{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Getting to Main directory\n",
    "import os\n",
    "os.chdir(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "SystemError",
     "evalue": "initialization of _internal failed without raising an exception",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSystemError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20872\\186520048.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mumap\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Generate some random data (e.g., 100 samples with 10 features)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Rajarshi Roy\\anaconda3\\lib\\site-packages\\umap\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mwarnings\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcatch_warnings\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msimplefilter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mumap_\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mUMAP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mcatch_warnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Rajarshi Roy\\anaconda3\\lib\\site-packages\\umap\\umap_.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtril\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msparse_tril\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtriu\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msparse_triu\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcsgraph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mumap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistances\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Rajarshi Roy\\anaconda3\\lib\\site-packages\\numba\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;31m# Re-export vectorize decorators and the thread layer querying function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m from numba.np.ufunc import (vectorize, guvectorize, threading_layer,\n\u001b[0m\u001b[0;32m     43\u001b[0m                             get_num_threads, set_num_threads)\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Rajarshi Roy\\anaconda3\\lib\\site-packages\\numba\\np\\ufunc\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecorators\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mVectorize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGUVectorize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvectorize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mguvectorize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_internal\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPyUFunc_None\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPyUFunc_Zero\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPyUFunc_One\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mufunc\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_internal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marray_exprs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Rajarshi Roy\\anaconda3\\lib\\site-packages\\numba\\np\\ufunc\\decorators.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0minspect\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mufunc\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_internal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mufunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparallel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mParallelUFuncBuilder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mParallelGUFuncBuilder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mSystemError\u001b[0m: initialization of _internal failed without raising an exception"
     ]
    }
   ],
   "source": [
    "import umap\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate some random data (e.g., 100 samples with 10 features)\n",
    "data = np.random.rand(100, 10)\n",
    "\n",
    "# Create a UMAP reducer (default: 2-dimensional embedding)\n",
    "reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2)\n",
    "\n",
    "# Fit the model and transform the data\n",
    "embedding = reducer.fit_transform(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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