drmingle/make simulated data for clusters.ipynb

## make simulated data for clusters.ipynb
{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"Make Simulated Data For Clustering\"\n",
    "author: \"Damian Mingle\"\n",
    "date: 05/01/2018\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Simulated Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make features and targets with 500 samples,\n",
    "features, target = make_blobs(n_samples = 500,\n",
    "                  # two feature variables,\n",
    "                  n_features = 3,\n",
    "                  # four clusters,\n",
    "                  centers = 4,\n",
    "                  # with .65 cluster standard deviation,\n",
    "                  cluster_std = 0.65,\n",
    "                  # shuffled,\n",
    "                  shuffle = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B5nsGBwd1fHw80XmJutGGnd9uuWmXG4ha2cYtqSwHwVUje40Tv6+Obp3vy2kr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\n7EREOfP/AZO4aLDvF6mMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1feb41a54e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot of first two features\n",
    "plt.scatter(features[:,0],\n",
    "            features[:,1])\n",
    "\n",
    "# View scatterplot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
	{
	"cells": [
	{
	"cell_type": "raw",
	"metadata": {},
	"source": [
	"---\n",
	"title: \"Make Simulated Data For Clustering\"\n",
	"author: \"Damian Mingle\"\n",
	"date: 05/01/2018\n",
	"---"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Preliminaries"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from sklearn.datasets import make_blobs\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Make Simulated Data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"# Make features and targets with 500 samples,\n",
	"features, target = make_blobs(n_samples = 500,\n",
	" # two feature variables,\n",
	" n_features = 3,\n",
	" # four clusters,\n",
	" centers = 4,\n",
	" # with .65 cluster standard deviation,\n",
	" cluster_std = 0.65,\n",
	" # shuffled,\n",
	" shuffle = True)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## View Data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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B5nsGBwd1fHw80XmJutGGnd9uuWmXG4ha2cYtqSwHwVUje40Tv6+Obp3vy2kr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\n7EREOfP/AZO4aLDvF6mMAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x1feb41a54e0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"# Create a scatterplot of first two features\n",
	"plt.scatter(features[:,0],\n",
	" features[:,1])\n",
	"\n",
	"# View scatterplot\n",
	"plt.show()"
	]
	}
	],
	"metadata": {
	"anaconda-cloud": {},
	"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": "3.5.4"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 1
	}