lhk/sigmoid

## sigmoid
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-10,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sigmoid function\n",
    "# x/2 stretches it a bit\n",
    "y = 1 / (1 + np.exp(x/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z+6Kc2+/lg6/4d9Y3U7ZzN6U769lc1UD5rt2UVzWwtbqBJZt2srW6kaaWry9K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\nkQikchcRiUD/D+jwMPcHqh8HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f4f1a4390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"x = np.linspace(-10,10,100)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 17,
	"metadata": {},
	"outputs": [],
	"source": [
	"# sigmoid function\n",
	"# x/2 stretches it a bit\n",
	"y = 1 / (1 + np.exp(x/2))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 18,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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z+6Kc2+/lg6/4d9Y3U7ZzN6U769lc1UD5rt2UVzWwtbqBJZt2srW6kaaWry9K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\nkQikchcRiUD/D+jwMPcHqh8HAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f5f4f1a4390>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.plot(x,y)\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"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": "3.5.2"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}