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 > > -- I'm proud of http://ANNEvolve.sourceforge.net. If you want to write software, or articles, or do testing or research for ANNEvolve, let me know. Humans may know that my email address is: (but remove the 3 digit number) zenguy at shaw666 dot ca -- http://mail.python.org/mailman/listinfo/python-list
