Comments (4)
Hi @aspassiani, could you give me an example with small example data?
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Hi @felixriese, not quite sure the best example to give, but if I were to set weights_per_datapoint and X as random matrixes,
weights_per_datapoint =[[8,5,0,4,5,7,0,2,0,2]
[4,0,7,4,8,0,4,2,2,8]
[5,3,1,2,7,9,9,8,8,7]
[8,6,8,7,7,5,4,7,1,1]
[6,2,3,5,7,5,9,5,9,0]]
X = [[8,9,7,8,6,3,0,4,1,6]
[3,2,1,2,5,7,0,0,3,3]
[0,7,5,1,4,5,7,5,8,5]
[9,1,1,7,5,9,8,9,3,3]
[2,7,7,2,3,3,3,3,3,3]]
then,
quantization_errors = np.linalg.norm(np.subtract(weights_per_datapoint, X))
would give me a single value of 25.729360660537214
making return np.mean(quantization_errors)
unnecessary, whereas,
quantization_errors = np.linalg.norm(np.subtract(weights_per_datapoint, X), axis = 1 )
would return a vector of values, [10.9087,12.2066,10,11.0905,13.0767]
and a mean of 11.45650021348017
.
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@aspassiani Thanks for the data! Makes sense. I added a fix in the PR #40. Please have a look
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The revised quantization error looks correct, thanks!
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