Python OLS calculation

Question

Is there any good library to calculate linear least squares OLS (Ordinary Least Squares) in python?

Thanks.

Edit:

Thanks for the SciKits and Scipy. @ars: Can X be a matrix? An example:

y(1) = a(1)*x(11) + a(2)*x(12) + a(3)*x(13)
y(2) = a(1)*x(21) + a(2)*x(22) + a(3)*x(23)
...........................................
y(n) = a(1)*x(n1) = a(2)*x(n2) + a(3)*x(n3)

Then how do I pass the parameters for Y and X matrices in your example?

Also, I don't have much background in algebra, I would appreciate if you guys can let me know a good tutorial for that kind of problems.

Thanks much.

Answer 1

Try the statsmodels package. Here's a quick example:

import pylab
import numpy as np
import statsmodels.api as sm

x = np.arange(-10, 10)
y = 2*x + np.random.normal(size=len(x))

# model matrix with intercept
X = sm.add_constant(x)

# least squares fit
model = sm.OLS(y, X)
fit = model.fit()

print fit.summary()

pylab.scatter(x, y)
pylab.plot(x, fit.fittedvalues)

Update In response to the updated question, yes it works with matrices. Note that the code above has the x data in array form, but we build a matrix X (capital X) to pass to OLS. The add_constant function simply builds the matrix with a first column initialized to ones for the intercept. In your case, you would simply pass your X matrix without needing that intermediate step and it would work.