parameter scaling by n_samples pb

gistfile1.py

import numpy as np
from scipy import linalg

from sklearn import datasets, svm, linear_model
from sklearn.svm import l1_min_c

iris = datasets.load_iris()
X = iris.data
y = iris.target

X = X[y != 2]
y = y[y != 2]

X -= np.mean(X, 0)

X2 = np.r_[X, X]
y2 = np.r_[y, y]

clfs = [linear_model.LogisticRegression(penalty='l1', tol=1e-6),
        linear_model.LogisticRegression(penalty='l2', tol=1e-6),
        svm.SVR(kernel='linear', tol=1e-6)]

for clf in clfs:
    for scaling, scaling2 in [(1., 1.), (len(X), len(X2))]:
        clf.C = 10. / scaling
        coef_ = clf.fit(X, y).coef_
        clf.C = 10. / scaling2
        coef2_ = clf.fit(X2, y2).coef_
        print "Error : %s" % (linalg.norm(coef2_ - coef_) / linalg.norm(coef_))

clfs = [linear_model.Lasso(tol=1e-6)]

for clf in clfs:
    for scaling, scaling2 in [(1., 1.), (len(X), len(X2))]:
        clf.alpha = 0.001 * scaling
        coef_ = clf.fit(X, y).coef_
        clf.alpha = 0.001 * scaling2
        coef2_ = clf.fit(X2, y2).coef_
        print "Error : %s" % (linalg.norm(coef2_ - coef_) / linalg.norm(coef_))

# Output:
# Error : 0.13608933675
# Error : 8.56854960126e-16
# Error : 0.124357027952
# Error : 3.02427897402e-16
# Error : 0.000213002674373
# Error : 2.40941736763e-07
# Error : 4.07295351853e-12
# Error : 0.16694490818