95 lines
2 KiB
Text
95 lines
2 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import scipy.spatial"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"clusters = 3\n",
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"mean = np.random.randint(5, size=clusters)\n",
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"sd = [0.25, 0.25, 0.3]\n",
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"dim = 2\n",
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"nb = 50\n",
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"K= clusters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def gen_points(mean=1,sd=0.5, nb=100, dim=2, clusters=2):\n",
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" size = []\n",
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" # for i in range(0,dim):\n",
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" size.append(nb)\n",
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" size.append(dim)\n",
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" points = np.random.normal(mean[0],sd[0],size=size)\n",
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" for i in range(1,clusters):\n",
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" points = np.concatenate((points,np.random.normal(mean[i],sd[i],size=size)),axis=0)\n",
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" \n",
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" return points"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def distance(points,Pc): \n",
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" return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"points = gen_points(mean,sd,nb,dim,clusters)\n",
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"dist = distance(points,points)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.10 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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