Javadoc clean-up

src/com/jwetherell/algorithms/data_structures/AVLTree.java

@@ -12,9 +12,9 @@
  * trees are more rigidly balanced, they are faster than red-black trees for
  * lookup intensive applications. However, red-black trees are faster for
  * insertion and removal.
- * 
- * http://en.wikipedia.org/wiki/AVL_tree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/AVL_tree">AVL Tree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 public class AVLTree<T extends Comparable<T>> extends BinarySearchTree<T> {

src/com/jwetherell/algorithms/data_structures/BTree.java

@@ -14,9 +14,9 @@
  * two children. Unlike self-balancing binary search trees, the B-tree is
  * optimized for systems that read and write large blocks of data. It is
  * commonly used in databases and file-systems.
- * 
- * http://en.wikipedia.org/wiki/B-tree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/B-tree">B-Tree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/BinaryHeap.java

@@ -16,9 +16,9 @@
  * the tree is not complete, the nodes of that level are filled from left to
  * right. 2) The heap property: each node is right than or equal to each of its
  * children according to a comparison predicate defined for the data structure.
- * 
- * http://en.wikipedia.org/wiki/Binary_heap
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Binary_heap">Binary Heap (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/BinarySearchTree.java

@@ -19,9 +19,9 @@
  * keys less than the node's key. 2) The right subtree of a node contains only
  * nodes with keys greater than the node's key. 3) Both the left and right
  * subtrees must also be binary search trees.
- * 
- * http://en.wikipedia.org/wiki/Binary_search_tree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Binary_search_tree">Binary Search Tree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/CompactSuffixTrie.java

@@ -8,9 +8,9 @@
  * string in a way that allows for a particularly fast implementation of many
  * important string operations. This implementation is based upon a patricia
  * trie which IS a compact trie.
- * 
- * http://en.wikipedia.org/wiki/Suffix_trie
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Suffix_trie">Suffix Trie (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/DisjointSet.java

@@ -3,14 +3,14 @@
 /**
  * In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of 
  * elements partitioned into a number of disjoint (non-overlapping) subsets. 
- * 
- * It supports two useful operations:
+ * <p>
+ * It supports two useful operations:<br>
  *     Find: Determine which subset a particular element is in. Find typically returns an item from this set that serves as its "representative"; by comparing the 
- *           result of two Find operations, one can determine whether two elements are in the same subset.
- *     Union: Join two subsets into a single subset.
- * 
- * http://en.wikipedia.org/wiki/Disjoint-set_data_structure
- * 
+ *           result of two Find operations, one can determine whether two elements are in the same subset.<br>
+ *     Union: Join two subsets into a single subset.<br>
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Disjoint-set_data_structure">Disjoint Set (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/FenwickTree.java

@@ -10,13 +10,13 @@
  * for calculation and manipulation of the prefix sums of a table of values. Fenwick trees 
  * primarily solve the problem of balancing prefix sum calculation efficiency with element 
  * modification efficiency. 
- * 
- * http://en.wikipedia.org/wiki/Fenwick_tree
- * 
+ * <p>
  * This class is meant to be somewhat generic, all you'd have to do is extend
  * the Data abstract class to store your custom data. I've included a range sum
  * implementations.
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Fenwick_tree">Fenwick Tree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/Graph.java

@@ -10,9 +10,9 @@
  * Graph. Could be directed or undirected depending on the TYPE enum. A graph is
  * an abstract representation of a set of objects where some pairs of the
  * objects are connected by links.
- * 
- * http://en.wikipedia.org/wiki/Graph_(mathematics)
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Graph_(mathematics)">Graph (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/HashArrayMappedTrie.java

@@ -10,12 +10,12 @@
  * A hash array mapped trie (HAMT) is an implementation of an associative 
  * array that combines the characteristics of a hash table and an array mapped 
  * trie. It is a refined version of the more general notion of a hash tree.
- * 
+ * <p>
  * This implementation is 32-bit and steps in 5-bit intervals, maximum tree
  * height of 7.
- * 
- * http://en.wikipedia.org/wiki/Hash_array_mapped_trie
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Hash_array_mapped_trie">Hash Array Mapped Trie (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/HashMap.java

@@ -9,9 +9,9 @@
  * Hash Map using either chaining or probing. hash map is a data structure that
  * uses a hash function to map identifying values, known as keys, to their
  * associated values.
- * 
- * http://en.wikipedia.org/wiki/Hash_table
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Hash_table">Hash Map/Table (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/ImplicitKeyTreap.java

@@ -9,18 +9,18 @@
 /**
  * A Treap is a self-balancing binary search tree that uses randomization to maintain 
  * a low height. In this version, it is used emulate the operations of an array and linked list.
- * 
+ * <p>
  * Time Complexity: Assuming the join/merge functions have constant complexity.
  * add(value), add(index,value), remove(index), set(index,value), get(index) all have O(log N).
  * remove(value), get(value), contains(value) all have O(N).
- * 
+ * <p>
  * Space Complexity: O(N)
- * 
+ * <p>
  * Note: This implementation is 0-based, meaning that all
  * indices from 0 to size() - 1, inclusive, are accessible.
- * 
- * http://en.wikipedia.org/wiki/Treap
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Treap">Treap (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/IntervalTree.java

@@ -12,9 +12,9 @@
  * An interval tree is an ordered tree data structure to hold intervals.
  * Specifically, it allows one to efficiently find all intervals that overlap
  * with any given interval or point. 
- *
- * http://en.wikipedia.org/wiki/Interval_tree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Interval_tree">Interval Tree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 public class IntervalTree<O extends Object> {

src/com/jwetherell/algorithms/data_structures/KdTree.java

@@ -21,9 +21,10 @@
  * useful data structure for several applications, such as searches involving a
  * multidimensional search key (e.g. range searches and nearest neighbor
  * searches). k-d trees are a special case of binary space partitioning trees.
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/K-d_tree">K-D Tree (Wikipedia)</a>
  * <br>
  * @author Justin Wetherell <[email protected]>
- * @see <a href="http://en.wikipedia.org/wiki/K-d_tree">K-d_tree (Wikipedia)</a>
  */
 public class KdTree<T extends KdTree.XYZPoint> implements Iterable<T> {
 

src/com/jwetherell/algorithms/data_structures/LCPArray.java

@@ -7,32 +7,33 @@
  * data structure to the suffix array. It stores the lengths of the longest common
  * prefixes (LCPs) between all pairs of consecutive suffixes in a sorted suffix array.
  * <p>
- * https://en.wikipedia.org/wiki/LCP_array
+ * @see <a href="https://en.wikipedia.org/wiki/LCP_array">LCP Array (Wikipedia)</a>
  * <br>
  * @author Jakub Szarawarski <[email protected]>
  * @author Justin Wetherell <[email protected]>
  */
-public class LCPArray {
+public class LCPArray<C extends CharSequence> {
 
     private static final char DEFAULT_END_SEQ_CHAR = '$';
 
-    private char END_SEQ_CHAR;
-    private SuffixArray suffixArrayBuilder;
-    private ArrayList<Integer> LCP;
+    private final char endSeqChar;
 
-    public LCPArray(CharSequence sequence){
+    private SuffixArray suffixArray;
+    private ArrayList<Integer> lcp;
+
+    public LCPArray(C sequence){
         this(sequence, DEFAULT_END_SEQ_CHAR);
     }
 
-    public LCPArray(CharSequence sequence, char endChar) {
-        END_SEQ_CHAR = endChar;
-        suffixArrayBuilder = new SuffixArray(sequence, END_SEQ_CHAR);
+    public LCPArray(C sequence, char endChar) {
+        endSeqChar = endChar;
+        suffixArray = new SuffixArray(sequence, endSeqChar);
     }
 
     public ArrayList<Integer> getLCPArray() {
-        if (LCP == null)
+        if (lcp == null)
             LCPAlgorithm();
-        return LCP;
+        return lcp;
     }
 
     private void LCPAlgorithm() {
@@ -41,20 +42,20 @@ private void LCPAlgorithm() {
     }
 
     private ArrayList<Integer> getLCPR() {
-        final ArrayList<Integer> KMRArray = suffixArrayBuilder.getKMRarray();
-        final ArrayList<Integer> suffixArray = suffixArrayBuilder.getSuffixArray();
-        final String string = suffixArrayBuilder.getString();
-        final int length = KMRArray.size();
+        final ArrayList<Integer> KMRArrayList = suffixArray.getKMRarray();
+        final ArrayList<Integer> suffixArrayList = suffixArray.getSuffixArray();
+        final String string = suffixArray.getString();
+        final int length = KMRArrayList.size();
         final ArrayList<Integer> LCPR = new ArrayList<Integer>();             // helper array, LCP[i] = LCPR[suffixArray[i]]
 
         int startingValue = 0;
         for (int i=0; i<length; i++) {
-            if(KMRArray.get(i).equals(0)) {
+            if(KMRArrayList.get(i).equals(0)) {
                 LCPR.add(0);
                 startingValue = 0;
             } else {
                 int LCPRValue = startingValue;
-                final int predecessor = suffixArray.get(KMRArray.get(i)-1);
+                final int predecessor = suffixArrayList.get(KMRArrayList.get(i)-1);
                 while (string.charAt(i+LCPRValue) == string.charAt(predecessor+LCPRValue))
                     LCPRValue++;
                 LCPR.add(LCPRValue);
@@ -66,12 +67,12 @@ private ArrayList<Integer> getLCPR() {
     }
 
     private void getLCPfromLCPR(ArrayList<Integer> LCPR) {
-        final ArrayList<Integer> suffixArray = suffixArrayBuilder.getSuffixArray();
-        final int length = suffixArray.size();
+        final ArrayList<Integer> suffixArrayList = suffixArray.getSuffixArray();
+        final int length = suffixArrayList.size();
 
-        LCP = new ArrayList<Integer>();
-        LCP.add(null);                                                  //no value for LCP[0]
+        lcp = new ArrayList<Integer>();
+        lcp.add(null);                                                  //no value for LCP[0]
         for (int i=1; i<length; i++)
-            LCP.add(LCPR.get(suffixArray.get(i)));
+            lcp.add(LCPR.get(suffixArrayList.get(i)));
     }
 }

src/com/jwetherell/algorithms/data_structures/List.java

@@ -4,16 +4,24 @@
 
 import com.jwetherell.algorithms.data_structures.interfaces.IList;
 
+/**
+ * In mathematics, a sequence is an enumerated collection of objects in which repetitions are allowed. Like a set, it contains members (also called elements, or terms). The number of elements 
+ * (possibly infinite) is called the length of the sequence. Unlike a set, order matters, and exactly the same elements can appear multiple times at different positions in the sequence.
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Sequence">Sequence (Wikipedia)</a>
+ * <br>
+ * @author Justin Wetherell <[email protected]>
+ */
 @SuppressWarnings("unchecked")
 public abstract class List<T> implements IList<T> {
 
     /**
      * A dynamic array, growable array, resizable array, dynamic table, or array
      * list is a random access, variable-size list data structure that allows
      * elements to be added or removed.
-     * 
-     * http://en.wikipedia.org/wiki/Dynamic_array
-     * 
+     * <p>
+     * @see <a href="https://en.wikipedia.org/wiki/Dynamic_array">Dynamic Array (Wikipedia)</a>
+     * <br>
      * @author Justin Wetherell <[email protected]>
      */
     public static class ArrayList<T> extends List<T> {
@@ -280,9 +288,9 @@ public T set(int index, T value) {
     /**
      * Linked List (Singly link). A linked list is a data structure consisting
      * of a group of nodes which together represent a sequence.
-     * 
-     * http://en.wikipedia.org/wiki/Linked_list
-     * 
+     * <p>
+     * @see <a href="https://en.wikipedia.org/wiki/Linked_list">Linked List (Wikipedia)</a>
+     * <br>
      * @author Justin Wetherell <[email protected]>
      */
     public static class SinglyLinkedList<T> extends List<T> {
@@ -477,9 +485,9 @@ public String toString() {
     /**
      * Linked List (singly link). A linked list is a data structure consisting
      * of a group of nodes which together represent a sequence.
-     * 
-     * http://en.wikipedia.org/wiki/Linked_list
-     * 
+     * <p>
+     * @see <a href="https://en.wikipedia.org/wiki/Linked_list">Linked List (Wikipedia)</a>
+     * <br>
      * @author Justin Wetherell <[email protected]>
      */
     public static class JavaCompatibleSinglyLinkedList<T> extends java.util.AbstractSequentialList<T> {
@@ -688,9 +696,9 @@ public T previous() {
     /**
      * Linked List (doubly link). A linked list is a data structure consisting
      * of a group of nodes which together represent a sequence.
-     * 
-     * http://en.wikipedia.org/wiki/Linked_list
-     * 
+     * <p>
+     * @see <a href="https://en.wikipedia.org/wiki/Linked_list">Linked List (Wikipedia)</a>
+     * <br>
      * @author Justin Wetherell <[email protected]>
      */
     public static class DoublyLinkedList<T> extends List<T> {

src/com/jwetherell/algorithms/data_structures/Matrix.java

@@ -7,9 +7,9 @@
 /**
  * Matrx. This Matrix implementation is designed to be more efficient 
  * in cache. A matrix is a rectangular array of numbers, symbols, or expressions.
- * 
- * http://en.wikipedia.org/wiki/Matrix_(mathematics)
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Matrix_(mathematics)">Matrix (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/PatriciaTrie.java

@@ -9,9 +9,9 @@
  * non-terminating (black) node with only one child is merged with its child.
  * The result is that every internal non-terminating (black) node has at least
  * two children. Each terminating node (white) represents the end of a string.
- * 
- * http://en.wikipedia.org/wiki/Radix_tree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Radix_tree">Radix Tree / Patricia Trie (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")

src/com/jwetherell/algorithms/data_structures/QuadTree.java

@@ -11,9 +11,9 @@
  * A quadtree is a tree data structure in which each internal node has exactly four children. Quadtrees 
  * are most often used to partition a two dimensional space by recursively subdividing it into four 
  * quadrants or regions. The regions may be square or rectangular, or may have arbitrary shapes.
- * 
- * http://en.wikipedia.org/wiki/Quadtree
- * 
+ * <p>
+ * @see <a href="https://en.wikipedia.org/wiki/Quadtree">QuadTree (Wikipedia)</a>
+ * <br>
  * @author Justin Wetherell <[email protected]>
  */
 @SuppressWarnings("unchecked")