Naotoshi Seo

[Feature Request] Support timestamp in json data type

7

I think what users (including me) want is a kind of recursive data type, not `JSON` in fact. See bigquery data type. https://cloud.google.com/bigquery/preparing-data-for-bigquery#datatypes. BigQuery has `STRING`, `BYTES`, `INTEGER`, `FLOAT`, `BOOLEAN`,...

YARD not working on C++ sources

5

YARD does not successfully parse my C++ sources. ## Steps to reproduce ``` rb_define_singleton_method(mMemoryPool, "enable", (VALUE(*)(ANYARGS))rb_memory_pool_enable, 0); # the (VALUE(*)(ANYARGS)) cast is necessary for C++ ``` ## Actual Output No...

Lots of memcpy are issued on a.dot(b.transpose)

6

With numo-linalg (I did not investigate without numo-linalg), ```ruby a = Numo::SFloat.new(3, 4).seq(0) b = Numo::SFloat.new(3, 4).seq(0) a.dot(b.transpose) ``` issues 12 times of memcpy at https://github.com/ruby-numo/numo-narray/blob/7bba089c3114ff08d110e47ba8bc448aa775f0d4/ext/numo/narray/ndloop.c#L1127 This drastically slows down...

[Propose] Stop making a view of NArray at #aref (or Stop using idx)

1

In Numo, advanced indexing returns a view of a Numo NArray. ``` irb(main):007:0> a = Numo::Int32.new(3,4).seq => Numo::Int32#shape=[3,4] [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]...

[Propose] Return 0-dimensional array instead of Ruby numeric object

1

This is a feedback from Cumo implementation. https://speakerdeck.com/sonots/fast-numerical-computing-and-deep-learning-in-ruby-with-cumo?slide=42 Numo: ``` irb(main):015:0> a = Numo::Int32.new(3,4).seq => Numo::Int32#shape=[3,4] [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] irb(main):016:0> a.sum...

In the case of numpy, we can create a contiguous array from a broadcasted array as: ``` In [11]: a = np.arange(3).reshape(3, 1) In [12]: a.strides Out[12]: (8, 8) In...

[PLAN] Support conversion between Numo::NArray and Cumo::NArray

1

POLICY: NO MONKEY PATCH to Numo. Assume numo_a is an Numo::Int32, and cumo_a is Cumo::Int32. (1) ```ruby Cumo::DFloat.cast(a_cumo) #=> Cumo::DFloat Cumo::DFloat.cast(a_numo) #=> Cumo::DFloat (Note that casting dtype) a_cumo.cast_to(Cumo::DFloat) #=> Cumo::DFloat...

1. Stop using ndloop and compute an operation with one CUDA kernel using indexer 2. Compact dimension to make computation of indexer fast Element-wise (binary ops) is already done at...

as CuPy does http://docs-cupy.chainer.org/en/stable/tutorial/kernel.html using NVIDIA NVRTC.

NVIDIA GPU supports float16.