nikie napisal(a): > 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?
Well, it works for me: x = Matrix([[1, 1], [1, 2], [1, 3]]) y = Matrix([[1], [2], [4]]) print linear_least_squares(x, y) Make sure the dimensions are right. X should be n*k, Y should (unless you know what you are doing) be n*1. So the first dimension must be equal. If you wanted to: y = Matrix([1, 2, 4]) it won't work because it'll have dimensions 1*3. You would have to transpose it: y = transpose(Matrix([1, 2, 4])) Hope this helps. -- http://mail.python.org/mailman/listinfo/python-list
