So say I have the below matrix
[1, 2, 3,
4, 5, 6,
7, 8, 9]
And I have a vector [1,3]
I want to access the 1st and 3rd row which would return
[1,2,3
7,8,9]
I need to be able to scale this up to about 1000 rows being grabbed based on values in the vector.
if A is your matrix and v your vector of index, you just have to do A(v,:)
Related
I am using scala spark. I have a dataframe that 2 column each containing a Vector with the same cardinality/size. I want to find the distance between each element of the 2 Vectors and put the results in a vector in another column of the dataframe.
Example: [1, 3, 5, -2] - [-2, 5, 0, 1] = [3, 2, 5, 3]
I found sqdist method that can get me the sum of the square distances between 2 Vectors but how do I get the individual distances of each elements in the vector.
I have a very large sparse matrix of the type 'scipy.sparse.coo.coo_matrix'. I can convert to csr with .tocsr(), however .todense() will not work since the array is too large. I want to be able to extract elements from the matrix as I would do with a regular array, so that I may pass row elements to a function.
For reference, when printed, the matrix looks as follows:
(7, 0) 0.531519363001
(48, 24) 0.400946334437
(70, 6) 0.684460955022
...
Make a matrix with 3 elements:
In [550]: M = sparse.coo_matrix(([.5,.4,.6],([0,1,2],[0,5,3])), shape=(5,7))
It's default display (repr(M)):
In [551]: M
Out[551]:
<5x7 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in COOrdinate format>
and print display (str(M)) - looks like the input:
In [552]: print(M)
(0, 0) 0.5
(1, 5) 0.4
(2, 3) 0.6
convert to csr format:
In [553]: Mc=M.tocsr()
In [554]: Mc[1,:] # row 1 is another matrix (1 row):
Out[554]:
<1x7 sparse matrix of type '<class 'numpy.float64'>'
with 1 stored elements in Compressed Sparse Row format>
In [555]: Mc[1,:].A # that row as 2d array
Out[555]: array([[ 0. , 0. , 0. , 0. , 0. , 0.4, 0. ]])
In [556]: print(Mc[1,:]) # like 2nd element of M except for row number
(0, 5) 0.4
Individual element:
In [560]: Mc[1,5]
Out[560]: 0.40000000000000002
The data attributes of these format (if you want to dig further)
In [562]: Mc.data
Out[562]: array([ 0.5, 0.4, 0.6])
In [563]: Mc.indices
Out[563]: array([0, 5, 3], dtype=int32)
In [564]: Mc.indptr
Out[564]: array([0, 1, 2, 3, 3, 3], dtype=int32)
In [565]: M.data
Out[565]: array([ 0.5, 0.4, 0.6])
In [566]: M.col
Out[566]: array([0, 5, 3], dtype=int32)
In [567]: M.row
Out[567]: array([0, 1, 2], dtype=int32)
I have a matrix like this:
A = [1, 2, 3, 4, 5, NaN, NaN, NaN, NaN, NaN;
1, 2, 3, 4, 5, 6, 7, NaN, NaN, NaN;
1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
I would like to know how I can count the number of values in each row excluding any NaNs.
So I would get an output like:
output = [5;
7;
10;]
If A is a 2D array, e.g.
A = [1, 2, 3, 4, 5, NaN, NaN, NaN, NaN, NaN;
1, 2, 3, 4, 5, 6, 7, NaN, NaN, NaN;
1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
and you want to count the number of NaN entries on each row of A, you can simply use
>> sum(~isnan(A), 2)
ans =
5
7
10
Breakdown
isnan(A) returns a logical array of the same size as A, in which (logical1 indicates a NaN and 0 a non-NaN.
Note that you have to use the isnan function, here. In particular, the expression A == ~NaN is useless: it would simply return a logical array of the same size as A but full of (logical) 0's. Why? Because, according to floating-point arithmetic, NaN == NaN always returns "false" (i.e. logical 0, in MATLAB).
Then, by applying MATLAB's not operator (~) to that, you get a logical array of the same size as A, in which 1 indicates a non-NaN and 0 a NaN.
Finally, sum(~isnan(A), 2) returns a column vector in which the i-th entry corresponds to the number of logical 1's on the i-th row of ~isnan(A).
The resulting column vector is exactly what you want: a count, row by row, of the non-NaN entries in A.
How can I create an index-matrix that specifies which elements of a matrix to address?
So for example I have a matrix A which is 80 by 50. I know that A(1:5,:) addresses only the first 5 elements, but what if I want to multiply A with another matrix which also changes the elements to be addressed? So I want to multiply B(1,:) with A(1:5,:), and B(2,:) with A(10:15,:) and so on. Is there a smart way to specify this index-matrix where the information (1:5; 10:15, etc.) is stored?
Yes you can certainly define indices into a matrix using another matrix. Here is a simple example using a cell array to store the index list:
X =[1,2,3,4,5,6]
Idx = { [1, 2, 3], [4, 5, 6] }
Y = X( Idx{1} ) .* X( {Idx{2} )
Y = [ 4, 10, 18]
This question already has answers here:
Reshaping of Array in MATLAB
(3 answers)
Closed 7 years ago.
Here is what I have:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
And here is what I want to get:
[
1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12
]
The number of rows and columns (3 and 4 in example) is already known.
How would I do that?
reshape
b = reshape(a, 4, 3)' will would work for your example. Elements are taken from the original and inserted into the new matrix column-wise.
Furthermore, reshape is a built-in MATLAB function. There exists other solutions such as vec2mat that require the communications toolbox.
This guide says
mat = vec2mat(vec,matcol) converts the vector vec into a matrix with matcol columns, creating one row at a time. If the length of vec is not a multiple of matcol, then extra zeros are placed in the last row of mat. The matrix mat has ceil(length(vec)/matcol) rows.