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lab work #178

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777faee
Create task1 disha
dishaay2898 Oct 12, 2020
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dishaay2898 Oct 12, 2020
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dishaay2898 Oct 21, 2020
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dishaay2898 Oct 21, 2020
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dishaay2898 Oct 21, 2020
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dishaay2898 Oct 21, 2020
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dishaay2898 Dec 6, 2020
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119 changes: 119 additions & 0 deletions ANDNOT logic functions using numpy neuron..ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Y is initiallised [[0]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[0]\n",
" [1]\n",
" [0]\n",
" [0]]\n",
"MODEL IS NOT TRAINED\n",
"Enter New Theta : 1\n",
"Enter Weight : 1\n",
"Enter Weight : -1\n",
"Y is initiallised [ 0. 1. -1. 0.]\n",
"Calculated y [[0.]\n",
" [1.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[0]\n",
" [1]\n",
" [0]\n",
" [0]]\n",
"MODEL IS TRAINED \n",
"\n",
"Output : \n",
" [[0.]\n",
" [1.]\n",
" [0.]\n",
" [0.]]\n",
"\n",
"weights : [ 1. -1.] \n",
"\n",
"theta : 1\n"
]
}
],
"source": [
"import numpy as np\n",
"x=np.array([[1,1],[1,0],[0,1],[0,0]])\n",
"t=np.array([[0],[1],[0],[0]])\n",
"w=np.array([[0],[0]])\n",
"theta=1\n",
"yin=np.zeros(shape=(4,1))\n",
"y=np.zeros(shape=(4,1))\n",
"yin=np.dot(x,w)\n",
"i=0\n",
"found=0\n",
"while(found==0):\n",
" i=0\n",
" yin=np.dot(x,w)\n",
" print(\"Y is initiallised\",yin)\n",
" while(i<4):\n",
" if yin[i]>=theta:\n",
" y[i]=1 \n",
" i=i+1\n",
" else:\n",
" y[i]=0\n",
" i=i+1\n",
" print(\"Calculated y\",y)\n",
" print(\"Expected Target t\",t)\n",
" if (y==t).all():\n",
" print(\"MODEL IS TRAINED \")\n",
" print(\"\\nOutput : \\n\",y)\n",
" print(\"\\nweights : \",w,\"\\n\")\n",
" print(\"theta : \",theta)\n",
" found=1\n",
" else:\n",
" print(\"MODEL IS NOT TRAINED\")\n",
" w=np.zeros(shape=(0,0))\n",
" theta=int(input(\"Enter New Theta : \"))\n",
" for k in range(int(2)):\n",
" w1=int(input(\"Enter Weight : \"))\n",
" w=np.append(w,w1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
159 changes: 159 additions & 0 deletions ANDlogic functions using numpy neuron..ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Y is initiallised [[0]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[0.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"MODEL IS NOT TRAINED\n",
"Enter New Theta : 2\n",
"Enter Weight : 1\n",
"Enter Weight : 1\n",
"Y is initiallised [2. 1. 1. 0.]\n",
"Calculated y [[1.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[1.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"Calculated y [[1.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"Expected Target t [[1]\n",
" [0]\n",
" [0]\n",
" [0]]\n",
"MODEL IS TRAINED \n",
"\n",
"Output : \n",
" [[1.]\n",
" [0.]\n",
" [0.]\n",
" [0.]]\n",
"\n",
"weights : [1. 1.] \n",
"\n",
"theta : 2\n"
]
}
],
"source": [
"import numpy as np\n",
"x=np.array([[1,1],[1,0],[0,1],[0,0]])\n",
"t=np.array([[1],[0],[0],[0]])\n",
"w=np.array([[0],[0]])\n",
"theta=1\n",
"yin=np.zeros(shape=(4,1))\n",
"y=np.zeros(shape=(4,1))\n",
"yin=np.dot(x,w)\n",
"i=0\n",
"found=0\n",
"while(found==0):\n",
" i=0\n",
" yin=np.dot(x,w)\n",
" print(\"Y is initiallised\",yin)\n",
" while(i<4):\n",
" if yin[i]>=theta:\n",
" y[i]=1 \n",
" i=i+1\n",
" else:\n",
" y[i]=0\n",
" i=i+1\n",
" print(\"Calculated y\",y)\n",
" print(\"Expected Target t\",t)\n",
" if (y==t).all():\n",
" print(\"MODEL IS TRAINED \")\n",
" print(\"\\nOutput : \\n\",y)\n",
" print(\"\\nweights : \",w,\"\\n\")\n",
" print(\"theta : \",theta)\n",
" found=1\n",
" else:\n",
" print(\"MODEL IS NOT TRAINED\")\n",
" w=np.zeros(shape=(0,0))\n",
" theta=int(input(\"Enter New Theta : \"))\n",
" for k in range(int(2)):\n",
" w1=int(input(\"Enter Weight : \"))\n",
" w=np.append(w,w1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
74 changes: 74 additions & 0 deletions BFS .ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"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",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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