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Sorting (Merge Sort vs Insertion Sort)
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package sort | |
func mergeSort(arr []int) (c []int) { | |
sz := len(arr) | |
if sz <= 1 { | |
return arr | |
} | |
s := int(sz / 2) | |
a, b := arr[:s], arr[s:] | |
la, lb := len(a), len(b) | |
if la > 1 || lb > 1 { | |
a = mergeSort(a) | |
b = mergeSort(b) | |
} else { | |
c = make([]int, la+lb) | |
for i, j, k := 0, 0, 0; k < la+lb; k++ { | |
if i < la && j < lb { | |
if a[i] < b[j] { | |
c[k] = a[i] | |
i++ | |
} else if b[j] < a[i] { | |
c[k] = b[j] | |
j++ | |
} else { | |
c[k] = b[j] | |
c[k+1] = a[i] | |
k++ | |
i++ | |
j++ | |
} | |
} else if i < la && j >= lb { | |
c[k] = a[i] | |
i++ | |
} else { | |
c[k] = b[j] | |
j++ | |
} | |
} | |
} | |
return | |
} | |
func insertionSort(arr []int) []int { | |
for i := 1; i < len(arr); i++ { | |
x := arr[i] | |
j := i - 1 | |
for j >= 0 && arr[j] > x { | |
arr[j+1] = arr[j] | |
j-- | |
} | |
arr[j+1] = x | |
} | |
return arr | |
} |
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package sort | |
import ( | |
"flag" | |
"math/rand" | |
"os" | |
"testing" | |
) | |
var ( | |
smallArr = []int{} | |
mediumArr = []int{} | |
largeArr = []int{} | |
) | |
func TestInsertionSortEven(t *testing.T) { | |
arr := []int{5, 8, 2, 4, 1, 7, 6, 3} | |
expected := []int{1, 2, 3, 4, 5, 6, 7, 8} | |
arr = insertionSort(arr) | |
t.Logf("arr = %v", arr) | |
for i := 0; i < len(arr); i++ { | |
if arr[i] != expected[i] { | |
t.Errorf("Expected %v, actual %v", expected, arr) | |
} | |
} | |
} | |
func TestMergeSortEven(t *testing.T) { | |
arr := []int{5, 8, 2, 4, 1, 7, 6, 3} | |
expected := []int{1, 2, 3, 4, 5, 6, 7, 8} | |
arr = mergeSort(arr) | |
t.Logf("arr = %v", arr) | |
for i := 0; i < len(arr); i++ { | |
if arr[i] != expected[i] { | |
t.Errorf("Expected %v, actual %v", expected, arr) | |
} | |
} | |
} | |
func TestInsertionSortOdd(t *testing.T) { | |
arr := []int{5, 8, 2, 4, 1, 9, 7, 6, 3} | |
expected := []int{1, 2, 3, 4, 5, 6, 7, 8, 9} | |
arr = insertionSort(arr) | |
t.Logf("arr = %v", arr) | |
for i := 0; i < len(arr); i++ { | |
if arr[i] != expected[i] { | |
t.Errorf("Expected %v, actual %v", expected, arr) | |
} | |
} | |
} | |
func TestMergeSortOdd(t *testing.T) { | |
arr := []int{5, 8, 2, 4, 1, 9, 7, 6, 3} | |
expected := []int{1, 2, 3, 4, 5, 6, 7, 8, 9} | |
arr = mergeSort(arr) | |
t.Logf("arr = %v", arr) | |
for i := 0; i < len(arr); i++ { | |
if arr[i] != expected[i] { | |
t.Errorf("Expected %v, actual %v", expected, arr) | |
} | |
} | |
} | |
func TestMain(m *testing.M) { | |
flag.Parse() | |
if !testing.Short() { | |
for i := 0; i < 10000; i++ { | |
smallArr = append(smallArr, rand.Intn(9999)) | |
} | |
for i := 0; i < 100000; i++ { | |
mediumArr = append(mediumArr, rand.Intn(9999)) | |
} | |
for i := 0; i < 500000; i++ { | |
largeArr = append(largeArr, rand.Intn(9999)) | |
} | |
} | |
os.Exit(m.Run()) | |
} | |
func BenchmarkSmallMS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
mergeSort(smallArr) | |
} | |
} | |
func BenchmarkSmallIS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
insertionSort(smallArr) | |
} | |
} | |
func BenchmarkMediumMS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
mergeSort(mediumArr) | |
} | |
} | |
func BenchmarkMediumIS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
insertionSort(mediumArr) | |
} | |
} | |
func BenchmarkLargeMS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
mergeSort(largeArr) | |
} | |
} | |
func BenchmarkLargeIS(b *testing.B) { | |
for i := 0; i < b.N; i++ { | |
insertionSort(largeArr) | |
} | |
} |
Quick update to exit early if there's no work to be done.
And another benchmark result:
PASS
BenchmarkSmallMS-4 3000 481411 ns/op
BenchmarkSmallIS-4 100000 22081 ns/op
BenchmarkMediumMS-4 300 4320245 ns/op
BenchmarkMediumIS-4 1 4906207100 ns/op
BenchmarkLargeMS-4 50 26784680 ns/op
BenchmarkLargeIS-4 1 127993339800 ns/op
ok github.com/xorith/lessons/merge-sort 139.990s
As you can clearly see, the NS/OP for MS (merge sort) has dropped nicely. :)
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So I optimized a bit more. The additional call did not help the run time at all.
I also made the data sets larger, and now you can clearly see the benefit of merge sort over insertion sort.
Small = 10k
Medium = 100k
Large = 500k