pyarray | node module for manipulating arrays | Computer Vision library

In order to get multi dimensional slices you have to insert the slices in a tuple, the call the get item on that tuple. Something like:

I found the answer.

Looks like Pythons Extension class from distutils.core module hass two additional input arguments for libraries which are library_dirs and libraries.

So I just had to change my setup.py code as below:

The xtensor-python equivalent of xtensor is pytensor just like pyarray is the xtensor-pyrhonequivalent of xarray. Notice that xtensor and pytensor are different types, even if they accept the same kind of template arguments. pytensor can be assigned a numpy array while xtensor cannot (the same stands for xarray and pyarray).

Also regarding the ability to call your code form R, you're right, pyarray and pytensor are not the appropriate types. A way to solve this problem is to put your implementation in generic function accepting any kind of expressions, and then make interface for each language, taht accept the appropriate types and forward to the implementation.

You can find more details about writing bindings of your C++ code to other languages in xtensor documentation or in this blogpost

Specifying the data type in your python deceleration for a,b,c as dtype=np.float64. Double in C parlance is 64 bit float. using np.array like the way you've used it usually returns np.int64. using np.array like so will return a np.float64

Your confusion seems to stem from a misunderstanding of what npy_intp is. It's not a typedef for int *. It's an integer type big enough to hold a pointer.

It makes no sense to train the network with data from a single class (digit) until it converges, then add another class and so on.

If you only train with one class, the desired output will always be the same and the network will probably converge quickly. It will probably produce this output for all kinds of input patterns, not just the ones you used for training.

What you need to do is present inputs from all classes during training, for example in random order. This way the network will be able to find the boundaries between the different classes.

The reason for the inconsistent and poor performances of numpy.fromiter() and numpy.array() that I had encountered earlier appears to be associated to the number of CPUs used by concurrent.futures.ProcessPoolExecutor. I had earlier used 6 CPUs. Below diagrams shows the corresponding performances of numpy.fromiter() and numpy.array() when 2, 4, 6, and 8 CPUs were used. These diagrams show that there exists an optimum number of CPUs that can be used. Using too many CPUs (i.e. >4 CPUs) can be detrimental for small array sizes (<512 elements). Example, >4 CPUs can cause slower performances (by a factor of 1/2) and even inconsistent performances when compared to a serial run.

wow this is a coincidence! I am working on exactly this speedup!

xtensor's sum is a lazy operation -- and it doesn't use the most performant iteration order for (auto-)vectorization. However, we just added a evaluation_strategy parameter to reductions (and the upcoming accumulations) which allows you to select between immediate and lazy reductions.

Immediate reductions perform the reduction immediately (and not lazy) and can use a iteration order optimized for vectorized reductions.

You can find this feature in this PR: https://github.com/QuantStack/xtensor/pull/550

In my benchmarks this should be at least as fast or faster than numpy. I hope to get it merged today.

Btw. please don't hesitate to drop by our gitter channel and post a link to the question, we need to monitor StackOverflow better: https://gitter.im/QuantStack/Lobby

I think the issue is that you're passing a Python list as the second argument to PyArray_SimpleNewFromData when it expects a pointer to an integer. I'm a little surprised this compiles.

Try: