*Last updated: 2023-03-16.*
# tf_quant_finance.math.piecewise.PiecewiseConstantFunc
View source
Creates a piecewise constant function.
```python
tf_quant_finance.math.piecewise.PiecewiseConstantFunc(
jump_locations, values, dtype=None, name=None
)
```
#### Args:
* `jump_locations`: A real `Tensor` of shape
`batch_shape + [num_jump_points]`. The locations where the function
changes its values. Note that the values are expected to be ordered
along the last dimension. Repeated values are allowed but it is
up to the user to ensure that the corresponding `values` are also
repeated.
* `values`: A `Tensor` of the same `dtype` as `jump_locations` and shape
`batch_shape + [num_jump_points + 1] + event_shape`. Defines
`values[batch_rank * slice(None), i]` on intervals
`(jump_locations[..., i - 1], jump_locations[..., i])`. Here
`event_shape` allows for array-valued piecewise constant functions
and `batch_rank = len(batch_shape)`.
* `dtype`: Optional dtype for `jump_locations` and `values`.
Default value: `None` which maps to the default dtype inferred from
`jump_locations`.
* `name`: Python `str` name prefixed to ops created by this class.
Default value: `None` which is mapped to the default name
`PiecewiseConstantFunc`.
#### Raises:
* `ValueError`: If `jump_locations` and `values` have different batch shapes or,
in case of static shapes, if the event shape of `values` is different
from `num_jump_points + 1`.
## Methods
__call__
View source
```python
__call__(
x, left_continuous=True, name=None
)
```
Computes value of the piecewise constant function.
Returns a value of the piecewise function with jump locations and values
given by the initializer.
#### Args:
* `x`: A real `Tensor` of shape `batch_shape + [num_points]`. Points at which
the function has to be evaluated.
* `left_continuous`: Python `bool`. Whether the function is left- or right-
continuous, i.e., at the `jump_locations[..., i]` left-continuity means
that the function has the same value
`values[batch_rank * slice(None), i]`, whereas for
right-continuity, the value is
`values[batch_rank * slice(None), i + 1]`.
Default value: `True` which means that the function is left-continuous.
* `name`: Python `str` name prefixed to ops created by this method.
Default value: `None` which is mapped to the default name
`self.name() + _call`.
#### Returns:
A `Tensor` of the same `dtype` as `x` and shape
`batch_shape + [num_points] + event_shape` containing values of the
piecewise constant function.
dtype
View source
```python
dtype()
```
The underlying data type.
integrate
View source
```python
integrate(
x1, x2, name=None
)
```
Integrates the piecewise constant function between end points.
Returns a value of the integral on the interval `[x1, x2]` of a piecewise
constant function with jump locations and values given by the initializer.
#### Args:
* `x1`: A real `Tensor` of shape `batch_shape + [num_points]`. Left end points
at which the function has to be integrated.
* `x2`: A `Tensor` of the same shape and `dtype` as `x1`. Right end points at
which the function has to be integrated.
* `name`: Python `str` name prefixed to ops created by this method.
Default value: `None` which is mapped to the default name
`self.name() + `_integrate``.
#### Returns:
A `Tensor` of the same `dtype` as `x` and shape
`batch_shape + [num_points] + event_shape` containing values of the
integral of the piecewise constant function between `[x1, x2]`.
jump_locations
View source
```python
jump_locations()
```
The jump locations of the piecewise constant function.
name
View source
```python
name()
```
The name to give to the ops created by this class.
values
View source
```python
values()
```
The value of the piecewise constant function between jump locations.