Neha Sharma(170409)C2

Neha Sharma(170409)C2/BFS and DFS.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "enter the number you want to search-5\n",
+      "[1, 2, 3, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bfs(graph,start,search):\n",
+    "    explored = []\n",
+    "    queue = [start]\n",
+    "    found = 1\n",
+    "    while found:\n",
+    "        node = queue.pop(0)\n",
+    "        if(node == search):\n",
+    "            found = 0\n",
+    "        if node not in explored:\n",
+    "            explored.append(node)\n",
+    "            neighbours = graph[node]\n",
+    "        for neighbour in neighbours:\n",
+    "            queue.append(neighbour)\n",
+    "    print(explored)\n",
+    "\n",
+    "search = int(input(\"enter the number you want to search-\"))\n",
+    "graph = {1: [2, 3, 5],\n",
+    "         2: [1,4, 5],\n",
+    "         3: [1, 6, 7],\n",
+    "         4: [2],\n",
+    "         5: [1, 2,4],\n",
+    "         6: [3],\n",
+    "         7: [3]}\n",
+    "bfs(graph,1,search)\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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Neha Sharma(170409)C2/Back Prpogation.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter X1: 0\n",
+      "0.0\n",
+      "Enter X2: 1\n",
+      "1.0\n",
+      "Enter bias 1: 1\n",
+      "Enter bias 2: 1\n",
+      "Enter bias 3: 1\n",
+      "Enter alpha: 0.25\n",
+      "Enter target: 1\n",
+      "phase 1\n",
+      "zin1= 0.19999999999999998\n",
+      "z1= 0.549833997312478\n",
+      "fzin1= 0.24751657271185995\n",
+      "zin2= 0.9\n",
+      "z2= 0.7109495026250039\n",
+      "fzin2= 0.2055003073422635\n",
+      "yin= 0.09102854918749159\n",
+      "y= 0.5227414361305817\n",
+      "fyin= 0.24948282708271868\n",
+      "phase 2\n",
+      "dell1= 0.11906781576358075\n",
+      "delta_w11= 0.01636688327313882\n",
+      "delta_w21= 0.021162801098940833\n",
+      "dellin1= 0.0476271263054323\n",
+      "dellin2= 0.011906781576358076\n",
+      "delta1= 0.011788503071235473\n",
+      "delta2= 0.002446847273398785\n",
+      "delta_w01= 0.029766953940895187\n",
+      "phase 3\n",
+      "delta_v11= 0.0\n",
+      "delta_v12= 0.0\n",
+      "delta_v21= 0.0029471257678088682\n",
+      "delta_v22= 0.0006117118183496963\n",
+      "delta_v01= 0.0029471257678088682\n",
+      "delta_v02= 0.0006117118183496963\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x1 = float(input(\"Enter X1: \"))\n",
+    "print(x1) \n",
+    "x2 = float(input(\"Enter X2: \")) \n",
+    "print(x2) \n",
+    "b1 = float(input(\"Enter bias 1: \"))\n",
+    "b2 = float(input(\"Enter bias 2: \"))\n",
+    "b3 = float(input(\"Enter bias 3: \"))\n",
+    "alpha = float(input(\"Enter alpha: \"))\n",
+    "t = float(input(\"Enter target: \"))\n",
+    "\n",
+    "a = [0.6,0.3,-0.1,-0.3,0.4,0.5,0.4,0.1,-0.2]\n",
+    "print('phase 1')           \n",
+    "zin1 = float(b1*a[1]+x1*a[0]+x2*a[2])\n",
+    "print('zin1=',zin1)\n",
+    "zp1 = 1/(1+np.exp(-zin1))\n",
+    "print('z1=',zp1)\n",
+    "fzin1= zp1*(1-zp1)\n",
+    "print('fzin1=',fzin1)\n",
+    "\n",
+    "zin2= float(a[3]*x1+a[4]*x2+a[5]*b2)\n",
+    "print('zin2=',zin2)\n",
+    "zp2 = 1/(1+np.exp(-zin2))\n",
+    "print('z2=',zp2)\n",
+    "fzin2= zp2*(1-zp2)\n",
+    "print('fzin2=',fzin2)\n",
+    "\n",
+    "yin=float(zp1*a[6]+zp2*a[7]+b3*a[8])\n",
+    "print('yin=',yin)\n",
+    "y = 1/(1+np.exp(-yin))\n",
+    "print('y=',y)\n",
+    "fyin= y*(1-y)\n",
+    "print('fyin=',fyin)\n",
+    "\n",
+    "print('phase 2')\n",
+    "dell1=(t-y)*fyin\n",
+    "print('dell1=',dell1)\n",
+    "delta_w11=alpha*dell1*zp1\n",
+    "print('delta_w11=',delta_w11)\n",
+    "delta_w21=alpha*dell1*zp2\n",
+    "print('delta_w21=',delta_w21)\n",
+    "\n",
+    "dellin1=dell1*a[6]\n",
+    "print('dellin1=',dellin1)\n",
+    "dellin2 = dell1*a[7]\n",
+    "print('dellin2=',dellin2)\n",
+    "\n",
+    "delta1=dellin1*fzin1\n",
+    "print('delta1=',delta1)\n",
+    "delta2=dellin2*fzin2\n",
+    "print('delta2=',delta2)\n",
+    "delta_w01=alpha*dell1\n",
+    "print('delta_w01=',delta_w01)\n",
+    "\n",
+    "print('phase 3')\n",
+    "delta_v11=alpha*delta1*x1\n",
+    "print('delta_v11=',delta_v11)\n",
+    "delta_v12=alpha*delta2*x1\n",
+    "print('delta_v12=',delta_v12)\n",
+    "delta_v21=alpha*delta1*x2\n",
+    "print('delta_v21=',delta_v21)\n",
+    "delta_v22=alpha*delta2*x2\n",
+    "print('delta_v22=',delta_v22)\n",
+    "\n",
+    "delta_v01 = alpha*delta1\n",
+    "print('delta_v01=',delta_v01)\n",
+    "delta_v02 = alpha*delta2\n",
+    "print('delta_v02=',delta_v02)\n",
+    "\n",
+    "\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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Neha Sharma(170409)C2/Bidirectional associative memory network.ipynb

@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Weight Matrix is:\n",
+      "\n",
+      "[[ 0  2]\n",
+      " [ 0  2]\n",
+      " [ 0  2]\n",
+      " [ 2  0]\n",
+      " [-2  0]\n",
+      " [ 2  0]\n",
+      " [ 2  0]\n",
+      " [-2  0]\n",
+      " [ 2  0]\n",
+      " [ 2  0]\n",
+      " [ 0  2]\n",
+      " [ 2  0]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\yukta\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:26: RuntimeWarning: overflow encountered in long_scalars\n",
+      "C:\\Users\\yukta\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:28: RuntimeWarning: overflow encountered in long_scalars\n",
+      "C:\\Users\\yukta\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:30: RuntimeWarning: overflow encountered in long_scalars\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x1=np.array([[1,1,1,-1,1,-1,-1,1,-1,-1,1,-1]])\n",
+    "x2=np.array([[1,1,1,1,-1,1,1,-1,1,1,1,1]])\n",
+    "x3=np.array([[1,1,1,-1,1,-1,-1,1,-1,1,1,1]])\n",
+    "t1 = np.array([[-1],[1]])\n",
+    "t2 = np.array([[1],[1]])\n",
+    "w1=np.zeros((12,2),dtype=int)\n",
+    "w2=np.zeros((12,2),dtype=int)\n",
+    "w=np.zeros((12,2),dtype=int)\n",
+    "i=0\n",
+    "while(i!=12):\n",
+    "   w1[i][0]=x1[0][i]*t1[0][0]\n",
+    "   w1[i][1]=x1[0][i]*t1[1][0]\n",
+    "   w2[i][0]=x2[0][i]*t2[0][0]\n",
+    "   w2[i][1]=x2[0][i]*t2[1][0]\n",
+    "   i=i+1\n",
+    "   w=w1+w2 \n",
+    "print('The Weight Matrix is:\\n')\n",
+    "print(w)\n",
+    "Yin11=Yin12=Yin21=Yin22=Yin31=Yin32=0\n",
+    "y1=0\n",
+    "y2=0\n",
+    "i=0\n",
+    "while(i!=12):\n",
+    "    Yin11=Yin11+(x1[0][i]*w[i][0])\n",
+    "    Yin12=Yin12+(x1[0][i]*w[i][1])\n",
+    "    Yin21=Yin21+(x2[0][i]*w[i][0])\n",
+    "    Yin22=Yin22+(x2[0][i]*w[i][1])\n",
+    "    Yin31=Yin31+(x3[0][i]*w[i][0])\n",
+    "    Yin32=Yin32+(x3[0][i]*w[i][1])\n",
+    "i=i+1\n",
+    "if(Yin11>0):\n",
+    "    Yin11=1\n",
+    "else:\n",
+    "    Yin11=-1\n",
+    "if(Yin12>0):\n",
+    "    Yin12=1\n",
+    "else:\n",
+    "    Yin12=-1    \n",
+    "if(Yin21>0):\n",
+    "    Yin21=1\n",
+    "else:\n",
+    "    Yin21=-1\n",
+    "if(Yin22>0):\n",
+    "    Yin22=1\n",
+    "else:\n",
+    "    Yin22=-1\n",
+    "if(Yin31>0):\n",
+    "    Yin31=1\n",
+    "else:\n",
+    "    Yin31=-1\n",
+    "if(Yin32>0):\n",
+    "    Yin32=1\n",
+    "else:\n",
+    "    Yin32=-1\n",
+    "\n",
+    "if((Yin11==-1) and (Yin12==1)):\n",
+    "   print('Pattern T is recognized for Y-Layer')\n",
+    "else:\n",
+    "   print('Pattern T is not recognized for Y-Layer')   \n",
+    "if((Yin21==1) and (Yin22==1)):\n",
+    "   print('Pattern O is recognized for Y-Layer')\n",
+    "else:\n",
+    "   print('Pattern O is not recognized for Y-Layer')\n",
+    "\n",
+    "i=0\n",
+    "Xin1=np.zeros((12,1),dtype=int)\n",
+    "Xin2=np.zeros((12,1),dtype=int)\n",
+    "while(i!=12):\n",
+    "    Xin1[i][0]=Xin1[i][0]+((Yin11*w[i][0])+(Yin12*w[i][1])) \n",
+    "if(Xin1[i][0]>0):\n",
+    "  Xin1[i][0]=1\n",
+    "else:\n",
+    "  Xin1[i][0]=-1\n",
+    "  Xin2[i][0]=Xin2[i][0]+((Yin21*w[i][0])+(Yin22*w[i][1]))\n",
+    "if(Xin2[i][0]>0):\n",
+    "   Xin2[i][0]=1\n",
+    "else:\n",
+    "  Xin2[i][0]=-1\n",
+    "i=i+1\n",
+    "Xin1=Xin1.T\n",
+    "Xin2=Xin2.T\n",
+    "print('\\n')\n",
+    "if((Xin1==x1).all()):\n",
+    "  print('Pattern T is recognized for X-Layer')\n",
+    "else:\n",
+    "  print('Pattern T is not recognized for X-Layer')   \n",
+    "if((Xin2==x2).all()):\n",
+    "  print('Pattern O is recognized for X-Layer')\n",
+    "else:\n",
+    "  print('Pattern O is not recognized for X-Layer')\n",
+    "  print('Testing of I \\n Values for I are:', Yin31 ,'\\t',Yin32)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}