diff --git a/online_judges/max_profit/__init__.py b/online_judges/max_profit/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/online_judges/max_profit/max_profit_challenge.ipynb b/online_judges/max_profit/max_profit_challenge.ipynb new file mode 100644 index 0000000..3c0e540 --- /dev/null +++ b/online_judges/max_profit/max_profit_challenge.ipynb @@ -0,0 +1,175 @@ +{ + "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 list of stock prices, find the max profit from 1 buy and 1 sell.\n", + "\n", + "See the [LeetCode](https://leetcode.com/problems/) problem page.\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", + "* Are all prices positive ints?\n", + " * Yes\n", + "* Is the output an int?\n", + " * Yes\n", + "* If profit is negative, do we return the smallest negative loss?\n", + " * Yes\n", + "* If there are less than two prices, what do we return?\n", + " * Exception\n", + "* Can we assume the inputs are valid?\n", + " * No\n", + "* Can we assume this fits memory?\n", + " * Yes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Cases\n", + "\n", + "* None -> TypeError\n", + "* Zero or one price -> ValueError\n", + "* No profit\n", + " * [8, 5, 3, 2, 1] -> -1\n", + "* General case\n", + " * [5, 3, 7, 4, 2, 6, 9] -> 7" + ] + }, + { + "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": false + }, + "outputs": [], + "source": [ + "class Solution(object):\n", + "\n", + " def find_max_profit(self, prices):\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_max_profit.py\n", + "from nose.tools import assert_equal, assert_raises\n", + "\n", + "\n", + "class TestMaxProfit(object):\n", + "\n", + " def test_max_profit(self):\n", + " solution = Solution()\n", + " assert_raises(TypeError, solution.find_max_profit, None)\n", + " assert_raises(ValueError, solution.find_max_profit, [])\n", + " assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1)\n", + " assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)\n", + " print('Success: test_max_profit')\n", + "\n", + "\n", + "def main():\n", + " test = TestMaxProfit()\n", + " test.test_max_profit()\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 +} diff --git a/online_judges/max_profit/max_profit_solution.ipynb b/online_judges/max_profit/max_profit_solution.ipynb new file mode 100644 index 0000000..933249d --- /dev/null +++ b/online_judges/max_profit/max_profit_solution.ipynb @@ -0,0 +1,207 @@ +{ + "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": [ + "# Solution Notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem: Given a list of stock prices, find the max profit from 1 buy and 1 sell.\n", + "\n", + "* [Constraints](#Constraints)\n", + "* [Test Cases](#Test-Cases)\n", + "* [Algorithm](#Algorithm)\n", + "* [Code](#Code)\n", + "* [Unit Test](#Unit-Test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constraints\n", + "\n", + "* Are all prices positive ints?\n", + " * Yes\n", + "* Is the output an int?\n", + " * Yes\n", + "* If profit is negative, do we return the smallest negative loss?\n", + " * Yes\n", + "* If there are less than two prices, what do we return?\n", + " * Exception\n", + "* Can we assume the inputs are valid?\n", + " * No\n", + "* Can we assume this fits memory?\n", + " * Yes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Cases\n", + "\n", + "* None -> TypeError\n", + "* Zero or one price -> ValueError\n", + "* No profit\n", + " * [8, 5, 3, 2, 1] -> -1\n", + "* General case\n", + " * [5, 3, 7, 4, 2, 6, 9] -> 7" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algorithm\n", + "\n", + "We'll use a greedy approach and iterate through the prices once.\n", + "\n", + "* Loop through the prices\n", + " * Update current profit (price = min_price)\n", + " * Update the min price\n", + " * Update the max profit\n", + "* Return max profit\n", + "\n", + "Complexity:\n", + "* Time: O(n)\n", + "* Space: O(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "\n", + "class Solution(object):\n", + "\n", + " def find_max_profit(self, prices):\n", + " if prices is None:\n", + " raise TypeError('prices cannot be None')\n", + " if len(prices) < 2:\n", + " raise ValueError('prices must have at least two values')\n", + " min_price = prices[0]\n", + " max_profit = -sys.maxsize\n", + " for index, price in enumerate(prices):\n", + " if index == 0:\n", + " continue\n", + " profit = price - min_price\n", + " min_price = min(price, min_price)\n", + " max_profit = max(profit, max_profit)\n", + " return max_profit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unit Test" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting test_max_profit.py\n" + ] + } + ], + "source": [ + "%%writefile test_max_profit.py\n", + "from nose.tools import assert_equal, assert_raises\n", + "\n", + "\n", + "class TestMaxProfit(object):\n", + "\n", + " def test_max_profit(self):\n", + " solution = Solution()\n", + " assert_raises(TypeError, solution.find_max_profit, None)\n", + " assert_raises(ValueError, solution.find_max_profit, [])\n", + " assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1)\n", + " assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7)\n", + " print('Success: test_max_profit')\n", + "\n", + "\n", + "def main():\n", + " test = TestMaxProfit()\n", + " test.test_max_profit()\n", + "\n", + "\n", + "if __name__ == '__main__':\n", + " main()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success: test_max_profit\n" + ] + } + ], + "source": [ + "%run -i test_max_profit.py" + ] + } + ], + "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 +} diff --git a/online_judges/max_profit/test_max_profit.py b/online_judges/max_profit/test_max_profit.py new file mode 100644 index 0000000..26e9b12 --- /dev/null +++ b/online_judges/max_profit/test_max_profit.py @@ -0,0 +1,21 @@ +from nose.tools import assert_equal, assert_raises + + +class TestMaxProfit(object): + + def test_max_profit(self): + solution = Solution() + assert_raises(TypeError, solution.find_max_profit, None) + assert_raises(ValueError, solution.find_max_profit, []) + assert_equal(solution.find_max_profit([8, 5, 3, 2, 1]), -1) + assert_equal(solution.find_max_profit([5, 3, 7, 4, 2, 6, 9]), 7) + print('Success: test_max_profit') + + +def main(): + test = TestMaxProfit() + test.test_max_profit() + + +if __name__ == '__main__': + main() \ No newline at end of file