bottle alternatives and similar shards
Based on the "Science and Data analysis" category.
Alternatively, view bottle alternatives based on common mentions on social networks and blogs.

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They vary from L1 to L5 with "L5" being the highest.
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README
Num.cr is the core shard needed for scientific computing with Crystal
 Website: https://crystaldata.github.io/num.cr
 API Documentation: https://crystaldata.github.io/num.cr/
 Source code: https://github.com/crystaldata/num.cr
 Bug reports: https://github.com/crystaldata/num.cr/issues
It provides:
 An ndimensional
Tensor
data structure  Efficient
map
,reduce
andaccumulate
routines  GPU accelerated routines backed by
OpenCL
 Linear algebra routines backed by
LAPACK
andBLAS
Prerequisites
Num.cr
aims to be a scientific computing library written in pure Crystal.
All standard operations and data structures are written in Crystal. Certain
routines, primarily linear algebra routines, are instead provided by a
BLAS
or LAPACK
implementation.
Several implementations can be used, including Cblas
, Openblas
, and the
Accelerate
framework on Darwin systems. For GPU accelerated BLAS
routines,
the ClBlast
library is required.
Num.cr
also supports Tensor
s stored on a GPU
. This is currently limited
to OpenCL
, and a valid OpenCL
installation and device(s) are required.
Installation
Add this to your applications shard.yml
dependencies:
num:
github: crystaldata/num.cr
Several thirdparty libraries are required to use certain features of Num.cr
.
They are:
 BLAS
 LAPACK
 OpenCL
 ClBlast
While not at all required, they provide additional functionality than is provided by the basic library.
Just show me the code
The core data structure implemented by Num.cr
is the Tensor
, an Ndimensional
data structure. A Tensor
supports slicing, mutation, permutation, reduction,
and accumulation. A Tensor
can be a view of another Tensor
, and can support
either Cstyle or Fortranstyle storage.
Creation
There are many ways to initialize a Tensor
. Most creation methods can
allocate a Tensor
backed by either CPU
or GPU
based storage.
[1, 2, 3].to_tensor
Tensor.from_array [1, 2, 3]
Tensor(UInt8).zeros([3, 3, 2])
Tensor.random(0.0...1.0, [2, 2, 2])
ClTensor(Float32).zeros([3, 2, 2])
ClTensor(Float64).full([3, 4, 5], 3.8)
Operations
A Tensor
supports a wide variety of numerical operations. Many of these
operations are provided by Num.cr
, but any operation can be mapped across
one or more Tensor
s using sophisticated broadcasted mapping routines.
a = [1, 2, 3, 4].to_tensor
b = [[3, 4, 5, 6], [5, 6, 7, 8]].to_tensor
# Convert a Tensor to a GPU backed Tensor
acl = a.astype(Float64).gpu
puts Num.add(a, b)
# a is broadcast to b's shape
# [[ 4, 6, 8, 10],
# [ 6, 8, 10, 12]]
When operating on more than two Tensor
s, it is recommended to use map
rather than builtin functions to avoid the allocation of intermediate
results. All map
operations support broadcasting.
a = [1, 2, 3, 4].to_tensor
b = [[3, 4, 5, 6], [5, 6, 7, 8]].to_tensor
c = [3, 5, 7, 9].to_tensor
a.map(b, c) do i, j, k
i + 2 / j + k * 3.5
end
# [[12.1667, 20 , 27.9 , 35.8333],
# [11.9 , 19.8333, 27.7857, 35.75 ]]
Mutation
Tensor
s support flexible slicing and mutation operations. Many of these
operations return views, not copies, so any changes made to the results might
also be reflected in the parent.
a = Tensor.new([3, 2, 2]) { i i }
puts a.transpose
# [[[ 0, 4, 8],
# [ 2, 6, 10]],
#
# [[ 1, 5, 9],
# [ 3, 7, 11]]]
puts a.reshape(6, 2)
# [[ 0, 1],
# [ 2, 3],
# [ 4, 5],
# [ 6, 7],
# [ 8, 9],
# [10, 11]]
puts a[..., 1]
# [[ 2, 3],
# [ 6, 7],
# [10, 11]]
puts a[1..., {..., 1}]
# [[[ 6, 7],
# [ 4, 5]],
#
# [[10, 11],
# [ 8, 9]]]
puts a[0, 1, 1].value
# 3
Linear Algebra
Tensor
s provide easy access to power Linear Algebra routines backed by
LAPACK and BLAS implementations, and ClBlast for GPU backed Tensor
s.
a = [[1, 2], [3, 4]].to_tensor.map &.to_f32
puts a.inv
# [[2 , 1 ],
# [1.5 , 0.5]]
puts a.eigvals
# [0.372281, 5.37228 ]
acl = a.opencl
bcl = a.opencl
puts acl.gemm(bcl).cpu
# [[7 , 10],
# [15, 22]]
puts a.matmul(a)
# [[7 , 10],
# [15, 22]]
Machine Learning
Num::Grad
provides a purecrystal approach to find derivatives of
mathematical functions. Use a Num::Grad::Variable
with a Num::Grad::Context
to easily compute these derivatives.
ctx = Num::Grad::Context(Tensor(Float64)).new
x = ctx.variable([3.0])
y = ctx.variable([2.0])
# f(x) = x ** y
f = x ** y
puts f # => [9]
f.backprop
# df/dx = y * x = 6.0
puts x.grad # => [6.0]
Num::NN
contains an extension to Num::Grad
that provides an easytouse
interface to assist in creating neural networks. Designing and creating
a network is simple using Crystal's block syntax.
ctx = Num::Grad::Context(Tensor(Float64)).new
x_train = [[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]].to_tensor
y_train = [[0.0], [1.0], [1.0], [0.0]].to_tensor
x = ctx.variable(x_train)
net = Num::NN::Network.new(ctx) do
input [2]
# A basic network with a single hidden layer using
# a ReLU activation function
linear 3
relu
linear 1
# SGD Optimizer
sgd 0.7
# Sigmoid Cross Entropy to calculate loss
sigmoid_cross_entropy_loss
end
500.times do epoch
y_pred = net.forward(x)
loss = net.loss(y_pred, y_train)
puts "Epoch: #{epoch}  Loss #{loss}"
loss.backprop
net.optimizer.update
end
# Clip results to make a prediction
puts net.forward(x).value.map { el el > 0 ? 1 : 0}
# [[0],
# [1],
# [1],
# [0]]
Review the documentation for full implementation details, and if something is missing, open an issue to add it!