def entropy_binary_vector(P):
"""
if P[i,j] represents the probability
of some binary random variable X[i,j] being 1
then rval[i] gives the entropy of the random vector
X[i,:]
"""
oneMinusP = 1.-P
PlogP = xlogx(P)
omPlogOmP = xlogx(oneMinusP)
term1 = - T.sum( PlogP , axis=1)
assert len(term1.type.broadcastable) == 1
term2 = - T.sum( omPlogOmP , axis =1 )
assert len(term2.type.broadcastable) == 1
rval = term1 + term2
for plp, olo, t1, t2, rv in get_debug_values(PlogP, omPlogOmP, term1, term2, rval):
debug_assert(not np.any(np.isnan(plp)))
debug_assert(not np.any(np.isinf(olo)))
debug_assert(not np.any(np.isnan(plp)))
debug_assert(not np.any(np.isinf(olo)))
debug_assert(not np.any(np.isnan(t1)))
debug_assert(not np.any(np.isnan(t2)))
debug_assert(not np.any(np.isnan(rv)))
return rval
def test_basic(self):
x = as_tensor_variable([1, 0])
y = xlogx(x)
f = theano.function([], [y])
assert numpy.all(f() == numpy.asarray([0, 0.0]))
# class Dummy(object):
# def make_node(self, a):
# return [xlogx(a)[:,2]]
utt.verify_grad(xlogx, [numpy.random.rand(3, 4)])
def entropy_binary_vector(P):
"""
.. todo::
WRITEME properly
if P[i,j] represents the probability
of some binary random variable X[i,j] being 1
then rval[i] gives the entropy of the random vector
X[i,:]
"""
for Pv in get_debug_values(P):
assert Pv.min() >= 0.0
assert Pv.max() <= 1.0
oneMinusP = 1. - P
PlogP = xlogx(P)
omPlogOmP = xlogx(oneMinusP)
term1 = -T.sum(PlogP, axis=1)
assert len(term1.type.broadcastable) == 1
term2 = -T.sum(omPlogOmP, axis=1)
assert len(term2.type.broadcastable) == 1
rval = term1 + term2
for plp, olo, t1, t2, rv in get_debug_values(PlogP, omPlogOmP, term1,
term2, rval):
debug_assert(not np.any(np.isnan(plp)))
debug_assert(not np.any(np.isinf(olo)))
debug_assert(not np.any(np.isnan(plp)))
debug_assert(not np.any(np.isinf(olo)))
debug_assert(not np.any(np.isnan(t1)))
debug_assert(not np.any(np.isnan(t2)))
debug_assert(not np.any(np.isnan(rv)))
return rval
def entropy_binary_vector(P):
"""
.. todo::
WRITEME properly
If P[i,j] represents the probability of some binary random variable X[i,j]
being 1, then rval[i] gives the entropy of the random vector X[i,:]
"""
for Pv in get_debug_values(P):
assert Pv.min() >= 0.0
assert Pv.max() <= 1.0
oneMinusP = 1. - P
PlogP = xlogx(P)
omPlogOmP = xlogx(oneMinusP)
term1 = - T.sum(PlogP, axis=1)
assert len(term1.type.broadcastable) == 1
term2 = - T.sum(omPlogOmP, axis=1)
assert len(term2.type.broadcastable) == 1
rval = term1 + term2
debug_vals = get_debug_values(PlogP, omPlogOmP, term1, term2, rval)
for plp, olo, t1, t2, rv in debug_vals:
debug_assert(isfinite(plp))
debug_assert(isfinite(olo))
debug_assert(not contains_nan(t1))
debug_assert(not contains_nan(t2))
debug_assert(not contains_nan(rv))
return rval
def test0(self):
x = as_tensor_variable([1, 0])
y = xlogx(x)
f = theano.function([], [y])
self.assertTrue(numpy.all(f() == numpy.asarray([0, 0.])))
def test0(self):
x = as_tensor_variable([1, 0])
y = xlogx(x)
f = theano.function([], [y])
self.assertTrue(numpy.all(f() == numpy.asarray([0, 0.])))