Say I have predictor array x:(n,px) and a predicted array y:(n, py). What would be the best way in Python to calculate all regression coefficients (linear) from x to each dimension of y (1...py)? The output of the whole thing would be a matrix (py, px) (for each output, px parameters). I could easy iterate over output dimensions, however that would be inefficient as I will recalculate the pseudo inverse matrix of x. Is there any efficient implementation out there?
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Best Answer
There is a sklearn library in python, which (among others) implements Ordinary Least Squares Linear Regression
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
Sample usage:
from sklearn import linear_model #creating a regression object regr = linear_model.LinearRegression() #runnin OLS on your data, assuming that you already have arrays x and y regr.fit( x, y ) #displaying coefficients matrix print regr.coef_ Similar Posts:
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