{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Given a knapsack with a total weight capacity and a list of items with weight w(i) and value v(i), determine the max total value you can carry.\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Can we replace the items once they are placed in the knapsack?\n", " * Yes, this is the unbounded knapsack problem\n", "* Can we split an item?\n", " * No\n", "* Can we get an input item with weight of 0 or value of 0?\n", " * No\n", "* Do we need to return the items that make up the max total value?\n", " * No, just the total value\n", "* Can we assume the inputs are valid?\n", " * No\n", "* Are the inputs in sorted order by val/weight?\n", " * Yes\n", "* Can we assume this fits memory?\n", " * Yes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* items or total weight is None -> Exception\n", "* items or total weight is 0 -> 0\n", "* General case\n", "\n", "
\n", "total_weight = 8\n", "items\n", " v | w\n", " 0 | 0\n", "a 1 | 1\n", "b 3 | 2\n", "c 7 | 4\n", "\n", "max value = 14 \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Refer to the [Solution Notebook](). If you are stuck and need a hint, the solution notebook's algorithm discussion might be a good place to start." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Item(object):\n", "\n", " def __init__(self, label, value, weight):\n", " self.label = label\n", " self.value = value\n", " self.weight = weight\n", "\n", " def __repr__(self):\n", " return self.label + ' v:' + str(self.value) + ' w:' + str(self.weight)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Knapsack(object):\n", "\n", " def fill_knapsack(self, input_items, total_weight):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following unit test is expected to fail until you solve the challenge.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load test_knapsack_unbounded.py\n", "from nose.tools import assert_equal, assert_raises\n", "\n", "\n", "class TestKnapsack(object):\n", "\n", " def test_knapsack(self):\n", " knapsack = Knapsack()\n", " assert_raises(TypeError, knapsack.fill_knapsack, None, None)\n", " assert_equal(knapsack.fill_knapsack(0, 0), 0)\n", " items = []\n", " items.append(Item(label='a', value=1, weight=1))\n", " items.append(Item(label='b', value=3, weight=2))\n", " items.append(Item(label='c', value=7, weight=4))\n", " total_weight = 8\n", " expected_value = 14\n", " results = knapsack.fill_knapsack(items, total_weight)\n", " total_weight = 7\n", " expected_value = 11\n", " results = knapsack.fill_knapsack(items, total_weight)\n", " assert_equal(results, expected_value)\n", " print('Success: test_knapsack')\n", "\n", "def main():\n", " test = TestKnapsack()\n", " test.test_knapsack()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook]() for a discussion on algorithms and code solutions." ] } ], "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }