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MadhuraWani803
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Phase 3/phase3_task0b.ipynb
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Phase 3/phase3_task0b.ipynb
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import *\n",
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"warnings.filterwarnings('ignore')\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"fd_collection = getCollection(\"team_5_mwdb_phase_2\", \"fd_collection\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"#same function as 0a\n",
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"def pca_inherent_dimensionality(data, threshold):\n",
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" \n",
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" # Calculate the mean of the data\n",
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" mean = np.mean(data, axis=0)\n",
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" # Center the data by subtracting the mean\n",
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" centered_data = data - mean\n",
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" # Normalize the data\n",
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" normalized_data = centered_data / np.std(centered_data, axis=0)\n",
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"\n",
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" # Reshape the centered data to ensure compatible dimensions\n",
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" if(len(normalized_data.shape)==3):\n",
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" reshaped_normalized_data = normalized_data.reshape(normalized_data.shape[0], normalized_data.shape[2])\n",
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" else:\n",
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" reshaped_normalized_data=normalized_data\n",
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"\n",
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" # Calculate the covariance matrix\n",
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" covariance_matrix = np.dot(reshaped_normalized_data.T, reshaped_normalized_data) / (reshaped_normalized_data.shape[0] - 1)\n",
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"\n",
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" # Compute the eigenvalues and eigenvectors of the covariance matrix\n",
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" eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)\n",
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"\n",
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" \n",
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" # Determine the number of eigenvalues greater than the threshold\n",
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" significant_eigenvalues = eigenvalues[eigenvalues > threshold]\n",
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" inherent_dimensionality = len(significant_eigenvalues)\n",
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"\n",
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" return inherent_dimensionality"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Inherent dimensionality associated with label 0 : 140\n",
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"Inherent dimensionality associated with label 1 : 134\n",
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"Inherent dimensionality associated with label 2 : 75\n",
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"Inherent dimensionality associated with label 3 : 182\n",
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"Inherent dimensionality associated with label 4 : 27\n",
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"Inherent dimensionality associated with label 5 : 186\n",
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"Inherent dimensionality associated with label 6 : 20\n",
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"Inherent dimensionality associated with label 7 : 20\n",
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"Inherent dimensionality associated with label 8 : 22\n",
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"Inherent dimensionality associated with label 9 : 26\n",
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"Inherent dimensionality associated with label 10 : 22\n",
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"Inherent dimensionality associated with label 11 : 16\n",
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"Inherent dimensionality associated with label 12 : 63\n",
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"Inherent dimensionality associated with label 13 : 48\n",
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"Inherent dimensionality associated with label 14 : 20\n",
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"Inherent dimensionality associated with label 15 : 42\n",
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"Inherent dimensionality associated with label 16 : 44\n",
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"Inherent dimensionality associated with label 17 : 24\n",
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"Inherent dimensionality associated with label 18 : 21\n",
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"Inherent dimensionality associated with label 19 : 60\n",
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"Inherent dimensionality associated with label 20 : 23\n",
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"Inherent dimensionality associated with label 21 : 28\n",
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"Inherent dimensionality associated with label 22 : 30\n",
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"Inherent dimensionality associated with label 23 : 53\n",
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"Inherent dimensionality associated with label 24 : 22\n",
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"Inherent dimensionality associated with label 25 : 34\n",
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"Inherent dimensionality associated with label 26 : 35\n",
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"Inherent dimensionality associated with label 27 : 34\n",
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"Inherent dimensionality associated with label 28 : 24\n",
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"Inherent dimensionality associated with label 29 : 25\n",
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"Inherent dimensionality associated with label 30 : 27\n",
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"Inherent dimensionality associated with label 31 : 33\n",
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"Inherent dimensionality associated with label 32 : 25\n",
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"Inherent dimensionality associated with label 33 : 31\n",
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"Inherent dimensionality associated with label 34 : 33\n",
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"Inherent dimensionality associated with label 35 : 37\n",
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"Inherent dimensionality associated with label 36 : 31\n",
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"Inherent dimensionality associated with label 37 : 25\n",
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"Inherent dimensionality associated with label 38 : 31\n",
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"Inherent dimensionality associated with label 39 : 42\n",
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"Inherent dimensionality associated with label 40 : 32\n",
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"Inherent dimensionality associated with label 41 : 33\n",
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"Inherent dimensionality associated with label 42 : 21\n",
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"Inherent dimensionality associated with label 43 : 16\n",
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"Inherent dimensionality associated with label 44 : 16\n",
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"Inherent dimensionality associated with label 45 : 25\n",
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"Inherent dimensionality associated with label 46 : 48\n",
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"Inherent dimensionality associated with label 47 : 49\n",
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"Inherent dimensionality associated with label 48 : 20\n",
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"Inherent dimensionality associated with label 49 : 26\n",
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"Inherent dimensionality associated with label 50 : 43\n",
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"Inherent dimensionality associated with label 51 : 39\n",
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"Inherent dimensionality associated with label 52 : 15\n",
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"Inherent dimensionality associated with label 53 : 31\n",
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"Inherent dimensionality associated with label 54 : 42\n",
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"Inherent dimensionality associated with label 55 : 56\n",
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"Inherent dimensionality associated with label 56 : 29\n",
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"Inherent dimensionality associated with label 57 : 40\n",
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"Inherent dimensionality associated with label 58 : 38\n",
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"Inherent dimensionality associated with label 59 : 19\n",
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"Inherent dimensionality associated with label 60 : 32\n",
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"Inherent dimensionality associated with label 61 : 21\n",
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"Inherent dimensionality associated with label 62 : 19\n",
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"Inherent dimensionality associated with label 63 : 41\n",
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"Inherent dimensionality associated with label 64 : 15\n",
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"Inherent dimensionality associated with label 65 : 37\n",
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"Inherent dimensionality associated with label 66 : 27\n",
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"Inherent dimensionality associated with label 67 : 16\n",
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"Inherent dimensionality associated with label 68 : 19\n",
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"Inherent dimensionality associated with label 69 : 22\n",
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"Inherent dimensionality associated with label 70 : 18\n",
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"Inherent dimensionality associated with label 71 : 22\n",
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"Inherent dimensionality associated with label 72 : 25\n",
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"Inherent dimensionality associated with label 73 : 16\n",
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"Inherent dimensionality associated with label 74 : 28\n",
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"Inherent dimensionality associated with label 75 : 39\n",
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"Inherent dimensionality associated with label 76 : 28\n",
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"Inherent dimensionality associated with label 77 : 24\n",
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"Inherent dimensionality associated with label 78 : 19\n",
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"Inherent dimensionality associated with label 79 : 30\n",
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"Inherent dimensionality associated with label 80 : 19\n",
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"Inherent dimensionality associated with label 81 : 41\n",
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"Inherent dimensionality associated with label 82 : 27\n",
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"Inherent dimensionality associated with label 83 : 17\n",
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"Inherent dimensionality associated with label 84 : 31\n",
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"Inherent dimensionality associated with label 85 : 21\n",
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"Inherent dimensionality associated with label 86 : 42\n",
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"Inherent dimensionality associated with label 87 : 29\n",
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"Inherent dimensionality associated with label 88 : 31\n",
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"Inherent dimensionality associated with label 89 : 16\n",
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"Inherent dimensionality associated with label 90 : 42\n",
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"Inherent dimensionality associated with label 91 : 23\n",
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"Inherent dimensionality associated with label 92 : 42\n",
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"Inherent dimensionality associated with label 93 : 37\n",
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"Inherent dimensionality associated with label 94 : 111\n",
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"Inherent dimensionality associated with label 95 : 18\n",
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"Inherent dimensionality associated with label 96 : 28\n",
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"Inherent dimensionality associated with label 97 : 16\n",
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"Inherent dimensionality associated with label 98 : 27\n",
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"Inherent dimensionality associated with label 99 : 19\n",
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"Inherent dimensionality associated with label 100 : 29\n"
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]
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}
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],
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"source": [
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"unique_labels=[]\n",
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"feature_space=[]\n",
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"unique_labels=fd_collection.distinct(\"true_label\")\n",
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"for i in range (len(unique_labels)):\n",
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" label_fds = [\n",
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" np.array(img_fds['fc_fd']).flatten() # get the specific feature model's feature vector\n",
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" for img_fds in fd_collection.find({\"true_label\": unique_labels[i]}) # repeat for all images\n",
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" ]\n",
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" threshold=0.5\n",
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" print(\"Inherent dimensionality associated with label\", unique_labels[i], \":\", pca_inherent_dimensionality(label_fds, threshold))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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