My problem is:
I have two large matrices: Matrix A is of rank 5 (namelly a Tensor), which I reshape to a Matrix B (NxM) of rank 2. At some point, my problem involved normalizing my matrices, so I was doing:
1) A*norm_scalar;
2) reshaping A to get B.
which is giving a different result than doing
1) reshaping A to get B
2) B*norm_scalar;
Both results should have the same output, as I am only multiplying by a scalar. My theory is that there is something related to rounding precision. If so, which one is the most recommended way to proceed?
In this sense, I was trying to get B with both methods, namelly B1 and B2 and compare them:
I have tryed:
julia> isequal(B1,B2)
false
So, yes, they are different.
I know that find(B1.==B2) will give me the indexes where B1 and B2 are equal. Now: Is there any command that gives me the indexes where B1 and B2 are different?. this would help me great deal!
find(B1.!=B2) should do what you want.
Related
I have a formula as:
in the above formula Delta(G) is a vector of 300 rows and 2 columns (Energy vs. distance) and D(z) is a vector of 30 rows and 2 columns (Diffusion vs. distance).
To solve this integral I have tried many strategies such as:
1- Fit an equation to D(z) named F1.
2- Fit another equation to Exponential named F2.
3- Divide F2 into F1 to give us F3 (from library 'pracma').
4- Integrate F3 (from library 'stats').
But my solution result is far away from experimental result.
I think my solution is not correct.
Plz, Could anyone elaborate on how to solve this in R.
I'm converting a rather complicated set of code from Matlab to R. I have zero experience in Matlab and am a functioning novice in R.
I have a segment of code which reads (in matlab):
dSii=(sum(tao.*Sik,1))'-(sum(m'))'.*Sii-beta.*Sii./N.*(Iii+sum(Iik)');
Which I've simplified and will focus on the first segment (if I can solve the first segment I'm confident I can perform the rest):
J = (sum(A.*B,1))' - ...
tao (or A) and Sik (or B) are matrices. So my assumption is I'm performing matrix multiplication here (A * B)and summing the resultant column. The '1' is what is throwing me off in that statement. In R, that 1 would likely indicate we're talking about a sum of rows as opposed to columns(indicated by 2). But I can't find any supporting documentation for that kind of Matlab statement.
I was thinking of using a statement like this (but of course, too many '1's and ',')
J<- (apply(A*B, 1), 1, sum)
Thanks for all your help. I searched for other examples here and elsewhere and couldn't find an answer. I'm willing to work for it but this is akin to me studying French (which I don't know) to translate in Spanish (which I'm moderate in) while interpreting the whole process in English. :D
Because of the different conventions in R and Matlab, the idiosyncrasies have to be learned for each (just like your language analogy!). The Matlab command sum(A.*B,1) means multiply A and B element-wise, so they must be the same shape, and then sum along dimension 1, i.e. add each row together to get the column sums. Dimension 1 is the default so, sum(A.*B) would do the same thing as sum(A.*B,1). Because R treats * as element-wise for matrix multiplication, the following Matlab and R codes will produce the same column of numbers in J:
Matlab:
A=[[1,2,3];[4,5,6];[7,8,9]];
B=[[10,11,12];[13,14,15];[16,17,18]];
J=sum(A.*B,1)'; %the ' means to transpose the column sums to be a 3x1 matrix
R:
A<-matrix(c(1,2,3,4,5,6,7,8,9),3,byrow=T)
B<-matrix(c(10,11,12,13,14,15,16,17,18),3,byrow=T)
J<-matrix(colSums(A*B)) # no transpose needed here: nrow(J)==3
I have two tables A1 and A2
(for example A1=[0.4472,-0.8944;-0.8944 0.4472] A2=[-0.5558 0.9101;0.8313 0.41420] )
and i want to check if the columns of A2 are optimally ordered and scalled
(its columns are least-squares estimates of the columns of A)
And if not , to make them.
Any help?
Thanks
I have a problem to solve in either Matlab or R (preferably in R).
Imagine I have a vector A with 10 elements.
I have also a vector B with 30 elements, of which 10 have value 'x'.
Now, I want to replace all the 'x' in B by the corresponding values taken from A, in the order that is established in A. Once a value in A is taken, the next one is ready to be used when the next 'x' in B is found.
Note that the sizes of A and B are different, it's the number of 'x' cells that coincides with the size of A.
I have tried different ways to do it. Any suggestion on how to program this?
As long as the number of x entries in B matches the length of A, this will do what you want:
B[B=='x'] <- A
(It should be clear that this is the R solution.)
MATLAB Solution
In MATLAB it's quite simple, use logical indexing:
B(B == 'x') = A;
I want to differentiate data vectors to find those that are similar. For example:
A=[4,5,6,7,8];
B=[4,5,6,6,8];
C=[4,5,6,7,7];
D=[1,2,3,9,9];
E=[1,2,3,9,8];
In the previous example I want to distinguish that A,B,C vectors are similar (not the same) to each other and D,E are similiar to each other. The result should be something like: A,B,C are similar and D,E are similar, but the group A,B,C is not similar to the group of D,E. Matlab can do this?
I was thinking using some classification algorithm or Kmeans,ROC,etc.. but I'm not sure which one will be the best one.
Any suggestion? Thanks in advance
One of my new favourite methods for this sort of thing is agglomerate clustering.
First, concatenate all your vectors into a matrix, where each row is a separate vector. This makes such methods much easier to use:
F = [A; B; C; D; E];
Then the linkages can be found:
Z = linkage(F, 'ward', 'euclidean');
This can be plotted using:
dendrogram(Z);
This shows a tree, where each leaf at the bottom is one of the original vectors. Lengths of the branches show similarities and dissimilarities.
As you can see, 1, 2 and 3 are shown to be very close, as are 4 and 5. This even gives a measure of closeness, and shows that vectors 1 and 3 are deemed to be closer than vectors 2 and 3 (in the sense that, percentagewise, 7 is closer to 8 than 6 is to 7).
If all the vectors you are comparing are of the same length, a suitable norm on pairwise differences may well be enough. The norm to choose will depend on your particular criteria of closeness, of course, but with the examples you show, simply summing the absolute values of the components of the pairwise differences gives:
A B C D E
A 0 1 1 12 11
B 0 2 13 12
C 0 13 12
D 0 1
E 0
which doesn't need a particularly well-tuned threshold to work.
You can use pdist(), this function gives you the pairwise distances.
Various distance (opposite of similarity) metrics are already implemented, 'euclidean' seems appropriate for your situation, although you may want to try out the effect of different metrics.
Here it goes the solution I propose based on your results:
Z = [A;B;C;D;E];
Y = pdist(Z);
matrix = SQUAREFORM(Y);
matrix_round = round(matrix);
Now that we have the vector we can set the threshold based on the maximun value and decide with which theshold is the most appropriate.
It would be nice to create some cluster plot showing the differences between them.
Best regards