georgehc/95865_recitation_clustering.ipynb Secret

## 95865_recitation_clustering.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 95-865: More on Clustering\n",
    "\n",
    "This demo covers clustering techniques on handwritten digits. We will look into K-means and GMM. We will also use dimensionality reduction with t-SNE and classification over the 2-dimensional data.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jDv8c_hpmA2t"
   },
   "source": [
    "# Part 1 - Clustering on Images, and a Sketch of How to Make Sense of the Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oe4cvczHV9BO"
   },
   "source": [
    "In this part, we visit the MNIST handwritten digits example that we saw earlier in the course with t-SNE. We will be looking at clustering techniques as well some visualizations to make sense of the clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the necessary elements plot our clusters as well as numpy for array manipulation. We also create some difinitions for our libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "9qPHRjokq2px"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "np.set_printoptions(formatter=None)  # This piece of code resets the print format for numpy to default\n",
    "plt.style.use('seaborn') # Matplotlib will print in seaborn style (http://seaborn.pydata.org/tutorial/aesthetics.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "<style>\n",
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       "    display: table-cell;\n",
       "    text-align: center;\n",
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       "}\n",
       "</style>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aligns our plots to the center\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
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    "\"\"\")"
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465,
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    },
    "id": "A12-HvC9rCMy",
    "outputId": "7def83b1-b8ae-4b2a-8555-dce0bd267a97"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# -------------------------------------------------------------------------------------------------------------\n",
    "# \"capture\" avoids printing the ouput of the cell. This cell will print the downloading results of the dataset. \n",
    "# If something is not working we can remove \"capture\" to see the output\n",
    "# -------------------------------------------------------------------------------------------------------------\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "train_dataset = torchvision.datasets.MNIST(root='data/',\n",
    "                                           train=True,\n",
    "                                           transform=transforms.ToTensor(),\n",
    "                                           download=True)\n",
    "images = np.array([image.numpy() for image, label in train_dataset]) # The images and labels are stored as numpys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the dataset\n",
    "The dataset has 6000 images. Each image has 28 by 28 pixels, which together can be visulized in the form of a handwritten number. We will start by reshaping the numpy matrix we have to a flatten numpy array. That means before we had 28 by 28 pixels. Now we will have a flat array with 784 elements in it (28x28)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z7R6ArMxmQ1Q",
    "outputId": "a4aed4e2-61a6-4776-b64f-c8548dedcc29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimentions before : (60000, 1, 28, 28)\n",
      "Dimentions after  : (60000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dimentions before :\", images.shape)\n",
    "num_images = images.shape[0] # total number of images\n",
    "feature_vectors = images.reshape(num_images, -1) # reshaping to a flat array\n",
    "print(\"Dimentions after  :\", feature_vectors.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this dataset we also obtain the labels. That we get the actual number that correspond to each of the handwritten numbers (each of the rows/images within the dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "4mIRgQ8Cm8Vl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The labels are: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "labels = np.array([label for image, label in train_dataset]) # obtain ing labels\n",
    "print(\"The labels are:\", np.unique(labels)) # looking at all the possible results (digits 0 to 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling and exploring\n",
    "We will be working with a **sub-sample** of data to make the experiment faster. We will be looking at 5000 sample rows chosen **ramdomly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "quIEYTqBmcj-"
   },
   "outputs": [],
   "source": [
    "np.random.seed(0) # seed makes random numbers to remain constant every time we run the \"random\" method of numpy\n",
    "random_sample = np.random.permutation(num_images)[:5000]  # we pick 5000 random indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='#FF7659'>**How can we look at this images from the numpy arrays?**</font> the method \"imshow\" from pyplot allows us to print the actual images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "L-j317PSmkze",
    "outputId": "908546f6-6704-4f7c-f28f-47bb391bab8d"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inline will make your plot outputs appear and be stored within the notebook\n",
    "%matplotlib inline\n",
    "# we need to reshape again before we use imshow\n",
    "plt.imshow(feature_vectors[np.random.randint(num_images)].reshape(28, 28)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XHxfTfUTmmcH"
   },
   "source": [
    "### Clustering in the original space with k-means\n",
    "\n",
    "Let's first cluster in the original high-dimensional space with k-means. We first import the library from sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only need the handwritten numbers (pixels) to start our clustering exercise. We will save our sample data on the variable **X**. The next step is to train the algorithm and fit the model to the data. n_cluster is then most important parameter and tells us how many clusters will be created fromk the data. Because we know we have 10 numbers, we will need the data to be divided in 10 parts; each part corresponding to a digit (0-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GpD9v89OmtUo",
    "outputId": "e03468b1-aca6-44ba-8b9b-7368f8be5f03"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=10, random_state=0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = feature_vectors[random_sample]\n",
    "\n",
    "kmeans = KMeans(n_clusters=10, n_init=10, random_state=0)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting our k-means model we can start looking at the cluster centers. The result of looking at the cluster_centers_ is a matrix that contains the coordinates of cluster centers. We have a matrix of 10 rows by 784 columns. Each row has 784 values that corresponds to a coordinate in the 784-space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gjeQ0yem4R1n",
    "outputId": "74185851-377e-4cb1-da8b-b98ee79822d9",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimensions: (10, 784)\n",
      "\n",
      "The matrix\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"The dimensions:\", kmeans.cluster_centers_.shape)\n",
    "print(\"\\nThe matrix\")\n",
    "print(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the cluster assignments we call the method \"predict\". This means that each handwritten number (row) will be assigned to a certain cluster (there are 10 cluster based on our n_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "K14t-p5loteO"
   },
   "outputs": [],
   "source": [
    "kmeans_cluster_assignments = kmeans.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For instance, this are the cluster assignments for the first 10 rows: [0 4 9 4 1 2 4 9 3 4]\n",
      "There are 10 clusters corresponding to  [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "print(\"For instance, these are the cluster assignments for the first 10 rows:\", kmeans_cluster_assignments[:10])\n",
    "print(\"There are {0} clusters corresponding to \".format(len(np.unique(kmeans_cluster_assignments))), \n",
    "      np.unique(kmeans_cluster_assignments))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='#FF7659'>**How do we figure out which points are in cluster 0?**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[kmeans_cluster_assignments == 0]\n",
    "\n",
    "# ---------------------------------------------------------------------------------------------------\n",
    "# kmeans_cluster_assignments == 0 gives us all the indices where the assignment is 0\n",
    "# then the indices are used with our original dataset to see all the numbers that were assigned to 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='#FF7659'>**Does cluster 0 actually correspond to digit 0?**</font>\n",
    "\n",
    "Hopefully it makes sense that the answer is, in general, **no**. K-means does not actually use the labels during the model fitting procedure. As such, it does not know what \"digit 0\" means. Moreover, the ordering of clusters that it determines is in an arbitrary order.\n",
    "\n",
    "Let's find out what cluster 0 actually corresponds to in this particular case. For instance, we can plot what the average image is across images in cluster 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x227e808f700>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[kmeans_cluster_assignments == 0].mean(axis=0).reshape(28, 28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing we can do is to plot the cluster center estimated by k-means for cluster 0. This will tell us what handwrittendigit has being assigned to be the center point in the particular cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kmeans.cluster_centers_[0].reshape(28, 28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster centers\n",
    "Let's now visualize all the **cluster centers**. We can then observe is we have all digits from 0 to 9 as centers or if there are centers that might have been confused with other numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "for cluster_idx in range(10):\n",
    "    ax = axes[cluster_idx]\n",
    "    ax.imshow(kmeans.cluster_centers_[cluster_idx].reshape(28, 28))\n",
    "    ax.set_title(f\"Cluster {cluster_idx +1}\", fontdict={\"fontsize\": 30})\n",
    "    fig.suptitle(\"Digits\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.90, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's immediately clear that k-means wasn't able to get all 10 digits as their own clusters!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Another visualization strategy:** we can figure out which images are closest to a cluster center.\n",
    "\n",
    "Let's look at the top 20 images closest to cluster 7. First, we calculated the distances of the points to the center. In this case, we look at the center of cluster 8 (index 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = np.array([np.linalg.norm(point - kmeans.cluster_centers_[7]) for point in X])\n",
    "sorted_indices = np.argsort(distances)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "for idx, (dist, point) in enumerate(zip(distances[sorted_indices], X[sorted_indices])):\n",
    "    ax = axes[idx]\n",
    "    ax.imshow(point.reshape(28, 28))\n",
    "    ax.set_title(\"#%d closest point to cluster 7's center\" % (idx + 1))\n",
    "    fig.suptitle(\"Digits\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.90, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the average image of cluster 7 earlier, it's clear that there's a confusion between 7's and 9's. Here's another visualization: we can compute the standard deviation across individual pixels, just for images in cluster 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATAAAAD3CAYAAACaYQhLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdA0lEQVR4nO3dfZBc1Xnn8e8MehlGmkESejcgyYv8IMxbeLdsggqRxcBitMLZqjW2y1FSjmNXCspJbMoBJ0XxR5xCbJb1xgkYSlUYbznByBVR61hUKYBBRrFZyayQOAIhSrxIQuhdGglJaPJHd19u99x7TnfPne4+0u9Tpaq+9+jcPt3T88y5t597nq7BwUFERGLU3e4BiIg0SwFMRKKlACYi0VIAE5FoKYCJSLRGteh59FWnyMjrGvYBurrq/l0dHBwc9vMNV6sCmIhEoKur7TGpIU0FMDPrBv4euBj4APgj59zrRQ5MRFovtgDW7DWwRUCPc+5TwF3A0sJGJCJt09XVVfe/TtBsAPsM8K8AzrkXgcsLG5GItE13d3fd/zpBs6PoB/altj80M11PE4lcbAGs2aCzH+hLbXc7544XMB4RaaNOOTWsV7Nh9AXgJgAzuxr4/4WNSETaJrZrYM3OwJYDv2dmqynlnvxBcUMSkXbplMBUr64WLaejRFaRkTfs6NPX11f37+qBAwfaHu104V1EErHNwBTARCTRKd8u1ksBTEQSmoGJSLQUwEQkWjqFFJFoaQYmItHSDExEoqUZmIhESwFMRKKlU0gRiZZmYCISLQUwEYlWUaeQ9dbNMLOHgN3OubuaqbWhACYd58SJE972I0eOeNvHjh1btX3aaafx4YcfJo+Hc+xjx45520ePHu1t7+np8ba3W4EzsEWU62aU1wxcCtya/g9m9sfAhcCz9fapFdcVOxEZUQUuKe2tm2FmnwKuBv6x3j6Z4637lYnISa/AFVlz62aY2Qzgr4Fv1Nsnj04hRSRR4Cmkr27G7wOTgf8LTAd6zezVQJ9MmoGJSKLAGVhu3Qzn3IPOucuccwuAvwF+7Jxb5uuTRzMwEUmEvuRowJC6GWb2BWC8c+6hevuEnkRr4kvH0beQTRv2+d+8efPq/l3duHFj25PGNAMTkYQSWaUwtbPjrq6uqn2+2cCYMWMaOnatgYEBb3vtTKanp6dq9uKbaXzwwQfeY+/Zs8fbHpoFjRpV/bGeMWMG7733HhB+3aGxhX7Bp02b5m3vdLoXUkSipRmYiESrwIv4LaEAJiIJzcBEJFq6BiYi0dIMTESipRmYiERLM7AO02i+07hx4zh06FCyffx4/r2kvjYI5yuFxlb7jdDUqVPZuXNnsu3LWapknucJjT00ttqM89mzZ7N9+/a6jh/KtdqxY4e3fffu3d722lnE4sWL+dWvfgXApEmTvH0PHDjgbZ8wYYK3PZRpn75LoLu7u+qug06Y/Zwy30Ka2Vo+Wvpii3MueN+SiHS2U2IGZmY9AOW7yUXkJNEJs8BGNDsDu5jSGj4ry8f4TnkFRRGJWGwzsKZWozCzCyktB/tDYC7wc8A8i49pNQqRkTfs6LNgwYK6f1efeeaZtke7Zmdgm4DXnXODwCYz2wXMAN4qbGQFOdku4lduSobOu4j/5ptv1nX8dlzEf/LJJ4GRv4g/d+5cb/vUqVOTx514ET+2GVizAWwJpWoiXzezmZTWst5W2KhEpC1OlW8hHwGWmdnzlE4Pl4TWrhaRzlfUDCxU49HMbgPuohQ/HnLO/bC8v6HshqYCmHPuKPCFZvo2w3c6EnrDQ6dKWaeQ6X379+/P7Rs6FQqt7rlv3z5v+7vvvlu1ffvtt/P0008n23v37s3t+/7773uPnc4na2ZstadaP/vZz7jzzjuTbd9rP3r0qPfYIZMnT/a2jx8/vmp78eLFPPXUU3X1TZ/iZTn33HO97dOnT/e29/b2Jo/7+/s5ePBgsl27kmytUHsRCjyNXUROjUczO43SWviXAweBDWb2s/LjhrIb2n/SLSIdo8CiHrk1Hp1zHwLznHP7gDMpfflwkFR2g5mtKgc+LwUwEUm0oi4kgHPuuJktBn4LPAccAwaA+4EbgK8Bj4fqQiqAiUjitNNOq/tfQLDGo3PuSeBjwBjgy5SyG37knBt0zm0CKtkNuRTARCTR3d1d97+A3BqPZtZvZs+a2Vjn3AngEHCCUnbD0vL/qSu74aS/mVtE6ldgHpi3LqSZPQ48Z2bHgJeBHwGn0WB2gwKYiCSK+hayPLP6Ws3uV1PtDwG1BW4/pMHshigCmO9r91AR1FB5sNp0gilTplRlu7/1Vv7NBb62etp37drlba/NSL/99ttZvnx5sr1tW/7sOpQmEco4D91FkJWe8swzz9TVP1T8NdReWzat1sSJE4fse/HF0q26oTQKM/O2h9IsQu9b+n3v7++v2g4Fj1akUZwqmfgichJSABORaHXC/ZiNUAATkYRmYCISLQUwEYmWApiIREsBTESipYv4TQgteeP7q5BePbWZ9qx8qfS+V155Jbevrw1g8+bN3va3337b2561lM+zzz6bPPbdj5aVC5UWWvZlz549DY8t/eE//fTTc/uG8pl8fSG8TFHW+1rZF1rt9YwzzvC2h/LnDh8+7G0fN25c1Xb6sx/KaWwFBTARiZZOIUUkWgpgIhItBTARiZYCmIhESxfxRSRamoGJSLQUwJoQWt/Jt6ZXqERXKJ8pq/xYel96bbBaofW8tm/fPqyxZeVLpd+rs846K7dvqIL0li1bvO2hsmpZuVihHKiK0HrqtWXRaoXWC8uq3B3KNawIjS1U8TxUaq/285re7oTTt3bWhQz1ydL+d0xEOkaBVYkWUa4LSSlQLa00pOpCXg98CvgLM5vs65NHAUxEEm2uC5nbJ48CmIgkCqxK1ExdSG+fzPE28uJE5ORW4AysmbqQwT61FMBEJFFgAGumLmRunzwd8S2kiHSGNteFHKztE3oSBTARSRQVwJqsC0lGH68oAlhPT09uW1bOT1oo/yfrYmR635gxY3L7htZvaqa2YlrW2lTp98L32rdu3eo9dugi7IwZM7ztfX19Q/ZddNFFyeOs/LqKUP5baGzD+SULva6ZM2d62325dwCTJk3ytk+ZMiV3u3atsHY4KRNZzewq4HvOuQVmdi6wjNJ0bz3wjXK0FZHIdUIybSOCozWzbwE/BCp/+h8A7nbOXUPpPPXWkRueiLRSgRfxW6KecLsZWJzavgyorGv8c0rZtCJyEogtgAVPIZ1zPzWz2aldXc65wfLjA4B/EfEC+K5DzZ4929s31J5l8eLFmY87QegexnZas2ZNu4eQy3c/bTuF7llttU4JTPVq5iJ++npXH7C3mKHk892w/e6773r7hoo4vPPOO1Xbixcv5sknn0y2165dm9t33bp13mOHin74LnQDnHnmmVXbW7ZsYc6cOcm273rFwYMHvccOXesIFQWpvYi/Zs0arrrqqmR7OBfxQ390QkU93nzzzartgYEBent7ATj77LO9fRcuXOhtv/56/wnH+eef721PF1OZMGECe/fuTbZDBUVaEVxOumtgGdaa2YLy4xuBXxY3HBFppwJvJWqJZmZgfwY8bGZjgI3AE8UOSUTa5aQ8hXTOvQlcXX68Cbi2yEGE8ql818BCa0eF1rXKyttJ7/NN60N5O6G1o0KvO+u6TXqfb/2tUL7T/Pnzve2zZs3ytmedol5zzTXJ41WrVuX2DV2Pmjx5srd9w4YN3vasupKVfXPnzvX2TZ+iZwmdgvb393vbaz+v6e1OCB6dMIZGRJHIKiKtoQAmItFSABORaCmAiUi0OuXbxXopgIlIQjMwEYmWZmBNCL1pvmVnQn8xQmkWWcdOZ5mH+jd67LRQmkU6Sztrn+82lKuvvtp77CuuuMLbHioPVpvtDtXZ976Scuecc4732KHSZqFlirLel8q+UBpEqD30echK4Uir/bx22oyn08YT0hEBTEQ6QwvrQv534E7gQ0orsn7dOXfCzNbyUWGPLc4576qsCmAikijwFHIR5RqP5fXtl1JeesvMTgfuAy50zg2Y2f8B/ouZrQRwzi2oe7xFjVZE4teKupCUZmTznXOVWzJGAUcozdZ6zWylma0qBz4vBTARSRQYwHJrPDrnTjjndgCY2Z8C44GngQHgfuAGSmvjPx6qC6lTSBFJFHgR31vjsXyN7G+BTwC3OecGzWwT8Hp5vcFNZrYLmAG8lfckmoGJSKIVdSHL/pHSMvWLUqeSSyhdK8PMZlKaxW3zPYlmYCKSaEVdSOA3wB9SWktwlZkB/E/gEWCZmT1PqWjQklBl7igCmG9F1rFjx3r7hn4gWTlH6X2Dg4ND2itCuVKhlUN9rwuyl9tJ9/HlHGXlkKWFln8+dOiQt/3ll18esm/16tXJ46yyaxWXXHJJw8dOCy1DlLUqamVfaJmgUI5aoyvV1qr9vIVy3lqtqPGE6kKSf/b3hUaeJ4oAJiKtoURWEYmWApiIREsBTESipQAmItFSABORaGk5HRGJlmZgI6Cnpye3LZRL5eub1z/dx7emVygfKcSXY1Y7jqx9+/fvz+374osveo893L+0WWNLr9N13XXX5fatVMnO41tLDMK5WOnq17X7Qut9+Ur4gb+UHYRzA9Ofp56enqpcwdBntRU0AxORaGkGJiLRUgATkWh12q1NIQpgIpLQDExEoqUAJiLRUgATkWgpgI0AX25KKBdr3Lhx3vb33ntvyL50bk661mGjfHUbIZwzFKobGVqzazhC9Q8vv/zyIfsuuOCC5PHcuXNz+4Zy1ELrqJUXwMs1Y8aM3H2jRvk/8qHPU+hntnv3bm97bQ5bqHZoq52UeWBmdhXwPefcAjO7FFgBvFZu/oFz7icjNUARaZ2iAlgzdSHLTbl9sgQDmJl9C/gSUPlzfynwgHNuaQOvR0QiUOAp5CIarAtJKR5l9slTT7jdDCxObV8G3Gxmz5nZI2bmX0NXRKLR3d1d97+AZupC+vpkCs7AnHM/NbPZqV3/DvzQOfeSmf0l8FfAn4eOM1JC99WFZK3Pnt4XWr+91UL34rXTihUr2j2EXPfee2+7h5ApdK2x1QqcgWXWhXTOHS+vl59VF/K/5fXJe5JmLuIvd87trTwG/lcTxyjMwMCAtz19g3GWLVu2VG1fcsklrFu3Ltlevnx5bt/0/8uyfv16b/uOHTu87bUX8Q8fPlxVyGO4N5P7hH6x5s+fX7W9YsUKbrnllmT705/+dG7f0EX8UMGRiy++2Nt+5ZVXVm3fe++9fPe73wXgk5/8pLfvnDlzvO39/f3e9tCXRumL+OPHj+fgwYNV2+3W5rqQ3j5Zmrli9wszq3xCFgIvNXEMEelAba4LGeozRDMzsD8Bvm9mR4HtwFebOIaIdKAC74Vspi7kkD6hJ6krgDnn3gSuLj/+f8B8b4cWCuX1hNYLyzrFTO8bPXp0bt/Jkyd7j53Oi8oyadIkb/vOnTuH7Js2bVry2JdDFLrIGjr9DK2Ldd5553n3+a7VhU69Q7UZ582b522/8MILc/eF6kKGfia+zwOE37famUunJY4WNZ5h1IWs7eMVRSKriLRGpwXUEAUwEUkogIlItE7KW4lE5NSgACYi0dIppIhESwGsxUJpFKH2vr6ht3Km911xxRW5fc866yzvsbOW6knzlUUDqrK0KxYtWpQ89i3tkpWCkfbrX//a2x56bb5UBYDHHnsst2/o9q/Pfvaz3vbauwBqZZVOq2TgZ/2800JpEOk7IbKMHTu2oeMP91a4oukUUkSipRmYiERLAUxEoqWyaiISrdhmYHFdsRMRSdEMTEQSsc3AFMBEJKEA1mKh5XJCpclqy1zV7vPlxWSV70oLLf8cKtG1b9++IfsWLlyYPHbO5fbdvHmz99ihcl7p58nym9/8pmr7y1/+ctW+DRs25Pb93Oc+N6znPvfcc73tWUveTJkyBQjnOQ33Inbofa3NE+u0gNFp4wmJPoCJSHGUyCoi0dIMTESiVVQACxW2Lf+fXkrViP7QOfdqed9aPqpMtMU5511WWgFMREbCIjxFas3scuAfgLNS+3oAnHML6n2SuE54RWREFViVKFSkdizwX6leJ/9ioNfMVprZqnLg81IAE5FEgZW5MwvbVjaccy84596q6TMA3A/cQKm4x+PpPll0CikiiVYVts2xCXjdOTcIbDKzXcAMoDbQJU76ABb6gWSt/5TeF1r/qdFjp2Wt95WWtaZXOu/tjTfeyO3ryxEDmDlzprc99Lqfeuqpqu0HH3ywap+vNNpnPvMZ77E/9rGPDWtsWe97Zd2t0Mwh9HkJ9Y8tDaFWgQHsBeAW4J/qLVILLAEuBL5uZjMpzeK2+Tqc9AFMROpXYADLLWzrnHsop88jwDIzex4YBJaEZm0KYCKSaGFh28r/W5B6fBT4QiPPE/d8V0ROaZqBiUgitmt4CmAiktCtRCISLQUwEYmWAliLnThxwtseqguZ1b+oH2LoOAMDA972jRs3evetXr06t++RI0e8x77gggu87U8//bS3/cCBA959t912W27fWbNmeY8det9Ca25l/cwrP+fQel89PT3Deu7YimLUOqkCmJmNBh4FZlO6d+k+YAOwjFKexnrgG+WvTEUkcrFdxA+N9ovALufcNcCNwPeBB4C7y/u6SN1hLiLSSqEA9s/APant48BlwLPl7Z8D14/AuESkDQpcjaIlugYHB4P/ycz6gH8BHgbud87NLO+/jlK6/xcDhwg/iYgM17CjysaNG+v+XZ03b17bo1jwIr6ZnU3pvqa/d8792Mz+NtXcB+wdobHVJXQhPKS2KMiECRPYu3dvsp11sboiVLQj9Mdhx44d3vbnnnuuavvuu+/mvvvuS7afeOKJ3L7btnnvgeWmm27ytr/99tve9nXr1lVt79y5MymcAfCVr3wlt++tt/qvOoRuNO/r6/O2116I7+vrS36OWQU/fH1rDfcifqfMXPJ0+vhqeU8hzWwasBL4tnPu0fLutWa2oPz4RuCXIzc8EWml2E4hQzOw7wATgXvMrHIt7A7gQTMbA2wE8qcBHSBUVi3rW5f0Pt8sKvTX2Dd7g6GlyWrVzsBq923dujW3b3o2lCW0lM/atWu97VOnTvU+5yc+8YncvsP98Id+plntlX2h5w6l5Qx3OZ5OF9u3kN4A5py7g1LAqnXtyAxHRKR+0SeyikhxYptBKoCJSEIBTEROec3UhaynT624rtiJyIgq8FvIRZTrQgJ3UaoLmSjXhXwO+E/19smiACYiiQLLqjVTFzLUZ+h463tZIiINaaYupLdPluivgQ03Lycr7yedTT1hwoTcvqE8r/Xr13vbf/vb33rbX311SA2Eqn2+1zZ27NhhjS10h8PcuXO9+3zZ8qFyc8NdkubYsWN17cty6NAhb3to7KHPY+hOgHZrc13IhvtoBiYiiQKvgb0A3ATQQF3IhvtEPwMTkY7UTF3IIX1CT6IAJiKJom4larIuZFYfLwUwEUnElsiqa2AiEi3NwEQkEdsMTAFMRBIKYC0WyhkK5f/41o4C/7pZoVVLs8qipb3yyive9qxcrPQ+34ctvapslv3793vbx40b520/77zzvPumTZuW2ze0Vll/f7+3PfRLlvWZqKy0GlolN9Qe+jyF8u86nQKYiERLAUxEohVbANO3kCISLc3ARCQR2wxMAUxEEgpgIhItBTARiZYCWIuFbj5tpj297/3338/t+8Ybb3iPvXv3bm97aO2prLqT6X2+nKTaiuONMjNv+5w5c7z7zjnnnNy+vjXWILxmVjPriVXy2prJC2xkbLHVVawVWwCL+90WkVNa9DMwESlObDMwBTARSSiAiUi0igpgoRqPZnYL8F3gOPCoc+7h8v61fFTYY4tzzrsqqwKYiIyERZRrPJbXt18K3ApgZqOB/wFcARwCXjCzFcBeqF6lNUQX8UUkUWBRD1+Nx3nA6865Pc65o8DzwDWUZmu9ZrbSzFaVA5+XApiIjARfjcfatgPAGcAAcD9wA6W18R8/6etChtYDC/2lOHz48JB96VyhI0eO5PYN5QSFxhbKh8rKE0uvlRXKI/OZPHmyt/3aa6/1tl900UXefb71xEK5UqE8r2ZqK1aec9Qo/0c+dOzQ5ym2i+C1WlQXsratj9Lp4yZKM7NBYJOZ7QJmALUFcBPen2b5XPVRYDalUuD3AW8DK4DXyv/tB865n9T1kkTkVPECcAvwTxk1HjcCc81sEnAQ+F1KM68lwIXA181sJqWZ2jbfk4RmYF8EdjnnvmRmZwJrgXuBB5xzSxt/TSLSyQqcgXnrQprZN4FfULqM9ahz7h0zewRYZmbPA4PAklBl7i7fErpmNh7ocs4dKAewX5ef1CgFv9eAO51zBwIvxr9Or4gUYdjRZ2BgoO7f1d7e3rafL3sDWIWZ9QH/AjxM6VTyZefcS2b2l8BE59yfBw7RtgB24sQJb/u+ffuqtidOnMiePXuS7ddee622S+Lll1/2HnvNmjXe9vXr13vb33nnnartrVu3Vt1jOJLXwD7/+c9722+++eaq7fnz57N69epk++Mf/3hu397eXu+xTz/9dG97M9fAKkL3Og73Glebr4GdcgEs+C2kmZ0N/BvwmHPux8By59xL5eblwO+M4PhEpIUKTKNoCW8AM7NpwErg2865R8u7f2FmV5YfLwReyuwsIjLCQhfxvwNMBO4xs3vK+74J/J2ZHQW2A18dwfENW+gUObScjq/E1/nnn+89dqjE1qxZs7ztb7019NvjG2+8MXnsS/GolBHLEyptdt1113nbZ86c6d3nO00MpTKM5JI0odSWkZa+pNHd3T1ku906ZWZVL+8nyTl3B3BHRtP8kRmOiEj9ok9kFZHixDYDa/+cVUSkSZqBiUhCMzARkRbRDExEErHNwBTARCQRWwCr61aiAnTsvZC15cfGjBlTtc9368kHH3zgPfZwPwy1S/1Mnz6d7du3J9sHDx7M7dvX15fbBuEctdDtPqElb6Qthh19jh8/Xvfv6qhRo9oe7TQDE5FEbDMwXcQXkWhpBiYiidhmYApgIpJQABORU14zdSFDfbLoGpiIJApcD2wR5bqQwF2U6kICVXUh/zNwLfBVM5vu65NHAUxERkIzdSF9fTK16hSyY0+ss/KZ6s1xCi19PFxnnHHGkH3Tp08f0eeUU15Rv6uZdSHLRTry6kL6+mTSDExERkIzdSF9fTIpgInISHgBuAnAVxfSzMZQqgv5q0CfTK26lUhETiGpbxQvolwXEriUj+pCVr6FrNSF/N9ZfZxzr/qeRwFMRKKlU0gRiZYCmIhESwFMRKLV0luJmrlVoJXMbC0f5aFscc79QZvHcxXwPefcAjM7F1hGaW219cA3nHMnfP1bOLZLgRXAa+XmHzjnftKGMY0GHgVmA2OB+4ANdMD7ljO2t+mA9y1mrb4XchHlWwXKX5MuBW5t8RgymVkPgHNuQZuHAoCZfQv4EnCovOsB4G7n3DNm9g+U3rflHTK2S4EHnHPBWz9G2BeBXc65L5nZmcBaYB2d8b5lje1eOuN9i1arTyEbvlWghS4Ges1spZmtKgfYdtoMLE5tXwY8W378c+D6lo/oI1lju9nMnjOzR8zMvxzsyPln4J7U9nE6533LG1snvG/RanUAy7xVoMVjyDMA3A/cAHwNeLydY3PO/RQ4ltrV5Zyr5LxUbr1oi4yx/TvwF8653wXeAP6qTeM66Jw7UA4ETwB30yHvW87YOuJ9i1mrA1jDtwq00CbgR865QefcJmAXMKPNY0pLX7ep3HrRKZY7516qPAZ+p10DMbOzgX8DHnPO/ZgOet8yxtYx71usWh3AGr5VoIWWUF6+w8xmUpotbmvriKqtNbMF5cc3Ar9s41hq/cLMriw/Xgi85PvPI8XMpgErgW875x4t7+6I9y1nbB3xvsWs1adIy4HfM7PVfHR7Qad4BFhmZs9T+sZqSQfNDgH+DHi4fO/YRkqnIZ3iT4Dvm9lRYDvw1TaN4zvAROAeM6tcb7oDeLAD3ressX0T+LsOeN+ipVuJRCRaSmQVkWgpgIlItBTARCRaCmAiEi0FMBGJlgKYiERLAUxEovUfDF2gVP/49iMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[kmeans_cluster_assignments == 7].std(axis=0).reshape(28, 28))\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above which pixels appear to have the most variability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0Hgl6TtIpPMd"
   },
   "source": [
    "### Clustering in the original space with GMMs\n",
    "\n",
    "Now, let's try GMM's. We start by clustering in the original space. As a word of caution, this will take some time to run since the original space is quite high-dimensional. We start by importing the GaussianMixture model form sklearn and as before we will call fit and predict to produce our final results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DcAyAUHXpQI-",
    "outputId": "c3d8753f-f50b-4f66-ea82-8bda8ba56a2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "execution time:  112.40421772003174\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "start_time = time.time() # This line helps us to set the start time when the model starts to fit the data\n",
    "gmm = GaussianMixture(n_components=10, n_init=10, random_state=0)\n",
    "gmm.fit(X) # Fitting the algorithm withe the original data\n",
    "end_time = time.time()\n",
    "print(\"execution time: \", end_time - start_time) # This line computes the final time\n",
    "gmm_cluster_assignments = gmm.predict(X) # Predicting the clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as with k-means, we can visualize cluster means:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print(gmm.means_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we plot the learned cluster means (the $\\mu_i$'s) and also standard deviations per pixel (technical detail: these come from taking the diagonal entries of the covariance matrices $\\Sigma_i$'s and then taking square root since the diagonal entries actually store variances and not standard deviations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "for cluster_idx in range(10):\n",
    "    ax = axes[cluster_idx]\n",
    "    ax.imshow(gmm.means_[cluster_idx].reshape(28, 28))\n",
    "    ax.set_title('Cluster %d mean' % cluster_idx)\n",
    "    fig.suptitle(\"Digits with GMM\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wLFQvw7Z7FDGu73m8MSI+1/B5/1geMqYejv7L0P1CSukDKaXXpJQGHkgeoyJVdAsFH+1pesWAYS+PQ/fw+UxEfDkOnaGzJCIu6h2cUpoXnUu3HdRGoeorDcasqSzPPsp1Th7l82ZH52yspv/OGMU6ei+9d0NK6XD3YDuWZzL1bsPyiHjRYca9Kg7dr+3TOefqz2ksnVtZvudIA+Sc78w5vyE6hc4fj4iPRcTWwtNmRMTbIuKebqHyeBltXr27snzOGGwLAACMGYUqAACOhQWV5Y2jiHFJz+Nbj+BeO8f13kI5570R8euV5jdG54D3symlz6SUfjGldGVKaWhkhKPWe/m/K1NKiyr9vcWrz+ScN0TEfT1t1QPtr4yIg5cG2xjlSxkeC483GFM9SH+0lzPbUlmee5TxxtI/xaFLWs6O/jPeIiKi+9l6c3fxQPQXlo5azvmuiLirp+lwl/87Lpf966reo2n9aAPlnJ/OOf9ZzvmbI2JhdC6j+fPR+f06XGF5aUR8PKX0wtGu9wgtqCxvbPi86vsy2nsGAgDAMaFQBQDAsbCgsvzsKGKc1vP4mSN43upRrOuo5Jz/MiL+a4y8P9b06BSCfisivhYRa1JK/zul9OqU0mjP4Kn6VETs7D6eFP1nokVl+dPd/z/T01YtVPUWQT7aLcQdb9tbWOcTleWVTZ6Uc/7XnPNQ3b+I+MbRbFj3rKTP9jQNKhK9OA79znyxe8+2sfa+nsffVv0Md8/0en13cUdEfPAYbEOvalF2rM5U3Jtz/krO+Xdzzjd01/P8iPjtGJmLpkXE/0kpTRuLdRcsqCw3zasbK8vV9w0AAFqlUAUAwLFwfmX53lHE6L282Y4jeF71Pi7HRc751yLiquicRXK4S4ctjYjviYj/FxF3ppReNgbr3R6HClARPWdQpZSeF4eKF4/knB/pPu4d/+KU0qTu+KGIeHVPXxuX/YsjOHtuLN1ZWb5o4Kj29J4h9c0ppeoZX8fjTKbebTgpRhY53xCHLsH44Zxz9Sy1sVa93OPmY7GSnPP+buHql6Jz6cbfrAxZFRHfeizWXTHavFot4B3tZTIBAGBMKVQBAHAsXFtZbnLPoaregtOcI3heawdhc8635py/MzoFqddExB9FpwAyqPDyvOhcNuzVA/qOVO/l/17Z87jvsn89jz8bEfu7jxdExGXdx1dExLLu490R8fEx2LaJ4stx6My0iM69vcaTf46IXd3HMyLihoMdKaWpEfGm7uKe6FwqcMzlnB+OiK/2NFXP7Ootlr33WGxDRfX3qnpG4wgppakppVNTSpeklKqFn6Kc846c86/EyGJV9UzGY6GaV29q+Lzq+7J/4CgAAGiJQhUAAGOqe9D8up6mLRFxxyhCre15vOywo0Y6aRTrGlM5550554/nnN+ec744Ik6Ozv2D3hP9BbipEfHnKaUpR7nKj8Shg/anp5TO7T4edNm/yDlvjIiv9/QdPLOr97J/nzkOZ8SMGznnbdG5jOJBK1NKL2lpc0bo/sx6C4e9RaJXx6HLuX0i5zzqezU10HtW1Ru7v+/RvTfawSLphjg+Rc7q53PWwFH9HouIJyPitoj426NY9+9Gf6Fs+VHEKkopLY6Ii3ua7ss5r2v49Grx/jnzew0AwMSgUAUAwFj7D9FfLHp/znnPKOLc2vP44u5l6Zq4dBTrOqZyzmtzzu/vnm21KvqLRGdGxJVHGf+piLilp+mV3cv5fVNP22f6n9V3+b+Dhapv6Wlr5bJ/LfvTyvKvtLIVh9dbJHpVSmlB9/FbetqP1WX/Dnp/HDojZ1EcOvPs26JTeI2I+Kec8+5jvB0RI+/RtKDBc57seXxJSmneaFbcLeL2Fop2Hm7sGPnPEdFb0P77I3jugsryxqPdGAAAGEsKVQAAjJnumUE/W2l+1yjDfa7n8byIeEnD571hlOsbpHivpJTS21NKH0op3ZtS+n+l8TnnZ2LkZcNOH+0G9qhe/u+SOHSA+u6c85rK+N5C1XUppRURcXl3+UBEfHgMtqmNe02NWs75kxHxpZ6ml6WUfnQMQo/V966PxKEz8qZFxOtTSrPi0GUAt8bY/NwOK+f8dET8e0/Tt3f/f3NP27Eulh30UGV5ZYPnfL7n8dSI+LHRrLhbJFzS03TXaOI0XNe86N/OvXFkZ4NV88uDR71RAAAwhhSqAAAYS++MQ/c7ioj4UM656X1Uqv4hInb0LP986QndS969qTTuCPTey+VwZ3RdEBGvj4jzIuL6lNIpDeJOrixvHsW2VfUWql4a/fen+nSM9MU4dM+jORHxX+PQa7y5e5bW0Wry/o03PxCH3peIiD9OKb31cIPrpJRmppT+V0SsGIsNyzlvj/4z3b49Il4bhy7t9sHumGOt98yuN3SLnC/uLj8WETceh22IiLivsryqwXP+d/QXUH8upZRGse6fjf7P9DG5L1jXuyLi1J7lP805P3EEz6++L/noNwkAAMaOQhUAAEctpTQppfRfI+Jnepo3RcRPjTZmznlD9J+N9aqU0i/WbMPi6FyWbPpo1zlA70H/w10irPeg/dSI+J8ppWohalj3nj5v72naERFfHu0GHpRzviUiDh68nlNZR/Wyf5Fz3hH9Zw99d8/jsbrsX5P3b1zJOd8bEd8Vh4pskyPiPSml/9n9jBV1fx/eHBF3R8QPVbqP9iyz3s/by6NTWDvovUcZu6kPRMTBy3kuiIg/i0PfLf8h53y8zqS7O/o/Y5cdbuBBOec7I+LdPU0LIuILKaXXNV1p9yy7X+hp+qec85ifUZVSmpFS+qvoXE71oMcj4teOMNTlPY+3xMgCHwAAtEqhCgCAUUkpTU4pnZdS+sno/IX+r8ahMwz2RsS35ZwfPcrV/HL031Pmt1JK708pXdSzHTO6RYGvx+D7Ux3NQfNneh6/NKV0XnVAzvkzEfGFnqYbIuKzKaXru0Wpg9s5JaX0ouic3XRdz/g/zzlvPIpt7PWvPY8Pntm1L/ov1dar90yr3u8GY1WoKr5/41HO+Z8i4juj/4y+/xwRj6WU/ial9C0ppd7LvkVKaWpK6YqU0q9ExAPRKSidWQl9R0SM6uysHp+IiA3dx9Mi4lXdx89ExL8dZexGukXkT/Q09d7b7Hhd9i+698HqPXvrqoZPfVv03wNvaUR8JKX0pZTST6SUzu7e421YSmllSul7Ukpfi/7C3GMxyssHDpJSmpZSujil9EvRubRhbyFyc0R8c8550xGG7b0H3r/nnPce7XYCAMBYmlIeAgDAc9DylNLh7mMyKSJmRcTiGDyffDYi3pxzPuqD5jnnzSmlb4nOGUELus3fHhHfnlLaFJ2zA06KzgH7g74YES/sWd4To3dLRLys+3h+RNyVUnoiOq//dTnnr3b7vjs6Zyct6y6/KDpFg90ppYP3hjopRp7t9YmIOOxZYqPwkYj44UrbLTWFsE/HyPtlPTyGZ4c0ff/GnZzzP6SU7o2Iv4pDB/pnRcT3dv9FSunZ6BQPpkbn53u471cbIuK3I+KPjrZIkHPek1L6QET8YKXr/ce5APEPEVE9C+nO7hlLx9PHo3NPtoiIa1NKs3PO2+qekHPellJ6WUT8Y/RfIvP53X9/FBH7UkrrolN0XxwRMwaEujc6haO1Dbf1mpq8Ojk6l3BcHIP/oPTxiPjWI31/u/e36i1UffxIng8AAMeDM6oAABhkSkScdZh/Z0bEyTHyoPze6Nz/5ZKc8yfHakNyzrdGxEsi4rZK1/yIOC0OFal2RsRPR8TvVcbtitH77xHRe6+myRFxenTOwBi+zFjO+ZHonCX1xcrzp0Xn/kQror9Itbsb+1u7Z4WMlU9HRPUg/aD7Ux30tRh5f6yxOpsqouH7N17lnG+LiGsi4j9GxKCi2sLovJ5TY3CR6u7oFA5X5Jx/fwwLSf8woO24ncnU9S/Rf9m9NrYhovNe7Os+nhaHCqO1usXbV0Wn6DioeDQ5OnlueYwsUm2JToH3iu7vflMz4vB59Yzo/F5Uv6PviIg/iIjLcs5fP4J1HfTyOPTZ3BOdy6MCAMC44owqAACO1IHoFEO2ROev/O+JiM9HxIdzzuuPxQpzzrenlK6MiG/r/rs8OgeRD0TENyLiYxHxFznnR7qXAex1pJfJ6l3vMymlK6Jz1tNr4lBBYl10zn7oHftQ99J+L4nOPWWuiIhV0bk3097oXJrtoeic0fB/x+CyiIO2d1dK6VMR8Yae5hH3p+oZvy+l9O/Rf+m2MStUHcn7N17lnPdH595P700pnRud1/GCiHhedF7P3OgUStZG53Xl6Fxq8XM553yMNuvfI+LpOHR5x4dyzjcdo3UN1D0r6SMR8R3dpgMxuIB2rLdjdUrp4xHxzd2mN0XnzMImzz0QEe9OKf1tdM6kelV0zj46KzpnR86KTuFoS3Qu8XdbRHwqIj6Uc946hi8jonNPtG3RKRw/Ep0i5+ci4qM55y1HEfdNPY8/lnNedxSxAADgmBg6cOB43ecWAACOvZTSD0fEX/Q0zSldCgyYuFJKr4iIg2dxbo2Ik3PO1bO9nnNSSnMjYnV0Cm4RES/NOX+uvS0CAIDBXPoPAIBxKaW0IqX0ypTSWSmlI7kSwHk9j59UpIITW875U9G5H1pExJzo3MeOiLfEoSLVVxWpAAAYrxSqAAAYr1ZFxCeic/+YHSmla0pPSCnNjojv6mn68jHaNmB8+bWexz/V1kaMFymloYh4e0/Tf2lpUwAAoEihCgCA8equ6NzbKaJzX6Pag88ppUkR8T8jYlFP83uOzaYB40nO+SPRuadTRMTFKaVXtbg548E3R+c+ahER/5Zz/nibGwMAAHUUqgAAGJdyzusj4sM9Td+RUvqXlNJ1KaWpBxtTSlNTSt8UER+LiO/sGf/vOecPHZ+tBcaBt8eh4vZvtLgdreqeTXXw9e8JZ5gBADDOKVQBADCevT0inuxZviEivhARO1NKT6WUHo+IbRHx7xHRewbFPeE+NfCcknO+PSJ+t7t4VUrpTW1uT4v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      "text/plain": [
       "<Figure size 2160x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "for cluster_idx in range(10):\n",
    "    ax = axes[cluster_idx]\n",
    "    ax.imshow(np.sqrt(np.diag(gmm.covariances_[cluster_idx])).reshape(28, 28))\n",
    "    ax.set_title('Cluster %d SD' % cluster_idx)\n",
    "    fig.suptitle(\"Digits with GMM (SD)\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as with k-means, we can also find which of the images in the dataset are closest to a cluster center, but now we should account for shape and not use raw Euclidean distances! How do we do this? The important basic idea here is that we work with probabilities. The probabilities serve as the similarity scores!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "probabilities_points_being_to_different_clusters = gmm.predict_proba(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we can look at which cluster has the **highest probability** for the handwritten number corresponding to row 493"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities for row 49:\n",
      "\n",
      " [0.00000000e+000 0.00000000e+000 0.00000000e+000 0.00000000e+000\n",
      " 0.00000000e+000 0.00000000e+000 0.00000000e+000 6.48930974e-229\n",
      " 1.00000000e+000 0.00000000e+000]\n"
     ]
    }
   ],
   "source": [
    "print(\"Probabilities for row 49:\\n\\n\", probabilities_points_being_to_different_clusters[492])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, for instance, we can look at the top 20 points (rows/digits) with the highest probability of belonging to cluster 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_indices = np.argsort(-probabilities_points_being_to_different_clusters[:, 0])[:20]\n",
    "\n",
    "fig, axes = plt.subplots(4, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "for idx, (dist, point) in enumerate(zip(distances[sorted_indices], X[sorted_indices])):\n",
    "    ax = axes[idx]\n",
    "    ax.imshow(point.reshape(28, 28))\n",
    "    ax.set_title('Cluster %d ' % idx)\n",
    "    fig.suptitle(\"Digits with high probability of belonging to cluster 0\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hsKq6OS12qJV"
   },
   "source": [
    "### Clustering in a 2D T-SNE space with k-means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5GMuYzDRtfF0"
   },
   "source": [
    "We can repeat what we did above but now apply clustering in a 2D t-SNE space. Keep in mind that t-SNE space is *not* unique in that depending on the hyper-parameters we run, it will change, including randomness. Thus, there are many possible 2D t-SNE spaces...\n",
    "\n",
    "We will just stick to using one of them. \n",
    "\n",
    "First, we start importing the model and the applying the classical fit_transform to get our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-YIY2T7LtpH3",
    "outputId": "6ad67e00-e884-48b4-80d6-4f013edc3730"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 5000 samples in 0.003s...\n",
      "[t-SNE] Computed neighbors for 5000 samples in 0.706s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 5000\n",
      "[t-SNE] Mean sigma: 2.321664\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 80.636673\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.479672\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=1000, random_state=0)\n",
    "feature_vectors_tsne2d = tsne.fit_transform(feature_vectors[random_sample])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot this result we will see all the data in a 2-dimensional space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "rQZ4I7XZt8Dd",
    "outputId": "b0dbb738-128e-4ecf-e08d-ce2c42c30c2f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(feature_vectors_tsne2d[:, 0], feature_vectors_tsne2d[:, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we can also give colors based on the labels that we got from our original dataset. This will show us how the different handwritten digits are distributed in our 2d space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "V12S3tpev208",
    "outputId": "911d381b-7609-46f0-f8cd-d943901dbacf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(feature_vectors_tsne2d[:, 0], feature_vectors_tsne2d[:, 1],\n",
    "            c=labels[random_sample], cmap='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe in this plot that some clear clusters are forming; however, for other digits, the clusters seem to be entangled/mixed, making it difficult to separate them. This problem will also occur when the model tries to distinguish one handwritten digit from another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now try to run our k-means algorithm as before, but know with the 2d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-72Ef3pxu3pC",
    "outputId": "52ca67b0-c741-4970-818d-c7694bdb1b63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=10, random_state=0)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_kmeans = KMeans(n_clusters=10, n_init=10, random_state=0)\n",
    "new_kmeans.fit(feature_vectors_tsne2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike when we worked in the original space, the cluster means in 2D are now just 2D points. **In particular, the cluster means in 2D t-SNE space are no longer images that we can readily visualize, but just pairs of numbers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gRrmkDrsvKOo",
    "outputId": "e16324d0-d2b3-4ce5-e56b-ea647e1f3a67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -0.77532744, -50.104446  ],\n",
       "       [ 33.20213   ,  33.30186   ],\n",
       "       [ -1.8330756 ,   7.493784  ],\n",
       "       [ 15.085539  , -26.490023  ],\n",
       "       [  8.037954  ,  54.72433   ],\n",
       "       [-63.774296  ,   6.232571  ],\n",
       "       [ 39.50086   , -32.76941   ],\n",
       "       [-36.0578    , -25.660137  ],\n",
       "       [ 38.542107  ,   8.014344  ],\n",
       "       [-22.14669   ,  26.542063  ]], dtype=float32)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the predictions now. We can compare our result to the original data by comparing the image generated after k-means has been applied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "N27JWMs8vSRE"
   },
   "outputs": [],
   "source": [
    "new_kmeans_cluster_assignments = new_kmeans.predict(feature_vectors_tsne2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharex=True)\n",
    "axes = axes.flatten() \n",
    "axes[0].scatter(feature_vectors_tsne2d[:, 0], feature_vectors_tsne2d[:, 1], c=labels[random_sample], cmap='Set2')\n",
    "axes[1].scatter(feature_vectors_tsne2d[:, 0], feature_vectors_tsne2d[:, 1], c=new_kmeans_cluster_assignments, cmap='Set2')\n",
    "fig.suptitle(\"Correct clusters vs k-means clusters\\n\\n\", fontsize=20)\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to what we did in lecture with t-SNE and the drug consumption data, the idea is to visualize things in the original space, but use cluster assignments from the 2D t-SNE space we found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "\n",
    "for cluster_idx in range(10):\n",
    "    ax = axes[cluster_idx]\n",
    "    ax.imshow(X[new_kmeans_cluster_assignments == cluster_idx].mean(axis=0).reshape(28, 28))\n",
    "    ax.set_title('Average image for cluster %d using 2D t-SNE clusters' % cluster_idx)\n",
    "    fig.suptitle(\"Digits with t-SNE\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 5, figsize=(30, 12), sharex=True)\n",
    "axes = axes.flatten()\n",
    "\n",
    "for cluster_idx in range(10):\n",
    "    ax = axes[cluster_idx]\n",
    "    ax.imshow(X[new_kmeans_cluster_assignments == cluster_idx].std(axis=0).reshape(28, 28))\n",
    "    ax.set_title('Std dev image for cluster %d using 2D t-SNE clusters' % cluster_idx)\n",
    "    fig.suptitle(\"Digits with t-SNE (SD)\", fontsize=40)\n",
    "\n",
    "plt.subplots_adjust(top=0.9, bottom=0.05, wspace=0.50, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again figure out what images are considered closest to a particular cluster (such as cluster 3) but this time using the distance in the t-SNE space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NGjXUoEEDPfnkk8qTJ4/lXv9evXLu3DnL/Tz173mBgYH69NNP9dJLL7k1lSTp6NGjmj9/vubPny8fHx898MADeuKJJ9SkSRO3FQ+3wqlTp1zH129ufzOpaYyl5Pq9aP69SfuNpPZ3KiO0bdtWJ0+e1MSJE5WUlOSKx8fHa/Pmzdq8ebPGjRun0NBQPfroo3rqqadUq1atW75/CwAAAGBX9nuGAgAAAHKEUqVKuZ3v27cvizL5/2JiYlzHQUFBqZobGBjodn7p0iXLmMcee0zLly9XREREim/mX758Wd9//7369Omjxx57TF988YVlzMWLF1OV242YVgGVKFFCS5YsUd++fVW2bFnjvOTkZG3dulVjxozRY489pmHDhrntc5PZrt+T5N8/+xsJDg5O972vfxRW7ty5PZ6XmjwzUo8ePTR//nw1adIkxd/rM2fOaOHCherSpYuaNGliebQdAAAAkFOxEgUAAABZ4uGHH9bChQtd57/99pvbfhxpMXr0aJ04cULVqlXTww8/rJIlS6Zq/vVvMKe2IfDvZkRKb5gXKFBAvXv3Vq9evfTHH39o7dq12rhxo7Zv327ZbP7cuXMaMmSIoqOjFRkZmeK1x48fryeffDJV+d5MQECAOnbsqI4dO+rQoUP68ccftWHDBm3ZssXSxElMTNSsWbO0b98+ffrpp/L398/QXExy5crlalSl5s/q3z/jtAgMDHQ13FLzKDpTY+1WKV++vMaOHasrV65o8+bNWrdunTZt2qR9+/bJ6XS6jf3777/VpUsXffzxx3rkkUeyKGMAAADAHmiiAAAAIEvUrl1bvr6+rkcMrVy5Ul27dk3z9ZKTk7VkyRJFR0dr2bJlcjgcWr16te68806PrxESEuI6jo6OTtX9o6Ki3M5v9ogph8OhihUrqmLFinr11Vd16dIl/frrr/rxxx+1fPlyt/tPnjxZzZs311133WXJU7r6mK3MVLx4cYWHhys8PFxJSUnauXOn1q9frxUrVmjPnj2ucZs2bdLixYvVunXrTM1HuvpYroMHD0qSTp8+7fG8tD727HqhoaGuJsr1jxW7mdTkmVly5cqlRx55xNUcOXPmjDZt2qQ1a9Zo9erVroZUQkKCBg8erJUrV8rPj382AgAAIOficV4AAADIEgULFlSdOnVc5zt27NC2bdvSfL0VK1a4NR4qVaqUqgaKJJUuXdp1HBUVZWmM3Mgff/zhdl6iRIlU3TswMFC1a9fW22+/re+//14tW7Z0fS05OVnffvut6/yee+5xm/vLL7+k6l7R0dGW1Qee8vX1VcWKFfXKK69oyZIlGjZsmNvXV6xYkabrptb1m8nv2rVLycnJHs27flP4tLp+c/o9e/Z4/LP8888/033vjBYaGqpGjRpp7NixWrVqlSpUqOD62pEjR7Rjx44szA4AAADIejRRAAAAkGUiIiLczkeOHOm2+bWnrly5ovHjx7vFXnjhhVRfp2rVqm7n33zzjcdzly1b5nb+0EMPuY5//PFHvfnmm3rmmWdUvXr1mz5Syt/fX3369HGLXb/aJDQ01K2RsmHDBstm8ynZuHGjateurYoVK+rJJ5/UnDlzXF/bv3+/+vXrp3bt2ql69erasmXLTa/XunVrlStXznV+7NgxyxiHw+FRbqlRo0YN13FsbKw2bNjg0bwff/wx3feuVq2a6zgmJkabN2/2aN6qVavSfe/UmD59ul555RU99dRT6ty5803H33777XrllVfcYqY/TwAAACAnoYkCAACALFOtWjXVrVvXdb5161bLyoabcTqdGjRokPbv3++KlStXTk8//XSq86lTp47bo7KmTZvm0eOfNm/erJ9++sl1XrVqVRUoUMB1fv78eX399dfauXOnzp49qzVr1tz0mv9eWfHvDdGbNm3qOr58+bI++OCDm17T6XTqww8/lCTFx8fr4MGDbhvH58mTRwsWLNBvv/2ms2fPavny5Te95r9zNW3c7uPj/s+OtK6Cud5TTz2lgIAA1/nEiRNvuhrlp59+ypCVFU8//bTbhvJTpky56ZwDBw64rSZKr+t/pin9PP/66y+tXr1a+/fv16ZNm3Ty5MmbXvffTcw8efKkL1EAAADAy9FEAQAAQJYaOnSo8ufP7zqfPXu2evTo4dHKigsXLug///mP2wb1uXPn1qhRo9K0+iEwMFDh4eGu85MnT+r111+/4ebhf/31l9588023N7JffvlltzH16tVz27R+9OjRN90f49NPP3U7v37lhSQ999xzyps3r+t87ty5mj59+g2v+d577+m3335znVetWlUPPvig6zwsLExVqlRxnc+bN89tvMmPP/6ovXv3ppinJLeGgyTXfiLpERoaqmeffdZ1/uuvv2r06NEpjj906JBldU9ahYSEuO37sn79ek2aNCnF8WfPntUbb7yh+Pj4DLm/dPV39ZqUfp7XNxITEhI0cODAG670SkhI0IwZM1znuXPnVuXKlTMgWwAAAMB70UQBAABAlgoLC9OkSZPcVjB8++23evzxxzV8+HBt3brV7c1np9Opffv26aOPPtJTTz3l9sgtf39/jRw50u3xUqnVtWtXVaxY0XW+adMmtWjRQl9//bVr023p6r4in3zyidq0aeO2F0vbtm1Vu3Ztt2vmzZtXHTp0cJ0fOXJErVu31ldffWV5A/zvv//WO++847a64b777lOtWrXcxuXLl08jRoxwi40YMUKvvvqqtm/f7tbU2bVrl3r06KFPPvnEFQsMDNSAAQMs33/37t1dxwkJCYqIiNDEiRMt+8OcOnVKU6ZM0WuvveaK5cmTR88//7zlmrfffrvb+fz582/6SDNPvPbaaypWrJjr/LPPPlOXLl3cVpvExsZq3rx5atOmTar2uLmZyMhIhYWFuc7HjRunyMhI7dmzxxW7cuWKli1bppYtWxr3Q0nPY85CQ0Ndx5s3b9bff/9tGVOjRg23ptiaNWvUvn17bdy40e3nn5iYqC1btqhjx4769ddfXfHnnntO+fLlS3OOAAAAQHbgcGbEWnoAAAAgnXbu3KnIyEgdOnTI8jV/f3/lz59fPj4+OnfunC5fvmwZExISonfffdfSwLhenz59tGjRIknSnXfemeJjtaKiotS1a1fLJuR+fn66/fbblZCQoLNnz1oeo9S4cWONGjVKfn5+lmvGx8frxRdftOwz4nA4VKBAAQUEBOjcuXOWVS933HGHZs+erSJFihhznTlzpnEvmaCgIOXPn1/nz5+3NGpy5cql999/X4899pjxmmPGjLGshJGu/ozz5MmjuLg4nT171u1r/v7+mjx5sqXZI11tfNWqVctt9U2ePHkUEhKi/Pnza8GCBcY8PPH333+rffv2OnXqlFs8b968ypMnj06dOqXExERJVx+BFR4e7rba4vqmxzXjx4/XRx99dMMx0tXmVIcOHXThwgXjvU+fPu3WrHjwwQfdVvbMmTPHbSVQau49atQoTZs2zXXu6+urwoUL69KlS5o8ebKrEXj48GG1a9fOrdEnXf3zuvbIudOnT1tWydSuXVsTJ050e2QaAAAAkBOxEgUAAAC2UKFCBS1atEjdunWz7KuRkJCgkydP6sSJE5YGir+/v1q2bKnly5ffsIGSGmFhYZozZ47atWsnf39/VzwxMVFRUVE6c+aMWwMlNDRUw4YN07vvvmtsoEhSQECApkyZomeeecZtBYLT6VR0dLSOHj1qaaA8/vjj+uKLL1JsoEhS+/btNWXKFN19991u8bi4OB09etTSQClbtqw+//zzFBsoktSzZ0/17t3b7ZFRknTu3DkdPXrU0kApW7aspk+fbmygSFcbRb1793b7vmNjY3X06FHt2rVLly5dSjGXm7nnnnu0YMECy2PELl68qBMnTrgaKEFBQRo5cqQeffTRNN/r38qXL69Zs2bp3nvvNd77WgMlV65c6tu3rzp16uQ2Lj0Nis6dO6tQoUKu86SkJB09elRnzpzRrl27XPFixYpp9uzZlmZNQkKCjh8/ruPHj7s1UPz9/dWpUydNmDCBBgoAAAAgyfwvPAAAACALBAcHKzIyUl27dtW6deu0YcMG7d27V0eOHFFsbKyuXLmi4OBg5c+fX/fee6+qVKmiRo0auT3aKKMEBQVp0KBB6ty5s5YuXaqffvpJBw8edDUQChYsqAoVKqhevXpq1KiRZd+PlK45YsQIde7cWUuWLNHWrVu1f/9+Xbx4UUlJScqfP78KFy6s6tWrq379+m6PFbuRWrVqadmyZVqzZo1++OEHbdu2TdHR0YqJiVFgYKDCwsJUsWJFNWjQQHXq1JGvr+9NrxkREaFmzZrp66+/1saNG7Vv3z6dPXtWV65cUb58+VSgQAFVqlRJ9erVU926dW96zWbNmqlgwYKaMWOGdu7cqbNnzyogIEBhYWE6ceKESpYs6dH3alK4cGFNnz5dGzdu1KJFi/Tbb78pKipKAQEBKlKkiOrWrat27dqpaNGi+umnn9J8H5Ny5cppwYIFWrlypVasWKFdu3a5VtzceeedeuSRR9SuXTsVK1ZMy5Ytc5t7/Z42qXX77bdr4cKFmjx5stauXauTJ08qMTFR+fPntzSlihcvrtmzZ2vz5s1asWKFdu7cqcOHDysmJkZ+fn4KDQ1V8eLFVadOHTVo0EBFixZNc14AAABAdsPjvAAAAADgFpgzZ44GDRrkOt+6dauCgoKyLiEAAAAAN8XjvAAAAAAgFY4fP67169fr0KFDrseFeWL//v2u47CwMBooAAAAgBfgcV4AAAAAkAqHDx927W/i5+en2bNn64EHHrjhnLi4OC1ZssR1XqlSpcxMEQAAAEAGYSUKAAAAAKRC6dKl5ed39fNoiYmJmj59+g3HJycna+DAgTp37pwr1rRp00zMEAAAAEBGoYkCAAAAAKmQP39+PfbYY67zb775Rt26ddOWLVuUkJDgiickJOiXX35Rly5d3FahPPzww3riiSduac4AAAAA0oaN5QEAAAAglY4fP662bdsqKir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