Perceptron trick code:
from sklearn.datasets import make_classification
import numpy as np
X, y = make_classification(n_samples=100, n_features=2, n_informative=1,n_redundant=0,
n_classes=2, n_clusters_per_class=1, random_state=41,hypercube=False,class_sep=10)
import matplotlib.pyplot as plt
plt.figure(figsize=(10,6))
plt.scatter(X[:,0],X[:,1],c=y,cmap='winter',s=100)
def perceptron(X,y):
X = np.insert(X,0,1,axis=1)
weights = np.ones(X.shape[1])
lr = 0.1
for i in range(1000):
j = np.random.randint(0,100)
y_hat = step(np.dot(X[j],weights))
weights = weights + lr*(y[j]-y_hat)*X[j]
return weights[0],weights[1:]
def step(z):
return 1 if z>0 else 0
intercept_,coef_ = perceptron(X,y)
print(coef_)
print(intercept_)
m = -(coef_[0]/coef_[1])
b = -(intercept_/coef_[1])
x_input = np.linspace(-3,3,100)
y_input = m*x_input + b
plt.figure(figsize=(10,6))
plt.plot(x_input,y_input,color='red',linewidth=3)
plt.scatter(X[:,0],X[:,1],c=y,cmap='winter',s=100)
plt.ylim(-3,2)
