two linked tori generator.ipynb

two-linked-tori-generator.ipynb

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      "name": "two linked tori generator.ipynb",
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        "<a href=\"https://colab.research.google.com/gist/NTT123/143a4b7cadc38fa1656c0732c1dcf3c5/two-linked-tori-generator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Two Linked Tori\n",
        "\n",
        "Our aim is to generate a dataset of 3d points with the following distribution\n",
        "\n",
        "<img src=\"http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/img/link.png\" alt=\"drawing\" width=\"400\"/>\n",
        "\n",
        "Source: Christopher Olah, http://colah.github.io/posts/2014-03-NN-Manifolds-Topology"
      ]
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      "source": [
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "\n",
        "def gen_torus(N, R=5., r=0.5):\n",
        "    import math\n",
        "    theta = np.random.rand(N) * 2. * math.pi\n",
        "    phi   = np.random.rand(N) * 2. * math.pi\n",
        "\n",
        "    data = np.empty((N, 3))\n",
        "    # https://en.m.wikipedia.org/wiki/Torus\n",
        "    data[:, 0] = (r * np.cos(theta) + R) * np.cos(phi)\n",
        "    data[:, 1] = (r * np.cos(theta) + R) * np.sin(phi)\n",
        "    data[:, 2] = r * np.sin(theta)\n",
        "    return data\n",
        "\n",
        "\n",
        "def make_two_linked_tori(n: int) -> np.ndarray :\n",
        "\n",
        "    data1 = gen_torus(n)\n",
        "    data1[:, 1] -= 2.5\n",
        "    data2 = gen_torus(n)\n",
        "    data2[:, 0], data2[:, 2] = np.copy(data2[:, 2]), np.copy(data2[:, 0])\n",
        "    data2[:, 1] += 2.5\n",
        "    label = np.concatenate((np.ones(n), np.zeros(n)))\n",
        "    data = np.concatenate((data1, data2))\n",
        "    \n",
        "    return data, label"
      ],
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      "source": [
        "\n",
        "data, label = make_two_linked_tori(1000)\n",
        "\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "_ = ax.scatter(data[:, 0], data[:, 1], data[:, 2], c = label)\n",
        "ax.axis(\"equal\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "lXqPEh_w-_Tm",
        "colab_type": "code",
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      },
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "wrt = SummaryWriter(log_dir=\"./logs\")\n",
        "wrt.add_embedding(data)\n",
        "wrt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
        "id": "g_HggZ9qKTPQ",
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      },
      "source": [
        "%load_ext tensorboard"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "metadata": {
        "id": "_6T-M2y9KWca",
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      },
      "source": [
        "%tensorboard --logdir ./logs --port 1246"
      ],
      "execution_count": 0,
      "outputs": []
    }
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}