Few Things on Scala

Original presentation at Computational Linguistics Lab, Nara Institute of Science and Technology.

Transcript

1. Few things on Scala
   ͍ΒͪͷͨΊͷ4DBMBೖ໳
   Image: typesafe.com
   Sorami Hisamoto
   09 July 2013 @ PG
   

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2. For me to digest, and to introduce some of the contents from
   l'VODUJPOBM1SPHSBNNJOH1SJODJQMFTJO4DBMBz
   a marvelous course by Martin Odersky (plus alpha).
   

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3. For me to digest, and to introduce some of the contents from
   l'VODUJPOBM1SPHSBNNJOH1SJODJQMFTJO4DBMBz
   a marvelous course by Martin Odersky (plus alpha).
   https://www.coursera.org/course/progfun
   

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4. For me to digest, and to introduce some of the contents from
   l'VODUJPOBM1SPHSBNNJOH1SJODJQMFTJO4DBMBz
   a marvelous course by Martin Odersky (plus alpha).
   https://www.coursera.org/course/progfun
   Disclaimar
   I’m just a novice.
   Watch the lectures by yourself!
   

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5. “Scala is so cool that
   nobody knows it.”
   Image:
   - Cal Henderson
   http://www.youtube.com/watch?v=i6Fr65PFqfk
   

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6. “I call Scala an old wine in a new bottle.”
   Image:
   - Venkat Subramaniam
   http://www.youtube.com/watch?v=LH75sJAR0hc
   

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7. Image:
   Programming Paradigms
   

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8. Image:
   Programming Paradigms
   ‣ Imperative ໋ྩܕ
   ‣ Functional ؔ਺ܕ
   ‣ Logic ࿦ཧϓϩάϥϛϯά

   

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9. Image:
   Programming Paradigms
   ‣ Imperative ໋ྩܕ
   ‣ Functional ؔ਺ܕ
   ‣ Logic ࿦ཧϓϩάϥϛϯά

   Orthogonal to
   Object Oriented
   Programming.
   

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10. Image:
    ij
    There's a strong correspondence between
    Imperative Programs and Computers.
    Memory cells, load/store instructions, etc...
    

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11. Image:
    ij
    There's a strong correspondence between
    Imperative Programs and Computers.
    Memory cells, load/store instructions, etc...
    Pure imperative programming 

    is limited by the
    "Von Neumann" bottleneck.
    

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12. Image:
    How can we avoid
    conceptualizing programs
    word by word?
    

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13. Image:
    How can we avoid
    conceptualizing programs
    word by word?
    High-level abstractions:
    ideally, develop theories of
    collections, shapes, strings, ...
    

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14. Image:
    How can we avoid
    conceptualizing programs
    word by word?
    High-level abstractions:
    ideally, develop theories of
    collections, shapes, strings, ...
    Let’s concentrate on
    defining theories for operators 

    expressed as functions.
    

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15. Image:
    Theory: data types & operations.
    Normally, a theory does not describe mutations.
    

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16. Image:
    Theory: data types & operations.
    Normally, a theory does not describe mutations.
    e.g. (a*x + b) + (c*x + d) = (a+c)*x + (b+d)
    

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17. Image:
    Functional
    Programming
    Restricted sense:
    programming without mutable variables, assignments,
    loops, and other imperative control structures.
    Wider sense:
    Focusing on the functions.
    

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18. Image:
    ‣ Assignment-less

    

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19. Image:
    Don't need to worry about
    thread safety.
    ‣ Assignment-less

    

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20. Image:
    Don't need to worry about
    thread safety.
    ‣ No side effects

    ‣ Assignment-less

    

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21. Image:
    Don't need to worry about
    thread safety.
    ‣ No side effects
 Multiple calls. 

    Support concurrency.
    ‣ Assignment-less

    

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22. Image:
    Don't need to worry about
    thread safety.
    ‣ No side effects

    ‣ Referential transparency

    Multiple calls. 

    Support concurrency.
    ‣ Assignment-less

    

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23. Image:
    Don't need to worry about
    thread safety.
    ‣ No side effects

    ‣ Referential transparency

    Multiple calls. 

    Support concurrency.
    Can move code up n' down. 

    Can distribute to multicore.
    ‣ Assignment-less

    

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24. Image:
    Don't need to worry about
    thread safety.
    ‣ No side effects

    ‣ Referential transparency

    Multiple calls. 

    Support concurrency.
    Can move code up n' down. 

    Can distribute to multicore.
    ‣ Functions are first-class, ...
    ‣ Assignment-less

    

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25. Why it matters?
    Image:
    

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26. Why it matters? Brevity.
    Easier to debug.
    Concurrency.
    Image:
    

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27. Image:
    ‣ 1959 Lisp
    ‣ 1975-77 ML, FP, Scheme
    ‣ 1978 Smalltalk
    ‣ 1986 Standard ML
    ‣ 1990 Haskell, Earlang
    ‣ 1999 XSLT
    ‣ 2000 OCaml
    ‣ 2003 Scala, XQuery
    ‣ 2005 F#
    ‣ 2007 Clojure
    

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28. Image:
    http://upload.wikimedia.org/wikipedia/commons/8/85/Scala_logo.png
    

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29. ‣ Includes full support of functional programming.

    Currying, pattern matching, lazy evaluation, tail recursion, immutability, etc.
    ‣ Static typing.

    “In Java, when they said typing they meant your fingers.”

    “You type less, because it is more typed. 

    In a language that is typed less, you type more.”

    - V. Subramaniam http://www.youtube.com/watch?v=LH75sJAR0hc
    Image:
    OO + FP
    

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30. Image:
    Image:
    Used in Twitter, LinkedIn, Foursquare,
    Sony, Amazon, Facebook, NASA, ...
    

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31. “
    ... if someone had shown me
    the Programming in Scala book
    [...] back in 2003
    I'd probably have never created Groovy.
    ”
    - James Strachan, the creator of Groovy

    http://macstrac.blogspot.jp/2009/04/

    scala-as-long-term-replacement-for.html
    

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32. “
    No other language on the JVM
    seems as capable of being a
    ‘replacement of Java’ as Scala, and
    the momentum behind Scala is now
    unquestionable.
    ”
    - Charles Nutter, the lead developer of JRuby
    http://blog.headius.com/2009/04/future-part-one.html
    

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33. “
    If I were to pick a language to
    use today other than Java,
    it would be Scala.
    ”
    - James Gosling, the father of Java
    Image:
    

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34. Sounds good, but it is also ...
    Image:
    

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35. Sounds good, but it is also ...
    a complex language.
    a ‘kitchen sink language’.
    Image:
    

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36. “
    The most depressing thing on Scala is [...]
    most people pick up Scala as
    ‘Java with more concise syntax’.
    ”

    - James Gosling
    https://twitter.com/akarazniewicz/status/11353944609521664
    

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37. Anyway, let’s get started.
    Image:
    

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38. Image:
    “Tool Setup” page in Odersky’s course page.

    https://class.coursera.org/progfun-002/wiki/view?page=ToolsSetup

    

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39. On IDEs,“worksheet” might be helpful.
    Image:
    

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40. On IDEs,“worksheet” might be helpful.
    Image:
    

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41. giter8
    A command line tool to apply
    templates defined on github.
    https://github.com/n8han/giter8
    sbt
    A build tool.
    http://www.scala-sbt.org/
    Image:
    

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42. Image:
    Natural Language Processing
    & Machine Learning?
    

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43. Image:
    Scalala - developed by Daniel Ramage, 

    who used to be under Chris Manning.
    Natural Language Processing
    & Machine Learning?
    

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44. Image:
    Scalala - developed by Daniel Ramage, 

    who used to be under Chris Manning.
    Other libraries: Breeze, OptiML, FACTORIE, and more.
    Natural Language Processing
    & Machine Learning?
    

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45. Image:
    Scalala - developed by Daniel Ramage, 

    who used to be under Chris Manning.
    Other libraries: Breeze, OptiML, FACTORIE, and more.
    “Scala and Machine Learning with Andrew McCallum”

    http://www.youtube.com/watch?v=DkRLTToCw-0
    Natural Language Processing
    & Machine Learning?
    

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46. Image:
    Scalala - developed by Daniel Ramage, 

    who used to be under Chris Manning.
    Other libraries: Breeze, OptiML, FACTORIE, and more.
    “Scala and Machine Learning with Andrew McCallum”

    http://www.youtube.com/watch?v=DkRLTToCw-0
    NAACL2013 “Tagger in 2hrs” paper - implementation in Scala.

    https://github.com/dhgarrette/low-resource-pos-tagging-2013
    Natural Language Processing
    & Machine Learning?
    

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47. Some Examples
    Image:
    

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48. First, some brevity.
    Image:
    

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52. You can drop the things that are obvious.
    

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54. View Slide

55. ɾ
    ɾ
    ɾ
    

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56. “for” is powerful in Scala.
    (For more usage, refer to “Programming in Scala” Chapter 7.)
    ɾ
    ɾ
    ɾ
    

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57. Square roots with
    Newton's method
    Image:
    

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58. ‣ To compute sqrt(x),
    ‣ Start with initial estimate y (say, 1).
    ‣ Repeatedly improve the estimate 

    by taking the mean of y and x/y.
    Image:
    

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60. Iterate until
    the guess is 

    good enough.
    

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61. Iterate until
    the guess is 

    good enough.
    Improve the guess

    by taking mean of

    x and guess.
    

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62. Image:
    Currying
    

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63. View Slide

64. Note that
    a and b are
    unchanged.
    

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65. Note that
    a and b are
    unchanged.
    

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66. Note that
    a and b are
    unchanged.
    These are special cases of for different values of f .
    

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67. Note that
    a and b are
    unchanged.
    These are special cases of for different values of f .
    Can we factor out
    the common pattern?
    

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68. View Slide

69. higher order
    function
    

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70. higher order
    function
    

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71. higher order
    function
    Or even better ...
    

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72. higher order
    function
    Or even better ...
    

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73. higher order
    function
    Or even better ...
    anonymous
    functions
    

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74. View Slide

75. Function
    returning
    function.
    

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76. Function
    returning
    function.
    

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77. Function
    returning
    function.
    

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78. Function
    returning
    function.
    

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79. Function
    returning
    function.
    

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80. Function
    returning
    function.
    Currying
    Transforming a function
    that takes multiple arguments
    in such a way that
    it can be called as a chain of functions,
    each with a single argument.
    

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81. Finding
    Fixed Points
    Image:
    

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82. Image:
    Number x is called a fixed point of a function f
    if f(x) = x.
    

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83. Image:
    Number x is called a fixed point of a function f
    if f(x) = x.
    For some f, we can locate the fixed points
    by repetitive way:
    x, f(x), f(f(x)), f(f(f(x))), ...
    until the value does not vary anymore.
    

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85. Image:
    Now, sqrt(x) = y such that y*y = x .
    

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86. Image:
    Now, sqrt(x) = y such that y*y = x .
    Or, sqrt(x) = y such that y = x /y .
    

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87. Image:
    Now, sqrt(x) = y such that y*y = x .
    Or, sqrt(x) = y such that y = x /y .
    Consequently, sqrt(x) is a fixed point of y → x /y .
    

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88. View Slide

89. Controls the oscillation.
    

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90. Rational Numbers
    ༗ཧ਺
    Image:
    

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91. Class
    

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92. Method
    

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93. Method
    

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94. ... and other things
    

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95. Operators
    

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96. Operators
    Relaxed identifiers.
    

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97. Operators
    Relaxed identifiers.
    

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98. Operators
    Relaxed identifiers.
    Precedence rules,
    just like the maths.
    

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99. Image:
    Binary Tree
    ೋ෼໦
    

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100. Image:
     (Time’s up)
     

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101. Image:
     (Time’s up)
     See Coursera week 3
     https://class.coursera.org/progfun-002/lecture/index
     - Class hierarchies
     - Polymorphism, etc.
     

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102. Some references
     for further study
     Image:
     

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103. “Why Scala?” Epic. Just watch it.

     Venkat Subramaniam. www.youtube.com/watch?v=LH75sJAR0hc

     Image:
     “Functional Programming Principles in Scala” A bliss online course.

     Martin Odersky. www.coursera.org/course/progfun

     Programming in Scala” The Bible, by the creator himself.

     Martin Odersky, Lex Spoon, & Bill Venners.

     Programming Scala” An O’Reilly book. First part publicly available online.

     Dean Wampler & Alex Payne. shop.oreilly.com/product/9780596155964.do

     “Pragmatic Real-World Scala” A nice overview presentation by Typesafe CTO.

     Jonas Bonér. www.slideshare.net/jboner/
     

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104. Image:
     END.
     Tanoshii
     desu kara
     

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