A piepline for creating polynomial degree 2 kernel matrix for and its vector norm

kernel.py

import numpy as np
X = np.array([[-0.5, 1], [-1, -1.5], [-1.5, 1.5], [1.5, -0.5], [0.5, -0.5]])
y = np.array([-1, 1, 1, 1, -1])
res = np.empty((5, 5))
def k(X):
    for i in range(X.shape[0]):
        for j in range(X.shape[0]):
            res[i][j] = np.dot(X[i], X[j]) ** 2
    return res
print(k(X))

def max_norm(X):
    norms = []
    for i in range(X.shape[0]):
        norms.append(np.linalg.norm(X[i]))
    print(norms)
    return max(norms) ** 2
max_norm = max_norm(X)
print(max_norm)