I want to insert a number in the following matrix: n x 1 matrix
6
103
104
660
579
750
300
299
300
750
579
661
580
760
302
301
302
760
580
662
581
How to I insert it in the middle and shift the remaining numbers? I tried the following code:
Idx=[723];
c=false(1,length(Element_set2)+length(Idx));
c(Idx)=true;
result=nan(size(c));
result(~c)=Element_set2;
result(c)=8
You are complicating things. Simply find the middle index by finding the length of the array, dividing by 2 and truncating any decimal points, then using simply indexing to update the new matrix. Supposing that result is the column vector that was created by you and number is the value you want to insert in the middle, do the following:
number = 8; %// Change to suit whatever number you desire
middle = floor(numel(result) / 2);
result = [result(1:middle); number; result(middle+1:end)];
In the future, please read this great MATLAB tutorial on indexing directly from MathWorks: http://www.mathworks.com/company/newsletters/articles/matrix-indexing-in-matlab.html. It's a good resource on the kinds of indexing operations one expects from starting out in MATLAB.
Related
Suppose I have a MATLAB table of the following type:
Node_Number Generation_Type Total_power(MW)
1 Wind 600
1 Solar 452
1 Tidal 123
2 Wind 200
2 Tidal 159
What I want to do is to produce a table with exactly same dimensions, with the only difference being the value of the data of the Total_Power column that corresponds to the Wind generation type being multiplied with 0.5. Hence the result that I would get would be:
Node_Number Generation_Type Total_power(MW)
1 Wind 300
1 Solar 452
1 Tidal 123
2 Wind 100
2 Tidal 159
What I believe that would do the trick is some code which would scan all the rows that have the string 'Wind', and then after locating the rows which have this string, to multiply the 3rd column of this row with 0.5. A for loop seems like a viable solution, though I am not sure how to implement this. Any help would be greatly appreciated.
Just find the index of rows with the category Wind, and then you could have access to them by calling T(index,:).
clc; clear;
T=readtable('data.txt');
rows = find(ismember(T.Generation_Type,'Wind'));
T(rows,:).Total_power_MW_=T(rows,:).Total_power_MW_*0.5
Output:
Node_Number Generation_Type Total_power_MW_
___________ _______________ _______________
1 'Wind' 300
1 'Solar' 452
1 'Tidal' 123
2 'Wind' 100
2 'Tidal' 159
My matrix currently looks like this
1 225 230 300
4 333 442 678
7 798 782 128
1 248 842 482
Coloumn 1 is a series of numbers which I have mapped to another set of numbers.
for example
KeySet = (1:42)
ValueSet = (333, 222, 4444, 7778 etc etc to 42 numbers)
mapObj = containers.Map(KeySet, ValueSet)
Now I want to create a new coloumn in my original matrix coloumn 5 which will be populated from the ValueSet with reference to the mapping - so row 1 coloumn 5 will be 333 and row 2 coloumn 5 will be 7778 and so on.
Its essentially a vlookup from coloumn 5 into the mapping.
It would look something like this I would guess
mat(:,5) = mapObj(mat(:,1))
You can't query a mapobject for multiple entries at once, i would use arrayfun:
arrayfun(#(ix)mapObj(ix),mat(:,1))
In your example the key set is 1:n, if this is always the case then use an array instead of a map, it's much faster and you can index multiple entries at once.
I am not an expert in statistics and data analysis, hence I can't understand if the behavior which I obtain is correct or not. I am here looking for your help.
Assume I have these samples which I would like to cluster (10 points in the plane - reduced version of the problem):
[X Y] =
266 450
266 400
258 168
290 442
295 438
273 432
294 158
318 161
250 423
253 413
To cluster them I can use a cluster tree
Z = linkage([ X Y ],'complete');
which is (by dendrogram(Z,10))
Now I would like to extract clusters on the basis of the distance attached to the nodes of the tree.
Say that my distance is 150, I would expect that the call
T = cluster(Z,'Cutoff',150);
returns me 2 clusters. But it gives me just one (I suppose), i.e.
T =
1
1
1
1
1
1
1
1
1
1
What am I missing?
Use inconsistent(Z,150) and look at the values in column 4. Increasing the cutoff from a small positive number steps you along the tree.
E.g.
cluster(Z,'cutoff',0.7)
does not give you what you want (I think)
but
cluster(Z,'cutoff',0.8)
does.
The criterion for cluster is inconsistency ('inconsistent') by default.
Since the height in dendrogram is distance, you can change the criterion to 'distance',
i.e:
T = cluster(Z, 'Cutoff', 150, 'criterion', 'distance');
I want to calculate the sum of the elements surrounding a given element in a matrix. So far, I have written these lines of code:
for i=1:m,
rij(1:n)=0
for j=1:n,
alive = tijdelijk(i-1,j)+tijdelijk(i+1,j)+tijdelijk(i-1,j-1)+tijdelijk(i+1,j-1)+tijdelijk(i,j+1)+tijdelijk(i,j-1)+tijdelijk(i-1,j+1)+tijdelijk(i+1,j+1)
This results in an error because, for example, i-1 becomes zero for i=1. Anyone got an idea how to do this without getting this error?
You can sum the elements via filtering. conv2 can be used for this manner.
Let me give an example. I create a sample matrix
>> A = reshape(1:20, 4, 5)
A =
1 5 9 13 17
2 6 10 14 18
3 7 11 15 19
4 8 12 16 20
Then, I create a filter. The filter is like a mask where you put the center on the current cell and the locations corresponding to the 1's on the filter are summed. For eight-connected neighbor case, the filter should be as follows:
>> B = [1 1 1; 1 0 1; 1 1 1]
B =
1 1 1
1 0 1
1 1 1
Then, you simply convolve the matrix with this small matrix.
>> conv2(A, B, 'same')
ans =
13 28 48 68 45
22 48 80 112 78
27 56 88 120 83
18 37 57 77 50
If you want four-connected neighbors, you can make the corners of your filter 0. Similarly, you can design any filter for your purpose, such as for averaging all neighbors instead of summing them.
For details, please see the convolution article in Wikipedia.
Two possibilities : change the limits of the loops to i=k:(m-k) and j=k:(n-k) or use blkproc
ex :
compute the 2-D DCT of each 8-by-8 block
I = imread('cameraman.tif');
fun = #dct2;
J = blkproc(I,[8 8],fun);
imagesc(J), colormap(hot)
There are lots of things you can do at the edges. Which you do depends very specifically on your problem and is different from usage case to usage case. Typical things to do:
If (i-1) or (i+1) is out of range, then just ignore that element. This is equivalent to zero padding the matrix with zeros around the outside and adjusting the loop limits accordingly
Wrap around the edges. In other words, for an MxN matrix, if (i-1) takes you to 0 then instead of taking element (i-1, j) = (0, j) you take element (M, j).
Since your code mentions "your teacher" I'd guess that you can ask what should happen at the edges (or working it out in a sensible manner may well be part of the task!!).
If I have 20 pairs of coordinates, whose x and y values are say :
x y
27 182
180 81
154 52
183 24
124 168
146 11
16 90
184 153
138 133
122 79
192 183
39 25
194 63
129 107
115 161
33 14
47 65
65 2
1 124
93 79
Now if I randomly generate 15 pairs of coordinates (x,y) and want to compare with these 20 pairs of coordinates given above, how can I do that most efficiently without nested loops?
If you're trying to see if any of your 15 randomly generated coordinate pairs are equal to any of your 20 original coordinate pairs, an easy solution is to use the function ISMEMBER like so:
oldPts = [...]; %# A 20-by-2 matrix with x values in column 1
%# and y values in column 2
newPts = randi(200,[15 2]); %# Create a 15-by-2 matrix of random
%# values from 1 to 200
isRepeated = ismember(newPts,oldPts,'rows');
And isRepeated will be a 15-by-1 logical array with ones where a row of newPts exists in oldPts and zeroes otherwise.
If your coordinates are 1) actually integers and 2) their span is reasonable (otherwise use sparse matrix), I'll utilize a simple truth table. Like
x_0= [27 180 ...
y_0= [182 81 ...
s= [200 200]; %# span of coordinates
T= false(s);
T(sub2ind(s, x_0, y_0))= true;
%# now obtain some other coordinates
x_1= [...
y_1= [...
%# and common coordinates of (x_0, y_0) and (x_1, y_1) are just
T(sub2ind(s, x_1, y_1))
If your original twenty points aren't going to change, you'd get better efficiency if you sorted them O(n log n); then you could see if each random point was in the list with a O(log n) search.
If your "original" points list changes (insertions / deletions), you could get equivalent performance with a binary tree.
BUT: If the number of points you're working with is really as low as in your question, your double loop might just be the fastest method! Algorithms with low Big-O curves will be faster as the amount of data gets really big, but it's often at the cost of a one-time slowdown (in your case, the sort) - and with only 15x20 data points... There won't be a human-perceptible difference; you might see one if you're timing it on your system clock. Or you might not.
Hope this helps!