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Want to do fast calculations in Ruby? NArray is your friend!
NArray οΌ Cumo
Numo::NArray
NArray is a powerful N-dimensional array library for science computing in Ruby. Machine learning libraries such as Rumale and Red Chainer use NArray.
Cumo
Cumo (pronounced "koomo") is a CUDA-aware, GPU-optimized numerical library that offers a significant performance boost over Ruby Numo, while (mostly) maintaining drop-in compatibility.
NArray Data types
| Integer | Unsigned Integer | Float | Complex number |
|---|---|---|---|
| Numo::Int8 | Numo::UInt8 | Numo::SFloat | Numo::SComplex |
| Numo::Int16 | Numo::UInt16 | Numo::DFloat | Numo::DComplex |
| Numo::Int32 | Numo::UInt32 | ||
| Numo::Int64 | Numo::UInt64 |
Import the Library
gem install numo-narray
require 'numo/narray'
include Numo
Creating Arrays
Int32[1,2,3]
[1, 2, 3]
Int32.new(3,3).seq
[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]
a = [[0, 1], [2, 3]]
Int32[*a]
[[0, 1],
[2, 3]]
Int32[1..5]
[1, 2, 3, 4, 5]
Int32[1...5]
[1, 2, 3, 4]
Initial Placeholders
Create an array of zeros
x = Int32.zeros(3,3)
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]
Create an array of ones
x = Int32.ones(3,3)
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]
Create an array of twos
z = y.fill 2
[[2, 2, 2],
[2, 2, 2],
[2, 2, 2]]
Create a 3x3 identity matrix
y = Int32.eye(3,3)
[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]
Create an array of evenly spaced values
f = DFloat.linspace(-20,20,11)
[-20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20]
Arithmetic operations
require 'numo/narray'
include Numo
a = Int32.new(3,3).seq
[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]
b = Int32.new(3,3).seq + 1
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
a + b
[[1, 3, 5],
[7, 9, 11],
[13, 15, 17]]
b - a
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]
a * b
[[0, 2, 6],
[12, 20, 30],
[42, 56, 72]]
a * 2
[[0, 2, 4],
[6, 8, 10],
[12, 14, 16]]
a * 0.1
[[0, 0.1, 0.2],
[0.3, 0.4, 0.5],
[0.6, 0.7, 0.8]]
Type conversion
a = UInt8[1,2,3]
SFloat.cast(a)
# => Numo::SFloat#shape=[3]
Dot product
c = Int32.new(2,2).seq
# [[0, 1],
# [2, 3]]
d = Int32.new(2,1).seq
# [[0],
# [1]]
c.dot d
# [[1],
# [3]]
install Numo::Linalg
Numo::Linalg.dot(c,d)
Numo::Linalg.matmul(c,d)
# [[1],
# [3]]
Inspecting NArray
e = DFloat.new(3,4).seq
# Array dimensions
e.shape # => [3, 4]
# Length of array
e.size # => 12
# Number of array dimensions
e.rank # => 2
e.ndim # => 2
# Byte size
e.byte_size # => 96
Convert NArray to string
s = UInt8.ones(2,2).to_string
# => "\x01\x01\x01\x01"
# [[1, 1],
# [1, 1]]
UInt8.from_string(d)
# => Numo::UInt8#shape=[4]
# [1, 1, 1, 1]
Convert NArray to Ruby Array
use to_a
a = UInt8[1,2,3]
a.to_a
NMath
sin cos tan log, etc.
f = DFloat.linspace(-20,20,11)
# [-20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20]
NMath.sin(f)
# [-0.912945, 0.287903, 0.536573, -0.989358, 0.756802, 0, -0.756802, ...]
NMath.cos(f)
# [-0.912945, 0.287903, 0.536573, -0.989358, 0.756802, 0, -0.756802, ...]
NMath.tanh(f)
# [-1, -1, -1, -1, -0.999329, 0, 0.999329, 1, 1, 1, 1]
NMath.log(f)
# [nan, nan, nan, nan, nan, -inf, 1.38629, 2.07944, 2.48491, 2.77259, ...]
NMath.log10(f)
# [nan, nan, nan, nan, nan, -inf, 0.60206, 0.90309, 1.07918, 1.20412, ...]
NMath.sqrt(f)
# [-nan, -nan, -nan, -nan, -nan, 0, 2, 2.82843, 3.4641, 4, 4.47214]
Subsetting, Slicing, Indexing
a = Int32.new(10,10).seq
# [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
# [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
# [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
# [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
# [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
# [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
# [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
# [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
# [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],
# [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]]
a[0,true]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
a[true, 0]
# [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
a[1..3, 2..4]
# [[12, 13, 14],
# [22, 23, 24],
# [32, 33, 34]]
a[1]
# 1
a[-1]
# 99
a.diagonal
# [0, 11, 22, 33, 44, 55, 66, 77, 88, 99]
a.transpose
# [[0, 10, 20, 30, 40, 50, 60, 70, 80, 90],
# [1, 11, 21, 31, 41, 51, 61, 71, 81, 91],
# [2, 12, 22, 32, 42, 52, 62, 72, 82, 92],
# [3, 13, 23, 33, 43, 53, 63, 73, 83, 93],
# [4, 14, 24, 34, 44, 54, 64, 74, 84, 94],
# [5, 15, 25, 35, 45, 55, 65, 75, 85, 95],
# [6, 16, 26, 36, 46, 56, 66, 76, 86, 96],
# [7, 17, 27, 37, 47, 57, 67, 77, 87, 97],
# [8, 18, 28, 38, 48, 58, 68, 78, 88, 98],
# [9, 19, 29, 39, 49, 59, 69, 79, 89, 99]]
a.reverse
# [[99, 98, 97, 96, 95, 94, 93, 92, 91, 90],
# [89, 88, 87, 86, 85, 84, 83, 82, 81, 80],
# [79, 78, 77, 76, 75, 74, 73, 72, 71, 70],
# [69, 68, 67, 66, 65, 64, 63, 62, 61, 60],
# [59, 58, 57, 56, 55, 54, 53, 52, 51, 50],
# [49, 48, 47, 46, 45, 44, 43, 42, 41, 40],
# [39, 38, 37, 36, 35, 34, 33, 32, 31, 30],
# [29, 28, 27, 26, 25, 24, 23, 22, 21, 20],
# [19, 18, 17, 16, 15, 14, 13, 12, 11, 10],
# [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]]
a.reshape(4,25)
# [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...],
# [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, ...],
# [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, ...],
# [75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, ...]
a.max
# 99
a.min
# 0
a.minmax
# [0, 99]
a.sum
# 4950
a.sum 0
# [450, 460, 470, 480, 490, 500, 510, 520, 530, 540]
a > 49
# => Numo::Bit#shape=[10,10]
# [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
bit = (a > 49)
bit.where
# => Numo::Int32#shape=[50]
# [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, ...]
a.eq 33
# [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
(a.eq 33).where
# => Numo::Int32#shape=[1]
# [33]
a[a > 90]
# [91, 92, 93, 94, 95, 96, 97, 98, 99]
a = Int32.new(10).seq - 5
# [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
a.abs
# a = Int32.new(10).seq - 5
# [5, 4, 3, 2, 1, 0, 1, 2, 3, 4]
b = Int32.new(10).seq
b.cumsum
# [0, 1, 3, 6, 10, 15, 21, 28, 36, 45]
Rnadom values
x = DFloat.new(1000).rand
y = DFloat.new(1000).rand
x = DFloat.new(1000).rand_norm
y = DFloat.new(1000).rand_norm
# rand_norm([mu,[sigma]])
Statistics
a = DFloat.new(100,100).rand_norm(1, 2)
# => Numo::DFloat#shape=[100,100]
a.size
# => 10000
a.mean
# => 0.9941970100670163
a.median
# => Numo::DFloat#shape=[]
# 1.0030267444162986
a.var
# => 3.9539947182922974
a.stddev
# => 1.9884654179271757
a.rms
# => 2.223067028599605
Sorting Arrays
na = Int32[1, 10, 2, 5, 9, 8, 12, 11, 3, 7, 4, 6]
na.sort
#=> Numo::Int32#shape=[12]
#[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
na.max_index
# => 6
na.min_index
# => 0
na.sort_index
=> Numo::Int32#shape=[12]
[0, 2, 8, 10, 3, 11, 9, 5, 4, 1, 7, 6]
View
a = Int8[1,2,3,4,5]
# [1, 2, 3, 4, 5]
b = a.view
# => Numo::Int8(view)#shape=[5]
# [1, 2, 3, 4, 5]
a[0] = 0
b
# => Numo::Int8(view)#shape=[5]
# [0, 2, 3, 4, 5]
b[1] = 0
a
# => Numo::Int8#shape=[5]
# [0, 0, 3, 4, 5]
Saving & Loading
a = Int32.new(2,2).seq
# save
s = Marshal.dump(a)
File.binwrite("data", s)
# load
b = Marshal.load(File.read("data"))
a == b
# true
Combining Arrays
a = Int32[1,2,3]
b = Int32[4,5,6]
a.append b
# [1,2,3,4,5,6]
a.concatenate b
# [1,2,3,4,5,6]
a.hstack b
Int32.hstack [a,b]
# [1,2,3,4,5,6]
Int32.dstack [b,b]
# => Numo::Int32#shape=[2,3,2]
# [[[1, 1],
# [2, 2],
# [3, 3]],
# [[4, 4],
# [5, 5],
# [6, 6]]]
Documents
This is just some of the features of NArray. If you want to know more, please refer to these webpages.
Have a nice day!
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