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The Insertion
          Sort
  Mr. Dave Clausen
La Cañada High School
Insertion Sort Description
The insertion sort uses a vector's partial ordering. On
the kth pass, the kth item should be inserted into its
place among the first k items in the vector.
After the kth pass (k starting at 1), the first k items of
the vector should be in sorted order.
This is like the way that people pick up playing cards
and order them in their hands. When holding the first
(k - 1) cards in order, a person will pick up the kth
card and compare it with cards already held until its
sorted spot is found.

                  Mr. Dave Clausen          2
Insertion Sort Algorithm
For each k from 1 to n - 1 (k is the index of
  vector element to insert)
   Set item_to_insert to v[k]
   Set j to k - 1
   (j starts at k - 1 and is decremented until
  insertion position is found)
   While (insertion position not found) and (not
  beginning of vector)
      If item_to_insert < v[j]
         Move v[j] to index position j + 1
         Decrement j by 1
      Else
         The insertion position has been found
                Mr. Dave Clausen    3
     item_to_insert should be positioned at index
C + + Code For Insertion Sort
 void Insertion_Sort(apvector<int> &v)
 {
    int item_to_insert, j;       // On the kth
 pass, insert item k into its correct
    bool still_looking;       // position among the
 first k entries in vector.
    for (int k = 1; k < v.length(); ++k)
    {     // Walk backwards through list, looking
 for slot to insert v[k]
       item_to_insert = v[k];
       j = k - 1;
       still_looking = true;
       while ((j >= 0) && still_looking )
          if (item_to_insert < v[j])
          {
               v[j + 1] = v[j];
               --j;
            }
          else Dave Clausen
            Mr.                    4
              still_looking = false;       // Upon
Insertion Sort Example
  The Unsorted Vector:                     80
                                           40
For each pass, the index j begins at
                                           32
the (k - 1)st item and moves that          84
item to position j + 1 until we find
the insertion point for what was
                                           61
originally the kth item.

We start with k = 1
and set j = k-1 or 0 (zero)


                        Mr. Dave Clausen   5
The First Pass
                                            K=2
80   Insert 40,     80          Insert 40    40
40   compare        80                      80
     & move
32                  32                      32
84                  84                      84
61                  61                      61


                     item_to_insert
                           40

                   Mr. Dave Clausen              6
The Second Pass
                                 K=3
40                 40    Compare  40     Insert 32   32
                         & move
80   Insert 32,    80               40               40
32   compare       80               80               80
     & move
84                 84               84               84
61                 61               61               61


                        item_to_insert
                             32

                    Mr. Dave Clausen        7
The Third Pass
K=4
 32
40
80    Insert 84?
84    compare
      & stop
61


                      item_to_insert
                            84

                    Mr. Dave Clausen   8
The Fourth Pass
                                   K=5
32                 32               32               32
40                                       Compare
                   40               40               40
                                         & stop
80                 80    Compare    80   Insert 61   61
                         & move
84   Insert 61,    84               80               80
61   compare       84               84               84
     & move


                        item_to_insert
                             61

                   Mr. Dave Clausen         9
What “Moving” Means
item_to_insert
                                    80
                                    40
 40
                                    32
Place the second element            84
into the variable                   61
item_to_insert.



                 Mr. Dave Clausen   10
What “Moving” Means
item_to_insert
                                    80
                                    80
 40
                                    32
Replace the second element          84
with the value of the first         61
element.



                 Mr. Dave Clausen   11
What “Moving” Means
item_to_insert
                                    40
                                    80
 40
                                    32
Replace the first element           84
(in this example) with the          61
variable item_to_insert.



                 Mr. Dave Clausen   12
C + + Examples of
                 The Insertion Sort
On the Net:
http://compsci.exeter.edu/Winter99/CS320/Resources/sortDemo.html


http://www.aist.go.jp/ETL/~suzaki/AlgorithmAnimation/index.html




                      Mr. Dave Clausen          13
Big - O Notation
Big - O notation is used to describe the efficiency
of a search or sort. The actual time necessary to
complete the sort varies according to the speed of
your system. Big - O notation is an approximate
mathematical formula to determine how many
operations are necessary to perform the search or
sort. The Big - O notation for the Insertion Sort is
O(n2), because it takes approximately n2 passes to
sort the “n” elements.
               Mr. Dave Clausen         14

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Insertion sort

  • 1. The Insertion Sort Mr. Dave Clausen La Cañada High School
  • 2. Insertion Sort Description The insertion sort uses a vector's partial ordering. On the kth pass, the kth item should be inserted into its place among the first k items in the vector. After the kth pass (k starting at 1), the first k items of the vector should be in sorted order. This is like the way that people pick up playing cards and order them in their hands. When holding the first (k - 1) cards in order, a person will pick up the kth card and compare it with cards already held until its sorted spot is found. Mr. Dave Clausen 2
  • 3. Insertion Sort Algorithm For each k from 1 to n - 1 (k is the index of vector element to insert) Set item_to_insert to v[k] Set j to k - 1 (j starts at k - 1 and is decremented until insertion position is found) While (insertion position not found) and (not beginning of vector) If item_to_insert < v[j] Move v[j] to index position j + 1 Decrement j by 1 Else The insertion position has been found Mr. Dave Clausen 3 item_to_insert should be positioned at index
  • 4. C + + Code For Insertion Sort void Insertion_Sort(apvector<int> &v) { int item_to_insert, j; // On the kth pass, insert item k into its correct bool still_looking; // position among the first k entries in vector. for (int k = 1; k < v.length(); ++k) { // Walk backwards through list, looking for slot to insert v[k] item_to_insert = v[k]; j = k - 1; still_looking = true; while ((j >= 0) && still_looking ) if (item_to_insert < v[j]) { v[j + 1] = v[j]; --j; } else Dave Clausen Mr. 4 still_looking = false; // Upon
  • 5. Insertion Sort Example The Unsorted Vector: 80 40 For each pass, the index j begins at 32 the (k - 1)st item and moves that 84 item to position j + 1 until we find the insertion point for what was 61 originally the kth item. We start with k = 1 and set j = k-1 or 0 (zero) Mr. Dave Clausen 5
  • 6. The First Pass K=2 80 Insert 40, 80 Insert 40 40 40 compare 80 80 & move 32 32 32 84 84 84 61 61 61 item_to_insert 40 Mr. Dave Clausen 6
  • 7. The Second Pass K=3 40 40 Compare 40 Insert 32 32 & move 80 Insert 32, 80 40 40 32 compare 80 80 80 & move 84 84 84 84 61 61 61 61 item_to_insert 32 Mr. Dave Clausen 7
  • 8. The Third Pass K=4 32 40 80 Insert 84? 84 compare & stop 61 item_to_insert 84 Mr. Dave Clausen 8
  • 9. The Fourth Pass K=5 32 32 32 32 40 Compare 40 40 40 & stop 80 80 Compare 80 Insert 61 61 & move 84 Insert 61, 84 80 80 61 compare 84 84 84 & move item_to_insert 61 Mr. Dave Clausen 9
  • 10. What “Moving” Means item_to_insert 80 40 40 32 Place the second element 84 into the variable 61 item_to_insert. Mr. Dave Clausen 10
  • 11. What “Moving” Means item_to_insert 80 80 40 32 Replace the second element 84 with the value of the first 61 element. Mr. Dave Clausen 11
  • 12. What “Moving” Means item_to_insert 40 80 40 32 Replace the first element 84 (in this example) with the 61 variable item_to_insert. Mr. Dave Clausen 12
  • 13. C + + Examples of The Insertion Sort On the Net: http://compsci.exeter.edu/Winter99/CS320/Resources/sortDemo.html http://www.aist.go.jp/ETL/~suzaki/AlgorithmAnimation/index.html Mr. Dave Clausen 13
  • 14. Big - O Notation Big - O notation is used to describe the efficiency of a search or sort. The actual time necessary to complete the sort varies according to the speed of your system. Big - O notation is an approximate mathematical formula to determine how many operations are necessary to perform the search or sort. The Big - O notation for the Insertion Sort is O(n2), because it takes approximately n2 passes to sort the “n” elements. Mr. Dave Clausen 14