Octave practice :: Plotting Data

Contents in the post based on the free Coursera Machine Learning course, taught by Andrew Ng.

1. define a matrix

>> t = [0:0.01:0.98];

Matrix t

2. plot

>> y1 = sin(2*pi*4*t)
>> plot(t,y1);

plot(t,y1);

>> y2 = cos(2*pi*4*t)
>> plot(t,y2);

3. hold on

>> plot(t,y1);
>> hold on;
>> plot(t,y2);

3.1 How to apply assigned color

>> plot(t, y1,'g');
>> hold on;
>> plot(t,y2,'y');

4. label

4.1 xlabel

>> xlabel('time')

4.2 ylabel

>> ylabel('value')

5. legend

>> legend('sin', 'cos')

6. title

>> title('my plot')

7. save

>> print -dpng 'myPlot.png'

8. figure

>> figure(1); plot(t,y1);

>> figure(2); plot(t,y2);

9. subplot

>> subplot(1,2,1);

>> plot(t,y1);

>> subplot(1,2,2);

>> plot(t,y2);

---

9.1 In the case of using subplot(1,2,2) first. (<-> Above we applied subplot(1,2,1) first)

>> subplot(1,2,2);

10. axis

>> axis([0.5 1 -1 1])

11. clf

12. imagesc

12.1 magic

>> A=magic(5)

12.2 imagesc

>> imagesc(A)

12.3 colorbar & colormap

>> imagesc(A), colorbar, colormap gray

- You could compare colormap to matrix A. It shows that a smaller number is related to a darker color on the map. - For example, Look at A(1,3) = 1. And we can verify that it matches with one of the darkest parts.

13. Comma VS Semicolon

>> a=1, b=2, c=3
a = 1
b = 2
c = 3

---

>> a=1;b=2;c=3;
>>

- By using 'Comma' you can carry out multiple commands simultaneously.