{
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# requirements:\n",
    "\n",
    "%pip install -q scikit-dimension pandas numba matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (32, 100)\n",
      "estimated IDs (DANCo, lPCA): 6.6024110947192325 5.0\n"
     ]
    }
   ],
   "source": [
    "import skdim\n",
    "import numpy as np\n",
    "\n",
    "#generate data : np.array (n_points x n_dim). Here a uniformly sampled 5-ball embedded in 10 dimensions\n",
    "n_rows = 32\n",
    "n_features = 100\n",
    "n_components = 5\n",
    "\n",
    "data = np.zeros((n_rows, n_features))\n",
    "\n",
    "data[:,:n_components] = skdim.datasets.hyperBall(n=n_rows, d=n_components, radius=2)\n",
    "\n",
    "# add some correlation\n",
    "data[:,8] = data[:, 1] + np.random.rand(n_rows)\n",
    "data[:,12] = data[:, 1] + np.random.rand(n_rows)\n",
    "data[:,20] = data[:, 2] + np.random.rand(n_rows)\n",
    "\n",
    "print('data shape:', data.shape)\n",
    "\n",
    "#estimate global intrinsic dimension\n",
    "danco = skdim.id.DANCo().fit(data)\n",
    "\n",
    "#estimate local intrinsic dimension (dimension in k-nearest-neighborhoods around each point):\n",
    "lpca = skdim.id.lPCA().fit_pw(data,\n",
    "                              n_neighbors=n_rows-1,\n",
    "                              n_jobs=1)\n",
    "\n",
    "#get estimated intrinsic dimension\n",
    "print('estimated IDs (DANCo, lPCA):', danco.dimension_, np.mean(lpca.dimension_pw_))"
   ]
  }
 ],
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