On Jan 21, 6:44 pm, nodrogbrown <[EMAIL PROTECTED]> wrote: > hi > i am using python to do some image data calculations..I use the > following numpy.ndarrays ,(i have given their shapes and ranks) > > weights=ndarray :shape(100,30),ndim=2 will have vals like > 2458121847.49 (of type 'numpy.float64') > input_weight=ndarray :shape(30,),ndim=1 (similar to above but diff > vals) > distance =ndarray :shape(30,),ndim=1 > mindistance==ndarray :shape(30,),ndim=1 > > now i am calculating the euclidian distance of 'input_weight' from > 'weight' > since this is the cumulative diff i do this in this way > > <code> > for image in range(100): > temp=0.0 > for j in range(30): > distance[j]=abs(input_weight[j]-weights[image,j]) > > if(image==0): > #at the start copy from distance to mindistance > mindistance=distance.copy() > if (sum(mindistance) > sum(distance)): > imgindex=image # i use this later to access a list > mindistance=distance.copy() > > # now normalise the mindistance > array > if (max(mindistance) > 0.0): > mindistance=mindistance/(max(mindistance)) > > dist=sum(mindistance) > > <code> > > this gives me the correct results
Are you sure? What happens if the vector with the smallest sum(distance) is the first one? > but i am worried if this is a bit > unpythonish? > (been a java programmer for a long time..) i wd like to know if there > is a better way 1. 'temp' is not used 2. Lose the superfluous parentheses in 'if' statements 3. Put space around operators 4. I've never used any of numpy & friends, but: (a) Can't you replace the inner loop with something like this: distance = abs(input_weight - weights[image, :]) (b) I doubt that you need the .copy() 5. Lose the hard-wired numbers like 30 and 100 6. Put it inside a function and *TEST* it The word you were looking for is 'unpythonic', but the principles behind the above apply to any language. HTH, John -- http://mail.python.org/mailman/listinfo/python-list
