tf_quant_finance.math.qmc.random_digital_shift

Last updated: 2023-03-16.

tf_quant_finance.math.qmc.random_digital_shift#

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Returns a Tensor drawn from a uniform distribution.

tf_quant_finance.math.qmc.random_digital_shift(
    dim, num_digits, seed, validate_args=False, dtype=None, name=None
)

The result can be can be passed to the sample_digital_net function to shift sampled points through a bitwise xor.

Examples#

import tf_quant_finance as tff

# Example: Creating a Digital shift which can randomize sampled 2D points.

dim = 2
num_digits = 10
seed = (2, 3)

tff.math.qmc.random_digital_shift(dim, num_digits, seed=seed)
# ==> tf.Tensor([586, 1011], shape=(2,), dtype=int32)

Args:#

  • dim: Positive scalar Tensor of integers with rank 0. The event size of the sampled points to shift.

  • num_digits: Positive scalar Tensor of integers with rank 0. the base-2 precision of the sampled points to shift.

  • seed: Positive scalar Tensor with shape [2] and dtype int32 used as seed for the random generator.

  • validate_args: Python bool indicating whether to validate arguments. Default value: False.

  • dtype: Optional dtype. The dtype of the output Tensor (either tf.int32 or tf.int64). Default value: None which maps to tf.int32.

  • name: Python str name prefixed to ops created by this function. Default value: None which maps to random_digital_shift.

Returns:#

A Tensor with shape (dim,).

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