More fun with Derivatives of Matrices

[PoulsenL at capecon.com]

I have a very complex function for which I would like to take the first
derivative.  The problem is that involves the inverse of a Matrix.
DerivVar chokes on Numpy's inverse because it is not implemented in Python.
Upon some advice from Konrad Hinsen I retrieved the matfunc.py module with
high hopes.  Unfortunately, these hopes were soon dashed due to the absence
of the __float__ attribute in the DerivVar object.
 
Any advice would be greatly appreciated.
 
C = matfunc.makeMat([[DerivVar(1.0,0),2],[3,5]])
>>> C.mmul(C.inverse())
Traceback (most recent call last):
  File "<interactive input>", line 1, in ?
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
183, in inverse
    return self.solve( eye(self.rows) )
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
143, in solve
    if isinstance(b,Mat): return makeMat( map(self.solve, b.tr()) ).tr()
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
145, in solve
    x = self._solve( b )
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
139, in _solve
    Q, R = self.qr()
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
127, in qr
    v, beta = R.tr()[i].house(i)
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
81, in house
    v = Vec( Elementwise([0]*index).concat(self[index:]) ).normalize()
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
72, in normalize
    def normalize( self ):  return self / self.norm()
  File "C:\Documents and Settings\PoulsenL\Desktop\PyMCMC\matfunc.py", line
71, in norm
    def norm( self ):  return math.sqrt(abs( self.dot(self.conjugate()) ))
AttributeError: DerivVar instance has no attribute '__float__'
>>>
 
Loren Poulsen
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