# Perceptron Implementations

### Implementing the Perceptron Algorithm Using Numpy

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import numpy as np
np.random.seed(42)
import matplotlib.pyplot as plt
def stepFunction(t):
if t >= 0:
return 1
return 0
def prediction(X, W, b):
return stepFunction((np.matmul(X,W)+b)[0])
def perceptronStep(X, y, W, b, learn_rate = 0.01):
for i in range(len(X)):
y_hat = prediction(X[i],W,b)
if y[i]-y_hat == 1:
W[0] += X[i][0]*learn_rate
W[1] += X[i][1]*learn_rate
b += learn_rate
elif y[i]-y_hat == -1:
W[0] -= X[i][0]*learn_rate
W[1] -= X[i][1]*learn_rate
b -= learn_rate
return W, b
def trainPerceptronAlgorithm(X, y, learn_rate = 0.01, num_epochs = 40):
x_min, x_max = min(X.T[0]), max(X.T[0])
y_min, y_max = min(X.T[1]), max(X.T[1])
W = np.array(np.random.rand(2,1))
b = np.random.rand(1)[0] + x_max
boundary_lines = []
for i in range(num_epochs):
# In each epoch, we apply the perceptron step.
W, b = perceptronStep(X, y, W, b, learn_rate)
boundary_lines.append((-W[0]/W[1], -b/W[1]))
return boundary_lines
data = np.loadtxt('perceptron-implementations/data.csv',
delimiter = ',')

X = data[:,:-1]
y = data[:,-1]

lines = trainPerceptronAlgorithm(X, y)

print('Epoch :\t\tW\t\tB')
for n, line in enumerate(lines):
print('{}:\t\t{}\t\t{}'
.format(str(n+1).zfill(2),
round(line[0][0],3),
round(line[1][0],3)))

plt.figure()

X_min = X[:,:1].min()
X_max = X[:,:1].max()

counter = len(lines)
for w, b in lines:
counter -= 1
color = [1 - 0.91 ** counter for _ in range(3)]
plt.plot([X_min-0.5, X_max+0.5],
[(X_min-0.5) * w + b, (X_max+0.5) * w + b],
color=color,
linewidth=0.75)

plt.scatter(X[:50,:1],
X[:50,1:],
c = 'blue',
zorder=3)
plt.scatter(X[50:,:1],
X[50:,1:],
c = 'red',
zorder=3)

plt.gca().set_xlim([-0.5,1.5])
plt.gca().set_ylim([-0.5,1.5]);
Epoch :     W       B
01:     -0.052      -2.049
02:     1.238       -2.908
03:     -39.477     23.579
04:     -13.132     7.065
05:     -9.116      4.575
06:     -7.791      3.969
07:     -6.775      3.505
08:     -5.972      3.138
09:     -5.321      2.841
10:     -4.783      2.595
11:     -4.331      2.388
12:     -4.091      2.393
13:     -3.803      2.259
14:     -3.544      2.14
15:     -3.312      2.032
16:     -3.102      1.935
17:     -2.911      1.847
18:     -2.736      1.766
19:     -2.577      1.692
20:     -2.43       1.624
21:     -2.294      1.561
22:     -2.169      1.503
23:     -2.052      1.449
24:     -1.944      1.399
25:     -1.843      1.352
26:     -1.748      1.309
27:     -1.708      1.296
28:     -1.67       1.283
29:     -1.64       1.277
30:     -1.611      1.271
31:     -1.582      1.265
32:     -1.553      1.259
33:     -1.509      1.184
34:     -1.474      1.173
35:     -1.44       1.162
36:     -1.406      1.152
37:     -1.373      1.141
38:     -1.347      1.136
39:     -1.321      1.131
40:     -1.296      1.126