In [1]:

import numpy as np
from sklearn.datasets import load_iris
from sklearn.metrics.pairwise import linear_kernel as sklinear_kernel

Implementation 1¶

brute force

In [2]:

def linear_kernel(X, Y=None):
    if Y is None:
        Y = X
    K = np.zeros((X.shape[0], Y.shape[0]))
    for i in range(X.shape[0]):
        for j in range(Y.shape[0]):
            K[i, j] = np.dot(X[i], Y[j])
    return K

In [3]:

X, _ = load_iris(return_X_y=True)
K1 = linear_kernel(X)
K2 = sklinear_kernel(X)
assert np.allclose(K1, K2)

Implementation 2¶

inner product
similar to scikit-learn

In [4]:

def linear_kernel(X, Y=None):
    if Y is None:
        Y = X
    return np.dot(X, Y.T)

In [5]:

X, _ = load_iris(return_X_y=True)
K1 = linear_kernel(X)
K2 = sklinear_kernel(X)
assert np.allclose(K1, K2)