mirror of
https://github.com/donnemartin/interactive-coding-challenges.git
synced 2024-03-22 13:11:13 +08:00
212 lines
5.2 KiB
Python
212 lines
5.2 KiB
Python
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Challenge Notebook"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 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",
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"\n",
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"* [Constraints](#Constraints)\n",
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"* [Test Cases](#Test-Cases)\n",
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"* [Algorithm](#Algorithm)\n",
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"* [Code](#Code)\n",
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"* [Unit Test](#Unit-Test)\n",
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"* [Solution Notebook](#Solution-Notebook)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Constraints\n",
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"\n",
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"* Can we replace the items once they are placed in the knapsack?\n",
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" * Yes, this is the unbounded knapsack problem\n",
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"* Can we split an item?\n",
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" * No\n",
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"* Can we get an input item with weight of 0 or value of 0?\n",
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" * No\n",
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"* Do we need to return the items that make up the max total value?\n",
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" * No, just the total value\n",
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"* Can we assume the inputs are valid?\n",
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" * No\n",
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"* Are the inputs in sorted order by val/weight?\n",
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" * Yes\n",
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"* Can we assume this fits memory?\n",
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" * Yes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test Cases\n",
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"\n",
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"* items or total weight is None -> Exception\n",
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"* items or total weight is 0 -> 0\n",
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"* General case\n",
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"\n",
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"<pre>\n",
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"total_weight = 8\n",
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"items\n",
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" v | w\n",
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" 0 | 0\n",
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"a 1 | 1\n",
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"b 3 | 2\n",
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"c 7 | 4\n",
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"\n",
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"max value = 14 \n",
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"</pre>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Algorithm\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Code"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class Item(object):\n",
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"\n",
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" def __init__(self, label, value, weight):\n",
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" self.label = label\n",
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" self.value = value\n",
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" self.weight = weight\n",
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"\n",
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" def __repr__(self):\n",
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" return self.label + ' v:' + str(self.value) + ' w:' + str(self.weight)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"class Knapsack(object):\n",
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"\n",
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" def fill_knapsack(self, input_items, total_weight):\n",
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" # TODO: Implement me\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Unit Test"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**The following unit test is expected to fail until you solve the challenge.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# %load test_knapsack_unbounded.py\n",
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"from nose.tools import assert_equal, assert_raises\n",
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"\n",
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"\n",
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"class TestKnapsack(object):\n",
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"\n",
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" def test_knapsack(self):\n",
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" knapsack = Knapsack()\n",
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" assert_raises(TypeError, knapsack.fill_knapsack, None, None)\n",
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" assert_equal(knapsack.fill_knapsack(0, 0), 0)\n",
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" items = []\n",
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" items.append(Item(label='a', value=1, weight=1))\n",
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" items.append(Item(label='b', value=3, weight=2))\n",
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" items.append(Item(label='c', value=7, weight=4))\n",
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" total_weight = 8\n",
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" expected_value = 14\n",
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" results = knapsack.fill_knapsack(items, total_weight)\n",
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" total_weight = 7\n",
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" expected_value = 11\n",
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" results = knapsack.fill_knapsack(items, total_weight)\n",
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" assert_equal(results, expected_value)\n",
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" print('Success: test_knapsack')\n",
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"\n",
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"def main():\n",
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" test = TestKnapsack()\n",
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" test.test_knapsack()\n",
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"\n",
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"\n",
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"if __name__ == '__main__':\n",
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" main()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Solution Notebook\n",
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"\n",
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"Review the [Solution Notebook]() for a discussion on algorithms and code solutions."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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