4 ms·

Hi tlarkworthy, thanks for sharing your code with me! - It's really hard to make valid comparisons against data I don't have access to. Do you have any open d

by onalark 13y ago

Hi tlarkworthy, thanks for sharing your code with me! - It's really hard to make valid comparisons against data I don't have access to. Do you have any open data sets to try this comparison against? - It looks like the meat of the work is being done here by scikit-learn. As was mentioned earlier, Numba at this stage is mostly useful for improving kernel performance, not large library routines. I'm planning on taking a deeper look into some of the scikit-learn kernels in the future. Keep an eye open for a blog post from Continuum on this.

tlarkworthy 13y ago
OK these are the images, feel free to replicate them distribute them, whatever, there is no licensing.:- http://img716.imageshack.us/img716/5808/frame0108.jpg http://img716.imageshack.us/img716/5808/frame0108.jpg and http://img254.imageshack.us/img254/9562/frame0109.jpg http://img254.imageshack.us/img254/9562/frame0109.jpg Yeah I use openCV and scikit learn to do the heavy lifting indeed. But then if I used Numba surely that is what you are advocating too?? I tried unrolling the inner kernel a few different ways before settling on the code you have before you. It doesn't compute the feature vector exactly how I would want, BUT ITS really FAST, which for sonar analysis to run on AUV in real time that's essential. I will fit my math to the library to meet my CPU budgets. Anyway I hope you can use this as a benchmark or something even if its implemented in a totally different way than you might do. feel free to email me tom dot larkworthy <at> gmail Tom
- tlarkworthy 13y ago
  oh I should also add that the clustering (kmeans) is very fast. The analogous part to the inner loop is "feature_vectors". Which has 2 main cases: case 1, in the case nothing has been computed it calculates the corner response images at all the different spatial scales (big operations). case 2, feature_vector just selects data from the corner images for the pixels demanded. Now my algorithm is sparse so its normally jsut selecting a subset of pixels, although as it calculated the spatial responses of the whole image it doesn't really make any odds to my runtime.
  - onalark 13y ago
    Thanks. I'll take a look.

by onalark 13y ago

tlarkworthy 13y ago

OK these are the images, feel free to replicate them distribute them, whatever, there is no licensing.:- http://img716.imageshack.us/img716/5808/frame0108.jpg http://img716.imageshack.us/img716/5808/frame0108.jpg and http://img254.imageshack.us/img254/9562/frame0109.jpg http://img254.imageshack.us/img254/9562/frame0109.jpg Yeah I use openCV and scikit learn to do the heavy lifting indeed. But then if I used Numba surely that is what you are advocating too?? I tried unrolling the inner kernel a few different ways before settling on the code you have before you. It doesn't compute the feature vector exactly how I would want, BUT ITS really FAST, which for sonar analysis to run on AUV in real time that's essential. I will fit my math to the library to meet my CPU budgets. Anyway I hope you can use this as a benchmark or something even if its implemented in a totally different way than you might do. feel free to email me tom dot larkworthy <at> gmail Tom

tlarkworthy 13y ago
oh I should also add that the clustering (kmeans) is very fast. The analogous part to the inner loop is "feature_vectors". Which has 2 main cases: case 1, in the case nothing has been computed it calculates the corner response images at all the different spatial scales (big operations). case 2, feature_vector just selects data from the corner images for the pixels demanded. Now my algorithm is sparse so its normally jsut selecting a subset of pixels, although as it calculated the spatial responses of the whole image it doesn't really make any odds to my runtime.
- onalark 13y ago
  Thanks. I'll take a look.