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POFast weighted euclidean distance between points in arrays
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copied!<p>I need to efficiently calculate the euclidean <em>weighted</em> distances for every <code>x,y</code> point in a given array to every other <code>x,y</code> point in another array. This is the code I have which works as expected:</p> <pre><code>import numpy as np import random def rand_data(integ): ''' Function that generates 'integ' random values between [0.,1.) ''' rand_dat = [random.random() for _ in range(integ)] return rand_dat def weighted_dist(indx, x_coo, y_coo): ''' Function that calculates *weighted* euclidean distances. ''' dist_point_list = [] # Iterate through every point in array_2. for indx2, x_coo2 in enumerate(array_2[0]): y_coo2 = array_2[1][indx2] # Weighted distance in x. x_dist_weight = (x_coo-x_coo2)/w_data[0][indx] # Weighted distance in y. y_dist_weight = (y_coo-y_coo2)/w_data[1][indx] # Weighted distance between point from array_1 passed and this point # from array_2. dist = np.sqrt(x_dist_weight**2 + y_dist_weight**2) # Append weighted distance value to list. dist_point_list.append(round(dist, 8)) return dist_point_list # Generate random x,y data points. array_1 = np.array([rand_data(10), rand_data(10)], dtype=float) # Generate weights for each x,y coord for points in array_1. w_data = np.array([rand_data(10), rand_data(10)], dtype=float) # Generate second larger array. array_2 = np.array([rand_data(100), rand_data(100)], dtype=float) # Obtain *weighted* distances for every point in array_1 to every point in array_2. dist = [] # Iterate through every point in array_1. for indx, x_coo in enumerate(array_1[0]): y_coo = array_1[1][indx] # Call function to get weighted distances for this point to every point in # array_2. dist.append(weighted_dist(indx, x_coo, y_coo)) </code></pre> <p>The final list <code>dist</code> holds as many sub-lists as points are in the first array with as many elements in each as points are in the second one (the weighted distances).</p> <p>I'd like to know if there's a way to make this code more efficient, perhaps using the <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html#scipy.spatial.distance.cdist" rel="nofollow">cdist</a> function, because this process becomes quite expensive when the arrays have lots of elements (which in my case they have) and when I have to check the distances for lots of arrays (which I also have)</p>

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