Understanding Scipy's Least Square Function With IRLS

I'm having a bit of trouble understanding how this function works. a, b = scipy.linalg.lstsq(X, w*signal)[0] I know that signal is the array representing the signal and currently

Solution 1:

If you product X and y with sqrt(weight) you can calculate weighted least squares. You can get the formula by following link:

http://en.wikipedia.org/wiki/Linear_least_squares_%28mathematics%29#Weighted_linear_least_squares

here is an example:

Prepare data:

import numpy as np
np.random.seed(0)
N = 20
X = np.random.rand(N, 3)
w = np.array([1.0, 2.0, 3.0])
y = np.dot(X, w) + np.random.rand(N) * 0.1

OLS:

from scipy import linalg
w1 = linalg.lstsq(X, y)[0]
print w1

output:

[ 0.98561405  2.0275357   3.05930664]

WLS:

weights = np.linspace(1, 2, N)
Xw = X * np.sqrt(weights)[:, None]
yw = y * np.sqrt(weights)
print linalg.lstsq(Xw, yw)[0]

output:

[ 0.98799029  2.02599521  3.0623824 ]

Check result by statsmodels:

import statsmodels.api as sm
mod_wls = sm.WLS(y, X, weights=weights)
res = mod_wls.fit()
print res.params

output:

[ 0.98799029  2.02599521  3.0623824 ]

Solution 2:

Create a diagonal matrix W from the elementwise square-roots of w. Then I think you just want:

scipy.linalg.lstsq(np.dot(W, X), np.dot(W*signal))

Following http://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)#Weighted_linear_least_squares