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correction in task 11 latent space sim matrix

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Kaushik Narayan R 2023-10-15 19:36:15 -07:00
parent 6a1c11a9fa
commit 04987d5aa8
1 changed files with 38 additions and 42 deletions
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80
Phase 2/task_11.ipynb
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@ -39,53 +39,51 @@
" def create_similarity_graph(\n",
" self, n, feature_model, semantic_data=None, dim_reduction_method=None\n",
" ):\n",
" import glob\n",
"\n",
" if semantic_data is None:\n",
" # Similarity graph from feature models\n",
" image_sim_matrix = find_image_image_similarity(fd_collection, feature_model)\n",
" file_prefix = f\"image_sim-{feature_model}-*.json\"\n",
" if len(glob.glob(file_prefix)) > 0:\n",
" matrix_data = json.load(open(glob.glob(file_prefix)[0]))\n",
" image_sim_matrix = matrix_data[\"sim-matrix\"]\n",
" if self.verbose:\n",
" print(\"Using image-image similarity matrix from semantic data\")\n",
" else:\n",
" # Similarity graph from feature models\n",
" image_sim_matrix = find_image_image_similarity(\n",
" fd_collection, feature_model\n",
" )\n",
" if self.verbose:\n",
" print(\"Image-image similarity matrix constructed from\", feature_model)\n",
" else:\n",
" # Similarity graph from image-semantic latent space\n",
" # LS3, LS4\n",
" if \"sim-matrix\" in semantic_data:\n",
" # for now, don't work with LS3\n",
" # TODO: do similar to task 7 and 10\n",
" image_sim_matrix = np.array(semantic_data[\"sim-matrix\"])\n",
" if image_sim_matrix.shape[0] != NUM_IMAGES:\n",
" raise TypeError(\n",
" \"Functionality to construct similarity graph from LS3 not yet done\"\n",
" )\n",
" if self.verbose:\n",
" print(\"Using image-image similarity matrix from semantic data\")\n",
" # LS1, LS2\n",
" else:\n",
" image_semantic = semantic_data[\"image-semantic\"]\n",
" # SVD, CP\n",
" if \"semantics-core\" in semantic_data:\n",
" semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
" if len(semantics_core.shape) == 1:\n",
" semantics_core = np.diag(semantics_core)\n",
" image_semantic = np.matmul(image_semantic, semantics_core)\n",
" image_semantic = semantic_data[\"image-semantic\"]\n",
" # SVD, CP\n",
" if \"semantics-core\" in semantic_data:\n",
" semantics_core = np.array(semantic_data[\"semantics-core\"])\n",
" if len(semantics_core.shape) == 1:\n",
" semantics_core = np.diag(semantics_core)\n",
" image_semantic = np.matmul(image_semantic, semantics_core)\n",
"\n",
" image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
" # Calculate half and fill the other\n",
" for i in range(NUM_IMAGES):\n",
" for j in range(i + 1, NUM_IMAGES):\n",
" # Note: lower the value, lower the distance => higher the similarity\n",
" distance_measure = (\n",
" kl_divergence_measure\n",
" if dim_reduction_method == \"lda\"\n",
" else euclidean_distance_measure\n",
" )\n",
" image_sim_matrix[j][i] = distance_measure(\n",
" np.array(image_semantic[i]),\n",
" np.array(image_semantic[j]),\n",
" )\n",
" image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
" if self.verbose:\n",
" print(\n",
" \"Image-image similarity matrix constructed from given image-semantic\"\n",
" image_sim_matrix = np.zeros((NUM_IMAGES, NUM_IMAGES))\n",
" # Calculate half and fill the other\n",
" for i in range(NUM_IMAGES):\n",
" for j in range(i + 1, NUM_IMAGES):\n",
" # Note: lower the value, lower the distance => higher the similarity\n",
" distance_measure = (\n",
" kl_divergence_measure\n",
" if dim_reduction_method == \"lda\"\n",
" else euclidean_distance_measure\n",
" )\n",
" image_sim_matrix[j][i] = distance_measure(\n",
" np.array(image_semantic[i]),\n",
" np.array(image_semantic[j]),\n",
" )\n",
" image_sim_matrix[i][j] = image_sim_matrix[j][i]\n",
" if self.verbose:\n",
" print(\n",
" \"Image-image similarity matrix constructed from given image-semantic\"\n",
" )\n",
"\n",
" # Create an unweighted directed similarity graph, with no self-loops\n",
" self.similarity_graph = []\n",
@ -103,8 +101,6 @@
" def personalized_pagerank(\n",
" self, label, m, damping_factor=0.85, max_iter=1000, tol=1e-6\n",
" ):\n",
" import time\n",
"\n",
" if self.similarity_graph is None:\n",
" raise ValueError(\n",
" \"Similarity graph not created. Call create_similarity_graph() first.\"\n",