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Eigen support for special matrix objects

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Functions returning specialized Eigen matrices like Eigen::DiagonalMatrix and
Eigen::SelfAdjointView--which inherit from EigenBase but not
DenseBase--isn't currently allowed; such classes are explicitly copyable
into a Matrix (by definition), and so we can support functions that
return them by copying the value into a Matrix then casting that
resulting dense Matrix into a numpy.ndarray.  This commit does exactly
that.
This commit is contained in:
Jason Rhinelander
2016-08-04 15:24:41 -04:00
parent 19637536ac
commit 9ffb3dda5f
5 changed files with 84 additions and 5 deletions
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18
example/eigen.py
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@@ -14,6 +14,7 @@ from example import double_mat_cm, double_mat_rm
from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
from example import diagonal, diagonal_1, diagonal_n
from example import block
from example import incr_diag, symmetric_upper, symmetric_lower
try:
import numpy as np
import scipy
@@ -88,3 +89,20 @@ for i in range(-5, 7):
print("block(2,1,3,3) %s" % ("OK" if (block(ref, 2, 1, 3, 3) == ref[2:5, 1:4]).all() else "FAILED"))
print("block(1,4,4,2) %s" % ("OK" if (block(ref, 1, 4, 4, 2) == ref[1:, 4:]).all() else "FAILED"))
print("block(1,4,3,2) %s" % ("OK" if (block(ref, 1, 4, 3, 2) == ref[1:4, 4:]).all() else "FAILED"))
print("incr_diag %s" % ("OK" if (incr_diag(7) == np.diag([1,2,3,4,5,6,7])).all() else "FAILED"))
asymm = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10,11,12],
[13,14,15,16]])
symm_lower = np.array(asymm)
symm_upper = np.array(asymm)
for i in range(4):
for j in range(i+1, 4):
symm_lower[i,j] = symm_lower[j,i]
symm_upper[j,i] = symm_upper[i,j]
print("symmetric_lower %s" % ("OK" if (symmetric_lower(asymm) == symm_lower).all() else "FAILED"))
print("symmetric_upper %s" % ("OK" if (symmetric_upper(asymm) == symm_upper).all() else "FAILED"))