Solution
Sandeep Kumar answered on
Nov 24 2021
{
"cells": [
{
"cell_type": "markdown",
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"source": [
"**COMP3670 Assignment 5 - Matrix Decomposition & Dimensionality Reduction**\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Enter Your Student ID:**\n",
"\n",
"**Your Name:** \n",
" \n",
"\n",
"**Submit:** You can write your answers in this file and submit a single Jupyter Notebook file (.ipynb) on Wattle. Rename this file with your student number as 'uXXXXXXX.ipynb'. Otherwise, you can write your programming questions in this file, and submit two files, 'uXXXXXXX.ipynb' for programming and 'uXXXXXXX.pdf' for theory. Please submit them separately instead of a zip file.\n",
" \n",
"**Enter Discussion Partner IDs Below:**\n",
"- \n",
"- \n",
"- \n",
" \n",
"\n",
"**Programming Section**\n",
"- 1 = 10%\n",
"- 2 = 15%\n",
"- 3 = 30%\n",
"- 4 = 10%\n",
"- 5 = 20%\n",
"- 6 = 15%"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"\n",
"**PROGRAMMING SECTION**\n",
"---\n",
"\n",
"For all of the following, program the solution yourself. Don't just call a li
ary function that does the whole question for you, or you'll get zero (no, that doesn't mean you can't use any li
ary functions, but it does mean that you have to show you understand how to compute the answer yourself).\n",
"\n",
"**All written answers** should be between 50 and 500 words. If you can describe all the necessary information in 50 words, that's better. However, you'll only be graded on whether you describe the necessary ideas.\n",
"\n",
"\n",
"-----------\n",
"\n",
" **TASK 0.1:** You know the drill. Import Numpy and PyPlot. We're also going to generate a dataset.\n",
"\n",
"\n",
"-----------"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D #This is for 3d scatter plots.\n",
"import math\n",
"import random\n",
"from scipy.stats import multivariate_normal\n",
"import os\n",
"from matplotlib.pyplot import imread\n",
"np.random.seed(13579201)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(77760, 135)\n"
]
},
{
"data": {
"image/png":...