mirror of
https://github.com/donnemartin/interactive-coding-challenges.git
synced 2024-03-22 13:11:13 +08:00
Add math ops challenge
This commit is contained in:
Donne Martin
parent
3395619d6e
commit
260ad2edc2
0
math_probability/math_ops/__init__.py
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0
math_probability/math_ops/__init__.py
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190
math_probability/math_ops/math_ops_challenge.ipynb
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190
math_probability/math_ops/math_ops_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: Create a class with an insert method to insert an int to a list. It should also support calculating the max, min, mean, and mode in O(1).\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 assume the inputs are valid?\n",
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" * No\n",
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"* Is there a range of inputs?\n",
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" * 0 <= item <= 100\n",
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"* Should mean return a float?\n",
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" * Yes\n",
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"* Should the other results return an int?\n",
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" * Yes\n",
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"* If there are multiple modes, what do we return?\n",
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" * Any of the modes\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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"* [] -> ValueError\n",
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"* [5, 2, 7, 9, 9, 2, 9, 4, 3, 3, 2]\n",
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" * max: 9\n",
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" * min: 2\n",
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" * mean: 55\n",
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" * mode: 9 or 2"
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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 __init__(self, upper_limit=100):\n",
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" # TODO: Implement me\n",
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" pass\n",
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"\n",
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" def insert(self, val):\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_math_ops.py\n",
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"from nose.tools import assert_equal, assert_true, assert_raises\n",
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"\n",
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"\n",
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"class TestMathOps(object):\n",
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"\n",
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" def test_math_ops(self):\n",
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" solution = Solution()\n",
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" assert_raises(TypeError, solution.insert, None)\n",
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" solution.insert(5)\n",
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" solution.insert(2)\n",
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" solution.insert(7)\n",
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" solution.insert(9)\n",
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" solution.insert(9)\n",
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" solution.insert(2)\n",
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" solution.insert(9)\n",
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" solution.insert(4)\n",
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" solution.insert(3)\n",
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" solution.insert(3)\n",
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" solution.insert(2)\n",
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" assert_equal(solution.max, 9)\n",
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" assert_equal(solution.min, 2)\n",
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" assert_equal(solution.mean, 5)\n",
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" assert_true(solution.mode in (2, 92))\n",
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" print('Success: test_math_ops')\n",
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"\n",
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"\n",
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"def main():\n",
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" test = TestMathOps()\n",
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" test.test_math_ops()\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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236
math_probability/math_ops/math_ops_solution.ipynb
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math_probability/math_ops/math_ops_solution.ipynb
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@ -0,0 +1,236 @@
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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: Create a class with an insert method to insert an int to a list. It should also support calculating the max, min, mean, and mode in O(1).\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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"* Can we assume the inputs are valid?\n",
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" * No\n",
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"* Is there a range of inputs?\n",
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" * 0 <= item <= 100\n",
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"* Should mean return a float?\n",
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" * Yes\n",
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"* Should the other results return an int?\n",
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" * Yes\n",
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"* If there are multiple modes, what do we return?\n",
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" * Any of the modes\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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"* [] -> ValueError\n",
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"* [5, 2, 7, 9, 9, 2, 9, 4, 3, 3, 2]\n",
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" * max: 9\n",
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" * min: 2\n",
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" * mean: 55\n",
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" * mode: 9 or 2"
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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 init our max and min to None. Alternatively, we can init them to -sys.maxsize and sys.maxsize, respectively.\n",
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"* For mean, we'll keep track of the number of items we have inserted so far, as well as the running sum.\n",
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"* For mode, we'll keep track of the current mode and an array with the size of the given upper limit\n",
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" * Each element in the array will be init to 0\n",
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" * Each time we insert, we'll increment the element corresponding to the inserted item's value\n",
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"* On each insert:\n",
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" * Update the max and min\n",
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" * Update the mean by calculating running_sum / num_items\n",
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" * Update the mode by comparing the mode array's value with the current mode\n",
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"\n",
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"Complexity:\n",
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"* Time: O(1)\n",
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"* Space: O(1), we are treating the 101 element array as a constant O(1), we could also see this as O(k)"
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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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"from __future__ import division\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 __init__(self, upper_limit=100):\n",
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" self.max = None\n",
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" self.min = None\n",
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" # Mean\n",
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" self.num_items = 0\n",
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" self.running_sum = 0\n",
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" self.mean = None\n",
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" # Mode\n",
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" self.array = [0] * (upper_limit + 1)\n",
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" self.mode_ocurrences = 0\n",
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" self.mode = None\n",
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"\n",
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" def insert(self, val):\n",
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" if val is None:\n",
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" raise TypeError('val cannot be None')\n",
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" if self.max is None or val > self.max:\n",
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" self.max = val\n",
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" if self.min is None or val < self.min:\n",
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" self.min = val\n",
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" # Calculate the mean\n",
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" self.num_items += 1\n",
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" self.running_sum += val\n",
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" self.mean = self.running_sum / self.num_items\n",
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" # Calculate the mode\n",
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" self.array[val] += 1\n",
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" if self.array[val] > self.mode_ocurrences:\n",
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" self.mode_ocurrences = self.array[val]\n",
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" self.mode = val"
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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_math_ops.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_math_ops.py\n",
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"from nose.tools import assert_equal, assert_true, assert_raises\n",
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"\n",
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"\n",
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"class TestMathOps(object):\n",
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"\n",
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" def test_math_ops(self):\n",
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" solution = Solution()\n",
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" assert_raises(TypeError, solution.insert, None)\n",
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" solution.insert(5)\n",
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" solution.insert(2)\n",
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" solution.insert(7)\n",
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" solution.insert(9)\n",
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" solution.insert(9)\n",
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" solution.insert(2)\n",
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" solution.insert(9)\n",
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" solution.insert(4)\n",
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" solution.insert(3)\n",
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" solution.insert(3)\n",
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" solution.insert(2)\n",
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" assert_equal(solution.max, 9)\n",
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" assert_equal(solution.min, 2)\n",
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" assert_equal(solution.mean, 5)\n",
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" assert_true(solution.mode in (2, 9))\n",
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" print('Success: test_math_ops')\n",
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"\n",
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"\n",
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"def main():\n",
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" test = TestMathOps()\n",
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" test.test_math_ops()\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_math_ops\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_math_ops.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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33
math_probability/math_ops/test_math_ops.py
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33
math_probability/math_ops/test_math_ops.py
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@ -0,0 +1,33 @@
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from nose.tools import assert_equal, assert_true, assert_raises
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class TestMathOps(object):
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def test_math_ops(self):
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solution = Solution()
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assert_raises(TypeError, solution.insert, None)
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solution.insert(5)
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solution.insert(2)
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solution.insert(7)
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solution.insert(9)
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solution.insert(9)
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solution.insert(2)
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solution.insert(9)
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solution.insert(4)
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solution.insert(3)
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solution.insert(3)
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solution.insert(2)
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assert_equal(solution.max, 9)
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assert_equal(solution.min, 2)
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assert_equal(solution.mean, 5)
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assert_true(solution.mode in (2, 9))
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print('Success: test_math_ops')
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def main():
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test = TestMathOps()
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test.test_math_ops()
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if __name__ == '__main__':
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main()
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