def testNdtri(self):
"""Verifies that ndtri computation is correct."""
if not special:
return
p = np.linspace(0., 1.0, 50).astype(np.float64)
# Quantile performs piecewise rational approximation so adding some
# special input values to make sure we hit all the pieces.
p = np.hstack((p, np.exp(-32), 1. - np.exp(-32), np.exp(-2),
1. - np.exp(-2)))
expected_x = special.ndtri(p)
x = special_math.ndtri(p)
self.assertAllClose(expected_x, self.evaluate(x), atol=0.)
def testNdtriDynamicShape(self):
"""Verifies that ndtri computation is correct."""
with self.test_session() as sess:
if not special:
return
p = array_ops.placeholder(np.float32)
p_ = np.linspace(0., 1.0, 50).astype(np.float32)
x = special_math.ndtri(p)
x_ = sess.run(x, feed_dict={p: p_})
expected_x_ = special.ndtri(p_)
self.assertAllClose(expected_x_, x_, atol=0.)
def testNdtriDynamicShape(self):
"""Verifies that ndtri computation is correct."""
with self.test_session() as sess:
if not special:
return
p = array_ops.placeholder(np.float32)
p_ = np.linspace(0., 1.0, 50).astype(np.float32)
x = special_math.ndtri(p)
x_ = sess.run(x, feed_dict={p: p_})
expected_x_ = special.ndtri(p_)
self.assertAllClose(expected_x_, x_, atol=0.)
def testNdtri(self):
"""Verifies that ndtri computation is correct."""
with self.test_session():
if not special:
return
p = np.linspace(0., 1.0, 50).astype(np.float64)
# Quantile performs piecewise rational approximation so adding some
# special input values to make sure we hit all the pieces.
p = np.hstack((p, np.exp(-32), 1. - np.exp(-32),
np.exp(-2), 1. - np.exp(-2)))
expected_x = special.ndtri(p)
x = special_math.ndtri(p)
self.assertAllClose(expected_x, x.eval(), atol=0.)
def _baseNdtriFiniteGradientTest(self, dtype):
"""Verifies that ndtri has finite gradients at interesting points."""
g = ops.Graph()
with g.as_default():
# Tests gradients at 0, 1, and piece-wise boundaries.
p = variables.Variable(
np.array([0.,
np.exp(-32.), np.exp(-2.),
1. - np.exp(-2.), 1. - np.exp(-32.),
1.]).astype(dtype))
value = special_math.ndtri(p)
grads = gradients_impl.gradients(value, p)
with self.test_session(graph=g):
variables.global_variables_initializer().run()
self.assertAllFinite(grads[0])
def _baseNdtriFiniteGradientTest(self, dtype):
"""Verifies that ndtri has finite gradients at interesting points."""
g = ops.Graph()
with g.as_default():
# Tests gradients at 0, 1, and piece-wise boundaries.
p = variables.Variable(
np.array([0.,
np.exp(-32.), np.exp(-2.),
1. - np.exp(-2.), 1. - np.exp(-32.),
1.]).astype(dtype))
value = special_math.ndtri(p)
grads = gradients_impl.gradients(value, p)
with self.test_session(graph=g):
variables.global_variables_initializer().run()
self.assertAllFinite(grads[0])
def _baseNdtriFiniteGradientTest(self, dtype):
"""Verifies that ndtri has finite gradients at interesting points."""
# Tests gradients at 0, 1, and piece-wise boundaries.
p = constant_op.constant(
np.array([
0.,
np.exp(-32.),
np.exp(-2.),
1. - np.exp(-2.),
1. - np.exp(-32.),
1.,
]).astype(dtype))
# Not having the lambda sanitizer means we'd get an `IndexError` whenever
# the user supplied function has default args.
_, grads = _value_and_gradient(lambda x: special_math.ndtri(x), p) # pylint: disable=unnecessary-lambda
self.assertAllFinite(self.evaluate(grads[0]))
def _baseNdtriFiniteGradientTest(self, dtype):
"""Verifies that ndtri has finite gradients at interesting points."""
# Tests gradients at 0, 1, and piece-wise boundaries.
p = constant_op.constant(
np.array([
0.,
np.exp(-32.),
np.exp(-2.),
1. - np.exp(-2.),
1. - np.exp(-32.),
1.,
]).astype(dtype))
# Not having the lambda sanitzer means we'd get an `IndexError` whenever
# the user supplied function has default args.
_, grads = _value_and_gradient(
lambda x: special_math.ndtri(x), p) # pylint: disable=unnecessary-lambda
self.assertAllFinite(self.evaluate(grads[0]))
def probit(x):
return self.evaluate(special_math.ndtri(x))
def probit(x, sess=sess): return self.evaluate(special_math.ndtri(x))
def _quantile(self, p): return self._inv_z(special_math.ndtri(p))
def probit(x, sess=sess): return sess.run(special_math.ndtri(x))
def probit(x, sess=sess):
return sess.run(special_math.ndtri(x))
def example_integrand(xarr, **kwargs):
"""Asian options test function"""
sum1 = tf.reduce_sum(ndtri(xarr), axis=1)
a = S0 * tf.exp((r-sigma2/2) + sigma*sqrtdt*sum1)
arg = 1 / d * tf.reduce_sum(a)
return e*tf.maximum(zero, arg-K)
def erfinv(x):
return special_math.ndtri((x + 1.) / 2.0) / tf.sqrt(2.)
def _quantile(self, p): return self._inv_z(special_math.ndtri(p))
def probit(x):
return special_math.ndtri(x)
def probit(x): return special_math.ndtri(x)