## Questions Exercise 1

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DreamFlasher
BASIC-Programmierer Beiträge: 102
Registriert: 12. Okt 2010 12:44

### Questions Exercise 1

Hi,

we should start using the forum for some questions of generel interest So our questions to exercise 1:
- 1.3: Why isn't the acceleration force for component q1: $$(m1+m2)*\ddot{q_1}$$ as both masses affect joint 1?

...TBC...
Marcel Ackermann
http://www.dreamflasher.de
Machine Learning, Natural Language Processing, Algorithms

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franzose
BASIC-Programmierer Beiträge: 146
Registriert: 9. Okt 2009 00:08

### Re: Questions Exercise 1

Please apologize if I haven't understood your question, but actually I think that in the solution the mass matrix for $$u_1$$ exactly does what you suggested (at least for the part that is dependent from $$\ddot{q_1}$$):

You have $$M(q) * \ddot{q}$$ so you have to multiply the matrix with the vector $$(\ddot{q_1}, \ddot{q_2})^T$$ and in the first row and first col of the matrix there is $$m_1 + m_2$$.

This results to $$u_1 = (m_1 + m_2) * \ddot{q_1} + k * \ddot{q_2}$$ with $$k$$ being the term in the first row and second col of the mass matrix.
Zuletzt geändert von franzose am 27. Nov 2012 00:05, insgesamt 1-mal geändert.

lustiz
Mausschubser Beiträge: 70
Registriert: 29. Apr 2009 10:28

### Re: Questions Exercise 1

Another question for you guys, concerning exercise 1.2 b) (Linear Least Squares):

Are we supposed to include a bias term? As far as I know you usually include a bias yet if we talk about 2 features, does that mean
a) [ sin(x); sin(2x) ] or
b) [ 1; sin(x); sin(2x) ] ?

Cheers!

lustiz
Mausschubser Beiträge: 70
Registriert: 29. Apr 2009 10:28

### Re: Questions Exercise 1

Also: Is the kernel function right in 1.2 (f) ?
I get pretty good results when I use
$$k(x_i, x_j) = e ^ \frac{- 0.5 || x_i - x_j ||^2}{\sigma^2}$$

However, there is no hope when I use the proposed one:
$$k(x_i, x_j) = e ^ \frac{|| x_i - x_j ||^2}{\sigma^2}$$

Any suggestions?

eesti
BASIC-Programmierer Beiträge: 116
Registriert: 6. Okt 2008 20:59
$$k(x_i, x_j) =e^\frac{-\|x_i−x_j\|^2}{\sigma^2}$$