def _runTest(self):
dmat = mfc.dMat()
dmat[0.] = mfc.nw.matrix([[1., 1.j], [2., 2.j]])
dmat[1.] = mfc.nw.matrix([[2., 2.j], [4., 4.j]])
dmat[2.] = mfc.nw.matrix([[4., 4.j], [8., 8.j]])
dMat_diff = dmat.gradient()
self._assert_matrices_close(dMat_diff[0.], [[1., 1.j], [2., 2.j]])
self._assert_matrices_close(dMat_diff[1.], [[1.5, 1.5j], [3., 3.j]])
self._assert_matrices_close(dMat_diff[2.], [[2., 2.j], [4., 4.j]])
def _runTest(self):
dmat1 = mfc.dMat({0.: mfc.nw.matrix([[1, 2], [3, 4], [5, 6]])})
self.assertEqual(dmat1.valsize(), 6)
self.assertEqual(dmat1.valshape(), (3, 2))
vec1 = dmat1.create_reduced_dim(0)
self.assertEqual(vec1.valsize(), 2)
self.assertEqual(vec1.valshape(), (1, 2))
mfc.nw.are_matrices_close(vec1[0][1], mfc.nw.vector([1, 2]))
vec2 = dmat1.create_reduced_dim(0, col=True)
self.assertEqual(vec2.valsize(), 3)
self.assertEqual(vec2.valshape(), (3, 1))
mfc.nw.are_matrices_close(vec2[0][1], mfc.nw.vector([1, 3, 5]))
dmat2 = mfc.dMat({0.: mfc.nw.matrix([[1, 2], [3, 4]])})
vec3 = dmat2.create_reduced_dim(diag=True)
self.assertEqual(vec3.valsize(), 2)
self.assertEqual(vec3.valshape(), (1, 2))
mfc.nw.are_matrices_close(vec3[0][1], mfc.nw.vector([1, 4]))
dsca = vec1.create_reduced_dim(0)
self.assertEqual(dsca.valsize(), 1)
self.assertEqual(dsca.valshape(), (1, 1))
def row_offset_col_gain_posImag(sz=100):
d = mfc.dMat()
_row_offset_col_gain_posImag(d, sz)
return d
def none_unitary():
d = mfc.dMat()
d[1.0] = _get_non_uni_mat()
d[2.0] = _get_non_uni_mat()
return d
def two_unitary():
d = mfc.dMat()
d[1.0] = _get_uni_mat()
d[2.0] = _get_uni_mat()
return d
def row_offset_col_gain_zeroimag_Smat(sz=100):
d = mfc.dMat()
_row_offset_col_gain_zeroimag(d, sz)
return d
def row_offset_col_gain_posNegImag(rg=10):
d = mfc.dMat()
_row_offset_col_gain_posNegImag(d, rg)
return d
def get_absolute_test():
d = mfc.dMat()
d[1.0] = get_identity()
d[2.0] = _get_complex1()
return d
