{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "HW 6", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name":...

Q2 and Q3


{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "HW 6", "provenance": [], "collapsed_sections": [] }, "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": "3.7.3" }, "test_info": { "id": "4adb3d5055da02e7ae8251ca99f4acfa901ab256" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "GTECSaaF2HIz" }, "source": [ "Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\\rightarrow$Run All).\n", "\n", "Make sure you fill in any place that says `YOUR CODE HERE` or \"YOUR ANSWER HERE\", as well as your name and collaborators below:" ] }, { "cell_type": "code", "metadata": { "id": "FgdGPJGQ2HI0" }, "source": [ "NAME = \"\"\n", "COLLABORATORS = \"\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "hdheqpHA2HI4" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "015255c8d63a81a6277731d085451e10", "grade": false, "grade_id": "cell-23f58e80afda25d4", "locked": true, "schema_version": 1, "solution": false }, "id": "q5_8md-x2HI5" }, "source": [ "# Homework 6: Stream Averages and Motion Detection\n", "\n", "## CSE 30 Fall 2020\n", "\n", "Copyright Luca de Alfaro, 2019-20. \n", "License: [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/)." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "e42300239388933dcf5ae54de0207866", "grade": false, "grade_id": "cell-ed51b034c970c018", "locked": true, "schema_version": 1, "solution": false }, "id": "HB7EBYTS2HI6" }, "source": [ "For how to work on this homework assignment, please refer to the instructions posted on Canvas. \n", "\n", "## Submission\n", "\n", "* **First, remove the output, or the notebook will be too large:**\n", "\n", " Edit > Clear all outputs\n", " \n", " \n", "* Then, download the .ipynb file:\n", "\n", " File > Download .ipynb\n", " \n", " \n", "* Finally, [submit the .ipynb to this Google Form](https://docs.google.com/forms/d/e/1FAIpQLSfUqr_ibrn1NKW8hKVT3eoomMogY6Q4kOJ7z2HfL0takEvrVw/viewform?usp=sf_link).\n", "\n", "Deadline: Wednesday November 4, 11pm (check on Canvas for updated information)." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "2177f64fe3d728669a088c66163e0e5e", "grade": false, "grade_id": "cell-dd3c62597369770a", "locked": true, "schema_version": 1, "solution": false }, "id": "8XIJ_qZs2HI7" }, "source": [ "## About this homework\n", "\n", "This homework notebook has many cells, as it is derived from the chapter, but there are only three questions. \n", "Each question is marked\n", "\n", " ### Question n:\n", " \n", "for $n = 1, 2, 3$. \n", "The questions are: \n", "\n", "* Implementing a sliding window averagerator\n", "* Implementing a class to clean data streams\n", "* Implementing a class to perform motion detection." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "232d22754eec312f64dbf32844db108c", "grade": false, "grade_id": "cell-e435450e249172c7", "locked": true, "schema_version": 1, "solution": false }, "id": "5_tJZtWP2HI8" }, "source": [ "Suppose you have a series of numbers, and you need to compute their average and standard deviation. What is a good way for doing this? \n", "The obvious way is to use the [numpy library](https://www.numpy.org), which offers a wealth of functions to operate on matrices, arrays, and much more. \n", "Numpy is one of the fundamental packages of Python, and you would be well advised to browse its documentation and familiarize yourself with what it can do. \n", "With numpy, we can compute average and standard deviation of a list of numbers very simply: \n" ] }, { "cell_type": "code", "metadata": { "deletable": false, "editable": false,