalar_functor.MULT,beta)
cp.apply_scalar_functor(self.baserate_bias,cp.scalar_functor.MULT,1-beta)
cp.matrix_plus_col(self.v,self.baserate_bias)
cp.apply_scalar_functor(self.baserate_bias,cp.scalar_functor.MULT,1.0/(1-beta))
cp.apply_scalar_functor(self.v,cp.scalar_functor.SIGM)
#if step % 100 == 0:
#plt.figure(1)
#self.v_=self.v.np
#showthis = self.v_.copy()
#plt.matshow(showthis[:,0].reshape((28,28)))
#plt.draw()
#if not os.path.exists("/tmp/%s"%os.getlogin()):
#os.mkdir("/tmp/%s"%os.getlogin())
#plt.savefig("/tmp/%s/chain_%05d.png"%(os.getlogin(),step))
cp.rnd_binarize(self.v)
def sample(self):
if self.unit_type == UnitType.gaussian or self.unit_type == UnitType.cont:
cp.add_rnd_normal(self.act)
elif self.unit_type == UnitType.binary:
cp.rnd_binarize(self.act)
def sample(self):
if self.unit_type == UnitType.gaussian or self.unit_type == UnitType.cont:
cp.add_rnd_normal(self.act)
elif self.unit_type == UnitType.binary:
cp.rnd_binarize(self.act)
