Create Ocatve_Cheatsheet.txt

Author: Shadow977

Octave_Cheatsheet.txt

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+% octave cheatsheet
+% not equals(~=)
+1 ~=2 % => 1
+
+% change prompt
+PS1('>> ');
+
+% semicolon suppresses output
+
+
+% print
+disp( 1+1)
+disp(sprintf('2 decimals: %0.2f', 3.146))
+format long
+format short
+
+
+% matrix
+A = [1 2; 3 4 ; 5 6]
+A =
+
+   1   2
+   3   4
+   5   6
+
+% vector
+v = [1; 2; 3]
+
+% range fat vector
+v = 1: 0.1: 1.5
+v =
+
+    1.0000    1.1000    1.2000    1.3000    1.4000    1.5000
+
+% matrix of ones
+ones(2,3)
+ans =
+
+   1   1   1
+   1   1   1
+
+
+zeroes(1,3) % matrix of zeroes
+rand(3,3)   % matrix of random numbers
+eye(3)      # identity matrix
+ans =
+
+Diagonal Matrix
+
+   1   0   0
+   0   1   0
+   0   0   1
+
+v = [1,2,3,4]
+% length returns longest dimension
+length(v) % =>  4
+
+% size returns matrix of size
+size(v)   % => [1,4]
+
+
+% working with files
+  load file
+  load featuresX.dat
+
+  who   % shows variables in current scope
+  whos  % shows variables and size
+
+  clear featuresX % removes variable featuresX
+
+  % save to disk
+  v = [1;2;3;4;5]
+
+  save hello.mat v; % saves v to file hello.mat
+
+  clear % removes all variables in workspace
+
+  save hello.mat v -ascii; % save as text
+
+A(3,2) % gets the value on row 3, column 2
+A(2,:) % get every element in row 2
+
+A = [A, [100; 101; 102]] % append another column to a
+
+% ==============================
+
+A = [1 2 ; 3 4 ; 5 6]
+B = [11 12; 13 14; 15 16]
+C = [1 1; 2 2]
+
+% element wise multiplication (.*)
+
+A .* B
+ans =
+
+   11   24
+   39   56
+   75   96
+
+abs(A) % absolute value
+
+% transpose
+A'
+ans =
+
+   1   3   5
+   2   4   6
+
+A = magic(3) % magic squares, all rows columns and diagonals add up to the same thing
+
+[r,c] = find(A >= 7)
+r =
+  1
+  3
+  2
+
+c =
+  1
+  2
+  3
+
+
+sum(A, 1) % sums rows
+sum(A, 2) % sums columns
+prod
+floor
+ceil
+
+
+
+flipud(A) % flips matrix up down
+
+pinv(A)   % gives the inverse of A
+
+
+% plotting
+t = [0:0.01: 0.98];
+y1 = sin(2*pi*4*t);
+
+plot(t, y1);
+
+
+y2 = cos(2*pi*4*t)
+plot(t,y1);
+hold on
+plot(t,y2);
+plot(t,y2, 'r');
+xlabel('time')
+ylabel('value')
+legend('sin', 'cos')
+title('my plot')
+
+print -dpng 'myplot.png'
+close
+
+figure(1); plot(t,y1);
+figure(2); plot(t,y2);
+subplot(1,2,1); %divides plot a 1x2 grid access first element
+plot(t, y1);
+subplot(1,2,2)
+plot(t, y2)
+
+clf % clears figure
+
+
+imagesc(A), colorbar, colormap gray;
+
+
+% =================================
+
+v = zeros(10,1)
+
+for i = 1:10,
+  v(i) = 2^i;
+end
+
+
+indicies = 1:10
+for i = indices,
+  disp(i);
+end;
+
+i = 1;
+while i <= 5,
+  v(i) = 100;
+  i = i+1;
+end;
+
+i = 1;
+while true,
+  v(i) = 999;
+  i = i + 1;
+  if i == 6,
+    break;
+  end;
+end;
+
+
+v(1) = 2
+if v(1) = 2;
+  disp('the value is one');
+elseif v(1) == 2,
+  disp('the value is true');
+else
+  disp("the value is not one or two.");
+end;
+
+
+squareThisNumber.m
+function y = squareThisNmber(x)
+y = x^2;
+
+suareAndCuebeThisNumber.m
+function [y1,y2] = squareAndCubeThisNumber(x)
+y1 = x^2;
+y2 = x^3;
+
+
+X = [1 1; 1 2; 1 3]
+y = [1; 2; 3]
+
+theta = [0;1];
+
+costFunctionJ.m
+  function J = costFunctionJ(X, y, theta)
+
+  % X is the 'design matrix' containing our training examples
+  % y is the class labels
+
+  m           = size(X, 1)            % number of training examples
+  predictions = X*theta;              % predictions of hypothesis on examples
+  sqrErrors   = (predictions - y).^2; % squared errors
+
+  J = 1/(2*m) * sum(sqrErrors);
+
+
+J % => 0
+
+
+
+Theta1 = ones(10,11);
+Theta2 = ones(10,11);
+Theta3 = 3*ones(10,11);
+
+thetaVec = [ Theta1(:); Theta2(:); Theta3(:)];
+
+Theta1 == reshape(thetaVec(1:110), 10, 11)
+
+gradApprox = (J(theta = EPSILON) - J(theta - EPSILON))/(2*EPSILON)
+
+for i = 1:n,
+  thetaPlus = theta;
+  thetaPlus(i) = thetaPlus(i) + EPSILON;
+  thetaMinus = theta;
+  thetaMinus(i) = thetaMinus(i) - EPSILON;
+  gradApprox(i) = (J(thetaPlus) - J(thetaMinus))/(2*EPSILON);
+end
+
+% check that gradApprox ~ DVec
+
+% - implement backprom to compute DVec (unrolled D1, D2, D3)
+% - implement numerical gradient check to compute gradApprox
+% - make sure they give similar values
+% - TURN OFF gradient checking using backprob code for learning with
+%   NO gradient checking
+
+optTheta = fminunc(@costFunction, initialTheta, options);
+
+initialTheta = zeros(n,1); % can we do better?
+
+% INIT_EPSILON is unrelated to EPSILON
+Theta1 = rand(10,11) * (2*INIT_EPSILON) - INIT_EPSILON;
+Theta1 = rand(1,11) * (2*INIT_EPSILON) - INIT_EPSILON;
+
+load('ex3data1.mat');