standalone: add linalg methods and tests

nac3standalone/demo/interpret_demo.py

@@ -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

nac3standalone/demo/src/ndarray.py

@@ -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