Hi everyone, I have been a lurker for a while now and reading many of the questions, but now signing up to post my question as I have been working towards my own implementation of Sparse Vectors.I am trying to encode images to enable an SDR to recognize , say cats and dogs - a canonical problem in machine vision.
I followed the guidelines in the [encoding data for htm] (Encoding Data for HTM Systems) and encoding the pixel levels in each of the
w=50 and n=1000. Here is the python code for generate the feature vector:
I trained it on cats and dogs image samples and as suggested in the paper,
v = int(pixel/256*1000)-> value bit
I set the value bit -> value + w bit and get recommended sparsity (<5%),
To make predictions I compare a new image against by
oring against the SDRs and taking a Jaccard i.e. and_bits.count(1)/or_bits.count(1), where and_bits = query_sdr & ref_sdr
however the prediction results are less than thrilling.
Am I doing this correctly? I seem to have followed the instructions, but no dice
Can anyone help?