PYTHON DATASET given x = np.array([i*np.pi/180 for i in range(60,300,4)]) np.random.seed(10) #Setting seed for reproducibility y = 4*x + 7 + np.random.normal(0,3,len(x)) Write a function inspired by sklearn’s polynomial preprocessing: (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) your function should have: degree and include bias parameters only. For this assignment, assume that input is a 1-dimensional numpy array. For example, if an input sample is np.array([a, b]), the degree-2 polynomial features with "include_bias=True" are [1, a, b, a2, b2].
PYTHON DATASET given x = np.array([i*np.pi/180 for i in range(60,300,4)]) np.random.seed(10) #Setting seed for reproducibility y = 4*x + 7 + np.random.normal(0,3,len(x)) Write a function inspired by sklearn’s polynomial preprocessing: (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) your function should have: degree and include bias parameters only. For this assignment, assume that input is a 1-dimensional numpy array. For example, if an input sample is np.array([a, b]), the degree-2 polynomial features with "include_bias=True" are [1, a, b, a2, b2].
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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PYTHON DATASET given
x = np.array([i*np.pi/180 for i in range(60,300,4)]) np.random.seed(10) #Setting seed for reproducibility y = 4*x + 7 + np.random.normal(0,3,len(x))
Write a function inspired by sklearn’s polynomial preprocessing: (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) your function should have: degree and include bias parameters only. For this assignment, assume that input is a 1-dimensional numpy array. For example, if an input sample is np.array([a, b]), the degree-2 polynomial features with "include_bias=True" are [1, a, b, a2, b2].
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