Simple Introduction to Clojure with Analysis By: Shannon Birchell

1. Simple Introduction to Clojure with Analysis By: Shannon Birchell

2. Foundation • First Class Functions • Inherits concepts from Lisp • Every expression is a function • Functions attempt to be pure (not possible) • Concurrency is very stable • Software Transaction Memory(STM) • Uses JVM • Hybrid • Dynamically run code • REPL • Inherits all Java classes (4k+)

3. Types • Primitives Not so interesting: Number, String, Boolean and char • Coercion takes place on the JVM in byte code • Interesting Lisp primitives • List: Linked List without element removal capacities • Vector: like linked list but more efficient (less capabilities) • Keyword: used for Map as key • Map: equivalent to Hashmap • Set: same as mathematics

4. Variables • Dynamic type binding • Variable Scope • Stack Dynamic • Explicitly stated global scopes • Value bindings are immutable • Values cannot be changed • Mapping areused to transform variables • Enhanced stability and reliability

5. Functions • Function Declaration • Anonymous function (fn [<parameters>]( <element> <element> … )) • Explicit function (defn <name> [<parameters>] ( <element> <element…)) • Functions can be self documented using • Function closure • A function and all its current values can be stored as a variable. It is said, “The function is closed over”

6. Function Cont. • Some functions • String: (str“hello” “world”) -> hello world • (cons 4 ‘(1 2 3)) -> 1 2 3 4 • (first ‘(1 3 5)) ->1 • (doc someFunct) -> description • First class functions • Parameters can be functions (* (+ 1 1) (+ 1 1)) = (* 2 (+ 1 1))=(* 2 2)=4 • Return values can be functions • (defn f1 [p1] (fn [p2] ( str p1 p2)))

7. Java integration • Java integration • (javax.swing.JOptionPane/showMessageDialog nil (str "Hello Everyone")) • Is equivalent to java code: javax.swing.JOptionPane.showMessageDialog(null, "Hello Everyone“); • Dot operator “.” performs java functinality (. String split “,”) -> String.split(“,”); • Most java objects do not work in the dynamic environment

8. Demonstration

9. Characteristics • Readability • Orthogonal structures. (. String contains “hi”) -> String.contains(“hi”); • iteration is done through recursion and is cryptic • Writeability • Highly nested structures require substantial forethought (if(< (+ 1 1) (3/4) “write”)) • Highly nested structures unify code into cohesive chunk. • Cost • Different than main stream – learning curve • Free software with good libraries

10. Overview and analysis of R By Shannon Birchell

11. Foundation • Domain specific • Statistical and numerical analysis • Used as a scripting language • Run commands real time in R environment • R Based on S syntax • Created by Bell Laboratories in 1976 • Alternative to Fortran • Static scoped • Imperative language • Class Oriented • Like OO but uses predefined classes

12. Variables • Primitives • Vectors logical, integer, double, complex, character, or raw • Coercion is used for different vectors of different size <1,2> + <1,3,5,7> = <1+1,2+3,1+5,2+7> Logical Error checking reduced!!! • Type coercion is character, integer, double, logic • Implicit type binding • Lists • Behave like a vector without enforcing a type • Keys/Names can be associated with entries • Use brackets with name string to accesses elements

13. Variables cont. • Matrix • Is a vector that is mapped to the specified dimensions • Coercion done when dim doesn’t match • An associated array • Indexes start with 1  • Tables • Matrix with elaborate row and column names • Inherits additional methods: colnames & rownames

14. Objects and functions • Objects are black box • unlike Java • Objects are implicitly associated with a class • Auto inherit mode and Attribute methods • More that one class may be inherited • There are many built in functions • Associated with classes but for all practical purposes act completely like functions

15. Operations • Important Functions • Function c() creates a vector • Function list() creates a vector of generic types • Function dim() maps vector to an array • Function plot() graphs a dataset • Creating a objects • “<-” assign to an object • Can assign values, functions or other objects • Creating a vector: x <-c(1,2,3,4) • Creating a list x <- list( “one”, 1, three= ”three” )

16. Expressions • Creating a matrix • array( c(1,0,0,1), dim=c(2,2))-> a : 1 0 0 1 • Creating a function • Default values as parameters x <- function(a , b = 1 ){ return( a + b );} x(1,2) is 3 x(4) is 5 • Creating a graph • Plot, box plot, density… etc

17. Demonstration

18. Characteristics • Readability • Black box objects make it difficult to understand what objects can do • Familiar syntax • Writeability • Large number of operators make full language use difficult • Large number of keywords is problematic • Consistent use of expressions make tasks easy • Cost • Syntax is familiar to C and C++ • Software and packages are free

19. References • Micheal J. Crawley, “The R Book” , Wiley, 2007 • The R Project, www.r-project.org

20. Simple Introduction to Prolog with Analysis By: Shannon Birchell

21. Foundation • Languages is based on first order logic • Queries perform complex analysis on truth statements • Can display true of false at the simplest level • Can display all values that are true

22. Data • Atom • A string of characters that is tokenized as an id • Has no other purpose • Scope is global • Numbers • Floats and Integers only • Variables • Starts with a capital letter • Anonymous variable “_” • Scope is rule

23. Rules • An Axiom is a statement of truth • Immutable – always true “There is a dog call red” • There must be an animal dog : animal(dog). • There must be a name red: name(red). • Rules are axioms with relationships Given the previous example “There is a dog called red” • The relationship that bind “dog” and “red” the verb called Therefore called(x):= name(x), animal(x).

24. Unification & Backtracking • Unification is how prolog derives the truth of a query • This is done by checking all axioms against rules Object is to make the implication query equal: axiom1, axiom2 axiomN…. rule1, rule2… query ? Rule LHS <- rule RHS <- axioms • Backtracking is the process of performing unification over all possibilities

25. Demonstration

26. Characteristics • Readability • Difficult to understand the relationships • But this is the point!!! • Writeability • Simple statements of fact • Simple syntax • Reliability • Bad query catastrophically fail • Unintended consequences of statements unrealized