def test_besseli_boundary(self):
self.assertAllClose(1., special_math_ops.bessel_i0(0.))
self.assertAllClose(1., special_math_ops.bessel_i0e(0.))
self.assertAllClose(0., special_math_ops.bessel_i1(0.))
self.assertAllClose(0., special_math_ops.bessel_i1e(0.))
self.assertTrue(np.isnan(self.evaluate(special_math_ops.bessel_i0(np.nan))))
self.assertTrue(
np.isnan(self.evaluate(special_math_ops.bessel_i0e(np.nan))))
self.assertTrue(np.isnan(self.evaluate(special_math_ops.bessel_i1(np.nan))))
self.assertTrue(
np.isnan(self.evaluate(special_math_ops.bessel_i1e(np.nan))))
def test_i0j0_even(self, dtype):
x = np.random.uniform(-100., 100., size=int(1e4)).astype(dtype)
self.assertAllClose(
self.evaluate(special_math_ops.bessel_i0(x)),
self.evaluate(special_math_ops.bessel_i0(-x)))
self.assertAllClose(
self.evaluate(special_math_ops.bessel_i0e(x)),
self.evaluate(special_math_ops.bessel_i0e(-x)))
self.assertAllClose(
self.evaluate(special_math_ops.bessel_j0(x)),
self.evaluate(special_math_ops.bessel_j0(-x)))
def test_besseli_larger(self, dtype):
x = np.random.uniform(1., 20., size=int(1e4)).astype(dtype)
try:
from scipy import special # pylint: disable=g-import-not-at-top
self.assertAllClose(
special.i0e(x), self.evaluate(special_math_ops.bessel_i0e(x)))
self.assertAllClose(
special.i1e(x), self.evaluate(special_math_ops.bessel_i1e(x)))
except ImportError as e:
tf_logging.warn('Cannot test special functions: %s' % str(e))
def kaiser_window(window_length, beta=12., dtype=dtypes.float32, name=None):
"""Generate a [Kaiser window][kaiser].
Args:
window_length: A scalar `Tensor` indicating the window length to generate.
beta: Beta parameter for Kaiser window, see reference below.
dtype: The data type to produce. Must be a floating point type.
name: An optional name for the operation.
Returns:
A `Tensor` of shape `[window_length]` of type `dtype`.
[kaiser]:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.kaiser.html
"""
with ops.name_scope(name, 'kaiser_window'):
window_length = _check_params(window_length, dtype)
window_length_const = tensor_util.constant_value(window_length)
if window_length_const == 1:
return array_ops.ones([1], dtype=dtype)
# tf.range does not support float16 so we work with float32 initially.
halflen_float = (math_ops.cast(window_length, dtype=dtypes.float32) -
1.0) / 2.0
arg = math_ops.range(-halflen_float,
halflen_float + 0.1,
dtype=dtypes.float32)
# Convert everything into given dtype which can be float16.
arg = math_ops.cast(arg, dtype=dtype)
beta = math_ops.cast(beta, dtype=dtype)
one = math_ops.cast(1.0, dtype=dtype)
two = math_ops.cast(2.0, dtype=dtype)
halflen_float = math_ops.cast(halflen_float, dtype=dtype)
num = beta * math_ops.sqrt(one - math_ops.pow(arg, two) /
math_ops.pow(halflen_float, two))
window = math_ops.exp(num - beta) * (special_math_ops.bessel_i0e(num) /
special_math_ops.bessel_i0e(beta))
return window