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https://github.com/donnemartin/interactive-coding-challenges
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Add k max profit challenge
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Donne Martin
240
recursion_dynamic/max_profit_k/max_profit_challenge.ipynb
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240
recursion_dynamic/max_profit_k/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 on each consecutive day, determine the max profits with k transactions.\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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"* Is k the number of sell transactions?\n",
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" * Yes\n",
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"* Can we assume the prices input is an array of ints?\n",
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" * Yes\n",
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"* Can we assume the inputs are valid?\n",
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" * No\n",
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"* If the prices are all decreasing and there is no opportunity to make a profit, do we just return 0?\n",
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" * Yes\n",
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"* Should the output be the max profit and days to buy and sell?\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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"<pre>\n",
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"* Prices: None or k: None -> None\n",
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"* Prices: [] or k <= 0 -> []\n",
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"* Prices: [0, -1, -2, -3, -4, -5]\n",
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" * (max profit, list of transactions)\n",
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" * (0, [])\n",
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"* Prices: [2, 5, 7, 1, 4, 3, 1, 3] k: 3\n",
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" * (max profit, list of transactions)\n",
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" * (10, [Type.SELL day: 7 price: 3, \n",
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" Type.BUY day: 6 price: 1, \n",
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" Type.SELL day: 4 price: 4, \n",
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" Type.BUY day: 3 price: 1, \n",
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" Type.SELL day: 2 price: 7, \n",
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" Type.BUY day: 0 price: 2])\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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"from enum import Enum # Python 2 users: Run pip install enum34\n",
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"\n",
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"\n",
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"class Type(Enum):\n",
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" SELL = 0\n",
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" BUY = 1\n",
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"\n",
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"\n",
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"class Transaction(object):\n",
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"\n",
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" def __init__(self, type, day, price):\n",
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" self.type = type\n",
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" self.day = day\n",
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" self.price = price\n",
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"\n",
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" def __eq__(self, other):\n",
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" return self.type == other.type and \\\n",
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" self.day == other.day and \\\n",
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" self.price == other.price\n",
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"\n",
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" def __repr__(self):\n",
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" return str(self.type) + ' day: ' + \\\n",
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" str(self.day) + ' price: ' + \\\n",
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" str(self.price)"
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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 StockTrader(object):\n",
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"\n",
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" def find_max_profit(self, prices, k):\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\n",
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"from nose.tools import assert_raises\n",
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"from nose.tools import assert_true\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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" stock_trader = StockTrader()\n",
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" assert_raises(TypeError, stock_trader.find_max_profit, None, None)\n",
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" assert_equal(stock_trader.find_max_profit(prices=[], k=0), [])\n",
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" prices = [5, 4, 3, 2, 1]\n",
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" k = 3\n",
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" assert_equal(stock_trader.find_max_profit(prices, k), (0, []))\n",
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" prices = [2, 5, 7, 1, 4, 3, 1, 3]\n",
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" profit, transactions = stock_trader.find_max_profit(prices, k)\n",
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" assert_equal(profit, 10)\n",
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" assert_true(Transaction(Type.SELL,\n",
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" day=7,\n",
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" price=3) in transactions)\n",
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" assert_true(Transaction(Type.BUY,\n",
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" day=6,\n",
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" price=1) in transactions)\n",
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" assert_true(Transaction(Type.SELL,\n",
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" day=4,\n",
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" price=4) in transactions)\n",
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" assert_true(Transaction(Type.BUY,\n",
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" day=3,\n",
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" price=1) in transactions)\n",
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" assert_true(Transaction(Type.SELL,\n",
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" day=2,\n",
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" price=7) in transactions)\n",
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" assert_true(Transaction(Type.BUY,\n",
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" day=0,\n",
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" price=2) in transactions)\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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