diff --git a/Phase 2/task_9.ipynb b/Phase 2/task_9.ipynb
index fe438e2..b501307 100644
--- a/Phase 2/task_9.ipynb	
+++ b/Phase 2/task_9.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,14 +45,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cm_fd-cp-10-semantics.json loaded\n"
+      "image_sim-cm_fd-lda-10-model.joblib loaded\n"
      ]
     }
    ],
@@ -88,28 +88,45 @@
     "\n",
     "match selected_latent_space:\n",
     "    case \"\":\n",
-    "        if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
-    "            data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
-    "            print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
-    "        else:\n",
-    "          print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
+    "        if selected_dim_reduction_method == \"lda\":\n",
+    "            if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\"):\n",
+    "                    model = load(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
+    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib and json loaded\")\n",
+    "                else:\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
+    "            else:\n",
+    "                if os.path.exists(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                    data = json.load(open(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "                else:\n",
+    "                    print(f\"{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n",
     "    case \"cp\":\n",
     "        if os.path.exists(f\"{selected_feature_model}-cp-{k}-semantics.json\"):\n",
     "            data = json.load(open(f\"{selected_feature_model}-cp-{k}-semantics.json\"))\n",
     "            print(f\"{selected_feature_model}-cp-{k}-semantics.json loaded\")\n",
-    "        else:          \n",
+    "        else:\n",
     "          print(f\"{selected_feature_model}-cp-{k}-semantics.json does not exist\")\n",
     "    case _:\n",
-    "        if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
-    "            data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
-    "            print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "        if selected_dim_reduction_method == \"lda\":\n",
+    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\") and os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                model = load(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib\")\n",
+    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib loaded\")\n",
+    "            else:\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-model.joblib does not exist\")\n",
     "        else:\n",
-    "          print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")\n"
+    "            if os.path.exists(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"):\n",
+    "                data = json.load(open(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json\"))\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json loaded\")\n",
+    "            else:\n",
+    "                print(f\"{selected_latent_space}-{selected_feature_model}-{selected_dim_reduction_method}-{k}-semantics.json does not exist\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,10 +186,18 @@
     "            label_vectors.append(sim_matrix[i])\n",
     "        label_rep = [sum(col) / len(col) for col in zip(*label_vectors)]\n",
     "\n",
-    "\n",
     "      for centroid in S:\n",
     "        comparison_vector.append(math.dist(label_rep, centroid))\n",
     "\n",
+    "    case \"lda\":\n",
+    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
+    "      label_vectors = []\n",
+    "      length = len(comparison_feature_space)\n",
+    "      for i in range(length):\n",
+    "        if all_images[i][\"true_label\"] == label:\n",
+    "          label_vectors.append(comparison_feature_space[i])\n",
+    "      comparison_vector = [sum(col) / len(col) for col in zip(*label_vectors)]      \n",
+    "\n",
     "  n = len(comparison_feature_space)\n",
     "\n",
     "  distances = []\n",
@@ -200,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,6 +279,11 @@
     "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
     "      comparison_vector = comparison_feature_space[label]\n",
     "\n",
+    "    case \"lda\":\n",
+    "      comparison_feature_space = np.array(data[\"image-semantic\"])\n",
+    "      comparison_vector = comparison_feature_space[label]      \n",
+    "\n",
+    "\n",
     "  n = len(comparison_feature_space)\n",
     "  distances = []\n",
     "  for i in range(n):\n",
@@ -268,23 +298,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'label': 2, 'distance': 0.9999999999999999}\n",
-      "{'label': 4, 'distance': 0.9999999999999999}\n",
-      "{'label': 6, 'distance': 0.9999999999999999}\n",
-      "{'label': 7, 'distance': 0.9999999999999999}\n",
-      "{'label': 8, 'distance': 0.9999999999999999}\n",
-      "{'label': 9, 'distance': 0.9999999999999999}\n",
-      "{'label': 10, 'distance': 0.9999999999999999}\n",
-      "{'label': 11, 'distance': 0.9999999999999999}\n",
-      "{'label': 13, 'distance': 0.9999999999999999}\n",
-      "{'label': 14, 'distance': 0.9999999999999999}\n"
+      "{'image_id': 2641, 'label': 46, 'distance': 0.013618215122607105}\n",
+      "{'image_id': 1686, 'label': 16, 'distance': 0.015215365128880378}\n",
+      "{'image_id': 2310, 'label': 35, 'distance': 0.015383486193179943}\n",
+      "{'image_id': 3781, 'label': 84, 'distance': 0.01541886635507712}\n",
+      "{'image_id': 1483, 'label': 11, 'distance': 0.015474891099448796}\n",
+      "{'image_id': 2719, 'label': 48, 'distance': 0.01960489858697963}\n",
+      "{'image_id': 3787, 'label': 85, 'distance': 0.02006387165132467}\n",
+      "{'image_id': 3877, 'label': 87, 'distance': 0.02050382578938892}\n",
+      "{'image_id': 3719, 'label': 82, 'distance': 0.02293235381986182}\n",
+      "{'image_id': 3403, 'label': 70, 'distance': 0.024912695992711693}\n"
      ]
     }
    ],
@@ -303,6 +333,13 @@
     "\n",
     "    extract_similarities_ls2(data, label)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Phase 2/utils.py b/Phase 2/utils.py
index 5153773..18508b6 100644
--- a/Phase 2/utils.py	
+++ b/Phase 2/utils.py	
@@ -1117,7 +1117,7 @@ def extract_latent_semantics_from_sim_matrix(
 
             dump(
                 model,
-                f"{sim_type}-{feature_model}-{dim_reduction_method}-{k}-model.joblib",
+                f"{sim_type}_sim-{feature_model}-{dim_reduction_method}-{k}-model.joblib",
             )
 
             # for each latent semantic, sort object-weight pairs by weights in descending order