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MATLAB supports the use of logical masking in order to perform selection on a matrix without the use of for loops or if statements. A logical mask is defined as a matrix composed of only 1 and 0 . For example:

Sum MATLAB/Octave Python Description sum(a) a.sum(axis=0) Sum of each column sum(a') a.sum(axis=1) Sum of each row sum(sum(a)) a.sum() Sum of all elements a.trace(offset=0) Sum along diagonal cumsum(a) a.cumsum(axis=0) Cumulative sum (columns) Sorting MATLAB/Octave Python Description a = [ 4 3 2 ; 2 8 6 ; 1 4 7 ] a = array([[4,3,2],[2,8,6], [1 ...

If the inputs are all arrays of the same size, then bitconcat performs element-wise concatenation of the bits and returns an array of the same size. Extended Capabilities C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

If A is a vector, sum(A) returns the sum of the elements. If A is a matrix, sum(A) treats the columns of A as vectors, returning a row vector of the sums of each column. If A is a multidimensional array, sum(A) treats the values along the first non-singleton dimension as vectors, returning an array of row vectors. 24: sum(A,dim) Sums along the ...

If you want to do this with arrays with 100.000 elements, you should use numpy: In [1]: import numpy as np In [2]: vector1 = np.array([1, 2, 3]) In [3]: vector2 = np.array([4, 5, 6]) Doing the element-wise addition is now as trivial as. In [4]: sum_vector = vector1 + vector2 In [5]: print sum_vector [5 7 9] just like in Matlab.

The dot product multiplies the elements in corresponding positions in the two vectors, and then takes the sum, returning a scalar value. To perform a dot product, the row vector must be listed before the column vector (otherwise MATLAB will perform an outer product , returning a matrix).