Created
September 22, 2015 19:56
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machine learning
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| MATRIX / MATLAB | |
| I = v < 0.05 # I contains 1 or 0 depending on logical criteria | |
| v(I) = 0 # element vise assign 0 where I is 1 (True) | |
| A .* B # element wise multiplication | |
| 1 . / v # element wise division (1 / element) | |
| v + 1 # element wise | |
| a < 3 # element wise comparision | |
| log(v), exp(v), abs(v) # element wise | |
| sum(a), prod(a), floor(a) | |
| rand(3) | |
| [val, index] = max(a) # column wise maximum | |
| find(a < 3) | |
| magic(n) | |
| [row, column] = find(A >= 7) # row / col contains index pairs that match the predicate | |
| max(A, [], 1) # per column wise max | |
| max(A, [], 2) # per row wise max | |
| max(max(A)) == max(A(:)) # max value in A | |
| sum(A, 1) # column wise sum | |
| sum(A, 2) # row wise sum | |
| flipud (A) # flip the matrix :) | |
| pinv(A) # pseudo inverse of A | |
| PLOT | |
| hold on; # plot in same figure | |
| close; # close figure | |
| xlabel(‘x’) | |
| ylabel(‘y’) | |
| legend(‘abc’) | |
| title(‘zxy’) | |
| print -dpng ‘plot.png’ | |
| figure(1); plot(x, y) | |
| subplot(1,2,1) # divides plot a 1x2 grid, access first element | |
| axis([0.5 1 -1 1]) # set x and y axis | |
| clf # clear figure | |
| imagesc(A) the # color stuff | |
| imagesc(A), colorbar, colormap gray |
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