Perceptron Convergence Analysis

preceptron.py

#!/usr/bin/python
"""
Is Convergence rate of perceptron update dependent on the input dimensionality?
"""
import numpy as np

N = 100
lr = 1
for sz in [5, 10, 100, 500, 1000, 5000, 10000]:
    dat = np.random.normal(scale=10, size=[N, sz])
    worig = np.random.normal(scale=10, size=[sz])
    y = np.where(np.dot(dat, worig)>0, np.ones([N]), -np.ones([N]))
    nsteps = 0
    na = 10
    ne = 0
    for attempt in range(na):
        w = np.random.normal(size=[sz])
        for epoch in range(100000):
            br = True
            for i, x in enumerate(dat):
                if y[i]*np.dot(x, w)<0:
                    w += lr*y[i]*x
                    br = False
            if br:
                #print "Dimension: %d Converged in %d epochs" % (sz, epoch)
                ne += epoch 
                break
    ne /= float(na)
    print "Dimension: %d Converged in %f epochs" % (sz, ne)