my-asu-archive/CSE515_MWDB_Project
1
0
Fork 0
mirror of https://github.com/20kaushik02/CSE515_MWDB_Project.git synced 2025-12-06 09:24:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

handle ls2

Browse Source
This commit is contained in:
Niraj Sonje 2023-10-13 20:50:03 -07:00
parent c652a6606e
commit b657e75c42
1 changed files with 55 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

67
Phase 2/task_10.ipynb
View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 13,
"metadata": {},
"outputs": [
{
@ -21,7 +21,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@ -35,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@ -45,14 +45,14 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"label_sim-cm_fd-kmeans-10-semantics.json loaded\n"
"cm_fd-cp-10-semantics.json loaded\n"
]
}
],
@ -69,12 +69,13 @@
"if k < 1:\n",
" raise ValueError(\"k should be a positive integer\")\n",
"\n",
"selected_dim_reduction_method = str(\n",
"if selected_latent_space != 'cp':\n",
" selected_dim_reduction_method = str(\n",
" input(\n",
" \"Enter dimensionality reduction method - one of \"\n",
" + str(list(valid_dim_reduction_methods.keys()))\n",
" )\n",
")\n",
" )\n",
"\n",
"label = int(input(\"Enter label: \"))\n",
"if label < 0 and label > 100:\n",
@ -109,7 +110,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@ -187,7 +188,39 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def extract_similarities_ls2(data, label):\n",
"\n",
" LS_f = np.array(data[\"feature-semantic\"])\n",
" LS_i = np.array(data[\"image-semantic\"])\n",
" S = np.array(data[\"semantics-core\"])\n",
"\n",
" if len(S.shape) == 1:\n",
" S = np.diag(S)\n",
"\n",
" label_rep = calculate_label_representatives(fd_collection, label, selected_feature_model)\n",
" comparison_feature_space = np.matmul(label_rep, LS_f)\n",
" comparison_vector = np.matmul(comparison_feature_space, S)\n",
"\n",
" comparison_image_space = np.matmul(LS_i, S)\n",
" distances = []\n",
"\n",
" n = len(comparison_image_space)\n",
" for i in range(n):\n",
" distances.append({\"image\": i, \"distance\": math.dist(comparison_vector, comparison_image_space[i])})\n",
" \n",
" distances = sorted(distances, key=lambda x: x[\"distance\"], reverse=False)[:knum]\n",
"\n",
" for x in distances:\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@ -237,18 +270,18 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'image_id': 2309, 'distance': 4.117664288663269}\n",
"{'image_id': 1930, 'distance': 4.117664288663269}\n",
"{'image_id': 1940, 'distance': 4.117664288663269}\n",
"{'image_id': 1929, 'distance': 4.117664288663269}\n",
"{'image_id': 2250, 'distance': 4.117664288663269}\n"
"{'image': 823, 'distance': 4006.335159603778}\n",
"{'image': 809, 'distance': 4006.3942621209867}\n",
"{'image': 806, 'distance': 4006.421689986329}\n",
"{'image': 832, 'distance': 4006.422683206996}\n",
"{'image': 830, 'distance': 4006.44733072835}\n"
]
}
],
@ -262,6 +295,10 @@
" case \"label_sim\":\n",
"\n",
" extract_similarities_ls3(selected_dim_reduction_method, data, label)\n",
"\n",
" case \"cp\":\n",
"\n",
" extract_similarities_ls2(data, label)\n",
" "
]
}