forked from M-Labs/nac3
standalone: add linalg methods and tests
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@ -5,6 +5,7 @@ import importlib.util
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import importlib.machinery
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import math
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import numpy as np
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import scipy as sp
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import numpy.typing as npt
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import pathlib
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@ -226,6 +227,19 @@ def patch(module):
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module.sp_spec_j0 = special.j0
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module.sp_spec_j1 = special.j1
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# Linalg functions
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module.np_dot = np.dot
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module.np_linalg_matmul = np.matmul
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module.np_linalg_cholesky = np.linalg.cholesky
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module.np_linalg_qr = np.linalg.qr
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module.np_linalg_svd = np.linalg.svd
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module.np_linalg_inv = np.linalg.inv
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module.np_linalg_pinv = np.linalg.pinv
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module.sp_linalg_lu = lambda x: sp.linalg.lu(x, True)
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module.sp_linalg_schur = sp.linalg.schur
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module.sp_linalg_hessenberg = lambda x: sp.linalg.hessenberg(x, True)
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def file_import(filename, prefix="file_import_"):
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filename = pathlib.Path(filename)
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modname = prefix + filename.stem
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@ -1429,6 +1429,104 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
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output_ndarray_float_2(nextafter_x_zeros)
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output_ndarray_float_2(nextafter_x_ones)
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def test_ndarray_dot():
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x: ndarray[float, 1] = np_array([5.0, 1.0])
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y: ndarray[float, 1] = np_array([5.0, 1.0])
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z = np_dot(x, y)
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output_ndarray_float_1(x)
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output_ndarray_float_1(y)
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output_float64(z)
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def test_ndarray_linalg_matmul():
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x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
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y: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
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z = np_linalg_matmul(x, y)
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m = np_argmax(z)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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output_ndarray_float_2(z)
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output_int64(m)
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def test_ndarray_cholesky():
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x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
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y = np_linalg_cholesky(x)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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def test_ndarray_qr():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
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y, z = np_linalg_qr(x)
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output_ndarray_float_2(x)
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# QR Factorization is not unique and gives different results in numpy and nalgebra
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# Reverting the decomposition to compare the initial arrays
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a = np_linalg_matmul(y, z)
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output_ndarray_float_2(a)
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def test_ndarray_linalg_inv():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
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y = np_linalg_inv(x)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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def test_ndarray_pinv():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
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y = np_linalg_pinv(x)
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output_ndarray_float_2(x)
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output_ndarray_float_2(y)
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def test_ndarray_schur():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
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t, z = sp_linalg_schur(x)
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output_ndarray_float_2(x)
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# Schur Factorization is not unique and gives different results in scipy and nalgebra
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# Reverting the decomposition to compare the initial arrays
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a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
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output_ndarray_float_2(a)
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def test_ndarray_hessenberg():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 5.0, 8.5]])
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h, q = sp_linalg_hessenberg(x)
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output_ndarray_float_2(x)
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# Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
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# Reverting the decomposition to compare the initial arrays
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a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
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output_ndarray_float_2(a)
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def test_ndarray_lu():
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x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
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l, u = sp_linalg_lu(x)
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output_ndarray_float_2(x)
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output_ndarray_float_2(l)
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output_ndarray_float_2(u)
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def test_ndarray_svd():
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w: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
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x, y, z = np_linalg_svd(w)
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output_ndarray_float_2(w)
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# SVD Factorization is not unique and gives different results in numpy and nalgebra
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# Reverting the decomposition to compare the initial arrays
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a = np_linalg_matmul(x, z)
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output_ndarray_float_2(a)
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output_ndarray_float_1(y)
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def run() -> int32:
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test_ndarray_ctor()
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test_ndarray_empty()
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@ -1608,4 +1706,14 @@ def run() -> int32:
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test_ndarray_nextafter_broadcast_lhs_scalar()
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test_ndarray_nextafter_broadcast_rhs_scalar()
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test_ndarray_dot()
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test_ndarray_linalg_matmul()
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test_ndarray_cholesky()
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test_ndarray_qr()
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test_ndarray_svd()
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test_ndarray_linalg_inv()
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test_ndarray_pinv()
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test_ndarray_lu()
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test_ndarray_schur()
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test_ndarray_hessenberg()
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return 0
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