From 40e5144d53a8e2558870a00450acc2a66457b204 Mon Sep 17 00:00:00 2001
From: MadhuraWani803 <103093329+MadhuraWani803@users.noreply.github.com>
Date: Sat, 25 Nov 2023 14:04:34 -0700
Subject: [PATCH] Delete Phase 3/phase3_task0a.ipynb

---
 Phase 3/phase3_task0a.ipynb | 124 ------------------------------------
 1 file changed, 124 deletions(-)
 delete mode 100644 Phase 3/phase3_task0a.ipynb

diff --git a/Phase 3/phase3_task0a.ipynb b/Phase 3/phase3_task0a.ipynb
deleted file mode 100644
index f6e4ca2..0000000
--- a/Phase 3/phase3_task0a.ipynb	
+++ /dev/null
@@ -1,124 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from utils import *\n",
-    "warnings.filterwarnings('ignore')\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pca_inherent_dimensionality(data, threshold):\n",
-    "\n",
-    "    # Calculate the mean of the data\n",
-    "    mean = np.mean(data, axis=0)\n",
-    "    # Center the data by subtracting the mean\n",
-    "    centered_data = data - mean\n",
-    "    # Normalize the data\n",
-    "    normalized_data = centered_data / np.std(centered_data, axis=0)\n",
-    "\n",
-    "    # Reshape the centered data to ensure compatible dimensions\n",
-    "    if(len(normalized_data.shape)==3):\n",
-    "        reshaped_normalized_data = normalized_data.reshape(normalized_data.shape[0], normalized_data.shape[2])\n",
-    "    else:\n",
-    "        reshaped_normalized_data=normalized_data\n",
-    "\n",
-    "    # Calculate the covariance matrix\n",
-    "    #covariance_matrix = np.dot(reshaped_normalized_data.T, reshaped_normalized_data)\n",
-    "    covariance_matrix = np.dot(reshaped_normalized_data.T, reshaped_normalized_data) / (reshaped_normalized_data.shape[0] - 1) #to bring the values in the range of 0 to 1\n",
-    "\n",
-    "    # Compute the eigenvalues and eigenvectors of the covariance matrix\n",
-    "    eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\n",
-    "    # Sort the eigenvalues in descending order\n",
-    "    #sorted_indices = np.argsort(eigenvalues)[::-1]\n",
-    "    # Sort the eigenvectors accordingly\n",
-    "    #sorted_eigenvectors = eigenvectors[:, sorted_indices]\n",
-    "    #print(sorted_eigenvectors)\n",
-    "    #print(sorted_indices)\n",
-    "\n",
-    "    # Calculate the mean of each subarray- the sorted_eigenvectors are in the form of subarrays, while computing the inherent dimensionality, each value is compared with \n",
-    "    #the threshold, hence mean of each subarray is computed and then it is compared with the threshold value (I am not sure if we can do this?)\n",
-    "    #means = np.mean(sorted_eigenvectors, axis=1)\n",
-    "    \n",
-    "    # Determine the number of eigenvalues greater than the threshold\n",
-    "    #inherent_dimensionality = np.sum(means>threshold)\n",
-    "    #inherent_dimensionality = len(significant_eigenvalues)\n",
-    "    significant_eigenvalues = eigenvalues[eigenvalues > threshold]\n",
-    "    inherent_dimensionality = len(significant_eigenvalues)\n",
-    "    #significant_eigenvalues = sorted_eigenvectors[sorted_indices][eigenvalues > threshold]\n",
-    "\n",
-    "    return inherent_dimensionality"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Inherent dimensionality associated with the even numbered images:  260\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Retrieve all feature spaces from the database\n",
-    "data = []\n",
-    "for document in fd_collection.find():\n",
-    "    feature_space = document[\"fc_fd\"]\n",
-    "    data.append(feature_space)\n",
-    "\n",
-    "threshold=0.5\n",
-    "print(\"Inherent dimensionality associated with the even numbered images: \", pca_inherent_dimensionality(data, threshold))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}