From 47f46e6e9cdc4b3581613b1f6940e7f0f0a9bdc7 Mon Sep 17 00:00:00 2001 From: Fabian Pedregosa <fabian.pedregosa@inria.fr> Date: Tue, 5 Jan 2010 13:53:48 +0000 Subject: [PATCH] Easier way to select random sample of data for RBF centers. From: fred.mailhot <fred.mailhot@cb17146a-f446-4be1-a4f7-bd7c5bb65646> git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@35 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8 --- scikits/learn/ann/rbf.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/scikits/learn/ann/rbf.py b/scikits/learn/ann/rbf.py index 025be8da48..b3d692a1ed 100644 --- a/scikits/learn/ann/rbf.py +++ b/scikits/learn/ann/rbf.py @@ -3,6 +3,7 @@ # 2006/08/20 import numpy as N +import random from scipy.optimize import leastsq class rbf: @@ -80,12 +81,8 @@ class rbf: (ii) set fixed variance from max dist between centers (iii) learn output weights using scipy's leastsq optimizer """ - # set centers - self.centers = N.zeros((len(X)/10,X.shape[1])) - for i in range(len(X)): - if i%10 == 0: - self.centers[i/10] = X[i] - # set variance + # set centers & variance + self.centers = N.array(random.sample(X,len(X)/10)) d_max = 0.0 for i in self.centers: for j in self.centers: -- GitLab