diff --git a/Phase 2/task_10.ipynb b/Phase 2/task_10.ipynb
index ac1e8e8..7f06f5a 100644
--- a/Phase 2/task_10.ipynb	
+++ b/Phase 2/task_10.ipynb	
@@ -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",
-    "    input(\n",
-    "        \"Enter dimensionality reduction method - one of \"\n",
-    "        + str(list(valid_dim_reduction_methods.keys()))\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",
     "    "
    ]
   }