example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1ddgCM_qEHZa",
    "outputId": "d8ad88e7-59b9-4097-cbd7-70668b9fec88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kmeans-pytorch in /usr/local/lib/python3.6/dist-packages (0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install kmeans-pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R8_IiKZZEU9i"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from kmeans_pytorch import kmeans, kmeans_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iyoljrh1FCxJ"
   },
   "outputs": [],
   "source": [
    "# set random seed\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tcnoTA16FHbJ"
   },
   "outputs": [],
   "source": [
    "# data\n",
    "data_size, dims, num_clusters = 1000, 2, 3\n",
    "x = np.random.randn(data_size, dims) / 6\n",
    "x = torch.from_numpy(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ar-lcW3OFTXI"
   },
   "outputs": [],
   "source": [
    "# set device\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda:0')\n",
    "else:\n",
    "    device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "KsM9zQZ5FYKp",
    "outputId": "c37d7629-560f-4191-bbee-2f5523d2cda2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[running kmeans]: 7it [00:00, 200.48it/s, center_shift=0.000068, iteration=7, tol=0.000100]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running k-means on cuda:0..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# k-means\n",
    "cluster_ids_x, cluster_centers = kmeans(\n",
    "    X=x, num_clusters=num_clusters, distance='euclidean', device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 797
    },
    "colab_type": "code",
    "id": "IdzkYHBEFdja",
    "outputId": "3bd48cc3-487c-40f7-826a-c8fccecfce89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2, 0, 2, 0, 1, 0, 1, 0, 1, 1, 2, 2, 0, 1, 0, 0, 0, 1, 2, 2, 0, 2, 1, 1,\n",
      "        2, 0, 1, 2, 2, 1, 2, 0, 1, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 0, 1, 1, 2, 2,\n",
      "        0, 1, 0, 2, 1, 0, 0, 2, 2, 1, 0, 1, 0, 2, 1, 1, 1, 0, 2, 1, 2, 1, 2, 1,\n",
      "        1, 2, 2, 1, 0, 2, 1, 1, 1, 2, 1, 1, 1, 0, 2, 2, 1, 2, 2, 1, 0, 0, 2, 1,\n",
      "        1, 0, 0, 0, 1, 1, 1, 0, 2, 1, 0, 2, 1, 2, 0, 0, 1, 0, 2, 2, 2, 1, 1, 1,\n",
      "        1, 0, 1, 0, 2, 1, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 2,\n",
      "        2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2, 1, 0, 2, 0, 0, 1, 0, 2, 2, 0, 0,\n",
      "        2, 1, 0, 1, 0, 2, 2, 0, 0, 0, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 2, 0, 1, 0,\n",
      "        2, 2, 1, 1, 0, 0, 2, 2, 1, 0, 2, 0, 2, 1, 2, 1, 1, 0, 2, 0, 0, 2, 2, 2,\n",
      "        0, 1, 0, 1, 1, 2, 1, 2, 1, 0, 0, 2, 2, 2, 2, 0, 1, 1, 1, 2, 1, 0, 2, 0,\n",
      "        0, 2, 2, 1, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 0, 2, 1,\n",
      "        1, 0, 1, 2, 1, 2, 0, 2, 2, 0, 2, 1, 0, 1, 1, 1, 2, 0, 1, 2, 2, 1, 1, 1,\n",
      "        0, 1, 0, 1, 2, 0, 2, 1, 2, 1, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 0, 2, 1,\n",
      "        0, 0, 2, 0, 2, 0, 1, 2, 1, 2, 0, 0, 2, 1, 1, 1, 1, 0, 2, 0, 2, 2, 1, 0,\n",
      "        1, 2, 2, 1, 1, 1, 2, 2, 0, 0, 1, 2, 1, 1, 0, 1, 2, 1, 2, 0, 0, 2, 0, 1,\n",
      "        1, 1, 2, 2, 1, 2, 0, 2, 0, 0, 2, 0, 2, 1, 2, 1, 1, 2, 2, 0, 1, 0, 0, 0,\n",
      "        0, 1, 0, 2, 2, 1, 0, 2, 0, 0, 2, 2, 2, 0, 1, 2, 0, 2, 2, 1, 2, 1, 2, 1,\n",
      "        0, 0, 0, 2, 0, 2, 2, 2, 0, 1, 1, 0, 2, 2, 0, 2, 2, 1, 0, 0, 2, 2, 0, 0,\n",
      "        1, 0, 1, 2, 0, 2, 0, 1, 0, 0, 0, 1, 2, 2, 1, 1, 2, 1, 1, 1, 0, 0, 2, 0,\n",
      "        0, 0, 2, 1, 1, 1, 2, 2, 2, 2, 0, 0, 1, 2, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2,\n",
      "        0, 0, 0, 0, 2, 1, 0, 2, 1, 1, 2, 1, 0, 2, 0, 2, 0, 2, 1, 1, 2, 1, 0, 0,\n",
      "        0, 1, 2, 1, 1, 0, 2, 0, 2, 1, 2, 1, 1, 2, 0, 1, 0, 0, 2, 0, 2, 2, 2, 1,\n",
      "        1, 2, 1, 1, 2, 1, 1, 1, 1, 0, 0, 2, 1, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 1,\n",
      "        1, 1, 1, 1, 0, 0, 2, 2, 1, 0, 1, 2, 2, 0, 1, 0, 2, 0, 2, 2, 2, 0, 1, 2,\n",
      "        2, 0, 1, 2, 1, 1, 2, 1, 2, 1, 0, 2, 0, 2, 1, 0, 2, 0, 1, 2, 1, 2, 1, 0,\n",
      "        1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 2, 0, 0, 2, 1, 0, 1, 1, 1, 0, 0,\n",
      "        2, 1, 0, 2, 1, 1, 0, 2, 1, 2, 0, 2, 2, 1, 1, 0, 0, 2, 0, 2, 1, 1, 0, 1,\n",
      "        1, 0, 2, 2, 2, 1, 0, 0, 2, 1, 1, 1, 2, 1, 0, 1, 1, 1, 2, 2, 1, 1, 2, 1,\n",
      "        0, 1, 0, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 1, 2, 1, 1, 1, 1, 0, 1, 0, 0, 2,\n",
      "        1, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 2, 2, 2, 0,\n",
      "        2, 2, 0, 2, 2, 1, 1, 1, 1, 0, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 0, 2,\n",
      "        2, 0, 2, 2, 0, 2, 1, 0, 0, 2, 0, 0, 1, 0, 2, 2, 0, 1, 2, 0, 0, 1, 1, 2,\n",
      "        2, 2, 0, 1, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 2, 2, 0, 2, 1, 0, 1, 1, 2, 1,\n",
      "        0, 2, 1, 1, 0, 1, 1, 0, 2, 2, 2, 2, 1, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 2,\n",
      "        1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 2, 1, 2, 1, 2, 0, 0, 2, 1, 0, 1, 0, 0, 2,\n",
      "        2, 2, 2, 0, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 1, 0, 2, 0, 2, 0, 2, 0, 0, 1,\n",
      "        0, 0, 0, 2, 0, 2, 1, 2, 0, 1, 0, 2, 0, 0, 0, 1, 0, 1, 1, 1, 0, 2, 2, 0,\n",
      "        1, 2, 0, 1, 1, 2, 2, 1, 2, 1, 0, 1, 0, 2, 1, 1, 2, 1, 1, 2, 2, 0, 1, 0,\n",
      "        2, 2, 0, 2, 2, 2, 1, 1, 0, 1, 2, 0, 2, 1, 0, 2, 1, 0, 1, 0, 2, 2, 2, 2,\n",
      "        2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 0, 0, 1, 1, 2, 1, 0, 1, 1, 1, 0, 2, 2,\n",
      "        2, 2, 1, 2, 0, 1, 2, 0, 1, 1, 1, 1, 2, 2, 0, 0, 2, 1, 1, 1, 0, 2, 0, 2,\n",
      "        2, 2, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 0])\n",
      "tensor([[-0.1075, -0.1522],\n",
      "        [ 0.1544, -0.0137],\n",
      "        [-0.0833,  0.1454]])\n"
     ]
    }
   ],
   "source": [
    "# cluster IDs and cluster centers\n",
    "print(cluster_ids_x)\n",
    "print(cluster_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "32XHxknWFayP"
   },
   "outputs": [],
   "source": [
    "# more data\n",
    "y = np.random.randn(5, dims) / 6\n",
    "y = torch.from_numpy(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "gQB3jVTKFfsN",
    "outputId": "1f2f634a-4a32-4b05-ca7f-231165e974ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicting on cuda:0..\n"
     ]
    }
   ],
   "source": [
    "# predict cluster ids for y\n",
    "cluster_ids_y = kmeans_predict(\n",
    "    y, cluster_centers, 'euclidean', device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "CGoD8s6_FiDp",
    "outputId": "7137fa6a-f5a8-4096-f5d2-fc74d45eaf2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 2, 0, 1, 2])\n"
     ]
    }
   ],
   "source": [
    "print(cluster_ids_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "colab_type": "code",
    "id": "5V0vxpaUEnFd",
    "outputId": "a97b6261-034e-493a-d8a6-cf955ab5e8fd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tpuBltPrjOeVOBupT3HsA8Be8EsE2rK8HKrmLJeWNt87gxs/Cry6B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\ngg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(4, 3), dpi=160)\n",
    "plt.scatter(x[:, 0], x[:, 1], c=cluster_ids_x, cmap='cool')\n",
    "plt.scatter(y[:, 0], y[:, 1], c=cluster_ids_y, cmap='cool', marker='X')\n",
    "plt.scatter(\n",
    "    cluster_centers[:, 0], cluster_centers[:, 1],\n",
    "    c='white',\n",
    "    alpha=0.6,\n",
    "    edgecolors='black',\n",
    "    linewidths=2\n",
    ")\n",
    "plt.axis([-1, 1, -1, 1])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BHkQ-zw9EnsN"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "example.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}