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| import numpy as np | |
| def perceptron(features,labels): | |
| theta=np.array([0]*len(features[0])) | |
| theta0=0 | |
| t=10 | |
| count=1 | |
| sum_theta=theta | |
| sum_theta_0=theta0 | |
| while(t): | |
| t=t-1 | |
| for i in range(len(features)): | |
| if (np.dot(features[i],theta)+theta0)*labels[i]<=0: | |
| #print('mistake') | |
| theta=theta+np.multiply(labels[i],features[i]) | |
| theta0=theta0+labels[i] | |
| sum_theta=sum_theta+theta | |
| sum_theta_0=sum_theta_0_theta_0 | |
| count+=1 | |
| #print(theta) | |
| #We return Average theta value and Average theta0 value | |
| return (list(theta/count),theta0/count) | |
| if __name__=="__main__": | |
| x=[[1,2],[2,3],[3,4],[4,5]] | |
| features=x | |
| y=[1,1,-1,-1] | |
| theta,theta0=perceptron(features,y) | |
| print('theta={},theta0={}'.format(theta,theta0)) |
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