def partialsumV(self, actv, acth, row):
"""
sums out hidden variables for given v
exp( log(exp(bh + actv*W)+1).sum(axis=0) + (v*bv).sum(axis=0) )
"""
# acth = bv + actv*W
cp.prod(acth, self.weight, actv, "t", "n")
cp.matrix_plus_col(acth, self.bh)
# acth = log(exp(acth)+1)
cp.apply_scalar_functor(acth, cp.scalar_functor.RECT, 1.0)
# row = actv.sum(axis=0)
cp.reduce_to_row(row, acth, cp.reduce_functor.ADD)
# row += h*bh
cp.matrix_times_col(actv, self.bv)
cp.reduce_to_row(row, actv, cp.reduce_functor.ADD, 1.0, 1.0)
# exp(row)
m = row.np.astype("float64")
return math.fsum(m.flatten()) / actv.shape[1]
def partialsumV(self, actv, acth, row):
"""
sums out hidden variables for given v
exp( log(exp(bh + actv*W)+1).sum(axis=0) + (v*bv).sum(axis=0) )
"""
# acth = bv + actv*W
cp.prod(acth, self.weight, actv, 't', 'n')
cp.matrix_plus_col(acth, self.bh)
# acth = log(exp(acth)+1)
cp.apply_scalar_functor(acth, cp.scalar_functor.RECT, 1.0)
# row = actv.sum(axis=0)
cp.reduce_to_row(row, acth, cp.reduce_functor.ADD)
# row += h*bh
cp.matrix_times_col(actv, self.bv)
cp.reduce_to_row(row, actv, cp.reduce_functor.ADD, 1.0, 1.0)
# exp(row)
m = row.np.astype("float64")
return math.fsum(m.flatten()) / actv.shape[1]
def partialsum(self, acth, actv, row):
"""
sums out visible variables for given hidden variables
exp( log(exp(bv + acth*W)+1).sum(axis=0) + (h*bh).sum(axis=0) )
"""
# actv = bv + acth*W
cp.prod(actv, self.weight, acth, "n", "n")
cp.matrix_plus_col(actv, self.bv)
# actv = log(exp(actv)+1)
cp.apply_scalar_functor(actv, cp.scalar_functor.RECT, 1.0)
# row = actv.sum(axis=0)
cp.reduce_to_row(row, actv, cp.reduce_functor.ADD)
# row += h*bh
cp.matrix_times_col(acth, self.bh)
cp.reduce_to_row(row, acth, cp.reduce_functor.ADD, 1.0, 1.0)
# cp.prod(row,self.bv,actv,'t','n',1.0,1.0)
# exp(row)
m = row.np.astype("float64")
return math.fsum(np.exp(m).flatten())
def partialsum(self, acth, actv, row):
"""
sums out visible variables for given hidden variables
exp( log(exp(bv + acth*W)+1).sum(axis=0) + (h*bh).sum(axis=0) )
"""
# actv = bv + acth*W
cp.prod(actv, self.weight, acth, 'n', 'n')
cp.matrix_plus_col(actv, self.bv)
# actv = log(exp(actv)+1)
cp.apply_scalar_functor(actv, cp.scalar_functor.RECT, 1.0)
# row = actv.sum(axis=0)
cp.reduce_to_row(row, actv, cp.reduce_functor.ADD)
# row += h*bh
cp.matrix_times_col(acth, self.bh)
cp.reduce_to_row(row, acth, cp.reduce_functor.ADD, 1.0, 1.0)
#cp.prod(row,self.bv,actv,'t','n',1.0,1.0)
# exp(row)
m = row.np.astype("float64")
return math.fsum(np.exp(m).flatten())