zonopy.batchPolyZonotope

class zonopy.batchPolyZonotope(Z, n_dep_gens=0, expMat=None, id=None, copy_Z=True, dtype=None, device=None)

Bases: object

Batched 1D polynomial zonotope

Batched form of the polyZonotope class. This class is used to represent a batch of polynomial zonotopes over the same domain with arbitrary batch dimensions. It follows a similar formulation from the polyZonotope class as the batchZonotope class did from zonotope.

This results in a $\mathbf{Z} \in \mathbb{R}^{B_1 \times B_2 \times \cdots \times B_b \times (N+M+1) \times d}$ tensor

Refer to the polyZonotope class for more information polynomial zonotops.

__init__(Z, n_dep_gens=0, expMat=None, id=None, copy_Z=True, dtype=None, device=None)

Constructor for the batchPolyZonotope class

Parameters:

• Z (torch.Tensor) – The center and generator matrix of the polynomial zonotope. The shape of Z should be $(B_1, B_2, \cdots, B_b, N+M+1, d)$ where $B_1, B_2, \cdots, B_b$ are the batch dimensions, $N$ is the number of dependent generators, $M$ is the number of independent generators, and $d$ is the dimension of the zonotope.

• n_dep_gens (int, optional) – The number of dependent generators in the polynomial zonotope. Default is 0.

• expMat (torch.Tensor, optional) – The exponent matrix of the dependent generators. If None, it will be the identity matrix. Default: None

• id (torch.Tensor, optional) – The integer identifiers for the dependent generators. If None, it will be the range of the number of dependent generators. Default: None

• copy_Z (bool, optional) – If True, it will copy the input Z value. Default: True

• dtype (torch.dtype, optional) – The data type of the polynomial zonotope. If None, it will be inferred. Default: None

• device (torch.device, optional) – The device of the polynomial zonotope. If None, it will be inferred. Default: None

Raises:

• AssertionError – If the dimension of Z input is not 3 or more.

• AssertionError – If the exponent matrix does not seem to be valid for the given dependent generators or ids.

• AssertionError – If the number of dependent generators does not match the number of ids.

• AssertionError – If the exponent matrix is not a non-negative integer matrix.

Methods

__init__(Z[, n_dep_gens, expMat, id, ...])	Constructor for the batchPolyZonotope class
center_slice_all_dep(val_slc)
combine_bpz(bpzlist, idxs)
compress(compression_level)
cpu()
deleteZerosGenerators([eps])
exactCartProd(other)	self: <polyZonotope> other: <polyZonotope> return <polyZonotope>
from_pzlist(pzlist[, batch_shape])
grad_center_slice_all_dep(val_slc)
hess_center_slice_all_dep(val_slc)
ones(batch_size, dims[, dtype, device])
project([dim])
reduce(order[, option])
reduce_indep(order[, option])
slice_all_dep(val_slc)	Slice polynomial zonotpe in depdent generators id_slc: id to slice val_slc: indeterminant to slice c: <torch.Tensor>, shape [b1,b2,...,nx] grad_c: <torch.Tensor>, d c/d val_slc shape [b1,b2,...,nx,n_ids]
slice_dep(id_slc, val_slc)	Slice polynomial zonotpe in depdent generators id_slc: id to dlice val_slc: indeterminant to slice
split_dep_indep([center_on_dep])
to([dtype, itype, device])
to_batchZonotope()
to_interval([method])
zeros(batch_size, dims[, dtype, device])

Attributes

G
Grest
batch_shape
c
device
dimension
dtype
input_pairs
itype
n_generators
n_indep_gens
shape

property G

property Grest

property batch_shape

property c

center_slice_all_dep(val_slc)

static combine_bpz(bpzlist, idxs)

compress(compression_level)

cpu()

deleteZerosGenerators(eps=0)

property device

property dimension

property dtype

exactCartProd(other)

self: <polyZonotope> other: <polyZonotope> return <polyZonotope>

static from_pzlist(pzlist, batch_shape=None)

grad_center_slice_all_dep(val_slc)

hess_center_slice_all_dep(val_slc)

property input_pairs

property itype

property n_generators

property n_indep_gens

static ones(batch_size, dims, dtype=None, device=None)

project(dim=[0, 1])

reduce(order, option='girard')

reduce_indep(order, option='girard')

property shape

slice_all_dep(val_slc)

Slice polynomial zonotpe in depdent generators id_slc: id to slice val_slc: indeterminant to slice c: <torch.Tensor>, shape [b1,b2,…,nx] grad_c: <torch.Tensor>, d c/d val_slc shape [b1,b2,…,nx,n_ids]

slice_dep(id_slc, val_slc)

Slice polynomial zonotpe in depdent generators id_slc: id to dlice val_slc: indeterminant to slice

split_dep_indep(center_on_dep=True)

to(dtype=None, itype=None, device=None)

to_batchZonotope()

to_interval(method='interval')

static zeros(batch_size, dims, dtype=None, device=None)