mirror of
https://github.com/donnemartin/interactive-coding-challenges.git
synced 2024-03-22 13:11:13 +08:00
Add max profit challenge
This commit is contained in:
Donne Martin
parent
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commit
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0
online_judges/max_profit/__init__.py
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0
online_judges/max_profit/__init__.py
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175
online_judges/max_profit/max_profit_challenge.ipynb
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175
online_judges/max_profit/max_profit_challenge.ipynb
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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 list of stock prices, find the max profit from 1 buy and 1 sell.\n",
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"\n",
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"See the [LeetCode](https://leetcode.com/problems/) problem page.\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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"* Are all prices positive ints?\n",
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" * Yes\n",
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"* Is the output an int?\n",
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" * Yes\n",
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"* If profit is negative, do we return the smallest negative loss?\n",
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" * Yes\n",
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"* If there are less than two prices, what do we return?\n",
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" * Exception\n",
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"* Can we assume the inputs are valid?\n",
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" * No\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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"* None -> TypeError\n",
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"* Zero or one price -> ValueError\n",
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"* No profit\n",
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" * [8, 5, 3, 2, 1] -> -1\n",
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"* General case\n",
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" * [5, 3, 7, 4, 2, 6, 9] -> 7"
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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": false
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},
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"outputs": [],
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"source": [
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"class Solution(object):\n",
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"\n",
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" def find_max_profit(self, prices):\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_max_profit.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 TestMaxProfit(object):\n",
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"\n",
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" def test_max_profit(self):\n",
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" solution = Solution()\n",
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" assert_raises(TypeError, solution.find_max_profit, None)\n",
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" assert_raises(ValueError, solution.find_max_profit, [])\n",
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" assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1)\n",
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" assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)\n",
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" print('Success: test_max_profit')\n",
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"\n",
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"\n",
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"def main():\n",
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" test = TestMaxProfit()\n",
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" test.test_max_profit()\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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207
online_judges/max_profit/max_profit_solution.ipynb
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online_judges/max_profit/max_profit_solution.ipynb
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@ -0,0 +1,207 @@
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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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"# 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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"## Problem: Given a list of stock prices, find the max profit from 1 buy and 1 sell.\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)"
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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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"* Are all prices positive ints?\n",
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" * Yes\n",
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"* Is the output an int?\n",
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" * Yes\n",
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"* If profit is negative, do we return the smallest negative loss?\n",
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" * Yes\n",
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"* If there are less than two prices, what do we return?\n",
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" * Exception\n",
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"* Can we assume the inputs are valid?\n",
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" * No\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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"* None -> TypeError\n",
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"* Zero or one price -> ValueError\n",
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"* No profit\n",
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" * [8, 5, 3, 2, 1] -> -1\n",
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"* General case\n",
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" * [5, 3, 7, 4, 2, 6, 9] -> 7"
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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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"We'll use a greedy approach and iterate through the prices once.\n",
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"\n",
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"* Loop through the prices\n",
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" * Update current profit (price = min_price)\n",
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" * Update the min price\n",
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" * Update the max profit\n",
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"* Return max profit\n",
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"\n",
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"Complexity:\n",
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"* Time: O(n)\n",
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"* Space: O(1)"
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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": 1,
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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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"import sys\n",
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"\n",
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"\n",
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"class Solution(object):\n",
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"\n",
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" def find_max_profit(self, prices):\n",
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" if prices is None:\n",
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" raise TypeError('prices cannot be None')\n",
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" if len(prices) < 2:\n",
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" raise ValueError('prices must have at least two values')\n",
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" min_price = prices[0]\n",
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" max_profit = -sys.maxsize\n",
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" for index, price in enumerate(prices):\n",
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" if index == 0:\n",
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" continue\n",
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" profit = price - min_price\n",
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" min_price = min(price, min_price)\n",
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" max_profit = max(profit, max_profit)\n",
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" return max_profit"
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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": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting test_max_profit.py\n"
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]
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}
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],
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"source": [
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"%%writefile test_max_profit.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 TestMaxProfit(object):\n",
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"\n",
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" def test_max_profit(self):\n",
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" solution = Solution()\n",
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" assert_raises(TypeError, solution.find_max_profit, None)\n",
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" assert_raises(ValueError, solution.find_max_profit, [])\n",
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" assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1)\n",
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" assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)\n",
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" print('Success: test_max_profit')\n",
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"\n",
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"\n",
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"def main():\n",
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" test = TestMaxProfit()\n",
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" test.test_max_profit()\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": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Success: test_max_profit\n"
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]
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}
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],
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"source": [
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"%run -i test_max_profit.py"
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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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21
online_judges/max_profit/test_max_profit.py
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21
online_judges/max_profit/test_max_profit.py
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@ -0,0 +1,21 @@
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from nose.tools import assert_equal, assert_raises
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class TestMaxProfit(object):
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def test_max_profit(self):
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solution = Solution()
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assert_raises(TypeError, solution.find_max_profit, None)
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assert_raises(ValueError, solution.find_max_profit, [])
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assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1)
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assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)
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print('Success: test_max_profit')
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def main():
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test = TestMaxProfit()
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test.test_max_profit()
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if __name__ == '__main__':
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main()
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