I'm trying to create a symbolic matrix (S) of general size (let's say LxL), and I want to set each element of the matrix as a function of the indices, i.e.:
S[m,n] = (u+i/2*(n-m))/(u-i/2*(n-m)) * (u+i/2*(n+m))/(u-i/2*(n+m))
I tried running this in sympy, and I got
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-a456d47e99e7> in <module>()
2 S_l = MatrixSymbol('S_l',2*l+1,2*l+1)
3 S_k = MatrixSymbol('S_k',2*k+1,2*k+1)
----> 4 S_l[m,n] = (u+i/2*(n-m))/(u-i/2*(n-m)) * (u+i/2*(n+m))/(u-i/2*(n+m))
TypeError: 'MatrixSymbol' object does not support item assignment
Searching through Stack Exchange I found this question from last year:
Sympy - Dense Display of Matrices of Arbitrary Size
Which is unanswered and not exactly the same. Is it the same issue, or am I just trying to do an impossible thing in sympy (or computers in general)?
I know this is ancient, but I came across the same issue and figured I'd share a solution that works for me. You'll need to use a FunctionMatrix object instead of a MatrixSymbol. For background, I'm using SymPy 1.6.1 on Python 3.5.2.
Here's an example. Using the code below, I've setup some iteration symbols and the function f(i,j) I'd like to use for the elements of my matrix u.
# Import SymPy for symbolic computations
import sympy as sym
# Index variables
i,j = sym.symbols('i j', integer=True);
N = sym.Symbol('N', real=True, integer=True, zero=False, positive=True);
# The function we'll use for our matrix
def f(i,j):
# Some arbitrary function...
return i + j;
# Define a function matrix where elements of the matrix
# are a function of the indices
U = sym.FunctionMatrix(N, N, sym.Lambda((i,j), f(i,j)));
Now, let's try using the elements in the matrix by summing them all up...
U_sum = sym.Sum(u[i,j], (i, 0, N), (j, 0, N));
U_sum
>>>
N N
___ ___
╲ ╲
╲ ╲
╱ ╱ (i + j)
╱ ╱
‾‾‾ ‾‾‾
j = 0 i = 0
Then, let's tell SymPy to calculate the summation
our_sum.doit().simplify()
>>> N * ( N**2 + 2*N + 1 )
This certainly can be done. The docs offer some examples. Here's one
>>> Matrix(3, 4, lambda i,j: 1 - (i+j) % 2)
Matrix([
[1, 0, 1, 0],
[0, 1, 0, 1],
[1, 0, 1, 0]])
Related
I'm trying to figure out how to solve a problem that seems a tricky variation of a common algorithmic problem but require additional logic to handle specific requirements.
Given a list of coins and an amount, I need to count the total number of possible ways to extract the given amount using an unlimited supply of available coins (and this is a classical change making problem https://en.wikipedia.org/wiki/Change-making_problem easily solved using dynamic programming) that also satisfy some additional requirements:
extracted coins are splittable into two sets of equal size (but not necessarily of equal sum)
the order of elements inside the set doesn't matter but the order of set does.
Examples
Amount of 6 euros and coins [1, 2]: solutions are 4
[(1,1), (2,2)]
[(1,1,1), (1,1,1)]
[(2,2), (1,1)]
[(1,2), (1,2)]
Amount of 8 euros and coins [1, 2, 6]: solutions are 7
[(1,1,2), (1,1,2)]
[(1,2,2), (1,1,1)]
[(1,1,1,1), (1,1,1,1)]
[(2), (6)]
[(1,1,1), (1,2,2)]
[(2,2), (2,2)]
[(6), (2)]
By now I tried different approaches but the only way I found was to collect all the possible solution (using dynamic programming) and then filter non-splittable solution (with an odd number of coins) and duplicates. I'm quite sure there is a combinatorial way to calculate the total number of duplication but I can't figure out how.
(The following method first enumerates partitions. My other answer generates the assignments in a bottom-up fashion.) If you'd like to count splits of the coin exchange according to coin count, and exclude redundant assignments of coins to each party (for example, where splitting 1 + 2 + 2 + 1 into two parts of equal cardinality is only either (1,1) | (2,2), (2,2) | (1,1) or (1,2) | (1,2) and element order in each part does not matter), we could rely on enumeration of partitions where order is disregarded.
However, we would need to know the multiset of elements in each partition (or an aggregate of similar ones) in order to count the possibilities of dividing them in two. For example, to count the ways to split 1 + 2 + 2 + 1, we would first count how many of each coin we have:
Python code:
def partitions_with_even_number_of_parts_as_multiset(n, coins):
results = []
def C(m, n, s, p):
if n < 0 or m <= 0:
return
if n == 0:
if not p:
results.append(s)
return
C(m - 1, n, s, p)
_s = s[:]
_s[m - 1] += 1
C(m, n - coins[m - 1], _s, not p)
C(len(coins), n, [0] * len(coins), False)
return results
Output:
=> partitions_with_even_number_of_parts_as_multiset(6, [1,2,6])
=> [[6, 0, 0], [2, 2, 0]]
^ ^ ^ ^ this one represents two 1's and two 2's
Now since we are counting the ways to choose half of these, we need to find the coefficient of x^2 in the polynomial multiplication
(x^2 + x + 1) * (x^2 + x + 1) = ... 3x^2 ...
which represents the three ways to choose two from the multiset count [2,2]:
2,0 => 1,1
0,2 => 2,2
1,1 => 1,2
In Python, we can use numpy.polymul to multiply polynomial coefficients. Then we lookup the appropriate coefficient in the result.
For example:
import numpy
def count_split_partitions_by_multiset_count(multiset):
coefficients = (multiset[0] + 1) * [1]
for i in xrange(1, len(multiset)):
coefficients = numpy.polymul(coefficients, (multiset[i] + 1) * [1])
return coefficients[ sum(multiset) / 2 ]
Output:
=> count_split_partitions_by_multiset_count([2,2,0])
=> 3
(Posted a similar answer here.)
Here is a table implementation and a little elaboration on algrid's beautiful answer. This produces an answer for f(500, [1, 2, 6, 12, 24, 48, 60]) in about 2 seconds.
The simple declaration of C(n, k, S) = sum(C(n - s_i, k - 1, S[i:])) means adding all the ways to get to the current sum, n using k coins. Then if we split n into all ways it can be partitioned in two, we can just add all the ways each of those parts can be made from the same number, k, of coins.
The beauty of fixing the subset of coins we choose from to a diminishing list means that any arbitrary combination of coins will only be counted once - it will be counted in the calculation where the leftmost coin in the combination is the first coin in our diminishing subset (assuming we order them in the same way). For example, the arbitrary subset [6, 24, 48], taken from [1, 2, 6, 12, 24, 48, 60], would only be counted in the summation for the subset [6, 12, 24, 48, 60] since the next subset, [12, 24, 48, 60] would not include 6 and the previous subset [2, 6, 12, 24, 48, 60] has at least one 2 coin.
Python code (see it here; confirm here):
import time
def f(n, coins):
t0 = time.time()
min_coins = min(coins)
m = [[[0] * len(coins) for k in xrange(n / min_coins + 1)] for _n in xrange(n + 1)]
# Initialize base case
for i in xrange(len(coins)):
m[0][0][i] = 1
for i in xrange(len(coins)):
for _i in xrange(i + 1):
for _n in xrange(coins[_i], n + 1):
for k in xrange(1, _n / min_coins + 1):
m[_n][k][i] += m[_n - coins[_i]][k - 1][_i]
result = 0
for a in xrange(1, n + 1):
b = n - a
for k in xrange(1, n / min_coins + 1):
result = result + m[a][k][len(coins) - 1] * m[b][k][len(coins) - 1]
total_time = time.time() - t0
return (result, total_time)
print f(500, [1, 2, 6, 12, 24, 48, 60])
I found this problem in a programming forum Ohjelmointiputka:
https://www.ohjelmointiputka.net/postit/tehtava.php?tunnus=ahdruu and
https://www.ohjelmointiputka.net/postit/tehtava.php?tunnus=ahdruu2
Somebody said that there is a solution found by a computer, but I was unable to find a proof.
Prove that there is a matrix with 117 elements containing the digits such that one can read the squares of the numbers 1, 2, ..., 100.
Here read means that you fix the starting position and direction (8 possibilities) and then go in that direction, concatenating the numbers. For example, if you can find for example the digits 1,0,0,0,0,4 consecutively, you have found the integer 100004, which contains the square numbers of 1, 2, 10, 100 and 20, since you can read off 1, 4, 100, 10000, and 400 (reversed) from that sequence.
But there are so many numbers to be found (100 square numbers, to be precise, or 81 if you remove those that are contained in another square number with total 312 digits) and so few integers in a matrix that you have to put all those square numbers so densely that finding such a matrix is difficult, at least for me.
I found that if there is such a matrix mxn, we may assume without loss of generalty that m<=n. Therefore, the matrix must be of the type 1x117, 3x39 or 9x13. But what kind of algorithm will find the matrix?
I have managed to do the program that checks if numbers to be added can be put on the board. But how can I implemented the searching algorithm?
# -*- coding: utf-8 -*-
# Returns -1 if can not put and value how good a solution is if can be put. Bigger value of x is better.
def can_put_on_grid(grid, number, start_x, start_y, direction):
# Check that the new number lies inside the grid.
x = 0
if start_x < 0 or start_x > len(grid[0]) - 1 or start_y < 0 or start_y > len(grid) - 1:
return -1
end = end_coordinates(number, start_x, start_y, direction)
if end[0] < 0 or end[0] > len(grid[0]) - 1 or end[1] < 0 or end[1] > len(grid) - 1:
return -1
# Test if new number does not intersect any previous number.
A = [-1,-1,-1,0,0,1,1,1]
B = [-1,0,1,-1,1,-1,0,1]
for i in range(0,len(number)):
if grid[start_x + A[direction] * i][start_y + B[direction] * i] not in ("X", number[i]):
return -1
else:
if grid[start_x + A[direction] * i][start_y + B[direction] * i] == number[i]:
x += 1
return x
def end_coordinates(number, start_x, start_y, direction):
end_x = None
end_y = None
l = len(number)
if direction in (1, 4, 7):
end_x = start_x - l + 1
if direction in (3, 6, 5):
end_x = start_x + l - 1
if direction in (2, 0):
end_x = start_x
if direction in (1, 2, 3):
end_y = start_y - l + 1
if direction in (7, 0, 5):
end_y = start_y + l - 1
if direction in (4, 6):
end_y = start_y
return (end_x, end_y)
if __name__ == "__main__":
A = [['X' for x in range(13)] for y in range(9)]
numbers = [str(i*i) for i in range(1, 101)]
directions = [0, 1, 2, 3, 4, 5, 6, 7]
for i in directions:
C = can_put_on_grid(A, "10000", 3, 5, i)
if C > -1:
print("One can put the number to the grid!")
exit(0)
I also found think that brute force search or best first search is too slow. I think there might be a solution using simulated annealing, genetic algorithm or bin packing algorithm. I also wondered if one can apply Markov chains somehow to find the grid. Unfortunately those seems to be too hard for me to implemented at current skills.
There is a program for that in https://github.com/minkkilaukku/square-packing/blob/master/sqPackMB.py . Just change M=9, N=13 from the lines 20 and 21.
Say you have a vertical game board of length n (being the number of spaces). And you have a three-sided die that has the options: go forward one, stay and go back one. If you go below or above the number of board game spaces it is an invalid game. The only valid move once you reach the end of the board is "stay". Given an exact number of die rolls t, is it possible to algorithmically work out the number of unique dice rolls that result in a winning game?
So far I've tried producing a list of every possible combination of (-1,0,1) for the given number of die rolls and sorting through the list to see if any add up to the length of the board and also meet all the requirements for being a valid game. But this is impractical for dice rolls above 20.
For example:
t=1, n=2; Output=1
t=3, n=2; Output=3
You can use a dynamic programming approach. The sketch of a recurrence is:
M(0, 1) = 1
M(t, n) = T(t-1, n-1) + T(t-1, n) + T(t-1, n+1)
Of course you have to consider the border cases (like going off the board or not allowing to exit the end of the board, but it's easy to code that).
Here's some Python code:
def solve(N, T):
M, M2 = [0]*N, [0]*N
M[0] = 1
for i in xrange(T):
M, M2 = M2, M
for j in xrange(N):
M[j] = (j>0 and M2[j-1]) + M2[j] + (j+1<N-1 and M2[j+1])
return M[N-1]
print solve(3, 2) #1
print solve(2, 1) #1
print solve(2, 3) #3
print solve(5, 20) #19535230
Bonus: fancy "one-liner" with list compreehension and reduce
def solve(N, T):
return reduce(
lambda M, _: [(j>0 and M[j-1]) + M[j] + (j<N-2 and M[j+1]) for j in xrange(N)],
xrange(T), [1]+[0]*N)[-1]
Let M[i, j] be an N by N matrix with M[i, j] = 1 if |i-j| <= 1 and 0 otherwise (and the special case for the "stay" rule of M[N, N-1] = 0)
This matrix counts paths of length 1 from position i to position j.
To find paths of length t, simply raise M to the t'th power. This can be performed efficiently by linear algebra packages.
The solution can be read off: M^t[1, N].
For example, computing paths of length 20 on a board of size 5 in an interactive Python session:
>>> import numpy
>>> M = numpy.matrix('1 1 0 0 0;1 1 1 0 0; 0 1 1 1 0; 0 0 1 1 1; 0 0 0 0 1')
>>> M
matrix([[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1]])
>>> M ** 20
matrix([[31628466, 51170460, 51163695, 31617520, 19535230],
[51170460, 82792161, 82787980, 51163695, 31617520],
[51163695, 82787980, 82792161, 51170460, 31628465],
[31617520, 51163695, 51170460, 31628466, 19552940],
[ 0, 0, 0, 0, 1]])
So there's M^20[1, 5], or 19535230 paths of length 20 from start to finish on a board of size 5.
Try a backtracking algorithm. Recursively "dive down" into depth t and only continue with dice values that could still result in a valid state. Propably by passing a "remaining budget" around.
For example, n=10, t=20, when you reached depth 10 of 20 and your budget is still 10 (= steps forward and backwards seemed to cancelled), the next recursion steps until depth t would discontinue the 0 and -1 possibilities, because they could not result in a valid state at the end.
A backtracking algorithms for this case is still very heavy (exponential), but better than first blowing up a bubble with all possibilities and then filtering.
Since zeros can be added anywhere, we'll multiply those possibilities by the different arrangements of (-1)'s:
X (space 1) X (space 2) X (space 3) X (space 4) X
(-1)'s can only appear in spaces 1,2 or 3, not in space 4. I got help with the mathematical recurrence that counts the number of ways to place minus ones without skipping backwards.
JavaScript code:
function C(n,k){if(k==0||n==k)return 1;var p=n;for(var i=2;i<=k;i++)p*=(n+1-i)/i;return p}
function sumCoefficients(arr,cs){
var s = 0, i = -1;
while (arr[++i]){
s += cs[i] * arr[i];
}
return s;
}
function f(n,t){
var numMinusOnes = (t - (n-1)) >> 1
result = C(t,n-1),
numPlaces = n - 2,
cs = [];
for (var i=1; numPlaces-i>=i-1; i++){
cs.push(-Math.pow(-1,i) * C(numPlaces + 1 - i,i));
}
var As = new Array(cs.length),
An;
As[0] = 1;
for (var m=1; m<=numMinusOnes; m++){
var zeros = t - (n-1) - 2*m;
An = sumCoefficients(As,cs);
As.unshift(An);
As.pop();
result += An * C(zeros + 2*m + n-1,zeros);
}
return result;
}
Output:
console.log(f(5,20))
19535230
I've got 2 matrices, first of which is sparse with integer coefficients.
import sympy
A = sympy.eye(2)
A.row_op(1, lambda v, j: v + 2*A[0, j])
The 2nd is symbolic, and I perform an operation between them:
M = MatrixSymbol('M', 2, 1)
X = A * M + A.col(1)
Now, what I'd like is to get the element-wise equations:
X_{0,0} = A_{0,0}
X_{0,1} = 2*A_{0,0} + A_{0,1}
One way to do this is specifying a matrix in sympy with each element being an individual symbol:
rows = []
for i in range(shape[0]):
col = []
for j in range(shape[1]):
col.append(Symbol('%s_{%s,%d}' % (name,i,j)))
rows.append(col)
M = sympy.Matrix(rows)
Is there a way to do it with the MatrixSymbol above, and then get the resulting element-wise equations?
Turns out, this question has a very obvious answer:
MatrixSymbols in sympy can be indexed like a matrix, i.e.:
X[i,j]
gives the element-wise equations.
If one wants to subset more than one element, the MatrixSymbol must first be converted to a sympy.Matrix class:
X = sympy.Matrix(X)
X # lists all indices as `X[i, j]`
X[3:4,2] # arbitrary subsets are supported
Note that this does not allow all operations of a numpy array/matrix (such as indexing with a boolean equivalent), so you might be better of creating a numpy array with sympy symbols:
ijstr = lambda i,j: sympy.Symbol(name+"_{"+str(int(i))+","+str(int(j))+"}")
matrix = np.matrix(np.fromfunction(np.vectorize(ijstr), shape))
I need help optimizing this loop. matrix_1 is a (nx 2) int matrix and matrix_2 is a (m x 2), m & n very.
index_j = 1;
for index_k = 1:size(Matrix_1,1)
for index_l = 1:size(Matrix_2,1)
M2_Index_Dist(index_j,:) = [index_l, sqrt(bsxfun(#plus,sum(Matrix_1(index_k,:).^2,2),sum(Matrix_2(index_l,:).^2,2)')-2*(Matrix_1(index_k,:)*Matrix_2(index_l,:)'))];
index_j = index_j + 1;
end
end
I need M2_Index_Dist to provide a ((n*m) x 2) matrix with the index of matrix_2 in the first column and the distance in the second column.
Output example:
M2_Index_Dist = [ 1, 5.465
2, 56.52
3, 6.21
1, 35.3
2, 56.52
3, 0
1, 43.5
2, 9.3
3, 236.1
1, 8.2
2, 56.52
3, 5.582]
Here's how to apply bsxfun with your formula (||A-B|| = sqrt(||A||^2 + ||B||^2 - 2*A*B)):
d = real(sqrt(bsxfun(#plus, dot(Matrix_1,Matrix_1,2), ...
bsxfun(#minus, dot(Matrix_2,Matrix_2,2).', 2 * Matrix_1*Matrix_2.')))).';
You can avoid the final transpose if you change your interpretation of the matrix.
Note: There shouldn't be any complex values to handle with real but it's there in case of very small differences that may lead to tiny negative numbers.
Edit: It may be faster without dot:
d = sqrt(bsxfun(#plus, sum(Matrix_1.*Matrix_1,2), ...
bsxfun(#minus, sum(Matrix_2.*Matrix_2,2)', 2 * Matrix_1*Matrix_2.'))).';
Or with just one call to bsxfun:
d = sqrt(bsxfun(#plus, sum(Matrix_1.*Matrix_1,2), sum(Matrix_2.*Matrix_2,2)') ...
- 2 * Matrix_1*Matrix_2.').';
Note: This last order of operations gives identical results to you, rather than with an error ~1e-14.
Edit 2: To replicate M2_Index_Dist:
II = ndgrid(1:size(Matrix_2,1),1:size(Matrix_2,1));
M2_Index_Dist = [II(:) d(:)];
If I understand correctly, this does what you want:
ind = repmat((1:size(Matrix_2,1)).',size(Matrix_1,1),1); %'// first column: index
d = pdist2(Matrix_2,Matrix_1); %// compute distance between each pair of rows
d = d(:); %// second column: distance
result = [ind d]; %// build result from first column and second column
As you see, this code calls pdist2 to compute the distance between every pair of rows of your matrices. By default this function uses Euclidean distance.
If you don't have pdist2 (which is part of the the Statistics Toolbox), you can replace line 2 above with bsxfun:
d = squeeze(sqrt(sum(bsxfun(#minus,Matrix_2,permute(Matrix_1, [3 2 1])).^2,2)));