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 -- http://mail.python.org/mailman/listinfo/python-list
