Archive

Posts Tagged ‘functional’

JS 101 – Week 3

February 15, 2011 Leave a comment

In Javascript, functions are first class objects. What does it mean to be a first class object?

A function can be used anywhere an object can be used; in particular, you can pass functions as method arguments. (We did this in last week’s homework by passing the print function into the iterateAndOperate method). This is in contrast to a language like Java where functions are not first class objects, and the programmer must use interfaces and anonymous classes to get around this problem. For instance, in Java, to delay the invocation of a function, you might do the following:

SwingUtilities.invokeLater(new Runnable() {
    @Override
    public void run() {
        System.out.println("I was run!");
    }
})

whereas in Javascript, you could do something like

invokeLater(function() { print("I was run") });

assuming there was an invokeLater function.

Functions and variables share the same namespace. What does this mean and what important implication does this have?

This means that you must take care in not defining variables in the global namespace that have the same name as a function. For instance,

// global variable
x = 5;

function x(args) {
    alert(args);   
}

// The global variable is shadowing the function name;
// the function is not called
x(5);

Douglas Crockford equates Javascript functions with Lambdas, and also mentions that they are a secure construct. Can you do some research and reflect on what he means by ‘secure construct’?

A lambda basically means that you can pass a function as an argument to another function; this is due to the aforementioned first class nature of functions. They are a secure construct because the scope of the functions is such that private variables in function A cannot be accessed by function B when A is passed into B. In other words,

function outer(func) {
  // "undefined", since the ‘privateVariable’
  // field is scoped only to the inner function.
  return func.privateVariable;
}

function inner(a,b,c) {
  var privateVariable = 25;
  return a + ", " + b + ", " + c;
}

alert(outer(inner("cat","dog","bear")));

f

Can you explain the concept of a closure.

A closure means that an inner function continues to have access to variables defined inside of an outer function, even after the outer function has finished. I wrote an example you can view on jsfiddle.

What is the difference between a function and a method?

A method is a function that is bound to an object, and thus it has an implicit “self” reference.

In Javascript there is no implicit type checking of arguments passed to functions. This could lead to bugs if programmers are not careful about what they pass to functions. If you create a function, how would you protect it from well meaning but careless programmers?

You can do defensive checks within the function. For instance, if a method is supposed to take in a number, you could do the following:

function numericalFunction(x) {
    if (typeof(x) != "number") {
        throw("Expected numerical value for numericalFunction; got " + x);
    }
    // Proceed as normal
}

You can also use the arguments implicit variable in the function to check that the number of arguments the user passed in is equal to the number of arguments that the method expects.

Javascript functions have implicit access to something called this. this points to different things depending on how the function was created. Can you explain why we need this, and what it represents in different type of functions.

If a function is created globally, its this pointer will be to the DOMObject. If it’s created and bound to an object, the this pointer points to that object. The following illustrates this:

function f() {
  return "f's this: " + this;
}

function nested() {
  function inner() {
    return "inner's this:" + this;
  }
  return inner();
}

// o is an object; add a method do it
var o = {};
o.f = f;
o.nested = nested;

o.newFunc = function() { return "newFunc's this: " + this; };
o.nestedFunc = function() { 
  var inner = function() {
    return "nestedFunc's inner this: " + this;
  }
  return inner();
}



print(f());
print(nested());
print(o.f());
print(o.nested());
print(o.newFunc());
print(o.nestedFunc());


// prints
// f's this: [object DOMWindow]
// inner's this:[object DOMWindow]
// f's this: [object Object]
// inner's this:[object DOMWindow]
// newFunc's this: [object Object]
// nestedFunc's inner this: [object DOMWindow]
//

Note that only the non nested functions that have been bound to object o point to that object as opposed to the DOMWindow.

The reason we want the this pointer is to be able to introspect on the type and contents of the object that is calling the function. For instance, in my last assignment, I used the this variable to produce a nice representation of an object:

nick = {name:"Nick",age:23};

// This function returns a string representation of whatever
// is invoking it
function selfStringRep() {
  var str = "";
  for (var v in this) {
    // This is not an inherited property
    if (this.hasOwnProperty(v)) {
      str += "’" + v + "’: " + this[v] +"\n";
    }
  }
  return str;
}
Object.prototype.toString = selfStringRep;
print(nick.toString());
// prints
//’name’: Nick
//’age’: 23

4.1

The solution for the cat problem talks about a ‘set’ of names. A set is a collection of values in which no value may occur more than once. If names are strings, can you think of a way to use an object to represent a set of names?

Show how a name can be added to this set, how one can be removed, and how you can check whether a name occurs in it.

var cat_set = {};
// Add a cat
cat_set["Tigger"] = true;
// remove a cat
delete cat_set["Tigger"];

// Check if cat exists
cat_set["Frog"] != undefined

// Wrap it all up nicely: in an object with functions:
var cat_set = {};
cat_set.add_cat = function(cat) {
    this[cat] = true;
}
cat_set.remove_cat = function(cat) {
    delete this[cat];
}
cat_set.cat_exists = function(cat) {
    return this[cat] != undefined;
}

cat_set.add_cat("Frisky");
// prints "true"
show(cat_set.cat_exists("Frisky"))
cat_set.remove_cat("Frisky");
// "false"
show(cat_set.cat_exists("Frisky"))

4.2

Write a function range that takes one argument, a positive number, and returns an array containing all numbers from 0 up to and including the given number.

An empty array can be created by simply typing []. Also remember that adding properties to an object, and thus also to an array, can be done by assigning them a value with the = operator. The length property is automatically updated when elements are added.

// Assumes n >= 0
function range(n) {
    var array = [];
    for (var i = 0; i <= n; i++) {
        array[i] = i;
    }
    return array;
}

4.3

split and join are not precisely each other’s inverse. string.split(x).join(x) always produces the original value, but array.join(x).split(x) does not. Can you give an example of an array where .join(" ").split(" ") produces a different value?

[" ", " ", " "].join(" ").split(" ");
// equals ["", "", "", "", "", ""]

4.4

Write a function called startsWith that takes two arguments, both strings. It returns true when the first argument starts with the characters in the second argument, and false otherwise.

function startsWith(string, pattern) {
    return string.slice(0,pattern.length) === pattern;
}

4.5

Can you write a function catNames that takes a paragraph as an argument and returns an array of names?

Strings have an indexOf method that can be used to find the (first) position of a character or sub-string within that string. Also, when slice is given only one argument, it will return the part of the string from the given position all the way to the end.

It can be helpful to use the console to ‘explore’ functions. For example, type "foo: bar".indexOf(":") and see what you get.

// The sentence is in the form "Blah blah: cat1, cat2, …, catN."
function catNames(paragraph) {
    var colonIndex = paragraph.indexOf(":");
    // Skip the colon and the following space
    var catListString = paragraph.slice(colonIndex + 2);
    return catString.split(", ");
}

/*#p2pu-Jan2011-javascript101*/

Advertisements

Car Talk Puzzler #2: What do these words have in common? An imperative and functional approach using Java and Scala

May 15, 2010 1 comment

Here’s the second in my series of Car Talk puzzler solutions. Today I’m going to be illustrating a solution first in Java, and then in Scala in order to show how functional programming features  can drastically reduce the number of lines of code, while still expressing the developer’s intent.

Puzzler:
What do these words have in common: pig, table, cab, real, yet, and ride?

Answer:

RAY: Here’s the answer. If you notice, the first letter in each of the words is from farther down in the alphabet than all the subsequent letters. And if we assigned a number to every letter in the alphabet, so that A is one, B is two, C is three and so on…

TOM: Yeah, yeah, yeah.

RAY: Then the numerical place in the alphabet of the first letter is equal to the sum of the numerical places of all the other letters. For example, Table: T is 20, A is 1, B is 2, L is 12, E is 5, so 1 plus 2 plus 12 plus 5 –

TOM: Is 20.

So, we have the answer.  But as the guys mention on the show, there are probably other words as well.  Let’s do a brute force search through the dictionary to find all such words.  The dictionary I”ll be using is available here, as a new line separated list of words, one per line.  This makes it very easy to deal with.

Here’s the rather straightforward solution in Java:

 import java.io.FileReader;
 import  java.io.BufferedReader;
 import java.io.FileNotFoundException;
 import java.io.IOException;

/**
* Determines all the words such that the first letter's ordinal position
* equals the sum of the remaining letters' ordinal positions.  For instance,
* "table"'s ordinal numbers are [20,1,2,12,5], and 20 = 1+2+12+5.  Thus
* "table" has this property.
*/
public class OrdinalPositionPuzzlerSolver {

    /**
    * Assumes the string consists just of A-Z, a-z.
    */
    public static boolean hasProperty(String s) {
        if (s.length() < 2) {
            return false;
        }
        char[] letters = s.toCharArray();
        int sum = 0;
        for (int i = 1; i < letters.length; i++) {
            int ordinal = ordinal(letters[i]) ;
            sum += ordinal;
        }
        return sum == ordinal(letters[0]);
    }

    /**
    * @return the 1 based ordinal position of the letter.  For instance,
    * a is 1, b is 2, etc.
    */
    public static int ordinal(char letter) {
        return Character.toLowerCase(letter) - 'a' + 1;
    }

    public static void main(String[] args) {
        String dictionaryPath = "dict.txt";
        /**
        http://www.javapractices.com/topic/TopicAction.do?Id=42
        */
        try {
            BufferedReader input = new BufferedReader(new FileReader(dictionaryPath));
            try {
                String line = null;
                while (( line = input.readLine()) != null){
                    if (hasProperty(line)) {
                        System.out.println(line);
                    }
                }
            }
            catch (FileNotFoundException e) {
                System.err.println("Error, file " + dictionaryPath + " didn't exist.");
            }
            finally {
                input.close();
            }
        }
        catch (IOException e) {
            System.err.println("IO Error:" + e);
        }

    }
}
 

The only place that bears some explanation might be the ordinal function:

/**
 * @return the 1 based ordinal position of the letter.  For instance,
 * a is 1, b is 2, etc.
 */
public static int ordinal(char letter) {
    return Character.toLowerCase(letter) - 'a' + 1;
}

Basically, letters (characters) in Java are really just numbers under the hood which get mapped back into letters when printed to the screen.  So, we can do arithmetic using letters.  ‘a’ + 1 = ‘b’, ‘b’ + 2 = ‘d’, and so on.  (This only works because the letters a-z and A-Z are mapped to a continuous range of integers).  So if we want to convert a->1, first we figure out a’s position from the start of the alphabet (‘a’ -‘a’ == 0, it’s the first), and add 1 to get the 1-based number.

The other thing to notice is how much junk code there is just to read the lines from our file:

try {
    BufferedReader input = new BufferedReader(new FileReader(dictionaryPath));
    try {
        String line = null;
        while (( line = input.readLine()) != null){
            if (hasProperty(line)) {
                System.out.println(line);
            }
        }
    }
    catch (FileNotFoundException e) {
        System.err.println("Error, file " + dictionaryPath + " didn't exist.");
    }
    finally {
        input.close();
    }
}
catch (IOException e) {
    System.err.println("IO Error:" + e);
}

Believe it or not, this is relatively idiomatic Java code to accomplish this task.  First we wrap a file reader in a buffered reader so that we do not continuously access the file; rather we read chunks into the file and buffer them for in-memory access.  That’s 20 lines of code just to iterate over the lines of a file, determine if they match a pattern, and print it to the terminal.

It really should not be this hard.

As I’ve said before, Java’s verbosity is one of my biggest complaints about it.  The program works, and it’s relatively straightforward, but it can’t really be described as elegant.  In that same post I mentioned my interest in Scala as an alternative to Java.  It’s a compiled language that runs in the JVM, but through its type inferencing and functional language aspects, it is much more concise; the creator Martin Odersky claims a typical Source Line of Code (SLOC) reduction of 2x between equivalent Scala and Java programs. Let’s reimplement the program in Scala, and see if this claim rings true.  (Note: I’m not an expert in scala so there’s probably even more concise, idiomatic ways to accomplish what I’m trying to do.  But let’s start somewhere).

The syntax of scala is very similar to that of Java.  One of the main differences is that type declarations occur after the variable rather than before.

 int x = 0; // Java
 val x: Int = 0 // Scala

Another syntactic difference is that generic type arguments occur within square brackets rather than angle brackets

 List<Integer> intList = ...; // java
 val intList: List[Int] =  ..// scala

Because of this, array indexing is done through use of parentheses rather than square brackets.

 // Java
 int[] x = {1,2,3,4};
 x[0] // 1
 // Scala
 val x: List[Int] = List(1,2,3,4)
 x(0) // 1

Due to type inferencing, you often don’t need to declare the type of your variables; the compiler can figure it out.  Thus the last declaration could be rewritten as

 val x = List(1,2,3,4)

I highly recommend you watch Martin Odersky’s introduction to Scala video; it’s about an hour long and goes over the design considerations and differences from Java.

There are no static functions in Scala; rather you create a singleton object through the keyword ‘object’ and access the method in the same way.

Finally, there is a read/eval/execute interpreter built-in, accessible by calling ‘scala‘ in the command line.  This allows you to experiment without having to explicitly compile any code; it allowed me to create the pieces iteratively before plunking them into a file for later compilation.

 scala
 Welcome to Scala version 2.7.3.final (Java HotSpot(TM) Client VM, Java  1.5.0_22).
 Type in expressions to have them evaluated.
 Type  :help for more information.

 scala> 1+1
 res0: Int = 2

 scala> print("hello world")
 hello world
 scala>
 

OK, enough beating around the bush.  Let’s solve this puzzler!

We’ll tackle this in 3 parts.
1) Summing a list of numbers
2) Completing the ordinal number function
3) Iterating over all the lines in the dictionary file

Summing a list of numbers

A first pass at a sum function might be as follows:

def sum(nums: Iterable[Int]): Int = {
    var sum = 0
    for (num <- nums) {
        sum += num
    }
    sum
}

Note the declaration of sum as a var as opposed to a val; a val is equivalent to a final value while a var can be reassigned.  Why did I declare the nums argument to be of type Iterable rather than List?  Well, a List is iterable.  So is an array.  So are any number of things.  We want to keep this function as generic as possible. Pasting this into our interpreter, we can test this out.

scala> def sum(nums: Iterable[Int]): Int = {
     |     var sum = 0
     |     for (num <- nums) {
     |         sum += num
     |     }
     |     sum
     | }
sum: (Iterable[Int])Int

scala> sum(List())
res4: Int = 0

scala> sum(List(1,2,3,4,5))
res5: Int = 15

scala> sum(Array(1,2,3,4))
res6: Int = 10

This works, but let’s look at another way to solve this problem – a functional rather than imperative solution.  Java is inherently imperative, meaning you’ll see a lot of explicit while and for loops.  Here’s just a quick excerpt of each the wikipedia articles to define each programming paradigm:

In computer science, functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It emphasizes the application of functions, in contrast to the imperative programming style, which emphasizes changes in state.

In computer science, imperative programming is a programming paradigm that describes computation in terms of statements that change a program state. In much the same way that imperative mood in natural languages expresses commands to take action, imperative programs define sequences of commands for the computer to perform.

So whereas the imperative manner is completely explicit in how the sum is created, using a mutable state variable (sum), a more functional approach will eliminate the use of mutable state altogether.

The reduce function is absolutely crucial to understand in order to program in a functional manner; let’s examine Python’s definition of it before applying it to the problem at hand.

Here is the Python definition of reduce function:

reduce(function, iterable[, initializer])

Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x, y:x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned.

And the description of the built-in sum function is as follows:

sum(iterable[, start])

Sums start and the items of an iterable from left to right and returns the total. start defaults to 0. The iterable‘s items are normally numbers, and are not allowed to be strings. The fast, correct way to concatenate a sequence of strings is by calling ”.join(sequence). Note that sum(range(n), m) is equivalent to reduce(operator.add,range(n), m) To add floating point values with extended precision, see math.fsum().

While Scala doesn’t have a built-in sum function, it does have support for reducing a list through use of a function.  It has both a reduce left (left to right reduction) and reduce right (right to left reduction).  Here is Scala’s definition of reduceLeft:

def reduceLeft[B >: A](op : (B, A) => B) : B
Combines the elements of this iterable object together using the binary operator op, from left to right
Notes Will not terminate for infinite-sized collections.
Parameters
op – The operator to apply
Returns op(… op(a0,a1), …, an) if the iterable object has elements a0, a1, …, an.
Throws
Predef.UnsupportedOperationException – if the iterable object is empty.

This might be a little confusing but bear with me.

First, we need to write a binary function for our sum operator – after all, we want to pairwise reduce the list by using an add operator.  A first pass is as follows:

scala> def add(x: Int, y: Int): Int = { x + y }
add: (Int,Int)Int

scala> List(1,2,3,4).reduceLeft(add)
res12: Int = 10
scala> List().reduceLeft(add)
java.lang.UnsupportedOperationException: Nil.reduceLeft
at scala.List.reduceLeft(List.scala:1093)
at .(:6)
at .()
at RequestResult$.(:3)
at RequestResult$.()
at RequestResult$result()
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethod...
scala> List(-5,5).reduceLeft(add)
res14: Int = 0

Note that you cannot call sum on an empty list any more; doing so results in an UnsupportedOperationException.  This is arguably more correct than returning 0, but we will have to ensure that we don’t attempt to sum an empty iterable object.

So a working sum definition would be

def sum(nums: Iterable[Int]): Int = {
    def add(x: Int, y: Int): Int = { x + y }
    nums.reduceLeft(add)
}

Note that you can nest function definitions, as functions are objects in Scala, and thus can be defined wherever a variable could.  Note too that I never have to explicitly return a value; the last evaluated value is implicitly returned by the function.

Finally, if you want to be even more terse, you can replace the explicitly defined add function with an anonymous one:

def sum(nums: Iterable[Int]): Int = {
    nums.reduceLeft(_+_)
}

Read the Daniel Spiewak’s excellent blog post for a better explanation for what’s going on here.  Here’s a quick explanation:

(_+_) is actually a Scala shorthand for an anonymous function taking two parameters and returning the result obtained by invoking the + operator on the first passing the second.  Less precisely: it’s addition.  Likewise, (_*_) would define multiplication.

Completing the ordinal number functional

Recall the function that we had in Java for computing the ordinal position of each letter:

    public static boolean hasProperty(String s) {
        if (s.length() < 2) {
            return false;
        }
        char[] letters = s.toCharArray();
        int sum = 0;
        for (int i = 1; i < letters.length; i++) {
            int ordinal = ordinal(letters[i]) ;
            sum += ordinal;
        }
        return sum == ordinal(letters[0]);
    }

    /**
    * @return the 1 based ordinal position of the letter.  For instance,
    * a is 1, b is 2, etc.
    */
    public static int ordinal(char letter) {
        return Character.toLowerCase(letter) - 'a' + 1;
    }

Again, we could directly translate this code into Scala but doing so would not be making full use of Scala’s functional underpinnings.  We are converting from characters of the string to their ordinal positions in the alphabet.  While the Java code does this one letter at a time, keeping a sum, it’s just as correct to do the entire mapping process and then sum over the new list.  Pictorially:

Just as there is a reduce function to convert an iterable object into a single entry, so too is there a function to convert an Iterable of one type into an Interable of another; it is the map function.

// The underscore in an anonymous function like  this is a placeholder for the entry in the iterable collection currently  being processed
scala> List(1,3,4).map(_+5)
res19: List[Int] =  List(6, 8, 9)
scala> List("hi","there").map(_.toUpperCase())
res22:  List[java.lang.String] = List(HI, THERE)

The above examples map from Int-> Int and String->String but there’s nothing stopping us from converting types.

scala> List(100,300,400).map(_.toString())
res23:  List[java.lang.String] = List(100, 300, 400)

Finally, let’s apply the ordinal function to a string:

scala> "cat".map(Character.toLowerCase(_) - 'a' +  1)
res24: Seq[Int] = ArrayBufferRO(3, 1, 20)

Thus the entire function for determining if the word is a match is

[sourecode language=”scala”]
/**
* Determines if the word has the desired property of the ordinal position
* of first letter equalling the sum of the ordinal positions of the other
* letters
* Expects letters in the range [a-zA-Z];
*/
def matches(word: String): Boolean = {
if (word.length() < 2) {
false
}
else {
val ordinals = word.map(Character.toLowerCase(_) – 'a' + 1)
val ordinalSum = sum(ordinals.drop(1))
ordinals(0) == ordinalSum
}
}
[/sourecode]

Iterating over the lines in a file

Remember how it took 20 lines to iterate over all the lines in a file?  Fortunately Scala makes it incredibly easy:

val lines =  scala.io.Source.fromFile("dict.txt").getLines

The variable is actually not an array or a list; it is an iterator over the lines in the file.  But iterators are still iterable, so all of our previously mentioned functions for dealing with iterable objects still work.  Again, rather than looping over each element, explicitly testing, and printing whether it matched, we will use a functional approach, namely the ‘filter’ function.

scala>  List(1,2,3,4).filter(_%2==0)
res25: List[Int] = List(2, 4)

The function passed into filter must return Boolean; since our matches() function does that, we can insert it right in:

val lines =  scala.io.Source.fromFile("dict.txt").getLines
val matching =  lines.filter(matches)
matching.foreach(print(_))

When we run the complete code, we get the following list of matches:

  • acknowledgements
    approximation
    bumptiously
    capitalizations
    circuitously
    conceptualizing
    consciousness
    consistently
    credulousness
    crystallizing
    disconcertingly
    discriminatory
    electrocutions
    excommunication
    existentialism
    experimentally
    exploitations
    expressively
    extraneously
    governmentally
    impersonations
    incomprehensibly
    indispensability
    initializations
    intercommunicated
    interpolations
    notwithstanding
    organizationally
    oversimplifying
    phenomenologically
    sportswriting

Wait a minute… what’s going on here?  These obviously don’t follow the pattern.  Let’s open a terminal and do some testing.

First we paste our two helper method definitions in

 Welcome to Scala version 2.7.3.final (Java  HotSpot(TM) Client VM, Java 1.5.0_22).
 Type in expressions to have  them evaluated.
 Type :help for more information.

 scala>  def sum(nums: Iterable[Int]): Int = {
 |          nums.reduceLeft[Int](_+_)
 |     }
 sum: (Iterable[Int])Int

 scala>

 scala>     /**
 |      * Determines  if the word has the desired property of the ordinal position
 |      * of first letter equalling the sum of the ordinal positions of  the other
 |      * letters
 |      * Expects letters in  the range [a-zA-Z];
 |      */

 scala>     def  matches(word: String): Boolean = {
 |         val ordinals =  word.map(Character.toLowerCase(_) - 'a' + 1)
 |         val  ordinalSum = sum(ordinals.drop(1))
 |         ordinals(0) ==  ordinalSum
 |     }
 matches: (String)Boolean

 scala> matches("table")
 res0: Boolean = true

 scala>  matches("chair")
 res1: Boolean = false

 scala>  matches("consistently")
 res2: Boolean = false
 

This is very strange.  “consistently” is one of the words on the list, but it doesn’t pass the test on its own.  In fact, none of them do.

What’s the problem?  After googling and debugging for a bit, I realized that the precondition I was assuming in my matches method was being violated; the letters were not in the range [A-Za-z].  How could that be?  Well, it turns out that the getLines() method returns the lines in the file – with new lines included!

scala> matches("consistently\r\n")
res5: Boolean = true

(The file had been created on Windows, and thus has a carriage feed as well as a new line marker).

Fortunately there is a method to eliminate whitespace (including control characters like these); it’s known as trim.

We can put the trim in one of two places, either in the matches() method itself,

def matches(word: String): Boolean = {
     val ordinals = word.trim().map(Character.toLowerCase(_) - 'a' + 1)
     val ordinalSum = sum(ordinals.drop(1))
     ordinals(0) == ordinalSum
}

scala> matches("consistently\r\n")
res6: Boolean = false

scala> matches("consistently")
res7: Boolean = false

Or we can introduce the trim while iterating over the lines in the collection.  I’ve opted for this second approach; I’m making it a precondition of calling the method matches() that the input is already sanitized.  We introduce an implicit mapping from the string to the trimmed version of the string:

// An iterator of lines in the file; note that they have newline control characters
val lines = scala.io.Source.fromFile("dict.txt").getLines
// Filter the lines, eliminating the new lines through trim mapping
val matching = lines.filter(i => matches(i.trim()))
matching.foreach(print(_))

After making the change, the result is

  • cab
  • hag
  • jade
  • leaf
  • leg
  • maced
  • mica
  • mid
  • need
  • pig
  • real
  • ride
  • same
  • sand
  • seam
  • sod
  • table
  • toad
  • toe
  • vial
  • ward
  • whack
  • who
  • wick
  • win
  • yeas
  • yet
  • zebra
  • zinc

Tada!

The final version of the program is

object OrdinalPositionSolver {
    /**
     * Determines if the word has the desired property of the ordinal position
     * of first letter equalling the sum of the ordinal positions of the other
     * letters
     * Expects letters in the range [a-zA-Z];
     */
     def matches(word: String): Boolean = {
         if (word.length() < 2) {
            false
         }
         else {
             val ordinals = word.map(Character.toLowerCase(_) - 'a' + 1)
             val ordinalSum = sum(ordinals.drop(1))
             ordinals(0) == ordinalSum
         }
     }

    def sum(nums: Iterable[Int]): Int = {
        nums.reduceLeft[Int](_+_)
    }

    def main(args: Array[String]) {
        // An iterator of lines in the file; note that they have newline control characters
        val lines = scala.io.Source.fromFile("dict.txt").getLines
        // Filter the lines, eliminating the new lines through trim mapping
        val matching = lines.filter(i => matches(i.trim()))
        matching.foreach(print(_))
    }
}

Conclusion

I have solved the puzzler in two different ways, once in an imperative Java style and once in a more functional Scala style.  The functional way is extremely concise and expressive, doing in 30 lines of code what it takes the Java code to do in over 60, confirming the claim that a typical Scala program is at least twice as compact as the corresponding Java program.  I have introduced some of the salient syntactic differences between Java and Scala. I have also introduced some of the mainstays of the functional programming paradigm, namely map, reduce, and filter.  While there are efforts to make Java more functional, the fact that you cannot pass functions as objects severely inhibits the use of functional programming paradigms.