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import numpy as np
class Perceptron:
    
    def __init__(self, input_length, weights=None):
        if weights is None:
            self.weights = np.ones(input_length) * 0.5
        else:
            self.weights = weights
        
    @staticmethod
    def unit_step_function(x):
        if x > 0.5:
            return 1
        return 0
        
    def __call__(self, in_data):
        weighted_input = self.weights * in_data
        weighted_sum = weighted_input.sum()
        return Perceptron.unit_step_function(weighted_sum)
    
p = Perceptron(-20, np.array([30, 30]))
for x in [np.array([0, 0]), np.array([0, 1]), 
          np.array([1, 0]), np.array([1, 1])]:
    y = p(np.array(x))
    print(x, y)



import numpy as np
import matplotlib.pyplot as plt

# function to plot graph for binary inputs
def plot_graph(data, idx_subplot, title):
	xs = np.repeat(range(len(data)), 2)
	ys = np.repeat(data, 2)
	xs = xs[1:]
	ys = ys[:-1]
	plt.subplot(5,1,idx_subplot)
	plt.title(title)
	plt.plot(xs, ys)
	plt.ylim(-0.5, 1.5)


    

    




[0 0] 0
[0 1] 1
[1 0] 1
[1 1] 1

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