Matt Crema wrote: > Robert Kern wrote: > >> nikie wrote: >> >>> I still don't get it... >>> My data looks like this: >>> x = [0,1,2,3] >>> y = [1,3,5,7] >>> The expected output would be something like (2, 1), as y[i] = x[i]*2+1 >>> >>> (An image sometimes says more than 1000 words, so to make myself clear: >>> this is what I want to do: >>> http://www.statistics4u.info/fundstat_eng/cc_regression.html) >>> >>> So, how am I to fill these matrices? >> >> >> >> As the docstring says, the problem it solves is min ||A*x - b||_2. In >> order to >> get it to solve your problem, you need to cast it into this matrix >> form. This is >> out of scope for the docstring, but most introductory statistics or >> linear >> algebra texts will cover this. >> >> In [201]: x = array([0., 1, 2, 3]) >> >> In [202]: y = array([1., 3, 5, 7]) >> >> In [203]: A = ones((len(y), 2), dtype=float) >> >> In [204]: A[:,0] = x >> >> In [205]: from numpy import linalg >> >> In [206]: linalg.lstsq(A, y) >> Out[206]: >> (array([ 2., 1.]), >> array([ 1.64987674e-30]), >> 2, >> array([ 4.10003045, 1.09075677])) >> > > I'm new to numpy myself. > > The above posters are correct to say that the problem must be cast into > matrix form. However, as this is such a common technique, don't most > math/stats packages do it behind the scenes? > > For example, in Matlab or Octave I could type: > polyfit(x,y,1) > > and I'd get the answer with shorter, more readable code. A one-liner! > Is there a 'canned' routine to do it in numpy? > > btw, I am not advocating that one should not understand the concepts > behind a 'canned' routine. If you do not understand this concept you > should take <Robert Kern>'s advice and dive into a linear algebra book. > It's not very difficult, and it is essential that a scientific > programmer understand it. > > -Matt
Hi again, I guess I should have looked first ;) m,b = numpy.polyfit(x,y,1) -Matt -- http://mail.python.org/mailman/listinfo/python-list
