k_means_starter.ipynb

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        "<a href=\"https://colab.research.google.com/github/wingated/cs180_labs/blob/main/k_means_starter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "%matplotlib inline"
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLRplV1_Yjlo"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from tqdm import tqdm\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [10,8]"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
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        "id": "8Y1kZzWFYlJQ"
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      "source": [
        "CNT = 100\n",
        "data1 = 0.5*np.random.randn(CNT,2)\n",
        "data2 = 0.5*np.random.randn(CNT,2) + np.atleast_2d([2,2])\n",
        "data = np.vstack([data1,data2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# TODO\n",
        "I want you to code up K-means in your group.  Here's the high level outline:\n",
        "\n",
        "*   Initialize centroids randomly\n",
        "\n",
        "*   Repeat until done:\n",
        "  *   Calculate distances from every point to every centroid\n",
        "  *   Label each point with the nearest centroid\n",
        "  *   Recalculate centroid values\n",
        "\n",
        "*   Optional (but highly recommended): plot the data, colored by centroid; plot the centroids\n",
        "\n",
        "Here's the visualization we saw in class:\n",
        "\n",
        "https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html\n",
        "\n",
        "Hint:\n",
        "\n",
        "*   Make your distance function a parameter.\n",
        "*   What changes if you use L1 vs. L2 distances?\n",
        "\n",
        "If you're looking for an extra challenge, maybe parameterize some of this code with different interactive elements:\n",
        "https://colab.research.google.com/notebooks/forms.ipynb#scrollTo=WPib9hvO8Pmb\n"
      ]
    },
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      },
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        "import ipywidgets as widgets\n",
        "from IPython.display import display, clear_output\n",
        "button = widgets.Button(description=\"Click Me!\")\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
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        "def on_button_clicked(b):\n",
        "  #\n",
        "  # ANIMATE STUFF HERE\n",
        "  #\n",
        "\n",
        "  clear_output()\n",
        "  plt.imshow(np.random.rand(50,50,3))\n",
        "  display(button)\n",
        "\n",
        "button.on_click(on_button_clicked)\n",
        "display(button)"
      ],
      "execution_count": 4,
      "outputs": [
        {
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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}