machine learning

gistfile1.txt

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