Initial commit
This commit is contained in:
commit
905f3bc7b7
|
|
@ -0,0 +1,477 @@
|
|||
### venv template
|
||||
# Virtualenv
|
||||
# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
|
||||
.Python
|
||||
[Bb]in
|
||||
[Ii]nclude
|
||||
[Ll]ib
|
||||
[Ll]ib64
|
||||
[Ll]ocal
|
||||
[Ss]cripts
|
||||
pyvenv.cfg
|
||||
.venv
|
||||
pip-selfcheck.json
|
||||
|
||||
### TeX template
|
||||
## Core latex/pdflatex auxiliary files:
|
||||
*.aux
|
||||
*.lof
|
||||
*.log
|
||||
*.lot
|
||||
*.fls
|
||||
*.out
|
||||
*.toc
|
||||
*.fmt
|
||||
*.fot
|
||||
*.cb
|
||||
*.cb2
|
||||
.*.lb
|
||||
|
||||
## Intermediate documents:
|
||||
*.dvi
|
||||
*.xdv
|
||||
*-converted-to.*
|
||||
# these rules might exclude image files for figures etc.
|
||||
# *.ps
|
||||
# *.eps
|
||||
# *.pdf
|
||||
|
||||
## Generated if empty string is given at "Please type another file name for output:"
|
||||
.pdf
|
||||
|
||||
## Bibliography auxiliary files (bibtex/biblatex/biber):
|
||||
*.bbl
|
||||
*.bcf
|
||||
*.blg
|
||||
*-blx.aux
|
||||
*-blx.bib
|
||||
*.run.xml
|
||||
|
||||
## Build tool auxiliary files:
|
||||
*.fdb_latexmk
|
||||
*.synctex
|
||||
*.synctex(busy)
|
||||
*.synctex.gz
|
||||
*.synctex.gz(busy)
|
||||
*.pdfsync
|
||||
|
||||
## Build tool directories for auxiliary files
|
||||
# latexrun
|
||||
latex.out/
|
||||
|
||||
## Auxiliary and intermediate files from other packages:
|
||||
# algorithms
|
||||
*.alg
|
||||
*.loa
|
||||
|
||||
# achemso
|
||||
acs-*.bib
|
||||
|
||||
# amsthm
|
||||
*.thm
|
||||
|
||||
# beamer
|
||||
*.nav
|
||||
*.pre
|
||||
*.snm
|
||||
*.vrb
|
||||
|
||||
# changes
|
||||
*.soc
|
||||
|
||||
# comment
|
||||
*.cut
|
||||
|
||||
# cprotect
|
||||
*.cpt
|
||||
|
||||
# elsarticle (documentclass of Elsevier journals)
|
||||
*.spl
|
||||
|
||||
# endnotes
|
||||
*.ent
|
||||
|
||||
# fixme
|
||||
*.lox
|
||||
|
||||
# feynmf/feynmp
|
||||
*.mf
|
||||
*.mp
|
||||
*.t[1-9]
|
||||
*.t[1-9][0-9]
|
||||
*.tfm
|
||||
|
||||
#(r)(e)ledmac/(r)(e)ledpar
|
||||
*.end
|
||||
*.?end
|
||||
*.[1-9]
|
||||
*.[1-9][0-9]
|
||||
*.[1-9][0-9][0-9]
|
||||
*.[1-9]R
|
||||
*.[1-9][0-9]R
|
||||
*.[1-9][0-9][0-9]R
|
||||
*.eledsec[1-9]
|
||||
*.eledsec[1-9]R
|
||||
*.eledsec[1-9][0-9]
|
||||
*.eledsec[1-9][0-9]R
|
||||
*.eledsec[1-9][0-9][0-9]
|
||||
*.eledsec[1-9][0-9][0-9]R
|
||||
|
||||
# glossaries
|
||||
*.acn
|
||||
*.acr
|
||||
*.glg
|
||||
*.glo
|
||||
*.gls
|
||||
*.glsdefs
|
||||
*.lzo
|
||||
*.lzs
|
||||
*.slg
|
||||
*.slo
|
||||
*.sls
|
||||
|
||||
# uncomment this for glossaries-extra (will ignore makeindex's style files!)
|
||||
# *.ist
|
||||
|
||||
# gnuplot
|
||||
*.gnuplot
|
||||
*.table
|
||||
|
||||
# gnuplottex
|
||||
*-gnuplottex-*
|
||||
|
||||
# gregoriotex
|
||||
*.gaux
|
||||
*.glog
|
||||
*.gtex
|
||||
|
||||
# htlatex
|
||||
*.4ct
|
||||
*.4tc
|
||||
*.idv
|
||||
*.lg
|
||||
*.trc
|
||||
*.xref
|
||||
|
||||
# hyperref
|
||||
*.brf
|
||||
|
||||
# knitr
|
||||
*-concordance.tex
|
||||
# TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files
|
||||
# *.tikz
|
||||
*-tikzDictionary
|
||||
|
||||
# listings
|
||||
*.lol
|
||||
|
||||
# luatexja-ruby
|
||||
*.ltjruby
|
||||
|
||||
# makeidx
|
||||
*.idx
|
||||
*.ilg
|
||||
*.ind
|
||||
|
||||
# minitoc
|
||||
*.maf
|
||||
*.mlf
|
||||
*.mlt
|
||||
*.mtc[0-9]*
|
||||
*.slf[0-9]*
|
||||
*.slt[0-9]*
|
||||
*.stc[0-9]*
|
||||
|
||||
# minted
|
||||
_minted*
|
||||
*.pyg
|
||||
|
||||
# morewrites
|
||||
*.mw
|
||||
|
||||
# newpax
|
||||
*.newpax
|
||||
|
||||
# nomencl
|
||||
*.nlg
|
||||
*.nlo
|
||||
*.nls
|
||||
|
||||
# pax
|
||||
*.pax
|
||||
|
||||
# pdfpcnotes
|
||||
*.pdfpc
|
||||
|
||||
# sagetex
|
||||
*.sagetex.sage
|
||||
*.sagetex.py
|
||||
*.sagetex.scmd
|
||||
|
||||
# scrwfile
|
||||
*.wrt
|
||||
|
||||
# svg
|
||||
svg-inkscape/
|
||||
|
||||
# sympy
|
||||
*.sout
|
||||
*.sympy
|
||||
sympy-plots-for-*.tex/
|
||||
|
||||
# pdfcomment
|
||||
*.upa
|
||||
*.upb
|
||||
|
||||
# pythontex
|
||||
*.pytxcode
|
||||
pythontex-files-*/
|
||||
|
||||
# tcolorbox
|
||||
*.listing
|
||||
|
||||
# thmtools
|
||||
*.loe
|
||||
|
||||
# TikZ & PGF
|
||||
*.dpth
|
||||
*.md5
|
||||
*.auxlock
|
||||
|
||||
# titletoc
|
||||
*.ptc
|
||||
|
||||
# todonotes
|
||||
*.tdo
|
||||
|
||||
# vhistory
|
||||
*.hst
|
||||
*.ver
|
||||
|
||||
# easy-todo
|
||||
*.lod
|
||||
|
||||
# xcolor
|
||||
*.xcp
|
||||
|
||||
# xmpincl
|
||||
*.xmpi
|
||||
|
||||
# xindy
|
||||
*.xdy
|
||||
|
||||
# xypic precompiled matrices and outlines
|
||||
*.xyc
|
||||
*.xyd
|
||||
|
||||
# endfloat
|
||||
*.ttt
|
||||
*.fff
|
||||
|
||||
# Latexian
|
||||
TSWLatexianTemp*
|
||||
|
||||
## Editors:
|
||||
# WinEdt
|
||||
*.bak
|
||||
*.sav
|
||||
|
||||
# Texpad
|
||||
.texpadtmp
|
||||
|
||||
# LyX
|
||||
*.lyx~
|
||||
|
||||
# Kile
|
||||
*.backup
|
||||
|
||||
# gummi
|
||||
.*.swp
|
||||
|
||||
# KBibTeX
|
||||
*~[0-9]*
|
||||
|
||||
# TeXnicCenter
|
||||
*.tps
|
||||
|
||||
# auto folder when using emacs and auctex
|
||||
./auto/*
|
||||
*.el
|
||||
|
||||
# expex forward references with \gathertags
|
||||
*-tags.tex
|
||||
|
||||
# standalone packages
|
||||
*.sta
|
||||
|
||||
# Makeindex log files
|
||||
*.lpz
|
||||
|
||||
# xwatermark package
|
||||
*.xwm
|
||||
|
||||
# REVTeX puts footnotes in the bibliography by default, unless the nofootinbib
|
||||
# option is specified. Footnotes are the stored in a file with suffix Notes.bib.
|
||||
# Uncomment the next line to have this generated file ignored.
|
||||
#*Notes.bib
|
||||
|
||||
### Python template
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# poetry
|
||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
|
||||
#poetry.lock
|
||||
|
||||
# pdm
|
||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
||||
#pdm.lock
|
||||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
||||
# in version control.
|
||||
# https://pdm.fming.dev/#use-with-ide
|
||||
.pdm.toml
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
|
||||
|
||||
# Cython debug symbols
|
||||
cython_debug/
|
||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
.idea/
|
||||
|
||||
/UWB-LOS-NLOS-Data-Set/
|
||||
|
|
@ -0,0 +1,269 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# CSC 3105 Project"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "cda961ffb493d00c"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# Importing the libraries\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-02-25T02:29:46.399745088Z",
|
||||
"start_time": "2024-02-25T02:29:46.386566147Z"
|
||||
}
|
||||
},
|
||||
"id": "bcd6cbaa5df10ce8",
|
||||
"execution_count": 39
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Load the data into a pandas dataframe"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "bab890d7b05e347e"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" NLOS RANGE FP_IDX FP_AMP1 FP_AMP2 FP_AMP3 STDEV_NOISE CIR_PWR \\\n",
|
||||
"0 1.0 6.18 749.0 4889.0 13876.0 10464.0 240.0 9048.0 \n",
|
||||
"1 1.0 4.54 741.0 2474.0 2002.0 1593.0 68.0 6514.0 \n",
|
||||
"2 1.0 4.39 744.0 1934.0 2615.0 4114.0 52.0 2880.0 \n",
|
||||
"3 1.0 1.27 748.0 16031.0 17712.0 10420.0 64.0 12855.0 \n",
|
||||
"4 0.0 1.16 743.0 20070.0 19886.0 15727.0 76.0 11607.0 \n",
|
||||
"\n",
|
||||
" MAX_NOISE RXPACC ... CIR1006 CIR1007 CIR1008 CIR1009 CIR1010 \\\n",
|
||||
"0 3668.0 1024.0 ... 818.0 938.0 588.0 277.0 727.0 \n",
|
||||
"1 1031.0 1024.0 ... 289.0 228.0 107.0 487.0 491.0 \n",
|
||||
"2 796.0 1024.0 ... 123.0 281.0 483.0 97.0 272.0 \n",
|
||||
"3 1529.0 323.0 ... 169.0 138.0 219.0 94.0 225.0 \n",
|
||||
"4 2022.0 296.0 ... 87.0 43.0 358.0 308.0 132.0 \n",
|
||||
"\n",
|
||||
" CIR1011 CIR1012 CIR1013 CIR1014 CIR1015 \n",
|
||||
"0 367.0 803.0 819.0 467.0 768.0 \n",
|
||||
"1 404.0 334.0 210.0 102.0 0.0 \n",
|
||||
"2 73.0 125.0 169.0 182.0 0.0 \n",
|
||||
"3 155.0 172.0 278.0 318.0 0.0 \n",
|
||||
"4 131.0 102.0 126.0 163.0 0.0 \n",
|
||||
"\n",
|
||||
"[5 rows x 1031 columns]\n",
|
||||
"Index(['NLOS', 'RANGE', 'FP_IDX', 'FP_AMP1', 'FP_AMP2', 'FP_AMP3',\n",
|
||||
" 'STDEV_NOISE', 'CIR_PWR', 'MAX_NOISE', 'RXPACC',\n",
|
||||
" ...\n",
|
||||
" 'CIR1006', 'CIR1007', 'CIR1008', 'CIR1009', 'CIR1010', 'CIR1011',\n",
|
||||
" 'CIR1012', 'CIR1013', 'CIR1014', 'CIR1015'],\n",
|
||||
" dtype='object', length=1031)\n",
|
||||
"No missing values\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def load_data(dataset_dir):\n",
|
||||
" # Get all file paths in the directory\n",
|
||||
" file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
|
||||
"\n",
|
||||
" # Load and concatenate all dataframes\n",
|
||||
" data = pd.concat([pd.read_csv(file_path) for file_path in file_paths])\n",
|
||||
"\n",
|
||||
" return data\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"data = load_data(DATASET_DIR)\n",
|
||||
"\n",
|
||||
"print(data.head())\n",
|
||||
"\n",
|
||||
"# Print Headers\n",
|
||||
"print(data.columns)\n",
|
||||
"\n",
|
||||
"# Check that there are no missing values\n",
|
||||
"assert data.isnull().sum().sum() == 0\n",
|
||||
"print(\"No missing values\")\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-02-25T02:29:51.084821398Z",
|
||||
"start_time": "2024-02-25T02:29:46.405675293Z"
|
||||
}
|
||||
},
|
||||
"id": "dd9657f5ec6d7754",
|
||||
"execution_count": 40
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"The selected code is performing data standardization, which is a common preprocessing step in many machine learning workflows. \n",
|
||||
"\n",
|
||||
"The purpose of standardization is to transform the data such that it has a mean of 0 and a standard deviation of 1. This is done to ensure that all features have the same scale, which is a requirement for many machine learning algorithms.\n",
|
||||
"\n",
|
||||
"The mathematical formulas used in this process are as follows:\n",
|
||||
"\n",
|
||||
"1. Calculate the mean (μ) of the data:\n",
|
||||
"\n",
|
||||
"$$\n",
|
||||
"\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i\n",
|
||||
"$$\n",
|
||||
"Where:\n",
|
||||
"- $n$ is the number of observations in the data\n",
|
||||
"- $x_i$ is the value of the $i$-th observation\n",
|
||||
"- $\\sum$ denotes the summation over all observations\n",
|
||||
"\n",
|
||||
"2. Standardize the data by subtracting the mean from each observation and dividing by the standard deviation:\n",
|
||||
"\n",
|
||||
"$$\n",
|
||||
"\\text{Data}_i = \\frac{x_i - \\mu}{\\sigma}\n",
|
||||
"$$\n",
|
||||
"Where:\n",
|
||||
"- $\\text{Data}_i$ is the standardized value of the $i$-th observation\n",
|
||||
"- $\\sigma$ is the standard deviation of the data\n",
|
||||
"- $x_i$ is the value of the $i$-th observation\n",
|
||||
"- $\\mu$ is the mean of the data\n",
|
||||
"\n",
|
||||
"The `StandardScaler` class from the `sklearn.preprocessing` module is used to perform this standardization. The `fit_transform` method is used to calculate the mean and standard deviation of the data and then perform the standardization.\n",
|
||||
"\n",
|
||||
"**Note:** By setting the explained variance to 0.95, we are saying that we want to choose the smallest number of principal components such that 95% of the variance in the original data is retained. This means that the transformed data will retain 95% of the information of the original data, while potentially having fewer dimensions.\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "2c13064e20601717"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The number of principle components after PCA is 868\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"\n",
|
||||
"# Standardize the data\n",
|
||||
"data_std = StandardScaler().fit_transform(data)\n",
|
||||
"\n",
|
||||
"# Initialize PCA with the desired explained variance\n",
|
||||
"pca = PCA(0.95)\n",
|
||||
"\n",
|
||||
"# Fit PCA to your data\n",
|
||||
"pca.fit(data_std)\n",
|
||||
"\n",
|
||||
"# Get the number of components\n",
|
||||
"num_components = pca.n_components_\n",
|
||||
"\n",
|
||||
"print(f\"The number of principle components after PCA is {num_components}\")"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-02-25T02:29:58.267018142Z",
|
||||
"start_time": "2024-02-25T02:29:51.084440279Z"
|
||||
}
|
||||
},
|
||||
"id": "7f9bec73a42f7bca",
|
||||
"execution_count": 41
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Perform Dimensionality Reduction with PCA\n",
|
||||
"\n",
|
||||
"We can use the `transform` method of the `PCA` object to project the original data onto the principal components. This will give us the transformed data with the desired number of components."
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"id": "dc9f8c0e194dd07d"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Original number of components: 1031\n",
|
||||
"Number of components after PCA: 868\n",
|
||||
"PCA has successfully reduced the number of components.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Project original data to PC with the highest eigenvalue\n",
|
||||
"data_pca = pca.transform(data_std)\n",
|
||||
"\n",
|
||||
"# Create a dataframe with the principal components\n",
|
||||
"data_pca_df = pd.DataFrame(data_pca, columns=[f\"PC{i}\" for i in range(1, num_components + 1)])\n",
|
||||
"\n",
|
||||
"# Print the number of components in the original and PCA transformed data\n",
|
||||
"print(f\"Original number of components: {data.shape[1]}\")\n",
|
||||
"print(f\"Number of components after PCA: {num_components}\")\n",
|
||||
"\n",
|
||||
"# Compare the number of components in the original and PCA transformed data\n",
|
||||
"if data.shape[1] > num_components:\n",
|
||||
" print(\"PCA has successfully reduced the number of components.\")\n",
|
||||
"elif data.shape[1] < num_components:\n",
|
||||
" print(\"Unexpectedly, PCA has increased the number of components.\")\n",
|
||||
"else:\n",
|
||||
" print(\"The number of components remains unchanged after PCA.\")"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-02-25T02:29:59.029369440Z",
|
||||
"start_time": "2024-02-25T02:29:58.266576678Z"
|
||||
}
|
||||
},
|
||||
"id": "96c62c50f8734a01",
|
||||
"execution_count": 42
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# CSC 3105 Project
|
||||
|
||||
This project involves data preprocessing and dimensionality reduction using Principal Component Analysis (PCA). The data is first standardized to have a mean of 0 and a standard deviation of 1. Then, PCA is applied to reduce the dimensionality of the data while retaining 95% of the original variance.
|
||||
|
||||
## File Structure
|
||||
|
||||
The project consists of the following files and directories:
|
||||
|
||||
- `Project.ipynb`: This is the main Jupyter notebook file where all the data processing.
|
||||
- `./UWB-LOS-NLOS-Data-Set/dataset`: This directory contains the dataset used in the project.
|
||||
|
||||
# Installation and Setup
|
||||
|
||||
```bash
|
||||
git clone <REPO_URL>
|
||||
cd <REPO_NAME>
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
Loading…
Reference in New Issue