Here is a subset of a 988x3 matrix (vertices of a 3D object):
2 3 4
1 2 3
8 5 2
6 2 4
7 8 9
9 5 1
3 5 8
6 5 7
1 2 8
. . .
Let suppose the 7 nearest neighbors of the first vertex are v(2), v(20), v(5), v(15), v(19), v(50), and v(23). We choose another vertex and find its 7 nearest neighbors according to this condition: The new vertex and its 7 nearest neighbors should not be from the last chosen nearest neighbors. I short, I want to make clusters of 8 distinct vertices from a list of 988 vertices based on the knnsearch. how can we do it in MATLAB?
kd-trees could help here.
It has a function called kdtree_k_nearest_neighbors.
In pseudocode
construct kd-tree
Pick random point
find k-nearest neighbors
store indices
remove point and k-nearest neighbors from set
*repeat*
Related
I have two vectors that describe a histogram: bin values/labels and bin count.
I'd like to import those into a Matlab histogram object so that I can change parameters more easily, such as the number of bins.
Here is a simplification what I have:
Center bin values:
BinValues(1:21) = [-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10];
and count values per bin
CountValues(1:21) = [ 16 12 3 21 7 8 2 6 0 3 4 6 1 8 3 25 16 5 7 10 16];
My actual histogram has 800000 bins, and I want to vary the number of bins.
In my simplified example above, it would be something like reducing the bins from 21 to 15 or 21 to 11.
What I have can be graphed as a simple bar graph.
bar(BinValues,CountValues);
But, if I want to experiment with different numbers of bins, I think it would be best to use the histogram function/object in Matlab, but I'm not sure how I do this from what I have.
OK Update:
I tried this:
h=histogram('BinEdges',[-10.5:10.5] , 'BinCounts', CountValues);
Now I have a nice histogram, but if I use fewerbins(h), I get an error saying I can't do that while BinCountsMode is in "manual". If I modify it to be in "auto" mode, then my original histogram disappears.
The brute force solution would be to generate a huge array with repeated values as dictated by binCount values, but that seems like a dumb way to solve this problem.
The graph is represented in the format as below:
MAX 12
NODE 1 1
NODE 2 2
NODE 3 3
NODE 4 4
NODE 5 5
NODE 6 6
NODE 7 7
NODE 9 9
NODE 8 8
NODE 10 10
NODE 11 11
NODE 12 12
EDGE 1 2
EDGE 2 3
EDGE 3 4
EDGE 4 5
EDGE 5 6
EDGE 6 7
EDGE 7 8
EDGE 8 9
EDGE 9 10
EDGE 10 11
EDGE 11 12
EDGE 1 12
EDGE 1 3
EDGE 1 4
EDGE 1 6
EDGE 1 8
EDGE 1 11
EDGE 1 10
EDGE 6 10
EDGE 3 6
EDGE 4 6
EDGE 5 7
EDGE 9 11
I need to use the adjacent list to read in those edges. But if I want to use it as an undirected graph, that is , ignore all the directness of all edges. How could I know the connectivity of each pair of nodes?
For example, the shortest distance between (NODE 2, NODE 8) is 2 (2->1>8) in the undirected graph, but using the Dijkstra's algorithm to this graph gets 4 (2->3->6->7->8). How could I represent the undirected graph while still using the same technique to read in edges?
If you really don't want to change the technique of reading in the edges you'd have to iterate over all the other nodes to see if your node is in their adjacency-list instead of the other way around.
This will increase your running time by quite a bit while not saving you much storage so I'd advise to just change the technique of reading in the edges.
I need help in Matlab: I need to find out how to Crossover any two sequences for genetic alghorithm in FlowShop, e.g.
1st sequence = 1 5 4 7 3 2 9 8 10 6
2nd sequence = 7 8 9 10 5 4 2 1 3 6
after crossover, the off-springs should be
offspring 1 = 1 5 4 7 3 2 8 9 10 6
offspring 2 = 7 8 9 10 1 5 4 3 2 6
Crossover should be such that each number doesn't repeat itself in the offspring sequence. Can anyone tell me how to do this?
There are a number of existing crossovers defined for permutation encodings. Among them the following would be useful for you:
Cyclic Crossover
Partially Matched Crossover
Uniform-like Crossover
Position-based Crossover
These crossovers aim to preserve the position of the job in the permutation. You can find implementations in C# in the PermutationEncoding plugin of HeuristicLab. Browse the source files and you can also find references to scientific articles that describe these crossovers.
I have a set of around 35000 data. These data are the signal strengths received only from a single location for different time interval of time. I want to plot a Histogram using these data. My X-axis will give the information about "Signal Strengths" and my Y-axis will give the information about "Probability". My histogram will consists of different bars which will give information about the signal strength and probabilities.
For example, suppose I have the following data
a= [ 1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 4 4 4 5 6 6 6 6 6 6 6 6 6 6 6]
How can I plot the graph using data at X-axis and Probability at Y-axis? Any help will be appreciated. Thanks!
This should work just fine if you don't want to use some predefined functions:
una=unique(a);
normhist=hist(a,size(unique(a),2))/sum(hist(a));
figure, stairs(una,normhist)
Una has only the unique values of a, normhist is now between 0 and 1 and it's the probability of occurring of the individual signal because you divide it by the number of elements included in the data.
The Mean Square Error(MSE), is a method used to define the difference in between two blocks, and can be calculated as follow: a and b two blocks equal size
MSE = sqrt(sum(sum((a-b).^2)))/size(a or b)
If the MSE is less than a given threshold, than the two blocks are not different.
Given a matrix A, already reshaped to be contain blocks all in the same raw,
the purpose is to extract all blocks where the MSE is less than a given threshold (based on the first block), then return the mean of those blocks. again, extract the second group of blocks which the MSE is less than the given threshold where the blocks that already assigned to be a part of other group of blocks must not be extracted again. Better than that, it must be deleted to reduce the search time. and so on till all blocks of the matrix A are assigned to be part of a group. the blocks of the resulted matrix should be organized based on the number of block within the group, from the biggest number of blocks to the lowest. And here is an example :
Given matrix A where the size of A is 2 by 14:
A= [1 1 2 2 9 9 4 4 6 6 5 5 3 3
1 1 2 2 9 9 4 4 6 6 5 5 3 3];
PS: its not necessary the blocks contain the same numbers, it is just to make the example clear.
blocks size is : 2 by 2
the threshold is 2
now we extract all blocks where the MSE is less than the threshold starting from the first block in the matrix A. so the blocks are:
1 1 2 2 3 3
1 1 2 2 3 3
the mean of those blocks is
Result= [ 2 2
2 2];
again. we extract all blocks where the MSE is less than the threshold, but we need to avoid the blocks that already extracted, so the second group of blocks is :
9 9
9 9
the mean of this block is it self, so:
Result= [2 2 9 9
2 2 9 9];
again. we extract all blocks where the MSE is less than the threshold, but we need to avoid the blocks that already extracted, so the third group of blocks is :
4 4 6 6 5 5
4 4 6 6 5 5
the block
3 3
3 3
is not a part of this group even if the MSE is less then the threshold because is already extracted to be part of the first group.
the mean of those blocks is:
5 5
5 5
therefore the result should be:
Result= [2 2 5 5 9 9
2 2 5 5 9 9 ];
there are any fast way to apply that ?
PS: that Datasize is huge, therefore , there are a need for a fast way to do that.