nikie wrote: > I'm a little bit stuck with NumPy here, and neither the docs nor > trial&error seems to lead me anywhere: > I've got a set of data points (x/y-coordinates) and want to fit a > straight line through them, using LMSE linear regression. Simple > enough. I thought instead of looking up the formulas I'd just see if > there isn't a NumPy function that does exactly this. What I found was > "linear_least_squares", but I can't figure out what kind of parameters > it expects: I tried passing it my array of X-coordinates and the array > of Y-coordinates, but it complains that the first parameter should be > two-dimensional. But well, my data is 1d. I guess I could pack the X/Y > coordinates into one 2d-array, but then, what do I do with the second > parameter? > > Mor generally: Is there any kind of documentation that tells me what > the functions in NumPy do, and what parameters they expect, how to call > them, etc. All I found was: > "This function returns the least-squares solution of an overdetermined > system of linear equations. An optional third argument indicates the > cutoff for the range of singular values (defaults to 10-10). There are > four return values: the least-squares solution itself, the sum of the > squared residuals (i.e. the quantity minimized by the solution), the > rank of the matrix a, and the singular values of a in descending > order." > It doesn't even mention what the parameters "a" and "b" are for...
Look at the docstring. (Note: I am using the current version of numpy from SVN, you may be using an older version of Numeric. http://numeric.scipy.org/) In [171]: numpy.linalg.lstsq? Type: function Base Class: <type 'function'> String Form: <function linear_least_squares at 0x1677630> Namespace: Interactive File: /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy-0.9.6.2148-py2.4-macosx-10.4-ppc.egg/numpy/linalg/linalg.py Definition: numpy.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. -- Robert Kern [EMAIL PROTECTED] "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
