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Update Issues with covaraince matrix authored by Soumen Roy's avatar Soumen Roy
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Issues-with-covaraince-matrix.md
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...@@ -14,5 +14,5 @@ In the above figures, the quantity 'inv' refers to the `numpy.linalg.inv()` func ...@@ -14,5 +14,5 @@ In the above figures, the quantity 'inv' refers to the `numpy.linalg.inv()` func
To resolve this issue, we propose to use the Moore-Penrose pseudo-inverse method. This method calculates the generalized inverse of a matrix using its singular-value decomposition (SVD) and including all large singular values. We use the numpy inbuild function [`numpy.linalg.pinv()`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html). At this moment, let us focus on the above comparison as we showed for the To resolve this issue, we propose to use the Moore-Penrose pseudo-inverse method. This method calculates the generalized inverse of a matrix using its singular-value decomposition (SVD) and including all large singular values. We use the numpy inbuild function [`numpy.linalg.pinv()`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html). At this moment, let us focus on the above comparison as we showed for the
`numpy.linalg.inv()` function. `numpy.linalg.inv()` function.
<img src="uploads/4692d03ff868fcc538ab06803102a7c3/pinv.png" width="440" > <img src="uploads/4692d03ff868fcc538ab06803102a7c3/pinv.png" width="440" ><img src="uploads/8d64ddc718f1fe06d54b62ca6028d123/pinv_off_diag.png" width="440" >