Do we obtain the same result if we apply K-means and sequential K-means methods to the same dataset with the same initial settings? Explain your reasons.
Personally I think the answer is No. The result obtained by sequential K-means depends on the presentation order of the data points. And the ending condition is not the same.
Here attaches the pseudo code of the two clustering algorithms.
K-means
Make initial guesses for the means m1, m2, ..., mk
Until there is no change in any mean
Assign each data point to the cluster whose mean is the nearest.
Calculate the mean of each cluster.
For i from 1 to k
Replace mi with the mean of all examples for cluster i.
end_for
end_until
Sequential K-means
Make initial guesses for the means m1, m2, ..., mk
Set the counts n1, n2, ..., nk to zero
Until interrupted
Acquire the next example, x
If mi is closest to x
Increment ni
Replace mi by mi + (1/ni)*(x - mi)
end_if
end_until
Correct, the results may be different.
Points: x1 = (0,0), x2 = (1,1), x3 = (0.75,0), x4 = (0.25,1); m1 = (0,0.5), m2 = (1,0.5). K-means assigns x1 and x4 to the m1-cluster, x2 and x3 to the m2-cluster. The new means are m1' = (0.125,0.5) and m2' = (0.875,0.5), and no reassignment takes place. With sequential K-means, after x1 is assigned, m1 moves to (0,0), x2 moves m2 to (1,1). Then m1 is closest mean to x3, so m1 moves to (0.375,0). Finally, m2 is closest to x4, so m2 moves to (0.625,1). This is again a stable configuration.
Related
I'm currently trying to solve this puzzle houses using only constraints provided by the clpfd prolog library, which means I cannot use backtracking!
Basically I want to find out which pairs of houses should be made in order to only have 2 distances between all the connections.
My input is a list of coordinates like this [[0,0],[0,3],[2,0],[3,0],[2,1],[3,1],[2,2],[3,3]] And a solution for it would be:
[
[[0,0],[0,3]],
[[2,0],[3,1]],
[[2,1],[3,0]],
[[2,2],[3,3]]
]
My current progress is this one:
connect(Houses):-
%There are only 2 distances and they're different
length(Distances, 2),
all_distinct(Distances),
%One connection per 2 houses (pairs) means half the number of houses as connections
length(Houses, NHouses),
NConnections #= NHouses // 2,
length(Connections, NConnections),
restrictDistances(Connections, Distances), %restrict every connection to have one of the two distances
%All the houses must be connected
append(Connections, ConnectedHouses),
ensureAllConnected(Houses, ConnectedHouses), %table
removeSymmetries(Connections), %avoid symmetries
%flatten list and labeling
append(ConnectedHouses, HousesCoordinates),
labeling([], HousesCoordinates),
write(Connections).
/*
All distances of all connections are one of the two distances
Distance is kept squared to keep it an integer i.e. dist(connection) = dist([[x1, y1], [x2, y2]]) = (x2-x1)^2 + (y2-y1)^2
*/
restrictDistances([], _).
restrictDistances([[[X1, Y1], [X2, Y2]]|Connections], Distances):-
DiffX #= X2 - X1,
DiffY #= Y2 - Y1,
Dis #= DiffX * DiffX + DiffY * DiffY,
% element(Idx, Distances, Dis), %element
member(Dis, Distances), %element
restrictDistances(Connections, Distances).
/*
Ensures all houses are connected
*/
ensureAllConnected([], _).
ensureAllConnected([H|Houses], ConnectedHouses):-
member(H, ConnectedHouses),
% element(_, ConnectedHouses, H),
ensureAllConnected(Houses, ConnectedHouses).
/*
Remove symmetries and connection permutations in final result
*/
removeSymmetries([_]).
removeSymmetries([[[X1, _], [X2, _]], [[X3, Y3], [X4, Y4]]|Connections]):-
X1 #=< X2,
X1 #=< X3,
X3 #=< X4,
removeSymmetries([[[X3, Y3], [X4, Y4]]|Connections]).
The worst part is that this code works, however the predicate member cannot be used because it uses backtracking... And yes, the predicate element exists, but I am unable to replace with it because if I replace first one the output is different, and if I replace the second one I get an instantiation error.
Strictly speaking, the problem is underspecified, because there are more than one kind of distance, e.g. Euclidean distance and Hamiltonian distance. Apparently, Euclidean distances are intended, otherwise you get multiple solutions for this instance.
For this puzzle, it is useful to think about what subtasks might be encoded with global constraints. Here are some hints:
You need to find a matching - that can be encoded with
assignment(Xs,Xs).
You can use table/2 to encode the (house,house,distance) relation.
You can use nvalue/2 to constrain
the number of distinct distances.
These are global constraints in SICStus Prolog.
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How do I know the cross product of A x B is perpendicular to B.
I'm little confused because there are 3 vectors instead of 2.
A = (0, -2, 5)
B = (2, 2, -5)
C= ( 7, -4, -5)
On R2 plane, (a x b) * b = 0 proves that a x b is perpendicular to b , but how do I find that on R3.
SO, after some of Research I finally figured out how to prove the vectors are perpendicularly to each other on R3.
A= (a1, a2, a3)
B= (b1, b2, b3)
C= (c1, c2, c3)
(AB x AC )* AB = 0
(AB x AC )* AC = 0
I don't think you understand what the cross product does. It gives a vector orthogonal to the two vectors.
The cross product a × b is defined as a vector c that is perpendicular
(orthogonal) to both a and b, with a direction given by the right-hand
rule and a magnitude equal to the area of the parallelogram that the
vectors span.
you can simply show this by using the definition of orthogonality which is from their dot products being zero.
Questions like this come down to precisely what you take to be your definitions.
For instance, one way to define the cross-product A x B is this:
By R^3 we mean three dimensional real space with a fixed orientation.
Observe that two linearly independent vectors A and B in R^3 span a plane, so every vector perpendicular to them lies on the (unique) line through the origin perpendicular to this plane.
Observe that for any positive magnitude, there are precisely two vectors along this line with that magnitude.
Observe that if we consider the ordered basis {A, B, C} of R^3, where C is one of the two vectors from the previous step, then one choice matches the orientation of R^3 and the other does not.
Define A x B as the vector C from the previous step for which {A, B, C} matches the orientation of R^3.
For instance, this is how the cross product is defined in the Wikipedia article:
"The cross product a × b is defined as a vector c that is perpendicular (orthogonal) to both a and b, with a direction given by the right-hand rule and a magnitude equal to the area of the parallelogram that the vectors span."
If this is your definition, then there is literally nothing to prove, because the definition already has the word "perpendicular" in it.
Another definition might go like this:
By R^3 we mean three-dimensional real space with a fixed orientation.
For an ordered basis { e1, e2, e3 } of R^3 with the same orientation as R^3, we can write any two vectors A and B as A = a1 e1 + a2 e2 + a3 e3 and B as B = b1 e1 + b2 e2 + b3 e3.
Observe that, regardless of the choice of { e1, e2, e3 } we make in step 2, the vector C := (a2 b3 - b2 a3) e1 - (a1 b3 - b3 a1) e2 + (a1 b2 - b1 a2) e3 is always the same.
Take the vector C from the previous step as the definition of A x B.
This isn't a great definition, because step 3 is both a lot of work and complete black magic, but it's one you'll commonly see. If this is your definition, the best way to prove that A x B is perpendicular to A and B would be to show that the other definition gives you the same vector as this one, and then the perpendicularity comes for free.
A more direct way would be to show that vectors with a dot product of zero are perpendicular, and then to calculate the dot product by doing a bunch of algebra. This is, again, a fairly popular way to do it, but it's essentially worthless because it doesn't offer any insight into what's going on.
I have a question regarding an algorithm:
We have a fixed point in 2D space let's call it S(x,y) and the length of two links joining (L1 and L2). These two links are connected at a common joint called E(x,y). And we have another point in the space which is end point of the L2 which we call F(x,y).
So we L1 have two end points S and E where as L2 has E and F.
When we are given a point P(x,y) in space. How can we find the coordinate of F(x,y) which is closest to P? I wanted to find the angle of θ1 and θ2 which takes the links L1 and L2 to that point?
See this link to get the graphical representation of my problem
See this pic http://postimage.org/image/qlekcv1qz/, where you will be able to see the real problem I have right now.
So I have formulated this as optimization problem. Where the Objective function is:
* arg min |P-F|
with constraints θ1 and θ2 where θ1 ∈ [ O , π] and θ2 ∈ [ O , π/2].
So we have,
* xE = xS + L1 * Cosθ1 and yE = yS + L1 * Sinθ1
* xF = xE + L2 * Cos (θ1 + θ2 ) and yF = yE + L2 * sin ( θ1 + θ2)
Here we have length of L1 = 105 and L2 = 113.7 and Point S is the origin i.e xS = O and yS = O.
Can you give a hint how code up my function or any optimization problem which gives me the values of θ1 and θ2, such that the distance between Point F and point P is minimized.
So if I understand correctly, your description is equivalent of having two rigid rods of length L1 and L2, with one end of L1 fixed at S, the other end connected to L2 by a flexible joint (at some undefined point E), and you want to get the other end of L2 (point F) as close to some point P as possible. If this is the case then:
If |L1-L2| < |P-S| < |L1+L2| then F = P
If |L1-L2| > |P-S| then F = S + (P-S)*|L1-L2|/|P-S|
If |P-S| > |L1+L2| then F = S + (P-S)*|L1+L2|/|P-S|
Is that what you want?
See imnage
http://postimage.org/image/l1ktt0qtb/
If point P is closer to point S than the distance |L1-L2| (assuming they are unequal), then point F cannot 'reach' point P, even with the angle at E bent to 180 ndegrees. Then the closest you can get is somewhere on the the circle with radius |L1-L2| and centre S. In this case the best F is given by the vector with direction (P-S), and magnitude |L1-L2|, my case 2 above and Figure A below. Note that if L1=L2 this will never be the case.
If point P is further from point S than the distance |L1+L2|, then point F cannot 'reach' point P, even with the angle at E straightened to 0 degrees. Then the closest you can get is aomewhere on the the circle with radius |L1+L2| and centre S. In this case the best F is given by the vector with direction (P-S), and magnitude |L1+L2|, my case 3 above and Figure B below.
If point P is betwen the two limiting circles, then there will be two solutions (one as shown in Figure 3 below, and the other with L1 and L2 reflected in the mirror line formewd by the vector P-S. In this case the 'best' F is equal to point P.
If you want to know the angles Theta1 and Theta 2 then that is a different question (I see you have added that now).
Use the cosine rule for triangles with no right angle.
The rule is
C = acos[(a^2 + b^2 - c^2)/(2ab)]
where a triangle has sides of length a,b, and c, and C is the angle between sides a and b. You are trying to produce a triangle with sides l1, l2, and d=|S-P|, which will be possible so as long as no two of the lengths are shorter (in sum) than the third one.
By substituting l1, l2, and d for a,b, anc c appropriately you will be able to solve for each of the internal angles, A, B, and C. Then you can use these angles A,B,C plus the angle between the vector P-S and horizontal (call that D perhaps?) to calculate your theta1 and theta2.
I'm trying to set up a linear program in which the objective function adds extra weight to the max out of the decision variables multiplied by their respective coefficients.
With this in mind, is there a way to use min or max operators within the objective function of a linear program?
Example:
Minimize
(c1 * x1) + (c2 * x2) + (c3 * x3) + (c4 * max(c1*x1, c2*x2, c3*x3))
subject to
#some arbitrary integer constraints:
x1 >= ...
x1 + 2*x2 <= ...
x3 >= ...
x1 + x3 == ...
Note that (c4 * max(c1*x1, c2*x2, c3*x3)) is the "extra weight" term that I'm concerned about. We let c4 denote the "extra weight" coefficient. Also, note that x1, x2, and x3 are integers in this particular example.
I think the above might be outside the scope of what linear programming offers. However, perhaps there's a way to hack/reformat this into a valid linear program?
If this problem is completely out of the scope of linear programming, perhaps someone can recommend an optimization paradigm that is more suitable to this type of problem? (Anything that allows me to avoid manually enumerating and checking all possible solutions would be helpful.)
Add in an auxiliary variable, say x4, with constraints:
x4 >= c1*x1
x4 >= c2*x2
x4 >= c3*x3
Objective += c4*x4
I hope this hasn't been asked before, if so I apologize.
EDIT: For clarity, the following notation will be used: boldface uppercase for matrices, boldface lowercase for vectors, and italics for scalars.
Suppose x0 is a vector, A and B are matrix functions, and f is a vector function.
I'm looking for the best way to do the following iteration scheme in Mathematica:
A0 = A(x0), B0=B(x0), f0 = f(x0)
x1 = Inverse(A0)(B0.x0 + f0)
A1 = A(x1), B1=B(x1), f1 = f(x1)
x2 = Inverse(A1)(B1.x1 + f1)
...
I know that a for-loop can do the trick, but I'm not quite familiar with Mathematica, and I'm concerned that this is the most efficient way to do it. This is a justified concern as I would like to define a function u(N):=xNand use it in further calculations.
I guess my questions are:
What's the most efficient way to program the scheme?
Is RecurrenceTable a way to go?
EDIT
It was a bit more complicated than I tought. I'm providing more details in order to obtain a more thorough response.
Before doing the recurrence, I'm having problems understanding how to program the functions A, B and f.
Matrices A and B are functions of the time step dt = 1/T and the space step dx = 1/M, where T and M are the number of points in the {0 < x < 1, 0 < t} region. This is also true for vector the function f.
The dependance of A, B and f on x is rather tricky:
A and B are upper and lower triangular matrices (like a tridiagonal matrix; I suppose we can call them multidiagonal), with defined constant values on their diagonals.
Given a point 0 < xs < 1, I need to determine it's representative xn in the mesh (the closest), and then substitute the nth row of A and B with the function v( x) (transposed, of course), and the nth row of f with the function w( x).
Summarizing, A = A(dt, dx, xs, x). The same is true for B and f.
Then I need do the loop mentioned above, to define u( x) = step[T].
Hope I've explained myself.
I'm not sure if it's the best method, but I'd just use plain old memoization. You can represent an individual step as
xstep[x_] := Inverse[A[x]](B[x].x + f[x])
and then
u[0] = x0
u[n_] := u[n] = xstep[u[n-1]]
If you know how many values you need in advance, and it's advantageous to precompute them all for some reason (e.g. you want to open a file, use its contents to calculate xN, and then free the memory), you could use NestList. Instead of the previous two lines, you'd do
xlist = NestList[xstep, x0, 10];
u[n_] := xlist[[n]]
This will break if n > 10, of course (obviously, change 10 to suit your actual requirements).
Of course, it may be worth looking at your specific functions to see if you can make some algebraic simplifications.
I would probably write a function that accepts A0, B0, x0, and f0, and then returns A1, B1, x1, and f1 - say
step[A0_?MatrixQ, B0_?MatrixQ, x0_?VectorQ, f0_?VectorQ] := Module[...]
I would then Nest that function. It's hard to be more precise without more precise information.
Also, if your procedure is numerical, then you certainly don't want to compute Inverse[A0], as this is not a numerically stable operation. Rather, you should write
A0.x1 == B0.x0+f0
and then use a numerically stable solver to find x1. Of course, Mathematica's LinearSolve provides such an algorithm.