Skip to content

MAINT: generate random positive definite matrices, not just stacks of identities #296

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

MAINT: generate random positive definite matrices, not just stacks of identities #296

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 10 additions & 6 deletions array_api_tests/hypothesis_helpers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
from . import xps
from ._array_module import _UndefinedStub
from ._array_module import bool as bool_dtype
from ._array_module import broadcast_to, eye, float32, float64, full
from ._array_module import broadcast_to, float32, float64, full
from .stubs import category_to_funcs
from .pytest_helpers import nargs
from .typing import Array, DataType, Scalar, Shape
Expand Down Expand Up @@ -314,16 +314,20 @@ def symmetric_matrices(draw, dtypes=real_floating_dtypes, finite=True, bound=10.

@composite
def positive_definite_matrices(draw, dtypes=floating_dtypes):
# For now just generate stacks of identity matrices
# TODO: Generate arbitrary positive definite matrices, for instance, by
# using something like
# https://github.com/scikit-learn/scikit-learn/blob/844b4be24/sklearn/datasets/_samples_generator.py#L1351.
base_shape = draw(shapes())
n = draw(integers(0, 8)) # 8 is an arbitrary small but interesting-enough value
shape = base_shape + (n, n)
assume(prod(i for i in shape if i) < MAX_ARRAY_SIZE)
dtype = draw(dtypes)
return broadcast_to(eye(n, dtype=dtype), shape)

import numpy as np
rng = np.random.default_rng(1234567)
a = rng.uniform(size=(n, n))
q, r = np.linalg.qr(a)
arr = q.T @ (np.diag(3 + rng.uniform(size=n))) @ q
arr = xp.asarray(arr, dtype=dtype)

return broadcast_to(arr, shape)

@composite
def invertible_matrices(draw, dtypes=floating_dtypes, stack_shapes=shapes()):
Expand Down
Loading