[Numpy-discussion] Identifying Colinear Columns of a Matrix

[Mark Janikas]

Hello All,

I am trying to identify columns of a matrix that are perfectly collinear.  It is not that difficult to identify when two columns are identical are have zero variance, but I do not know how to ID when the culprit is of a higher order. i.e. columns 1 + 2 + 3 = column 4.  NUM.corrcoef(matrix.T) will return NaNs when the matrix is singular, and LA.cond(matrix.T) will provide a very large condition number.... But they do not tell me which columns are causing the problem.   For example:

zt = numpy. array([[ 1.  ,  1.  ,  1.  ,  1.  ,  1.  ],
                           [ 0.25,  0.1 ,  0.2 ,  0.25,  0.5 ],
                           [ 0.75,  0.9 ,  0.8 ,  0.75,  0.5 ],
                           [ 3.  ,  8.  ,  0.  ,  5.  ,  0.  ]])

How can I identify that columns 0,1,2 are the issue because: column 1 + column 2 = column 0?

Any input would be greatly appreciated.  Thanks much,

MJ

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