Created
April 18, 2023 22:49
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Weightspace for perceptron training
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# Based on https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/homeworks/hw2.pdf, 1. b. | |
import numpy | |
import pylab | |
import random | |
# Training Data | |
x = numpy.array([ | |
[1, -2], | |
[0, -1] | |
]) | |
N = len(x) | |
i = 0 | |
w = [0, 0] | |
t = [1, -1] | |
lastnudge = N - 1 | |
path = numpy.array([w]) | |
for safety in range(1, 100): | |
zi = numpy.matmul(numpy.transpose(w), x[i]) | |
if zi * t[i] <= 0: | |
#print ("Nudge") | |
w = w + t[i] * x[i] | |
#print (w) | |
lastnudge = i | |
path = numpy.append(path, [w], axis=0) | |
else: | |
# print("No nudge") | |
if (lastnudge == i): | |
break | |
i = (i + 1) % N | |
#plotting stuff: | |
pylab.title("Weight Space of Perceptron Training ($n = " + str(len(path)) + "$ steps)") | |
pylab.plot(path[:,0],path[:,1]) | |
pylab.xlim(-5, 5) | |
pylab.ylim(-5, 5) | |
pylab.axhline(0, color='black', linewidth=.5) | |
pylab.axvline(0, color='black', linewidth=.5) | |
pylab.savefig("rand_walk"+str(n)+".png",bbox_inches="tight",dpi=600) | |
pylab.show() |
Author
mcapodici
commented
Apr 18, 2023
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