ch03_Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 간단한 그래프 그리기\n",
    "### numpy 와 matplotlib \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(-1,2,0.1)\n",
    "y = 3*x + 2\n",
    "\n",
    "x0 = [0,]*len(x)\n",
    "y0 = [0,]*len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f07887ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y, label=\"y=3x+2\")\n",
    "plt.plot(x0,y, 'k')\n",
    "plt.plot(x,y0, 'k')\n",
    "plt.legend()\n",
    "\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AND(x1, x2):\n",
    "    w1, w2, theta = 0.5, 0.5, 0.7\n",
    "    tmp = x1*w1 + x2*w2\n",
    "    if tmp <= theta:\n",
    "        return 0\n",
    "    else :\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   0   0   1\n"
     ]
    }
   ],
   "source": [
    "print(AND(0,0), \" \", AND(0,1), \" \", AND(1,0), \" \", AND(1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 수식 수정 \n",
    "\n",
    "y = \\begin{cases} 0 ~ ( b + w_1 \\times x_1 + w_2 \\times x_2  \\le  0 ) \\\\ 1 ~ ( b + w_1 \\times x_1 + w_2 \\times x_2  >  0 ) \\end{cases}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AND(x1, x2):\n",
    "    x = np.array([x1,x2])\n",
    "    w = np.array([0.5, 0.5])\n",
    "    b = -0.7\n",
    "    tmp = np.sum(w*x) + b\n",
    "    if tmp <= 0:\n",
    "        return 0\n",
    "    else :\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   0   0   1\n"
     ]
    }
   ],
   "source": [
    "print(AND(0,0), \" \", AND(0,1), \" \", AND(1,0), \" \", AND(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AND(0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NAND(x1,x2):\n",
    "    x = np.array([x1,x2])\n",
    "    w = np.array([-0.5, -0.5])\n",
    "    b = 0.7\n",
    "    \n",
    "    tmp = np.sum(w*x)+b\n",
    "    if tmp <= 0:\n",
    "        return 0\n",
    "    else :\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OR(x1,x2):\n",
    "    x = np.array([x1,x2])\n",
    "    w = np.array([0.5, 0.5])\n",
    "    b = -0.2\n",
    "    \n",
    "    tmp = np.sum(w*x)+b\n",
    "    if tmp <= 0:\n",
    "        return 0\n",
    "    else :\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(OR(0,0),OR(0,1), OR(1,0), OR(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Hi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def AND(x1,x2):\n",
    "    if x1 != 0 and x2 != 0:\n",
    "        res = 1\n",
    "    else :\n",
    "        res = 0\n",
    "    return res\n"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}