import random
i went to add another function to calculate the number guesses
def guess(x):
randomNumb = random.randint(1, x)
guess = 0
while guess != randomNumb :
guess = int(input(f'entre number between 1 and {x} : '))
print(guess)
if guess < randomNumb :
print('guess is low')
elif guess > randomNumb :
print('guess is high')
print(f'guess is right {randomNumb}')
guess(100)
In my app people give grades to each other, out of ten point. Each day, an algorithm computes a match for as much people as possible (it's impossible to compute a match for everyone). It makes a graph where vertexes are users and edges are the grades
I simplify the problem by saying that if 2 people give a grade to each other, there is an edge between them with a weight of their respective grade average. But if A give a grade to B, but B doesnt, their is no edge between them and they can never match : this way, the graph is not oriented anymore
I would like that, in average everybody be happy, but in the same time, I would like as few as possible of people that have no match.
Being very deterministic, I made an algorithm that find ALL maximal matchings in a graph. I did that because I thought I could analyse all these maximal matchings and apply a value function that could look like :
V(Matching) = exp(|M| / max(|M|)) * sum(weight of all Edge in M)
That is to say, a matching is high-valued if its cardinal is close to the cardinal of the maximum matching, and if the sum of the grade between people is high. I put an exponential function to the ratio |M|/max|M| because I consider it's a big problem if M is lower that 0.8 (so the exp will be arranged to highly decrease V as |M|/max|M| reaches 0.8)
I would have take the matching where V(M) is maximal. Though, the big problem is that my function that computes all maximal matching takes a lot of time. For only 15 vertex and 20 edges, it takes almost 10 minutes...
Here is the algorithm (in Swift) :
import Foundation
struct Edge : CustomStringConvertible {
var description: String {
return "e(\(v1), \(v2))"
}
let v1:Int
let v2:Int
let w:Int?
init(_ arrint:[Int])
{
v1 = arrint[0]
v2 = arrint[1]
w = nil
}
init(_ v1:Int, _ v2:Int)
{
self.v1 = v1
self.v2 = v2
w = nil
}
init(_ v1:Int, _ v2:Int, _ w:Int)
{
self.v1 = v1
self.v2 = v2
self.w = w
}
}
let mygraph:[Edge] =
[
Edge([1, 2]),
Edge([1, 5]),
Edge([2, 5]),
Edge([2, 3]),
Edge([3, 4]),
Edge([3, 6]),
Edge([5, 6]),
Edge([2,6]),
Edge([4,1]),
Edge([3,5]),
Edge([4,2]),
Edge([7,1]),
Edge([7,2]),
Edge([8,1]),
Edge([9,8]),
Edge([11,2]),
Edge([11, 8]),
Edge([12,13]),
Edge([1,6]),
Edge([4,7]),
Edge([5,7]),
Edge([3,5]),
Edge([9,1]),
Edge([10,11]),
Edge([10,4]),
Edge([10,2]),
Edge([10,1]),
Edge([10, 12]),
]
// remove all the edge and vertex "touching" the edges and vertex in "edgePath"
func reduce (graph:[Edge], edgePath:[Edge]) -> [Edge]
{
var alreadyUsedV:[Int] = []
for edge in edgePath
{
alreadyUsedV.append(edge.v1)
alreadyUsedV.append(edge.v2)
}
return graph.filter({ edge in
return alreadyUsedV.first(where:{ edge.v1 == $0 }) == nil && alreadyUsedV.first(where:{ edge.v2 == $0 }) == nil
})
}
func findAllMaximalMatching(graph Gi:[Edge]) -> [[Edge]]
{
var matchings:[[Edge]] = []
var G = Gi // current graph (reduced at each depth)
var M:[Edge] = [] // current matching being built
var Cx:[Int] = [] // current path in the possibilities tree
// eg : Cx[1] = 3 : for the depth 1, we are at the 3th edge
var d:Int = 0 // current depth
var debug_it = 0
while(true)
{
if(G.count == 0) // if there is no available edge in graph, it means we have a matching
{
if(M.count > 0) // security, if initial Graph is empty we cannot return an empty matching
{
matchings.append(M)
}
if(d == 0)
{
// depth = 0, we cannot decrement d, we have finished all the tree possibilities
break
}
d = d - 1
_ = M.popLast()
G = reduce(graph: Gi, edgePath: M)
}
else
{
let indexForThisDepth = Cx.count > d ? Cx[d] + 1 : 0
if(G.count < indexForThisDepth + 1)
{
// depth ended,
_ = Cx.popLast()
if( d == 0)
{
break
}
d = d - 1
_ = M.popLast()
// reduce from initial graph to the decremented depth
G = reduce(graph: Gi, edgePath: M)
}
else
{
// matching not finished to be built
M.append( G[indexForThisDepth] )
if(indexForThisDepth == 0)
{
Cx.append(indexForThisDepth)
}
else
{
Cx[d] = indexForThisDepth
}
d = d + 1
G = reduce(graph: G, edgePath: M)
}
}
debug_it += 1
}
print("matching counts : \(matchings.count)")
print("iterations : \(debug_it)")
return matchings
}
let m = findAllMaximalMatching(graph: mygraph)
// we have compute all the maximal matching, now we loop through all of them to find the one that has V(Mi) maximum
// ....
Finally my question is : how can I optimize this algorithm to find all maximal matching and to compute my value function on them to find the best matching for my app in a polynomial time ?
I may be missing something since the question is quite complicated, but why not simply use maximum flow problem, with every vertex appearing twice and the edges weights are the average grading if exists? It will return the maximal flow if configured correctly and runs polynomial time.
I'm using numba's #guvectorize to change two different arrays. The code is:
#guvectorize([(int64[:], int64[:], int64[:], int64[:])], '(n),(n)->(n),(n)', target= 'parallel')
def g(x, y, res, res_two):
res = x
for i in range(x.shape[0]-1):
var = np.random.poisson((2),1)[0]
res_two[i] = var
res[i+1] = res[i] + res_two[i]
print("res[i+1] is", res[i+1], "for x[i] is", x[i])
q = (np.arange(5)) * 0
q[0] = 5
r = (np.arange(5)) * 0
g(q,r)
print("q is", q)
print("r is", r)
And the results printed out are:
As one can see, q is changing, but r isn't.
What must I do to use guvectorize to input two arrays and change those two arrays?
Consider the following problem:
We are given two arrays A and B such that A and B are sorted
except A has B.length additional 0s appended to its end. For instance, A and B could be the following:
A = [2, 4, 6, 7, 0, 0, 0]
B = [1, 7, 9]
Our goal is to create one sorted list by inserting each entry of B
into A in place. For instance, running the algorithm on the above
example would leave
A = [1, 2, 4, 6, 7, 7, 9]
Is there a clever way to do this in better than O(n^2) time? The only way I could think of is to insert each element of B into A by scanning linearly and performing the appropriate number of shifts, but this leads to the O(n^2) solution.
Some pseudo-code (sorta C-ish), assuming array indexing is 0-based:
pA = A + len(A) - 1;
pC = pA; // last element in A
while (! *pA) --pA; // find the last non-zero entry in A
pB = B + len(B) - 1;
while (pA >= A) && (pB >= B)
if *pA > *pB
*pC = *pA; --pA;
else
*pC = *pB; --pB;
--pC
while (pB >= B) // still some bits in B to copy over
*pC = *pB; --pB; --pC;
Not really tested, and just written off the top of my head, but it should give you the idea... May not have the termination and boundary conditions exactly right.
You can do it in O(n).
Work from the end, moving the largest element towards the end of A. This way you avoid a lot of trouble to do with where to keep the elements while iterating. This is pretty easy to implement:
int indexA = A.Length - B.Length - 1;
int indexB = B.Length - 1;
int insertAt = A.Length;
while (indexA > 0 || indexB > 0)
{
insertAt--;
A[insertAt] = max(B[indexB], A[indexA]);
if (A[indexA] <= B[indexB])
indexB--;
else
indexA--;
}
I'm new to Matlab and now learning the basic grammar.
I've written the file GetBin.m:
function res = GetBin(num_bin, bin, val)
if val >= bin(num_bin - 1)
res = num_bin;
else
for i = (num_bin - 1) : 1
if val < bin(i)
res = i;
end
end
end
and I call it with:
num_bin = 5;
bin = [48.4,96.8,145.2,193.6]; % bin stands for the intermediate borders, so there are 5 bins
fea_val = GetBin(num_bin,bin,fea(1,1)) % fea is a pre-defined 280x4096 matrix
It returns error:
Error in GetBin (line 2)
if val >= bin(num_bin - 1)
Output argument "res" (and maybe others) not assigned during call to
"/Users/mac/Documents/MATLAB/GetBin.m>GetBin".
Could anybody tell me what's wrong here? Thanks.
You need to ensure that every possible path through your code assigns a value to res.
In your case, it looks like that's not the case, because you have a loop:
for i = (num_bins-1) : 1
...
end
That loop will never iterate (so it will never assign a value to res). You need to explicitly specify that it's a decrementing loop:
for i = (num_bins-1) : -1 : 1
...
end
For more info, see the documentation on the colon operator.
for i = (num_bin - 1) : -1 : 1