PiecewiseConstant¶

class safe_learning.PiecewiseConstant(discretization, vertex_values=None)¶

A piecewise constant function approximator.

Parameters:	discretization : instance of discretization For example, an instance of GridWorld. vertex_values: arraylike, optional A 2D array with the values at the vertices of the grid on each row.
Attributes:	`limits` Return the discretization limits. `nindex` Return the number of discretization indices. `output_dim` Return the output dimensions of the function. `parameters` Return the vertex values. scope_name

Methods

`__call__`(self, \args, \\*kwargs)	Evaluate the function using the template to ensure variable sharing.
`build_evaluation`(self, points)	Return the function values.
`copy_parameters`(self, other_instance)	Copy over the parameters of another instance.
`gradient`(self, points)	Return the gradient.
`parameter_derivative`(self, points)	Obtain function values at points from triangulation.

build_evaluation(self, points)¶

Return the function values.

Parameters:	points : ndarray The points at which to evaluate the function. One row for each data points.
Returns:	values : ndarray The function values at the points.

copy_parameters(self, other_instance)¶: Copy over the parameters of another instance.

gradient(self, points)¶

Return the gradient.

The gradient is always zero for piecewise constant functions!

Parameters:	points : ndarray The points at which to evaluate the function. One row for each data points.
Returns:	gradient : ndarray The function gradient at the points.

limits¶: Return the discretization limits.

nindex¶: Return the number of discretization indices.

output_dim¶: Return the output dimensions of the function.

parameter_derivative(self, points)¶

Obtain function values at points from triangulation.

This function returns a sparse matrix that, when multiplied with the vector with all the function values on the vertices, returns the function values at points.

Parameters:	points : ndarray A 2d array where each row represents one point.
Returns:	values A sparse matrix B so that evaluate(points) = B.dot(parameters).

parameters¶: Return the vertex values.