explain these python codes with comments , explain briefly   >>> for iteration_num in range(5): ... correct_answers = 0 ... for idx, sample in enumerate(sample_data): ... input_vector = np.array(sample) ... weights = np.array(weights) ... activation_level = np.dot(input_vector, weights) +\ ... (bias_weight * 1) ... if activation_level > activation_threshold: ... perceptron_output = 1 ... else: ... perceptron_output = 0 ... if perceptron_output == expected_results[idx]: ... correct_answers += 1 ... new_weights = [] ... for i, x in enumerate(sample): ... new_weights.append(weights[i] + (expected_results[idx] -\ ... perceptron_output) * x) ... bias_weight = bias_weight + ((expected_results[idx] -\ ... perceptron_output) * 1) ... weights = np.array(new_weights) ... print('{} correct answers out of 4, for iteration {}'\ ... .format(correct_answers, iteration_num)) 3 correct answers out of 4, for iteration 0 2 correct answers out of 4, for iteration 1 3 correct answers out of 4, for iteration 2 4 correct answers out of 4, for iteration 3 4 correct answers out of 4, for iteration 4

Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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explain these python codes with comments , explain briefly

 

>>> for iteration_num in range(5):

... correct_answers = 0

... for idx, sample in enumerate(sample_data):

... input_vector = np.array(sample)

... weights = np.array(weights)

... activation_level = np.dot(input_vector, weights) +\

... (bias_weight * 1)

... if activation_level > activation_threshold:

... perceptron_output = 1

... else:

... perceptron_output = 0

... if perceptron_output == expected_results[idx]:

... correct_answers += 1

... new_weights = []

... for i, x in enumerate(sample):

... new_weights.append(weights[i] + (expected_results[idx] -\

... perceptron_output) * x)

... bias_weight = bias_weight + ((expected_results[idx] -\

... perceptron_output) * 1)

... weights = np.array(new_weights)

... print('{} correct answers out of 4, for iteration {}'\

... .format(correct_answers, iteration_num))

3 correct answers out of 4, for iteration 0

2 correct answers out of 4, for iteration 1

3 correct answers out of 4, for iteration 2

4 correct answers out of 4, for iteration 3

4 correct answers out of 4, for iteration 4

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