PML_R.ipynb

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      "name": "PML_R.ipynb",
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      "source": [
        "## Created by Sreya Dhar\n",
        "## Dated: 04/18/20\n",
        "\n",
        "rm(list = ls());\n",
        "\n",
        "#load libraries\n",
        "library(caret)\n",
        "library(rpart)\n",
        "library(rpart.plot)\n",
        "library(RColorBrewer)\n",
        "library(rattle)\n",
        "library(e1071)\n",
        "library(randomForest)\n",
        "library(readr)\n",
        "library(party)\t\t\t\t\t# Alternative decision tree algorithm\n",
        "library(partykit)\t\t\t\t# Convert rpart object to BinaryTree\n",
        "\n",
        "\n",
        "# set the seed\n",
        "set.seed(100)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "# the url of the files incase you want to read them online \n",
        "train.url <- \"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv\"\n",
        "test.url <-  \"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv\"\n",
        "\n",
        "# read the csv files (saved in hard drive)\n",
        "train_orig <- read.csv(train.url, header=T)\n",
        "test_orig <- read.csv(test.url, header=T)\n",
        "\n",
        "# Drop the first 7 columns as they're unnecessary for predicting.\n",
        "train_clean <- train_orig[,8:length(colnames(train_orig))]\n",
        "test_clean <- test_orig[,8:length(colnames(test_orig))]\n",
        "\n",
        "# Drop colums with NAs\n",
        "train_clean <- train_clean[, colSums(is.na(train_clean)) == 0] \n",
        "test_clean <- test_clean[, colSums(is.na(test_clean)) == 0] \n",
        "\n",
        "# Check for near zero variance predictors and clean them \n",
        "nzv <- nearZeroVar(train_clean,saveMetrics=TRUE)\n",
        "zero.var.ind <- sum(nzv$nzv)\n",
        "\n",
        "if ((zero.var.ind>=0)) {\n",
        "  train_clean <- train_clean[,nzv$nzv==FALSE]\n",
        "}\n",
        "\n",
        "# data partitioning in training set (80%) and validation set (20%) \n",
        "train_part <- createDataPartition(train_clean$classe, p=0.80, list=F)\n",
        "train_final <- train_clean[train_part, ]\n",
        "validate_final <- train_clean[-train_part, ]\n",
        "\n",
        "# 5-fold cross-validation performed on Random Forest algorithm\n",
        "controlPara <- trainControl(method=\"cv\", 5)\n",
        "\n",
        "# Random Forest for training data set, 300 trees performed \n",
        "mod_rf <- train(classe ~ ., data=train_final, method=\"rf\",\n",
        "                  trControl=controlPara, ntree=300)\n",
        "# model summary\n",
        "mod_rf"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Random Forest \n",
              "\n",
              "15699 samples\n",
              "   52 predictor\n",
              "    5 classes: 'A', 'B', 'C', 'D', 'E' \n",
              "\n",
              "No pre-processing\n",
              "Resampling: Cross-Validated (5 fold) \n",
              "Summary of sample sizes: 12558, 12560, 12558, 12561, 12559 \n",
              "Resampling results across tuning parameters:\n",
              "\n",
              "  mtry  Accuracy   Kappa    \n",
              "   2    0.9921653  0.9900887\n",
              "  27    0.9925476  0.9905725\n",
              "  52    0.9864966  0.9829188\n",
              "\n",
              "Accuracy was used to select the optimal model using the largest value.\n",
              "The final value used for the model was mtry = 27."
            ]
          },
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        "id": "l8OzV9w42-F1",
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      "source": [
        "# predict the trained model on validation set\n",
        "pred_rf <- predict(mod_rf, validate_final)\n",
        "\n",
        "# confusion matrix on validation set\n",
        "confusionMatrix(validate_final$classe, pred_rf)"
      ],
      "execution_count": 0,
      "outputs": [
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            "text/plain": [
              "Confusion Matrix and Statistics\n",
              "\n",
              "          Reference\n",
              "Prediction    A    B    C    D    E\n",
              "         A 1114    2    0    0    0\n",
              "         B    4  755    0    0    0\n",
              "         C    0    4  678    2    0\n",
              "         D    0    0    3  640    0\n",
              "         E    0    0    0    0  721\n",
              "\n",
              "Overall Statistics\n",
              "                                          \n",
              "               Accuracy : 0.9962          \n",
              "                 95% CI : (0.9937, 0.9979)\n",
              "    No Information Rate : 0.285           \n",
              "    P-Value [Acc > NIR] : < 2.2e-16       \n",
              "                                          \n",
              "                  Kappa : 0.9952          \n",
              "                                          \n",
              " Mcnemar's Test P-Value : NA              \n",
              "\n",
              "Statistics by Class:\n",
              "\n",
              "                     Class: A Class: B Class: C Class: D Class: E\n",
              "Sensitivity            0.9964   0.9921   0.9956   0.9969   1.0000\n",
              "Specificity            0.9993   0.9987   0.9981   0.9991   1.0000\n",
              "Pos Pred Value         0.9982   0.9947   0.9912   0.9953   1.0000\n",
              "Neg Pred Value         0.9986   0.9981   0.9991   0.9994   1.0000\n",
              "Prevalence             0.2850   0.1940   0.1736   0.1637   0.1838\n",
              "Detection Rate         0.2840   0.1925   0.1728   0.1631   0.1838\n",
              "Detection Prevalence   0.2845   0.1935   0.1744   0.1639   0.1838\n",
              "Balanced Accuracy      0.9979   0.9954   0.9969   0.9980   1.0000"
            ]
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        }
      },
      "source": [
        "# accuracy of the predicted model\n",
        "accuracy <- postResample(pred_rf, validate_final$classe)\n",
        "accu_out <- accuracy[1]\n",
        "accu_out"
      ],
      "execution_count": 0,
      "outputs": [
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            "text/plain": [
              " Accuracy \n",
              "0.9961764 "
            ],
            "text/latex": "\\textbf{Accuracy:} 0.9961763956156",
            "text/markdown": "**Accuracy:** 0.9961763956156",
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              "<strong>Accuracy:</strong> 0.9961763956156"
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      },
      "source": [
        "# Overall out of sample error:\n",
        "OSE <- 1 - as.numeric(confusionMatrix(validate_final$classe, pred_rf)\n",
        "                 $overall[1])\n",
        "OSE"
      ],
      "execution_count": 0,
      "outputs": [
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              "[1] 0.003823604"
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            "text/latex": "0.00382360438439966",
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      },
      "source": [
        "# trained model applied to test data for results\n",
        "pred_test <- predict(mod_rf, test_clean[, -length(names(test_clean))])\n",
        "pred_test"
      ],
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            "text/plain": [
              " [1] B A B A A E D B A A B C B A E E A B B B\n",
              "Levels: A B C D E"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item B\n\\item A\n\\item B\n\\item A\n\\item A\n\\item E\n\\item D\n\\item B\n\\item A\n\\item A\n\\item B\n\\item C\n\\item B\n\\item A\n\\item E\n\\item E\n\\item A\n\\item B\n\\item B\n\\item B\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item 'A'\n\\item 'B'\n\\item 'C'\n\\item 'D'\n\\item 'E'\n\\end{enumerate*}\n",
            "text/markdown": "1. B\n2. A\n3. B\n4. A\n5. A\n6. E\n7. D\n8. B\n9. A\n10. A\n11. B\n12. C\n13. B\n14. A\n15. E\n16. E\n17. A\n18. B\n19. B\n20. B\n\n\n\n**Levels**: 1. 'A'\n2. 'B'\n3. 'C'\n4. 'D'\n5. 'E'\n\n\n",
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              "<ol class=list-inline><li>B</li><li>A</li><li>B</li><li>A</li><li>A</li><li>E</li><li>D</li><li>B</li><li>A</li><li>A</li><li>B</li><li>C</li><li>B</li><li>A</li><li>E</li><li>E</li><li>A</li><li>B</li><li>B</li><li>B</li></ol>\n",
              "\n",
              "<details>\n",
              "\t<summary style=display:list-item;cursor:pointer>\n",
              "\t\t<strong>Levels</strong>:\n",
              "\t</summary>\n",
              "\t<style>\n",
              "\t.list-inline {list-style: none; margin:0; padding: 0}\n",
              "\t.list-inline>li {display: inline-block}\n",
              "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "\t</style>\n",
              "\t<ol class=list-inline><li>'A'</li><li>'B'</li><li>'C'</li><li>'D'</li><li>'E'</li></ol>\n",
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      },
      "source": [
        "# Building the regression tree\n",
        "mytree <- rpart(classe ~ ., data=train_final, method=\"class\")\n",
        "\n",
        "# Plot the tree \n",
        "fancyRpartPlot(mytree, caption = 'https://github.com/sreyadhar') +\n",
        "title('Regression Tree Visualization',adj = 0.0)"
      ],
      "execution_count": 0,
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jMQs7VZ+/8efuK1918NkYb4+7PpR9DrDO3qbtzGBXAd4T12yOoAAFhramqakJoS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JYrFUVlbKnSI+tGTmed7j8bjdbofD0dHR\nUVJSIncijkNhBzMIBoOBQECv18sdJIloOXSQkFkayKxAGJ4QCR2jpORlvu2JNNGam5uTtGZW\nYXgiJgxPzAcyk5CZRGNmhWD8jB2N3ZfITJ6xS17r29jYWE5OztyfjzN2GJ6ICcMT84HM0kDm\nlMJ4YQc0kqawixcKOwAAUD7GL8UCJITBYJA7AgAAwO0xfruT5KGx+5LGzGNjY2VlZXKnAJgR\nhieUCZmlgcwKxHhh19DQsH379mSsOakdo8jMcZzJZOI4zul0JqnNorGxUbhtHszRrVu3li5d\nKncKxXG5XP39/XV1dXIHwaHjPZCZhMwkDE/QjcbuS2TmeZ7jOI/Hk6gVxjQ8PJyfnx/vq1K5\nxw7DEzFheGI+kFkayJxSGC/sgFJCbadAKVvVAQAAFVDYAQDEp7W19Q9/+ENFRYXD4eB5XqfT\n8TwfCARMJtMdd9yxevVquQMCQOrCVKxIFotF7ghxQ2aAhBgcHKyvr//whz/8wAMPbNu2Tfiz\nvr6+rKxsdHRU7nSJR+NuiMzSQGYFYrywo/HW1chMojEzqxj+5ImJiYnReIyPjzc0NFgsljNn\nzkxOTgp/dnZ2lpSUjI2NxbWqBN6pEbshCZlJyEzC8ATdaOy+RGZp0JhZXqwOT5w+fbqlZTgn\nN45JmlAoODzsiPlQbm6+RqOd+6rG3WOLF2fcc889c3/JTGjcpJFZGsicUhgv7AAAZhGJRJ77\n9q/WrNkseg2/+92PFy2qTk9PX79e5K0ZOjtaH3xwa15enugMAABRjF+KBQCYRSgUSk+f1+08\ns7NzN23aNTw8JHoNmQZjsm/uAwCpA4WdSDR2XyIzQMK5xkbOnz9hLiiez0r8fj8/q0SlFYfG\n3RCZpYHMCsR4YUdj9yUyk2jMzCqGhyfm4+FHHt+6dc/adVvns5LBwcH+WDwez9xP5mE3JCEz\nCZlJGJ6gG43dl8gsDRozy4vJ4YlgMPiD//PyypVTP1yutbWpv797bHSkZlld9NETJw5P+nx3\n3f3AT3/y7Kef+P/IZxoNJltf94c+9Alh4VtvvR4KBtLSNaFgYPeefeTLc3MLbH3dDz/8WPR7\n2e29pix3cXGMc35ms1n4bL25fEoKjZs0MksDmVMK42fsaNwskFkaNGaWF3tV3SyWL18diXDr\n128bGxuJLlSp1Hn5hWfPvrFkSe2UZ65ZuyVDp48uHBrq12XohT+nvHxF7dpwKDjl2/l8Pu80\nHMc5HA632z3HzDRu0sgsDWROKfPqGgYAYE9vr4XnJy2WNp1WN+Swh0KhtLS03l5LKBRyOuwG\nQ5atrzsQ8Gs02ugzbbaumppVkUhYpVL39lpKS8sdQ/aS4gWOIXt0ofDyllCQU6mEhbPH8Hq9\nBoNBmn8yADCD8UuxyWOxWCorK+VOER9kBphipkuxUrLbezXpg4WFhdMfMhgMwtXYuVyKTR4a\nd0NklgYyKxDjl2Jp7L5EZhKNmVmF4QmFw25IQmYSMpOYH55g/FJsTU1NktacvM/5RmYSjZlZ\nFQgE5I6gLNEJiby8gokJ76ZNu+TNg92QhMwkZCYlL7NCMH7GjsbuS2SWBo2Z5ZVSwxNzIcxG\nFOQX9vX1aDVau71X3jw0btLILA1kTimMF3YAALNIT0+fnJwU91phQqJ8UfWCBRUu9+jQUL+4\n9TgcA9nZ2eJeCwAwBYYnRKKx+xKZAaZrbW197bXGCCfPkVDFqbKzJ6urq2M+iuEJ0ZBZGsis\nQIyfsaOx+xKZSTRmZhWrwxPLly8/cODvnjrwsX/67CMPfmjHHTuXVS42rF1Tmp8X7LU2dnac\nv3L56PWWkznZfEvziacOfOypAx+LLnzqwMcGB66Fgv3Ll+UtqTY9deBj99y91pDp+ftP3leQ\nH8rJ5i+8fXh0pK2/70pVpfHC24c3va+iuEi1Yf3C6H/r1y+YqaqLF3ZDEjKTkJnE/PAE42fs\naLx1NTJLg8bM8mLykydIPM/39/c7HI6RkRG9Xn/s2LGqqqq1a9dGnxCJRGw2G8dxhYWFOp1O\nxLcIBAJz/4gwLs4zdjRu0sgsDWROKYwXdgAAcxQt7IRPfVAChVyKBQCKMH67EwAQLRKJ9PT0\nhMPh6Q+VlJTo9frpywEAQF6M99glj8VikTtC3JAZ4nLt2rUjR4709fU1NjaePXtW+PPUqVNn\nzpw5e/as3OmoFAgEnE6n3CniRuNuiMzSQGYFYrywo7H7EplJNGZmRigUqqmp2bp16+bNm6uq\nqoQ/jUbj4sWLtVqt3OmSaGRkpL9f5L1LZhcOh0XfXeW2sBuSkJmEzCQMT9CNxu5LZJYGjZnn\nY3Jy8ty5c3Ht74ODgw0NDXfeeWdLS0t+fv7w8HB+fn5vb+/ChQvD4fCKFSvmviqVSrVs2bKy\nsrL4g0uHHJ4IhUKZmZlyJ8LwhBIhszRozKwQjBd2ACD4+X//5M5796hUKnEvHxke+ewnP//L\nw78QHeDsqYYP1H9QCdXSTITCjuM4h8PBcZzsIxQGg4HjOAxPAEBcMDwBkBKMWUbRVR3Hca/8\n9rA+U99vs5cuKBG3hrUb17a2tq5fv150BgmYTCa32y2cJ1DI2QKTySR3BACgCQo7kWi8dTUy\ng2i3Wtv3P7TvjSNvfuKJj4tbQ5pa7fP5eJ6f5Tnibg6XWEktpPx+//DwcElJfMWx0WhMUp45\nonE3RGZpILMCMV7YNTQ0bN++PRlrTmrHKDJH0ZiZSf/xvW/MfyU+n2+W2/PKXr5EMwwODnq9\n3mQc+n0+n0qlStK/FLshCZlJyEzC8ATdaOy+RGZp0Jh5Pl5+47dbdmyWMYBvwtfc0LZ69eqY\njwrnyRTSSebz+YLBYFZWltxB4kPjJo3M0kDmlML4GTsaNwtklgaNmRPuxNGTlo5uLhJZvX7V\n+7ZuJBdu2LSuy9L9wCP7hYVX3rnaZem+b//7v3nwmX99+svkMyurKywd3Y9/5pPkM0PBkGfc\nM+W67ejoqDCXMIXZbHa73cppJqP03ss0btLILA1kTimM38cOAGax7Y6t+syMHXu2DztHpixc\nu3ENeTpf+PLkm6fr1q6c8kzhzynPDIVC09vpJicnvbHErPYAAEAEFHYi0XjramQGUme75QfP\nvVBUXNh5y2Kz9gWDQXLhtSvNrc1twueJdbZbWpqutza3eb3ero4uP+8nn/m9Q/+nqLhwyjPD\n4XBGRkbMjyNLZTzP9/X1yZ0ibjTuhsgsDWRWIMYvxdLYfYnMJBoz06JqSeWTX/78LAtXra2L\nLuQ4buXq2pjP3Pv+O8mF059JnYGBAa/XW1VVlfA1h8PhiYmJhK9WgN2QhMwkZCYxPzyRdvDg\nQbkzJJFerxdu8plwxcXFyVgth8zvRWNmZWrtvL5w0QIZAwQDwc4b3QUFBdMf0mq1BoNBp9Mp\n5PbFaWlper0+Gfde0Wg0yRsQwW5IQmYSMpOSl1khGD9jR2P3JTJLg8bMsvjNL37HcZzRaDAX\nmb0e7+676+VOlHQYnpAMMksDmVMKeuwAYDYLysts1j5zkbnb0qPVaXu6rOLWEwyF0tLSEpsN\nAACmQGEnEo3dl8icynQBgzD0EK8J70RxSdGKuuWLqypGnKP9tn5xAS42XKyoqBD3WmZgeEIy\nyCwNZFYgxi/F0th9icwkGjNLr7W1NRQKrVy5cpYv77333vPnz/M8//zzz3u9Xq1WW1VVNTg4\nWFRUZLfbS0pKhoaGPv3pT3Mc9/3vfz8SifT19VVUVPT09KxevbqiouI3L7588eLFjIyM9vb2\nJUuW2O32devWDQ0NcRwnvOob3/jGhg0b7rrrrq6urubm5vr6+m9+85urVq0KBoMDAwP19fXL\nly9vaWmpqqqK3q/uypUr5Jdnz57dsWOHEu5RjOGJKWjcDZGZhMwk5ocn8MkTioPM0qAxc1JF\nIpHOzk6O48rKyubeZDbHV506dWqWlRgMBrPZbDKZlFDVcfjkCQkhszSQOaUwXtgBwBQ8z3s8\nns7OTq/XK3eWdymtsAMAoBd67ABAKRwOh8/nkzsFAADFUNiJRGP3JTKz6uLFi3JHSAyXy+X3\ni5nwoAWGJySDzNJAZgVivLBraGhI0pqT2jGapDUjMyl5maW3cOFCuSMkRnV1dXZ2ttwpuIGB\nAaFxMOGSPTyRpDXTuBsiMwmZSRieoBuN3ZfILA0aMycEeuxuC8MTkkFmaSBzSmG8sAOAKaKF\nHcdxCqntlFbYAQDQC4UdQGoRCju32+1wOOTO8i7h93KTyRQOh41GI6Wf6AUAoASM36A4eSwW\nS2Vlpdwp4oPMrLp48eKmTZvifZWiLnMItyl2uVxarZbhwo7neafTWVZWJneQ+NC4GyKzNJBZ\ngRgv7Gi8dTUyk2jMLL14hyeMRmOSksyH0WhUyKVYfPLEFDTuhshMQmYShifoRmP3JTJLg8bM\nicLzvNwRYtDpdHJHeBeGJySDzNJA5pTCeGEHAAp35syZtra2YDAYDoezs7PVanVHR0d1dXV7\ne/s999yzefNmuQMCANCE8UuxAKBw4XC4vr7e7XZfunRp48aN4+Pj+/bta2tr27dv36VLl+RO\nBwBAGRR2ItHYfYnMrBI3PJEMfr//woULcX16RHNz80svvfTggw/abLbjx48HAgGXy9Xc3Oxy\nuYQzeXNflV6v37Jli1qt3PuuY3hCMsgsDWRWIMYLOxq7L5GZRGNm6Snnkyde/caB+zav1aSn\nzf0l9XX5XN09HOdpH7hVqfctX1RazvXX1+VzXH/9MhPH9c99Vb4x/6tf/+/9//Lt+IO/B4Yn\npqBxN0RmEjKTmB+eSDt48KDcGZJIr9cbDIZkrLm4uDgZq+WQ+b1ozCw9hbT52+12o7OrKNck\n7uXnr7cX5WaXF+UbMkROUWjS0wZHXYs21atUKnFrEKSlpcWFvCAAACAASURBVOn1+mQMc2g0\nmuRN/mI3JCEzCZlJycusEIyfsaNxpgaZpUFjZoULBALa9PkeUlzeCXO2+Do1Ta32+XzpM8eY\nS7lG6Y30aNykkVkayJxSGC/sAIAWBx5+f0LW4/F4ZirsjEYjz/PKua8KAEDCKbfLWOEsFovc\nEeKGzKy6ePGi3BEUZHx83D0Dj8cjbzae5/v6+uTNIAKNuyEySwOZFYjxwq6hoSFJa05qx2iS\n1ozMpORllp5yhiem+9WJt3/02mk+EPzii7+JLnz1/OVnf//6f5/5y69Pvj3lmX9p6/zVib8u\nFL78zstvnGu5FV0orG3KwijHDOaeeWBgoLOzM75/59wke3giSWumcTdEZhIykzA8QTcauy+R\nmURjZukpZHjC5XKFem7kZr3nXb3e05dvMlyz9BbnZdctfrcALc3PvdjW6fFNFuWYlpWXks/c\nvLy61dq/qvLdZ5YV5F3r6t1Wu6TXMRJ95msXrhRkZ22trSYXCnqHhidzy4LBYOC9tFrtxMRE\nenp6VlbWLB14AgxPTEHjbojMJGQmYXiCbjR2XyKzNGjMTJ2bvfZgKGRzjGYb9O19g3wgqNOk\n3+y1t1r7VSrV4mJzr2MkHImoVaroM2/22pstveHd7y708f5mS69eq+11DAdDofS0tJu99gne\n3943qFapBkZdwsLbJvF6vXP/IYHhCckgszSQOaXgI8UAIDGsViv/1u8rS+Q8HJ9rueVfuSst\nVqlnMBjMZnNpaSmGJwCAYYz32CUPjd2XyMwqDE/QAsMTkkFmaSCzAjFe2NHYfYnMJBozS0/J\nwxNzFB2naGi++fpfmuQNg+GJKWjcDZGZhMwkDE/QjcbuS2Qm0ZhZegoZnhgfH/d3NedlGUW8\nVhinWFmxoL1vMN9kjHDclCGMObIOOiPFlTE/eUKr1RoMBgxPiEDjbojMJGQmMT88wfgZOxq7\nL5FZGjRmVriSkpLOviFxrz1x5bpKpVq+qHRJWZHTNd47NCxuPe6JSbV6voc1vV6vkFo5LjRu\n0sgsDWROKYxPxQKAZNLS0lZ/7POn29oikUggEHC73fn5+c8+++z9999/5syZQCBgt9tLSko+\n/elPcxz30ksvTU5O2u32nTt3vvnmm+Xl5dnZ2b/uDb7+5mW9Xt/efnbJkpt9fX1lZWWRSCQt\nLe3Tn/70b37zm5ycnPr6+snJyRdeeKG2traxsfGLX/xiV1dXIBAQMlStSMqnhgMA0AKFnUgW\ni6WyslLuFPFBZlZdvHhx06ZNcqfgOI4rKysrKyvjOI7n+eHh4dLS0qNHjz7xxBNWqzUSiQSD\nwUWLFt15550cxx0+fDgSifA8H4lEysrKduzYsXLlymAwWFpaajAYhoaGFixY4Pf777jjjp6e\nHpVKdeeddxYVFZ09e3bLli0//OEPS0pKSktL169fv2/fvvPnz/M8L/c/fU54nnc6ncJbRBEa\nd0NklgYyKxDjtztpaGjYvj0pv8E3NTWtXr06GWtGZhKNmaXX399fWlp6++dJiOd5u93ucrna\n2tqKiooyMjKE5ZFIRBgLNZvN82xiGx4e1ul0RqNxcnJyLsexud/uZGBgwOv1VlVVzSdeTD6f\nz2azLVmyJOFr5rAbvhcyk5CZlLzMCsF4YedwOKi7To/M0qAxM0V4nu/v73c4HAMDA5mZmTFv\nLCexuRd2Pp8vGAxS12ZH4yaNzNJA5pTCeGEHwKqTJ0+Gw+Hpy4uLi1euXCl9nimihZ3X65U7\ny7twg2IASAWMT8UCsOratWuRSCQUCmVnZ+fl5fX19Ql/dzqdckcDAADZoLATicZbVyMzSwoK\nCvLy8np6erKyskKh0AMPPCD8Xe5c0nG5XMm72W+S4JMnJIPM0kBmBWJ8KjZ53ZdJvd02MkfR\nmDle4+Pjb7/99mjAEderfvDC97besaW7q+fClfPBYLC8YmHr9bYLV85XVi8e9MZROqjV6sLM\n0jvuuCPO1HPV2dlZWlqq1+sTvma/3x/zRsTzl7zhiWR/8gR2wyhkJiEziflPnmC8x47G7ktk\nloZyMr/xxht1O5aJfvnI8MhnP/n5Xx7+heg1jI6M+gaCGzZsEL2G6YQeO47jLBaLQoYnhP/d\nGJ5QFGSWBjKnFMbP2NG4WSCzNJST2cONzeflr/z2sD5T32+zly4oEbeG3LxcR0fXfDLEZDKZ\n3G63ou4XZTKZ5vK0ZJxflIByNum5Q2ZpIHNKYbywA2Derdb2/Q/te+PIm5944uOiVxIIBGa/\nx6+4SdI5FlIAAJAoKOxEovHW1cjMpP/43jfmvxKfz+fxeGZ61Gg08jwfb21nNBrnnWs2w8PD\nGo2GrtoRnzwhGWSWBjIrEONTsQ0NDUlac1I7RpO0ZmQmJS8zjbxer3tms9R8M9H9j7a2tkAg\noEuCcDisVqtFvPC24QcGBjo7O0W9kbeR7OGJJK2Zxt0QmUnITMLwBN1o7L5EZmkoJ/PLb/x2\ny47NUxa+ceRYV2fXgoVlgWDwgw/fLyy88s7VLku3wWDo6ux64nOfEhaeOHrS0tG9YdO6Lkv3\nA4/sJxeWlBaRL//Db/844Z0wF5rJlwte/cXr69atixnPbDabTCaj0TiXkmi60dHRrKys9HSa\nLg5geEIyyCwNZE4pjJ+xo3GzQGZpKDzztl1bg8FQn62fbORfu3FNJBIRHvrrM+/Yqs/MEB6a\nsnDKy9UqdVFx4fpN68iXC3w+nzeW+f9DcnNz6arqOI7T6/XUVXWc4jfpmJBZGsicUig74AKk\ngs52y63WdpWKW7hoYb+tX7jm2Nlu8U34WpvbMjIyVCouuvCPv3t11dqV1640tza3hR9+z8JA\nIEi+PBgK2vsGgsHG6Mvl/ocCAECCMX4pNnlo7L5EZmWKeSlWYr998Q9r1qyZvlz4fNX5XIpN\nHqfTqdVqMTwhARp3Q2SWBjIrEOO/stPYfYnMJBozA6mpqUnE7MVcTE5OBgKBZKwZwxNT0Lgb\nIjMJmUkYnqAbjd2XyCwN5WQWfcbuyB9eCwSDmvR0c5HZ6/HuvrtedIbknbHD8IRklLNJzx0y\nSwOZUwrjZ+xo3CyQWRrKyRwOh8W9UJiNKCop6rb0aHXani6r6AzJ+wUPwxOSUc4mPXfILA1k\nTimUHXAB2FNTVtt44VLV0sp4pxnMhQWWdkvtquWecY/N2ufzTebkZsf73flJ/uqlpqVLl8b7\nQgAAUCAUdiLR2H2JzArk8/mGhoa2rN4xPj7u9/u1Wu3vf//7GzduBAIBj8ej0WjWr1/f2Nh4\n6NAhjuOefvppjUYTiUQ6OztfeOGFprevl5aWfufff+D1ejUazTPPPPP0vz2dm5u7dOnSUCi0\nd+/eP/3pTwaDwe/3e73evXv3vvnmm1ar1ePxFBYWdnV1bd++PT09PTc3d2HR4sHBQYPBIPeb\nER8MT0iGxt0QmaWBzArEeGHX0NCwffv2ZKw5qR2jyBxFY+a46PX6O++8k+M4s9ksfGxXTk7O\n448/TmYTKjmO47773e+S96X70Y9+NGVtU5Z87nOfI7+c0kV3+fJll8sl/D15Hf1NTU1VVVXJ\n+HixyclJlUqV8NVyHDcwMOD1equqqhK+5mQPT2A3jEJmEjKTMDxBNxq7L5FZGgrMzPO8x+Nx\nu90OhyMhNweePwxPUESBm/RtIbM0kDmlMF7YAShcMBiMFj1MFnYAACAlxqdiARQOt9MDAIAE\nQmEnksVikTtC3JBZgdavXy93BI7jOLvdLneEuDmdTrfbLXeK+PA839fXJ3eKuNG4GyKzNJBZ\ngRgv7Gi8dTUyk2jMHBeFNHIldXgCnzwRhU+emAKZSchMojGzQjDeY0dj9yUyS0OBmZnsscPw\nhGQUuEnfFjJLA5lTCuOFHYDCzTQ8IW+qKOHAiuEJAABa0PRrNAB7Ghoadu3aRS5R2h13lZYH\nAABmgTN2ItF462pkVqDx8fHoJT+e5zmOS1JH2ux6enoWLVo006PKPF2HT56QDI27ITJLA5kV\niPEzdjTeuhqZSTRmjgvZyCUUT7KUUDabLT8/PxlrxidPkPDJE1MgMwmZSTRmVgjGz9jR2H2J\nzNKgMTONMDwhGRo3aWSWBjKnFMYLOwCFI4cnAAAA5onx+9gBKJxCbqcHAABsQGEnEo23rkZm\nBVLIJ0/Q+D7jkyckQ+PmgczSQGYFYrywo/HW1chMojFzXBTSyJW89xmfPEHCJ09MgcwkZCbR\nmFkhGO+xo7H7EpmlQWNmGmF4QjI0btLILA1kTimMF3YACofhCQAASCDGL8UCKJxCrggDAAAb\nUNiJRGP3JTIrEIYnRMPwhGRo3DyQWRrIrECMF3Y0dl8iM4nGzHFRSCMXhidIGJ6YgsbdEJlJ\nyEzC8ATdaOy+RGZp0JiZRhiekAyNmzQySwOZUwrjhR2AwmF4AgAAEojxS7EACqeQK8IAAMAG\nFHYi0dh9icwKhOEJ0TA8IRkaNw9klgYyKxDjhR2N3ZfITKIxc1wU0siF4QkShiemoHE3RGYS\nMpMwPEE3GrsvkVkaNGamEYYnJEPjJo3M0kDmlMJ4YQegcBieAACABGL8UiyAwinkijAAALAB\nhZ1INHZfIrMCYXhCNAxPSIbGzQOZpYHMCsR4YUdj9yUyk2jMHBeFNHJheIKE4YkpaNwNkZmE\nzCQMT9CNxu5LZJaGjJmvXLny9NNPr1u3rr+/f2Jiwu12r1u3zmq1+ny+559/3mQyyZIqSTA8\nIRnshtJAZmnQmFkhGC/sABTo9OnTDodj1apVer3earXa7fZVq1ap1er29vaampqqqiq5AwIA\nAK0YvxQLoExZWVlOp/Py5csajUb4+3/+538uWLBA7lwAAEA3nLETyWKxVFZWyp0iPsicDEeP\nHu3wtsa1H406R4f6HdOXq1Sq6hVV6rQ4ft2KRCJ7VtxTW1s795fEpPz3eTqn06nVaum6cs3z\nvNPpLCsrkztIfGjcPJBZGsisQDQ1vojQ0NCwffv2ZKw5qR2jyByl8MzXrl3jFvEb8teJe/nY\niOvg//7ad37zzHwynDzzxvwLu6QOT1RVVRmNxoSveXJyUqVSJXy1HMcNDAx4vd5kXBNP9vBE\nau6GMSEzCZlJzA9PpB08eFDuDEmk1+sNBkMy1lxcXJyM1XLI/F4Kz9za2mpcpBf98lf/6zXH\ngLPufSuNWeKTDPYNbVy2WfTLBcl7n3U6ncFgUKsT3/VhMpkyMzMTvlqO49LS0vR6vU6nS/ia\nNRpNfn5+wlcrSNndMCZkJiEzKXmZFYLxHjsaZ2qQWRpKyNx1s/uuD+45+/q5ea6Hv52EpBUn\nNzeXrpFYjuP0ej11I7GcMjbpeCGzNJA5pVB2wAVgyf976MmErGf2G8Ul4zIoAAAoEwo7kWjs\nvkRmafzmxd8vX7NswDbgm5jc/3cfEBZev9xq67ItrFzQ8Mb5x774CeHS5LljF2xdtgWLF9i6\nbI986kHhmW+8fMw3ManVaiY8vgf//oPkwjxzXq/F9tH//ciU7xjzbrrC77tznC2g8X3G8IRk\naNw8kFkayKxAjF+KpfHW1chMojHz++7YMDY8plarC4ry+cl3L4PWrlseiXBLVlYvW7Ms2nC2\nYcdanV4n/Bl9ufDCCa+P5/1TFtZtWBEKhqZ/R28sDkeMwduZ4JMnSPjkiSlo3A2RmYTMJAxP\n0I3G7ktkJik8c3d3d7r5PYOZ1o7eng7rgG1QnZbmHhuv21ArLBzsG3rn7CVjlmFh5YKsbKOw\n8JWfv1petfDEH08tqi5fsLhMpVJZO3oH+4dcIy6dTstx3LLVNeTC8bFx96i7bmMtOQ1q7x0w\nZxRNzxYIBAKBQF5enlarvW2XG4YnSBiemELhu2FMyExCZhLzwxO4jx2AeKdPn85YkZQ7bszd\n5fNXl2evivmQwWAQbjWSjBoFAAAUiPFLsQAAAACpA4WdSBaLRe4IcUNmRQn4A987+MOTr56+\neuHa+eMX5A1D4/vsdDrdbrfcKeLD83xfX5/cKeJG4+aBzNJAZgVivLCjsfsSmUk0Zp6jt09c\nrFm1pKC4wNbVp9Vp+3r6ZQyD4QkShiemoHE3RGYSMpMwPEE3GrsvkZmk8Mzj4+MDE32ZBjEf\nPnGz+Vavxba5fiPHcSOOkXAoXLJQzD/W1tV38tW31qxZEw6Hpzyk1WoxPCEChiemUPhuGBMy\nk5CZhOEJAIht27ZttbW1Ho9n64c2yrUfRSKRq69fV6lUjz76aDAYnPIohicAAFINblAMIFJm\nZmZlZeXKlSuDweDRo0fdbnckEsnNzbVYLI8++ugLL7zwwAMPWK3Wp556qqio6MCBA1qtNhKJ\ntLW1vfLKKwcOHCgvL79+/fr4+LhWq/3Zz3524MCBgoICYW379+/nOO4jH/nI3Xff/eijj/76\n17/OysrieX58fHz//v2vvPKK8B1ra2ttNltGRkYkEvH7/ck4KwYAAHTBGTuRaLx1NTInTyAQ\n8Pl83//+95uamj7ykY9UVFQIyyORiM1m4ziusLBwjqfNRLwkLS1tdHQ0FJp67+K5n7Gj5X0m\n4ZMnJEPj5oHM0kBmBWL8V3wauy+RmURL5nA4HAgEPvKRjzz55JNOp3P0f4yNjRmNRqPRODEx\nMTo3s7zkzJkzMV/idDqnV3VxwfAECcMTU9CyG5KQmYTMJOaHJxg/Y+dwOIRPzKQIMksjsZl5\nnvd4PG632+FweL3eRK12irGxsZycnLk/Xwk9dqOjo1lZWbed3lAUn88XDAazsrLkDhIf7IbS\nQGZp0JhZIRgv7ACkIU1hFy8lFHYAACAlxi/FAgAAAKQOFHYi0XjramSWht1ulztC3Gh8n/HJ\nE5KhcfNAZmkgswIxXtjR2H2JzCQaMyevvaG5uXnuTzYYDHPvUMHwBAnDE1PQuBsiMwmZSRie\noBuN3ZfILI0kDU8Ia07UaqcYHR3Nzc2d45OFf53JZMLwRLwwPCEZZJYGMqcUxgs7AGnwPM9x\nXJJOTc0ThicAAFIHCjuAxBBqO2VCYQcAkCIY77FLHhq7L5E5qXT/o6+vT6cwtw1P0fscheEJ\nydC4eSCzNJBZgRgv7GjsvkRmEjKTaMyM4QkShiemQGYSMpNozKwQjF+KpbH7EpmlgczSwPCE\nZGjcPJBZGsicUhgv7AAAAABSB+OXYgEAAABSBwo7kWjsvkRmaSCzNDA8IRkaNw9klgYyKxDj\nhR2N3ZfITEJmEo2ZMTxBwvDEFMhMQmYSjZkVgvEeOxq7L5FZGsgsDQxPSIbGzQOZpYHMKYXx\nwg4AAAAgdTB+KRYAAAAgdaCwE4nG7ktklgYySwPDE5KhcfNAZmkgswIxXtjR2H2JzCRkJtGY\nGcMTJAxPTIHMJGQm0ZhZIRjvsaOx+xKZpYHM0sDwhGRo3DyQWRrInFIYL+wAAAAAUgfjl2IB\nAAAAUgcKO5Fo7L5EZmkgszQwPCEZGjcPZJYGMisQ44Udjd2XyExCZhKNmTE8QcLwxBTITEJm\nEo2ZFYLxHjsauy+RWRrILA0MT0iGxs0DmaWBzCmF8cIOAAAAIHUwfikWAAAAIHWgsBOJxu5L\nZJYGMksDwxOSoXHzQGZpILMCMV7Y0dh9icwkZCbRmBnDEyQMT0yBzCRkJtGYWSEY77GjsfsS\nmaWBzNLA8IRkaNw8kFkayJxSGC/sAAAAAFIH45diAQAAAFIHCjuRaOy+RGZpILM0MDwhGRo3\nD2SWBjIrEOOFHY3dl8hMQmYSjZkxPEHC8MQUyExCZhKNmRWC8R47GrsvkVkayCwNDE9IhsbN\nA5mlgcwphfHCDgAAACB1MH4pFgAAACB1oLATicbuS2SWBjJLA8MTkqFx80BmaSCzAjFe2NHY\nfYnMJGQm0ZgZwxMkDE9MgcwkZCbRmFkhGO+xo7H7EpmlgczSwPCEZGjcPJBZGsicUhgv7AAA\nAABSB+OXYgEAAABSBwo7kWjsvkRmaSCzNDA8IRkaNw9klgYyKxDjhR2N3ZfITEJmEo2ZMTxB\nwvDEFMhMQmYSjZkVgvEeOxq7L5FZGsgsDQxPSIbGzQOZpYHMKYXxwg4AAAAgdTB+KRYAAAAg\ndaCwE4nG7ktklgYySwPDE5KhcfNAZmkgswIxXtjR2H2JzCRkJtGYGcMTJAxPTIHMJGQm0ZhZ\nIRjvsaOx+xKZpYHM0sDwhGRo3DyQWRrInFIYL+wAAAAAUgfjl2IBAAAAUgcKO5Fo7L5EZmkg\nszQwPCEZGjcPZJYGMisQ44Udjd2XyExCZhKNmTE8QcLwxBTITEJmEo2ZFYLxHjsauy+RWRrI\nLA0MT0iGxs0DmaWBzCmF8cIOAAAAIHUwfikWAAAAIHWgsBOJxu5LZJYGMksDwxOSoXHzQGZp\nILMCMV7Y0dh9icwkZCbRmBnDEyQMT0yBzCRkJtGYWSEY77GjsfsSmaWBzNLA8IRkaNw8kFka\nyJxSGC/sAAAAAFIH45diAQAAAFIHCjuRaOy+RGZpILM0MDwhGRo3D2SWBjIrEOOFHY3dl8hM\nQmYSjZkxPEHC8MQUyExCZhKNmRWC8R47GrsvkVkayCwNDE9IhsbNA5mlgcwphfHCDgAAACB1\nMH4pFgAAACB1oLATicbuS2SWBjJLA8MTkqFx80BmaSCzAjFe2NHYfYnMJGQm0ZgZwxMkDE9M\ngcwkZCbRmFkhGO+xo7H7EpmlgczSwPCEZGjcPJBZGsicUhgv7AAAAABSB02/RivB0NDQd7/7\n3aKiIo7jNBpNdnb2Bz7wAaPRKHeu2UQikZ///Ocul0utVut0uuHh4YcffriqqkruXDP64x//\n2NPTU1VVNTg4ODk5mZGRMTk5GYlEHA7Hv/3bv6nViusfOHbs2I0bN6qqqrq6ujQaTVZWVkdH\nR15enkajqaioeOWVV1588UW5M8bw5ptvNjU1VVdXj4yMTE5OTkxMCH93uVy7d+9es2aN3AFj\nOHXqVGNj45TMHR0d1dXV+fn5O3fulDtgDOfOnWtpaQmFQuFwODs7W61Wd3R0mEymnJyc8vLy\nPXv2yB1wRidOnGhpaVGr1UuWLLl06dLSpUsfeughuUPNprW19ZVXXlm1apXL5ero6CgpKQkG\ng4WFhQ8++KDc0WbT1NR09OjRzMzMvLy8jo6OioqKj3/843KHuo3BwcHXXnvNbrdrtdrq6mqf\nz3f//fcbDAa5c92GzWa7evXq4OCgz+dbs2bN9u3b5U6UFCjs4hMIBMxm86pVqyKRiFqtzsrK\nCoVCcoe6jUgkUllZabfbi4uLS0tLu7u7/X6/3KFmk5OTU1hYuGnTpr6+PqvVWl5ebrVab9y4\nsXTpUmWeYNZoNBs3bty0adPZs2fVarVer9+3b9+lS5fUavWNGzfq6urkDhibVqutqKhYsWKF\nXq+3Wq12u134e3t7O8/zcqeLTaVSTc8cDAZXrFhht9vlThdbIBDYvXu32+2+dOnSxo0bx8fH\n9+3bd+3atYGBgbS0NLnTzSYtLa24uLi4uLi1tXXXrl1J6mhMIJ7nN2zYsGfPnsuXL+/bt29g\nYMDtdiu/ocrv95eUlCxatCgzM1M4dMid6PYCgUBfX9/u3bv7+/tXrFjh9XqV/6OQ4zi/379q\n1SqO46xWazAYlDtOsjBe2DU0NMxUkl+/fn1gYCDeQsHj8dy8eTMtLa2vr6+qqkqn0/X39+t0\nurhWolarS0tLly1bFm/m9vb23t7ecDgc17eLRCI2m62trW3hwoUvvvjiPffc43Q6RYzmFRYW\nCrtEXJmtVmtHR0dcmXmef/nll+12e0tLS35+/okTJ/Lz84eGhjiOO3nypEqlmvuqcnJyNmzY\nEG/mwcHBlpaWuW8bLS0tnZ2ddru9sbGxqqpqfHzc5XKdOHGiqqrq+vXrJpPp+PHjc8+cmZm5\nZcuWmP/MWTKPjY1dunQpru25ubl5cnKyv79/bGwsPz9/fHxc+LtwMimuH4darXbbtm0xy5Sm\npqaqqqqYZ7UnJiYuXLgQ17bR1tYWzRnNPDEx0d/fr9Vq4zpSp6WlbdmyJSMjY/pDAwMDXq83\n5lntQCBw7ty5uL6RxWJ58cUXH3roIZvNdvz48UAg4HK5GhoaFixYYLPZ4lqVWq3euHHjTM1/\nM20e4XD43LlzIor169ev9/f3L1y48ObNmzzPl5eXx7UlC1Qq1Zo1a/Lz8+PKzHHchQsX4h27\nGRgYaGhoGB8ft1qtLpfrpz/96YMPPqhWq+ONrVara2pqysrK4s185cqV4eHhuL4Xx3E2m625\nudntdgvbRl9fn4j3Wa1WL168ePHixTEfnSlzZ2dnT09PvD9TOI4bHx/v6upqbGwU9r68vLyB\ngQGtVisidmVlZUVFxdwzc2J/FHIcNzo6qlKprl+/np+fn56eLq62KyoqUuyv6wLGe+xm6r68\ndOnSW+PHC0pjH2tua3zU8+KBHx/4yedFBxvsGbq76L6YddJMmVtbW5+5/Gx+hcjMPpfvtX8/\n8uCz4q+keByejxf/3ebNm6c/NFNmq9X6pTe+UrKiRNx3nH/mSffkAxn76+vrpz80U+axsbFv\nHv7qii3LxX3H+W8bvI9faK+65557pj80U+ZAIPDN//jPrVtFXtT73e9+vGhRdXp6+vr1Ii9M\nBIMBfvLWvn37pj80y/DEy4/ff39ddTyF+l8dOtFYV1qwvDi/PFfkiEM4EvlTc+cDPzo8/aFZ\nhie++tUXN22uF32mbf5v9blzx77y5U/FbEiYafP485//fEx/Il0r8tf4+e+GXRcsv/iHn2dm\nZk5/aKbMx44dU5mXGYwi/+f+6sffy803V9WsWLoi9u+it9XUeOGD9WuFrpspZsrc0NAwOKTN\nyRF5iJ7/ttHZ0bp797Lq6urpD8XMfPPmzcmDj9fmiR8SevaKZWV+1vI8Y7lRL3olVxyugv/4\nv9NL0ll+FIa++sSyXJEdUPPP3OedtD/x9Zg/ChVCce1KiTXTTM34+Ljoqo7juAt/uqjVa0fs\nI6LXULSocGQk9stnyjw4OCi6quM4rvXNGxpd+viQJ4y81wAAIABJREFU+KsSRrPxO43Px3xo\npswWi0V0VcclInOGKePF6y/FfGimzFevXl2+OfbJ1LmY/7ah0+vOOE/EfGimzG1tbStXrhf9\nHbOzczdt2jU8PCR6Denpmo7O2CdXcnNzY1Z1w8PD1QU54qq6KL1G/DUHtUpVlGWYnJyMsVq9\nfqazYgZj1nyun87/ra6qWm61WmM+NNPm8QvL/xVd1XGJ2A3L1y+6evVqzIdmynxriBdd1XEc\np1KpIpFIXGf3p1i9YXNra2vMh2bK/Jd3rKKrOi4h20b1cpvNFvOhmJntdvt8qroo/fw6Ctaa\ns3t6eqYvn+VHoeiqLmo+mcsMGd5viv/VXQKMX4pNEltH/+b73nf5xNU7/9du0SsJBAKzXxyJ\n9wrv7JxdzmV7V3ScbV/7oXXzWU8qZJ7Pz4OEbBtcnJmDweB8qg3X2Mj58yfMBcWi1yCIP/N8\nf7F0+XjzPE4VpKepJiYmZvnfndjtmUvEW52Wlj4xMTHLW53wzPPfDdVpap/PJ2XmcCSck5c/\n7BhaIvLMO8fd7hCtwG2Dk/zHisDtD5r1cV+EJUkfe/6ZpX+f5w6FnRif+Pe/m/9KfD7fLB0k\nRqOR5/kEbhx3ffHuhKwHmWeXkG2Dkzbzw488npD1SJn5wJ7YrZPx8nq9s7ejJPYAnZC3emJi\nYvbms8RmTshuOPvhjkt05o8+9k/zX4nEmROybUj8Y+XJtZUJWY+UsROVWcr3OV6MF3azdF/K\nzuv1xvxApMbGxt27d3s8HmXeRYWlzMJcBTInSszMra2tixYtKi4uVmzm6Q3UDofD5/OtXLlS\nlki35fF4ZtkNpc8zF7c93HFyn+SYjsnMytwNY8YWDncmk0mZsWf6PEMlBGa8sKupqZn7ky3X\nuq6caipcYOZ9fPQ6WmdT15B1KBQMTXonowvf/tNFfoJfuGzhkHVoywc2kQtNBabB7sF7//7u\nWZ4pcLlcDodjegyTyeR2u00m01wyN7/WzHFcuiYtMBlYtW+1sLD1zRuByUDtvSvPvnhm12fq\nyYWaDM30Z+qyMsLB0PK9K2Z5ZlSiMhcsLug817Ht77er1KrZ4xkLjKO20fUPb7jtP8RcXThm\nG11+14o5Zp5LWoGwbVTWLR7qddz98TuFhcK2ocvUTf8/nluUM9AzNOWZHMfxE/yuR3aSz5yy\nwSQw84kThyd9vgy9ftLn+5v7PkwuzM0tsPV1P/zwY8LC1tam/v7usrKKt98++eijn1Op1P9/\ne2ce30Zx/v9Z3ZctW4fl+75vJ87thJzQQAjhbJK2oUCAGChH+bWFHkCPLy3ND3oE0r5oISlX\ny9WSfBMgB8EhJiTO5cSx49uWZVuyZB22rFu7+/1jglAkWdZKsg533n/4ZT2anf3s7Mzso51n\nZgAAnZ0Xv/rq2OJFq06d/txlhIdff8Ntr7/24oM7ng5EM5vNDvzh987ZKya7Q5bA79Hon1x9\n9Y63yJW9GkOhNKlXY9haX+aeks9imuyO+5deDZD/uL2/R2Mokib1aAyPrbw6bvjBhW4HTtxW\nW/SLA82/33SdxxnHx8e5XM/BXJvNJhaLJycnA+ydYbHkF5SNjg6uWXOLu9GjrGBRr1ix3t0I\nizovr5jA8dVrNvo5HKLT6XxORMjPz4cPm0AKvP2Ty3aznZPACa5D8NPJYHQaAKDqpmumDRoM\nBp/VA2oOsGIf2v++1WwqLKsclg/csPHqZI5/7dldVjXvStv5sqp5NfVX49lPNh1WDPalZ+Yo\nBvu2bv+B++F0BgMAsOGO714th4tnh+UDCYlCxWDft7/f6H666bpoSprhfSwoLHNvXLAacDl8\n92bo844fP/4J7nTY7TYAwLfW3+mekslimc2mTZuuGSII/bHyTveo2enkMRhmp3N7eXboxpYx\nQ++EeWtxus/0fmQnJiZCY+Cyq8WJ8FxhuZa9V74JWPx+WabHGX2Ws0v5jIJnlf/SyRM+yS7L\nyqvMJQHpsH2zXFNBTR5JkgRBuBsxDBNKhVklme6DONBYWFuA44T/lBCLxWLyRXJycuCahamJ\nk6oJAABfzHfarr54wGgYX8zvOd4tK/lmShc0Oh24d0r9kI7JYfpP6SJcmqWF0tTSVOjV+ZeX\nXpkxozxolBZIfQ6rha4Z1o2KpeVszjdhGbBulC8u9b7jhXUF3inhV3ar45qU11aYMGrGMJpI\nnOKw20XiFJvN6m4sr6gj8G/ubFlZDUmCwsLykuIq+OAB4OrHouIKdyM8/MSJQ0VFFQFqTkhI\nmG6ekDc0DEtLFCzOTXO6lcnCnDSSvPrXI6XNiaclCiyOq9eyqjibx2LAv66UQ3ojj8X4tGOg\nLtPHDEez2eytmc1mU9rRFRZLfn6Ju0KfZQWLuqXluLsRFrVGo2JzuP4P96MZAEBpITGCIJ12\n59XWRL1D8NPJ8JJ5k6oJkrimJVqtVp/Vg5JmGo0mTkktLKkEbgW9cNkqg24c/nUZ5y9eweFw\n5y9Z4Z4/PDxZLFWNfLNSRkVNPSBJmN7jdNN10ZQ0w/uYlZXv3o5gNaitW+LeDH3ecbV6lM3h\nymQZY2OjJEm4p7RYzNDhC0Rz4F0HDQOpPI4dJ1J5HIsTD924UJZEAnK69H5kU1o8BWZeJU6A\n5wrLtWQncOVGS6aAIzdaCK8ni89yDlzwrDLH39gFjnJAZZ4wZ5dmdZy6wuSwSILEaJhyQGW3\n2BVdw6m5MncjjuP6Mb2yX6noGvYw9pzvxTDMT8owatYN6RwWh0AiIEnSqDYy2AxoJHDCqDay\nBWy9Qo87cDqT7jKyeCzvlJJ8yeSYEcrzmXI2NGt61NLCFJfRj7zzH5yTlcj8yHMZdXKtpk8T\n9nJ21Y2Df/sktyLHo26w2CzvO374jc+8U2YUppvHzCwOE+bpXWFmVGIymQL8raJQ9OM4Pq5R\ncrj8cY2Szea4Gy/jToBhJElgGE2h6LfZrP39nWlpWfkFpa7Dp4yT+QWlPd3t7kZ4OJ+fMDwy\n6HDYmcyZe16TyRTgevRdar2TIIYNRidBYBhGkCQNw7rUeovd0TaqOa8QtY1qCLIUGmFKAZs5\nbDDCubFdav275zrnZcleONJSn53qOrxAIlQYjMlcTt+4webE2YyZZ5kErhm4FYtC0d/f30mS\nN8NS9S4rV1Hn55eOjspdRljUJCA1aqXrplAtakqadUM6AACDzcAduNVopdoh+O9kEqQJAokg\nvG0QADA00IM7nWrVqLy/u7ezfd3NBI1GGxroUQz2qUYVOO7UjClx3EmnM4YGeo7874cllbUt\nzccwDBDE1ZTwcKksTSJLhevFDA30WC2W3s72oYHesup5MGUYNbvu45C8z70dwWrAZrHdm6HP\nCpOenq1RK6UpaWJJCvQLXSnZLDYAAB4eLsHdBpOTIEemrHwmfWTKymXQQze2jk+2aY2d+inv\nr8Iuu8tgatMaYdsP/VpMTmc6n23B8XQ+mxbiBP7IMsfXsZuOpqamofTe6GrQHjT63LWJz+dL\npdLExESBQOA+pNLU1HSQ+UkEBfpAcWHowYoHvO3xqBkAkJub61Nz1OtGZ0vXuowbfX7lU/OF\nCxcUwxifH81N6y9ePF1X63tpGz6fD9codtc8Njam2fVUmUwUKYE+ODuk0q3+rs81imGVTk9P\n9xjWfOkP79TULPJOHzE0GpXTMeBz7VxXM/RYDXjzy1uy6rK900cMkiCzzmf4XLZzuq7jlbcP\nzFsU5fDoU/+7h1IXHfW6AQC4dHF/4Jqbmpoa3t8ZWYG+2VW4Nr5kHx/R0h77rbd9uroRYeb4\nUGxzc3O0JVDm4sWL0ZZAmXjU3NbWFm0JlIlHzX19fRaLJdoqqKHT6aZbDyyWicdmiDRHhnjU\nHI/dXYwwx4diKU2emBH3YHlxhlgoEVpN1prrwry1SHZ2+H9b957o0Q/rE1OFfBHfbrbnLwnP\nfG8Xs6G5/2SfflgvkCbMkuasrKzwZggAOP9Z66zWjdnQfPrU58MjgxJJqkgkMZtNixatDG/+\nKSkpQWw05B84SSJDKEhN5Btt9vXlvrdRCho+n+/zNV4ofPjBnpLSKq1WM0vlDGahGSo7lIZh\nPY1Jn6U2CGZB84mjHysG+1IzssRSmXlqasnKdeHNH8yCZofD/vprL5aW1cZR3YBTIlh0LJXH\nNtrx9TkUwtkDZDa6uze6hqfseAqPNXuyY4E5/saO0uSJGXEPlk9OSVLL1UwWQ6OYdmpMcFAK\nkA+Q7Pk5BE4IJALDsJ7Bok+MGsKb/yxpZnKYs6c5KSkpvBkCAGa7bkynmU6nE0SQO3DX1i3h\nsLkSccrIiJzFZCmVihAE+iAhwfdWDXQ6PYitHiFwekS6UNCrMbAZ9H7tRHD54ATpM6aKzWb7\nnHYaCvPnLzMYdKGXM0Hg08WBhb0ZppWnkSQZehskpylnMAua4YQJSUrasLyfyWKNKgbDmz+Y\nBc1wMs3stUEwC5rhlIh0Hqdvwsym0wYmKUw2CpDZ6KIJAlhxfFZlxwJz3LGbDg6HM6mlvEOO\nvGNI0TV8ubkDw7D0wnRZrsyon9IGtXmUfszgvciCf0Qi0YQyyAeY/KwcwzBxrjg5K9lssEyO\n+V6Axz9Wo3UpsYTSIVlZWeP94zOn80XL26f5Yn6Imh1WR72V2l5bFRUVPRf6gjgXAKD95JXQ\n6waOE4nD1DYmKi4u7uoMctji3X+9KhKnZOcUZmbmTkzq1erRIDIhSdJkorZfu1gsHtAGc08B\nADuPnklLFJSliopSksanLApdkPmojWaqbxNNU0HurDU8Mjg2NpKVXRBKOQMABgd7UlJSKB1i\ntzg85qgGiLp7TNOnCbENAgCGLw3n51N721cgZji8pnwGwpkvmzAM5BWWZOYUGPRa1WiQQ+pd\n7Rd9BjL6oa42zWwOZl6kzWoZHhnMyMwNsW4MDw9QcuAkEknQng2cElEmEhQK+eNW+5Ax+HCL\nTv2URCIJPL1IJBoM4XRcBj1E2Xqb4/KqLUELiAD/pZMnAADnzp3T6/UOh8NisZjNZoPB4HPX\nyFlCKBT63KoZ+I2+bGtrU6vVdrs9Kpp5PF5Zme8Nevxo7urqGh4ejpZmFotVWVk53Z5RPici\nAAAGBgb6+/ujpZlOp1dVVU23Rdh0mpVKZUdHB0mSk5OTkdeMYVhlZeV0TpLPyRMAAJ1Od+HC\nBZIkHQ6HVquNvOaSkpLpZpJON3liamrq9OnTULPFYhkdHY2w5vz8/OneZEw3ecJutzc3NxME\nES3NmZmZ0zmj03UdBEGcOHHC4XC4umiVShUpyQDDMJlMNp1j56e7+/LLLy0WS1QeKxiGiUSi\n3Nxcn99Op/ny5ctjY2PR6usAABiGicXinJwcn99OJzseH4WRZI47djPuPGGz2eAy7hqNJkYW\noent7V27dq2fmoE0h4W2trabb755jmkGAGi12pjS3NfXV1RUVFlZ6UezzWYbHR2NHc06nQ7D\nsLq6Om/HzgWsHn19fTGiGbg1Qw/HzkUsa56xGQ4ODkZcnW/isbvzrzkGBQMA2traFi9e7MdP\nikHZMeLYockTMcdsTESYbeJR82xE5s428ah5NiZPzDazMXkiAsRjM0SaI0M8ao7H7i5GmOMx\nduGdPBEZZmMiwmwTj5pnIzJ3tolHzdNNnohlZmPyRASIx2aINEeGeNQcj91djDBn39hpNBq5\nXI5hmM1mmzdvXuz//iZJ8vLly2w2m8vljo2N0en05cuXx9oO0x50d3fDf6BmPp9Po9EaGhoY\njBitV0NDQ+6hGFNTU3w+n8vl4ji+ZMmSGPQ/NBqNXq93t7g0s1gsi8WybNmy6SIIo4harTYY\nvplByeVyp6amuFwug8HIy8ujFCgdMfR6vVqtdhUmrNIqlYrFYqWmplKNo48YWq1Wq9XC/13N\nMDMzs7i4OLrC/DM+Pg43moOaBQLB0qVLoy3KH3a7vaenh8lkArdyFggECxcujLY0f9hstr6+\nPgaDATXTaLS1a9dGW9QMuB6FAAAulzs0NLRs2bJoi4ozYvQBHDrt7e3FxcVNTU0NDQ2jo6NU\n52RFHovF0tDQoFQq6XQ6juN8Pl+j0SQkRHMvgRkpKSkxmUyVlZWdnZ0ikUin001MTOh0Oqqz\n9iKGTqcrLi6urKxUKBRms5nH45nNZjabffDgwcrKyhj8gTgyMgIFd3R0sNnspKSktrY26DeP\njo6SJGmxWGLw3dLo6Gh2dnZVVZVarTabzUajMTMzUyAQNDU1mUymlStXRlugD+RyeV1dncPh\nIAhCJBLR6fS2tjYajUan041GY8w6dgqFYsGCBa7KbDQaS0pKRkdHY9yxGx4eLi4uptPpJpMJ\nCo62ohkYHx9PSEhYuXKlVqtta2tLTk42m82UNhSOCiqVqqGhob29feXKlU1NTZmZmUGvMRQx\nzGbz8uXLJyYmHA4Hl8vt6enRarUiUTS3qIk75vJQ7P79+6ebaxOzwBcbycnJqamp0dYSEMnJ\nyZmZmXK5PCEhQS6XB75bZbSAgrOzs3k8HvyL4/jq1aujrWtaoGD4ugvH8dtuuw3+TxBELM98\ngvJgCcP/Y789ZmRkiEQiWJlhUcP2GG1dM+BemWO8VrgDyzaOBAuFwszMTFgxYGlHW1FAHDp0\nKCcnJ/ZbnwewMWZnZ8d+A4xBYnpW7NTU1IkTJ/Z2/yMIidZJ65UjHQKpIEGaIC1MoTMpj7Jh\nGJhnqVuwYAGloS6n03n+/Hl+ehlBUvthRBBEZ9uF3q72ZJFkTDmSX1xWPX8Rg8GklAkNo02N\ndNTV1VEKVydJ8uzZsyetKSRFzVp592j7GXFu6eSYgs1PtJkmWVx+7oLVNIpjmiSB31+fKhAI\nKB3V2tpa3fM6pd+f/Vrrx526mnT+oM6WyKFPWvFEDt1JkFlCdn2WgNI2zwQJpq5/jurvyPb2\n9lLtURIEWqPHDJb3T/bW5Iq7RyfSRTyzzZknS7zQP54u4k2Y7HQ6dseSAkr1c7x4M9XfDN3d\n3b14IqDSUUxoNZ3nvmKwWDaLmcsT2G1WBotl0IwlS2Wl85fyEhIpnB7DKvl2SqHfSqXyI81+\nCqcAAABgmbBc3NeaWZs1MWJgJ7AJJyFMTxrrUvGSeemVGbwkak9xDIANopsyMzMDP2RkZETW\n/AJF1WDc5PikU++qzBYHwabTajP4eSJqwScYBnqqHyotLaV0lEajSdKfCyIYQKOf3Pf5eWly\nosVqY7OYRTmplYUUygqCAdBhTa+qora/y8TEBKfrJNU93W0O56sHjtUV5qoNk3mp0nb5SCKX\nU5gpK8+h9iqXRsNagWT+fGqLa1oslra2tvGEbErNEADgsFmPf/RPkSxNrxlLlsqSU9Lyymso\n5QAAwGg08aQ8iEfhuXPnGMxMqo8VgiC6u9vOnztZVb1ApRrmcLhLlqyhGt6DYTTcOVJbWxvh\nmVsxMis2ph27w4cPH+F/FvThlgnLwV8duOPFO4POAXfgxZ2F8+bNC/yQM2fO1K7dTNWzcfHO\n33cli6UFJeXF5T42zA4EkiTPH3qbUuTHxYsXzyXUM9hBhiFe2LeHJxSJcoqleb6X9gmEkuGj\nixZR2D+7u7t7bf/LKQJqjq+Lj9q0XCaNy6KtyBcGlwMA4EiXIfm+1wJPr1KpCofeLc0IMor5\no9P9XBaDy2asKE8PLgcAwJFWRfJNPws8vdlsPtpvKKii0ATc+fStVxNF4syC0uySiuByAAC0\nn/7iroUUBhZ3fvli7sLcoE8HwtF1AAAGWwZ/tOzJwNPrX7tvXUnwwQChV+kLI1Pj1/+OUpS9\n/vyH65ZUBnc6AMBHx85x2Uwuh71ifpDLFyhUutMTadMt3uYT3ecfrJsfvOb//97HVXlZZTnp\n2SnUVhF3MWGy7NOxKiootIjTp08LFt8S3OlAOJqh0+HALx2h+igsLllHowX5KHzvvb/n5BQy\nGIz58/0tWOYHkiQ7r3wa+SDIWHDsYjrG7vXuPVl1wU/SvnK4g8lmGNXGhJQgI9XoTLpQKKQ0\ntVYkEgXt1QEAMAwjSTKUcHi43iMlzQKBIGivDgCAYYAkSQyEFMJ/zJC4gYrmvr6+oL06AMCE\n1bmpShb04RAmA6NUzgMDA8XpwT+5J0z2TYtCDRVlMmiUNMvlcpEsLejTwfoMQpveQWewKGmm\nM0ONMAm964AyKMmeYoRUSqFX6XwxR+lwUNNMfSTEnYkp86bVy0PJIStVdFKHUyznMDz1uOzg\n3wMJ+VyOmUlJ8zBTRu1V6rWE3gwZTKYgiEdhsF4dAEAoTF60aOXhw/8OOocgHoVzhph27EJk\nfGC8dF1574meutuDfN8AAOBwOP5jKTxeEYcYZEaQRJJIrNWoi4J/+RWU5iA3GgUAAJIguYnJ\nZkOQW4e5iGQ5C9j0A+06FgO7viSkVQBmjLNxl83n84E9+HMJuMwDZwZZTNr1tSEtSUVJs0Ag\nAKbgKwdJEAlJogmtGoDyoDMBFDWHTli6DhBZ2WGp0lS7jhARcDkHjl9gMRnXL6U2nOpOhDVD\nJkxmqTB4pz/CmsPSDCP8KJww6E6e/EwqCSnWPCp1A8BuM6rMZcfu+h/fMNungJXGfRQ/xEjP\n72x/NFRNAcBisTw1U9vn8xrqNt0bBk0z4UNzCNxeHYkVN3g8nqfmEBy725cUhEHTTHho5vF4\nAAS5OyoA4FvfezAcombAQ3PoRKDrAOGWHYEqHfZyvn3dgjDm5hPvLjpE/t9dN4Yrq+kIu+YI\nNMOwPwrv+vb9oWoKAI/HShiJ7lJlc9mxCwtWq9XPnHYGg+ExlA4XOoou/jUnJiayWKxY0wwA\nQJojAyXNMbJpBCXNsUPcyfbTdSQmJtrt9hhcgIlqFx0L/DdoZjKZeLRXVqH6KJwzxJ9j13aw\nzfV/1U1XX+BfOdzhsDqkhSmGYX3Z9eXuRiaH6bA6qjfWUDVCVCqV9x6F8CVz4IP37RfPDssH\nMnPyhuUDN2y8GpHtsNv++tJvVq+/xd14aP/7VrOpsKzS3Xji6MeKwb7cwhLFYN+3v9/onqde\nqymrmldTvzjsmru/OOCwmgVimUEpr9mwDRo7P//IYTGVr7vz1Nt/Wvb9H7mnlOSVDZ79fOHm\nRzCM5jKWrtrkndIjTxeha37nvMZkx2vS+Qc6dM/dkEPDvjGa7Hh9VkJD3tX5mG+cURttuCyB\n6cDJLfOk7odnCFk9GutjK9LdjXwW3WTH71/sOS4QBs1fdJusjupcSZ9qYuuKq1MEPj4n71Ea\nMkR8B05sWX7V+MHJXgdOFKQKD5wdfG7zQjitDx6eIRb0KA2Pbahxz1OWxOtRGp68pS7smk99\n+pHNYrZZzHnlNUW1V1+6DLS3tp44Wt2w5lLzZ5se+CFGo7lSJqekjg0NrNtyn5/D+y9fUA/L\neQmJ7ikD0Tw5OTnjwIeyQwl7hhD7BKq9R7iqdHU6v2/cuvXairqtPuXnn8h33pwHjbBKJ7Dp\nRhv+cEOae0qZgNkzbn1yZYafwyGjo6MeI2juHxMTA5rO/M7HJ00Wm0ws7JGrnrz76vut0219\nfYqx29cu/PnL7+/84RZo/ONbn9ZX5OsmprxTspgMhxPfsn6Ju1Gtm6yvyG+ou2YmjXd359JM\noZw/+8pktdUWZB84deHZu2+Djauls693RK02TC4oyVtWefWk0FiYkdI7ot66Zom70eZwAADu\n+dYKaLQ5nL94/YPKvEyjxfrwLdcsCxyWLhq2I16iEHc6F99wdWoFbIYZBSUEQbiMMGV2cXnr\niaOBtE0anQEAWL7xrrBr/uyzfVaLhcPlWi2WmzZshsYrVy6Ojg4yGCzc6Vi9ZqOflJ2dF7/6\n6tjWrY179/zhwR1Pux8OAHBPGUbN8Uj8rWMnTE2cVE0kpiRMqiZI4uqUXoyG8cV8aYHUfZIv\nNDodOF/Md9qcVI1+oLrfcEVNPSBJ+Ndl/Or40ZKKGg8jjUYTp6QWllS6G+cvWYHj+PzFKzgc\nrkeeC5etMugCCm6jqhnDMH6yVFZSy2B9M6+CJAinwy4/f0JaUOaRMjkjN6WgEnp1LmP/qSPe\nKT3yDKNmGgbSElglKdz6TAENu8a4LE84PuVwpcRJ0uokhvQ2HovmcfjinERvo81JpCWwLI6Z\nf4FS14ylJfOrc8XuVXdVVQaPxRgan+KxvvnpBT/W5knqC1JcizXAwxeXyNxTfm1MdeIBzXmH\nmjUaTYCaMQwTSlKKahcYDTqXMau4IresOqekMresGj45XCkLquax3Kquz8PzK+tIkiytX+qe\n0r9mk8kUoOa08jRYvCH2CaH3HpRkg6+rX3Ua3716QON/2rTzMr7xaGGVhn89Ui7OTXQSMxzu\nX3CAar/OH0uTJC2uLnS6vbFZVFVAkuDTLy/OK8t1GdcurhzXG1ctKPdOOaTU8jgsDyNMPyua\naViaWFiclTa/OM/VuBaWFpCAXDe/QjPxzUmhEf71MObIJENjWuLrO/Vpy8W6ohycIKx2BwiA\nILpooSRFJe9nue2rBJuhXq1yN8KUqTkFAbbNRJFYqxomA1hKirpmmkic4rDbReIUm+3qJkBl\nZTUkCdTqUfY1SnykLCwsLymuOnv2RFHRN9N74eEwvStlGDXHI3H2xk43pHNYHAKJwGlzCiQC\njIZBI4ETRrVRJ9dq+jQkQWI0zGVk8VhGtZHBZrinnNEYRoYGeqwWS29ne05Ba29n+7qbCRqN\nNjTQY7VaFIN97a1n3Y2406lWjcr7u92NAz1dGAbe+OtLZdXzCOLrwy2W3s52NoczphzBcSed\nHk7ZhpEBgsCntGOXDr6Vkl9OkgSG0QwjAwAABovtsJonVUO4w05nslwp9cP9ktwSj8NZfMHE\nqNwjpXueYdTcpbY4CXJ4wnZlzFKdzvcwOglSrrc5CZJBw7rUFgAAl0lLS2QpDDaCBDTsm5S7\nTozOzxR4GAVs+vCEjRvYdEuTyRTgz8GuEYOTIIa1U50j+kty7RaSpGFY14jhX83d8/NTJDih\n0E4RXxsLZIkK7dTZXk11rsTj8F0HL83PT3H54d+qAAAYyklEQVSlhEZnJ4FhABoD0RxgsLNK\n3o/juF6tJHBcqxwmcCeNzlDJ+03GiazicnnX5azico+UR9/dk1NaRRIERqNNd7jdalH0XlEN\n9eeV18CUgYgJEHX3mKZPox3UhtInRL73cFW/TrX5ktK85do6KeQwesYtNifBZtBcVZogSS6T\n5lF7nQMkBgAx/eHh1DyodOLE8JjOiRMYBgiCpNGwrkGl2Wq/1D1UXZzdpxiz2Z1sFqNrUNk7\nNCZXjh86eck75eLqQoVK52HkspmKMZ0Txxlh3f2vS6F04viwRt85NFpdkO0yWmz2tn4Fl8VS\naLTwpC7j+ezBtn4FsZqkYZjLKOCw08RJsLl1KZRmm71nZCwlKZHLZgbYDAPH1Y4yCop1Y0pX\n44LNkASkuxGmHB3sDbBtJktTkySy8LZBAIBC0Y/j+LhGyeHyxzVKNpsDjTabtb+/s7ysVqNW\nwufCdCmnjJP5BaUd7eeHRwYdDjuTyXIdnptb5EqJiOl17Da/vCWU5U7CQnpLam1trbd9unUI\nm5qa2Jk+0keSU/+7h6rmD/Shrv0RIiOXWx5t8HGv/Whecf4XERTog6a+CeyOP/n8Kjc311vz\nF198sXT83fD271RpujyCNfieoMPn8wsKCjw09/b27u/SJqcEv+JJ6HSeO/WtEt+OMqwe6enp\n7ppjod8AACguDD1Y8YDPr3wW9bGHq1cWBL+qYuhMWJ3vS+4pKiry/srVDMXiaxZvO/baT1fW\nhzCBPxz86dg4pe7u2Is/va4mlMVDwsCf27SUNP/kr++Vzl/snT6SGJo/pPpYwYmsCAr0waWL\n+ylpnjPE2Ru7AGn/5LLdbOeJ+HwR32625y+J9Y1iISebDisG+6SyNLFUZp6aWrJyXbQVzcxA\nyzGDUp4gTeclSRxWU868FdFWNDP7L2t7xq05yWxZAmvKhq8vC2nFk8hwunusTzXBYtBkSbwp\nq2P9vDjYHQiGzdGZTKFYajWZqpetiraimYHReDQmPY66jrirz+7xdouqCmRi4ZTZur6B8o4I\nEQbGzC0sy09NFhot1vULY10wAODLgx9YTaZEsSSO2iAAwOGwv/7ai6VltSKRxGw2LVq0MtqK\n4oz4i7ELBIIgnXanQCIwDOsZLPrEqCHaigICRtFJUtKG5f1MFmtUMRhtRTOTWbWIxJ18kXRC\nNURnsCbHFNFWNDOripKcBJmWyOobt7AZWL925rCMqLOoWEaSZJqI36eaYDPo/WOT0VY0MzBs\nLkkiUysGGSyWZmQo2opmBkbjxVfXEXf12T3eLk2a3KcYYzMZ/cPqaOuaARgzly5O7h1Vs5nM\nfmWggZJRhMAJh90WX20QANDScryoqEIiThkZkbOYLKUyDh4rMUVMO3YPlAe/kg2DzRDnipOz\nks0Gy2QIT0GqQ9UEpb1Lr+WtV/8oTknNKyzJzCkw6LWq0eHg8omkZsWlrwCGiTILk9JyLEa9\nUaMMLh+qmmk0miOwmQHefNZtwAAoT+UVSrnjJqfCYAsuH6qaExMTDaYgz3VhQHNJri3PFBWm\nJY0bLQpNkGvLUdWckJBgngryXENd7YreK+n5RbKsvCmDTqsaDS4fqntNkkTw4SUwGi88XQdF\nGUFHxYSrPhssONVFZYMr6gud8kvdQ1NmW49cVZQtK8xOHTdMKVTaILKy2OxUV+Shuou3Cxgz\nV5ghK8qQjU8YFepgBAMACOo3+obi4BcpZLE5obdBENnHis1qGR4ZzMjMzczMnZjUq9VBdx2x\nG2k2q8R0jF1/f/9P9j3NFUYtHHJqfOrukm0+J/lPN0ivUqneOXAiWRK1qdQGnTad70xJSfH+\najrNk5OTm59/IyG0Nb5DwWqavCOf6XPH9Ok02+32Aw/WZiSxaVGKWDNa8f55jxYU+F402GeM\nHUmS+366QZbEY9Cj84PKZHNcEa2bbqN3n4FfAIBPPvnkS5WdPjtLtM+Iw2ZLmRqqqfE97OUz\nxu7UqVN/Or+LyYlmnInT5lzsWDTd/vQ+i/rkyZOOdxrdZ2RHEidBfp5y15IlS3x+O12M3fnz\n5ydaPxRwo9NF4wSh1OgTq26m+Qrzn67raG9vV37yppA/w6YgswRJkgqNjr9ovc/Qruk09/f3\n7/74K74w+A0JQ8SgGVtdIKb0KFQqlXv2fpycHIml4H1iMGjz8/iUHoVzhph27AAAOI5PTk7a\nbDaTyWQ0GrVabSTnKvtZjdNPzSBJ0mAwREuzQCCYbqtZ/7XZYDBYrdaoaObz+T67ZjCT5snJ\nSbPZHBXNXC7Xz3Y0Ph07iNFodDqdOp0u8po5HI6fRZKnc+wAACaTyW63R0Wz/xVEfTp2AACb\nzWY2m2EbHBwcjPwCB2w228+bpOmK2mq1WiyWqMhmMBh+tgqYzrEDANjtdpPJ5OruhoYiN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5jxwrdt2/aXv/zlhz/84c6dO3k83r59+776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\nIBAIxBzh/wBkZCdVg//PCgAAAABJRU5ErkJggg==",
            "text/plain": [
              "Plot with title “Regression Tree Visualization”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    }
  ]
}