SciPy - I need an example of use of linalg.lstsq()

[I. Myself]

Robert Kern wrote:
> I. Myself wrote:
>   
>> And it has to run on Windows, so it can't use xplt.
>>     
>
> Huh?
>
> A. xplt runs on Windows, too.
> B. xplt has nothing to do with linalg.lstsq().
> C. xplt has been removed from scipy.
>   
Thank you.  I have been misled by the Scipy Tutorial by Travis 
Oliphant.  It has an example which uses xplt; it also mentions that xplt 
is only for x-windows. 

Your example below seems like just what I'm looking for.  I will try it out.

Thanks again.

Mitchell Timin
>   
>> I would prefer that it use the simplest multi-dimensional model, z = k + 
>> a*x1 + b*x2 + c*x3 + d*x4
>>     
>
> In [1]: import numpy as np
>
> In [2]: np.linalg.lstsq?
> Type:           function
> Base Class:     <type 'function'>
> String Form:    <function lstsq at 0x6d3f30>
> Namespace:      Interactive
> File:
> /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy-0.9.7.2476-py2.4-macosx-10.4-ppc.egg/numpy/linalg/linalg.py
> Definition:     np.linalg.lstsq(a, b, rcond=1e-10)
> Docstring:
>     returns x,resids,rank,s
>     where x minimizes 2-norm(|b - Ax|)
>           resids is the sum square residuals
>           rank is the rank of A
>           s is the rank of the singular values of A in descending order
>
>     If b is a matrix then x is also a matrix with corresponding columns.
>     If the rank of A is less than the number of columns of A or greater than
>     the number of rows, then residuals will be returned as an empty array
>     otherwise resids = sum((b-dot(A,x)**2).
>     Singular values less than s[0]*rcond are treated as zero.
>
>
> In [3]: z = np.rand(10)
>
> In [4]: x1 = np.rand(10)
>
> In [5]: x2 = np.rand(10)
>
> In [6]: x3 = np.rand(10)
>
> In [7]: x4 = np.rand(10)
>
> In [8]: A = np.column_stack([x1, x2, x3, x4, np.ones(10, float)])
>
> In [9]: A
> Out[9]:
> array([[ 0.07257264,  0.36544251,  0.68467294,  0.33813333,  1.        ],
>        [ 0.09520828,  0.27102091,  0.04673061,  0.12905473,  1.        ],
>        [ 0.839834  ,  0.46010114,  0.3949568 ,  0.38983012,  1.        ],
>        [ 0.49776387,  0.70666191,  0.85005579,  0.47738743,  1.        ],
>        [ 0.25457977,  0.93335912,  0.88441593,  0.05255062,  1.        ],
>        [ 0.85982216,  0.97920853,  0.27991214,  0.94230651,  1.        ],
>        [ 0.03224487,  0.1275237 ,  0.66943552,  0.320765  ,  1.        ],
>        [ 0.86807363,  0.63800103,  0.67153924,  0.69125023,  1.        ],
>        [ 0.26571213,  0.68845408,  0.06478114,  0.03657494,  1.        ],
>        [ 0.46615143,  0.99464106,  0.9303421 ,  0.61363703,  1.        ]])
>
> In [10]: np.linalg.lstsq(A, z)
> Out[10]:
> (array([-0.32421087, -0.23330787,  0.13369118, -0.28334431,  0.84010014]),
>  array([ 0.22958042]),
>  5,
>  array([ 4.59505886,  1.1181838 ,  0.85704672,  0.70211311,  0.4420187 ]))
>
>
> If you have more scipy questions, you will probably want to ask on the
> scipy-user list:
>
>   http://www.scipy.org/Mailing_Lists
>
>   


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