Given a=[123,45] and b=[232,64] we need to determine moves from a to b
a[0] to b[0] leads to:
increment by 1(1 to 2),
increment by 1(2 to 3),
decrement by 1(3 to 2)
so 3 move in total (1+1+1).
a[1] to b[1] leads to
increment by 2(4 to 6),
decrement by 1(5 to 4)
3 move in total (2 + 1)
Min moves = 3 + 3 = 6.
So for given 2 list we need to find the total moves to reach next list?
My program which is wrong is below
def sub(a,b):
s = 0
for x, y in zip(a,b):
s += x-y
return s
sub([123,45],[232,64])
-128
This should work:
def sub(a,b):
s = 0
for x, y in zip(a,b):
s += sum(abs(int(n) - int(m)) for n, m in zip(str(x), str(y)))
return s
print(sub([123,45],[232,64]))
Output:
6
Related
I am examining every contiguous 8 x 8 x 8 cube within a 50 x 50 x 50 cube. I am trying to create a collection (in this case a dictionary) of the subcubes that contain the same sum and a count of how many subcubes share that same sum. So in essence, the result would look something like this:
{key = sum, value = number of cubes that have the same sum}
{256 : 3, 119 : 2, ...}
So in this example, there are 3 cubes that sum to 256 and 2 cubes that sum to 119, etc. Here is the code I have thus far, but it only sums (at least I think it does):
an_array = np.array([i for i in range(500)])
cube = np.reshape(an_array, (8, 8, 8))
c_size = 8 # cube size
sum = 0
idx = None
for i in range(cube.shape[0] - cs + 2):
for j in range(cube.shape[1] - cs + 2):
for k in range(cube.shape[2] - cs + 2):
cube_sum = np.sum(cube[i:i + cs, j:j + cs, k:k + cs])
new_list = {cube_sum : ?}
What I am trying to make this do is iterate the cube within cubes, sum all cubes then count the cubes that share the same sum. Any ideas would be appreciated.
from collections import defaultdict
an_array = np.array([i for i in range(500)])
cube = np.reshape(an_array, (8, 8, 8))
c_size = 8 # cube size
sum = 0
idx = None
result = defaultdict(int)
for i in range(cube.shape[0] - cs + 2):
for j in range(cube.shape[1] - cs + 2):
for k in range(cube.shape[2] - cs + 2):
cube_sum = np.sum(cube[i:i + cs, j:j + cs, k:k + cs])
result[cube_sum] += 1
Explanation
The defaultdict(int), can be read as a result.get(key, 0). Which means that if a key doesn't exists it will be initialized with 0. So the line result[cube_sum] += 1, will either contain 1, or add 1 to the current number of cube_sum.
I have to arrange and/or fit 2d tiles into a 2d square or rectangular plane with AI algorithm using python program. Each tile has a length and width. For example if a plane is 4x3 and set of tiles is
S={(2,3),(1,2),(2,2)}
these tiles can be rotated 90 degrees in order to fit the matrix.
input
first line contains length and width of the plane
second line number of tiles
and then the length,width of the subsequent tiles
but the inputs should be tab seperated
for eg
4 3
3
2 3
1 2
2 2
output
for eg
1 1 2 2
1 1 3 3
1 1 3 3
I have trouble solving this as i have to use only standard libraries in python no NumPy and no CSP library
~Edit 2`
my code so far I cant figure out how to add algorithm without csp library or to generate grid
from sys import stdin
a = stdin.readline()
x = a.split()
rectangular_plane = [[0] * int(x[0]) for i in range(int(x[1]))]
num_of_rectangles = stdin.readline()
r_widths = []
r_lengths= []
for l in range(int(num_of_rectangles)):
b = stdin.readline()
y = b.split()
r_lengths.insert(l,y[0])
r_widths.insert(l,y[1])
I've solved task with backtracking approach and without any non-standard modules.
Try it online!
import sys
nums = list(map(int, sys.stdin.read().split()))
pw, ph = nums[0:2]
ts = list(zip(nums[3::2], nums[4::2]))
assert len(ts) == nums[2]
if sum([e[0] * e[1] for e in ts]) != pw * ph:
print('Not possible!')
else:
def Solve(*, it = 0, p = None):
if p is None:
p = [[0] * pw for i in range(ph)]
if it >= len(ts):
for e0 in p:
for e1 in e0:
print(e1, end = ' ')
print()
return True
for tw, th in [(ts[it][0], ts[it][1]), (ts[it][1], ts[it][0])]:
zw = [0] * tw
ow = [it + 1] * tw
for i in range(ph - th + 1):
for j in range(pw - tw + 1):
if all(p[k][j : j + tw] == zw for k in range(i, i + th)):
for k in range(i, i + th):
p[k][j : j + tw] = ow
if Solve(it = it + 1, p = p):
return True
for k in range(i, i + th):
p[k][j : j + tw] = zw
return False
if not Solve():
print('Not possible!')
Example input:
4 3
3
2 3
1 2
2 2
Output:
1 1 2 2
1 1 3 3
1 1 3 3
algorithm question:
Given n points on a 2D plane, find the maximum number of points that lie on the same straight line.
Example 1:
Input: [[1,1],[2,2],[3,3]]
Output: 3
Explanation:
^
|
| o
| o
| o
+------------->
0 1 2 3 4
Example 2:
Input: [[1,1],[3,2],[5,3],[4,1],[2,3],[1,4]]
Output: 4
the working python 3 code is below:
wondering
snippet 1 d[slope] = d.get(slope, 1) + 1 is working
but why this snippet 2 is not working correctly for example 2 even though snippet 1 and 2 are the same
if slope in d:
d[slope] += 1
else:
d[slope] = 1
def gcd(self, a, b):
if b == 0:
return a
return self.gcd(b, a%b)
def get_slope(self, p1, p2):
dx = p1[0] - p2[0]
dy = p1[1] - p2[1]
c = self.gcd(dx, dy)
dx /= c
dy /= c
return str(dy) + "/" + str(dx)
def is_same_points(self, p1:List[int], p2:List[int]):
return p1[0] == p2[0] and p1[1] == p2[1]
def maxPoints(self, points: List[List[int]]) -> int:
if not points:
return 0
n = len(points)
count = 1
for i in range(0, n):
d = {}
duped = 0
localmax = 1
p1 = points[i]
for j in range(i+1, n):
p2 = points[j]
if self.is_same_points(p1, p2):
duped += 1
else:
slope = self.get_slope(p1, p2)
# 1) not work: output is 3 in example 2
# if slope in d:
# d[slope] += 1
# else:
# d[slope] = 1
# 2) works: correct output 4 for example 2
d[slope] = d.get(slope, 1) + 1
localmax = max(localmax, d[slope]);
count = max(count, localmax + duped)
return count
Interesting problem and nice solution.
The reason why the commented out code doesn't work is because of that:
else:
d[slope] = 1 ## correct would be d[slope] = 2
Every 2 points are on the same line, you are counting only one point for the first two p1 p2, thus you get one less in the final answer.
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.
I am trying to find the sum of the multiples of 3 or 5 of all the numbers upto N.
This is a practise question on HackerEarth. I was able to pass all the test cases except 1. I get a time and memory exceeded error. I looked up the documentation and learnt that int can handle large numbers and the type bignum was removed.
I am still learning python and would appreciate any constructive feedback.
Could you please point me in the right direction so I can optimise the code myself?
test_cases = int(input())
for i in range(test_cases):
user_input = int(input())
sum = 0
for j in range (0, user_input):
if j % 3 == 0:
sum = sum + j
elif j % 5 == 0:
sum = sum + j
print(sum)
In such problems, try to use some math to find a direct solution rather than brute-forcing it.
You can calculate the number of multiples of k less than n, and calculate the sum of the multiples.
For example, with k=3 and n=13, you have 13 // 3 = 4 multiples.
The sum of these 4 multiples of 3 is 3*1 + 3*2 + 3*3 + 3*4 = 3 * (1+2+3+4)
Then, use the relation: 1+2+....+n = n*(n+1)/2
To sum the multiples of 3 and 5, you can sum the multiples of 3, add the sum of the multiples of 5, and subtract the ones you counted twice: the multiples of 15.
So, you could do it like this:
def sum_of_multiples_of(k, n):
"""
Returns the sum of the multiples of k under n
"""
# number of multiples of k between 1 and n
m = n // k
return k * m * (m+1) // 2
def sum_under(n):
return (sum_of_multiples_of(3, n)
+ sum_of_multiples_of(5, n)
- sum_of_multiples_of(15, n))
# 3+5+6+9+10 = 33
print(sum_under(10))
# 33
# 3+5+6+9+10+12+15+18 = 78
print(sum_under(19))
# 78