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‎Genetic_Algorithm_Assignment.ipynb‎

Lines changed: 103 additions & 46 deletions
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@@ -3525,6 +3525,33 @@
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "yuYxCX6CK8Yw",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"def details2(row):\n",
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" duration = 0\n",
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" size = 0\n",
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" fitness = 0\n",
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" for i in range(len(row)):\n",
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" if(row[i] == 1):\n",
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" size += df.loc[i,'size']\n",
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" duration += df.loc[i,'duration']\n",
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" \n",
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" if(size<=4500):\n",
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" fitness += duration\n",
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" else:\n",
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" fitness += duration*0.5\n",
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" \n",
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" return duration,size,fitness"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
@@ -3552,6 +3579,19 @@
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "nRcRCeWvKawF",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
@@ -3560,11 +3600,13 @@
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"colab": {}
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},
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"source": [
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"finalDuration = []\n",
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"finalFitness = []\n",
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"finalSize = []\n",
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"#Get Fitness and Size information for the Last Population\n",
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"for i in range(popSize):\n",
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" fitness,size = getFitness(population.iloc[i,1:].values)\n",
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" duration,size,fitness = details2(population.iloc[i,1:].values)\n",
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" finalDuration.append(duration)\n",
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" finalFitness.append(fitness)\n",
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" finalSize.append(size)"
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],
@@ -3576,91 +3618,106 @@
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"metadata": {
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"id": "yBBu8t91DGnF",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"nearst = 0\n",
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"index = -1\n",
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"for i in range(popSize):\n",
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" if(finalFitness[i] > nearst and finalSize[i] <=4500):\n",
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" nearst = final_size[i]\n",
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" index = i"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "McmqkVFHJaOQ",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "356b1b56-ac91-4007-bc10-8981b7f96039"
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"outputId": "2b721bb0-3042-4898-d4ca-65ab127cc012"
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},
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"source": [
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"max(finalFitness)"
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"finalFitness[index]"
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],
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"execution_count": 499,
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"execution_count": 521,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"573"
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"2795"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 499
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"execution_count": 521
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "qtqbDM4eDUIV",
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"id": "JHDsez3zJjc0",
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"colab_type": "code",
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"colab": {}
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "85a83391-b233-4711-fb36-13080406d1bf"
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},
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"source": [
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"#Getting the Optimum solution Information to Variables\n",
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"Best_Solution_id = finalFitness.index(max(finalFitness))\n",
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"Best_Solution_Size = finalSize[Best_Solution_id] \n",
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"Best_Solution_Duration = finalDuration[Best_Solution_id]\n",
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"Best_Solution = chromosome.iloc[Best_Solution_id]\n"
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"finalSize[index]"
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],
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"execution_count": 0,
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"outputs": []
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"execution_count": 522,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"4300"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 522
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "MBlGAsC6FGFb",
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"id": "51BpFkk_KtV_",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 289
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"height": 34
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},
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"outputId": "d80b3e48-b3ff-4879-ae71-ece12612a4ec"
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"outputId": "f3fdbc62-b780-42f8-c7fe-f66901af1c20"
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},
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"source": [
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"#Printing the Optimum Solution Information\n",
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"print(\"Optimum Solution ID : \" + str(Best_Solution_id))\n",
3635-
"print(\"Optimum Fitness : \" + str(finalFitness[Best_Solution_id]))\n",
3636-
"print(\"Optimum Solution File Size : \" + str(Best_Solution_Size) + \"MB\")\n",
3637-
"print(\"Optimum Solution Duration : \" + str(Best_Solution_Duration) + \" Minutes\")\n",
3638-
"print(\"Optimum Solution : \")\n",
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"print(str(Best_Solution[1:]))"
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"finalDuration[index]"
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],
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"execution_count": 501,
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"execution_count": 523,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Optimum Solution ID : 4\n",
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"Optimum Fitness : 573\n",
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"Optimum Solution File Size : 4225MB\n",
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"Optimum Solution Duration : 444 Minutes\n",
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"Optimum Solution : \n",
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"file 1 1\n",
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"file 2 0\n",
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"file 3 1\n",
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"file 4 1\n",
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"file 5 0\n",
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"file 6 0\n",
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"file 7 0\n",
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"file 8 0\n",
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"file 9 0\n",
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"file 10 1\n",
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"Name: 4, dtype: int64\n"
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],
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"name": "stdout"
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"575"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 523
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}
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]
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},

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