#21




Re: Perceptron Learning Algorithm
The homework asks you to choose whatever you observe. It is hard to comment on the correctness here before the deadline.
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When one teaches, two learn. 
#22




Re: Perceptron Learning Algorithm
I also had mixed feelings about the choices we were given on problem #9. My result from simulation was right between two answers, and, strictly speaking, if I were to take the absolute closest answer, I got an answer that I felt was wrong, so I chose the other one. I felt that the purpose of the question was to highlight what, if any, difference N makes in the number of steps to converge. So, based on the professor's response about choosing the absolute closest answer, I think I chose poorly.
Last edited by kurts; 04092012 at 12:32 PM. Reason: spelling 
#23




Re: Perceptron Learning Algorithm
Hello Professor,
I have a question regarding updating w on each PLA iteration. If we always assign x0 = 1, how are we reasonably updating w0? By the vector addition, it will always be updated by the value y * x0 = 1 or +1, and if the true w0 is not an integer the PLA will never be able to converge to that value. Would it be more appropriate to setup such that we divide each component of the true w by w0, so that w0 = 1 always? This way I know it is an integer and my PLA does not have to wander for a noninteger value. Or even, if I know w0 is always 1, I might not even include it in the PLA since I know it is 1 by setup. Thank you, Aaron 
#24




Re: Perceptron Learning Algorithm
Hmm, I have the same problem. The average number of iterations for the case where N = 10 and the case where N = 100 are clearly "different" in the way that one would expect, but with respect to the answer choices they are different relatively but identical absolutely.

#25




Re: Perceptron Learning Algorithm
Can you explain the math in terms of the points I listed?
(1,1,3), (1,3,7), (1,2,3), and (1,4,7). These map ++ and   What exactly happens? During the first iteration, what actual numbers does the computer compare? My eventual f vector should be (1,2,1),right? That represents the line y=2x when rewritten 0=2xy in matrix notation. Shouldn't my eventual result look pretty darn similar to that? I really am trying on this, and I've literally taken all of the required prereq. courses. It's just that I'm not an engineer. The notations don't make as much sense unless I see it in some sort of context. I copied a python PLA script from the net, but python isn't my "native" language, so I'm still trying to figure out exactly what it does. 
#26




Re: Perceptron Learning Algorithm
Quote:
http://en.wikipedia.org/wiki/Perceptron In particular, look at the section called Learning Algorithm Steps. It hopefully will get you started. 
#27




Re: Perceptron Learning Algorithm
Quote:
w_0 = w_0 + y_i However, there is usually a learning rate associated with the perceptron such as alpha, which would make the update on the intercept: w_0 = w_0 + alpha * y_i So you can see here that the algorithm would accommodate noninteger values. In our case, without a learning rate, we just have to hope it converges with an integer value intercept. I actually had one case myself where it wouldn't converge. To avoid biasing my average results, I'm going to just run the algorithm to 100k iterations and throw out anything that doesn't fully converge. 
#28




Re: Perceptron Learning Algorithm
Quote:
Assuming the data classes are linearly separable, the PLA should always converge. You might want to plot the training data for those cases where it won't converge. I had a problem during my initial implementation and after reviewing the plot realized that I had a bug in my update method. Last edited by tcristo; 04092012 at 09:28 PM. Reason: Grammer 
#29




Re: Perceptron Learning Algorithm
How do you calculate the desired output (dj), as shown on the wikipedia page?

#30




Re: Perceptron Learning Algorithm
Virginia 
Desired output is a set of 1/+1 based on the signum function on N (x,y) points compared to the original line in the xy plane. 
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