*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.digital_net_sample
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Constructs a digital net from a generating matrix.
```python
tf_quant_finance.math.qmc.digital_net_sample(
generating_matrices, num_results, num_digits, sequence_indices=None,
scrambling_matrices=None, digital_shift=None, apply_tent_transform=False,
validate_args=False, dtype=None, name=None
)
```
#### Examples
```python
import tf_quant_finance as tff
# Example: Sampling 1,000 points from 2D Sobol generating matrices.
dim = 2
num_results = 1000
num_digits = 10
tff.math.qmc.digital_net_sample(
tff.math.qmc.sobol_generating_matrices(dim, num_results, num_digits),
num_results,
num_digits)
# ==> tf.Tensor([
# [0., 0. ],
# [0.5, 0.5 ],
# [0.25, 0.75 ],
# ...
# [0.65527344, 0.9736328 ],
# [0.40527344, 0.7236328 ],
# [0.90527344, 0.22363281],
# ], shape=(1000, 2), dtype=float32)
```
#### Args:
* `generating_matrices`: Positive scalar `Tensor` of integers with rank 2. The
matrix from which to sample points.
* `num_results`: Positive scalar `Tensor` of integers with rank 0. The maximum
number of points to sample from `generating_matrices`.
* `num_digits`: Positive scalar `Tensor` of integers with rank 0. the base-2
precision of the points sampled from `generating_matrices`.
* `sequence_indices`: Optional positive scalar `Tensor` of integers with rank 1.
The elements of the sequence to return specified by their position in the
sequence.
Default value: `None` which corresponds to the `[0, num_results)` range.
* `scrambling_matrices`: Optional positive scalar `Tensor` of integers with the
same shape as `generating_matrices`. The left matrix scramble to apply to
the generating matrices.
Default value: `None`.
* `digital_shift`: Optional positive scalar `Tensor` of integers with shape
(`dim`) where `dim = tf.shape(generating_matrices)[0]`. The digital shift
to apply to all the sampled points via a bitwise xor.
Default value: `None`.
* `apply_tent_transform`: Python `bool` indicating whether to apply a tent
transform to the sampled points.
Default value: `False`.
* `validate_args`: Python `bool` indicating whether to validate arguments.
Default value: `False`.
* `dtype`: Optional `dtype`. The `dtype` of the output `Tensor` (either
`float32` or `float64`).
Default value: `None` which maps to `float32`.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` which maps to `sample_digital_net`.
#### Returns:
A `Tensor` of samples from the Sobol sequence with `shape`
`(num_samples, dim)` where `num_samples = min(num_results,
size(sequence_indices))` and `dim = tf.shape(generating_matrices)[0]`.