I just learned two things: 1. np.newaxis 2. Array dimension broadcasting rocks more than you think.
The x[:, np.newaxis] might not be the most intuitive solution but it's great and powerful. Intuitive would be to have x.T to transform [0,1,2,4] into [[0],[1],[2],[4]]. Thanks Warren :-) Samuel On 06.10.2011, at 14:18, Warren Weckesser wrote: > > > On Thu, Oct 6, 2011 at 7:08 AM, Neal Becker <ndbeck...@gmail.com> wrote: > Given a vector y, I want a matrix H whose rows are > > y - x0 > y - x1 > y - x2 > ... > > > where x_i are scalars > > Suggestion? > > > > In [15]: import numpy as np > > In [16]: y = np.array([10.0, 20.0, 30.0]) > > In [17]: x = np.array([0, 1, 2, 4]) > > In [18]: H = y - x[:, np.newaxis] > > In [19]: H > Out[19]: > array([[ 10., 20., 30.], > [ 9., 19., 29.], > [ 8., 18., 28.], > [ 6., 16., 26.]]) > > > Warren > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
