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6a0d858
insertion-sort-chapter
GuyPozner d479cec
Added a c implementation
GuyPozner 9fce769
fixed misspellings in md
GuyPozner 598f4ba
changed to array subscription and C99
GuyPozner f20099e
moved the i decleration into the for loop
GuyPozner 631e2f8
changed md to fit c code
GuyPozner 0ffcccc
deletion of searching_sorting.md in wrong dir
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41 changes: 41 additions & 0 deletions
41
chapters/sorting_searching/insertion_sort/c/insertion_sort.c
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#include <stdio.h> | ||
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void print_array(int *array, int arr_len){ | ||
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putchar('['); | ||
for(int i = 0; i < arr_len; i++){ | ||
if(i < (arr_len - 1)) | ||
printf("%d, ", array[i]); | ||
else | ||
printf("%d]\n", array[i]); | ||
} | ||
} | ||
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/*Insertion sort sorts the array inplace*/ | ||
void insertion_sort(int *array, int arr_len){ | ||
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/*loop through array[1:n], array[0] is already sorted*/ | ||
for(int j = 1; j < arr_len; ++j){ | ||
int current_element = array[j]; | ||
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/*Place the j-th element to the correct position in the sub array array[0...j] | ||
Keeping array[0...j] sorted*/ | ||
int i = j - 1; | ||
while((i >= 0) && (array[i] > current_element)){ | ||
array[i + 1] = array[i]; | ||
i -= 1; | ||
} | ||
array[i + 1] = current_element; | ||
} | ||
} | ||
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int main(){ | ||
int arr_len = 10; | ||
int array[] = {10, 1, 3, 4, 7, 2, 5, 9, 6, 8}; | ||
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printf("This is the array after sorting: "); | ||
insertion_sort(array, arr_len); | ||
print_array(array, arr_len); | ||
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return 0; | ||
} |
54 changes: 54 additions & 0 deletions
54
chapters/sorting_searching/insertion_sort/insertion_sort.md
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# Insertion Sort | ||
Insertion sort is the first algorithm usually taught in an introductory algorithms course since it is simple to understand as it is used regulary to sort decks of cards. Insertion sort has one important rule which is helpful to keep in mind, in the j-th iteration the subarray A[1...j-1] is sorted, it means that all the elements which the algorithm iterated over, are sorted. | ||
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The algorithm starts from the second element in the array, in this point the subarray A[1...j-1], which is A[1], is obviously sorted, since it holds only one element. | ||
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{% method %} | ||
{% sample lang="c" %} | ||
[import:18-19, lang:"c"](code/c/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:5-6, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
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In each iteration the current , j-th, element of A, A[j], is inserted into the correct position in the subarray A[1...j], moving each element that is bigger than A[j] one position to the right, leaving room for A[j]. | ||
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{% method %} | ||
{% sample lang="c" %} | ||
[import:23-28, lang:"c"](code/c/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:10-15, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
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||
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The worst input for insertion sort is the reverse sorted array, since in each iteration of the inner while loop will iterate over the entire A[1...j-1] array, moving each element one position to the right, this is why it has time complexity of $$\mathcal{O}(n^2)$$. | ||
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||
And the full code: | ||
{% method %} | ||
{% sample lang="c" %} | ||
[import:14-41, lang:"c"](code/python/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:1-24, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
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<script> | ||
MathJax.Hub.Queue(["Typeset",MathJax.Hub]); | ||
</script> | ||
$$ | ||
\newcommand{\d}{\mathrm{d}} | ||
\newcommand{\bff}{\boldsymbol{f}} | ||
\newcommand{\bfg}{\boldsymbol{g}} | ||
\newcommand{\bfp}{\boldsymbol{p}} | ||
\newcommand{\bfq}{\boldsymbol{q}} | ||
\newcommand{\bfx}{\boldsymbol{x}} | ||
\newcommand{\bfu}{\boldsymbol{u}} | ||
\newcommand{\bfv}{\boldsymbol{v}} | ||
\newcommand{\bfA}{\boldsymbol{A}} | ||
\newcommand{\bfB}{\boldsymbol{B}} | ||
\newcommand{\bfC}{\boldsymbol{C}} | ||
\newcommand{\bfM}{\boldsymbol{M}} | ||
\newcommand{\bfJ}{\boldsymbol{J}} | ||
\newcommand{\bfR}{\boldsymbol{R}} | ||
\newcommand{\bfT}{\boldsymbol{T}} | ||
\newcommand{\bfomega}{\boldsymbol{\omega}} | ||
\newcommand{\bftau}{\boldsymbol{\tau}} | ||
$$ |
24 changes: 24 additions & 0 deletions
24
chapters/sorting_searching/insertion_sort/python/insertion_sort.py
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def insertion_sort(array): | ||
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array_length = len(array) | ||
# loop through array[1:n], array[0] is already sorted | ||
for j in range(1, array_length): | ||
current_element = array[j] | ||
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# Place the j-th element to the correct position in the sub array array[0...j] | ||
# Keeping array[0...j] sorted | ||
i = j - 1 | ||
while((i >= 0) and (array[i] > current_element)): | ||
array[i + 1] = array[i] | ||
i -= 1 | ||
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array[i + 1] = current_element | ||
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return array | ||
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if __name__ == '__main__': | ||
array = [10, 1, 3, 4, 7, 2, 5, 9, 6, 8] | ||
sorted_array = insertion_sort(array) | ||
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print("This is the array of sorting: " + str(sorted_array)) |
54 changes: 54 additions & 0 deletions
54
chapters/sorting_searching/insertion_sort/sorting_searching.md
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---|---|---|
@@ -0,0 +1,54 @@ | ||
# Insertion Sort | ||
Insertion sort is the first algorithm usually taught in an introductory algorithms course since it is simple to understand as it is used regulary to sort decks of cards. Insertion sort has one important rule which is helpful to keep in mind, in the j-th iteration the subarray A[1...j-1] is sorted, it means that all the elements which the algorithm iterated over, are sorted. | ||
|
||
The algorithm starts from the second element in the array, in this point the subarray A[1...j-1], which is A[1], is obviously sorted, since it holds only one element. | ||
|
||
{% method %} | ||
{% sample lang="c" %} | ||
[import:19-20, lang:"c"](code/c/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:5-6, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
|
||
In each iteration the current , j-th, element of A, A[j], is inserted into the correct position in the subarray A[1...j], moving each element that is bigger than A[j] one position to the right, leaving room for A[j]. | ||
|
||
{% method %} | ||
{% sample lang="c" %} | ||
[import:24-29, lang:"c"](code/c/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:10-15, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
|
||
|
||
The worst input for insertion sort is the reverse sorted array, since in each iteration of the inner while loop will iterate over the entire A[1...j-1] array, moving each element one position to the right, this is why it has time complexity of $$\mathcal{O}(n^2)$$. | ||
|
||
And the full code: | ||
{% method %} | ||
{% sample lang="c" %} | ||
[import:14-42, lang:"c"](code/python/insertion_sort.c) | ||
{% sample lang="py" %} | ||
[import:1-24, lang:"python"](code/python/insertion_sort.py) | ||
{% endmethod %} | ||
|
||
<script> | ||
MathJax.Hub.Queue(["Typeset",MathJax.Hub]); | ||
</script> | ||
$$ | ||
\newcommand{\d}{\mathrm{d}} | ||
\newcommand{\bff}{\boldsymbol{f}} | ||
\newcommand{\bfg}{\boldsymbol{g}} | ||
\newcommand{\bfp}{\boldsymbol{p}} | ||
\newcommand{\bfq}{\boldsymbol{q}} | ||
\newcommand{\bfx}{\boldsymbol{x}} | ||
\newcommand{\bfu}{\boldsymbol{u}} | ||
\newcommand{\bfv}{\boldsymbol{v}} | ||
\newcommand{\bfA}{\boldsymbol{A}} | ||
\newcommand{\bfB}{\boldsymbol{B}} | ||
\newcommand{\bfC}{\boldsymbol{C}} | ||
\newcommand{\bfM}{\boldsymbol{M}} | ||
\newcommand{\bfJ}{\boldsymbol{J}} | ||
\newcommand{\bfR}{\boldsymbol{R}} | ||
\newcommand{\bfT}{\boldsymbol{T}} | ||
\newcommand{\bfomega}{\boldsymbol{\omega}} | ||
\newcommand{\bftau}{\boldsymbol{\tau}} | ||
$$ |
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This file
sorting_searching.md
seems to be a copy ofinsertion_sort.md
, is there a reason for the two files?