class MAT < $MAT{FLT,VEC,MAT}
****
Includes some functions that only work with FLTs Generalizing these functions is possible, but would require definitions of machine epsilon in the numeric classes


Flattened version is here

Ancestors
$MAT{_,_,_} NUMERIC_MAT{_,_} MAT{_,_} AREF{_}



Public


Features
inplace_affine_fit_of(vin,vout:ARRAY{VEC})
**** Fill vin `self' to be the best least squares affine map relating `in' to `out' by: `out[i]=self.affine_act_on(vin[i])'.
inplace_linear_fit_of(vin,vout:ARRAY{VEC}):MAT
**** Fill vin `self' to be the least squares best linear approximation relating `vin' to `vout' by: `out[i]=self.act_on(in[i])'. Return `self'.
inplace_uniform_random
**** Become self's entries uniform in `[0.,1.)'
inplace_weighted_affine_fit_of(vin,vout:ARRAY{VEC}, wt:ARRAY{FLT})
inplace_weighted_linear_fit_of(vin,vout:ARRAY{VEC}, wt:ARRAY{FLT})
**** Fill in `self' to be the least squares best linear approximation relating `vin' to `vout' by: `vout[i]=self.act_on(vin[i])'. `wt[i]' gives the weight which should be given to the ith example. (typically in `[0.,1.]' (`0.' means ignore, `1.' means full weight).
svd_back_sub(u:MAT, w:VEC, v:MAT, b,x:VEC)
**** Solves `a.x=b' for `x' when `a=u.d.v^T' is the svd of `a'.
svd_in(a:MAT, w:VEC, v:MAT)
**** Computes the singular value decomposition of `self = a w v^T'. `a' must be `max(nr,nc)' by `nc', `w' length `nc', `v' is `nc' by `nc'. `Self' is unchanged, `a', `w', `v' are altered.

Iters


Private


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