def testErfInvIntegerInput(self):
with self.test_session():
with self.assertRaises(TypeError):
x = np.array([1, 2, 3]).astype(np.int32)
special_math.erfinv(x)
with self.assertRaises(TypeError):
x = np.array([1, 2, 3]).astype(np.int64)
special_math.erfinv(x)
def testErfInvValues(self):
with self.test_session():
if not special:
return
x = np.linspace(0., 1.0, 50).astype(np.float64)
expected_x = special.erfinv(x)
x = special_math.erfinv(x)
self.assertAllClose(expected_x, x.eval(), atol=0.)
def diff(self, j):
'''
@return format [batch_size]
'''
t1 = tf.norm(self.pred_mean_pl - self.output_pl)
t3 = tf.sqrt(2.0)
t4 = erfinv(j)
return tf.cast(
tf.reduce_mean(t1 - tf.sqrt(self.pred_var_pl) * t3 * t4),
tf.float32)
def acc(self, j):
'''
@return format [batch_size]
'''
t1 = tf.norm(self.pred_mean_pl - self.output_pl)
t2 = tf.sqrt(self.pred_var_pl) * tf.sqrt(2.0) * erfinv(j)
t = t1 < t2
s = tf.count_nonzero(t)
b_size = tf.size(t)
b_size = tf.cast(b_size, tf.int64)
return tf.cast(s / b_size, tf.float32)
def _quantile(self, p):
return np.sqrt(2.0) * self.scale * special_math.erfinv(p)
def erfinv(y: TfExpressionEx) -> TfExpression:
"""Inverse of the error function."""
# pylint: disable=no-name-in-module
from tensorflow.python.ops.distributions import special_math
return special_math.erfinv(y)
def _quantile(self, p): return np.sqrt(2.0) * self.scale * special_math.erfinv(p)
