python Perceptron

 

And:

from typing import Any
import numpy as np  
 
class Perception:
 
    def __init__(self,input_length,weights=None,bias=None):
        if weights is None:
            self.weights =np.ones(input_length)*1
        else:
            self.weights=weights
        if bias is None:
            self.bias = -1
        else:
            self.bias=bias
 
    @staticmethod
    def activation_function(x):
        if x > 0:
            return 1
        return 0
    def __call__(self,input_data):
        weighted_input=self.weights*input_data
        weight_sum=weighted_input.sum()+self.bias
        return Perception.activation_function(weight_sum)
 
weights =np.array([1,1])
bias =-1
AND_Gate =Perception(2,weights,bias)    
input_data =[np.array([0,0]) , np.array([0,1]), np.array([1,0]),np.array([1,1]) ]
 
for x in input_data:
    out =AND_Gate(np.array(x))
    print(x,out)

OR:

from typing import Any
import numpy as np  
class Perception:
    
    def __init__(self,input_length,weights=None,bias=None):
        if weights is None:
            self.weights =np.ones(input_length)*1
        else:
            self.weights=weights
        if bias is None:
            self.bias = -1
        else:
            self.bias=bias
    @staticmethod
    def activation_function(x):
        if x > 0:
            return 1
        return 0
    def __call__(self,input_data):
        weighted_input=self.weights*input_data
        weight_sum=weighted_input.sum()+self.bias
        return Perception.activation_function(weight_sum)
    
weights =np.array([1,1])
bias =-0.5
OR_Gate =Perception(2,weights,bias)    
input_data =[np.array([0,0]) , np.array([0,1]), np.array([1,0]),np.array([1,1]) ]
for x in input_data:
    out =OR_Gate(np.array(x))
    print(x,out)