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December 29, 2020 06:34
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methodminik-means.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"name": "methodminik-means.ipynb", | |
"provenance": [], | |
"collapsed_sections": [], | |
"authorship_tag": "ABX9TyOl1pQsPO1a1rxrZ2XkaIi2", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/ahhzaky/a22f9272a68dfdbedab10924b8d104cc/methodminik-means.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "wwU0C9iIPCeD", | |
"outputId": "783d873e-2715-48cd-917b-c033122a99c5" | |
}, | |
"source": [ | |
"from google.colab import drive\r\n", | |
"drive.mount('/content/drive/')" | |
], | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"Mounted at /content/drive/\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "S78ouNhlPOf2", | |
"outputId": "6cd6410f-4ee2-4183-8504-6864d684aab0" | |
}, | |
"source": [ | |
"%cd '/content/drive/My Drive/Colab Notebooks/Dataset'\r\n", | |
"%ls" | |
], | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": [ | |
"/content/drive/My Drive/Colab Notebooks/Dataset\n", | |
" accent-mfcc-data-1.csv Mall_Customers.csv\n", | |
" data_banknote_authentication.csv Mall.ipynb\n", | |
" hayes-roth.csv 'Wholesale customers data.csv'\n", | |
" iris.csv\n" | |
], | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 281 | |
}, | |
"id": "YMAvr7tMem6J", | |
"outputId": "82889fd5-af32-4c63-e12e-e83539646427" | |
}, | |
"source": [ | |
"import pandas as pd\r\n", | |
"import numpy as np\r\n", | |
"import matplotlib.pyplot as plt\r\n", | |
"from sklearn.cluster import MiniBatchKMeans\r\n", | |
"from sklearn.preprocessing import MinMaxScaler\r\n", | |
"\r\n", | |
"driver = pd.read_csv(\"Wholesale customers data.csv\")\r\n", | |
"\r\n", | |
"# Menentukan variabel yang akan di klusterkan\r\n", | |
"driver_x = driver.iloc[:, 2:5]\r\n", | |
"\r\n", | |
"# Mengubah Variabel Data Frame Menjadi Array\r\n", | |
"x_array = np.array(driver_x)\r\n", | |
"\r\n", | |
"# --- Menstandarkan Ukuran Variabel ---\r\n", | |
"scaler = MinMaxScaler()\r\n", | |
"x_scaled = scaler.fit_transform(x_array)\r\n", | |
"\r\n", | |
"# --- Menentukan dan mengkonfigurasi fungsi Mini-Batch K-Means ---\r\n", | |
"minikmeans = MiniBatchKMeans(n_clusters = 3, random_state=123)\r\n", | |
"# --- Menentukan kluster dari data ---\r\n", | |
"minikmeans.fit(x_scaled)\r\n", | |
"\r\n", | |
"# --- Menambahkan Kolom \"kluster\" Dalam Data Frame Driver ---\r\n", | |
"driver[\"kluster\"] = minikmeans.labels_\r\n", | |
"\r\n", | |
"# --- Memvisualkan hasil kluster ---\r\n", | |
"output = plt.scatter(x_scaled[:,0], x_scaled[:,1], s = 100, c = driver.kluster, marker = \"o\", alpha = 1, )\r\n", | |
"centers = minikmeans.cluster_centers_\r\n", | |
"plt.scatter(centers[:,0], centers[:,1], c='red', s=200, alpha=1 , marker=\"s\");\r\n", | |
"plt.title(\"Hasil Klustering MINI-BATCH K-Means\")\r\n", | |
"plt.colorbar (output)\r\n", | |
"plt.show()" | |
], | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"image/png": 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CT6jqX0VkBPACMKQN4jXGmLyoQiLtvyTshZemjzHAXHfCa0TkceBYIDNRK9DN/bo78HUhgzTGmNZymj4230Q9EFic8XgJ8N0GZW7AWf78cqAUOLipE7lLo18IMGjQoJbGaowxrdJZRyYW6u3lVGCiqm4JHAE8JCKNzq2q41V1lKqO6tOnT4EubYwxudV3z/Oy+Y2XRL0U2Crj8ZbuvkznAU8AqOr/gCKgohABGmNMYThNH162nGcSmSAiy0WkyRXEReQAEVknIp+623UZx7J2zmiKl0T9ETBcRIaKSAQYB0xuUOYr4CA3iB1wEvUKLwEYY0x7SbvrJubaPJgIjM1R5m1V3cXdboRNOmccDowATnU7YGSVs41aVZMichnwMhAEJqjqTBG5EZiiqpOBq4G/iciVOJ8wzlFVzXVuY4xpL06vj8LM9aGqb4nIkDye6qVzRiOeBryo6gs4Xe4y912X8fUsYO8WBmyMMe2mhQNeKkRkSsbj8ao6voWX3FNEpuH0gvuxqs7EW+eMRmxkoslKNQGxNyG1CIhAdG8kNKyjwzImLx6bNQBWquqoVlzqY2Cwqla5AwCfAYbnezJL1KZZ6ZonoPI2IA0aAwJQKWh4B6THH5HgwI4O0RjP2nNSJlVdn/H1CyJyj4hU4K1zRiOds/e3aXPp6omw/jeglaDVQBKIAzFITEdXnoCmlnVskMa0UKF6feQiIluIiLhfj8HJtavw1jmjEatRm0Y0tQoqfw/EmimRBl2Prv8t0vOu9gzNmLypCskCjUwUkceAA3DaspcA1wNh5zp6L/B/wCUikgRqgXFuB4smO2fkup4latOI1k7yUCoFsdfR9Bok0LPNYzKmEArV9KGqp+Y4/hfgL80ca9Q5Ixdr+jCNxd6n+dp0BolC4ss2D8eYQujMIxOtRm2aYF3gzebJj0nYC6tRm8YiY4BI7nIag/C2bR6OMYVQ34+6M9aoLVGbRqTkFMjZ3zQA0f2QQK/2CMmYgijgEPJ2ZYnaNCLBvlB2CUhxcyVAypDyn7drXMa0hiok0wFPm99YG7VpkpRegkoxVN0JCGgNzvt6BIJbIT3/goS2ynEWY/zFj80aXliiNk0SEaT0+2jJqVD3MppcgEgUovsi4R07OjxjWqwzL25ridpkJVIExcf6sNXOmJZTS9TGGONvfrxR6IUlamNMl6BqbdTGGONzQsqHPTq8sERtjOkyrI3aGGN8rD3noy40S9TGmK5BnXbqzsgStTGmy7BeH8YY42NqNxONMcb/rOljM6fptWjNU1D3IhCD0DCk5CwI74q7NJoxxues18dmTOv+g669Gue+cZ2zMzkbrXsDIrtAj78igZKODNEYk4Nq4RK1iEwAjgKWq2qjyW9E5HTgZzjzBVcCl6jqNPfYQndfCkiq6qhc1+ucDTbtSONT0LVX4axPWZd5xNkX/xhde2nHBGeMaZECLhwwERib5fgCYH9V3Qm4CRjf4PiBqrqLlyQNlqhz0srb2DRBNxRzknViRnuFZIzJk6q3Lfd59C1gdZbj76nqGvfh+8CWrYnbEnUWmlwCiS88lIyhNQ+3eTzGmPwpQjod8LQBFSIyJWO7sBWXPg94cZNQ4BURmer1vNZGnU36a5CIszZg9oKQnN8uIRlj8teCTh8rvTZLZCMiB+Ik6n0ydu+jqktFpC/wqoh84dbQm2WJOqsokPZWVErbNJJ8aLoG6p5D614ArYXQcKTkDCS8fUeHZkz7K+DNRC9EZCRwP3C4qq7aEIbqUvf/5SLyNDAGsESdt/AIIOihYAlSdFRbR9MiGnsXXXuZ2+BW4+xMTEdrJ6PRvZEedzorthjTlbRTP2oRGQT8CzhTVWdn7C8FAqpa6X59KHBjrvN5aqMWkbEi8qWIzBWRa5opc7KIzBKRmSLyqKdX43MiYSg5EyjKVRCKj2yXmLzQxHR0zSWg1WxI0oDTG6gOYu+ia6/ooOiM6Tiq4mnLRUQeA/4HbCciS0TkPBG5WEQudotcB/QG7hGRT0Vkiru/H/COiEwDPgSeV9WXcl0vZ41aRILA3cAhwBLgIxGZrKqzMsoMB34O7K2qa9y2l82ClF2Cxj+AxGc07v0hQBHS416k2RW725+uz9VTpQ5i76GJWUh4RHuFZUyHUiCdLkzTh6qemuP4+cD5TeyfD+zc0ut5qVGPAeaq6nxVjQOPA8c2KHMBcHd9dxRVXd7SQPxKJIL0mghlF4P0ACkBKQMiENkH6f0YEv1uR4e5gaaWQWK6h5JxtOahNo/HGN9QQMXb5jNe2qgHAoszHi8BGmambQFE5F2cRt0bmqrOu11RLgQYNGhQPvF2CJEIUvYDtPQiSM4DYhAciAR6dXRojaUWe++pkpjbLiEZ4xddfa6PEDAcOACnY/dbIrKTqq7NLKSq43FH6IwaNarTfctEghDetqPDyE5a0lPFbiaaLqbTZR2Hl6aPpcBWGY+3dPdlWgJMVtWEqi4AZuMkbtPeQtuDpzl3i6Eo2whYYzY33m4k+nHiJi+J+iNguIgMFZEIMA6Y3KDMMzi1aUSkAqcpxEaAdACRCJScjtMHPFtBkOLj2iUmY3xDPW4+kzNRq2oSuAx4GfgceEJVZ4rIjSJyjFvsZWCViMwC3gB+ktnB27QvKbsMQtvSfLIuQnr8CQmUtWdYxnQsBU2Lp81vPLVRq+oLwAsN9l2X8bUCV7mb6WAiUej9KFp1F9Q8Vr8XNA7hEUj5NUhk1w6N0ZiO4b8k7IWNTNxMiUSR8p+gZVdAYhZoHYS2QoIDOzo0YzqOD5s1vLBEvZkTiTiLGxhjLFEbY4yv1Q946YQsURtjuoyuPuDF+IhqHOpeQmufA61xF+I9DQlv19GhGdOxfNijwwtL1JsZjX+MrrkQSLmz5wGJqWjt02h0L3d60xyzARqzmZJOWqO2pbg2I5qYja45F3T9xiQNbDK96ZpL0c76+c+Y1vA62MWHfx6WqDcjWvl7ZyWXZsUgPsXj7HrGbG48zpznwxuOlqg3E5peDfF3yV0diKE1D7RHSMb4TyetUVsbtQ9oeh3UvQa6GqQnFB2MBLq37CSp+oV44zkKpiE5J+9YjenUPE4s6TeWqDuQahxdfyPUPoszjXcciMD6G9DiY5Fu1zkDVjwJ470q4PWcxmxGOnE/amv66CCqKad3Ru1kIIaztmHS/T8GtZPRNRegmvJ2wtAwvL3vFkHRoXlGbUznJupty3kekQkislxEPmvmuIjIXe46s9NFZLeMY2eLyBx3O9tL3JaoO0rdy5D4lObXNqyDxDSoy7nuJQAiISg5i5wL8QJScpLnMI3ZrBSujXoikG1C98Nx5uQfjrOq1V8BRKQXcD3OKlljgOtFpGeui1mi7iBaPd4ZjJK1UA1a/TfP55SyiyC8PdmmN6X7Lf5cQsyYTkRV3wJWZylyLPCgOt4HeohIf+Aw4FVVXe2uMfsq2RM+YIm64yRneyz3pedTOgvxPgQlZ4KUOovwSjlQBKEdkJ73ESg+Mr94jdkMtKDpo0JEpmRsF7bwUk2tNTswy/6s7GZih2mbPkAiUaTbT9HyHzlNJ1oHwS2R0NA2uZ4xnYbSkiHkK1V1VBtG0yKWqDtKaGtv3eSC+SVYZ3rT0Xk915jNVvv1kW5urdmluMsWZux/M9fJrOmjg0jphUBxjlLFSFlLP3EZY5pTqF4fHkwGznJ7f+wBrFPVb3CWLTxURHq6NxEPdfdlZTXqjlJ0BNQ8AonPcbrnNRSF8LZOOWNMYRSoRi0ij+HUjCtEZAlOT44wgKrei7N04RHAXJw+t993j60WkZtwFg0HuFFVs92UBCxRdxiRMPR6AF37M4i9jrOWW4wNg1GiByA9bm/BgBdjTE4FStSqemqO4wpc2syxCcCEllzPEnUHEilGet6FppY580enVzld54oOR4JbdHR4xmxWCtis0e4sUfuABLeA0nM66frIxnQitnCAMcb4m9WojTHG7yxRG2OMj1kbtTHGdAKWqI0xxt/EFg7oGJr4HK3+h7MMlSYhtA1Sej5E90Mk2NHhGWMyVK+vYeXS1USLI/Qb3AeRztkLo7116kSdrvwjVP8DSOCstA0kPkLXzYTQ9tBzAhIo6cgQjTHAwpmLeeD6SXzw/MeEIiHSyRTdK7px8k+P4aiLDyUYbKdKVSdt+ui0c32kayZB9UScifcbrIKiNZD4DF37ww6IzBiTacbbn3P5Hj/n3Wc+JBFLUFtZS6w2zvLFK/nbzx7hhhN+RyrlcSWj1vA4z4cfbzh2ykStmoaqO4HaLKXiEP8ATc5rr7CMMQ3UVtfxy6Nvoa46hqYbZ8BYTYxPXpvOv+58vn0C6qSrkHtK1CIyVkS+dNf/uiZLuRNFREWkbedxTUx15lnOKYXWPJmzlKqi8Wmk1/2K9OrzSK/9GRp7H2e4vjEmX288+g7pJhJ0plhNnH/+bjLpdDvc6eukiTpnG7U4d+TuBg7BWY3gIxGZrKqzGpQrB64APmiLQDeR+tZjwSSklmQtoek16OoL3LmhY9SvJ6+xlyHQF3r9AwnmXIDBGNOE/zzyFnVVuStVtdUxFn62mK1HDm6zWITO2+vDS416DDBXVeerahx4HGc9sIZuAm6j+dVaCydQBp5mxhAIbLpupKZWoXUvobXPko5NQVedBslZOM0oGT9FrYHUV+iqk9H02gIGb0zXUVvpLR0EgwHqqts4dXTiNmovvT6aWuPru5kF3KXQt1LV50XkJ82dyF137EKAQYMGtTzaepE92CSpNnvBYqT4aAA0vRpddx3E/gsSBhQ0DiRp/rNOGtLr0ZqHkbLL8o/XmC5qwLB+zPt0AblaEROxBH22qmj7gHyYhL1odfc8EQkAfwDOyVVWVccD4wFGjRqV97dMpAgtPgVqHqP5CnwAAv0gPMpp3lh5AqSXA0lQZ6J+GT4XqfISxuXu5iovh/Xr8w3fmC7jmEvH8uGLn1BX3dTiGBtts+tQ+mzZu+0D6qSJ2kvTR3Nrf9UrB3YE3hSRhcAewOS2vqEo5VdDeBeaXs4qAoGeSM+/IyJo5e2QXoFTe844h6ck3YTKyvyeZ0wXM3K/EQzZcRDhSPN1wmhxhPNvPaNd4umsTR9eEvVHwHARGSrOciPjcNYDA0BV16lqhaoOUdUhwPvAMao6pZCBqsadbnkukQjSawKU/xyCW+GsghMFKYfSs5GK55HQlmi6CmqfxxkUY4xpTyLCLS9ey9CRgygqK9rkWKQoTKQ4wk8mXsZO++7QPgFtrr0+VDUpIpfhLMAYBCao6kwRuRGYoqqTs58hf5pahlZPhNpJoE6faY3shZRdjETGIBJCSsehJaeArgNNQaDHpkPHk7NAQr785hvTFZT1KOXP79/C1Fen8687n2PpnGVEisMccPJeHHnhIfTs16N9AtHC9foQkbHAn3By4v2qemuD438EDnQflgB9VbWHeywFzHCPfaWqx+S6nqc2alV9AWexxsx91zVT9gAv58x5zcRMdPWZbntyRm04/g66eipadgEB9wafiIA088PWBN56iBhj2kogEGD0Ybsw+rBdOjaQAlTYvHRZVtUrM8pfDuyacYpaVW3RN8KXIxM1XY2uPhu0iqabLGqh6m9o3Wu5TxYa5vbuMMZ0dQVqo/baZbneqcBjrYnbn4m69t9uTTibWrTqzznPJcEtILJrznLGmC7Aext1hYhMydguzDhLU12WmxwVJyKDgaHA6xm7i9xzvi8ix3kJ25+z59U+QfZ5PFzJuWhqBRLsk7WYlF+LrjqJ9hiLY4zxqZbdKFypqoXouTYOeFJVM2edGqyqS0Vka+B1EZmhqlknJfJljRr1OBJQws5NxJzFtnMHyRhjuiqhYE0fubosZxpHg2YPVV3q/j8feJNN26+b5M8adaAi5xwdgNM8Eui16a7Ut2jNYxB7B0g7fa1LzoD4e20TqzGm0yhQH+kNXZZxEvQ44LRG1xLZHugJ/C9jX0+gRlVjIlIB7A3cnuuCvkzUUnIaun62M99GNuGdkIxEna6eCJW/xxle7rZxJ2dC7eM0mrPaGNP1FCBRt6DL8jjgcd10Gs4dgPtEJI3TonFrwwnumuLLRE3R4VB5h9t3urnvbBFStnFhgHTNU1D5B5wZ8DIpDUckGmO6qAKNp/s55uQAACAASURBVPDSZVlVb2jiee8BO7X0er5soxaJIr0edps1ihocDTr7yn+BRPcEQDUFle0zcZ8xppPazGfP6xASGgoVLzsT/9c8BOmVIFEoOgQpORcJD99YOP5e7mYSY4zxYRL2wreJGkAC3ZCyc6Hs3KzlNP4p0PJBLVom+U3MVF7e8ucYYzpcZ104wNeJ2gtNr4Hqv7fsSYG+kF6OztmmiTfYIii7lEDZRQWK0BjjF35s1vDC14la0+vR2qeg5knQ9aj0IV18OqHSIxFx2q61+kGghc0emm2a0jqouhuN7ouER+QduzHGZ3w6M54Xvk3UGp+KrrkANMXby3pz3+ej+XBlf1QX0DP6e84cuRtn7XIA3avvb+GZizfMxNe8OFr9D6THHfmGb4zxI0vUhaPJr9A156HpGm76ZC/+uWB7alPhDcdXx4q49+PpPDzjSyYdGGVIefbVIzaK4K1nSBpib+YRuTHGr+pHJnZGvuyep9X3g8b454LtGiXperFUiDV1Cc7671Gk0h6nMY0ejPeXbAsNGLO5kbR62vzGd4laNQ21z6Ca4s8zRzWZpOulCbAuHuWNb7wslNsLuv8epMRbIIEB3soZYzoHrzPn+S9P+y9Ro9VAktnrerEuHs1ZvDoZ4Yn52+c+b8lxBAJBKDkNpwkkm2Kk9Dwv0RpjOpHOOuDFf4laioA0a+JRggFvnR5XxZpa4DZTEClzppOVkrPdWnVzzSVBCPaB4iO9RmyM6SysRl0YImGIjKZ3tJZEylt4/Yqzdc8TKD5uw+RNEqxAej/u9KWW0gZFSyA0FOn12Ibuf8aYzUdnrVH7steHlF7CNrGLiQRT1KWbb6MGiAYSjBuWZfKp4NZIt1832DeUD+MP8fHCh9m523v0jNYQCg2gT5/z6VF+oLMGozFm8+PDJOyFPxN1dC/WRc6nJpm7K11KA+zdt7m5q8PQaxIiG9ukq+Nxzp38L2auWE5tohjlIACiwSDCDG49uD/HbNdOS9cbY9pPAVchb2++a/qo9+zivQkFcr+PhANpPlvT1FJcRdDtFgLBbpvsvei5Z5n+7TJqEolN3lxjqRR1qSTXvPYK7y5e1LrgjTG+U8AVXtqdL2vUADOXL6culfs7JhJgXmUfdu5dhfOWGQApg/LrCRQfsknZ6d8u45NlXxNLNb+IQF0yya3vvMW/Tz2ztS+h4FKpFDPf/ZI1y9ZS0r0ETSsfvDCVmvW1DNp+IIeecyC9+/fs6DCN8S/1YRb2wLeJOhryFpqQIhzsDsVHQ2gHJDQMImMQafxh4eHpn2ZN0vXmrVnNwrVrGNLDH0lPVfn3va/w4A1PEK+Lo2klVhMnc+GISFGYh258ksPOOYDL/nwewVCwAyM2xp/8WFv2wrdNH98bsjWl4ew3EgGS6QBjKqZB7b8hMQ0i320ySQMsWLuGtId31HAgyNLK9S2Oua2M/+lD/O0nD7FuxXpqK+uoq46hDV5HvC5BIpbg1Yfe4nfn3dNBkRrjYzbgpfAOGDI0Z606KCn26LvU7Z5XS1318/x3+nHMm3co1cuOIL32KjT+6YakVhrJNdDFoaqUhMLu13Fnwdz0mkbJsT188eEc/v3XV6ir8TafSawmxttPvs+Cz75q48iM6Xwk7W3LeR6RsSLypYjMFZFrmjh+joisEJFP3e38jGNni8gcdzvbS9y+TdTBQIB7jzyC4mDTc26EJEWvaIxbRr+1YV9RMME+fT9naOlCiplLquZ50qvPRtecg6ZrOG67HTzV0gMBYcfeadLrrke/HY2uOARdvg+68lC05ilnmHs7eeJ3k4nXtWxRhEQ8yb/ufL6NIjKm8ypEohaRIHA3cDgwAjhVRJqaE3mSqu7ibve7z+0FXA98FxgDXO+uTJ6VbxM1wO594jz2vVfZtfcyooEkRcEEQUkjKD2idfxk5Pv0jm46ZWkgowt0MKAItRD/GF17CWOHDScY2PQll4XiHD/4Sy7Z4WPO2OYzBpfXcdWovgTXHAu1/wRqcWbcS0BqEbr+RnTtpc46je3g09dmoC2cJCadSjN7yrw2isiYTkpxbiZ62bIbA8xV1fmqGgceB471GMVhwKuqulpV1wCvAmNzPcm3NxMdaXbstZpbRv+X014/mupkmJQ6iXZlXSm//ngf/jJzdx4+4DkGlFYB8HVNKY/M+Q4vLx1KPB1kSNk6zt1uOvtuMY1o2SwmHHMCZz3zJPFknKt3ep8ztplJWoVoMEUiHeAXgfcJB4KgzdViayH2Llr9N6Ts4jb/DqRS+dXe7WaiMY214GZihYhMyXg8XlXHu18PBBZnHFuCU0Nu6EQR2Q+YDVypqoubee7AXMH4O1EHB7GyNsy4149lXTyCNvgAUJ2MUJsKccrrx/LS2Em8uHhrrv9kX1QhnnZe2tc15Xy6ui/blK/lgcMeZrf+d/DMyaexatml7Nh9FsWhjTXjUKD+61y15Tqo/jtaej4ibfst3HJ4f2ZPnd+i54SjYcYcsWsbRWRMJ+Y9Ua9U1VGtuNK/gcdUNSYiFwEPAN/L92Semj48NJxfJSKzRGS6iLwmIoPzDWiT8wZKmLjgGGqSoUZJul5aA6yNR7ljxhhu+GQfYqnQhiRdryYZ4Yt1vTj/PyWoKsPKFzK69xcUh5KtiC4JiRmteL43J/34GIrKWjbviAgcdfGhbRSRMZ1TAQe8LAW2yni8pbtvA1Vdpar1PQDuB3b3+tym5EzUHhvOPwFGqepI4Eng9lzn9erR2d0bJd6GalNhJs3fgbosc1fH0yFmrS3l02XfOAsTeFrpJZsAaFUrz5HbPid8ly2H9ycc8VZzj5ZEuPCOM6kY0KuNIzOmk1FviwZ4WDjgI2C4iAwVZ36KccDkzAIi0j/j4THA5+7XLwOHikhP9ybioe6+rLzUqHM2nKvqG6paP4Xd+zjvEq0WSyapintbaSWRzt0mG0sGeXjGNKe/dWs7S2oSAv1adw4PAsEAR15wMIFQ0z8qCQjRkijF5UX06t+Tq+6/hGMvPbzN4zKmUypAP2pVTQKX4STYz4EnVHWmiNwoIse4xX4oIjNFZBrwQ+Ac97mrgZtwkv1HwI3uvqy8VNO8NpzXOw94sakDInIhcCHAoEG5V2WZtXKFpwEqXqWBD5YsZv2IMN1a2bSswX5IaHhB4mpOKpXippP+wNRXpxGr2fTmpgSgqLiIs359MuW9yui/dT923Gd7AgFfd+QxpkMVamSiqr4AvNBg33UZX/8c+Hkzz50ATGjJ9Qr6Vy0iZwCjgCaX71bV8ao6SlVH9enT1ERKG7391ULO+NcTHq+sNL8QwKa+qapk7ItHsLS63OO5G6tNhvjxezvy79lf5H0OLybd9ixTXvmUuurGg100DbXVdTzym6fY+/gxjNxvhCVpY7JRIK3eNp/x8pftqfFbRA4GrgWOyWhEz0tNIsEPnp9MbbI1N/uapsDKujBnvnl0zp9Hw+PxlFCbDHLH9DE8u2gg17z2Ck/O+qzgMQKkkin++fvJjWrSDVWtreakfucz+Z6X2iQOYzYrnXQIuZcGgA0N5zgJehxwWmYBEdkVuA8Yq6rLWxvUs1/MasEkV95r0/XSCsvrSnj96yEcPHBhs+X+Mms39uq7lCHl64mnA7y2dAgPzNmRhVU9AGemvevefI2x22xLmcfh6Zm+nreMuZ8sQAIBths9jL5bVWw4NuXlaVSvy7ZyzUbJeJLxP30YgGN+kLPvvDFdVmedlClnolbVpIjUN5wHgQn1DefAFFWdjNPUUQb8010d5StVPabZk+bw/NzZ1CS93ETM/7telwoycc53OGjAQuoXdFGtr3EX8+GKLbjv8125a+borOcJiPDsF7M4feQunq+9YMYi7rx4PHM/WUAw7NwETcST7Lj39lx530VsMbQvf778/haNSIzVxBj/04c55Kz9KS7LtYakMV2Thx4dvuTplpqHhvODCxlUnecmj9YtmbU6VkxtKsi8dT1ZXN2Nz9b05vWvB/NNbTmptBDz0JOkJpHgg6VLPCfqOR/P5+oDrqe2yu0eWLfxDenT1z/jwp2v5gd3ncva5eta/HpE4PVH3+HICw/JXdiYrsanzRpe+HJk4vBevZm27BtSbTxb3eLKckY/cw5pFRIaoLWJPxdV5cb/+/3GJN2EWE2cuy4eTyrZ8qHjddUxZk+dx5FYojamIWfAS+fM1L7sJnDWyF0ItMMCs7XpMLF0iIQGyTdJl4TCjB6Qc6g+ADPe/pw1y9fmLJdPkq5nc3wYk0Xa4+YzvqxR79CnL0WhEIl4y6b3bLnWvxmkVTlu+6ZmOHQk4gne+deHvDzxDRbMWJSzF0drFJcXsev3dmqz8xvT2XXWGrUvEzU4bb+dwYW7j6I8Gm3y2IIZi/jpITcSq4lnbe4olGAoyJ7HtGYeGWM2Y9ZGXVjV8Xibt08XggBrY013GV+5dBVX7n8d1Wu9dbFrrWhxhGsfu5JQ2Jc/UmN8wNM8Hr7ky7/quWtyDn33BQUWrV3T5LEnfjeZWBMjCr2Kljj9shOxJOkm5qQOBAOEoyFEhN4DenHl+IvYef/v5H09Y7qETlABbIovE/U7ixZ2dAiedYs2noI0lUrx8oTXGHPQak6+dDnDR9YgAfhmYYTfXDSYBbOKydU+XlJezB/eupHHb3maNya9h4iQSqYYuM0WHHbugXTv3Y10Ks3QnQax7ahhSDvcfDWmU1Nv6yH6kS8T9XNz2nYOjUIpDYc5drsdGu2vXlvJT/70JbvuW0lx6cbfjC2Hxfnjs3M5f9/tWbksTHPJOlIc4czrT6KkvJi+g/vQe4ue1FbVUrFlL0688mj2O2lPItHcaz8aYxqwGnXhLFqbuwtbRxOgLBJl/8FDGh0rlvvYbb/1FJU0/qUoLlX+/OIcrjhqOMuXNj3sfI+jdqfPlr05a5vL0XSauDsoZu2K9dz1g7/xj189xh/fummTIecAcz9ZwKz/zSadTjN8t60Zsee2VtM2JlPnzNP+TNR1qfZZODZfQRFKwxEeOO7ERovlqtYSiD/aZJKu16tfkr+++iUXHLgdq7/dNFlLQPjigzm8/dT7TQ4hr62qo7aqjgtHXs19n/6OfoP7MPeTBdx65l0sW7gcZ4F0JRAM0KNvd077xQnM+Xg+X89dRlmPUr532r5898jd8u5vvfLr1dRV1dGzX3dKu5fmdQ5jOoqkO2fbhy8Ttd8dt/0IfrTHXgws77bJ/jkfz+fzt//K946IUdLELKorvwnx5rM9Wb0sRFnPFPsfvZan7++7SRlNK8u/Wpkzhup1NZyz7eWc/etTeOS3/6Kuie5/yxYs5w8X3IsEZEPS//ClTyguLeLWl3/J0J28rZimqrz2yNs8evNTLFu0glAoSDKRZPdDd+HsX5/MNrsM9XQeYzqU4svBLF5Yom6hcCBAPJXirYULKI9EGdarF5/O/YpnLnyQdfNWcugJyzjoqE1rwrFa4fdXbcV7L3UHhUQ8QCicbnVzWTKRYsK1j+Y8T2bNvLayjtrKOq7c77oNNfKsz1XlDxfcy5uT3t0wL3YCpynmg+em8MlrM7j+yasZPdYW0zX+JqgNeCmU9bG2HxjSGol0mn/P/mLjogEKJNN0Hxyk15dJ1q0OUV0Z5NN3SqipCtCrb4KH/7AFs6eVkIhtbCZJJgozej/f37vaqjoe/s2TXP23S7KWe+2RtzdJ0g2vHauJceNJv+fhBffQvaJbE2cwxkcsURfGV+taPmtchxIgHGDdIQOp2akX7901g6l7lRMMKaqQSkG8ru0nfGqpdCrNG4++w2V3nUu0uOmRlQCP3PxUk0l6k3OllRf//jrjfnZcocM0prAKlKhFZCzwJ5ypn+9X1VsbHL8KOB9IAiuAc1V1kXssBcxwi3qaEtp3kzI1vDnXaYiQqCji2xO2JlYXoKYqSG11kHhd/hM+tTUJCKu/ab6HzcqvV/PtohU5zxOvjfOfh/5byNCMKbz6NupWTsokIkHgbuBwYARwqog0nPDnE2CUqo4EngRuzzhWq6q7uJuneft9V6Me1rNXO16t5avDZBUOUL1Lb1JPLyJY0/plxJ7VZyih5eepIcSxkrt2m06lCUUa/wp89s7nTLpjMh+/Np1Enbc5V3LVulWVme9+wdN3vcBXny8lUhxm/5P3Yuy536Nbr/zXrzSmJQrU62MMMFdV5wOIyOPAscCs+gKq+kZG+feBM1pzQd8l6kgwiNDy7o7Tf3otZc3Mu5FNVTTKyNtvbvHzmqMppWZED8qn5O65kUs+Sbolz+tW0Y2KgRvfGFWV+378AM/d9x/itbEWfUrsO6ii2WO1VbX88uhbmT1lHrGaOOqeeNGsJTx4/RNcfvf5rFi8kk/fmAnAyP1HcNRFh9Bri57eA2ihlUtXMX/6V0hA2Hb3ra19vUvQQjV9DAQWZzxeAnw3S/nzgBczHheJyBScZpFbVfWZXBf0XaKG/Pqk55Ok658XkhRJLdA8ztEA8b6Nh5X7TbQkwklXHUWsNk60OIKI8Pz4V3n+vv8Qq2nZ97K4rIjjrziSZCJJ5eoqIkXhDX2sVZXrjrudzz+Y06h2Xj/l6+/OvYdQJEgy7vSf//z92Tx+2zOced1JnHrN8QV4tRst/nIpf/nhBD57+3PC7ujORCzBmCN249I/fZ+Kgb0Lej3jI0pLEnWFm0zrjVfV8S29pIicAYwC9s/YPVhVl4rI1sDrIjJDVedlO48vE3UkECSebr9BL5FgimSycDf81h44gB7/XUawuvCrqHsVjoZIxJq+fjgSIloc5b6fPMS9Vz/o1HDz+RgDBEMBulV0Y+qr07jtrD+j6TSpZJott+vPqdecwIBt+vH5+42TdEP1SRrYMBLzkd88RXFZlOMuO6LlgTVhwYxFXLHPr6irqkV143UA3nv2I2a8NYt7ptxG30HZuyyaTsx7y8dKVW1uzuClwFYZj7d0921CRA4GrgX2V9UNtR9VXer+P19E3gR2BbImal/eudu6Z9t95G1KANi6vEDD1kUgJCz/vyGFOV+efvbgD7n2sSsZMKwfRaVRSroVU9KtmHA0RDqtrF9dSTqV3tAMkU+SLi4roveAXlSuruSlCa8Tq4kRr0uQSqZYNHMJd158Hzefeiex2vw+7cRqYkz4xWPEY62fm1xVuf7426mtrG2yUpVOpalcU80tZ/4553k+e/cLbjr591yw01VcOuZnTLr9Gdavqmx1jKbtiaqnLYePgOEiMlREIsA4YPIm1xHZFbgPOEZVl2fs7ykiUffrCmBvMtq2m+PLGvVlY/bgshefa7frVSXDjCyuYn5lgd4gRKjdoUehb1W2yP4n7UkykSQQFP7z8FtUrakmFA0x7c2ZpFux1BdApCjMtqOHcewlY7n7RxOoWV/bZLm66hix2pWtnl/hvWc+5IBT9m7VOT575wvWfJu962c6lWb2R3P5et4yBgzbotHx2uo6rjvmNr74cM6mbe0zl/DQr//JNQ//kH2Oz9ZUaTpcAdqoVTUpIpcBL+N0z5ugqjNF5EZgiqpOBu4AyoB/uvPt1HfD2wG4T0TSOHXEW1W1cybq2kT7NxmsqC3svBUaCXZop7wZb3/O9SfcTjKWLOjqMiKw/8l78dOJl/Gfh/9LTWX2czc1X0lL1FbVsWT2N606B8CUVz7N2TMFQAIBPnltRpOJ+obj72Dme1+SaFDDj9U6be23nnEXt7z0S3bat/GMisYHVKGJud3zO5W+ALzQYN91GV8f3Mzz3gNavF6eL5s+7njv7Xa+ojCnULXp+jO2stbaWj8//GYqV1UVfAmwaGkRB522L/de/QB3fP9u4rVtva4lBflYEvMYp6bTTbbtfzllHrP+1zhJN7zG+J8+lHeMph2oett8xnc16rQqy2uqO+DKBaz/qlI6rWNXqWlpzw0vAsEAFQN78sjNTzF7yjzSqfb5hdYCXGfIiK0oKo3mrFUHw0G23G5Ao/3P3v2ipzel+dMXsWzhcrYY0jdnWdMBfJiEvfBdjboyz252ftPj9dZ/XPebUCTE/ifvxeyp8zzXUAHC0TDhojD5To0tgda/ie5/8p6emmGixVF2PWjHRvsXf/E1aQ/PD0dCfLsw92hO0wEUSKu3zWd8l6i/WNn5f8lDy2qIftM+i9q2J1XlmT+9sKH/s1cDtunHn//3W0aP3ZVwNExJt+ImR0Q2JRQJUVJenE+4myguK+bMG06mqKT5eU2iJRF+cOf3CQYb96kvLvPWN17TSlFp89cwHUlB0942n/Fd08fSyvUdHUKrFM1eS//7vuzoMNpEoi7heUh5vWhxhPN+ezrDdh7Czc//gnUr17P8q5VUrq7i2qNvIdlMX+96gYCw57HNdWdtmZN/fAzJeJJHb34K2NiPOloSRVW59E/ncuC4pnuXfO/Uffj8gzlNzvu9SbzBANvsavNz+5JSsJuJ7c13ibpfWVlHh5C36Pz19B//JYF2arv1u0hxhPNvO4M9j96YaLtXdNswXHvXA3bkkzc+IxlvOlmHIiF23HcH+g/tV5B4RITTrz2RI84/iOfHv8rM92YTCAi7HTKSw845kLIezff8OWDc3vz1qolZzx8pjnD8Dw/Pe/Uc0w46aRu17xL1Nu06KVNh9Z00n0Cyc/4iFFqfrXpz2yu/YqvtBjZb5ppHfsjZwy+nqplEDXDFPRcUPLae/Xpwxq9OatFzikqi3Pjsz/jlUbcQq2k8D0q0OMJ2Y7bhtGtPLGCkpuA6aaL2XRv1nFWrOjqEvETnrye8srA3QmvyfB/N93leBEMBgqHsvzZFZUVcff8lWZM0wLtPf7jJMO6GVJXbzs4+UrA97bz/d/jTuzcz+vDdNrS1F5UV0b2iG6f/8kRue+VXhMK+q/uYDTx2zfNhMvfdb9W6PHt9VEWjec+e1yppRRJpyj9aCQVu8vAyVWl769W/J3VVMarWVm8cfp4hUhRm2923ZteDsvfpT6fTTLxuUtYub6lEinmfLuLLKfPYbtSwVsdeCFuPHMzNz/18Q1t7pCjMltsNaPIGpPEZBTbnxW09rGYQBR4EdgdWAaeo6sJ8Asq36aOQU5W2VP/7Pqd4flWHXb89HXT6vhx2zoFcc9hvWL+qcsOAmmAoSDAcZNfv7cgvJ11FIMcCEF98MIfaqqaHnmdK1MV58f7XfJOo62W2tZtOxIe1ZS9yJuqM1QwOwZl39SMRmdxgfPp5wBpV3UZExgG3AafkE1BdO86aVxABYd2+WxD9ap4v2qeDwQCpNryzvXbFerbcdgAPzvsLn7w2g9cffYeqtdX037ofR1xwMIO2z97cUW/Nt+sQDx2r02llxeLWz+1tDBRuCHl781Kjzrmagfv4BvfrJ4G/iIhoU5+Nc0ikOlmiBqp3rWB5MEDfibORdMdNxBQIBjj0+wfy+qNvt7ivs1c165z+4YFAgN0P2ZndD9k5r/Nk62HRUPc+VnM1BaCgPuwj7YWXm4lNrWbQsNq0oYyqJoF1QKMZ2EXkQhGZIiJTVqxoemDL4B49/HeH04Pqkb1Y8pORaDjQ2sni8hYMBzn/ltP57QvXsvOB3yEUCVFUGiUUCTFyvxH8eMIPKO+Z/+RToUiIrTzWmHP5zt7b5WweAWegySFn7Z+znDGedNKRie16M9FdIWE8wKhRo5r8bvQpKWWvrQbxzuKv2jO0gkj0L2HJ1TuyxYTZhJfX5axZiwh7HLU7ex4zimfvfollC5ZTva7xiMZAQEirZp0uNBwJsfexo+nWu5yR+43gd6/dQNXaaipXV1HWs5Tynk7/9P1O2pMzh13KuuUtH1gkAeHw8w5q8fOaEgqHOPGqo3j81meanZckEAzQq39Pdjmw8ZBuY/LSSduovVRevaxmsKGMiISA7jg3FfNy7X4H+nTd7uxCEkAGlLH/r45i90N2yjlHRaQ4zLifH8/h5x3EvR/fwTNrHuDhBfdw/A+P2DBsOhx15tf4xSNXUFxe3GS7bigSos+gCq7464Wb7C/rUUr/rfttSNIAxaVF/HXK7WwxpK/nYdHg9BPe/6Q96Te4cKufnPrz4xk9dpcmh1yHoyG6V5Rz68u/9NSWbUxOqk6vDy+bz0iuZmQ38c4GDsJJyB8Bp6nqzIwylwI7qerF7s3EE1T15GznHTVqlE6ZMqXZ41OWLuHkpyZ5fiEtEQkESLg/jLJIhMp4fu25Bw5xhgovWb+ecDDIoVtvw6k7jaRPidO88Ngt/+KRm//VZI0xWhLl9F+emHVNwFQqtUm3ryWzv+ZvP3uYj17+lEg0jKqSTqU57PsH8v2bxm1Yp9CLRDzBu09/yNN/fpGVS1ZRXFbEwOH9+eilTwHdMNVnICCEiyKMHrsLv3j0CsKRsOdreJFOp/nvE//j8dueZuGMr5BAgJJuxRx72ViOv/wIuvW2FcoNiMjULEtjedI9WKF7lh7tqezLlRNbfb1CypmoAUTkCOBONq5mcHPmagYiUgQ8hLP212pgXP3Nx+bkStQAtfE4Rz3+EAvWbrpMlgD7DhrMHw87krV1tcxetYpPln1NTSLB1j17MXrAQF6YO5s5q1dRHAqxXUUfhvXoSb+yckb06UskGNzQB1hEWFNby6MzpjFx2iesqnWaHkpCIRRnVfQtysqojMVJpFNs2a075+6yO4cM24aIh76z703+iAeun8SSL78mFAmRjCfZavuBnP3rUzYZWt0S61auZ+mcbwiGggzZcSuixYWbBGjl0lVMvudlPnj+Y1LJFMN2GcoJPzqyXbrHxWMJkvEkxWVFVos2myhMou6texQd6ansKzUPdb5E3Ra8JOp6NYk4j302g2+rKhlQ3o0TdvgO3Vo7UKUZqXSagEjBE8XyxStZv7KSbhXl9N2qoqDnNmZzV5BEHeite0S9LZT8St3DvkrUvhuZ2JSScITzdt29Xa4V9NATIR99t6qwBG1MR9uMu+cZY0ynpzjzhXvZchGRsSLypYjMFZFrmjgeFZFJ7vEPRGRIxrGf5sl7DAAABJlJREFUu/u/FJHDvMRuidoY0zVoYRYOyBitfTgwAjhVREY0KLZhtDbwR5zR2rjlxgHfAcYC97jny8oStTGmy9BUytOWw4bR2qoaB+pHa2c6FnjA/fpJ4CBxbnwdCzyuqjFVXQDMdc+XVYe1UU+dOnWliCxqwVMqgM4+6YO9ho7X2eOHrvkaBrf2gpWsefk/+qTXG0VFIpLZ22G8O2APmh6t/d0Gz99ktLaI1I/WHgi83+C5OYf7dliiVtUWjZwQkSl+ugubD3sNHa+zxw/2GvKlqmPb83qFZE0fxhjTMq0Zre3luY1YojbGmJb5CBguIkNFJIJzc3BygzKTgbPdr/8PeN2dTXQyMM7tFTIUGA58mOuCnaIftWt87iK+Z6+h43X2+MFeQ4dy25wvA15m42jtmZmjtYG/Aw+JyFzc0druc2eKyBM400QngUtVNefdyw4bmWiMMcYba/owxhifs0RtjDE+57tE3ZqhmX7h4TVcJSKzRGS6iLwmIq3uI1pIueLPKHeiiKiI+K6rmJfXICInuz+HmSLyaHvHmIuH36NBIvKGiHzi/i55m3GonYjIBBFZLiKfNXNcROQu9/VNF5Hd2jvGTkNVfbPhNMzPA7YGIsA0YESDMj8A7nW/HgdM6ui483gNBwIl7teX+Ok1eInfLVcOvIXTeX9UR8edx89gOPAJ0NN93Lej487jNYwHLnG/HgEs7Oi4G8S3H7Ab8Fkzx48AXsSZuXgP4IOOjtmvm99q1K0ZmukXOV+Dqr6hqvVrbr2P05fSL7z8DABuwpm/oK49g/PIy2u4ALhbVdf8f3t37BpFFMRx/PsTCxvRIo2FECwCSiwEC+0sxDLWQhDhmjT+BWnEXjsLO7ERtJEDrSxCIBisAgGLVCLBoDamTvGzmBPCYe72krg7Z+YD2xx3MMPbG3bfvrcDYPtHyzGO0yQHA386/54DvrUY31i2V4kVDwe5C7x0WAfOS7rQTnTTJVuhPrZGuh1qksN+PeKqIoux8Q9uUS/aftdmYBNoMgZzwJykNUnrkrLtWmuSwyNgUdI28B542E5ox2bS/8qJNU3rqP87khaB68DUtNmWdAp4CjzoOJSjOk1Mf9wi7mhWJV21/Wvkr3K5B7yw/UTSTWLd7rw9pS9dLgfKdkXdeiPdf6DRFlFJt4FlYMH239twd2Nc/GeBeWBF0hdibrGf7IFikzHYBvq29xxvMdsiCncWTXLoAa8BbH8EzhAvO5oWh9pOfRJlK9RH2ZqZxdgcJF0DnhNFOtvc6Mj4be/anrE9a3uWmGNfsN2sr1o7mpxHb4mraSTNEFMhI/t8tqxJDl+JptNIukwU6p+tRnk0feD+YPXHDWDX9k7XQaXU9dPM4YN4ErxFPPFeHnz2mCgGECfjG+I9rp+AS13HfIgcPgDfgY3B0e865kniH/ruCslWfTQcAxFTOJ+BTaIhc+dxT5jDFWCNWBGyAdzpOuah+F8BO8AecQfTA5aApX1j8GyQ32bG8yjLUVvISykluWxTH6WUUoZUoS6llOSqUJdSSnJVqEspJbkq1KWUklwV6lJKSa4KdSmlJPcb+AvrnGbbi9YAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"tags": [], | |
"needs_background": "light" | |
} | |
} | |
] | |
} | |
] | |
} |
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