Hi all, I'm trying to calculate accumulated sum-of-squared-differences for an array in the following manner:
import numpy as np a = np.array([1, 2, 3, 49., 50, 51, 98, 99, 100], dtype=np.float32) # Calculate accumulated means over all elements means = np.add.accumulate(a) / (np.arange(a.size) + 1) # Create a matrix of squared differences (elements minus means) diff_sqr = np.asarray(np.matrix(a) - np.matrix(means).T) ** 2 # Sum the lower triangular elements ssd = np.tril(diff_sqr).sum(axis=1) Is there an easier or more terse way to do this? Ideally, I'd like to accumulate over all slices in 'a' as well, ie. not just [0:n], but also [1:n], [2:n], etc. I see some functions within scipy.stats.stats, but I'm struggling on figuring out how to put it together. thanks, matt _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
