I am using the finite element method and separating a unit isosceles triangle into triangles with six nodes. While calculating coordinates of the nodes I noticed that the variables in the for loop get messed up for some reason and I cannot figure out why. Here is my code:
PROGRAM triangle
IMPLICIT NONE
INTEGER, PARAMETER :: stepSize = 4
INTEGER, PARAMETER :: numberOfNodes = ((2*stepSize - 1) * (2*stepSize)) / 2
INTEGER :: i, j, counter
REAL, DIMENSION(2, numberOfNodes) :: nodeCoordinates
counter = 0
DO j = 1, 2*stepSize - 1
DO i = 1, 2*stepSize - 1 - counter
nodeCoordinates(1, i + (j-1)*(2*stepSize - 1)) = ((REAL(i-1)) / REAL(2*stepSize - 2))
nodeCoordinates(2, i + (j-1)*(2*stepSize - 1)) = ((REAL(j-1)) / REAL(2*stepSize - 2))
END DO
counter = counter + 1
END DO
END PROGRAM triangle
When I print the variable j in the inner for loop, the following is shown:
1
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
4
4
4
4
0
0
0
1
1
2
Related
I'm currently working on an OpenVibe Session in which I must program a Lua Script. My problem is generating a random table with 2 values: 1s and 2s. If the value in table is 1, then send Stimulus through output 1. And if it's 2, then through output 2.
My question is how I can generate in Lua code a table of 52 1s and 2s (44 1s and 8 2s which correspond to 85% 1s and 15% 2s) in a way that you have at least 3 1s before the next 2s? Somehow like this: 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2.
I´m not an expert in Lua. So any help would be most appreciated.
local get_table_52
do
local cached_C = {}
local function C(n, k)
local idx = n * 9 + k
local value = cached_C[idx]
if not value then
if k == 0 or k == n then
value = 1
else
value = C(n-1, k-1) + C(n-1, k)
end
cached_C[idx] = value
end
return value
end
function get_table_52()
local result = {}
for j = 1, 52 do
result[j] = 1
end
local r = math.random(C(28, 8))
local p = 29
for k = 8, 1, -1 do
local b = 0
repeat
r = r - b
p = p - 1
b = C(p - 1, k - 1)
until r <= b
result[p + k * 3] = 2
end
return result
end
end
Usage:
local t = get_table_52()
-- t contains 44 ones and 8 twos, there are at least 3 ones before next two
Here is the logic.
You have 8 2s. Before each 2 there is a string of 3 1s. That's 32 of your numbers.
Those 8 groups of 1112 separate 9 spots that the remaining 20 1s can go.
So your problem is to randomly distribute 20 1s to 9 random places. And then take that collection of numbers and write out your list. So in untested code from a non-Lua programmer:
-- Populate buckets
local buckets = {0, 0, 0, 0, 0, 0, 0, 0, 0}
for k = 1, 20 do
local bucket = floor(rand(9))
buckets[bucket] = buckets[bucket] + 1
end
-- Turn that into an array
local result = {}
local i = 0
for bucket = 0, 8 do
-- Put buckets[bucket] 1s in result
if 0 < buckets[bucket] do
for j = 0, buckets[bucket] do
result[i] = 1
i = i + 1
end
end
-- Add our separating 1112?
if bucket < 8 do
result[i] = 1
result[i+1] = 1
result[i+2] = 1
result[i+3] = 2
i = i + 4
end
end
I'm trying to figure out this algorithm that accepts an input of an int and should return an output of the sum of each element in the int.
# Input -> 4321
# output -> 10 (4+3+2+1)
def sum_func(n):
# Base case
if len(str(n)) == 1:
return n
# Recursion
else:
return n%10 + sum_func(n/10)
When Trying to break apart this algorithm this is what I come up with
1st loop -> 1 + 432 = 433
2nd loop -> 2 + 43 = 45
3rd loop -> 3 + 4 = 7
4th loop -> 4 + 4 = 8
How was it able to come up with the result of 10?
Unwinding, it would look like this:
sum_func(4321)
= 1 + sum_func(432)
= 1 + 2 + sum_func(43)
= 1 + 2 + 3 + sum_func(4)
= 1 + 2 + 3 + 4
When trying to understand recursion you'll have to clearly understand what is returned.
In this case function sum_func(n) returns the sum of the digits in it's argument n.
For concrete n task is divided into last_digit_of_n + sum_func(n_without_last_digit).
For example,
sum_func(4321) =
sum_func(432) + 1 =
sum_func(43) + 2 + 1 =
sum_func(4) + 3 + 2 + 1 =
4 + 3 + 2 + 1
Hope this helps.
(As a side note, checking if n has more than one digit using str is a bad idea. Better just to check if n <= 9)
You must reach the base case before the summation occurs:
Iteration 1: 1 + sum_func(432)
Iteration 2: 1 + 2 + sum_func(43)
Iteration 3: 1 + 2 + 3 + sum_func(4) = 1 + 2 + 3 + 4 = 10
f(N) = 0^0 + 1^1 + 2^2 + 3^3 + 4^4 + ... + N^N.
I want to calculate (f(N) mod M).
These are the constraints.
1 ≤ N ≤ 10^9
1 ≤ M ≤ 10^3
Here is my code
test=int(input())
ans = 0
for cases in range(test):
arr=[int(x) for x in input().split()]
N=arr[0]
mod=arr[1]
#ret=sum([int(y**y) for y in range(N+1)])
#ans=ret
for i in range(1,N+1):
ans = (ans + pow(i,i,mod))%mod
print (ans)
I tried another approach but in vain.
Here is code for that
from functools import reduce
test=int(input())
answer=0
for cases in range(test):
arr=[int(x) for x in input().split()]
N=arr[0]
mod=arr[1]
answer = reduce(lambda K,N: x+pow(N,N), range(1,N+1)) % M
print(answer)
Two suggestions:
Let 0^0 = 1 be what you use. This seems like the best guidance I have for how to handle that.
Compute k^k by multiplying and taking the modulus as you go.
Do an initial pass where all k (not exponents) are changed to k mod M before doing anything else.
While computing (k mod M)^k, if an intermediate result is one you've already visited, you can cut back on the number of iterations to continue by all but up to one additional cycle.
Example: let N = 5 and M = 3. We want to calculate 0^0 + 1^1 + 2^2 + 3^3 + 4^4 + 5^5 (mod 3).
First, we apply suggestion 3. Now we want to calculate 0^0 + 1^1 + 2^2 + 0^3 + 1^4 + 2^5 (mod 3).
Next, we begin evaluating and use suggestion 1 immediately to get 1 + 1 + 2^2 + 0^3 + 1^4 + 2^5 (mod 3). 2^2 is 4 = 1 (mod 3) of which we make a note (2^2 = 1 (mod 3)). Next, we find 0^1 = 0, 0^2 = 0 so we have a cycle of size 1 meaning no further multiplication is needed to tell 0^3 = 0 (mod 3). Note taken. Similar process for 1^4 (we tell on the second iteration that we have a cycle of size 1, so 1^4 is 1, which we note). Finally, we get 2^1 = 2 (mod 3), 2^2 = 1(mod 3), 2^3 = 2(mod 3), a cycle of length 2, so we can skip ahead an even number which exhausts 2^5 and without checking again we know that 2^5 = 2 (mod 3).
Our sum is now 1 + 1 + 1 + 0 + 1 + 2 (mod 3) = 2 + 1 + 0 + 1 + 2 (mod 3) = 0 + 0 + 1 + 2 (mod 3) = 0 + 1 + 2 (mod 3) = 1 + 2 (mod 3) = 0 (mod 3).
These rules will be helpful to you since your cases see N much larger than M. If this were reversed - if N were much smaller than M - you'd get no benefit from my method (and taking the modulus w.r.t. M would affect the outcome less).
Pseudocode:
Compute(N, M)
1. sum = 0
2. for i = 0 to N do
3. term = SelfPower(i, M)
4. sum = (sum + term) % M
5. return sum
SelfPower(k, M)
1. selfPower = 1
2. iterations = new HashTable
3. for i = 1 to k do
4. selfPower = (selfPower * (k % M)) % M
5. if iterations[selfPower] is defined
6. i = k - (k - i) % (i - iterations[selfPower])
7. clear out iterations
8. else iterations[selfPower] = i
9. return selfPower
Example execution:
resul = Compute(5, 3)
sum = 0
i = 0
term = SelfPower(0, 3)
selfPower = 1
iterations = []
// does not enter loop
return 1
sum = (0 + 1) % 3 = 1
i = 1
term = SelfPower(1, 3)
selfPower = 1
iterations = []
i = 1
selfPower = (1 * 1 % 3) % 3 = 1
iterations[1] is not defined
iterations[1] = 1
return 1
sum = (1 + 1) % 3 = 2
i = 2
term = SelfPower(2, 3)
selfPower = 1
iterations = []
i = 1
selfPower = (1 * 2 % 3) % 3 = 2
iterations[2] is not defined
iterations[2] = 1
i = 2
selfPower = (2 * 2 % 3) % 3 = 1
iterations[1] is not defined
iterations[1] = 2
return 1
sum = (2 + 1) % 3 = 0
i = 3
term = SelfPower(3, 3)
selfPower = 1
iterations = []
i = 1
selfPower = (1 * 3 % 0) % 3 = 0
iterations[0] is not defined
iterations[0] = 1
i = 2
selfPower = (0 * 3 % 0) % 3 = 0
iterations[0] is defined as 1
i = 3 - (3 - 2) % (2 - 1) = 3
iterations is blank
return 0
sum = (0 + 0) % 3 = 0
i = 4
term = SelfPower(4, 3)
selfPower = 1
iterations = []
i = 1
selfPower = (1 * 4 % 3) % 3 = 1
iterations[1] is not defined
iterations[1] = 1
i = 2
selfPower = (1 * 4 % 3) % 3 = 1
iterations[1] is defined as 1
i = 4 - (4 - 2) % (2 - 1) = 4
iterations is blank
return 1
sum = (0 + 1) % 3 = 1
i = 5
term = SelfPower(5, 3)
selfPower = 1
iterations = []
i = 1
selfPower = (1 * 5 % 3) % 3 = 2
iterations[2] is not defined
iterations[2] = 1
i = 2
selfPower = (2 * 5 % 3) % 3 = 1
iterations[1] is not defined
iterations[1] = 2
i = 3
selfPower = (1 * 5 % 3) % 3 = 2
iterations[2] is defined as 1
i = 5 - (5 - 3) % (3 - 1) = 5
iterations is blank
return 2
sum = (1 + 2) % 3 = 0
return 0
why not just use simple recursion to find the recursive sum of the powers
def find_powersum(s):
if s == 1 or s== 0:
return 1
else:
return s*s + find_powersum(s-1)
def find_mod (s, m):
print(find_powersum(s) % m)
find_mod(4, 4)
2
I have a vector
A = [ 1 1 1 2 2 3 6 8 9 9 ]
I would like to write a loop that counts the frequencies of values in my vector within a range I choose, this would include values that have 0 frequencies
For example, if I chose the range of 1:9 my results would be
3 2 1 0 0 1 0 1 2
If I picked 1:11 the result would be
3 2 1 0 0 1 0 1 2 0 0
Is this possible? Also ideally I would have to do this for giant matrices and vectors, so the fasted way to calculate this would be appreciated.
Here's an alternative suggestion to histcounts, which appears to be ~8x faster on Matlab 2015b:
A = [ 1 1 1 2 2 3 6 8 9 9 ];
maxRange = 11;
N = accumarray(A(:), 1, [maxRange,1])';
N =
3 2 1 0 0 1 0 1 2 0 0
Comparing the speed:
K>> tic; for i = 1:100000, N1 = accumarray(A(:), 1, [maxRange,1])'; end; toc;
Elapsed time is 0.537597 seconds.
K>> tic; for i = 1:100000, N2 = histcounts(A,1:maxRange+1); end; toc;
Elapsed time is 4.333394 seconds.
K>> isequal(N1, N2)
ans =
1
As per the loop request, here's a looped version, which should not be too slow since the latest engine overhaul:
A = [ 1 1 1 2 2 3 6 8 9 9 ];
maxRange = 11; %// your range
output = zeros(1,maxRange); %// initialise output
for ii = 1:maxRange
tmp = A==ii; %// temporary storage
output(ii) = sum(tmp(:)); %// find the number of occurences
end
which would result in
output =
3 2 1 0 0 1 0 1 2 0 0
Faster and not-looping would be #beaker's suggestion to use histcounts:
[N,edges] = histcounts(A,1:maxRange+1);
N =
3 2 1 0 0 1 0 1 2 0
where the +1 makes sure the last entry is included as well.
Assuming the input A to be a sorted array and the range starts from 1 and goes until some value greater than or equal to the largest element in A, here's an approach using diff and find -
%// Inputs
A = [2 4 4 4 8 9 11 11 11 12]; %// Modified for variety
maxN = 13;
idx = [0 find(diff(A)>0) numel(A)]+1;
out = zeros(1,maxN); %// OR for better performance : out(maxN) = 0;
out(A(idx(1:end-1))) = diff(idx);
Output -
out =
0 1 0 3 0 0 0 1 1 0 3 1 0
This can be done very easily with bsxfun.
Let the data be
A = [ 1 1 1 2 2 3 6 8 9 9 ]; %// data
B = 1:9; %// possible values
Then
result = sum(bsxfun(#eq, A(:), B(:).'), 1);
gives
result =
3 2 1 0 0 1 0 1 2
I wrote a program in SciLab that solves sudoku's.
But it can only solve sudoku's that always have a square with 1 possible value.
Like very easy and easy sudoku's on brainbashers.com .
Medium sudoku's always reach a point that they do not have a square with 1 possible value.
How can I modify my code to solve these, more difficult sudoku's?
///////////////////////////////////////////////////////////////////////////
////////////////////////// Check Sudoku ///////////////////////////////
///////////////////////////////////////////////////////////////////////////
function r=OneToNine(V) // function checks if the given vector V contains 1 to 9
r = %T // this works
u = %F
index = 1
while r == %T & index < 10
for i=1 : length(V)
if V(i)==index then
u = %T
end
end
index=index+1
if u == %F then r = %F
else u = %F
end
end
if length(V) > 9 then r = %F
end
endfunction
function y=check(M) // Checks if the given matrix M is a solved sudoku
y = %T // this works too
if size(M,1)<>9 | size(M,2)<>9 then // if it has more or less than 9 rows and columns
y = %F // we return false
end
for i=1 : size(M,1) // if not all rows have 1-9 we return false
if OneToNine(M(i,:)) == %F then
y = %F
end
end
endfunction
function P=PossibilitiesPosition(board, x, y)
// this one works
// we fill the vector possibilites with 9 zeros
// 0 means empty, 1 means it already has a value, so we don't need to change it
possibilities = [] // a vector that stores the possible values for position(x,y)
for t=1 : 9 // sudoku has 9 values
possibilities(t)=0
end
// Check row f the value (x,y) for possibilities
// we fill the possibilities further by puttin '1' where the value is not possible
for i=1 : 9 // sudoku has 9 values
if board(x,i) > 0 then
possibilities(board(x,i))=1
end
end
// Check column of the value (x,y) for possibilities
// we fill the possibilities further by puttin '1' where the value is not possible
for j=1 : 9 // sudoku has 9 values
if board(j, y) > 0 then
possibilities(board(j, y))=1
end
end
// Check the 3x3 matrix of the value (x,y) for possibilities
// first we see which 3x3 matrix we need
k=0
m=0
if x >= 1 & x <=3 then
k=1
else if x >= 4 & x <= 6 then
k = 4
else k = 7
end
end
if y >= 1 & y <=3 then
m=1
else if y >= 4 & y <= 6 then
m = 4
else m = 7
end
end
// then we fill the possibilities further by puttin '1' where the value is not possible
for i=k : k+2
for j=m : m+2
if board(i,j) > 0 then
possibilities(board(i,j))=1
end
end
end
P = possibilities
// we want to see the real values of the possibilities. not just 1 and 0
for i=1 : 9 // sudoku has 9 values
if P(i)==0 then
P(i) = i
else P(i) = 0
end
end
endfunction
function [x,y]=firstEmptyValue(board) // Checks the first empty square of the sudoku
R=%T // and returns the position (x,y)
for i=1 : 9
for j=1 : 9
if board(i,j) == 0 & R = %T then
x=i
y=j
R=%F
end
end
end
endfunction
function A=numberOfPossibilities(V) // this checks the number of possible values for a position
A=0 // so basically it returns the number of elements different from 0 in the vector V
for i=1 : 9
if V(i)>0 then
A=A+1
end
end
endfunction
function u=getUniquePossibility(M,x,y) // this returns the first possible value for that square
pos = [] // in function fillInValue we only use it
pos = PossibilitiesPosition(M,x,y) // when we know that this square (x,y) has only one possible value
for n=1 : 9
if pos(n)>0 then
u=pos(n)
end
end
endfunction
///////////////////////////////////////////////////////////////////////////
////////////////////////// Solve Sudoku ///////////////////////////////
///////////////////////////////////////////////////////////////////////////
function G=fillInValue(M) // fills in a square that has only 1 possibile value
x=0
y=0
pos = []
for i=1 : 9
for j=1 : 9
if M(i,j)==0 then
if numberOfPossibilities(PossibilitiesPosition(M,i,j)) == 1 then
x=i
y=j
break
end
end
end
if x>0 then
break
end
end
M(x,y)=getUniquePossibility(M,x,y)
G=M
endfunction
function H=solve(M) // repeats the fillInValue until it is a fully solved sudoku
P=[]
P=M
if check(M)=%F then
P=fillInValue(M)
H=solve(P)
else
H=M
end
endfunction
//////////////////////////////////////////////////////////////////////////////
So it solves this first one
// Very easy and easy sudokus from brainbashers.com get solved completely
// Very Easy sudoku from brainbashers.com
M = [0 2 0 0 0 0 0 4 0
7 0 4 0 0 0 8 0 2
0 5 8 4 0 7 1 3 0
0 0 1 2 8 4 9 0 0
0 0 0 7 0 5 0 0 0
0 0 7 9 3 6 5 0 0
0 8 9 5 0 2 4 6 0
4 0 2 0 0 0 3 0 9
0 1 0 0 0 0 0 8 0]
But it doens't solve this medium:
M2= [0 0 6 8 7 1 2 0 0
0 0 0 0 0 0 0 0 0
5 0 1 3 0 9 7 0 8
1 0 7 0 0 0 6 0 9
2 0 0 0 0 0 0 0 7
9 0 3 0 0 0 8 0 1
3 0 5 9 0 7 4 0 2
0 0 0 0 0 0 0 0 0
0 0 2 4 3 5 1 0 0]
Error code when trying to solve medium sudoku:
-->solve(M2)
!--error 21
Invalid index.
at line 14 of function PossibilitiesPosition called by :
at line 3 of function getUniquePossibility called by :
at line 20 of function fillInValue called by :
at line 182 of function solve called by :
at line 183 of function solve called by :
at line 183 of function solve called by :
at line 183 of function solve called by :
at line 183 of function solve called by :
solve(M2)
at line 208 of exec file called by :
_SCILAB-6548660277741359031.sce', 1
while executing a callback
Well, one of the easiest way to program a Sudoku solver (not the most efficient) could be to solve each cell with all the possible options recursively (which could be similar to the "Backtracking" algorithm) until a full answer is found.
Another options (I would say it's better) is to iterate trough all the squares solving all the "simple" squares and storing the possible answers in the others squares, then repeat (now you have some more solved), repeat the process until the Sudoku is solved or no more squares can be solved directly. Then you could try the rest with brute-force or Backtracking (maybe half or more of the Sudoku is already solved, so it may be relatively efficient)
Anyway,with a quick search I found this Wikipedia page where some Sudoku solving algorithms are explained with pseudo-code examples, hopefully these will be useful to you