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standalone: add linalg methods and tests

This commit is contained in:
abdul124 2024-07-25 12:17:32 +08:00
parent 2242c5af43
commit d6a4d0a634
2 changed files with 122 additions and 0 deletions

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@ -5,6 +5,7 @@ import importlib.util
import importlib.machinery
import math
import numpy as np
import scipy as sp
import numpy.typing as npt
import pathlib
@ -226,6 +227,19 @@ def patch(module):
module.sp_spec_j0 = special.j0
module.sp_spec_j1 = special.j1
# Linalg functions
module.np_dot = np.dot
module.np_linalg_matmul = np.matmul
module.np_linalg_cholesky = np.linalg.cholesky
module.np_linalg_qr = np.linalg.qr
module.np_linalg_svd = np.linalg.svd
module.np_linalg_inv = np.linalg.inv
module.np_linalg_pinv = np.linalg.pinv
module.sp_linalg_lu = lambda x: sp.linalg.lu(x, True)
module.sp_linalg_schur = sp.linalg.schur
module.sp_linalg_hessenberg = lambda x: sp.linalg.hessenberg(x, True)
def file_import(filename, prefix="file_import_"):
filename = pathlib.Path(filename)
modname = prefix + filename.stem

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@ -1429,6 +1429,104 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
output_ndarray_float_2(nextafter_x_zeros)
output_ndarray_float_2(nextafter_x_ones)
def test_ndarray_dot():
x: ndarray[float, 1] = np_array([5.0, 1.0])
y: ndarray[float, 1] = np_array([5.0, 1.0])
z = np_dot(x, y)
output_ndarray_float_1(x)
output_ndarray_float_1(y)
output_float64(z)
def test_ndarray_linalg_matmul():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
z = np_linalg_matmul(x, y)
m = np_argmax(z)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
output_ndarray_float_2(z)
output_int64(m)
def test_ndarray_cholesky():
x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
y = np_linalg_cholesky(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_qr():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
y, z = np_linalg_qr(x)
output_ndarray_float_2(x)
# QR Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(y, z)
output_ndarray_float_2(a)
def test_ndarray_linalg_inv():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
y = np_linalg_inv(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_pinv():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
y = np_linalg_pinv(x)
output_ndarray_float_2(x)
output_ndarray_float_2(y)
def test_ndarray_schur():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
t, z = sp_linalg_schur(x)
output_ndarray_float_2(x)
# Schur Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
output_ndarray_float_2(a)
def test_ndarray_hessenberg():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 5.0, 8.5]])
h, q = sp_linalg_hessenberg(x)
output_ndarray_float_2(x)
# Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
output_ndarray_float_2(a)
def test_ndarray_lu():
x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
l, u = sp_linalg_lu(x)
output_ndarray_float_2(x)
output_ndarray_float_2(l)
output_ndarray_float_2(u)
def test_ndarray_svd():
w: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
x, y, z = np_linalg_svd(w)
output_ndarray_float_2(w)
# SVD Factorization is not unique and gives different results in numpy and nalgebra
# Reverting the decomposition to compare the initial arrays
a = np_linalg_matmul(x, z)
output_ndarray_float_2(a)
output_ndarray_float_1(y)
def run() -> int32:
test_ndarray_ctor()
test_ndarray_empty()
@ -1608,4 +1706,14 @@ def run() -> int32:
test_ndarray_nextafter_broadcast_lhs_scalar()
test_ndarray_nextafter_broadcast_rhs_scalar()
test_ndarray_dot()
test_ndarray_linalg_matmul()
test_ndarray_cholesky()
test_ndarray_qr()
test_ndarray_svd()
test_ndarray_linalg_inv()
test_ndarray_pinv()
test_ndarray_lu()
test_ndarray_schur()
test_ndarray_hessenberg()
return 0