{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.datasets import load_iris\n", "from sklearn.metrics.pairwise import linear_kernel as sklinear_kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation 1\n", "- brute force" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def linear_kernel(X, Y=None):\n", " if Y is None:\n", " Y = X\n", " K = np.zeros((X.shape[0], Y.shape[0]))\n", " for i in range(X.shape[0]):\n", " for j in range(Y.shape[0]):\n", " K[i, j] = np.dot(X[i], Y[j])\n", " return K" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X, _ = load_iris(return_X_y=True)\n", "K1 = linear_kernel(X)\n", "K2 = sklinear_kernel(X)\n", "assert np.allclose(K1, K2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation 2\n", "- inner product\n", "- similar to scikit-learn" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def linear_kernel(X, Y=None):\n", " if Y is None:\n", " Y = X\n", " return np.dot(X, Y.T)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X, _ = load_iris(return_X_y=True)\n", "K1 = linear_kernel(X)\n", "K2 = sklinear_kernel(X)\n", "assert np.allclose(K1, K2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }