From 7570ce45aba4e9c2c5018b3d88a1412d90784153 Mon Sep 17 00:00:00 2001 From: MadhuraWani803 <103093329+MadhuraWani803@users.noreply.github.com> Date: Tue, 21 Nov 2023 13:07:29 -0700 Subject: [PATCH] Add files via upload --- Phase 3/phase3_task0a.ipynb | 382 ++++++++++++++++++++++++++++++++++++ 1 file changed, 382 insertions(+) create mode 100644 Phase 3/phase3_task0a.ipynb diff --git a/Phase 3/phase3_task0a.ipynb b/Phase 3/phase3_task0a.ipynb new file mode 100644 index 0000000..8e2138b --- /dev/null +++ b/Phase 3/phase3_task0a.ipynb @@ -0,0 +1,382 @@ +{ + "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": 6, + "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", + " reshaped_normalized_data = normalized_data.reshape(normalized_data.shape[0], normalized_data.shape[2])\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", + "\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 = means[sorted_eigenvectors > threshold]\n", + " #significant_eigenvalues = sorted_eigenvectors[sorted_indices][eigenvalues > threshold]\n", + "\n", + " return inherent_dimensionality" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.00552372 -0.00585585 0.0054683 ... 0.00213608 -0.00578668\n", + " -0.02901922]\n", + " [-0.00142398 -0.00531728 0.00500698 ... -0.00029204 -0.00168182\n", + " -0.02245712]\n", + " [ 0.00410895 -0.00136694 0.00755997 ... 0.01052992 0.01506127\n", + " -0.02160561]\n", + " ...\n", + " [-0.02672341 -0.03687741 0.01916759 ... 0.00180396 -0.02123255\n", + " -0.00149316]\n", + " [-0.05641969 0.01001905 0.01649787 ... -0.01201208 -0.00081941\n", + " -0.00218969]\n", + " [-0.00773203 0.03703142 0.00402409 ... -0.00618637 0.00476636\n", + " -0.012095 ]]\n", + "[ 4.54971200e-04 -1.46639936e-04 4.46222044e-04 9.86643620e-04\n", + " 1.46248769e-03 -1.76804539e-04 1.17091267e-03 -9.74455099e-04\n", + " 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-8.77297729e-04 1.20798804e-03 4.78743862e-05 -2.23655531e-03\n", + " -7.52775148e-05 8.66588845e-04 1.21198197e-04 2.13673547e-03\n", + " -6.92086405e-04 3.25506399e-04 1.74639317e-03 -9.74005782e-04\n", + " -9.62757679e-04 -1.84137807e-03 -4.51718367e-04 -1.11452965e-03\n", + " -1.61475756e-05 -1.59888640e-04 5.57448579e-04 3.61996088e-04\n", + " 1.70787900e-04 1.92241745e-04 1.23009434e-03 -8.35186518e-04\n", + " -1.21697352e-03 3.95687756e-04 -3.87286715e-05 1.45531592e-03\n", + " 2.68288114e-04 -3.73163651e-04 6.31135250e-04 -1.21595519e-03\n", + " -4.97821315e-04 1.26845610e-04 3.28939029e-04 -1.16599880e-03\n", + " -9.62473961e-04 5.33357930e-04 5.33948074e-05 -7.69960858e-04\n", + " 2.73844876e-04 -3.61908987e-04 -8.74590444e-04 -6.91055228e-04\n", + " -9.90859665e-04 1.15154765e-03 -6.89760895e-04 -7.00155222e-04\n", + " 1.99404774e-04 1.49928225e-03 -1.33344502e-03 -2.91712005e-04\n", + " 1.59746352e-04 -2.28708964e-04 -4.63731553e-04 6.95002008e-04\n", + " -3.56256399e-04 5.19370615e-05 1.18344245e-03 -4.22511586e-04\n", + " 6.53562410e-04 -3.54005595e-04 2.68993309e-04 -6.31011310e-04\n", + " -2.22264786e-04 6.52318487e-04 -2.33897922e-03 -8.62253885e-05\n", + " -8.93796643e-04 1.31183029e-03 6.62360621e-04 -8.74336534e-04\n", + " 4.32571486e-04 -6.37632867e-04 -1.65183041e-03 6.61224548e-05\n", + " 1.09280215e-03 4.63715739e-04 -2.77866950e-04 1.81800657e-03\n", + " 1.43014515e-04 3.42227766e-04 2.30016265e-04 5.18947405e-05\n", + " -4.65300615e-04 -1.20715290e-03 -8.52856536e-04 -1.58632617e-03\n", + " -1.16443070e-03 1.91875680e-03 -2.07279760e-03 1.65672841e-03\n", + " -7.66519812e-04 -9.25326043e-05 -6.86213845e-04 1.09324163e-04\n", + " -3.78846908e-04 6.62058797e-04 -2.67239111e-03 1.42595105e-03\n", + " -1.93109802e-03 -7.78773161e-04 -1.45826488e-03 6.98252748e-04\n", + " 4.37178492e-04 -2.19338680e-03 -1.24559090e-04 -1.79625532e-04\n", + " -3.59769827e-04 7.26871247e-04 -8.07276991e-04 -9.55698052e-04\n", + " 1.55391994e-04 -7.52188416e-04 -2.92048655e-05 1.37844594e-03\n", + " 1.89024593e-03 2.92464089e-04 2.27862916e-04 -7.25231397e-04\n", + " -8.12927609e-04 1.46915625e-03 -1.62948337e-04 1.11550089e-05\n", + " 1.01072043e-03 1.78149951e-03 1.44646178e-03 1.17282085e-03\n", + " -1.68472898e-03 -2.64668978e-04 -1.00679822e-03 1.67188636e-03\n", + " -7.46180324e-04 -1.78804438e-04 1.38862211e-03 -1.15319713e-04\n", + " -2.11947543e-04 1.11518110e-03 -1.42780741e-04 6.57531082e-05\n", + " 6.85036118e-04 5.57193024e-04 -2.18338073e-04 -1.00267564e-03\n", + " -2.38851207e-04 1.11136319e-03 1.38015997e-03 1.65601029e-03\n", + " -1.28179847e-03 -1.84837566e-04 2.25470127e-03 -6.99900723e-05\n", + " -3.21353944e-04 7.75113354e-04 1.79219889e-04 -9.67213939e-04\n", + " -1.93379647e-03 6.88409643e-04 -9.09593785e-04 -1.75387256e-04\n", + " 2.80085877e-04 -1.69768850e-04 -6.65184596e-04 -1.54962849e-03\n", + " 3.32534579e-04 -1.11931740e-03 1.18999906e-03 -1.28480889e-03\n", + " 5.14778725e-04 -3.69313703e-05 1.41115956e-03 1.66303692e-03\n", + " -1.74792009e-03 -1.58280004e-04 -1.10318386e-03 -5.13931654e-04\n", + " -1.21440237e-04 -8.71745666e-04 1.38498013e-03 8.02966837e-04\n", + " 1.55751222e-03 7.39101811e-04 -3.37886751e-04 -2.20364025e-04\n", + " -3.02639122e-04 4.92758368e-04 -1.08760478e-03 -8.50358277e-04\n", + " 5.54363205e-05 1.71474091e-04 7.30577593e-04 -1.99104634e-03\n", + " -5.01042075e-04 -2.04673251e-04 8.93615157e-04 -3.49378966e-05\n", + " -1.75618983e-03 8.90925755e-04 5.48596246e-04 2.16481076e-03\n", + " -6.87399376e-05 -4.19008003e-04 2.24366282e-03 -6.01177096e-04\n", + " 1.33475347e-03 1.87419065e-03 -2.64236346e-04 -2.08440755e-05\n", + " 5.33692331e-04 9.49887723e-04 -3.95823950e-04 -7.44351796e-04\n", + " -6.12055907e-04 -1.27975757e-03 2.92396564e-04 2.10638748e-04\n", + " -1.67123915e-04 6.35997032e-04 -3.61987067e-04 -9.63997862e-04\n", + " -1.02442386e-03 -1.05436267e-03 1.59547057e-03 2.82506975e-03\n", + " -7.24252012e-04 -6.16887708e-04 9.33747713e-04 -1.43908967e-03\n", + " -1.94335265e-03 1.65038272e-03 -1.90161380e-03 -4.56208402e-04\n", + " -5.87535035e-04 -1.06308678e-03 -7.68148177e-04 -1.31749795e-03\n", + " 6.16157322e-05 -1.03807509e-03 6.37837280e-04 7.32322495e-04\n", + " 3.20565967e-04 -1.47885503e-03 1.00067731e-03 4.57860536e-04\n", + " -1.59939350e-04 4.08605182e-04 1.13900147e-03 -7.28968971e-04\n", + " 7.88987646e-04 -2.41816663e-05 1.07391743e-03 1.28375917e-03\n", + " -3.46564970e-04 -1.41776286e-03 2.52672589e-04 7.53977903e-04\n", + " -6.65362605e-04 4.31517269e-04 -3.26928497e-03 -5.45040626e-04\n", + " 1.00497208e-04 -1.89392004e-03 -2.31285196e-03 7.76079213e-04\n", + " 1.32210567e-04 1.00385320e-03 -9.36689383e-04 7.96630552e-04\n", + " 1.08275384e-03 3.69926177e-04 -1.18170928e-03 -1.49616226e-03\n", + " -1.40277264e-03 -1.60690389e-03 -9.74496795e-04 2.49514852e-04\n", + " -7.07085622e-04 -1.36831778e-03 1.08343160e-03 6.39516852e-04\n", + " -3.83835603e-04 -5.32540505e-04 2.07664046e-04 -3.67543619e-04\n", + " 5.37413289e-04 -1.22192445e-03 -1.92460323e-03 -1.87198171e-03\n", + " -5.17398904e-04 -9.91662329e-04 1.18158834e-03 1.46575378e-05\n", + " 2.17566677e-03 -1.76594961e-03 9.23701823e-04 2.13758345e-04\n", + " -1.64276210e-03 -1.81764608e-04 1.68051541e-03 -1.91098714e-04\n", + " -6.52347822e-04 5.67064525e-04 -1.46669474e-04 -6.10477893e-04\n", + " -1.82155199e-04 1.93184236e-03 -5.34672676e-04 1.11055711e-03\n", + " 2.16701324e-04 -7.35006727e-04 -9.07442800e-04 1.47978475e-03]\n", + "Inherent dimensionality associated with the even numbered images: 150\n" + ] + } + ], + "source": [ + "# Retrieve all feature spaces from the database\n", + "data = []\n", + "for document in fd_collection.find():\n", + " feature_space = document[\"resnet_fd\"]\n", + " data.append(feature_space)\n", + "\n", + "threshold=1e-3\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 +}