Skip to content

Instantly share code, notes, and snippets.

@alinaselega
Created February 10, 2022 18:12
Show Gist options
  • Save alinaselega/6699fc9c3f65f5fa0f2eb4241cb2f331 to your computer and use it in GitHub Desktop.
Save alinaselega/6699fc9c3f65f5fa0f2eb4241cb2f331 to your computer and use it in GitHub Desktop.
Notebook for making a clustering illustration (adapted from sklearn)
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn import cluster, datasets, mixture\n",
"from sklearn.neighbors import kneighbors_graph\n",
"from sklearn.preprocessing import StandardScaler\n",
"from itertools import cycle, islice"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"n_samples = 1500\n",
"random_state = 170\n",
"# blobs with varied variances\n",
"varied = datasets.make_blobs(\n",
" n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state\n",
")\n",
"\n",
"#plt.figure(figsize=(9 * 2 + 3, 13))\n",
"#plt.subplots_adjust(\n",
"# left=0.02, right=0.98, bottom=0.001, top=0.95, wspace=0.05, hspace=0.01\n",
"#)\n",
"\n",
"plot_num = 1\n",
"\n",
"default_base = {\n",
" \"quantile\": 0.3,\n",
" \"eps\": 0.3,\n",
" \"damping\": 0.9,\n",
" \"preference\": -200,\n",
" \"n_neighbors\": 10,\n",
" \"n_clusters\": 3,\n",
" \"min_samples\": 20,\n",
" \"xi\": 0.05,\n",
" \"min_cluster_size\": 0.1,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"datasets = [\n",
" (\n",
" varied,\n",
" {\n",
" \"eps\": 0.18,\n",
" \"n_neighbors\": 2,\n",
" \"min_samples\": 5,\n",
" \"xi\": 0.035,\n",
" \"min_cluster_size\": 0.2,\n",
" },\n",
" )\n",
"]\n",
"\n",
"for i_dataset, (dataset, algo_params) in enumerate(datasets):\n",
" # update parameters with dataset-specific values\n",
" params = default_base.copy()\n",
" params.update(algo_params)\n",
"\n",
" X, y = dataset\n",
"\n",
" # normalize dataset for easier parameter selection\n",
" X = StandardScaler().fit_transform(X)\n",
"\n",
" # estimate bandwidth for mean shift\n",
" bandwidth = cluster.estimate_bandwidth(X, quantile=params[\"quantile\"])\n",
"\n",
" # connectivity matrix for structured Ward\n",
" connectivity = kneighbors_graph(\n",
" X, n_neighbors=params[\"n_neighbors\"], include_self=False\n",
" )\n",
" # make connectivity symmetric\n",
" connectivity = 0.5 * (connectivity + connectivity.T)\n",
"\n",
" # ============\n",
" # Create cluster objects\n",
" # ============\n",
" ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
" two_means = cluster.MiniBatchKMeans(n_clusters=params[\"n_clusters\"])\n",
"\n",
" clustering_algorithms = (\n",
" (\"MiniBatch\\nKMeans\", two_means),\n",
" )\n",
"\n",
" for name, algorithm in clustering_algorithms:\n",
"\n",
" # catch warnings related to kneighbors_graph\n",
" \n",
" algorithm.fit(X)\n",
"\n",
" if hasattr(algorithm, \"labels_\"):\n",
" y_pred = algorithm.labels_.astype(int)\n",
" else:\n",
" y_pred = algorithm.predict(X)\n",
"\n",
" plt.subplot(len(datasets), len(clustering_algorithms), plot_num)\n",
" #if i_dataset == 0:\n",
" # plt.title(name, size=18)\n",
"\n",
" colors = np.array(\n",
" list(\n",
" islice(\n",
" cycle(\n",
" [\n",
" \"#377eb8\",\n",
" \"#ff7f00\",\n",
" \"#4daf4a\",\n",
" \"#f781bf\",\n",
" \"#a65628\",\n",
" \"#984ea3\",\n",
" \"#999999\",\n",
" \"#e41a1c\",\n",
" \"#dede00\",\n",
" ]\n",
" ),\n",
" int(max(y_pred) + 1),\n",
" )\n",
" )\n",
" )\n",
" # add black color for outliers (if any)\n",
" colors = np.append(colors, [\"#000000\"])\n",
" plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])\n",
"\n",
" plt.xlim(-2.5, 2.5)\n",
" plt.ylim(-2.5, 2.5)\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
"\n",
" plot_num += 1\n",
"\n",
"plt.savefig(\"cluster_illustr.png\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAELCAYAAAAP/iu7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAgIUlEQVR4nO3de7QcZZnv8e/PECGImiBRTDQkLDQKcnWPMhMcLqMnOspVPToLFS8DXkbOgMIIylFEkBxRGUE9gs5ZOiIDM1ySKDOgJIiCICaGoCARkOsOjFwSlBAggef8UdWw0/St9q7qqu76fdbqtUlVV/Wza23q6fd9n3pfRQRmZmZFek7ZAZiZ2fBzsjEzs8I52ZiZWeGcbMzMrHBONmZmVrjNyg6gLNtss03Mnj277DDMzIbC8uXLH4iI6e321zbZzJ49m2XLlpUdhpnZUJB0Z6f97kYzM7PCOdmYmVnhnGzMzKxwTjZmZlY4JxszMytcbavRzOpq4YpRTrtsFavXrmfG1CkcO38uB+0+s+ywbMg52ZjVyMIVoxx/0W9Yv+FJAEbXruf4i34D4IRjhXI3mlmNnHbZqqcTTcP6DU9y2mWrSorI6sItG7MBl6VbbPXa9Zm2m+XFLRuzAdboFhtdu57gmW6xhStGW75/xtQpmbab5cXJxmyAZe0WO3b+XKZMnrTJtimTJ3Hs/LmFxWgG7kYzG2hZu8Ua3WuuRrN+c7IxG2Azpk5htEVi6dQtdtDuM51crO/cjWY2QQtXjDJvwVLmHHcJ8xYsbTteUgR3i9mgcMvGbALKfm7F3WI2KJxszCag0wB9v2747hazQeBuNLMJ8HMrZr1xy8YGWtnzfHUaoO81trJ/B7N+cLKxgVX2eAkkA/RjY4BkgH7fV03vKbZuv4MTkQ0Ld6PZwKrCPF8H7T6TUw/ZmZlTpyBg5tQpnHrIzlxx8/09xdbpd8g6O4BZlbllYwOrKuMlrQbojz7/+pbvbY6t0+9QheIDs7y4ZWMDq8rzfPUaW6f3VSWZmuXBycYGVpUfaOw1tk7vq3IyNcvK3Wg2sKr8QGOvsXV7X6vigyokU7OsFBFlx1CKkZGRWLZsWdlhmHXkajQbFJKWR8RIu/1u2ZhVmGcHsGHhZGNWYW7Z2LBwsjGrqCo8tGqWFycbs4oaxOds3BKzdpxszCqq1ZxrUN3nbNwSs078nI1ZBS1cMYra7KvqczZVmD7IqsstG7MKOu2yVbR6KEEwruds+tG95RkPrBO3bMwqqN0NOsjeJdWvCT0944F14mRjVkHtbtAzx3Hj7lf3VpWnD7LyOdmYkXz7n7dgKXOOu4R5C5aWPo1/njfufnVvtVtuwcUBBh6zMatkFVWe8751Wk00b57xwNpxsrG+quJzGFV9niWvG3e71UTdvWX95GRjfVPFFgQMfxVVlWfHtvpwsrG+qWoLop/dTGVx95aVzQUC1jdVbUG4isqseE421jdVfQ7DVVRmxXM3mvVNHgPVRRUYuJvJrFhONtY3Ex2ormqBgZl152RjfTWRFkRVCwzMrDsnGxsYVS0wqKLxdDdW8RkoGx5ONjYw6lCinIfxdDe6i9KK5mo0q6zm+cr2fdV0lyj3YDwTb3otGiuak41VUqtp8S9cPsrbXzvTJcpdjKe70V2UVjR3o1kltfumfcXN93P1cfuVFFU5so6ljKe70V2UVjS3bKyS/E07MZ6Fz8YzI4JnUbCiOdlYJVV1toF+G89YynhmRPAsClY0d6NZJXla/MR4W3jjeZ7JsyhYkdyysUryN+2EW3g2LNyyscryN2238Gx4ONmYVZgXPiueZ07oDycbs4pzC684njmhf5xszGwo9dJi8eSu/eNkY2ZDp9cWi5/n6p9M1WiS9pR0oqRLJd0g6RZJ10j6rqQPSJqWNQBJL5d0gaSHJf1J0kWSZvVw3ImSos3rsaxxmNnw6PX5JFf79U9PyUbSYZJ+A/wCOArYErgF+CWwBng98B1gNE08c3o875bAUuBVwGHAe4FXAFdIel6Xw78D/GXT643ARmBxL59vllXz5KCdnuS38vTaYvHMCf3TtRtN0krgxcC/Au8Dro+IaPG+FwJvAw4FbpT0gYg4v8vpDwe2B+ZGxK3peW4gSWQfBr7a7sCIuAe4pymG96a/0/e6/V5mWXkweXD0Otebq/36Ry3yxqZvkI4CvhURPXdNSdoV2DYiLuvyviXAFhExr2n7lQARsXevn5kedznwGuBlEbGx03tHRkZi2bJlWU5vNTdvwdKWN7CZU6fUbnLQ8epXmXHzFwNIWix1fDC4XyQtj4iRdvu7tmwi4p+zfmhErARW9vDWnYBFLbbfCLwzy2dKehmwL/DP3RKN9S6vm8MwPMswqIPJVbn2/WwZusVSPWVXo21NMubT7CEga7HBe0nGoNp2oUk6AjgCYNasrjUItZfXzWFYup8GcRr+Kl37fpcZ+/mkaqnC3Git+vE0jvO8D1gRETe0/aCIsyNiJCJGpk+fPo6PqJe8Vm8cllUgB3EwuUrXflBbhpaPcScbSXMkLZH0B0lflbTFmH3X9XiaNSStm2bTaN3iaRfL60gq2lwYkKO8bg7DcpMZxMlBq3TtXWZcbxPpRvsmcBFwLXAksETSmyPiz8DkHs9xI8m4TbMdgZsyxHIYScnzuRmOsS7y6jYaxO6ndgata6ZK196TitbbRLrRXhIR34iI5RHxfuBHJAnnhbTuGmtlMbCnpO0bGyTNBubR47Mykp4LvBv4z4i4P0P81kVe3UaD2P00LKp07QexZWj5mUjLZvOx/4iIUyVtAJYAz+/xHN8GPg4sknQCSZL6AnA3cFbjTZK2A24DToqIk5rO8TaSrjh3oRVg882e8/Q30WlbTuZz++80rkW5wJVBZZjotc+7ki3PlmFVquysNxNJNr+X9KaI+EljQ0R8WdJTwJd7OUFErJO0H3A68H2SwoAlwFER8ciYtwqYROuW2GEk1Ws/Gt+vYa20ek7hsQ1Pjft8g9b9NEzGe+2rVMnWrMqxWWtdH+pse6C0OUBEPN5i38yIqPQ8Hn6oszM/wGhV/htoF9vUKZN53uabubVTggk/1NnihPuTDOA/CKyUdENzwql6orHuqlTFZOWo8t9AuxjWrt/A2vUbALd2qibrrM9nkjzx/wXgbJJKtD+nM0B/V9KRBcRoJXCZqlX5b6DXGAbxea5hlbUa7VDgDGALkiKANwBHA9cBOwNfyjU6m7DxzlJcpSomK0cefwNFzZLdKrZ2qtASs+zdaE8AiyPiKWAdyZIDv2jslFT29Dc2xkQGUV1BZnlUshU1iN8qtkef2MiaRzc8671VaIlZxgIBSWcAD0XEiYVF1Cd1KBCo8gCvDb9+//15pudy5V0gcAJwuaSVEXHxxEKzolV5gNeGX7///twar7asyeZFJHOWXZAucnYZ8GuSCTBvyTs4m5gqTVVi9VPG35+f56qurAUC55BMJXMhsJpkpuXzgJslPdxY9MyqwYP8Vib//dlYWVs2uwNHRMQ5jQ2SXpJu3wPYLb/QbKLcrWBl8t+fjZW1QOBG4J8i4pLiQuqPOhQImJn1S7cCgazdaKcDH5pYSGZmVjdZk808YA9J50raoYiAzMxs+GQds9kDeCnJ+jHvknQXSTXa06+I+O98QzQzs0GXKdlExK6SJgOvISkKaLw+BWxFsh5Nb3NImJlZbWSeXiYiNgAr0hcAkgS8ElejmZlZC7nMZRZJSduq9GVmZraJrAUCZmZmmTnZmJlZ4ZxszMyscLklG0mflXS4pC3yOqeZmQ2HPFs2JwJnAXdK+lSO5zUzswGX58qac4DnAXsBf53jec3MbMDllmwi4s70P28Czs7rvGZmNvjybNlYDS1cMeop5M2sq0KSjaQXR8Qfizi3VUfzmu+ja9dz/EW/AXDCMbNNFFX67IViauC0y1Y9nWga1m94ktMu80QSZrapcbdsJB3QYbfLn2tgdYv15TttN6u7Onc7T6Qb7WLgSkAt9j1/Aue1ATFj6hRGWySWGVOnlBCNWbXVvdt5It1otwAfioh9m1/AAznFZxV27Py5TJm86YoSUyZP4tj5c0uKyKy66t7tPJGWzfeAbYDbW+z71gTOawOi8W2srt0CZlnUvdt53MkmIk7tsO+U8Z7XBstBu890cjHrQd27nT0Rp03YwhWjzFuwlDnHXcK8BUtZuGK07JDMKqfu3c6ZWzaS9gd2BB4EVgI3RMTjeQdmg6Hug55mvap7t3OmZCPpTOAfgI3psQE8Kelm4NfA8og4M/corbI6DXrW5X8is17Vuds5azfaocAZJM/RPB94A3A0cB2wM/ClXKOzyqv7oKeZ9SZrN9oTwOKIeApYB/wifQEgyXOt1UzdBz3NrDdZWzb/ToflAyJi48TCsUFT90FPM+tN1pbICcDlklZGxMVFBGSDpe6DnmbWm6zJ5kXAGuACSTcAl5EUBqyIiFvyDs4GQ50HPW141Hnesn7ImmzOAXYFLiRZlfN9wD8BIekR4PqI2DvfEM3MiuUS/uJlTTa7A0dExDmNDZJekm7fA9gtv9DMzPrDJfzFy5psbifpRntaRPw3cGn6MjMbOC7hL17WarTTgQ8VEYiZWVnaleq7hD8/WZPNPGAPSedK2qGIgMzM+s0l/MXL2o22B/BS4N3AuyTdRVKN9vQr7VYzMxsYLuEvniIi2wHSZOA1JEUBjdcuwFZARMSkDodXxsjISCxbtqzsMMzMhoKk5REx0m5/5ullImIDsCJ9NT5EwCtxNZqZWVt1fpYnl7nMImkerUpfZmbWpO7P8nQtEJC0SNLuvZ5Q0haSPiHpIxMLzcbDC5mZVVOnZ3nqoJeWzV3AtZKuB84Ffk6yYNrTk25KmgG8DtgfOAQYBT6Ye7TWUd2/OZlVWd2f5enasomII0lW5rwO+BzwK+AxSQ9JulfSY8DdwEXATsBRwC4RcV1hUVtLdf/mZFZldX+Wp6cxm4i4TdLRwE+AtcCewAySRdQeBG4GfhYRdxYUp/Wg7t+czKrs2PlzN+l5gHo9y9NzgUBEbJR0AfC2iMhtRU5JLyeZmeBNgIDLgaMi4q4ej381cBKwL8nkoHcB34yIr+UV46DwQmZm1VX3Z3myVqPdSvI8TS4kbQksBR4HDgMCOBm4QtIuEbGuy/Ej6fE/Bf4eeBh4RZ4xDpK6f3Myq7o6L8eRNdmcAfwvSRdH1qdBWzsc2B6YGxG3AqTr5NwCfBj4arsDJT0H+B6wJCIOHrPrihziGkh1/+ZkZtWVNdm8DtiBZLXOYyJiRbcDujgAuLaRaAAi4nZJVwMH0iHZAPuQFC64xHqMOn9zMrPqyjoR52uB6STjI8sk3S3pIkmfkfSWdG2bLHYCftti+40kiaSTvdKfW0i6VtIGSX+UdIYkD1KYmVVIppZNROzaZm60T5HOjQZkmRtta5rWx0k9BEzrcuyM9Of5wNeB44ARkmKBlwMHtznOzGwgDNP0Nj0nG0mbAfOBX6TdZ3nNjdZq7Ec9HNdolZ0TEZ9N//unkiYBCyTtGBE3bXJS6QjgCIBZs2aNI1Qzs/4Ytoe0e+5GS2cMuBj4ixb7IiJWRcT5GT9/DUnrptk0Wrd4xnow/fmTpu0/Tn/u1nxARJwdESMRMTJ9+vQscZqZ9dWwPaSddcwm19JnkrGZnVps3xG4qcX25mPh2S2jRqvoqQnEZWZWqmF7SDtrsmmUPvfSzdWLxcCekrZvbJA0m2RF0MVdjv0vkudz3ty0fX7604vVmNnAGrbpbbImm7Glzz3PBN3Bt4E7gEWSDpR0ALCIZK61sxpvkrSdpI2SGmMzRMSDwKnARyR9UdIbJR0HfBb43thyajOzQTNsS1Vnfc6mUfo8g6T0eTXJxJzLGcey0BGxTtJ+JNPVfJ+kC2wJyXQ1j4x5q0iq3JqT40nAn4GPAccA9wKnAV/I+HsVYpgqScysv4btIW0vC12Q5koSSL6VnHrIzhP6Y3ECM7Mq8rLQJelUSTLe5DBspZBmVh/jXhZa0nMj4gmo97LQ7VoaRVSSFJHAzMz6IVOBgKTNJJ0i6X5gvaQHJJ0v6a8Kiq/SGi2N0bXrCZ5paSxcMVpIJcmwlUKaWX1krUb7DPAJ4EKSKWp+QPJMzM/TSrBa6dTSKKKSZNhKIc2sPrJ2o70P+HREnD52o6QPAt+U9LuIWJRbdBXXqaVRRCWJ16sxs0EtEsqabF5KUuK8iYj4f5JeSVJ+XJtk021lzLyn+x+2Ukgzy2aQi4SyJptbSCrOrmyx71KS511qo4yWhterMauvQS4Syjpm8y/AZyXt1mLfy4BHWmwfWgftPpNTD9mZmVOnIGDm1CkTfo7GzKydVj0pnbZXSdaWzZnA3sAvJZ1PMj/ZfSRT2HyOpHVTK25pmFm/TJJ4ssWD+JNym66yOFkXTwtJ7wSOBv4ReM+Y3T8GPpljbGZmNkarRNNpe5VkSjaS3gK8ICK+AnwlLQqYDtwZEfcUEaCZmSVmtilKmjkAjz9kHbP5PLBn4x8R8fuIuDoi7pG0Q7pKppmZFWCQZ4LOmmxeDVzeZt8JJAUEZmZWgEEuSspaIPAUz6yE2ey/gP8zsXDMzKyTQS1KytqyWcEzK2E2uw94ycTCMTOzYZQ12XyDZGXMg1vs2wXoeeE0MzOrj6ylz/8h6fXAhZIWARcAq4GdgBPTf5uZmW1iPIunHSNpGUly+f6YXVeRzAptZma2iXEtnhYR5wHnSZoDzABWR8TtuUZmZmZDY9wrdQKkCcZJxszMOspaIGBmZpaZk42ZmRXOycbMzAo3oTEby8+gLvVqZtVS1XuJk00FDPJSr2ZWHVW+l7gbrQI6LfVqZtarKt9LnGwqYHWbJV3bbTcza6XK9xInmwqY0Wbho3bbzcxaqfK9xMmmAgZ5QSQzq44q30tcIFABjYG7KlaQmNngqPK9RBFRdgylGBkZiWXLlpUdhpnZUJC0PCJG2u13N5qZmRXOycbMzArnMRszsyFTxVkEnGzMzIZIVWcRcDeamdkQqeosAk42ZmZDpKqzCDjZmJkNkarOIuBkY2Y2RMYzi8DCFaPMW7CUOcddwrwFS1m4YjT3uFwgYGY2RLLOItCvggInGzOzIXPQ7jNbJopWJdGdCgqcbMzMLJN2LZjmRNOQd0GBx2zMzGqgXQtmktTy/XkXFDjZmJnVQLuWypMRfVmWwMnGzKwG2rVUZk6dwqmH7MzMqVPQmH/nPduAx2wyqOJ8Q2ZmvTh2/txnjdE0WjDtCgry5GTTo6rON2Rm1ouyF1ZzsulRv8oDzcyK0o8WTDses+lRVecbMjMbBE42ParqfENmZoPAyaZH45lvyMzMEh6z6VHZg2tmZoOs9GQj6eXA6cCbAAGXA0dFxF09HBttdu0eEdfnFmSqzME1M7NBVmqykbQlsBR4HDgMCOBk4ApJu0TEuh5O813grKZtv88zTjMzm5iyWzaHA9sDcyPiVgBJNwC3AB8GvtrDOUYj4triQjQzs4kqu0DgAODaRqIBiIjbgauBA0uLyszMclV2stkJ+G2L7TcCO/Z4jo9KelzSo5KWSnpDfuGZmVkeyk42WwNrWmx/CJjWw/HnAB8D3ggcAbwIWCppn5ziMzOzHJQ9ZgNJUUCz1gssNB8Y8d4x//y5pEUkLaWTgb2edVLpCJKkxKxZs7JHamZm41J2y2YNSeum2TRat3g6iog/A5cAf9Fm/9kRMRIRI9OnT896ejMzG6eyk82NJOM2zXYEbhrnOUXr1pKZmZWk7GSzGNhT0vaNDZJmA/PSfZlIegHwVuCXeQVoZmYTV/aYzbeBjwOLJJ1A0iL5AnA3Yx7UlLQdcBtwUkSclG47BpgLXAGsBrYDjgG2BQ4tMmgvomZmlk2pySYi1knaj2S6mu+TdIEtIZmu5pExbxUwiU1bYquAg9PXC4E/kTyf86GIuK6omL2ImplZdmW3bEjnQHt7l/fcQVOFWkT8EPhhcZG15kXUzMyyK3vMZuB4ETUzs+ycbDLyImpmZtk52WTkRdTMzLIrfcxm0HgRNTOz7JxsxsGLqJmZZeNuNDMzK5yTjZmZFc7JxszMCudkY2ZmhXOyMTOzwiminrPxS7ofuLNp8zbAAyWEUzW+Dr4G4GvQ4OvQ2zXYLiLaLhRW22TTiqRlETFSdhxl83XwNQBfgwZfh3yugbvRzMyscE42ZmZWOCebTZ1ddgAV4evgawC+Bg2+DjlcA4/ZmJlZ4dyyMTOzwjnZmJlZ4WqfbCS9XNIFkh6W9CdJF0maVXZc/STpHZIulHSnpPWSVkk6VdLzy46tTJIulRSSTi47ln6T9LeSfibpkfT/i2WS9is7rn6RNE/SjyX9Mf39fy3pg2XHVRRJL5N0pqRrJD2a/t3PbvG+aZK+I+kBSeskXS5p514+o9bJRtKWwFLgVcBhwHuBVwBXSHpembH12THAk8CngTcD/xf4KPATSbX8G5H0d8CuZcdRBkkfBhYBy4GDgXcC/wFsWWZc/SJpF+ByYDJwOPB24FfAv0j6aJmxFWgH4H8Ca4Cft3qDJAGLSe4RR5Jcl8kk98uXdf2EiKjtC/hHkpvsDmO2zQE2Ap8oO74+XofpLba9Dwhgv7LjK+F6TAXuA/4uvQYnlx1TH3/32cB64KiyYynxGnwReALYqmn7tcA1ZcdX0O/8nDH//ffp3/3spvccmG7fd8y2FwIPAWd0+4xafmsd4wDg2oi4tbEhIm4Hria5sLUQEfe32Pyr9GcdV4n7EnBjRPxb2YGU4IPAU8C3yg6kRM8FNpAk3bHWMqS9QRHxVA9vOwBYHRFXjDnuYeCH9HC/HMoLl8FOwG9bbL8R2LHPsVTN3unP35UaRZ9J2oukVfexsmMpyV7AzcC7Jd0maaOkWyX9Q9mB9dF3059nSJohaaqkw4G/AU4vL6zSdbpfzpK0VaeD674s9NYkfZTNHgKm9TmWypA0EzgJuDwilpUdT79ImgycBXw5IlaVHU9JZqSv00jG8G4jGbP5uqTNIuJrZQbXDxHxW0n7ABfzzJeODcBHIuK8suKqgK2BO1psfyj9OQ14pN3BdU82kPRBNlPfo6iI9NvJIpJxqw+UHE6/fQqYApxSdiAleg7wfOD9EXFRum1pWpl0vKQzIu2sH1aSXgFcSPKN/SMk3WkHAt+S9FhE/KDM+EokJnC/rHuyWUOSrZtNo3WLZ6hJ2oKk2mR7YO+IuKfkkPomLXf/DMng6OaSNh+ze3NJU4E/R8STZcTXRw+SVGT+pGn7j0mqkF4KrO53UH32RZKWzNsiYkO6bYmkFwFfk/RvPY5xDJuHaH+/hC73zLqP2dxI0g/ZbEfgpj7HUqq0C+lC4HXA30bEb0oOqd+2B7YAziH5n6bxgqQ0fA3Q0/MEA+7GNtsb317rcJPdGVg5JtE0XAe8CHhx/0OqhE73y7siom0XGjjZLAb2lLR9Y0PaXTAv3VcL6bM0PyAZAD0wIq4tOaQyXA/s2+IFSQLaF7i15ZHD5eL05/ym7fOBeyLivj7HU4b7gN0kPbdp++uBx3hmjKJuFgMzJTWKh5D0AmB/erhf1r0b7dvAx4FFkk4g6Y/8AnA3yUBxXXyDZBD4FGCdpD3H7LunDt1pEbEW+Gnz9uQ5Nu6MiGftG1L/CVwBnCVpG+APwDuA/0F9xvC+TvIQ6w8lfZNkzOYAkueuTo+IJ8oMriiS3pH+52vTn29JVzS+PyKuJEko1wDnSDqWpLV/PEmr90tdzz/kY31dpX31pwNvIrloS0geaLujzLj6SdIdwHZtdn8+Ik7sXzTVIimAUyLihLJj6Zf02+qpJElmGkkp9IKIOLfUwPpI0ltICkZ2IulevY1kmv2zhnXcLv1bb+XKiNgnfc/WwJeBg0iuyzUkD8Cv7Hr+uicbMzMrXt3HbMzMrA+cbMzMrHBONmZmVjgnGzMzK5yTjZmZFc7JxszMCudkY9ZHkraVtFjSQ+nSu0flfP6jJB2S5znN8uDnbMz6KH0i/VDg/cC9wB15TgGTPqB7VUS8J69zmuWh7tPVmPXbq0kmeby46zsrQtLmEfF42XHYYHM3mlmPJO0q6WJJD0paL2mVpOPTfZJ0dLrtCUn3Svp6OvULkman04HsA7wh7UKLdPt0SWdJ+r2kRyXdLencdBG7LDHcQTLt0KFjzv/dMce+WdI16XEPS1ooaW7T+X8q6SpJ+0taIelx6rtqqeXILRuzHkh6HclEnbcCRwP3kKz7skv6llNIJiX8Bsma7DuSTOq6azpL7r3AX5JM8Pokz9zA7wVmk8wmfDxwP8lKmZ8Erpb0qoh4rMcYDiaZSHMlcGK67f702DcDlwBLgXcBW5GsxnqVpN0iYnTMr/tK4Iw0/j9Q31mOLUceszHrgaSfAXOAuRHxaNO+rUkWFDsvIt4/Zvt7gO+TLNuwON12FbCxMbFhm8+aRJJw7gIOaXS5dYphzLF30GLMRtIy4IXAqyNiY7ptDvB74MyI+ES67afAXwN7RMT13a6LWa/cjWbWhaQtSdY4+kGbm/yewOYk696MdR7J8tp7P+uIZ3/GRyWtlPRIesxd6a65PcbQ6dzPA/YAzm8kGoCIuB24ukV8dzjRWN6cbMy6m0by/0q7dX0aS+XeO3ZjemN/kNZL6T5N0pHAN4HLgUNIVkttrCm0RY8xdDKNZPmMe1vsu69FfK3eZzYhHrMx624NyXLIzxqwTzXGNLZlzLLKkjYjWUb4wS7nfzewJCI+OebYORlj6GQNycKA27bYt22L+Ny3brlzy8asi7Tb6irgPZKmtHjLtcDjJEljrHeRfKG7sstHbAk0r3e/yaqYPcTQ8Diwyf6IWAcsB96ZjgcBIGk74K96iM9swtyyMevNMSQ35WskfYWkO2t7YLeIOFLSV4HjJa0jqQh7NXAySYK4pMu5LwU+JenTwHXAfiSrZGaKIX3PTSSl1W8j6SJ7IF119n+ncfwofbB0K+DzwMPAV7JeDLOs3LIx60FE/IpkgP5u4EyShHIsz4yhfAb4BPAW4EfAccC/Am+NiKe6nP4kkpLoo4GLSUqZ548jBkjKp1cB/w78irQEOiIuBd4KTE33fQv4HbBXRKzu5RqYTYRLn83MrHBu2ZiZWeGcbMzMrHBONmZmVjgnGzMzK5yTjZmZFc7JxszMCudkY2ZmhXOyMTOzwv1/XAhMBGenwpcAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams.update({'font.size': 16})\n",
"\n",
"train_x = np.random.rand(50)\n",
"train_y = -(train_x - .5)**2 + np.random.rand(train_x.size)*0.1 + .7\n",
"plt.plot(train_x*10, train_y, 'o')\n",
"plt.xlabel('cofactor')\n",
"plt.ylabel(r'$corr(m_1, m_2)$')\n",
"#plt.ylim((-0.2, 0.1))\n",
"plt.tight_layout()\n",
"plt.savefig('coexp1_illustr.png')"
]
},
{
"cell_type": "code",
"execution_count": 85,
"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.rcParams.update({'font.size': 16})\n",
"\n",
"train_x = np.random.rand(50)\n",
"train_y = np.random.rand(train_x.size)*0.1 + .5\n",
"plt.plot(train_x*10, train_y, 'o')\n",
"plt.xlabel('cofactor')\n",
"plt.ylabel(r'$corr(m_3, m_4)$')\n",
"plt.ylim((0.4, 0.75))\n",
"plt.tight_layout()\n",
"plt.savefig('coexp2_illustr.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.6.9"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@alinaselega
Copy link
Author

Has two random plots at the end too.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment