The svs are
1.99991695e-01, 1.99991682e-01, 6.84719250e-10
so if you try
>>> np.linalg.pinv(a,1e-5)
array([[ 0.41097834, 3.12024106, -3.26279309],
[-3.38526587, 0.30274957, 1.89394811],
[ 2.98692033, -2.30459609, 0.28627222]])
you at least get an answer that's not near-random.
w
On Wed, 10 Aug 2011, Nadav Horesh wrote:
> The matrix in singular, so you can not expect a stable inverse.
>
> Nadav.
>
> ________________________________
> From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org]
> On Behalf Of jp d [yo...@yahoo.com]
> Sent: 11 August 2011 03:50
> To: numpy-discussion@scipy.org
> Subject: [Numpy-discussion] matrix inversion
>
> hi,
> i am trying to invert matrices like this:
> [[ 0.01643777 -0.13539939 0.11946689]
> [ 0.12479926 0.01210898 -0.09217618]
> [-0.13050087 0.07575163 0.01144993]]
>
> in perl using Math::MatrixReal;
> and in various online calculators i get
> [ 2.472715991745 3.680743681735 -3.831392002314 ]
> [ -4.673105249083 -5.348238625096 -5.703193038649 ]
> [ 2.733966489601 -6.567940452290 -5.936617926811 ]
>
> using python , numpy and linalg.inv (or linalg.pinv) i get a divergent answer
> [[ 6.79611151e+07 1.01163031e+08 1.05303510e+08]
> [ 1.01163057e+08 1.50585545e+08 1.56748838e+08]
> [ 1.05303548e+08 1.56748831e+08 1.63164381e+08]]
>
> any suggestions?
>
> thanks
> jpd
>
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