spaces.GridAxis

spaces.GridAxis(low, high, n, shape=None)

Axis for systematic grid sampling over parameter bounds.

Generates linearly spaced values between low and high bounds using deterministic grid sampling.

Parameters

Name	Type	Description	Default
low	float	Lower bound for sampling.	required
high	float	Upper bound for sampling.	required
n	int	Number of grid points to generate.	required

Raises

Name	Type	Description
	ValueError	If n <= 0 or low >= high.

Examples

>>> grid = GridAxis(0.0, 1.0, 5)
>>> values = grid.generate_values()
>>> print(values)  # [0.0, 0.25, 0.5, 0.75, 1.0]

Attributes

Name	Description
size	Number of grid points.

Methods

Name	Description
generate_values	Generate linearly spaced grid values.

generate_values

spaces.GridAxis.generate_values(key=None)

Generate linearly spaced grid values.

Parameters

Name	Type	Description	Default
key	jax.random.PRNGKey	Random key. Ignored for deterministic grid sampling.	`None`

Returns

Name	Type	Description
	jnp.ndarray	Array of linearly spaced values from low to high. If shape is specified, values are broadcast to shape (n,) + shape with identical values across additional dimensions.

# spaces.GridAxis { #tvboptim.spaces.GridAxis } ```python spaces.GridAxis(low, high, n, shape=None) ``` Axis for systematic grid sampling over parameter bounds. Generates linearly spaced values between low and high bounds using deterministic grid sampling. ## Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|------------------|------------------------------------|------------| | low | [float](`float`) | Lower bound for sampling. | _required_ | | high | [float](`float`) | Upper bound for sampling. | _required_ | | n | [int](`int`) | Number of grid points to generate. | _required_ | ## Raises {.doc-section .doc-section-raises} | Name | Type | Description | |--------|----------------------------|---------------------------| | | [ValueError](`ValueError`) | If n <= 0 or low >= high. | ## Examples {.doc-section .doc-section-examples} ```python >>> grid = GridAxis(0.0, 1.0, 5) >>> values = grid.generate_values() >>> print(values) # [0.0, 0.25, 0.5, 0.75, 1.0] ``` ## Attributes | Name | Description | | --- | --- | | [size](#tvboptim.spaces.GridAxis.size) | Number of grid points. | ## Methods | Name | Description | | --- | --- | | [generate_values](#tvboptim.spaces.GridAxis.generate_values) | Generate linearly spaced grid values. | ### generate_values { #tvboptim.spaces.GridAxis.generate_values } ```python spaces.GridAxis.generate_values(key=None) ``` Generate linearly spaced grid values. #### Parameters {.doc-section .doc-section-parameters} | Name | Type | Description | Default | |--------|---------------------------------------------------------------------|------------------------------------------------------|-----------| | key | [jax](`jax`).[random](`jax.random`).[PRNGKey](`jax.random.PRNGKey`) | Random key. Ignored for deterministic grid sampling. | `None` | #### Returns {.doc-section .doc-section-returns} | Name | Type | Description | |--------|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | | [jnp](`jax.numpy`).[ndarray](`jax.numpy.ndarray`) | Array of linearly spaced values from low to high. If shape is specified, values are broadcast to shape (n,) + shape with identical values across additional dimensions. |