Sum of Two Arrays - Python - python-3.x

Two random integer arrays/lists have been given as ARR1 and ARR2 of size N and M respectively. Both the arrays/lists contain numbers from 0 to 9(i.e. single digit integer is present at every index). The idea here is to represent each array/list as an integer in itself of digits N and M.
You need to find the sum of both the input arrays/list treating them as two integers and put the result in another array/list i.e. output array/list will also contain only single digit at every index.
NOTE:
The sizes N and M can be different.
Output array/list(of all 0s) has been provided as a function argument. Its size will always be one more than the size of the bigger array/list. Place 0 at the 0th index if there is no carry.
No need to print the elements of the output array/list.
def sumOfTwoArrays(arr1, n, arr2, m, output) :
#Your code goes here
#Taking Input Using Fast I/O
def takeInput() :
n = int(stdin.readline().rstrip())
if n == 0 :
return list(), 0
arr = list(map(int, stdin.readline().rstrip().split(" ")))
return arr, n
#to print the array/list
def printList(arr, n) :
for i in range(n) :
print(arr[i], end = " ")
print()
#main
t = int(stdin.readline().rstrip())
while t > 0 :
arr1, n = takeInput()
arr2, m = takeInput()
outputSize = (1 + max(n, m))
output = outputSize * [0]
sumOfTwoArrays(arr1, n, arr2, m, output)
printList(output, outputSize)
t -= 1
Sample Input:
1
3
6 2 4
3
7 5 6
Sample Output:
1 3 8 0

This problem can be solved by a simple function like this:
(Note - you can made adjustment on the last line of return result if needed to meet the "strange requirement" of - 'place 0 at the 0th index if there is no carry'. It's left as a trivial exercise. )
def sum_two_array(L1, L2):
carry, total = 0, 0
m, n = len(L1), len(L2)
k = max(m, n)
result = [0] + [0] * k # add +1
for i in range(1, k+1):
a = L1[m-i] if m - i >= 0 else 0
b = L2[n-i] if n - i >= 0 else 0
total = a + b + carry
result[k-i + 1] = total % 10
carry = total // 10
if carry > 0: result[0] = carry
return result if result[0] != 0 else result[1:]
if __name__ == '__main__':
L1 = [6, 4, 4]
L2 = [7, 5, 6]
print(sum_two_array(L1, L2)) # [1, 4, 0, 0]
print(sum_two_array([6, 2, 4], [7, 5, 6])) # [1, 3, 8, 0]
print(sum_two_array([1, 2, 4], [8, 0])) # [2, 0, 4]

JAVA CODE:
import java.lang.Math;
public class Solution {
public static void sumOfTwoArrays(int arr1[], int arr2[], int output[]) {
int n = arr1.length; //size of arr1
int m = arr2.length; //size of arr2
int o = n+1; //size of output array
int sum1 = 0, sum2=0, totalSum=0;
//storing sum of arr1 in sum1
for(int i=0; i<n; i++)
{
sum1+= arr1[i] * Math.pow(10,(n-1-i));
}
//storing sum of arr2 in sum2
for(int i=0; i<m; i++)
{
sum2+= arr2[i] * Math.pow(10, (m-1-i));
}
totalSum = sum1+sum2;
//storing totalSum in reverse order in output array
for(int i=o-1; i>=0; i--)
{
output[i] = totalSum % 10;
totalSum = totalSum/10;
}
}
}
Explanation:
condition: arr1[n], arr2[m], output[n+1]
Instead of calculating sum of unit, tens, and so on digits of both the arrays.
we first calculate the sum1 of arr1, and sum2 of arr2, by using:
number at index * (10 ^ ((n-1) - index)) concept.
sum1 and sum2 are equal to the n and m sized numbers of respective arrays
we store totalSum = sum1+sum2
we store totalSum's every digit in output[n+1] array
we store it in reverse order by using % and / operations

Related

Calculating number of minimum swaps to sort array (selection sort is too slow) [duplicate]

I'm working on sorting an integer sequence with no identical numbers (without loss of generality, let's assume the sequence is a permutation of 1,2,...,n) into its natural increasing order (i.e. 1,2,...,n). I was thinking about directly swapping the elements (regardless of the positions of elements; in other words, a swap is valid for any two elements) with minimal number of swaps (the following may be a feasible solution):
Swap two elements with the constraint that either one or both of them should be swapped into the correct position(s). Until every element is put in its correct position.
But I don't know how to mathematically prove if the above solution is optimal. Anyone can help?
I was able to prove this with graph-theory. Might want to add that tag in :)
Create a graph with n vertices. Create an edge from node n_i to n_j if the element in position i should be in position j in the correct ordering. You will now have a graph consisting of several non-intersecting cycles. I argue that the minimum number of swaps needed to order the graph correctly is
M = sum (c in cycles) size(c) - 1
Take a second to convince yourself of that...if two items are in a cycle, one swap can just take care of them. If three items are in a cycle, you can swap a pair to put one in the right spot, and a two-cycle remains, etc. If n items are in a cycle, you need n-1 swaps. (This is always true even if you don't swap with immediate neighbors.)
Given that, you may now be able to see why your algorithm is optimal. If you do a swap and at least one item is in the right position, then it will always reduce the value of M by 1. For any cycle of length n, consider swapping an element into the correct spot, occupied by its neighbor. You now have a correctly ordered element, and a cycle of length n-1.
Since M is the minimum number of swaps, and your algorithm always reduces M by 1 for each swap, it must be optimal.
All the cycle counting is very difficult to keep in your head. There is a way that is much simpler to memorize.
First, let's go through a sample case manually.
Sequence: [7, 1, 3, 2, 4, 5, 6]
Enumerate it: [(0, 7), (1, 1), (2, 3), (3, 2), (4, 4), (5, 5), (6, 6)]
Sort the enumeration by value: [(1, 1), (3, 2), (2, 3), (4, 4), (5, 5), (6, 6), (0, 7)]
Start from the beginning. While the index is different from the enumerated index keep on swapping the elements defined by index and enumerated index. Remember: swap(0,2);swap(0,3) is the same as swap(2,3);swap(0,2)
swap(0, 1) => [(3, 2), (1, 1), (2, 3), (4, 4), (5, 5), (6, 6), (0, 7)]
swap(0, 3) => [(4, 4), (1, 1), (2, 3), (3, 2), (5, 5), (6, 6), (0, 7)]
swap(0, 4) => [(5, 5), (1, 1), (2, 3), (3, 2), (4, 4), (6, 6), (0, 7)]
swap(0, 5) => [(6, 6), (1, 1), (2, 3), (3, 2), (4, 4), (5, 5), (0, 7)]
swap(0, 6) => [(0, 7), (1, 1), (2, 3), (3, 2), (4, 4), (5, 5), (6, 6)]
I.e. semantically you sort the elements and then figure out how to put them to the initial state via swapping through the leftmost item that is out of place.
Python algorithm is as simple as this:
def swap(arr, i, j):
arr[i], arr[j] = arr[j], arr[i]
def minimum_swaps(arr):
annotated = [*enumerate(arr)]
annotated.sort(key = lambda it: it[1])
count = 0
i = 0
while i < len(arr):
if annotated[i][0] == i:
i += 1
continue
swap(annotated, i, annotated[i][0])
count += 1
return count
Thus, you don't need to memorize visited nodes or compute some cycle length.
For your reference, here is an algorithm that I wrote, to generate the minimum number of swaps needed to sort the array. It finds the cycles as described by #Andrew Mao.
/**
* Finds the minimum number of swaps to sort given array in increasing order.
* #param ar array of <strong>non-negative distinct</strong> integers.
* input array will be overwritten during the call!
* #return min no of swaps
*/
public int findMinSwapsToSort(int[] ar) {
int n = ar.length;
Map<Integer, Integer> m = new HashMap<>();
for (int i = 0; i < n; i++) {
m.put(ar[i], i);
}
Arrays.sort(ar);
for (int i = 0; i < n; i++) {
ar[i] = m.get(ar[i]);
}
m = null;
int swaps = 0;
for (int i = 0; i < n; i++) {
int val = ar[i];
if (val < 0) continue;
while (val != i) {
int new_val = ar[val];
ar[val] = -1;
val = new_val;
swaps++;
}
ar[i] = -1;
}
return swaps;
}
We do not need to swap the actual elements, just find how many elements are not in the right index (Cycle).
The min swaps will be Cycle - 1;
Here is the code...
static int minimumSwaps(int[] arr) {
int swap=0;
boolean visited[]=new boolean[arr.length];
for(int i=0;i<arr.length;i++){
int j=i,cycle=0;
while(!visited[j]){
visited[j]=true;
j=arr[j]-1;
cycle++;
}
if(cycle!=0)
swap+=cycle-1;
}
return swap;
}
#Archibald, I like your solution, and such was my initial assumptions that sorting the array would be the simplest solution, but I don't see the need to go through the effort of the reverse-traverse as I've dubbed it, ie enumerating then sorting the array and then computing the swaps for the enums.
I find it simpler to subtract 1 from each element in the array and then to compute the swaps required to sort that list
here is my tweak/solution:
def swap(arr, i, j):
tmp = arr[i]
arr[i] = arr[j]
arr[j] = tmp
def minimum_swaps(arr):
a = [x - 1 for x in arr]
swaps = 0
i = 0
while i < len(a):
if a[i] == i:
i += 1
continue
swap(a, i, a[i])
swaps += 1
return swaps
As for proving optimality, I think #arax has a good point.
// Assuming that we are dealing with only sequence started with zero
function minimumSwaps(arr) {
var len = arr.length
var visitedarr = []
var i, start, j, swap = 0
for (i = 0; i < len; i++) {
if (!visitedarr[i]) {
start = j = i
var cycleNode = 1
while (arr[j] != start) {
j = arr[j]
visitedarr[j] = true
cycleNode++
}
swap += cycleNode - 1
}
}
return swap
}
I really liked the solution of #Ieuan Uys in Python.
What I improved on his solution;
While loop is iterated one less to increase speed; while i < len(a) - 1
Swap function is de-capsulated to make one, single function.
Extensive code comments are added to increase readability.
My code in python.
def minimumSwaps(arr):
#make array values starting from zero to match index values.
a = [x - 1 for x in arr]
#initialize number of swaps and iterator.
swaps = 0
i = 0
while i < len(a)-1:
if a[i] == i:
i += 1
continue
#swap.
tmp = a[i] #create temp variable assign it to a[i]
a[i] = a[tmp] #assign value of a[i] with a[tmp]
a[tmp] = tmp #assign value of a[tmp] with tmp (or initial a[i])
#calculate number of swaps.
swaps += 1
return swaps
Detailed explanation on what code does on an array with size n;
We check every value except last one (n-1 iterations) in the array one by one. If the value does not match with array index, then we send this value to its place where index value is equal to its value. For instance, if at a[0] = 3. Then this value should swap with a[3]. a[0] and a[3] is swapped. Value 3 will be at a[3] where it is supposed to be. One value is sent to its place. We have n-2 iteration left. I am not interested what is now a[0]. If it is not 0 at that location, it will be swapped by another value latter. Because that another value also exists in a wrong place, this will be recognized by while loop latter.
Real Example
a[4, 2, 1, 0, 3]
#iteration 0, check a[0]. 4 should be located at a[4] where the value is 3. Swap them.
a[3, 2, 1, 0, 4] #we sent 4 to the right location now.
#iteration 1, check a[1]. 2 should be located at a[2] where the value is 1. Swap them.
a[3, 1, 2, 0, 4] #we sent 2 to the right location now.
#iteration 2, check a[2]. 2 is already located at a[2]. Don't do anything, continue.
a[3, 1, 2, 0, 4]
#iteration 3, check a[3]. 0 should be located at a[0] where the value is 3. Swap them.
a[0, 1, 2, 3, 4] #we sent 0 to the right location now.
# There is no need to check final value of array. Since all swaps are done.
Nicely done solution by #bekce. If using C#, the initial code of setting up the modified array ar can be succinctly expressed as:
var origIndexes = Enumerable.Range(0, n).ToArray();
Array.Sort(ar, origIndexes);
then use origIndexes instead of ar in the rest of the code.
Swift 4 version:
func minimumSwaps(arr: [Int]) -> Int {
struct Pair {
let index: Int
let value: Int
}
var positions = arr.enumerated().map { Pair(index: $0, value: $1) }
positions.sort { $0.value < $1.value }
var indexes = positions.map { $0.index }
var swaps = 0
for i in 0 ..< indexes.count {
var val = indexes[i]
if val < 0 {
continue // Already visited.
}
while val != i {
let new_val = indexes[val]
indexes[val] = -1
val = new_val
swaps += 1
}
indexes[i] = -1
}
return swaps
}
This is the sample code in C++ that finds the minimum number of swaps to sort a permutation of the sequence of (1,2,3,4,5,.......n-2,n-1,n)
#include<bits/stdc++.h>
using namespace std;
int main()
{
int n,i,j,k,num = 0;
cin >> n;
int arr[n+1];
for(i = 1;i <= n;++i)cin >> arr[i];
for(i = 1;i <= n;++i)
{
if(i != arr[i])// condition to check if an element is in a cycle r nt
{
j = arr[i];
arr[i] = 0;
while(j != 0)// Here i am traversing a cycle as mentioned in
{ // first answer
k = arr[j];
arr[j] = j;
j = k;
num++;// reducing cycle by one node each time
}
num--;
}
}
for(i = 1;i <= n;++i)cout << arr[i] << " ";cout << endl;
cout << num << endl;
return 0;
}
Solution using Javascript.
First I set all the elements with their current index that need to be ordered, and then I iterate over the map to order only the elements that need to be swapped.
function minimumSwaps(arr) {
const mapUnorderedPositions = new Map()
for (let i = 0; i < arr.length; i++) {
if (arr[i] !== i+1) {
mapUnorderedPositions.set(arr[i], i)
}
}
let minSwaps = 0
while (mapUnorderedPositions.size > 1) {
const currentElement = mapUnorderedPositions.entries().next().value
const x = currentElement[0]
const y = currentElement[1]
// Skip element in map if its already ordered
if (x-1 !== y) {
// Update unordered position index of swapped element
mapUnorderedPositions.set(arr[x-1], y)
// swap in array
arr[y] = arr[x-1]
arr[x-1] = x
// Increment swaps
minSwaps++
}
mapUnorderedPositions.delete(x)
}
return minSwaps
}
If you have an input like 7 2 4 3 5 6 1, this is how the debugging will go:
Map { 7 => 0, 4 => 2, 3 => 3, 1 => 6 }
currentElement [ 7, 0 ]
swapping 1 with 7
[ 1, 2, 4, 3, 5, 6, 7 ]
currentElement [ 4, 2 ]
swapping 3 with 4
[ 1, 2, 3, 4, 5, 6, 7 ]
currentElement [ 3, 2 ]
skipped
minSwaps = 2
Finding the minimum number of swaps required to put a permutation of 1..N in order.
We can use that the we know what the sort result would be: 1..N, which means we don't actually have to do swaps just count them.
The shuffling of 1..N is called a permutation, and is composed of disjoint cyclic permutations, for example, this permutation of 1..6:
1 2 3 4 5 6
6 4 2 3 5 1
Is composed of the cyclic permutations (1,6)(2,4,3)(5)
1->6(->1) cycle: 1 swap
2->4->3(->2) cycle: 2 swaps
5(->5) cycle: 0 swaps
So a cycle of k elements requires k-1 swaps to put in order.
Since we know where each element "belongs" (i.e. value k belongs at position k-1) we can easily traverse the cycle. Start at 0, we get 6, which belongs at 5,
and there we find 1, which belongs at 0 and we're back where we started.
To avoid re-counting a cycle later, we track which elements were visited - alternatively you could perform the swaps so that the elements are in the right place when you visit them later.
The resulting code:
def minimumSwaps(arr):
visited = [False] * len(arr)
numswaps = 0
for i in range(len(arr)):
if not visited[i]:
visited[i] = True
j = arr[i]-1
while not visited[j]:
numswaps += 1
visited[j] = True
j = arr[j]-1
return numswaps
An implementation on integers with primitive types in Java (and tests).
import java.util.Arrays;
public class MinSwaps {
public static int computate(int[] unordered) {
int size = unordered.length;
int[] ordered = order(unordered);
int[] realPositions = realPositions(ordered, unordered);
boolean[] touchs = new boolean[size];
Arrays.fill(touchs, false);
int i;
int landing;
int swaps = 0;
for(i = 0; i < size; i++) {
if(!touchs[i]) {
landing = realPositions[i];
while(!touchs[landing]) {
touchs[landing] = true;
landing = realPositions[landing];
if(!touchs[landing]) { swaps++; }
}
}
}
return swaps;
}
private static int[] realPositions(int[] ordered, int[] unordered) {
int i;
int[] positions = new int[unordered.length];
for(i = 0; i < unordered.length; i++) {
positions[i] = position(ordered, unordered[i]);
}
return positions;
}
private static int position(int[] ordered, int value) {
int i;
for(i = 0; i < ordered.length; i++) {
if(ordered[i] == value) {
return i;
}
}
return -1;
}
private static int[] order(int[] unordered) {
int[] ordered = unordered.clone();
Arrays.sort(ordered);
return ordered;
}
}
Tests
import org.junit.Test;
import static org.junit.Assert.assertEquals;
public class MinimumSwapsSpec {
#Test
public void example() {
// setup
int[] unordered = new int[] { 40, 23, 1, 7, 52, 31 };
// run
int minSwaps = MinSwaps.computate(unordered);
// verify
assertEquals(5, minSwaps);
}
#Test
public void example2() {
// setup
int[] unordered = new int[] { 4, 3, 2, 1 };
// run
int minSwaps = MinSwaps.computate(unordered);
// verify
assertEquals(2, minSwaps);
}
#Test
public void example3() {
// setup
int[] unordered = new int[] {1, 5, 4, 3, 2};
// run
int minSwaps = MinSwaps.computate(unordered);
// verify
assertEquals(2, minSwaps);
}
}
Swift 4.2:
func minimumSwaps(arr: [Int]) -> Int {
let sortedValueIdx = arr.sorted().enumerated()
.reduce(into: [Int: Int](), { $0[$1.element] = $1.offset })
var checked = Array(repeating: false, count: arr.count)
var swaps = 0
for idx in 0 ..< arr.count {
if checked[idx] { continue }
var edges = 1
var cursorIdx = idx
while true {
let cursorEl = arr[cursorIdx]
let targetIdx = sortedValueIdx[cursorEl]!
if targetIdx == idx {
break
} else {
cursorIdx = targetIdx
edges += 1
}
checked[targetIdx] = true
}
swaps += edges - 1
}
return swaps
}
Python code
A = [4,3,2,1]
count = 0
for i in range (len(A)):
min_idx = i
for j in range (i+1,len(A)):
if A[min_idx] > A[j]:
min_idx = j
if min_idx > i:
A[i],A[min_idx] = A[min_idx],A[i]
count = count + 1
print "Swap required : %d" %count
In Javascript
If the count of the array starts with 1
function minimumSwaps(arr) {
var len = arr.length
var visitedarr = []
var i, start, j, swap = 0
for (i = 0; i < len; i++) {
if (!visitedarr[i]) {
start = j = i
var cycleNode = 1
while (arr[j] != start + 1) {
j = arr[j] - 1
visitedarr[j] = true
cycleNode++
}
swap += cycleNode - 1
}
}
return swap
}
else for input starting with 0
function minimumSwaps(arr) {
var len = arr.length
var visitedarr = []
var i, start, j, swap = 0
for (i = 0; i < len; i++) {
if (!visitedarr[i]) {
start = j = i
var cycleNode = 1
while (arr[j] != start) {
j = arr[j]
visitedarr[j] = true
cycleNode++
}
swap += cycleNode - 1
}
}
return swap
}
Just extending Darshan Puttaswamy code for current HackerEarth inputs
Here's a solution in Java for what #Archibald has already explained.
static int minimumSwaps(int[] arr){
int swaps = 0;
int[] arrCopy = arr.clone();
HashMap<Integer, Integer> originalPositionMap
= new HashMap<>();
for(int i = 0 ; i < arr.length ; i++){
originalPositionMap.put(arr[i], i);
}
Arrays.sort(arr);
for(int i = 0 ; i < arr.length ; i++){
while(arr[i] != arrCopy[i]){
//swap
int temp = arr[i];
arr[i] = arr[originalPositionMap.get(temp)];
arr[originalPositionMap.get(temp)] = temp;
swaps += 1;
}
}
return swaps;
}
def swap_sort(arr)
changes = 0
loop do
# Find a number that is out-of-place
_, i = arr.each_with_index.find { |val, index| val != (index + 1) }
if i != nil
# If such a number is found, then `j` is the position that the out-of-place number points to.
j = arr[i] - 1
# Swap the out-of-place number with number from position `j`.
arr[i], arr[j] = arr[j], arr[i]
# Increase swap counter.
changes += 1
else
# If there are no out-of-place number, it means the array is sorted, and we're done.
return changes
end
end
end
Apple Swift version 5.2.4
func minimumSwaps(arr: [Int]) -> Int {
var swapCount = 0
var arrayPositionValue = [(Int, Int)]()
var visitedDictionary = [Int: Bool]()
for (index, number) in arr.enumerated() {
arrayPositionValue.append((index, number))
visitedDictionary[index] = false
}
arrayPositionValue = arrayPositionValue.sorted{ $0.1 < $1.1 }
for i in 0..<arr.count {
var cycleSize = 0
var visitedIndex = i
while !visitedDictionary[visitedIndex]! {
visitedDictionary[visitedIndex] = true
visitedIndex = arrayPositionValue[visitedIndex].0
cycleSize += 1
}
if cycleSize > 0 {
swapCount += cycleSize - 1
}
}
return swapCount
}
Go version 1.17:
func minimumSwaps(arr []int32) int32 {
var swap int32
for i := 0; i < len(arr) - 1; i++{
for j := 0; j < len(arr); j++ {
if arr[j] > arr[i] {
arr[i], arr[j] = arr[j], arr[i]
swap++
}else {
continue
}
}
}
return swap
}

Start counting indexes from 1 instead of 0 of a list

I created a program to get the the max value of a list and the position of its occurrences (list starting at indexing with 1 not 0) but I can't manage to find any useful solutions.
The input is always a string of numbers divided by zero.
This is my code:
inp = list(map(int,input().split()))
m = max(inp)
count = inp.count(m)
print(m)
def maxelements(seq): # #SilentGhost
return [i for i, j in enumerate(seq) if j == m]
print(maxelements(inp))
I expect to output the maximum value and then all the positions of its occurrences. (also is it possible to do without brackets as in the example below?)
Input: 4 56 43 45 2 56 8
Output: 56
2 6
If you want to shift index values, you could just do
return [i + 1 for i, j in enumerate(seq) if j == m]
more generally any transformation of i or j!
def f(i, j):
# do whatever you want, and return something
return i + 1
return [f(i, j) for i, j in enumerate(seq) if j == m]
Without brackets, as a string:
return " ".join(str(i + 1) for i, j in enumerate(seq) if j==m)
Specifiy start=1 with enumerate():
>>> l = [4, 56, 43, 45, 2, 56, 8]
>>> max_num = max(l)
>>> [i for i, e in enumerate(l, start=1) if e == max_num]
[2, 6]
By default enumerate() uses start=0, because indices start at 0.

How to find median of a list using indexing

I am trying to define a function, median, that consumes a list of numbers and returns the median number from the list. If the list is empty, then I want to return None. To calculate the median, I need to find the middle index of the list after it has been sorted. Do not use a built-in function.
SURVEY_RESULTS = [1.5, 1, 2, 1.5, 2, 3, 1, 1, 1, 2]
def median(SURVEY_RESULTS):
length = 0
order = sorted(SURVEY_RESULTS)
I'm not sure how to use indexing to now determine the median.
Here is my implementation:
def QuickSort(myList,start,end):
if start < end:
i,j = start,end
base = myList[i]
while i < j:
while (i < j) and (myList[j] >= base):
j = j - 1
myList[i] = myList[j]
while (i < j) and (myList[i] <= base):
i = i + 1
myList[j] = myList[i]
myList[i] = base
QuickSort(myList, start, i - 1)
QuickSort(myList, j + 1, end)
return myList
def median(l):
half = len(l) // 2
return (l[half] + l[~half])/2 # Use reverse index
SURVEY_RESULTS = [1.5, 1, 2, 1.5, 2, 3, 1, 1, 1, 2]
# Sort first
QuickSort(SURVEY_RESULTS, 0, len(SURVEY_RESULTS)-1)
result = median(SURVEY_RESULTS)
print (result)

Optimization of CodeWars Python 3.6 code: Integers: Recreation One

I need help optimizing my python 3.6 code for the CodeWars Integers: Recreation One Kata.
We are given a range of numbers and we have to return the number and the sum of the divisors squared that is a square itself.
"Divisors of 42 are : 1, 2, 3, 6, 7, 14, 21, 42. These divisors squared are: 1, 4, 9, 36, 49, 196, 441, 1764. The sum of the squared divisors is 2500 which is 50 * 50, a square!
Given two integers m, n (1 <= m <= n) we want to find all integers between m and n whose sum of squared divisors is itself a square. 42 is such a number."
My code works for individual tests, but it times out when submitting:
def list_squared(m, n):
sqsq = []
for i in range(m, n):
divisors = [j**2 for j in range(1, i+1) if i % j == 0]
sq_divs = sum(divisors)
sq = sq_divs ** (1/2)
if int(sq) ** 2 == sq_divs:
sqsq.append([i, sq_divs])
return sqsq
You can reduce complexity of loop in list comprehension from O(N) to O(Log((N)) by setting the max range to sqrt(num)+1 instead of num.
By looping from 1 to sqrt(num)+1, we can conclude that if i (current item in the loop) is a factor of num then num divided by i must be another one.
Eg: 2 is a factor of 10, so is 5 (10/2)
The following code passes all the tests:
import math
def list_squared(m, n):
result = []
for num in range(m, n + 1):
divisors = set()
for i in range(1, int(math.sqrt(num)+1)):
if num % i == 0:
divisors.add(i**2)
divisors.add(int(num/i)**2)
total = sum(divisors)
sr = math.sqrt(total)
if sr - math.floor(sr) == 0:
result.append([num, total])
return result
It's more the math issue. Two maximum divisors for i is i itself and i/2. So you can speed up the code twice just using i // 2 + 1 as the range stop instead of i + 1. Just don't forget to increase sq_divs for i ** 2.
You may want to get some tiny performance improvements excluding sq variable and sq_divs ** (1/2).
BTW you should use n+1 stop in the first range.
def list_squared(m, n):
sqsq = []
for i in range(m, n+1):
divisors = [j * j for j in range(1, i // 2 + 1 #speed up twice
) if i % j == 0]
sq_divs = sum(divisors)
sq_divs += i * i #add i as divisor
if ((sq_divs) ** 0.5) % 1 == 0: #tiny speed up here
sqsq.append([i, sq_divs])
return sqsq
UPD: I've tried the Kata and it's still timeout. So we need even more math! If i could be divided by j then it's also could be divided by i/j so we can use sqrt(i) (int(math.sqrt(i)) + 1)) as the range stop. if i % j == 0 then append j * j to divisors array. AND if i / j != j then append (i / j) ** 2.

Find multiples of a number in list

I have a list and I want to find all the multiples of that number within a certain tolerance as well as get their indices:
def GetPPMError(t, m):
"""
calculate theoretical (t) and measured (m) ppm
"""
return (((t - m) / t) * 1e6)
multiple = n*1.0033
a = 100
b = [100, 101, 101.0033,102, 102.0066,102.123,103.0099]
results = [p for p in b if abs(GetPPMError(a,b)) < 10]
So I want to find all the multiples like 102.0066 and 103.0099 etc.
where a = 100 + 1*1.0033, a = 100 + 2*1.0033, a = 100 + 3*1.0033 etc
So the result would be the indexes.
Results for the indexes:
[2, 4, 6]
and:
[101.0033, 102.0066, 103.0099]
for the values.
This works for your data:
multiple = 1.0033
a = 100
digits = 6
res = []
res_index = []
for n, x in enumerate(b):
diff = round((x - a) / multiple, digits)
if diff != 0 and diff == int(diff):
res.append(x)
res_index.append(n)
print(res)
print(res_index)
Output:
[101.0033, 102.0066, 103.0099]
[2, 4, 6]

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