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copied!<pre><code>from __future__ import division from itertools import izip, repeat import math def weighted_mean(values, weights=None): total = 0 number = 0 if weights is None: weights = repeat(1) for weight, value in izip(weights, values): total += weight * value number += weight return number and total / number xa = [1, 2, 3, 10, 1383, 0, 12, 9229, 2, 494, 10, 49] xb = [1, 1, 4, 12, 1100, 43, 9, 4848, 2, 454, 6, 9] print "Option 1, if you want bigger numbers to have a bigger effect on the score" weights = (math.sqrt(abs(a) * abs(b)) for a, b in izip(xa, xb)) scores = (abs(a) + abs(b) and abs(a - b) / (abs(a) + abs(b)) for a, b in izip(xa, xb)) final_score = weighted_mean(scores, weights) print "%.02f%%" % (final_score * 100) print "Option 2, if you want to have all numbers have the same effect on the score" scores = (abs(a) + abs(b) and abs(a - b) / (abs(a) + abs(b)) for a, b in izip(xa, xb)) final_score = weighted_mean(scores) print "%.02f%%" % (final_score * 100) </code></pre> <p>Of course, you can also use other kinds of weights, such as <code>(abs(a) + abs(b)) / 2</code>, depending on how you want to interpret a given difference.</p> <p>Loopless version of the second one:</p> <pre><code>xan = numpy.array(xa) xbn = numpy.array(xb) error_threshold = 0.000001 final_score = numpy.mean((abs(xan - xbn) + error_threshold) / (abs(xan) + abs(xbn) + error_threshold)) </code></pre> <p>Or the first:</p> <pre><code>scores = (abs(xan - xbn) + error_threshold) / (abs(xan) + abs(xbn) + error_threshold) weights = numpy.sqrt(abs(xan) * abs(xbn)) final_score = numpy.sum(scores * weights) / numpy.sum(weights) </code></pre>

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