I have a 1d vector d. I want compute the means of subsets of this vector. The subsets are selected by looking at another vector s or same shape as d. This can be done as: [np.mean (d[s == i]) for i in range (size)] But I think this could be done directly with numpy addressing, without resorting to list comprehension?
On Wed, Jan 8, 2014 at 11:12 AM, Neal Becker <ndbecker2@gmail.com> wrote:
I have a 1d vector d. I want compute the means of subsets of this vector. The subsets are selected by looking at another vector s or same shape as d.
This can be done as:
[np.mean (d[s == i]) for i in range (size)]
But I think this could be done directly with numpy addressing, without resorting to list comprehension?
You could get it done with np.bincount: d_sums = np.bincount(s, weights=d) d_counts = np.bincount(s) d_means = d_sums / d_counts Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.
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Neal Becker