Talk:Perceptron: Difference between revisions
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It would be helpful to know how long the java implementation had to "cook" before that screenshot was taken... --[[User:Rdm|Rdm]] ([[User talk:Rdm|talk]]) 04:10, 16 July 2016 (UTC)
: Not very long, maybe a few minutes, I can't remember exactly, but it wasn't very long. It stabilizes after about half a minute on my system, but I almost never get a perfect split (I believe the text at natureofcode also says that), so I got a bit lucky with the screenshot. I tried making it run longer but it doesn't seem to improve. [[User:Fwend|Fwend]] ([[User talk:Fwend|talk]]) 07:13, 16 July 2016 (UTC)
== Is the line on Java correct? ==
The line drawn here is from (0,0) to (x,f(x)). Should it not be from (0,f(0)) to (x, f(x))? This is the line we're partitioning acress? Surely?
<lang java>
int x = getWidth();
int y = (int) f(x);
//. [...]
// g.drawLine(0, 0, x, y);
g.drawLine(0, (int)f(0), x, y);
</lang>
--[[User:Tim-brown|Tim-brown]] ([[User talk:Tim-brown|talk]]) 16:59, 16 January 2017 (UTC)
== Tasks ==
Excellent: I was looking for some machine-learning style tasks, and found this one!
As it's still in draft, could I suggest some additional tasks to be done? I also suggest the image output be moved to 'extra credit': image output is difficult for languages without a natural gui library, and there also seem to be problems uploading images to the site, so results are hard to show. (Although I do like the Java version's dynamic graphical output.)
As a core set of tasks, how about simply showing the perceptron being trained and working:
# Create a set of training data of at least 5000 random points, classifying the points with a line of your choice.
# Train a perceptron from the training data.
# Show the final perceptron's weights
# Create a new test set of at least 1000 random points, and show the percentage correct achieved by the final perceptron.
As 'extra credit' tasks:
# Draw an image showing the target line and the decisions made by the perceptron on the test set.
# Show how performance on a fixed test set varies with an increasing range of training data, say 1000 to 20000 points in steps of 1000.
I shall add a Scheme example illustrating these core tasks and the second extra one.
-- [[User:Peter]] 17:15, 25 February 2017 (UTC)
: Do you feel like [[Rosetta_Code:Add_a_Task|drafting]] one or more of these tasks? --[[User:Rdm|Rdm]] ([[User talk:Rdm|talk]]) 18:59, 25 February 2017 (UTC)
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