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https://github.com/botkop/numsca
numsca is numpy for scala
https://github.com/botkop/numsca
deep-learning nd4j numpy scala
Last synced: 3 months ago
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numsca is numpy for scala
- Host: GitHub
- URL: https://github.com/botkop/numsca
- Owner: botkop
- License: bsd-2-clause
- Created: 2017-12-06T10:18:37.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2023-01-30T06:59:54.000Z (almost 2 years ago)
- Last Synced: 2024-05-02T10:18:19.589Z (6 months ago)
- Topics: deep-learning, nd4j, numpy, scala
- Language: Jupyter Notebook
- Size: 735 KB
- Stars: 182
- Watchers: 18
- Forks: 17
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scala - numsca - activity/y/botkop/numsca) (Table of Contents / Science and Data Analysis)
README
"What I cannot create, I do not understand." - Richard Feynman.
Numsca: Numpy for Scala
=========================
[](https://maven-badges.herokuapp.com/maven-central/be.botkop/numsca_2.13)
[](https://travis-ci.org/botkop/numsca)
Numsca is Numpy for Scala.
I invite you to have a look at [this notebook](https://nbviewer.jupyter.org/github/botkop/numsca/blob/master/notebooks/dl-from-scratch.ipynb),
which explains in simple terms how you can implement a neural net framework with Numsca.
(If nbviewer barfs, then you can try [this notebook](notebooks/dl-from-scratch.ipynb))
Here's the famous [neural network in 11 lines of Python](http://iamtrask.github.io/2015/07/12/basic-python-network/), translated to Numsca:
```scala
import botkop.{numsca => ns}
val x = ns.array(0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1).reshape(4, 3)
val y = ns.array(0, 1, 1, 0).T
val w0 = 2 * ns.rand(3, 4) - 1
val w1 = 2 * ns.rand(4, 1) - 1
for (j <- 0 until 60000) {
val l1 = 1 / (1 + ns.exp(-ns.dot(x, w0)))
val l2 = 1 / (1 + ns.exp(-ns.dot(l1, w1)))
val l2Delta = (y - l2) * (l2 * (1 - l2))
val l1Delta = l2Delta.dot(w1.T) * (l1 * (1 - l1))
w1 += l1.T.dot(l2Delta)
w0 += x.T.dot(l1Delta)
}
```
Another example: a Scala translation of Andrej Karpathy's
['Minimal character-level language model with a Vanilla Recurrent Neural Network'](src/main/scala/botkop/numsca/samples/MinCharRnn.scala).
(Compare with Andrej Karpathy's original [post](https://gist.github.com/karpathy/d4dee566867f8291f086).)
Also have a look at [Scorch](https://github.com/botkop/scorch), a neural net framework in the spirit of [PyTorch](http://pytorch.org/), which uses Numsca.
## Why?
I love Scala. I teach myself deep learning. Everything in deep learning is written in Python.
This library helps me to quickly translate Python and Numpy code to my favorite language.
I hope you find it useful.
Pull requests welcome.
## Disclaimer
This is far from an exhaustive copy of Numpy's functionality. I'm adding functionality as I go.
That being said, I think many of the most interesting aspects of Numpy like slicing, broadcasting and indexing
have been successfully implemented.
## Under the hood
Numsca piggybacks on [Nd4j](https://nd4j.org/). Thanks, people!
## Dependency
Add this to build.sbt:
For Scala 2.13:
```scala
libraryDependencies += "be.botkop" %% "numsca" % "0.1.7"
```
For Scala 2.11 and 2.12:
```scala
libraryDependencies += "be.botkop" %% "numsca" % "0.1.5"
```
## Importing Numsca
```scala
import botkop.{numsca => ns}
import ns.Tensor
```
## Creating a Tensor
```scala
scala> Tensor(3, 2, 1, 0)
[3.00, 2.00, 1.00, 0.00]
scala> ns.zeros(3, 3)
[[0.00, 0.00, 0.00],
[0.00, 0.00, 0.00],
[0.00, 0.00, 0.00]]
scala> ns.ones(3, 2)
[[1.00, 1.00],
[1.00, 1.00],
[1.00, 1.00]]
scala> val ta: Tensor = ns.arange(10)
[0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
scala> val tb: Tensor = ns.reshape(ns.arange(9), 3, 3)
[[0.00, 1.00, 2.00],
[3.00, 4.00, 5.00],
[6.00, 7.00, 8.00]]
scala> val tc: Tensor = ns.reshape(ns.arange(2 * 3 * 4), 2, 3, 4)
[[[0.00, 1.00, 2.00, 3.00],
[4.00, 5.00, 6.00, 7.00],
[8.00, 9.00, 10.00, 11.00]],
[[12.00, 13.00, 14.00, 15.00],
[16.00, 17.00, 18.00, 19.00],
[20.00, 21.00, 22.00, 23.00]]]
```
## Access
Single element
```scala
scala> ta(0)
res10: botkop.numsca.Tensor = 0.00
scala> tc(0, 1, 2)
res14: botkop.numsca.Tensor = 6.00
```
Get the value of a single element Tensor:
```scala
scala> ta(0).squeeze()
res11: Double = 0.0
```
Slice
```scala
scala> tc(0)
res7: botkop.numsca.Tensor =
[[0.00, 1.00, 2.00, 3.00],
[4.00, 5.00, 6.00, 7.00],
[8.00, 9.00, 10.00, 11.00]]
scala> tc(0, 1)
res8: botkop.numsca.Tensor = [4.00, 5.00, 6.00, 7.00]
```
## Update
In place
```scala
scala> val t = ta.copy()
t: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
scala> t(3) := -5
scala> t
res16: botkop.numsca.Tensor = [0.00, 1.00, 2.00, -5.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
scala> t(0) += 7
scala> t
res18: botkop.numsca.Tensor = [7.00, 1.00, 2.00, -5.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
Array wise
```scala
scala> val a2 = 2 * ta
val a2 = 2 * ta
a2: botkop.numsca.Tensor = [0.00, 2.00, 4.00, 6.00, 8.00, 10.00, 12.00, 14.00, 16.00, 18.00]
```
## Slicing
Note:
- negative indexing is supported
- Python notation ```t[:3]``` must be written as ```t(0 :> 3)``` or ```t(:>(3))```
Not supported (yet):
- step size
- ellipsis
### Single dimension
#### Slice over a single dimension
```scala
scala> val a0 = ta.copy().reshape(10, 1)
a0: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
scala> val a1 = a0(1 :>)
a1: botkop.numsca.Tensor = [1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
scala> val a2 = a0(0 :> -1)
a2: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00]
scala> val a3 = a1 - a2
a3: botkop.numsca.Tensor = [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]
scala> ta(:>, 5 :>)
res19: botkop.numsca.Tensor = [5.00, 6.00, 7.00, 8.00, 9.00]
scala> ta(:>, -3 :>)
res4: botkop.numsca.Tensor = [7.00, 8.00, 9.00]
```
#### Update single dimension slice
```scala
scala> val t = ta.copy()
t: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
Assign another tensor
```scala
scala> t(2 :> 5) := -ns.ones(3)
scala> t
res6: botkop.numsca.Tensor = [0.00, 1.00, -1.00, -1.00, -1.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
Assign a value
```scala
scala> t(2 :> 5) := 33
scala> t
res8: botkop.numsca.Tensor = [0.00, 1.00, 33.00, 33.00, 33.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
Update in place
```scala
scala> t(2 :> 5) -= 1
scala> t
res10: botkop.numsca.Tensor = [0.00, 1.00, 32.00, 32.00, 32.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
### Multidimensional slices
```scala
scala> tb
res11: botkop.numsca.Tensor =
[[0.00, 1.00, 2.00],
[3.00, 4.00, 5.00],
[6.00, 7.00, 8.00]]
scala> tb(2:>, :>)
res15: botkop.numsca.Tensor = [6.00, 7.00, 8.00]
```
Mixed range/integer indexing. Note that integers are implicitly translated to ranges,
and this differs from Python.
```scala
scala> tb(1, 0 :> -1)
res1: botkop.numsca.Tensor = [3.00, 4.00]
```
## Fancy indexing
### Boolean indexing
```scala
scala> val c = ta < 5 && ta > 1
c: botkop.numsca.Tensor = [0.00, 0.00, 1.00, 1.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00]
```
This returns a TensorSelection:
```scala
scala> val d = ta(c)
d: botkop.numsca.TensorSelection = TensorSelection([0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00],[[I@153ea1aa,None)
```
Which is implicitly converted to a Tensor when needed:
```scala
scala> val d: Tensor = ta(c)
d: botkop.numsca.Tensor = [2.00, 3.00, 4.00]
```
Or you can force it to become a Tensor:
```scala
scala> ta(c).asTensor
res10: botkop.numsca.Tensor = [2.00, 3.00, 4.00]
```
Updating:
```scala
scala> val t = ta.copy()
scala> t(ta < 5 && ta > 1) := -7
res6: botkop.numsca.Tensor = [0.00, 1.00, -7.00, -7.00, -7.00, 5.00, 6.00, 7.00, 8.00, 9.00]
```
Selection over multiple dimensions:
```scala
scala> val c: Tensor = tc(tc % 5 == 0)
c: botkop.numsca.Tensor = [0.00, 5.00, 10.00, 15.00, 20.00]
```
Updating over multiple dimensions:
```scala
scala> val t1 = tc.copy()
t1: botkop.numsca.Tensor =
[[[0.00, 1.00, 2.00, 3.00],
[4.00, 5.00, 6.00, 7.00],
[8.00, 9.00, 10.00, 11.00]],
[[12.00, 13.00, 14.00, 15.00],
[16.00, 17.00, 18.00, 19.00],
[20.00, 21.00, 22.00, 23.00]]]
scala> t1(t1 > 5 && t1 < 15) *= 2
res21: botkop.numsca.Tensor =
[[[0.00, 1.00, 2.00, 3.00],
[4.00, 5.00, 12.00, 14.00],
[16.00, 18.00, 20.00, 22.00]],
[[24.00, 26.00, 28.00, 15.00],
[16.00, 17.00, 18.00, 19.00],
[20.00, 21.00, 22.00, 23.00]]]
```
### List of location indexing
```scala
scala> val primes = Tensor(2, 3, 5, 7, 11, 13, 17, 19, 23)
scala> val idx = Tensor(3, 4, 1, 2, 2)
scala> primes(idx).asTensor
res23: botkop.numsca.Tensor = [7.00, 11.00, 3.00, 5.00, 5.00]
```
Reshape according to index:
```scala
scala> tb
res25: botkop.numsca.Tensor =
[[0.00, 1.00, 2.00],
[3.00, 4.00, 5.00],
[6.00, 7.00, 8.00]]
scala> primes(tb).asTensor
res24: botkop.numsca.Tensor =
[[2.00, 3.00, 5.00],
[7.00, 11.00, 13.00],
[17.00, 19.00, 23.00]]
```
Use as a look-up table:
```scala
scala> val numSamples = 4
val numClasses = 3
val x = ns.arange(numSamples * numClasses).reshape(numSamples, numClasses)
val y = Tensor(0, 1, 2, 1)
val z: Tensor = x(ns.arange(numSamples), y)
res26: botkop.numsca.Tensor = [0.00, 4.00, 8.00, 10.00]
```
Update along a single dimension:
```scala
scala> val primes = Tensor(2, 3, 5, 7, 11, 13, 17, 19, 23)
primes: botkop.numsca.Tensor = [2.00, 3.00, 5.00, 7.00, 11.00, 13.00, 17.00, 19.00, 23.00]
scala> val idx = Tensor(3, 4, 1, 2, 2)
idx: botkop.numsca.Tensor = [3.00, 4.00, 1.00, 2.00, 2.00]
scala> primes(idx) := 0
scala> primes
res1: botkop.numsca.Tensor = [2.00, 0.00, 0.00, 0.00, 0.00, 13.00, 17.00, 19.00, 23.00]
```
Multiple dimensions
```scala
scala> val a = ns.arange(6).reshape(3, 2) + 1
a: botkop.numsca.Tensor =
[[1.00, 2.00],
[3.00, 4.00],
[5.00, 6.00]]
scala> val s1 = Tensor(0, 1, 2)
s1: botkop.numsca.Tensor = [0.00, 1.00, 2.00]
scala> val s2 = Tensor(0, 1, 0)
s2: botkop.numsca.Tensor = [0.00, 1.00, 0.00]
scala> val r1: Tensor = a(s1, s2)
r1: botkop.numsca.Tensor = [1.00, 4.00, 5.00]
```
An index will be broadcast if needed:
```scala
scala> val y = ns.arange(35).reshape(5, 7)
y: botkop.numsca.Tensor =
[[0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00],
[7.00, 8.00, 9.00, 10.00, 11.00, 12.00, 13.00],
[14.00, 15.00, 16.00, 17.00, 18.00, 19.00, 20.00],
[21.00, 22.00, 23.00, 24.00, 25.00, 26.00, 27.00],
[28.00, 29.00, 30.00, 31.00, 32.00, 33.00, 34.00]]
scala> val r5: Tensor = y(Tensor(0, 2, 4), Tensor(1))
r5: botkop.numsca.Tensor = [1.00, 15.00, 29.00]
```
Update along multiple dimensions:
```scala
scala> val a = ns.arange(6).reshape(3, 2) + 1
a: botkop.numsca.Tensor =
[[1.00, 2.00],
[3.00, 4.00],
[5.00, 6.00]]
scala> val s1 = Tensor(1, 1, 2)
s1: botkop.numsca.Tensor = [1.00, 1.00, 2.00]
scala> val s2 = Tensor(0, 1, 0)
s2: botkop.numsca.Tensor = [0.00, 1.00, 0.00]
scala> a(s1, s2) := 0
res1: botkop.numsca.Tensor =
[[1.00, 2.00],
[0.00, 0.00],
[0.00, 6.00]]
```
## Broadcasting
```scala
scala> val x = ns.arange(4)
x: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00]
scala> val xx = x.reshape(4, 1)
xx: botkop.numsca.Tensor = [0.00, 1.00, 2.00, 3.00]
scala> val y = ns.ones(5)
y: botkop.numsca.Tensor = [1.00, 1.00, 1.00, 1.00, 1.00]
scala> val z = ns.ones(3, 4)
val z = ns.ones(3, 4)
[[1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00],
[1.00, 1.00, 1.00, 1.00]]
scala> (xx + y)
[[1.00, 1.00, 1.00, 1.00, 1.00],
[2.00, 2.00, 2.00, 2.00, 2.00],
[3.00, 3.00, 3.00, 3.00, 3.00],
[4.00, 4.00, 4.00, 4.00, 4.00]]
scala> x + z
[[1.00, 2.00, 3.00, 4.00],
[1.00, 2.00, 3.00, 4.00],
[1.00, 2.00, 3.00, 4.00]]
```
Outer sum:
```scala
scala> val a = Tensor(0.0, 10.0, 20.0, 30.0).reshape(4, 1)
a: botkop.numsca.Tensor = [0.00, 10.00, 20.00, 30.00]
scala> val b = Tensor(1.0, 2.0, 3.0)
b: botkop.numsca.Tensor = [1.00, 2.00, 3.00]
scala> a + b
res6: botkop.numsca.Tensor =
[[1.00, 2.00, 3.00],
[11.00, 12.00, 13.00],
[21.00, 22.00, 23.00],
[31.00, 32.00, 33.00]]
```
Vector Quantization from [EricsBroadcastingDoc](http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc):
```scala
scala> val observation = Tensor(111.0, 188.0)
scala> val codes = Tensor( 102.0, 203.0, 132.0, 193.0, 45.0, 155.0, 57.0, 173.0).reshape(4, 2)
codes: botkop.numsca.Tensor =
[[102.00, 203.00],
[132.00, 193.00],
[45.00, 155.00],
[57.00, 173.00]]
scala> val diff = codes - observation
diff: botkop.numsca.Tensor =
[[-9.00, 15.00],
[21.00, 5.00],
[-66.00, -33.00],
[-54.00, -15.00]]
scala> val dist = ns.sqrt(ns.sum(ns.square(diff), axis = -1))
dist: botkop.numsca.Tensor = [17.49, 21.59, 73.79, 56.04]
scala> val nearest = ns.argmin(dist).squeeze()
nearest: Double = 0.0
```