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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.
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,:)
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.
Waht i need to do is take a single dimensional array, ie:
[1, 1, 2, 2, 3, 3]
and turn it into an array of points:
[[1, 1], [2, 2], [3, 3]]
I am hoping for a simple native matlab way of doing it rather then a function. This will be going into sets of points ie:
[ [[1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6]],
[[7, 7], [7, 7], [8, 8]] ]
The reason this is going to happen is the points will be stored in a text file as a single stream and i need to turn them into something meaningful.
First note that a horizontal concatenation of row vectors will result in one larger row vector rather than in a row of pairs, that is [[1, 1], [2, 2], [3, 3]] is the same as [1 1 2 2 3 3]. Hence, you need to concatenate them vertically.
You can try
a = [1, 1, 2, 2, 3, 3];
b = reshape(a, 2, floor(length(a)/2))';
This will result in a matrix where each row represents the coordinates of one point.
b =
1 1
2 2
3 3
I'm just adding this answer for the sake of diversity:
Just as H.Muster said, concatenation of vectors will result in a larger vector or a matrix (depending on your operation). You can go with that.
But you can also use a cell array, which is a set of data containers called "cells". A cell can contain any type of data, regradless of what other cells contain in the same cell array.
In your case, creating a cell array can be done using a slightly different syntax (than H.Muster's answer):
a = [1, 1, 2, 2, 3, 3];
p = mat2cell(a, 1, 2 * ones(1, numel(a) / 2))
p is a cell array, each cell containing a 1-by-2 point vector. To access an element in a cell array, you'll have to use curly braces. For instance, the second point would be p{2} = [2, 2].
I have a 3 Dimensional array Val 4xmx2 dimension. (m can be variable)
Val{1} = [1, 280; 2, 281; 3, 282; 4, 283; 5, 285];
Val{2} = [2, 179; 3, 180; 4, 181; 5, 182];
Val{3} = [2, 315; 4, 322; 5, 325];
Val{4} = [1, 95; 3, 97; 4, 99; 5, 101];
I have a subscript vector:
subs = {1,3,4};
What i want to get as output is the average of column 2 in the above 2D Arrays (only 1,3 an 4) such that the 1st columns value is >=2 and <=4.
The output will be:
{282, 318.5, 98}
This can probably be done by using a few loops, but just wondering if there is a more efficient way?
Here's a one-liner:
output = cellfun(#(x)mean(x(:,1)>=2 & x(:,1)<=4,2),Val(cat(1,subs{:})),'UniformOutput',false);
If subs is a numerical array (not a cell array) instead, i.e. subs=[1,3,4], and if output doesn't have to be a cell array, but can be a numerical array instead, i.e. output = [282,318.5,98], then the above simplifies to
output = cellfun(#(x)mean(x(x(:,1)>=2 & x(:,1)<=4,2)),Val(subs));
cellfun applies a function to each element of a cell array, and the indexing makes sure only the good rows are being averaged.