paper_distance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SimonettoS\\AppData\\Roaming\\Python\\Python311\\site-packages\\weaviate\\warnings.py:121: DeprecationWarning: Dep005: You are using weaviate-client version 3.25.3. The latest version is 4.6.4.\n",
      "            Please consider upgrading to the latest version. See https://weaviate.io/developers/weaviate/client-libraries/python for details.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import weaviate\n",
    "import json\n",
    "import requests\n",
    "import json\n",
    "\n",
    "client = weaviate.Client(\n",
    "    url = \"http://localhost:8080\",  # Replace with your endpoint\n",
    "  # Replace w/ your Weaviate instance API key\n",
    "    additional_headers = {\n",
    "        \"X-OpenAI-Api-Key\": \"sk-...\"  # Replace with your inference API key\n",
    "    }\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnexpectedStatusCodeException",
     "evalue": "Create class! Unexpected status code: 422, with response body: {'error': [{'message': 'class name \"CVE_CWE\" already exists'}]}.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnexpectedStatusCodeException\u001b[0m             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 42\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# class_obj = {\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#     \"class\": \"CWE9\",\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m#     \"properties\": [\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# client.schema.create_class(class_obj)\u001b[39;00m\n\u001b[0;32m     24\u001b[0m class_obj \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m     25\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclass\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCVE_CWE\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvectorizer\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext2vec-openai\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     40\u001b[0m     ]\n\u001b[0;32m     41\u001b[0m }\n\u001b[1;32m---> 42\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclass_obj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\weaviate\\schema\\crud_schema.py:252\u001b[0m, in \u001b[0;36mSchema.create_class\u001b[1;34m(self, schema_class)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;124;03mCreate a single class as part of the schema in Weaviate.\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    248\u001b[0m \u001b[38;5;124;03m    If the 'schema_class' could not be validated against the standard format.\u001b[39;00m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    251\u001b[0m loaded_schema_class \u001b[38;5;241m=\u001b[39m _get_dict_from_object(schema_class)\n\u001b[1;32m--> 252\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_class_with_primitives\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloaded_schema_class\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_complex_properties_from_class(loaded_schema_class)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\weaviate\\schema\\crud_schema.py:816\u001b[0m, in \u001b[0;36mSchema._create_class_with_primitives\u001b[1;34m(self, weaviate_class)\u001b[0m\n\u001b[0;32m    814\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RequestsConnectionError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass may not have been created properly.\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconn_err\u001b[39;00m\n\u001b[0;32m    815\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m200\u001b[39m:\n\u001b[1;32m--> 816\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m UnexpectedStatusCodeException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreate class\u001b[39m\u001b[38;5;124m\"\u001b[39m, response)\n",
      "\u001b[1;31mUnexpectedStatusCodeException\u001b[0m: Create class! Unexpected status code: 422, with response body: {'error': [{'message': 'class name \"CVE_CWE\" already exists'}]}."
     ]
    }
   ],
   "source": [
    "# class_obj = {\n",
    "#     \"class\": \"CWE9\",\n",
    "#     \"properties\": [\n",
    "#         {\n",
    "#             \"name\": \"cve_id\",\n",
    "#             \"dataType\":[\"string\"],\n",
    "#             \"description\": \"cve_id\",\n",
    "#             \"moduleConfig\": {\n",
    "#                 \"text2vec-openai\": {\n",
    "#                     \"skip\": True\n",
    "#                 }\n",
    "#             }\n",
    "#         },\n",
    "#         {\n",
    "#             \"name\": \"cwe_id\",\n",
    "#             \"dataType\": [\"string\"],\n",
    "#             \"description\": \"Name of the CWE\",\n",
    "#         },\n",
    "\n",
    "#     ]\n",
    "# }\n",
    "# client.schema.create_class(class_obj)\n",
    "\n",
    "class_obj = {\n",
    "    \"class\": \"CVE_CWE\",\n",
    "    \"vectorizer\": \"text2vec-openai\",\n",
    "    \"properties\": [\n",
    "        {\n",
    "            \"name\": \"cwe_id\",\n",
    "            \"dataType\": [\"int\"],\n",
    "            \"description\": \"CWE ID\",\n",
    "            \"moduleConfig\": {\"text2vec-openai\": {\"skip\": True}}\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"cwe_description\",\n",
    "            \"dataType\": [\"string\"],\n",
    "            \"description\": \"Description of the CWE\",\n",
    "        },\n",
    "\n",
    "    ]\n",
    "}\n",
    "client.schema.create_class(class_obj)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class': 'CVE_CWE',\n",
       " 'invertedIndexConfig': {'bm25': {'b': 0.75, 'k1': 1.2},\n",
       "  'cleanupIntervalSeconds': 60,\n",
       "  'stopwords': {'additions': None, 'preset': 'en', 'removals': None}},\n",
       " 'moduleConfig': {'text2vec-openai': {'baseURL': 'https://api.openai.com',\n",
       "   'model': 'ada',\n",
       "   'modelVersion': '002',\n",
       "   'type': 'text',\n",
       "   'vectorizeClassName': True}},\n",
       " 'multiTenancyConfig': {'enabled': False},\n",
       " 'properties': [{'dataType': ['int'],\n",
       "   'description': 'CWE ID',\n",
       "   'indexFilterable': True,\n",
       "   'indexSearchable': False,\n",
       "   'moduleConfig': {'text2vec-openai': {'skip': True,\n",
       "     'vectorizePropertyName': False}},\n",
       "   'name': 'cwe_id'},\n",
       "  {'dataType': ['text'],\n",
       "   'description': 'Description of the CWE',\n",
       "   'indexFilterable': True,\n",
       "   'indexSearchable': True,\n",
       "   'moduleConfig': {'text2vec-openai': {'skip': False,\n",
       "     'vectorizePropertyName': False}},\n",
       "   'name': 'cwe_description',\n",
       "   'tokenization': 'whitespace'}],\n",
       " 'replicationConfig': {'factor': 1},\n",
       " 'shardingConfig': {'virtualPerPhysical': 128,\n",
       "  'desiredCount': 1,\n",
       "  'actualCount': 1,\n",
       "  'desiredVirtualCount': 128,\n",
       "  'actualVirtualCount': 128,\n",
       "  'key': '_id',\n",
       "  'strategy': 'hash',\n",
       "  'function': 'murmur3'},\n",
       " 'vectorIndexConfig': {'skip': False,\n",
       "  'cleanupIntervalSeconds': 300,\n",
       "  'maxConnections': 64,\n",
       "  'efConstruction': 128,\n",
       "  'ef': -1,\n",
       "  'dynamicEfMin': 100,\n",
       "  'dynamicEfMax': 500,\n",
       "  'dynamicEfFactor': 8,\n",
       "  'vectorCacheMaxObjects': 1000000000000,\n",
       "  'flatSearchCutoff': 40000,\n",
       "  'distance': 'cosine',\n",
       "  'pq': {'enabled': False,\n",
       "   'bitCompression': False,\n",
       "   'segments': 0,\n",
       "   'centroids': 256,\n",
       "   'trainingLimit': 100000,\n",
       "   'encoder': {'type': 'kmeans', 'distribution': 'log-normal'}}},\n",
       " 'vectorIndexType': 'hnsw',\n",
       " 'vectorizer': 'text2vec-openai'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.schema.get('CVE_CWE')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from collections import Counter\n",
    "valid_cwe=[]\n",
    "with open('cwe_57_with_descr.pickle', 'rb') as f:\n",
    "    test = pickle.load(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cwe_id': '416', 'cwe_descr': 'Referencing memory after it has been freed can cause a program to crash, use unexpected values, or execute code.'}\n",
      "{'cwe_id': '352', 'cwe_descr': 'The web application does not, or can not, sufficiently verify whether a well-formed, valid, consistent request was intentionally provided by the user who submitted the request.'}\n",
      "{'cwe_id': '22', 'cwe_descr': 'The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.'}\n",
      "{'cwe_id': '79', 'cwe_descr': 'The product does not neutralize or incorrectly neutralizes user-controllable input before it is placed in output that is used as a web page that is served to other users.'}\n",
      "{'cwe_id': '20', 'cwe_descr': 'The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.'}\n",
      "{'cwe_id': '119', 'cwe_descr': 'The product performs operations on a memory buffer, but it can read from or write to a memory location that is outside of the intended boundary of the buffer.'}\n",
      "{'cwe_id': '862', 'cwe_descr': 'The product does not perform an authorization check when an actor attempts to access a resource or perform an action.'}\n",
      "{'cwe_id': '787', 'cwe_descr': 'The product writes data past the end, or before the beginning, of the intended buffer.'}\n",
      "{'cwe_id': '798', 'cwe_descr': 'The product contains hard-coded credentials, such as a password or cryptographic key, which it uses for its own inbound authentication, outbound communication to external components, or encryption of internal data.'}\n",
      "{'cwe_id': '125', 'cwe_descr': 'The product reads data past the end, or before the beginning, of the intended buffer.'}\n",
      "{'cwe_id': '732', 'cwe_descr': 'The product specifies permissions for a security-critical resource in a way that allows that resource to be read or modified by unintended actors.'}\n",
      "{'cwe_id': '190', 'cwe_descr': 'The product performs a calculation that can produce an integer overflow or wraparound, when the logic assumes that the resulting value will always be larger than the original value. This can introduce other weaknesses when the calculation is used for resource management or execution control.'}\n",
      "{'cwe_id': '287', 'cwe_descr': 'When an actor claims to have a given identity, the product does not prove or insufficiently proves that the claim is correct.'}\n",
      "{'cwe_id': '835', 'cwe_descr': 'The product contains an iteration or loop with an exit condition that cannot be reached, i.e., an infinite loop.'}\n",
      "{'cwe_id': '400', 'cwe_descr': 'The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.'}\n",
      "{'cwe_id': '476', 'cwe_descr': 'A NULL pointer dereference occurs when the application dereferences a pointer that it expects to be valid, but is NULL, typically causing a crash or exit.'}\n",
      "{'cwe_id': '89', 'cwe_descr': 'The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component.'}\n",
      "{'cwe_id': '863', 'cwe_descr': 'The product performs an authorization check when an actor attempts to access a resource or perform an action, but it does not correctly perform the check. This allows attackers to bypass intended access restrictions.'}\n",
      "{'cwe_id': '120', 'cwe_descr': 'The product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer, leading to a buffer overflow.'}\n",
      "{'cwe_id': '908', 'cwe_descr': 'The product uses or accesses a resource that has not been initialized.'}\n",
      "{'cwe_id': '772', 'cwe_descr': 'The product does not release a resource after its effective lifetime has ended, i.e., after the resource is no longer needed.'}\n",
      "{'cwe_id': '74', 'cwe_descr': 'The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component.'}\n",
      "{'cwe_id': '522', 'cwe_descr': 'The product transmits or stores authentication credentials, but it uses an insecure method that is susceptible to unauthorized interception and/or retrieval.'}\n",
      "{'cwe_id': '326', 'cwe_descr': 'The product stores or transmits sensitive data using an encryption scheme that is theoretically sound, but is not strong enough for the level of protection required.'}\n",
      "{'cwe_id': '59', 'cwe_descr': 'The product attempts to access a file based on the filename, but it does not properly prevent that filename from identifying a link or shortcut that resolves to an unintended resource.'}\n",
      "{'cwe_id': '77', 'cwe_descr': 'The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.'}\n",
      "{'cwe_id': '611', 'cwe_descr': 'The product processes an XML document that can contain XML entities with URIs that resolve to documents outside of the intended sphere of control, causing the product to embed incorrect documents into its output.'}\n",
      "{'cwe_id': '427', 'cwe_descr': 'The product uses a fixed or controlled search path to find resources, but one or more locations in that path can be under the control of unintended actors.'}\n",
      "{'cwe_id': '362', 'cwe_descr': 'The product contains a code sequence that can run concurrently with other code, and the code sequence requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence that is operating concurrently.'}\n",
      "{'cwe_id': '94', 'cwe_descr': 'The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.'}\n",
      "{'cwe_id': '532', 'cwe_descr': 'Information written to log files can be of a sensitive nature and give valuable guidance to an attacker or expose sensitive user information.'}\n",
      "{'cwe_id': '200', 'cwe_descr': 'The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.'}\n",
      "{'cwe_id': '401', 'cwe_descr': 'The product does not sufficiently track and release allocated memory after it has been used, which slowly consumes remaining memory.'}\n",
      "{'cwe_id': '434', 'cwe_descr': \"The product allows the attacker to upload or transfer files of dangerous types that can be automatically processed within the product's environment.\"}\n",
      "{'cwe_id': '78', 'cwe_descr': 'The product constructs all or part of an OS command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended OS command when it is sent to a downstream component.'}\n",
      "{'cwe_id': '770', 'cwe_descr': 'The product allocates a reusable resource or group of resources on behalf of an actor without imposing any restrictions on the size or number of resources that can be allocated, in violation of the intended security policy for that actor.'}\n",
      "{'cwe_id': '347', 'cwe_descr': 'The product does not verify, or incorrectly verifies, the cryptographic signature for data.'}\n",
      "{'cwe_id': '843', 'cwe_descr': 'The product allocates or initializes a resource such as a pointer, object, or variable using one type, but it later accesses that resource using a type that is incompatible with the original type.'}\n",
      "{'cwe_id': '617', 'cwe_descr': 'The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.'}\n",
      "{'cwe_id': '295', 'cwe_descr': 'The product does not validate, or incorrectly validates, a certificate.'}\n",
      "{'cwe_id': '327', 'cwe_descr': 'The product uses a broken or risky cryptographic algorithm or protocol.'}\n",
      "{'cwe_id': '601', 'cwe_descr': 'A web application accepts a user-controlled input that specifies a link to an external site, and uses that link in a Redirect. This simplifies phishing attacks.'}\n",
      "{'cwe_id': '276', 'cwe_descr': 'During installation, installed file permissions are set to allow anyone to modify those files.'}\n",
      "{'cwe_id': '668', 'cwe_descr': 'The product exposes a resource to the wrong control sphere, providing unintended actors with inappropriate access to the resource.'}\n",
      "{'cwe_id': '269', 'cwe_descr': 'The product does not properly assign, modify, track, or check privileges for an actor, creating an unintended sphere of control for that actor.'}\n",
      "{'cwe_id': '312', 'cwe_descr': 'The product stores sensitive information in cleartext within a resource that might be accessible to another control sphere.'}\n",
      "{'cwe_id': '502', 'cwe_descr': 'The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid.'}\n",
      "{'cwe_id': '426', 'cwe_descr': \"The product searches for critical resources using an externally-supplied search path that can point to resources that are not under the product's direct control.\"}\n",
      "{'cwe_id': '284', 'cwe_descr': 'The product does not restrict or incorrectly restricts access to a resource from an unauthorized actor.'}\n",
      "{'cwe_id': '345', 'cwe_descr': 'The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.'}\n",
      "{'cwe_id': '755', 'cwe_descr': 'The product does not handle or incorrectly handles an exceptional condition.'}\n",
      "{'cwe_id': '918', 'cwe_descr': 'The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.'}\n",
      "{'cwe_id': '319', 'cwe_descr': 'The product transmits sensitive or security-critical data in cleartext in a communication channel that can be sniffed by unauthorized actors.'}\n",
      "{'cwe_id': '639', 'cwe_descr': \"The system's authorization functionality does not prevent one user from gaining access to another user's data or record by modifying the key value identifying the data.\"}\n",
      "{'cwe_id': '415', 'cwe_descr': 'The product calls free() twice on the same memory address, potentially leading to modification of unexpected memory locations.'}\n",
      "{'cwe_id': '134', 'cwe_descr': 'The product uses a function that accepts a format string as an argument, but the format string originates from an external source.'}\n",
      "{'cwe_id': '306', 'cwe_descr': 'The product does not perform any authentication for functionality that requires a provable user identity or consumes a significant amount of resources.'}\n",
      "{'cwe_id': '203', 'cwe_descr': 'The product behaves differently or sends different responses under different circumstances in a way that is observable to an unauthorized actor, which exposes security-relevant information about the state of the product, such as whether a particular operation was successful or not.'}\n"
     ]
    }
   ],
   "source": [
    "client.batch.configure(batch_size=50)  # Configure batch\n",
    "with client.batch as batch:\n",
    "    for d in test:\n",
    "        print(d)\n",
    "        properties = {\n",
    "            \"cwe_id\": int(d['cwe_id']),\n",
    "            \"cwe_description\": d['cwe_descr'],\n",
    "        }\n",
    "        batch.add_data_object(\n",
    "            properties,\n",
    "            \"CVE_CWE\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVE-2021-39275\n",
      "ap_escape_quotes() may write beyond the end of a buffer when given malicious input. No included modules pass untrusted data to these functions, but third-party / external modules may. This issue affects Apache HTTP Server 2.4.48 and earlier.\n",
      "787\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "n=37\n",
    "with open('cve_test.pickle', 'rb') as f:\n",
    "    test = pickle.load(f)\n",
    "print(test[n]['cve_id'])\n",
    "print(test[n]['cve_description'])\n",
    "print(test[n]['cwe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.batch.configure(batch_size=50)  # Configure batch\n",
    "with client.batch as batch:\n",
    "\n",
    "    properties = {\n",
    "        \"cwe_id\": int('202139275'),\n",
    "        \"cwe_description\": test[n]['cve_description'],\n",
    "    }\n",
    "    batch.add_data_object(\n",
    "        properties,\n",
    "        \"CVE_CWE_mapping\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "787\n",
      "125\n",
      "120\n",
      "617\n",
      "79\n",
      "352\n",
      "119\n",
      "502\n",
      "89\n",
      "601\n",
      "22\n",
      "611\n",
      "416\n",
      "327\n",
      "400\n",
      "345\n",
      "78\n",
      "77\n",
      "59\n",
      "94\n"
     ]
    }
   ],
   "source": [
    "additional_clause = {\n",
    "  \"featureProjection\": [\n",
    "    \"vector\"\n",
    "  ]\n",
    "}\n",
    "\n",
    "additional_setting = {\n",
    "  \"dimensions\": 3\n",
    "}\n",
    "\n",
    "nearText = {\"concepts\": [test[n]['cve_description']]}\n",
    "result = (\n",
    "    client.query\n",
    "    .get(\"CVE_CWE_mapping\", [\"cwe_id\"])\n",
    "    .with_near_text(nearText)\n",
    "    .with_additional(\n",
    "    (additional_clause, additional_setting)\n",
    "  )\n",
    "    .do()\n",
    ")\n",
    "    \n",
    "for i in range(20):\n",
    "    print(result['data']['Get']['CVE_CWE_mapping'][i]['cwe_id'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVE-2021-39275\n",
      "787\n"
     ]
    }
   ],
   "source": [
    "print(test[n]['cve_id'])\n",
    "print(test[n]['cwe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CVE_CWE': [{'_additional': {'featureProjection': {'vector': [83.08659, 55.668606, 52.227695]}}, 'cwe_id': 202139275}, {'_additional': {'featureProjection': {'vector': [84.37042, -69.53689, 44.270744]}}, 'cwe_id': 787}, {'_additional': {'featureProjection': {'vector': [105.83117, -19.756285, -15.4720125]}}, 'cwe_id': 120}, {'_additional': {'featureProjection': {'vector': [77.31858, -67.07185, 34.79526]}}, 'cwe_id': 125}, {'_additional': {'featureProjection': {'vector': [82.00587, -61.591003, 39.417564]}}, 'cwe_id': 125}, {'_additional': {'featureProjection': {'vector': [-99.82336, -38.742363, 13.538937]}}, 'cwe_id': 617}, {'_additional': {'featureProjection': {'vector': [-102.82738, -43.76121, 11.413918]}}, 'cwe_id': 617}, {'_additional': {'featureProjection': {'vector': [24.352713, 58.00724, 76.665405]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-23.297369, 67.539474, 55.01607]}}, 'cwe_id': 79}, {'_additional': {'featureProjection': {'vector': [-19.781927, 62.010254, 54.176754]}}, 'cwe_id': 79}, {'_additional': {'featureProjection': {'vector': [-59.836998, -58.71028, -93.69791]}}, 'cwe_id': 918}, {'_additional': {'featureProjection': {'vector': [-57.186302, -57.532238, -99.280266]}}, 'cwe_id': 918}, {'_additional': {'featureProjection': {'vector': [91.773895, 6.5556717, 57.23354]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [98.44526, 5.274533, 57.49251]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [21.81192, 4.8366427, 79.12751]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-58.0829, 91.02919, -68.34122]}}, 'cwe_id': 352}, {'_additional': {'featureProjection': {'vector': [-62.424164, 95.2656, -66.74752]}}, 'cwe_id': 352}, {'_additional': {'featureProjection': {'vector': [88.033585, -54.3001, 6.480049]}}, 'cwe_id': 119}, {'_additional': {'featureProjection': {'vector': [87.22862, -50.66579, 11.653479]}}, 'cwe_id': 119}, {'_additional': {'featureProjection': {'vector': [24.12154, 29.293343, 63.267616]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-20.13385, 12.110534, -122.377045]}}, 'cwe_id': 134}, {'_additional': {'featureProjection': {'vector': [-19.791576, 12.278863, -116.01643]}}, 'cwe_id': 134}, {'_additional': {'featureProjection': {'vector': [-33.727448, -49.903187, 62.554924]}}, 'cwe_id': 89}, {'_additional': {'featureProjection': {'vector': [17.458712, 27.5836, 58.13645]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-29.160736, -44.875626, 62.513714]}}, 'cwe_id': 89}, {'_additional': {'featureProjection': {'vector': [-28.521107, 17.675434, 58.89231]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-16.319511, -89.71239, -54.95115]}}, 'cwe_id': 611}, {'_additional': {'featureProjection': {'vector': [-19.252813, -94.236534, -51.764915]}}, 'cwe_id': 611}, {'_additional': {'featureProjection': {'vector': [-35.370163, 18.147223, 57.289276]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [41.19127, -82.92547, -50.786644]}}, 'cwe_id': 601}, {'_additional': {'featureProjection': {'vector': [41.349575, -83.0797, -58.096134]}}, 'cwe_id': 601}, {'_additional': {'featureProjection': {'vector': [-60.993183, -76.95716, -22.266989]}}, 'cwe_id': 22}, {'_additional': {'featureProjection': {'vector': [-61.75863, -83.18933, -24.268826]}}, 'cwe_id': 22}, {'_additional': {'featureProjection': {'vector': [45.585384, 36.893215, -66.83194]}}, 'cwe_id': 416}, {'_additional': {'featureProjection': {'vector': [45.511116, 32.33207, -71.2637]}}, 'cwe_id': 416}, {'_additional': {'featureProjection': {'vector': [-30.203575, -53.624203, 100.55666]}}, 'cwe_id': 78}, {'_additional': {'featureProjection': {'vector': [-28.49541, -59.02691, 103.548164]}}, 'cwe_id': 78}, {'_additional': {'featureProjection': {'vector': [-4.6705623, -85.46576, 70.65836]}}, 'cwe_id': 94}, {'_additional': {'featureProjection': {'vector': [-7.4164305, -87.31704, 62.539646]}}, 'cwe_id': 94}, {'_additional': {'featureProjection': {'vector': [64.77934, -17.36349, -69.36946]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-23.760948, -86.78414, 79.37026]}}, 'cwe_id': 74}, {'_additional': {'featureProjection': {'vector': [-28.872282, -79.82406, 79.22669]}}, 'cwe_id': 74}, {'_additional': {'featureProjection': {'vector': [-27.244967, 25.508171, 62.932]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [20.765133, -114.47372, 4.4837165]}}, 'cwe_id': 202231092}, {'_additional': {'featureProjection': {'vector': [18.723162, -109.16804, 8.083891]}}, 'cwe_id': 202231092}, {'_additional': {'featureProjection': {'vector': [-30.778336, 52.106216, 59.229233]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-25.842657, -48.342484, -25.561998]}}, 'cwe_id': 502}, {'_additional': {'featureProjection': {'vector': [-22.274452, -45.3808, -21.148937]}}, 'cwe_id': 502}, {'_additional': {'featureProjection': {'vector': [-24.24745, -72.60126, 88.609726]}}, 'cwe_id': 77}, {'_additional': {'featureProjection': {'vector': [-19.668629, -73.43555, 94.45325]}}, 'cwe_id': 77}, {'_additional': {'featureProjection': {'vector': [65.95066, -12.718936, -74.34953]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-24.327139, 122.65796, -16.265253]}}, 'cwe_id': 327}, {'_additional': {'featureProjection': {'vector': [-25.154282, 129.33937, -18.763565]}}, 'cwe_id': 327}, {'_additional': {'featureProjection': {'vector': [-28.579767, 64.23097, -40.05733]}}, 'cwe_id': 345}, {'_additional': {'featureProjection': {'vector': [-27.726755, 58.17609, -41.60145]}}, 'cwe_id': 345}, {'_additional': {'featureProjection': {'vector': [-5.681569, 45.56679, -21.210646]}}, 'cwe_id': 20}, {'_additional': {'featureProjection': {'vector': [-5.9784093, 50.124187, -16.52383]}}, 'cwe_id': 20}, {'_additional': {'featureProjection': {'vector': [88.582985, 41.30844, -40.234184]}}, 'cwe_id': 400}, {'_additional': {'featureProjection': {'vector': [89.88859, 35.213062, -39.563644]}}, 'cwe_id': 400}, {'_additional': {'featureProjection': {'vector': [-64.5694, 8.419433, 43.561897]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [96.695404, -54.412895, -77.07029]}}, 'cwe_id': 190}, {'_additional': {'featureProjection': {'vector': [92.508675, -59.450127, -73.59575]}}, 'cwe_id': 190}, {'_additional': {'featureProjection': {'vector': [64.81199, 25.42334, -24.211075]}}, 'cwe_id': 401}, {'_additional': {'featureProjection': {'vector': [64.45017, 19.0907, -24.240072]}}, 'cwe_id': 401}, {'_additional': {'featureProjection': {'vector': [-32.328533, 9.531116, 123.16166]}}, 'cwe_id': 415}, {'_additional': {'featureProjection': {'vector': [-38.483067, 9.796934, 124.352554]}}, 'cwe_id': 415}, {'_additional': {'featureProjection': {'vector': [-54.353416, 38.889038, -4.277665]}}, 'cwe_id': 200}, {'_additional': {'featureProjection': {'vector': [-60.090393, 42.754982, -4.311184]}}, 'cwe_id': 200}, {'_additional': {'featureProjection': {'vector': [-83.108734, 40.167465, -1.1634516]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [28.733826, 67.60325, 82.66118]}}, 'cwe_id': 201919517}, {'_additional': {'featureProjection': {'vector': [-90.18043, -52.50998, -48.09107]}}, 'cwe_id': 59}, {'_additional': {'featureProjection': {'vector': [-90.51074, -45.06735, -45.213802]}}, 'cwe_id': 59}, {'_additional': {'featureProjection': {'vector': [-86.7783, 26.007488, 88.23657]}}, 'cwe_id': 835}, {'_additional': {'featureProjection': {'vector': [-93.3133, 28.805504, 87.03599]}}, 'cwe_id': 835}, {'_additional': {'featureProjection': {'vector': [-26.993391, -9.926355, -72.278824]}}, 'cwe_id': 863}, {'_additional': {'featureProjection': {'vector': [-31.348602, -8.971283, -67.76165]}}, 'cwe_id': 863}, {'_additional': {'featureProjection': {'vector': [-14.496027, -13.576837, 7.110692]}}, 'cwe_id': 755}, {'_additional': {'featureProjection': {'vector': [-15.065561, -15.234328, 0.49032566]}}, 'cwe_id': 755}, {'_additional': {'featureProjection': {'vector': [17.194002, -38.763393, -76.06008]}}, 'cwe_id': 434}, {'_additional': {'featureProjection': {'vector': [10.222579, -36.86228, -72.94419]}}, 'cwe_id': 434}, {'_additional': {'featureProjection': {'vector': [-39.918583, 48.425354, -56.57685]}}, 'cwe_id': 347}, {'_additional': {'featureProjection': {'vector': [-38.904858, 53.704132, -60.435513]}}, 'cwe_id': 347}, {'_additional': {'featureProjection': {'vector': [-102.39409, 44.839348, 8.693666]}}, 'cwe_id': 532}, {'_additional': {'featureProjection': {'vector': [-96.857735, 42.011276, 10.644659]}}, 'cwe_id': 532}, {'_additional': {'featureProjection': {'vector': [28.510492, 96.90612, -25.672764]}}, 'cwe_id': 522}, {'_additional': {'featureProjection': {'vector': [22.32855, 98.135956, -23.937977]}}, 'cwe_id': 522}, {'_additional': {'featureProjection': {'vector': [-21.771265, 8.738202, -32.2652]}}, 'cwe_id': 284}, {'_additional': {'featureProjection': {'vector': [-17.428892, 7.946814, -36.92423]}}, 'cwe_id': 284}, {'_additional': {'featureProjection': {'vector': [-62.25373, 42.159245, -62.464397]}}, 'cwe_id': 295}, {'_additional': {'featureProjection': {'vector': [-62.68671, 47.967228, -65.24596]}}, 'cwe_id': 295}, {'_additional': {'featureProjection': {'vector': [21.753408, -17.672169, -2.0476844]}}, 'cwe_id': 269}, {'_additional': {'featureProjection': {'vector': [27.105211, -14.28919, -1.8562012]}}, 'cwe_id': 269}, {'_additional': {'featureProjection': {'vector': [20.14414, 101.83236, -45.417988]}}, 'cwe_id': 319}, {'_additional': {'featureProjection': {'vector': [15.973957, 99.090805, -49.056213]}}, 'cwe_id': 319}, {'_additional': {'featureProjection': {'vector': [91.297874, 88.455635, -10.892235]}}, 'cwe_id': 476}, {'_additional': {'featureProjection': {'vector': [85.66259, 84.66343, -11.980836]}}, 'cwe_id': 476}]}\n"
     ]
    }
   ],
   "source": [
    "del(result['data']['Get']['CVE_CWE'][2])\n",
    "print(result['data']['Get'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202139275\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "# print(list(result[0]))\n",
    "i=0\n",
    "vector=[]\n",
    "labels=[]\n",
    "for entry in result['data']['Get']['CVE_CWE']:\n",
    "    if i<= 3:\n",
    "        vector.append(entry['_additional']['featureProjection']['vector'] )\n",
    "        labels.append(entry['cwe_id'])\n",
    "    i+=1\n",
    "# for entry in result['data']['Get']['CWE11']]\n",
    "\n",
    "# Plotting\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plotting points\n",
    "ax.scatter([v[0] for v in vector], [v[1] for v in vector], [v[2] for v in vector])\n",
    "\n",
    "# Adding labels\n",
    "# for i, label in enumerate(labels):\n",
    "#     if i == 0:\n",
    "#         labell=str(label)\n",
    "#         ax.text(vector[i][0], vector[i][1], vector[i][2], f'CVE\\n{labell[:4]}-{labell[4:]}', color='red')\n",
    "#     else:\n",
    "#         ax.text(vector[i][0], vector[i][1], vector[i][2], f'CWE\\n{label}', color='blue')\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0:\n",
    "        label_str = str(label)\n",
    "        print(label)\n",
    "        ax.text(vector[i][0]-0.05, vector[i][1],  vector[i][2],f'CVE-{label_str[:4]}-{label_str[4:]}', color='red', ha='right')\n",
    "    else:\n",
    "        if label==119:\n",
    "            ax.text(vector[i][0]-0.05, vector[i][1], vector[i][2], f'CWE-{label}', color='blue', ha='left')    \n",
    "        else:\n",
    "            ax.text(vector[i][0]-0.05, vector[i][1], vector[i][2], f'CWE-{label}', color='blue', ha='right')\n",
    "\n",
    "# Set labels\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference label: 202139275\n",
      "Distance between 202139275 and 787: 125.46464648374584\n",
      "Distance between 202139275 and 120: 103.87242414290637\n",
      "Distance between 202139275 and 125: 124.10632243555224\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "vectors = []\n",
    "labels = []\n",
    "for entry in result['data']['Get']['CVE_CWE']:\n",
    "    if i <= 3:\n",
    "        vectors.append(entry['_additional']['featureProjection']['vector'])\n",
    "        labels.append(entry['cwe_id'])\n",
    "    i += 1\n",
    "\n",
    "# Divide each coordinate of the vectors by 2\n",
    "# vectors = [[v[0]/2, v[1]/2] for v in vectors]\n",
    "\n",
    "# Calculate and print distances\n",
    "reference_vector = np.array(vectors[0])\n",
    "for i, vector in enumerate(vectors):\n",
    "    if i == 0:\n",
    "        label_str = str(labels[0])\n",
    "        print(f'Reference label: {label_str}')\n",
    "    else:\n",
    "        distance = np.linalg.norm(reference_vector - np.array(vector))\n",
    "        print(f'Distance between {label_str} and {labels[i]}: {distance}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202139275\n"
     ]
    },
    {
     "data": {
      "image/png": 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AYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADYEI4AAABsCEcAAAA2hCMAAAAbwhEAAIAN4QgAAMCGcAQAAGBTZcPRO++8o8aNG6tmzZrq1KmT1q1b5+2SAABAOVAlw9H8+fM1evRoTZgwQRs3btQ111yjnj176ujRo94uDQAAeFmVDEevvvqqhgwZovvvv18tW7bU1KlTVbt2bU2fPt3bpQEAAC+rcuEoKytLGzZsUEJCgrutWrVqSkhIUGpqap7+mZmZcrlcHhsAAKi8qlw4+t///qcLFy4oPDzcoz08PFwZGRl5+iclJcnhcLi3qKiosioVAAB4QZULR8U1btw4OZ1O93bw4EFvlwQAAEqRr7cLKGv169dX9erVdeTIEY/2I0eOKCIiIk9/f39/+fv7l1V5AADAy6rcyJGfn5/at2+v5cuXu9uys7O1fPlyxcbGerEyAABQHlS5kSNJGj16tAYOHKjrr79eHTt21Ouvv67Tp0/r/vvv93ZpAADAy6pkOPrjH/+on376Sc8++6wyMjJ07bXXaunSpXkWaQMAgKrHx7Isy9tFVCQul0sOh0NOp1OBgYHeLgcAABRBcf5+V7k1RwAAAIUhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADYEI4AAABsCEcAAAA2hCMAAAAbwhEAAIAN4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsKk04Wjfvn0aPHiwoqOjVatWLTVp0kQTJkxQVlaWRx8fH58829q1a71YOQAAKE98vV1ASdm5c6eys7P13nvvqWnTptq6dauGDBmi06dP6+WXX/bou2zZMrVq1cr9OCQkpKzLBQAA5VSlCUe9evVSr1693I9jYmK0a9cuTZkyJU84CgkJUURERFmXCAAAKoBKM62WH6fTqeDg4Dztt912m8LCwnTTTTdp8eLFhR4jMzNTLpfLYwMAAJVXpQ1Hu3fv1ltvvaWHH37Y3Va3bl298sorWrBggT7//HPddNNN6tu3b6EBKSkpSQ6Hw71FRUWVRfkAAMBLfCzLsrxdRGHGjh2rF154odA+O3bsUPPmzd2PDx06pLi4OHXt2lUffPBBoc8dMGCA9u7dq2+//Tbf/ZmZmcrMzHQ/drlcioqKktPpVGBgYDHeCQAA8BaXyyWHw1Gkv9/lfs3RmDFjNGjQoEL7xMTEuH8/fPiw4uPj1blzZ/3973+/5PE7deqk5OTkAvf7+/vL39+/yPUCAICKrdyHo9DQUIWGhhap76FDhxQfH6/27dtrxowZqlbt0rOGaWlpioyM/K1lAgCASqLch6OiOnTokLp27apGjRrp5Zdf1k8//eTel3Nl2qxZs+Tn56d27dpJkhYuXKjp06dfcuoNAABUHZUmHCUnJ2v37t3avXu3GjZs6LHPvqzqueee0/79++Xr66vmzZtr/vz5+sMf/lDW5QIAgHKq3C/ILm+Ks6ALAACUD8X5+11pL+UHAAC4HIQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADYEI4AAABsCEcAAAA2hCMAAAAbwhEAAIAN4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADApsjh6PDhw6VZBwAAQLlQ5HDUqlUrzZ07tzRrAQAA8Loih6PJkyfr4YcfVv/+/XX8+PHSrAkAAMBrihyOhg0bps2bN+vYsWNq2bKllixZUpp1AQAAeIVvcTpHR0drxYoVevvtt9WvXz+1aNFCvr6eh9i4cWOJFggAAFCWihWOJGn//v1auHCh6tWrp9tvvz1POAIAAKjIipVs3n//fY0ZM0YJCQnatm2bQkNDS6uuy9K4cWPt37/foy0pKUljx451P968ebMSExO1fv16hYaGasSIEXryySfLulQAAFBOFTkc9erVS+vWrdPbb7+tAQMGlGZNv8lf/vIXDRkyxP04ICDA/bvL5VKPHj2UkJCgqVOnasuWLXrggQcUFBSkhx56yBvlAgCAcqbI4ejChQvavHmzGjZsWJr1/GYBAQGKiIjId9+cOXOUlZWl6dOny8/PT61atVJaWppeffXVAsNRZmamMjMz3Y9dLlep1A0AAMqHIl+tlpycXO6DkSQ9//zzCgkJUbt27fTSSy/p/Pnz7n2pqanq0qWL/Pz83G09e/bUrl279PPPP+d7vKSkJDkcDvcWFRVV6u8BAAB4T6X6+pBHH31U8+bN08qVK/Xwww/rb3/7m8d6ooyMDIWHh3s8J+dxRkZGvsccN26cnE6nezt48GDpvQEAAOB15f5Ss7Fjx+qFF14otM+OHTvUvHlzjR492t3Wtm1b+fn56eGHH1ZSUpL8/f0v6/X9/f0v+7kAAKDiKffhaMyYMRo0aFChfWJiYvJt79Spk86fP699+/apWbNmioiI0JEjRzz65DwuaJ0SAACoWsp9OAoNDb3sWwakpaWpWrVqCgsLkyTFxsbqz3/+s86dO6caNWpIMmupmjVrpnr16pVYzQAAoOKqNGuOUlNT9frrr2vTpk3673//qzlz5mjUqFG677773MHnnnvukZ+fnwYPHqxt27Zp/vz5euONNzym4wAAQNVW7keOisrf31/z5s3TxIkTlZmZqejoaI0aNcoj+DgcDn399ddKTExU+/btVb9+fT377LPc4wgAALj5WJZlebuIisTlcsnhcMjpdCowMNDb5QAAgCIozt/vSjOtBgAAUBIIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADYEI4AAABsCEcAAAA2hCMAAAAbwhEAAIAN4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbCpNOEpJSZGPj0++2/r16yVJ+/bty3f/2rVrvVw9AAAoL3y9XUBJ6dy5s9LT0z3axo8fr+XLl+v666/3aF+2bJlatWrlfhwSElImNQIAgPKv0oQjPz8/RUREuB+fO3dOn376qUaMGCEfHx+PviEhIR59C5OZmanMzEz3Y5fLVTIFAwCAcqnSTKtdbPHixTp27Jjuv//+PPtuu+02hYWF6aabbtLixYsLPU5SUpIcDod7i4qKKq2SAQBAOeBjWZbl7SJKw6233ipJ+uKLL9xt//vf//SPf/xDN954o6pVq6aPP/5YL774oj755BPddttt+R4nv5GjqKgoOZ1OBQYGlu6bAAAAJcLlcsnhcBTp73e5D0djx47VCy+8UGifHTt2qHnz5u7HP/74oxo1aqQPP/xQd955Z6HPHTBggPbu3atvv/22SPUU5+QCAIDyoTh/v8v9mqMxY8Zo0KBBhfaJiYnxeDxjxgyFhIQUOBpk16lTJyUnJ/+WEgEAQCVS7sNRaGioQkNDi9zfsizNmDFDAwYMUI0aNS7ZPy0tTZGRkb+lRAAAUImU+3BUXCtWrNDevXv14IMP5tk3a9Ys+fn5qV27dpKkhQsXavr06frggw/KukwAAFBOVbpwNG3aNHXu3NljDZLdc889p/3798vX11fNmzfX/Pnz9Yc//KGMqwQAAOVVuV+QXd6wIBsAgIqnOH+/K+19jgAAAC4H4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADYEI4AAABsCEcAAAA2hCMAAAAbwhEAAIAN4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgE2FCUeTJ09W586dVbt2bQUFBeXb58CBA+rdu7dq166tsLAwPfHEEzp//rxHn5SUFF133XXy9/dX06ZNNXPmzNIvHgAAVBgVJhxlZWWpf//+Gjp0aL77L1y4oN69eysrK0tr1qzRrFmzNHPmTD377LPuPnv37lXv3r0VHx+vtLQ0jRw5Ug8++KC++uqrsnobAACgnPOxLMvydhHFMXPmTI0cOVInTpzwaP/yyy/1+9//XocPH1Z4eLgkaerUqXrqqaf0008/yc/PT0899ZQ+//xzbd261f28u+66SydOnNDSpUuL9Poul0sOh0NOp1OBgYEl9r4AAEDpKc7f7wozcnQpqampatOmjTsYSVLPnj3lcrm0bds2d5+EhASP5/Xs2VOpqakFHjczM1Mul8tjAwAAlVelCUcZGRkewUiS+3FGRkahfVwul3755Zd8j5uUlCSHw+HeoqKiSqF6AABQXng1HI0dO1Y+Pj6Fbjt37vRmiRo3bpycTqd7O3jwoFfrAQAApcvXmy8+ZswYDRo0qNA+MTExRTpWRESE1q1b59F25MgR976cnzlt9j6BgYGqVatWvsf19/eXv79/kWoAAAAVn1fDUWhoqEJDQ0vkWLGxsZo8ebKOHj2qsLAwSVJycrICAwPVsmVLd58vvvjC43nJycmKjY0tkRoAAEDFV2HWHB04cEBpaWk6cOCALly4oLS0NKWlpenUqVOSpB49eqhly5b605/+pE2bNumrr77SM888o8TERPfIzyOPPKL//ve/evLJJ7Vz5069++67+vDDDzVq1ChvvjUAAFCOVJhL+QcNGqRZs2blaV+5cqW6du0qSdq/f7+GDh2qlJQU1alTRwMHDtTzzz8vX9/cAbKUlBSNGjVK27dvV8OGDTV+/PhLTu3ZcSk/AAAVT3H+fleYcFReEI4AAKh4quR9jgAAAEoC4QgAAMCGcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAFAmMjKkESOkmBjJ31+KipL69JGWL5fuukvq1cuz/9Klko+PNHGiZ/vEidKVV5rf9+0zffLb1q4tuJauXfN/Tu/euX1OnZKGD5caNpRq1ZJatpSmTs3dX9hrL1hw2acJ5YCvtwsAAFR++/ZJN94oBQVJL70ktWkjnTsnffWVlJgojRolPf64dP685PvrX6aVK02ASknxPNbKlVJ8vGfbsmVSq1aebSEhBdezcKGUlZX7+Ngx6ZprpP79c9tGj5ZWrJBmz5YaN5a+/loaNkxq0EC67TZTW3q653H//nfz/m655dLnBOUX4QgAUOqGDTMjKuvWSXXq5La3aiU98IB09KgZqfn+e+mGG8y+lBRp7FhpzBjp7FmpZk3z87vvpPvv9zx+SIgUEVH0eoKDPR/PmyfVru0ZjtaskQYONKNMkvTQQ9J775n3cNttUvXqeV9z0SLp//5Pqlu36LWg/GFaDQBQqo4fN1NkiYmewShHUJB09dVmRGblStN28qS0caMJK40bS6mppn3NGikzM+/I0W81bZqZ2rPX17mztHixdOiQZFmmtv/8R+rRI/9jbNggpaVJgweXbG0oe4QjAECp2r3bhIvmzQvvFx+fO4X27bcmMIWGSl265LanpEjR0VKjRp7P7dzZjNbYt6Jat07aulV68EHP9rfeMuuMGjaU/PzMmqh33jH15GfaNKlFC1PLhWxLqXuO6dO0Q0rdc0wXsq2iFwSvY1oNAFCqrCLmgq5dpZEjzVqklJTc6ay4ODOdJZn2/EaN5s83weRiBw6YgJPj6afNZjdtmlkD1bGjZ/tbb5lF3YsXmzD2zTdm9KtBAykhwbPvL79Ic+dK48dLS7ema9KS7Up3nnXvj3TU1IQ+LdWrdeSlTwS8jnAEAChVV11l1hvt3Fl4v/h46fRpaf16M4X1xBOmPS7OrEs6ftysN3r44bzPjYqSmjbN296ggZnqynHxWqPTp816o7/8xbP9l19MiFq0KPcKtrZtzbFefjlvOProI+nMGemKDkc0dPZGXZwHM5xnNXT2Rk257zoCUgXAtBoAoFQFB0s9e5opqdOn8+4/ccL8bNLEhJzFi00IiYsz7VdcYbZXXjFXmBVnvZGvrwlNOdvF4WjBArOG6b77PNvPnTNbtYv+SlavLmVn532dadOkPn0svfHvrXmCkSR326Ql25liqwAIRwCAUvfOO9KFC2bq6uOPpR9+kHbskN58U4qNze0XHy+9+64JMuHhue1xcWaaK2fh9sWOHTP3UbJvZ8/m7XexadOkvn3zXvYfGGhe84knzFTe3r3SzJnSP/4h3XGHZ9/du82U2029T3pMpV3MkpTuPKt1e49fujB4FeEIAFDqYmLM1Wfx8ebS/NatpZtvNjeAnDIlt198vLlSLWe9UY64ONNe0KhRQoIUGem5ffJJ4TXt2iWtXl3w1WXz5kkdOkj33mvWLT3/vDR5svTII579pk83i7YbX3uy8Bf81dGTRUht8CofyyrqUjlIksvlksPhkNPpVGBgoLfLAQCUE6l7junu9wu5Lfev/jXkBsU2KeQOlSgVxfn7zcgRAAAloGN0sCIdNeVTwH4fmavWOkYHF9AD5QXhCACAElC9mo8m9DH3Dbg4IOU8ntCnpapXKyg+obwgHAEAUEJ6tY7UlPuuU4Sjpkd7hKMml/FXINznCACAEtSrdaRubhmhdXuP6+jJswoLMFNpjBhVHIQjAABKWPVqPiy6rsAqzLTa5MmT1blzZ9WuXVtBQUF59m/atEl33323oqKiVKtWLbVo0UJvvPGGR5+UlBT5+Pjk2TIyMsroXQAAUHQZGdKIEeZWCP7+5iaZffqYWyDcdZf5vje7pUvN3cgnTvRsnzhRuvJK8/u+faZPftvaQi6227ZNuvNO80XAPj7S66/n7ZOUZG5/EBAghYWZe0jt2uXZp2vXvK978e0RvK3CjBxlZWWpf//+io2N1bRp0/Ls37Bhg8LCwjR79mxFRUVpzZo1euihh1S9enUNHz7co++uXbs8LuMLCwsr9foBACiOffukG2+UgoKkl14y3/927pz01VfmO95GjZIef1w6f97cCVwyX7sSFZX7Rb05Vq7Me4+oZcukVq082y6+GabdmTMmpPXvb147P6tWmdo6dDB1Pf201KOHtH27VKdObr8hQzy/sqV27UJOhBdUmHA0adIkSdLMmTPz3f/AAw94PI6JiVFqaqoWLlyYJxyFhYXlO/oEAEB5MWyYGVVZt84zWLRqZb5r7uhR6dQp6fvvpRtuMPtSUqSxY82NNs+elWrWND+/+066/37P44eESBERRa+nQwezSeY18rN0qefjmTPNCNKGDVKXLrnttWsX77XLWoWZVrscTqdTwRd/kY6ka6+9VpGRkbr55pv173//u9BjZGZmyuVyeWwAAJSm48dN0EhM9AxGOYKCcr9KZeVK03bypLkLef/+ZuorNdW0r1ljvj+uON9JV1KcTvPz4j/Fc+ZI9eubO6WPG2dGpcqTShuO1qxZo/nz5+uhhx5yt0VGRmrq1Kn6+OOP9fHHHysqKkpdu3bVxo0bCzxOUlKSHA6He4uKiiqL8gEAVdju3ZJlSc2bF94vPj53Cu3bb01gCg01ozQ57SkpUnS01KiR53M7d5bq1vXcSlJ2tjRypJkabN06t/2ee6TZs02oGzdO+uc/837xr7d5dVpt7NixeuGFFwrts2PHDjW/1KfjIlu3btXtt9+uCRMmqEePHu72Zs2aqVmzZu7HnTt31p49e/Taa6/pn//8Z77HGjdunEaPHu1+7HK5CEgAgFJV1C/26trVBJBz50wIyvlOurg46b33zO8pKfmPGs2fL7Vokbf9wAHzXXI5nn7abMWVmCht3Wq+v87ONmahNm3M9+B17y7t2SM1jrbKxS0QvBqOxowZo0GDBhXaJyYmpljH3L59u7p3766HHnpIzzzzzCX7d+zYUasv/pez8ff3l7+/f7FqAADgt7jqKrPeaOfOwvvFx0unT0vr15uRmCeeMO1xcWZd0vHjZr3Rww/nfW5UlNS0ad72Bg2ktLTcx/msTrmk4cOlzz6TvvnGfClvYTp1Mj/nLTumz46nKd2Z+8W8kY6amtCnZZnfPNOr4Sg0NFShoaEldrxt27apW7duGjhwoCZPnlyk56SlpSkykjuWAgDKj+BgqWdP6Z13pEcfzbvu6MQJs+6oSRMTchYvNoEmLs7sv+IKs73yipSVVbz1Rr6++YemorAsc+uBRYtyp/MuJSeIvfvdNtUIO+uxL8N5VkNnbyzzu4tXmKvVDhw4oOPHj+vAgQO6cOGC0n49m02bNlXdunW1detWdevWTT179tTo0aPd9y6qXr26O4C9/vrrio6OVqtWrXT27Fl98MEHWrFihb7++mtvvS0AAPL1zjtmvU7Hjuay97ZtzeXxycnSlCnSjh2mX3y89O67JtCEh+c+Py5Oeuut3IXbFzt2zNxHyS4oyFzhlp+sLHNJfs7vhw6ZYFO3bm6YSkyU5s6VPv3U3Oso5/gOh1Srlpk6mztXuvVWc7Xc5s3SqFGWAhqfUI2wk3le05L5XrpJS7br5pYRZTbFVmEWZD/77LNq166dJkyYoFOnTqldu3Zq166dvv/+e0nSRx99pJ9++kmzZ89WZGSke+uQc92hzL2SxowZozZt2iguLk6bNm3SsmXL1L17d2+9LQAA8hUTY64+i483l+a3bi3dfLO5AeSUKbn94uPNlWo5641yxMWZ9oJGjRISzHof+/bJJwXXc/iw1K6d2dLTpZdfNr8/+GBunylTzBVqXbt6Hnf+fLPfz8/cX6lHD7PYfMwYKbb7WQX1XVfg61qS0p1ntW7v8ULOVsnysayiLvuCZBZkOxwOOZ1OjxtJAgCA4vs07ZAem5d2yX5v3HWtbr/2ist+neL8/a4wI0cAAKDyCQsoYB7vMvuVBMIRAADwmo7RwYp01FRBq4l8ZK5a6xh9GZfNXSbCEQAA8Jrq1Xw0oY+5sdLFASnn8YQ+Lcv0fkeEIwAA4FW9Wkdqyn3XKcLhOXUW4ahZ5pfxSxXoUn4AAFB59WodqZtbRnCHbAAAgBzVq/kotkmIt8tgWg0AAMCOcAQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hqJzKyJBGjJBiYiR/fykqSurTR1q+XLrrLqlXL8/+S5dKPj7SxIme7RMnSldeaX7ft8/0yW9bu7bgWrZtk+68U2rc2PR9/fW8fZKSpA4dpIAAKSxM6ttX2rXLs8/Zs1JiohQSItWta4555EhxzgoAAKWPcFQO7dsntW8vrVghvfSStGWLCT/x8SZcxMdL//63dP587nNWrjQBKiXF81grV5r+dsuWSenpnlv79gXXc+aMCWnPPy9FROTfZ9UqU9vatVJysnTunNSjh3T6dG6fUaOkJUukBQtM/8OHpX79inNmAAAofXx9SDk0bJgZoVm3TqpTJ7e9VSvpgQeko0elU6ek77+XbrjB7EtJkcaOlcaMMSM0NWuan999J91/v+fxQ0IKDjn56dDBbJJ5jfwsXer5eOZMM4K0YYPUpYvkdErTpklz50rdupk+M2ZILVqYQJXzPgAA8DZGjsqZ48dN0EhM9AxGOYKCpKuvlho0MKNCknTypLRxo9S/v5n6Sk017WvWSJmZeUeOyoLTaX4GB5ufGzaY0aSEhNw+zZubKb+cegEAKA8IR+XEhWxLqXuOadoXR2VZ0tVXW4X2j4/PnUL79lsTmEJDzShNTntKihQdLTVq5Pnczp3Nmh/7VpKys6WRI6Ubb5RatzZtGRmSn58Jd3bh4WYfAADlBdNq5cDSrematGS70p1nlXk4SFKYnl60RXWahapX68h8n9O1qwkg586ZENS1q2mPi5Pee8/8npKS/6jR/PlmOutiBw5ILVvmPn76abMVV2KitHWrtHp18Z8LAIC3EY68bOnWdA2dvVE540S+9U5LsnT0oJ+Gzt6oKfddl29Aio83i53XrzfTa088Ydrj4sy6pOPHzXqjhx/O+5pRUVLTpnnbGzSQ0tJyH+dMiRXH8OHSZ59J33wjNWyY2x4RIWVlSSdOeI4eHTlSvPVPAACUNqbVvOhCtqVJS7bLPoFWvdY51Yz+SSc3NlJ2VnVNWrJdF7Jze5w4YX42aWJCzuLFJtDExZn2K64w2yuvmDBSnPVGvr4mNOVsxQlHlmWC0aJF5iq76GjP/e3bSzVqmFsR5Ni1y4xWxcYW/XUAAChtjBx50bq9x5XuPJunPbjHVh2Z3Vnp/7hRZ373H3288oTaNKin5GRpyhRpxw7TLz5eevddE2TCw3OfHxcnvfVW7sLtix07lnedT1CQucItP1lZ0vbtub8fOmQCWd26uSNQiYnmSrRPPzX3Oso5vsMh1aplfg4eLI0ebUJXYKC5j1NsLFeqAQDKF0aOvOjoybzBSJJqBP2iiEGrVbPRMf28ooXuvSVIN99sRl2mTMntFx9vrlTLWW+UIy7OtBc0apSQIEVGem6ffFJwnYcPS+3amS09XXr5ZfP7gw/m9pkyxVyh1rWr53Hnz8/t89pr0u9/b27+2KWLmU5buLCwMwQAQNnzsSyr8Mui4MHlcsnhcMjpdCowMPA3HSt1zzHd/X4ht6b+1b+G3KDYJiG/6bUAAKjKivP3m5EjL+oYHaxIR035FLDfR1Kko6Y6Rl/GymgAAHBZCEdeVL2ajyb0MdfOXxyQch5P6NNS1asVFJ8AAEBJIxx5Wa/WkZpy33WKcHiuho5w1CzwMn4AAFB6uFqtHOjVOlI3t4zQur3HdfTkWYUFmKk0RowAACh7hKNyono1HxZdAwBQDjCtBgAAYEM4AgAAsCEcAQAA2FSYcDR58mR17txZtWvXVpD9m0ttfHx88mzz5s3z6JOSkqLrrrtO/v7+atq0qWbOnFn6xQMAgAqjwoSjrKws9e/fX0OHDi2034wZM5Senu7e+vbt6963d+9e9e7dW/Hx8UpLS9PIkSP14IMP6quvvirl6gEAQEVRYa5WmzRpkiRdcqQnKChIERER+e6bOnWqoqOj9corr0iSWrRoodWrV+u1115Tz549S7ReAABQMVWYkaOiSkxMVP369dWxY0dNnz5d9q+OS01NVUJCgkf/nj17KjU1tcDjZWZmyuVyeWwAAKDyqjAjR0Xxl7/8Rd26dVPt2rX19ddfa9iwYTp16pQeffRRSVJGRobCw8M9nhMeHi6Xy6VffvlFtWrVynPMpKQk96gVAACo/Lw6cjR27Nh8F1Hbt507dxb5eOPHj9eNN96odu3a6amnntKTTz6pl1566TfVOG7cODmdTvd28ODB33Q8AABQvnl15GjMmDEaNGhQoX1iYmIu+/idOnXSc889p8zMTPn7+ysiIkJHjhzx6HPkyBEFBgbmO2okSf7+/vL393c/zpmmY3oNAICKI+fvtn25TUG8Go5CQ0MVGhpaasdPS0tTvXr13OEmNjZWX3zxhUef5ORkxcbGFvmYJ0+elCRFRUWVXKEAAKBMnDx5Ug6Ho9A+FWbN0YEDB3T8+HEdOHBAFy5cUFpamiSpadOmqlu3rpYsWaIjR47ohhtuUM2aNZWcnKy//e1vevzxx93HeOSRR/T222/rySef1AMPPKAVK1boww8/1Oeff17kOho0aKCDBw8qICBAPj6V44thXS6XoqKidPDgQQUGBnq7nDJX1d+/xDmQOAcS50DiHFTm929Zlk6ePKkGDRpcsm+FCUfPPvusZs2a5X7crl07SdLKlSvVtWtX1ahRQ++8845GjRoly7LUtGlTvfrqqxoyZIj7OdHR0fr88881atQovfHGG2rYsKE++OCDYl3GX61aNTVs2LDk3lg5EhgYWOn+x1AcVf39S5wDiXMgcQ4kzkFlff+XGjHK4WMVZfINlZrL5ZLD4ZDT6ayU/2O4lKr+/iXOgcQ5kDgHEuegqr//HJXuPkcAAAC/BeEI8vf314QJEzyuyqtKqvr7lzgHEudA4hxInIOq/v5zMK0GAABgw8gRAACADeEIAADAhnAEAABgQzgCAACwIRxVEd9884369OmjBg0ayMfHR5988onHfsuy9OyzzyoyMlK1atVSQkKCfvjhB+8UW0oudQ4GDRqU54uPe/Xq5Z1iS0FSUpI6dOiggIAAhYWFqW/fvtq1a5dHn7NnzyoxMVEhISGqW7eu7rzzzjzfR1iRFeUcdO3aNc/n4JFHHvFSxSVvypQpatu2rfsmf7Gxsfryyy/d+yv7Z0C69Dmo7J+Biz3//PPy8fHRyJEj3W1V4XNQGMJRFXH69Gldc801euedd/Ld/+KLL+rNN9/U1KlT9d1336lOnTrq2bOnzp49W8aVlp5LnQNJ6tWrl9LT093bv/71rzKssHStWrVKiYmJWrt2rZKTk3Xu3Dn16NFDp0+fdvcZNWqUlixZogULFmjVqlU6fPiw+vXr58WqS1ZRzoEkDRkyxONz8OKLL3qp4pLXsGFDPf/889qwYYO+//57devWTbfffru2bdsmqfJ/BqRLnwOpcn8G7NavX6/33ntPbdu29WivCp+DQlmociRZixYtcj/Ozs62IiIirJdeesndduLECcvf39/617/+5YUKS9/F58CyLGvgwIHW7bff7pV6vOHo0aOWJGvVqlWWZZl/8xo1algLFixw99mxY4clyUpNTfVWmaXq4nNgWZYVFxdnPfbYY94rygvq1atnffDBB1XyM5Aj5xxYVtX5DJw8edK66qqrrOTkZI/3XJU/BzkYOYL27t2rjIwMJSQkuNscDoc6deqk1NRUL1ZW9lJSUhQWFqZmzZpp6NChOnbsmLdLKjVOp1OSFBwcLEnasGGDzp075/E5aN68ua688spK+zm4+BzkmDNnjurXr6/WrVtr3LhxOnPmjDfKK3UXLlzQvHnzdPr0acXGxlbJz8DF5yBHVfgMJCYmqnfv3h7/3lLV/P+Ci1WYL55F6cnIyJAkhYeHe7SHh4e791UFvXr1Ur9+/RQdHa09e/bo6aef1i233KLU1FRVr17d2+WVqOzsbI0cOVI33nijWrduLcl8Dvz8/BQUFOTRt7J+DvI7B5J0zz33qFGjRmrQoIE2b96sp556Srt27dLChQu9WG3J2rJli2JjY3X27FnVrVtXixYtUsuWLZWWllZlPgMFnQOpanwG5s2bp40bN2r9+vV59lW1/y/ID+EI+NVdd93l/r1NmzZq27atmjRpopSUFHXv3t2LlZW8xMREbd26VatXr/Z2KV5T0Dl46KGH3L+3adNGkZGR6t69u/bs2aMmTZqUdZmlolmzZkpLS5PT6dRHH32kgQMHatWqVd4uq0wVdA5atmxZ6T8DBw8e1GOPPabk5GTVrFnT2+WUS0yrQREREZKU50qEI0eOuPdVRTExMapfv752797t7VJK1PDhw/XZZ59p5cqVatiwobs9IiJCWVlZOnHihEf/yvg5KOgc5KdTp06SVKk+B35+fmratKnat2+vpKQkXXPNNXrjjTeq1GegoHOQn8r2GdiwYYOOHj2q6667Tr6+vvL19dWqVav05ptvytfXV+Hh4VXmc1AQwhEUHR2tiIgILV++3N3mcrn03XffeczBVzU//vijjh07psjISG+XUiIsy9Lw4cO1aNEirVixQtHR0R7727dvrxo1anh8Dnbt2qUDBw5Ums/Bpc5BftLS0iSp0nwO8pOdna3MzMwq8RkoSM45yE9l+wx0795dW7ZsUVpamnu7/vrrde+997p/r6qfgxxMq1URp06d8vivnr179yotLU3BwcG68sorNXLkSP31r3/VVVddpejoaI0fP14NGjRQ3759vVd0CSvsHAQHB2vSpEm68847FRERoT179ujJJ59U06ZN1bNnTy9WXXISExM1d+5cffrppwoICHCvHXA4HKpVq5YcDocGDx6s0aNHKzg4WIGBgRoxYoRiY2N1ww03eLn6knGpc7Bnzx7NnTtXt956q0JCQrR582aNGjVKXbp0yXOpc0U1btw43XLLLbryyit18uRJzZ07VykpKfrqq6+qxGdAKvwcVIXPQEBAgMc6O0mqU6eOQkJC3O1V4XNQKG9fLoeysXLlSktSnm3gwIGWZZnL+cePH2+Fh4db/v7+Vvfu3a1du3Z5t+gSVtg5OHPmjNWjRw8rNDTUqlGjhtWoUSNryJAhVkZGhrfLLjH5vXdJ1owZM9x9fvnlF2vYsGFWvXr1rNq1a1t33HGHlZ6e7r2iS9ilzsGBAwesLl26WMHBwZa/v7/VtGlT64knnrCcTqd3Cy9BDzzwgNWoUSPLz8/PCg0Ntbp37259/fXX7v2V/TNgWYWfg6rwGcjPxbcvqAqfg8L4WJZllWUYAwAAKM9YcwQAAGBDOAIAALAhHAEAANgQjgAAAGwIRwAAADaEIwAAABvCEQAAgA3hCAAAwIZwBAAAYEM4AlClXbhwQZ07d1a/fv082p1Op6KiovTnP//ZS5UB8Ba+PgRAlfef//xH1157rd5//33de++9kqQBAwZo06ZNWr9+vfz8/LxcIYCyRDgCAElvvvmmJk6cqG3btmndunXq37+/1q9fr2uuucbbpQEoY4QjAJBkWZa6deum6tWra8uWLRoxYoSeeeYZb5cFwAsIRwDwq507d6pFixZq06aNNm7cKF9fX2+XBMALWJANAL+aPn26ateurb179+rHH3/0djkAvISRIwCQtGbNGsXFxenrr7/WX//6V0nSsmXL5OPj4+XKAJQ1Ro4AVHlnzpzRoEGDNHToUMXHx2vatGlat26dpk6d6u3SAHgBI0cAqrzHHntMX3zxhTZt2qTatWtLkt577z09/vjj2rJlixo3buzdAgGUKcIRgCpt1apV6t69u1JSUnTTTTd57OvZs6fOnz/P9BpQxRCOAAAAbFhzBAAAYEM4AgAAsCEcAQAA2BCOAAAAbAhHAAAANoQjAAAAG8IRAACADeEIAADAhnAEAABgQzgCAACwIRwBAADY/D/odf9tBIHMcgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Extracting data\n",
    "i = 0\n",
    "vector = []\n",
    "labels = []\n",
    "for entry in result['data']['Get']['CVE_CWE']:\n",
    "    if i <=3:\n",
    "        vector.append(entry['_additional']['featureProjection']['vector'])\n",
    "        labels.append(entry['cwe_id'])\n",
    "    i += 1\n",
    "\n",
    "# Divide each coordinate of the vectors by 2\n",
    "# vector = [[v[0]/2, v[1]/2] for v in vector]\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plotting points\n",
    "ax.scatter([v[0] for v in vector], [v[1] for v in vector])\n",
    "\n",
    "# Adding labels\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0:\n",
    "        label_str = str(label)\n",
    "        print(label)\n",
    "        ax.text(vector[i][0]-0.05, vector[i][1], f'CVE-{label_str[:4]}-{label_str[4:]}', color='red', ha='left')\n",
    "    else:\n",
    "        if (label==787 or label==125):\n",
    "            ax.text(vector[i][0]-0.05, vector[i][1], f'CWE-{label}', color='blue', ha='right')    \n",
    "        else:\n",
    "            ax.text(vector[i][0]-0.05, vector[i][1], f'CWE-{label}', color='blue', ha='left')\n",
    "\n",
    "# Set labels\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference label: 202139275\n",
      "Distance between 202139275 and 787: 125.46464648374584\n",
      "Distance between 202139275 and 120: 103.87242414290637\n",
      "Distance between 202139275 and 125: 124.10632243555224\n"
     ]
    },
    {
     "data": {
      "image/png": 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rldvtlsfjYb4ZAAABorZ8fgfM5H8AAIDajmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYImACWaTJk1S165d1ahRI0VHR+u2225TZmamT5sTJ04oNTVVTZs2VcOGDTVgwADl5OT4qWIAAICaCZhgtnr1aqWmpmrdunVatmyZCgoK1KdPH/34448lbR577DEtWrRICxYs0OrVq5WVlaX+/fv7sWoAAIDqczmO4/i7iDNx8OBBRUdHa/Xq1br++uvl8XgUFRWld955R7/+9a8lSTt27FC7du2Ulpama665plrn9Xq9crvd8ng8ioiIOJcvAQAAnCW15fM7YHrMTufxeCRJTZo0kSRlZGSooKBAvXv3LmnTtm1bXXTRRUpLS/NLjQAAADUR4u8CzkRRUZEeffRRXXvtterYsaMkKTs7W6GhoYqMjPRpGxMTo+zs7ArPlZeXp7y8vJK/vV7vOakZAACgKgHZY5aamqqtW7fqvffe+9nnmjRpktxud8kWFxd3FioEAACouYALZg8++KAWL16slStXqmXLliX7Y2NjlZ+fr6NHj/q0z8nJUWxsbIXnGzNmjDweT8m2d+/ec1U6AABApQImmDmOowcffFALFy7UihUrFB8f73M8MTFR9erV0/Lly0v2ZWZmas+ePUpKSqrwvGFhYYqIiPDZAAAA/CFg5pilpqbqnXfe0YcffqhGjRqVzBtzu92qX7++3G63hg0bppEjR6pJkyaKiIjQQw89pKSkpGrfkQkAAOBPAbNchsvlKnf/nDlzNHToUElmgdlRo0bp3XffVV5envr27as33nij0qHM09WW220BAKhLasvnd8AEs/OltvzDAgBQl9SWz++AmWMGAABQ2xHMAAAALEEwAwAAsATBDAAAwBIEMwAAAEsQzAAAACxBMAMAALAEwQwAAMASBDMAAABLEMwAAAAsQTADAACwBMEMAADAEgQzAAAASxDMAAAALEEwAwAAsATBDAAAwBIEMwAAAEsQzAAAACxBMAMAALAEwQwAAMASBDMAAABLEMwAAAAsQTADAACwBMEMAADAEgQzAAAASxDMAAAALEEwAwAAsATBDAAAwBIEMwAAAEsQzAAAACxBMAMAALAEwQwAAMASBDMAAABLEMwAAAAsQTADAACwBMEMAADAEgQzAAAASxDMAAAALEEwAwAAsATBDAAAwBIEMwAAAEsQzAAAACxBMAMAALBErQxmf/7zn9W6dWuFh4erW7duSk9P93dJAAAAVap1wWz+/PkaOXKkxo0bp40bN+qKK65Q3759deDAAX+XBgAAUKlaF8ymTp2q4cOH65577lH79u01Y8YMNWjQQLNnz/Z3aQAAAJWqVcEsPz9fGRkZ6t27d8m+oKAg9e7dW2lpaeU+Ji8vT16v12cDAADwh1oVzP7v//5PhYWFiomJ8dkfExOj7Ozsch8zadIkud3uki0uLu58lAoAAFBGrQpmZ2LMmDHyeDwl2969e/1dEgAAqKNC/F3A2dSsWTMFBwcrJyfHZ39OTo5iY2PLfUxYWJjCwsLOR3kAAACVqlU9ZqGhoUpMTNTy5ctL9hUVFWn58uVKSkryY2UAAABVq1U9ZpI0cuRIDRkyRFdddZWuvvpqvfLKK/rxxx91zz33+Ls0AACAStW6YPab3/xGBw8e1HPPPafs7GxdeeWVWrp0aZkbAgAAAGzjchzH8XcRNvF6vXK73fJ4PIqIiPB3OQAAoBpqy+d3rZpjBgAAEMgIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYotrBLCsr61zWAQAAUOdVO5h16NBB77zzzrmsBQAAoE6rdjCbOHGi7r//fg0cOFCHDx8+lzUBAADUSdUOZr///e+1efNmHTp0SO3bt9eiRYvOZV0AAAB1To2+xDw+Pl4rVqzQ66+/rv79+6tdu3YKCfE9xcaNG89qgQAAAHVFjYKZJH3//fd6//331bhxY/Xr169MMAMAAMCZqVGqmjlzpkaNGqXevXtr27ZtioqKOld1AQAA1DnVDmYpKSlKT0/X66+/rsGDB5/LmgAAAOqkagezwsJCbd68WS1btjyX9QAAANRZ1Q5my5YtO5d1AAAA1Hl8JRMAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAOO+ys6WHHpISEqSwMCkuTrr1Vmn5cumOO6SUFN/2S5dKLpc0frzv/vHjpYsuKv6rldzuCLlcKrOtW1dxLdu2SQMGSK1bm7avvFK2zaRJUteuUqNGUnS0dNttUmamb5sTJ6TUVKlpU6lhQ3POnJwaXBQRzAAAwHm2e7eUmCitWCFNmSJt2WKCV3KyCTbJydLnn0snT5Y+ZuVKE95WrfI918qVpv2pPv1U2r/fd0tMrLie48dNQJw8WYqNLb/N6tWmtnXrpGXLpIICqU8f6ccfS9s89pi0aJG0YIFpn5Ul9e9fkysjuRzHcWr2kNrN6/XK7XbL4/EoIiLC3+UAAFDr/PKX0ubNpsfpggt8jx09Kh04IF12mZSWJl1zjdnfrZs0ZIg0apR05IgUHm56qCIjpRkzpP79vXK7L5e0W198IV155ZnV1rq19OijZqvMwYOm52z1aun66yWPR4qKkt55R/r1r02bHTukdu18X0dV6DEDAADnzeHDpncsNbVsKJNM0Lr0UqlFC9MbJkm5udLGjdLAgSY4paWZ/WvXSnl5ZXvMzgePx/xs0sT8zMgwvWi9e5e2advWDLMW11sdBDMAAHDOFRY5Svv2kGZ9dECOI116aeUDdsnJpcOWn31mwlpUlOmdKt6/apUUHy+1auX72O7dzRyvU7ezqajI9Khde63UsaPZl50thYaaYHmqmBhzrLpCzlaRAAAA5Vm6db8mLNqu/Z4TysuKlBStpxdu0QWXRSmlY/NyH9Ozpwk/BQUmgPXsafb36CG9+ab5fdWq8nvL5s83Q4in27NHat++9O+nnzZbTaWmSlu3SmvW1PyxVQmIHrPdu3dr2LBhio+PV/369dWmTRuNGzdO+fn5Pu02b96s6667TuHh4YqLi9NLL73kp4oBAIBkQtmIuRu133NCkhTS+EdJjg7sDdWIuRu1dOv+ch+XnGwm1m/YYIY0e/Qw+3v0kNavN0Oi69dLN9xQ9rFxcdLFF/tukhke3bSpdHvggZq/ngcflBYvNjW1bFm6PzZWys83c+ROlZNT8Q0F5QmIYLZjxw4VFRXpzTff1LZt2zRt2jTNmDFDT58Sc71er/r06aNWrVopIyNDU6ZM0fjx4/XWW2/5sXIAAOquwiJHExZt16mDlsH1CxQef1C5G1upKD9YExZtV2FRaYviYNOmjQlY//qXCVHFwezCC832xz+aIFST+WUhIb5hrXh+WHU4jgllCxeau0nj432PJyZK9eqZ5T6KZWaaXrqkpBrUWP2m/pOSkqKUUxY0SUhIUGZmpqZPn66XX35ZkjRv3jzl5+dr9uzZCg0NVYcOHbRp0yZNnTpV9913n79KBwCgzkrfdbikp+xUTfpsVc7c7tr/12t1/Lqv9c+VR9WpRWMtWyZNny599ZVpl5wsvfGGCVExMaWP79FD+tOfSm8SON2hQ2XndUVGmjs5y5OfL23fXvr7vn0mDDZsWNrblppq7rj88EOzllnx+d1uqX5983PYMGnkSBP4IiLMOm1JSdW/I1MKkB6z8ng8HjU5JeqmpaXp+uuvV2hoaMm+vn37KjMzU0eOHKnwPHl5efJ6vT4bAAD4+Q7klg1lklQv8ifFDl2j8FaHdGRFOw26KVI33mh6m6ZPL22XnGzuyCyeX1asRw+zv6Lest69pebNfbcPPqi4zqwsqXNns+3fL738svn9d78rbTN9urkTs2dP3/POn1/aZto06ZZbzMKy119vhjDff7+yK1RWQK5jtnPnTiUmJurll1/W8OHDJUl9+vRRfHy83iyeEShp+/bt6tChg7Zv36525c0ClDR+/HhNmDChzH7WMQMA4OdJ+/aQ7pxZyZL7//Hu8GuU1Kbpz3qu2rIOqV97zEaPHi2Xy1XptmPHDp/H7Nu3TykpKRo4cGBJKPs5xowZI4/HU7Lt3bv3Z58TAABIV8c3UXN3uFwVHHdJau4O19XxNZjsVcv5dY7ZqFGjNHTo0ErbJCQklPyelZWl5ORkde/evcyk/tjYWOWc9oVUxX/HVnI7RFhYmMLCwmpYOQAAqEpwkEvjbm2vEXM3yiX53ARQHNbG3dpewUEVRbe6x6/BLCoqSlFRUdVqu2/fPiUnJysxMVFz5sxRUJBvZ19SUpLGjh2rgoIC1atXT5K0bNkyXXbZZWrcuPFZrx0AAFQtpWNzTb+rS8k6ZsVi3eEad2v7Ctcxq6sCYo7Zvn371LNnT7Vq1Upvv/22goODS44V94Z5PB5ddtll6tOnj5566ilt3bpV9957r6ZNm1ajuzJryxg1AAA2KSxylL7rsA7knlB0IzN8eTZ7ymrL53dALJexbNky7dy5Uzt37lTLU1dzk1ScK91utz755BOlpqYqMTFRzZo103PPPcdSGQAAWCA4yPWzJ/jXBQHRY3Y+1ZbEDQBAXVJbPr8Ddh0zAACA2oZgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGCJgAtmeXl5uvLKK+VyubRp0yafY5s3b9Z1112n8PBwxcXF6aWXXvJPkQAAAGcg4ILZk08+qRYtWpTZ7/V61adPH7Vq1UoZGRmaMmWKxo8fr7feessPVQIAANRciL8LqIklS5bok08+0T//+U8tWbLE59i8efOUn5+v2bNnKzQ0VB06dNCmTZs0depU3XfffX6qGAAAoPoCpscsJydHw4cP19/+9jc1aNCgzPG0tDRdf/31Cg0NLdnXt29fZWZm6siRI+ezVAAAgDMSEMHMcRwNHTpUDzzwgK666qpy22RnZysmJsZnX/Hf2dnZFZ47Ly9PXq/XZwMAAPAHvwaz0aNHy+VyVbrt2LFDf/rTn5Sbm6sxY8ac9RomTZokt9tdssXFxZ315wAAAKgOl+M4jr+e/ODBgzp06FClbRISEnT77bdr0aJFcrlcJfsLCwsVHBysQYMG6e2339bgwYPl9Xr1wQcflLRZuXKlbrjhBh0+fFiNGzcu9/x5eXnKy8sr+dvr9SouLk4ej0cRERE/7wUCAIDzwuv1yu12B/znt18n/0dFRSkqKqrKdq+99pr+8Ic/lPydlZWlvn37av78+erWrZskKSkpSWPHjlVBQYHq1asnSVq2bJkuu+yyCkOZJIWFhSksLOxnvhIAAICfLyDuyrzooot8/m7YsKEkqU2bNmrZsqUk6be//a0mTJigYcOG6amnntLWrVv16quvatq0aee9XgAAgDMREMGsOtxutz755BOlpqYqMTFRzZo103PPPcdSGQAAIGD4dY6ZjWrLGDUAAHVJbfn8DojlMgAAAOoCghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgCYIZAACAJQhmAAAAlgioYPY///M/6tatm+rXr6/GjRvrtttu8zm+Z88e3XzzzWrQoIGio6P1xBNP6OTJk/4pFgAAoIZC/F1Adf3zn//U8OHD9eKLL+qGG27QyZMntXXr1pLjhYWFuvnmmxUbG6u1a9dq//79Gjx4sOrVq6cXX3zRj5UDAABUj8txHMffRVTl5MmTat26tSZMmKBhw4aV22bJkiW65ZZblJWVpZiYGEnSjBkz9NRTT+ngwYMKDQ2t1nN5vV653W55PB5FRESctdcAAADOndry+R0QQ5kbN27Uvn37FBQUpM6dO6t58+a66aabfHrM0tLS1KlTp5JQJkl9+/aV1+vVtm3bKjx3Xl6evF6vzwYAAOAPARHMvvvuO0nS+PHj9cwzz2jx4sVq3LixevbsqcOHD0uSsrOzfUKZpJK/s7OzKzz3pEmT5Ha7S7a4uLhz9CoAAAAq59dgNnr0aLlcrkq3HTt2qKioSJI0duxYDRgwQImJiZozZ45cLpcWLFjws2oYM2aMPB5PybZ3796z8dIAAABqzK+T/0eNGqWhQ4dW2iYhIUH79++XJLVv375kf1hYmBISErRnzx5JUmxsrNLT030em5OTU3KsImFhYQoLCzuT8gEAAM4qvwazqKgoRUVFVdkuMTFRYWFhyszM1C9+8QtJUkFBgXbv3q1WrVpJkpKSkjRx4kQdOHBA0dHRkqRly5YpIiLCJ9ABAADYKiCWy4iIiNADDzygcePGKS4uTq1atdKUKVMkSQMHDpQk9enTR+3bt9fdd9+tl156SdnZ2XrmmWeUmppKjxgAAAgIARHMJGnKlCkKCQnR3XffrZ9++kndunXTihUr1LhxY0lScHCwFi9erBEjRigpKUkXXHCBhgwZoueff97PlQMAAFRPQKxjdj7VlnVQAACoS2rL53dALJcBAABQFxDMAAAALEEwAwAAsITfgll2tvTQQ1JCghQWJsXFSbfeKi1fLt1xh5SS4tt+6VLJ5ZLGj/fdP368dNFF5vfdu02b8rZ16yquZds2acAAqXVrye2OkPRImTaTJkldu0qNGknR0dJtt0mZmb5tevYs+7wPPFCTqwIAAOoyvwSz3bulxERpxQppyhRpyxYTvJKTpdRU8/Pzz6WTJ0sfs3KlCW+rVvmea+VK0/5Un34q7d/vuyUmVlzP8eMmIE6eLMXEFJXbZvVqU9u6ddKyZVJBgdSnj/Tjj77thg/3fd6XXqr2ZQEAAHWcX5bL+P3vTW9Serp0wQWl+zt0kO69VzpwQDp2TPrf/5WuucYcW7VKGj1aGjVKOnFCCg83P9evl+65x/f8TZtKlSz2X0bXrmaTpKeeKr/N0qW+f//lL6bnLCNDuv760v0NGtTsuQEAAIqd9x6zw4dNyElN9Q1lxSIjpUsvlVq0ML1hkpSbK23cKA0caIYb09LM/rVrpby8sj1m54PHY342aeK7f948qVkzqWNHacwY0xsHAABQHee9x2znTslxpLZtK2+XnGx6ycaMkT77zIS1qCjTO7VqVenx+HjpP9/KVKJ7dynotMh57NjZew1FRdKjj0rXXmsCWLHf/tbU0qKFtHmz6X3LzJQW/MNR+q7DOpB7QtGNwnV1fBMFB7nOXkEAAKBWOO/BrLrL2fbsacJPQYEJYD17mv09ekhvvml+Lw5op5s/X2rXruz+PXukU7828+mnzVZTqanS1q3SmjW++++7r/T3Tp2k5s2lXr2kq55M05GQIyXHmrvDNe7W9krp2LzmTw4AAGqt8z6UecklZn7Zjh2Vt0tONhPrN2wwQ5o9epj9PXqYeWWHD5ufN9xQ9rFxcdLFF/tukunJ2rSpdDuTOyYffFBavNjU1LJl5W29DbMlSVl7gn32Z3tOaMTcjVq6dX/NC7CATXfUlncnrMsl3XxzaZtjx8y/W8uWUv36JpzPmFF6vLLnXrDgjC8TAAA1dt57zJo0kfr2lf78Z+nhh8vOMzt61Mwza9PGfOD/618mRBUHswsvNNsf/yjl59dsfllISGlIqynHMWFk4cLSIdTKFBY5emb2D5JiFdwwz/dcklySJizarhvbxwbUsObu3WYINzLS3FHbqZPp1fz4Y9OT+Nhj0uOPmztqQ/7z7qrpHbUdOvjua9q04nref9+8D4odOiRdcYWZj1hs5EhzB/DcuWaO4iefmBtQWrSQfvUrU9v+0zLyW2+Z13fTTVVfEwAAzha/3JX55z+bD/err5aef166/HLzQb5smTR9uvTVV6ZdcrL0xhsmTMXElD6+Rw/pT38qvUngdIcOmV6dU0VGmjs5y5OfL23fXvq7dKE2bw5SbGxpkEtNld55R/rwQ7OWWfH53W7TC/Ptt+b4L39pgsTfP85V5vx2Cos7pNDo3DLP6Uja7zmh9F2HldSmkuRhGdvuqD395ov33jN3xp4azNaulYYMKR0Ov+8+Mxyenm6CWXBw2edcuFC6/XapYcPq1wIAwM/ll3XMEhLMXZbJyebDumNH6cYbzVDY9Oml7ZKTzR2ZxR+oxXr0MPsr6i3r3dvM7zp1++CDiuvJypI6dzZbdnaQpCd03XUN9bvflbaZPt3cidmzp+955883x0NDTW9Pnz7mxoZXJjZQg0uzFT3gfyu9FgdyT1R63CaBcEftrFlmOPXU+rp3Nz2v+/aZns+VK6Wvvzb/VuXJyDC9tMOGnd3aAACoistxqjsdv244W99On/btId05s5LJUf/x7vBrAqbHLD1d6tbNDB/+139V3O6uu6SDB83w5kcfSU88Yb5d4f77Tc/UhAnSc8+ZocXvvjOP2b3bDA/Xr3/md9QW17d+vemNLZaXZ3rJ/vpXM7waFCTNnCkNHlz+eX7/e9PLV9yLCgCw39n6/PY3vivzHLk6vomau8NV0ewxl8zdmVfHN6mghX1qckft55+Xf0dt8Tyzyu6oPfUGjU2bzP49e8ywYvH24otlHztrlpnzdmook8yw97p1ptcsI8PMT0xNNT2cp/vpJzMkfWpvWWGRo7RvD+nDTfuU9u0hFRbx/zIAgHPDL3PM6oLgIJfG3dpeI+ZulEtmTlmx4rA27tb2ATXx/0zvqH3iCbO/Rw8zD634jtr77y/72OI7ak9XfEdtsdPnlv34o5lf9vzzvvt/+sksibJwYemdmpdfbs718stm2PtU//iHWRS4uDdt6db9mrBou/Z7SoecWe4EAHCu0GN2DqV0bK7pd3VRrNv3roNYd7im39Ul4D7YT72j9vTvCJXMHbXSub2jtng7PZgtWGCGLO+6y3d/QYHZTh8eDQ42CwWfbtYsc0NAVJQJZSPmbvQJZVLgL3cCALAXPWbnWErH5rqxfWytWfnftjtqi82aJd12W9mlNSIizHM+8YSZv9aqlflC+r/+VZo61bftzp3Sv/9t5sUVFjmasGi7yhu0DOTlTgAAdqPH7DwIDnIpqU1T9bvyQiW1aRrQH+S23VErma+9WrOm4rso33vPfEn9oEFmcdnJk6WJE8suMDx7tlmEtk8fKX3X4TI9Zac6dbkTAADOFu7KPE1tuasDP8+Hm/bpkfc2Vdnu1TuuVL8rLzz3BQEAKlVbPr/pMQPKEd2oirHTGrYDAKA6CGZAOWrjcicAAPsRzIByFC93IqlMOAvU5U4AAPYjmAEVqG3LnQAA7MdyGUAlattyJwAAuxHMgCoUL3cCAMC5xlAmAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlWPn/NI7jSJK8Xq+fKwEAANVV/Lld/DkeqAhmp8nNzZUkxcXF+bkSAABQU7m5uXK73f4u44y5nECPlmdZUVGRsrKy1KhRI7lcde+Lqr1er+Li4rR3715FRET4u5yAw/U7c1y7M8e1O3NcuzNn27VzHEe5ublq0aKFgoICd6YWPWanCQoKUsuWLf1dht9FRERY8R9aoOL6nTmu3Znj2p05rt2Zs+naBXJPWbHAjZQAAAC1DMEMAADAEgQz+AgLC9O4ceMUFhbm71ICEtfvzHHtzhzX7sxx7c4c1+7cYPI/AACAJegxAwAAsATBDAAAwBIEMwAAAEsQzAAAACxBMKujCgsL9eyzzyo+Pl7169dXmzZt9MILL/h8x5jjOHruuefUvHlz1a9fX71799Y333zjx6rtUJ1rN3ToULlcLp8tJSXFj1XbIzc3V48++qhatWql+vXrq3v37tqwYUPJcd53Favq2vG+M/7973/r1ltvVYsWLeRyufTBBx/4HK/Oe+zw4cMaNGiQIiIiFBkZqWHDhunYsWPn8VX4x9m4dq1bty7zPpw8efJ5fBUBzkGdNHHiRKdp06bO4sWLnV27djkLFixwGjZs6Lz66qslbSZPnuy43W7ngw8+cL788kvnV7/6lRMfH+/89NNPfqzc/6pz7YYMGeKkpKQ4+/fvL9kOHz7sx6rtcfvttzvt27d3Vq9e7XzzzTfOuHHjnIiICOeHH35wHIf3XWWquna874yPPvrIGTt2rPP+++87kpyFCxf6HK/OeywlJcW54oornHXr1jmfffaZc/HFFzt33nnneX4l59/ZuHatWrVynn/+eZ/34bFjx87zKwlcBLM66uabb3buvfden339+/d3Bg0a5DiO4xQVFTmxsbHOlClTSo4fPXrUCQsLc959993zWqttqrp2jmM+IPv163eeK7Pf8ePHneDgYGfx4sU++7t06eKMHTuW910lqrp2jsP7rjynh4vqvMe2b9/uSHI2bNhQ0mbJkiWOy+Vy9u3bd95q97czuXaOY4LZtGnTzmOltQtDmXVU9+7dtXz5cn399deSpC+//FJr1qzRTTfdJEnatWuXsrOz1bt375LHuN1udevWTWlpaX6p2RZVXbtiq1atUnR0tC677DKNGDFChw4d8ke5Vjl58qQKCwsVHh7us79+/fpas2YN77tKVHXtivG+q1x13mNpaWmKjIzUVVddVdKmd+/eCgoK0vr16897zbaoyX+fkydPVtOmTdW5c2dNmTJFJ0+ePN/lBiy+xLyOGj16tLxer9q2bavg4GAVFhZq4sSJGjRokCQpOztbkhQTE+PzuJiYmJJjdVVV106SUlJS1L9/f8XHx+vbb7/V008/rZtuuklpaWkKDg72Y/X+1ahRIyUlJemFF15Qu3btFBMTo3fffVdpaWm6+OKLed9VoqprJ/G+q47qvMeys7MVHR3tczwkJERNmjSp0+/D6v73+fDDD6tLly5q0qSJ1q5dqzFjxmj//v2aOnXqea03UBHM6qi///3vmjdvnt555x116NBBmzZt0qOPPqoWLVpoyJAh/i7PatW5dnfccUdJ+06dOunyyy9XmzZttGrVKvXq1ctfpVvhb3/7m+69915deOGFCg4OVpcuXXTnnXcqIyPD36VZr6prx/sONhg5cmTJ75dffrlCQ0N1//33a9KkSXx9UzUwlFlHPfHEExo9erTuuOMOderUSXfffbcee+wxTZo0SZIUGxsrScrJyfF5XE5OTsmxuqqqa1eehIQENWvWTDt37jyPldqpTZs2Wr16tY4dO6a9e/cqPT1dBQUFSkhI4H1XhcquXXl435VVnfdYbGysDhw44HP85MmTOnz4cJ1+H57pf5/dunXTyZMntXv37nNZXq1BMKujjh8/rqAg33/+4OBgFRUVSZLi4+MVGxur5cuXlxz3er1av369kpKSzmuttqnq2pXnhx9+0KFDh9S8efNzXV7AuOCCC9S8eXMdOXJEH3/8sfr168f7rprKu3bl4X1XVnXeY0lJSTp69KhPL+6KFStUVFSkbt26nfeabXGm/31u2rRJQUFBZYaHUQF/330A/xgyZIhz4YUXliz58P777zvNmjVznnzyyZI2kydPdiIjI50PP/zQ2bx5s9OvXz+WLXCqvna5ubnO448/7qSlpTm7du1yPv30U6dLly7OJZdc4pw4ccLP1fvf0qVLnSVLljjfffed88knnzhXXHGF061bNyc/P99xHN53lans2vG+K5Wbm+t88cUXzhdffOFIcqZOnep88cUXzvfff+84TvXeYykpKU7nzp2d9evXO2vWrHEuueSSOrFcxs+9dmvXrnWmTZvmbNq0yfn222+duXPnOlFRUc7gwYP9+bICCsGsjvJ6vc4jjzziXHTRRU54eLiTkJDgjB071snLyytpU1RU5Dz77LNOTEyMExYW5vTq1cvJzMz0Y9V2qOraHT9+3OnTp48TFRXl1KtXz2nVqpUzfPhwJzs728+V22H+/PlOQkKCExoa6sTGxjqpqanO0aNHS47zvqtYZdeO912plStXOpLKbEOGDHEcp3rvsUOHDjl33nmn07BhQyciIsK55557nNzcXD+8mvPr5167jIwMp1u3bo7b7XbCw8Oddu3aOS+++GKd+5+Dn8PlOKcsVw4AAAC/YY4ZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghmAWquwsFDdu3dX//79ffZ7PB7FxcVp7NixfqoMAMrHyv8AarWvv/5aV155pWbOnKlBgwZJkgYPHqwvv/xSGzZsUGhoqJ8rBIBSBDMAtd5rr72m8ePHa9u2bUpPT9fAgQO1YcMGXXHFFf4uDQB8EMwA1HqO4+iGG25QcHCwtmzZooceekjPPPOMv8sCgDIIZgDqhB07dqhdu3bq1KmTNm7cqJCQEH+XBABlMPkfQJ0we/ZsNWjQQLt27dIPP/zg73IAoFz0mAGo9dauXasePXrok08+0R/+8AdJ0qeffiqXy+XnygDAFz1mAGq148ePa+jQoRoxYoSSk5M1a9Yspaena8aMGf4uDQDKoMcMQK32yCOP6KOPPtKXX36pBg0aSJLefPNNPf7449qyZYtat27t3wIB4BQEMwC11urVq9WrVy+tWrVKv/jFL3yO9e3bVydPnmRIE4BVCGYAAACWYI4ZAACAJQhmAAAAliCYAQAAWIJgBgAAYAmCGQAAgCUIZgAAAJYgmAEAAFiCYAYAAGAJghkAAIAlCGYAAACWIJgBAABYgmAGAABgif8PxaFKNNZylfkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Extracting data\n",
    "i = 0\n",
    "vectors = []\n",
    "labels = []\n",
    "for entry in result['data']['Get']['CVE_CWE']:\n",
    "    if i <= 3:\n",
    "        vectors.append(entry['_additional']['featureProjection']['vector'])\n",
    "        labels.append(entry['cwe_id'])\n",
    "    i += 1\n",
    "\n",
    "\n",
    "reference_vector = np.array(vectors[0])\n",
    "for i, vector in enumerate(vectors):\n",
    "    if i == 0:\n",
    "        label_str = str(labels[0])\n",
    "        print(f'Reference label: {label_str}')\n",
    "    else:\n",
    "        distance = np.linalg.norm(reference_vector - np.array(vector))\n",
    "        print(f'Distance between {label_str} and {labels[i]}: {distance}')\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plotting points\n",
    "ax.scatter([v[0] for v in vectors], [v[1] for v in vectors])\n",
    "\n",
    "# Adding labels\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0:\n",
    "        label_str = str(label)\n",
    "        ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CVE-{label_str[:4]}-{label_str[4:]}', color='red', ha='left')\n",
    "    else:\n",
    "        if label == 787 or label == 125:\n",
    "            ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CWE-{label}', color='blue', ha='right')\n",
    "        else:\n",
    "            ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CWE-{label}', color='blue', ha='left')\n",
    "\n",
    "# Set labels\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference label: 202139275\n",
      "Distance between 202139275 and 787: 125.45807785002293\n",
      "Distance between 202139275 and 120: 101.35168759299195\n",
      "Distance between 202139275 and 125: 123.97221192314494\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Extracting data\n",
    "i = 0\n",
    "vectors = []\n",
    "labels = []\n",
    "for entry in result['data']['Get']['CVE_CWE']:\n",
    "    if i <= 3:\n",
    "        # Take only the last two dimensions of the vector\n",
    "        vectors.append(entry['_additional']['featureProjection']['vector'][-2:])\n",
    "        labels.append(entry['cwe_id'])\n",
    "    i += 1\n",
    "\n",
    "# Calculate and print distances\n",
    "reference_vector = np.array(vectors[0])\n",
    "for i, vector in enumerate(vectors):\n",
    "    if i == 0:\n",
    "        label_str = str(labels[0])\n",
    "        print(f'Reference label: {label_str}')\n",
    "    else:\n",
    "        distance = np.linalg.norm(reference_vector - np.array(vector))\n",
    "        print(f'Distance between {label_str} and {labels[i]}: {distance}')\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plotting points\n",
    "ax.scatter([v[0] for v in vectors], [v[1] for v in vectors])\n",
    "\n",
    "# Adding labels\n",
    "for i, label in enumerate(labels):\n",
    "    if i == 0:\n",
    "        label_str = str(label)\n",
    "        ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CVE-{label_str[:4]}-{label_str[4:]}', color='red', ha='left')\n",
    "    else:\n",
    "        if label == 787 or label == 125:\n",
    "            ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CWE-{label}', color='blue', ha='right')\n",
    "        else:\n",
    "            ax.text(vectors[i][0] - 0.05, vectors[i][1], f'CWE-{label}', color='blue', ha='left')\n",
    "\n",
    "# Set labels\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}