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    "# Activation Functions\n",
    "\n",
    "In D.L., our **objective** is, almost always, to find a **set of weights** that **minimizes error.** All of these sets of weights are **linear operations** and hence, if performed alone, we would attain just a simple **multiple linear regression model.** \n",
    "\n",
    "##### What’s the Problem with Linear Models?\n",
    "\n",
    "If inputs are left untouched, they are not **flexible** as they can only model linear relationships while most data out there has a **non-linear patterns.** Hence, we need to find a way to force our model to be able to **learn non-linear patterns.** \n",
    "\n",
    "##### How do we do this?\n",
    "\n",
    "After a set of linear operations, we apply to the new **input** created by the linear operations ($Ax = \\hat{y}$) a **non-linear activation function**.\n",
    "\n",
    "Suppose we have a simple linear model $\\hat{y}=ax+b$. These $\\hat{y}$ form a linear operation such as below:\n",
    "\n",
    "\n",
    "\n",
    "Well, given our orange line ($\\hat{y}$), we then apply a **non-linear activation function** so as to **transform** our linear model into a **fixed non-linear model** such as below:\n",
    "\n",
    "<img src=\"https://www.researchgate.net/profile/Hoon_Chung2/publication/309775740/figure/fig1/AS:538049215381504@1505292337270/The-most-common-nonlinear-activation-functions.png\" alt=\"The most common nonlinear activation functions. | Download ...\" style=\"zoom: 50%;\" />\n",
    "\n",
    "**Why is this a fixed non-linear operation?**\n",
    "\n",
    "Because whatever formula we use for our non-linear operation, **we do NOT** have a set of weights on it that try to learn an optimal non-linear representation. It will always follow a **fixed, single transformation.**\n",
    "\n",
    "##### Well, isn’t our purpose to find an optimal non-linear operation?\n",
    "\n",
    "Yes and no. We find an optimal non-linear operation by letting our set of **linear weights** learn a **representation of the data** that, **once fed to the non-linear operation**, will **correctly identify the new pattern.** Hence, the objective of our linear weights now becomes to **find a representation of the data that, once fed to the non-linear activation, will correctly learn the non-linear patterns.**\n",
    "\n",
    "##### How do we backpropagate these non-linear activations?\n",
    "\n",
    "Given that these non-linear activations are in fact non-linear, we are unable to just take the input as the gradient as we can do with linear operations (3x => 3). Hence, we will need **two things**:\n",
    "\n",
    "1. The **input ($\\hat{y}$) that was fed to the non-linear activation** and\n",
    "2. The **derivative equation of the non-linear function.**\n",
    "\n",
    "Given that we want to apply the non-linear operation to every input, we can classify these operations as element-wise. \n",
    "\n",
    "This has important implications on how we can calculate our gradient. \n",
    "\n",
    "**First**, as we learned on the \"Linear Layer\" tutorial, the dimension of the incoming gradient from our subsequent operation will equal the dimension of the output from our non-linear operation. \n",
    "\n",
    "Now, since the output of the non-linear operations equals the dimension of the input, we are able to calculate the corresponding chain-gradient with a simple Hadamard product (element-wise multiplication) between our incoming gradient and our current non-linear operation. In other words,\n",
    "\n",
    "```input.shape == output.shape == incoming_grad.shape```\n",
    "\n",
    "**Second**, given that there are no weight parameters to these operations holds two implications:\n",
    "\n",
    "i) from a backward perspective, these operations are only intermediate variables and \n",
    "\n",
    "ii) we can just apply the derivative of the equation to each input such as shown below\n",
    "\n",
    "$$z = \\sigma(y)=\\sigma(x_ow_0+x_1w_1+x_2w_2+x_3w_3) = \\sigma(x_ow_0)+\\sigma(x_1w_1)+\\sigma(x_2w_2)+\\sigma(x_3w_3)$$\n",
    "\n",
    "Hence\n",
    "\n",
    "$$\\frac{\\partial z}{\\partial y} = \\frac{\\partial z}{\\partial y}(x_ow_0+x_1w_1+x_2w_2+x_3w_3) = \\frac{\\partial z}{\\partial y}(x_ow_0)+\\frac{\\partial z}{\\partial y}(x_1w_1)+\\frac{\\partial z}{\\partial y}(x_2w_2)+\\frac{\\partial z}{\\partial y}(x_3w_3)$$\n",
    "\n",
    "Now that we generally understand how to implement non-linear operations, it begs to ask, **what are some common non-linear operations?**\n",
    "\n",
    "Some common activation functions are shown below:\n",
    "\n",
    "* ReLU\n",
    "* Hyperbolic Tangent (tanh)\n",
    "* Leaky ReLU\n",
    "\n",
    "Each has their own unique properties and usually, finding the best best corresponding non-linear activation to a model is left to trial and error."
   ]
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU\n",
    "In this tutorial, we will first focus on implementing the ReLU layer and towards the end, for comparison purposes, we will define alternative activation functions.\n",
    "\n",
    "ReLU is a piece-wise linear, vector valued function that adds non-linearity to our model. The effects that this simple piece-wise function has had on the DL sphere have been astonishing. \n",
    "\n",
    "The ReLU's forward and backward pass can both be seen as \"gates\" that either inhibit or advance the flow of either operations. \n",
    "\n",
    "During the forward pass, ReLU either retains the original content of the input if its greater than zero or else, turns it to zero.\n",
    "\n",
    "```python\n",
    "[x if x > 0 else 0 for x in input]\n",
    "```\n",
    "\n",
    "For the inputs that were \"cut\" to zero, its gradients are turned to zero while the rest of the values become 1. Hence, and given that ReLU is an intermediate operation, ReLU either restricts values of the incoming gradients or lets them \"flow\". This process is graphed below:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Such simple conditions make ReLU a \"lightweight\" operation as it does not take much to compute its forward and backward method\n",
    "\n",
    "Such properties, and its surprising effectiveness to model non-linearity, have made ReLU a very popular choice of option for most DL architectures.\n",
    "\n",
    "Let us model this process in PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.6169, -0.8602],\n",
       "        [ 0.2214, -0.4084]], device='cuda:0')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "torch.randn((2,2)).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom ReLU function \n",
    "# Remember that:\n",
    "# input.shape == out.shape == incoming_gradient.shape\n",
    "\n",
    "class ReLU_layer(torch.autograd.Function):\n",
    "    \n",
    "    @staticmethod\n",
    "    def forward(self, input):\n",
    "        # save input for backward() pass \n",
    "        self.save_for_backward(input) # wraps in a tuple structure\n",
    "        activated_input = torch.clamp(input, min = 0)\n",
    "        return activated_input\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(self, incoming_grad):\n",
    "        \"\"\"\n",
    "        In the backward pass we receive a Tensor containing the \n",
    "        gradient of the loss with respect to our f(x) output, \n",
    "        and we now need to compute the gradient of the loss\n",
    "        wrt the input.\n",
    "        \"\"\"\n",
    "        # keep in mind that the gradient of ReLU is binary = {0,1}\n",
    "        # hence, we will either keep the element of the output_grad_wrt_loss\n",
    "        # or turn it to zero\n",
    "        input, = self.saved_tensors\n",
    "        output_grad = incoming_grad.clone()\n",
    "        output_grad[input < 0] = 0\n",
    "        return output_grad "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrap ReLU_layer function in nn.module\n",
    "class ReLU(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        \n",
    "    def forward(self, input):\n",
    "        output = ReLU_layer.apply(input)\n",
    "        return output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2316]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test function with linear + relu layer\n",
    "dummy_input= torch.ones((1,2)) # input \n",
    "\n",
    "# forward pass\n",
    "linear = nn.Linear(2,3)\n",
    "relu = ReLU()\n",
    "linear2 = nn.Linear(3,1)\n",
    "\n",
    "output1 = linear(dummy_input)\n",
    "output2 = relu(output1)\n",
    "output3 = linear2(output2)\n",
    "output3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# backward pass\n",
    "output3.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1558, 0.1558],\n",
       "        [0.0000, 0.0000],\n",
       "        [0.0000, 0.0000]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check computed gradients of 1st linear layaer\n",
    "list(linear.parameters())[0].grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have validated our operation, let's us apply ReLU to the MNIST dataset by building a standard neural network with the following linear parameters:\n",
    "\n",
    "```[128, 64, 10]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "    def __init__(self, num_units = 128, activation = ReLU()):\n",
    "        super().__init__()\n",
    "        \n",
    "        # fully-connected layers\n",
    "        self.fc1 = nn.Linear(784,num_units)\n",
    "        self.fc2 = nn.Linear(num_units , num_units//2)\n",
    "        self.fc3 = nn.Linear(num_units // 2, 10)\n",
    "        \n",
    "        # init activation\n",
    "        self.activation = activation\n",
    "        \n",
    "    def forward(self,x):\n",
    "        \n",
    "        # 1st layer\n",
    "        output = self.activation(self.fc1(x))\n",
    "        \n",
    "        # 2nd layer\n",
    "        output = self.activation(self.fc2(output))\n",
    "        \n",
    "        # 3rd layer\n",
    "        output = self.fc3(output)\n",
    "        \n",
    "        # output.shape = (B, 10)\n",
    "        return output\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NeuralNet(\n",
       "  (fc1): Linear(in_features=784, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=64, bias=True)\n",
       "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
       "  (activation): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate model and feed it to GPU\n",
    "device = torch.device('cuda')\n",
    "model = NeuralNet().to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define optimizer\n",
    "from torch import optim\n",
    "optimizer = optim.SGD(model.parameters(), lr = .01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define criterion\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([60000, 28, 28])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import training MNIST dataset\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import numpy as np\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.utils import make_grid \n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "root = r'C:\\Users\\erick\\PycharmProjects\\untitled\\3D_2D_GAN\\MNIST_experimentation'\n",
    "train_mnist = torchvision.datasets.MNIST(root = root, \n",
    "                                      train = True, \n",
    "                                        transform = transforms.ToTensor(),\n",
    "                                      download = False, \n",
    "                                  )\n",
    "\n",
    "train_mnist.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000, 28, 28])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import evaluation MNIST dataset\n",
    "\n",
    "eval_mnist = torchvision.datasets.MNIST(root = root, \n",
    "                                      train = False,\n",
    "                                      transform = transforms.ToTensor(),\n",
    "                                      download = False, \n",
    "                                  )\n",
    "eval_mnist.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cb924fc2e8>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DBgHu5kulANpwRSowvvfee9Ncp2Jybcy8RglV2ggvXLgQgE2bNgHBhIv0SORGL1bIZR26COZ0lHzVs2fPdM9LDzzwQNye39yhhmEYRtLi2RQJ7XSUUCJXodLRtePzI0ooGDFiBBBUVyrMVVGnArjHqrgiBcqPBfVfBdfdHC/0/pQqVYqNGzcCbgGzX1EPx1mzZqW5bfTo0YDr1vca7Y4HDRrklBGpv6RchPv27XOOV1G53J/hjQuGDBni9Gr0C3ITZkUZqY1adiR37tzZwhuWFeTVkvfi1FNPBYL9RcNRwuTBgwfjZo8pQcMwDCNp8UwJKgFGPn8lfii1+/3333fU1JgxYwD/NClWskpoQehll10G+K+BdzRWr14dk8dRMtAFF1wAuEkaoQkXinUoruZX9BqUQARu2z8VnnuNCqv79OkDBH8XUoDt27ePeJ8qVao4EzzkfREzZ84E3PZ92YnbbruNQoUKAWmLsEMbTqxYsQIgYQ0ijpUjR4745nyXGeRJ6dq1K+B69kJR6VGk16fYtZrUv/POO0Bqj0asMSVoGIZhJC2eT5ZXoatGb7z88stAcCeh3YR2ekpJV+alV6icQzvPpUuXxkwBqohd8YB4Fs0XL1486u316tVz7NE8wFNOOQVwW1Jde+21zjHaran1ndKZ8+bN64yG8itSUJpHCW5RuUolwjOavULvfWgLMGV1nnzyyQB0794dcEfR1K5d22nPpx24LtXKLrQ8yW+omFwNNBQPDfXGhP92xI4dO5z34/Dhw3G3NRmR6lZMOau5DMuWLQPgxRdfjI1hGcCUoGEYhpG0eK4Ehcb5qP5n5MiRjvrQOCGNslGdVqJrztT2rX79+oC7k37zzTdj9hzhAyeVHRUrpNYCgYAzqPKee+6JeGzdunUdJaoi2H///ReAr7/+GoAJEyY4sVupYTWTVoHrcccd59uG2dGyQTdv3gz4rzm2MkFVy1eyZEm2bNkCpB833759uxNvUfG/alrfeuutuNqbVfLly+eMY9PnI9v1Pd6xY4cT71M8V6pR5MmThyuuuAJw47rxGlad7Oh8Ec2DlZ5iB/ccK4WvmGA88c0iKL744gsAOnXqRLt27QDXRXrTTTcBbk9A9QpMFCp3kDtKhbrTp08/psdVyUVoGrjS8BUgjhVKpti2bRtnnXVW1GN/+OEHx72hRe/jjz8+6nOosFlTCrSY+BGVEkT6QYa6Rv2Ekovkwn377bcd17bCC/rc1IHkzz//dBpRaCHR335Dv68LLriA2bNnp7pt8ODBgPv7WL58ufPadV34ZPmSJUs6c+5++OEHwO1iFY8OJLEgUomE5kH6sWOMztuagqPkOCVs/ffff+neV13Dwqe0JApzhxqGYRhJS65AlDxcv0z/Dk2wANc117ZtW6ePYyJQH8OpU6cCbn+80LZgmUEKUK2tBg0a5Lh4paa0k8pOSBnr/Ro+fLijuPyCXNpys4UH8ufOnUvHjh0Tble8aN68ueOulsLQdHY1AfAaFUs//PDDgDt9BmD+/PmAqzCkhkuWLOm4zFS6JFenSj5q167tlDCJxYsXpzrmr7/+cm7TVBcvOXz4cLqubZXvyDuT3VHLSc2dBTehK1bu0GjlJqYEDcMwjKTFdzFB7XI6duzozNGSAhTaAX344YeJNS6MrCbESIVop6tu/nPnzqVDhw6xMc5HhM8W8wNqyxfetV7lHSrZySkcd9xxaZKu/BITzJMnD+A2VOjfvz8QLNlQay15X6QAdW4YPXq0kzyjWZW9e/cGgg03INjMQfFvteyS0ghtz3isnp1Y8sILLzg5EOHISyQln91p27atp89vStAwDMNIWjxXgmrorMwgTZJOSUlJc6wKXVUsn+gGs+Hpv8rOy+jk+379+gFuDFC+cLWzUvNtI/6cdNJJQNrvkFr07d27N+E2xRM/x5albKQAVYZz0003OUqtSZMmgNsEQCn0BQsWdGKIyiIPn2W3Z88eJ6aoy6uvvhpIPaLszjvvjOXLOib8WlIUimK4bdq0cTJzM9PeTKPKRo0aFXvjMoEpQcMwDCNp8UQJpqSkcM011wDQt29fwC1ajoSKsVUkH8vi9MwQ3m5KavWZZ55hwoQJgJvhpJ2rWr/Vq1fPaTmmWiXtzjUANachxVy1alVfNS5++eWXnYLdcFR4ndPwOu4SjfCBqYoRDhgwwKmdrVKlSsT7PvTQQ04NYGZaoinGqEu/MXr0aMc7plFDQp6n0aNHO3WhiaRZs2aA22SjdevWThw12kR51XNKxav9ZGhzAynJeDbMDichi6AmPKvv3+jRo6lRo0bU+6xatYrhw4cDbuGv3+Zr6cfap08fJ6FFXTlU0B+KFgK5DuI5LdkPaLOQ3oKTaJSQ1Lp1a+e7pHR6uUH91h0mVoSfSP3EL7/8ArjNFVQ6VK9ePecYpcorGU7JVlu3bs2x/UA177Ny5cqprvf6PKiSmtCmBHfffTcQfV6ompuolCW8bOGDDz7g+eefB9ykpkTgj7OTYRiGYXhAXJSgZO/YsWMBdwcevqMJRW4oSeQFCxYkVBJnBCk5zeFTmja4rlGpXiH36LRp0zKcQJPTaNq0qdO+y0s0hy/0M1JzAiVl5FSWLVsWtWejl6gdmBLNpBR+++03J8ygYvZk6vmpSQpqH+lnVJaSGdR2Ur1rb7/99qjt1eKFKUHDMAwjaYmZEmzcuDEQDGY3atQIgLJly6Z7vFSeurprUoSfZ5ppKoI60quYVSUPoeh1aVKDCnmTCb+03TOCDY71HZRHRnFCTaPwCsWRJk2alOoy2VFTkG+++QaAmjVremmOg8pUbrnlFsCdtxmN77//3il90czAcePGAW7zba8wJWgYhmEkLTFroK2xM6FNb4V2MvL9Hj58mBEjRgBuGyQj56CWY4rnjBs3Lt0WUIlEcdvp06dzzjnnADhz+NJLwc9J6HMZP3484M5/VCp+TmnIbCQGZfF269aNIUOGAG4bQmXvLlq0CAhm+CsL2AusgbZhGIZhRCBbjFIyDOPYKVKkCACvv/46AK1atQJwBtd2797d1zF5w8gq0ZSgLYKGkWRoMVQHJqW3161b11yiRo7E3KGGYRiGEQFTgoZhGEaOxpSgYRiGYUTAFkHDMAwjabFF0DAMw0habBE0DMMwkhZbBA3DMIykxRZBwzAMI2mxRdAwDMNIWmwRNAzDMJKWuEyWNwzD8Ipq1aoBMH/+fADy5MlDhQoVvDTJ8DGmBA3DMIykxZSgYRg5gtGjRwPQuXNnAIoXLw7A22+/7ZlNhv8xJWgYhmEkLdZA2+csWbIEcD+L8847zxM7atWqBcAll1wCQM+ePQFYvXo169evT3XsqFGjADhw4EACLTSSkVKlSjnzEJs0aQK4zZK//PJLAM4//3z++OMPbww0fIE10DYMwzCMCHgeE8yXLx8AZ511FgDDhg0D4Oyzz/bMJj/w1FNPAe77MnHiRE/suOmmmwAYPnw4AIULF051+6mnnspVV12V6ro1a9YA8N577yXAwpxN4cKFnRjXf//9B8CZZ54JwAknnADAtddeywcffADAzz//nO5j/fLLLwDMnTsXcD+n7IgyQEeMGEHjxo1T3TZo0CDAfX3ZQQXK0zN16lQALrroIsf78tNPP3lmVzLguTu0RIkSAPz222+A+0M944wznP8nE4899hgAt99+OwAHDx4EoEePHgC8/vrrCbVHyQWaOH7yyScf9T67du0CcBbHhQsXxsm6nM8TTzxB//79Y/qYR44cAdzPdNq0aUDwBLxly5aYPle8aNq0KQDLli1zrtP5qkuXLoC7oGQHjj/+eAA2btwIQJkyZejVqxcA48eP98yunIK5Qw3DMAwjAp67Q8NJSUlxLpNRCSq4LzfxRx99BCReAYo///wTgIceeggIup/A3bn+8MMPlC9fPtV9ihUrBkDbtm2BnK0EVYR93HHHAXD11VcD0Lt3b+eYefPmAdC9e/dMP/4VV1yR7m1y833++efpHvPtt98CUL16dedzOf300wGoXbs2AEOGDAHgs88+870SlBt08uTJQGpvld4ruXuzE//++y+QWglmxOuSnbnrrrsAyJ8/PwA1a9bk2muvTXXMhg0bADjttNPiZocpQcMwDCNp8Z0SzCllGc2bNwfg3nvvBYIKQaoqPa6++mpnd/79998DxDwelFVeeOEFwE2UqVevHgB79uxJ9z5jxoyJv2Ee0KpVKyCoPKT8ihYtCkSOPUjdZ4W2bdtSvXp1wFV1Quphx44dGXosJdJ88cUXAGkU/KWXXuqoVr/StWtXwLX9nXfe4eabbwaiJwVlF/SbadmyJTVq1PDYmtjRokUL59zWokULAC6//HIg9Tk//PdTtWpVwI1fK1kolpgSNAzDMJIW32WHirPPPpuVK1fG/fnjhXzZ2sm0aNHCie+lx5dffun4vhXfeOONN+JoZebp2LEjAPfccw8A9evXT/dYvZZvvvkm/obFEWXn1alTB4CGDRumOebvv/8G3FiV0vOnTJnilDZ4zTXXXAPAa6+9lur6/fv3A0HvxerVqxNuV0ZYsWIF4H7ftm/fDsCFF17Id99955ldsaZcuXIAbNu2zWk2UalSJSDjit8LSpcuDbgZuZUrV051e9GiRSlUqBDgritr164FgpUAR0MqP6uN0KNlh/rOHSrOPPPMbL0IylWlN79gwYLpHqsfdvny5Z309WjHe8nMmTMBN2FnwYIFzuIQzsMPPwzAlVdemRjjYshJJ53Eo48+CsANN9wAuElC+vE+9thjTleSffv2AcFEIT+hpINnnnmG6667LuIxqkVdt25dwuzKKJdddhmAUwuo39OMGTMA933PaeTKlcv57C699FIAxo4d66VJ6dKqVSvGjRsHuIt4NOTS/P333wFXCJUpU4aXX34ZgFNOOSXVfeQOjQfmDjUMwzCSFs+V4KFDhwDYvXs34CYYnHrqqZ7ZdCw88sgjgOs6k1v0s88+S3Os3AMDBw4EgmUHH3/8MeAqLr+hFOa6desCbpp9JJYvX54Qm+LB/fffz4033gi40wmU5LR3717P7Moo6jGrwvFu3bo5t6kBw2233Qb4111drFgxmjVrFvG2v/76C4jeTUUNJ0LViV8SzY5GqPtOitCv3H333ekqQLnaBw4cyKpVq4C0CV4q9bn99tvTKMCtW7cCbkJUPDAlaBiGYSQtnitBtdhS+yNNKchuaCek6QpSuH379gVg586dae7z5JNPAm7MbPv27b7rmao0bXXqr1KlCgB58x79q/Pmm2/Gz7AYoaJ/qXHtOO+44w7ef/99IBj3BHyT4BKNRo0aAa7NefLkSXOMVMaPP/4IwOHDhxNkXeY4fPiw0yc1d+7gfl0x8w8//DDN8f369QPc13frrbcCqZMpVKAtxZETyiq8ok2bNkDkEiDFxvV7yohXKFwFgtv4QPHDeGBK0DAMw0haPFeCOYE6deo4SkmZToojLV26NM3xikuExmkAhg4dGkcrs0bNmjUBN007IwpQ3HHHHYAbe/Ij9913H+AqQbWnW7hwYbZQfuF06tQJiKwAhWJMmriuco633nqLOXPmAG5BvZe0aNHCiQlKAUphhE6GUHb1OeecA7jZlOKff/5xYodqPKCYu5q8b9u2LS6vIScjVS1vCrilLIMHDwaiK8ATTzwRCJa5gNtgJPRx3nnnnRhaHBlTgoZhGEbS4lsleNJJJ3ltQrpIDSnz7qWXXkoTs9CoFxWVjxw50hlLpBigikY1K9CPdUAq1pdS0qinjNQxqoDWz2j2nOJIKvbNjioQ3NitFLwK++WhiESDBg2cywcffBCAUaNGAcFRTpC2mUU8UXs3eR/ALRSfNGkSgFMgX61aNQYMGAC4NYWKHy1atAgI/vaKFCkCuDMulYXuV3LlyhW1wNsPvPjii0Dwu6XsfjVkyMjwA7W7U0Y9wFdffQW4Ho1EDFHw7SIY7tLwE3KhqJNIIBBwFr9NmzYBqU8sEHw9ZcuWBdzFQckyKsb2M8888wzgnnw0kQDcTYFcwDrhZAc++eQTwP2cnn32WSBYhK2TaHZCbqSLL74YcHtslihRglKlSgFuNyJ970I7Q2kzpyQTJaacf/75gLvJiydya2qwNLgnXDVg0GsZMWIEFxn5dHoAACAASURBVF10EeB27VEhvdx1VatWdXrf6hgthn51g/p9AQSYNWtWqsuM0q5dOwAeeOCBVNcfOnTIEQKJnCBk7lDDMAwjafG8d6i48847gaDrAoLTCULVhh/o3Lkz4PZeVBnErl27HDeAinj1OtQxHdz3U2+5LrXradmypTM9Ijuh1yVXmnZ4ei2tWrXyxY5brbfWrVvn9GWUi1rJO/fffz8QLIhX6rdfi8mPFTU+UCmByisi8b///Q9w3aPxRK730ESx8IQsJVzoMwVXrSoZLdL0ebl5/Vo0H9o7VJx77rlA5CS77IhKcsKXnj59+jiKP9bYZHnDMAzDiIBvYoLhjYfz5cvnFLn6QUWAO0tPtmqnOmHChDTHanetnU2kglIpKBVlZ0cVCG7KfbiPX+25vCrGVuxVpQCKj915552OmldTbMUCpQQLFy7spHDnVDTxYvr06QAsXrw4VZp6KGqSkAjkAcqVK1eaKfEqh6hYsaJzjGJ/UkqRps/rGCnB7ER2PS+EM2zYMCBt4wPhldI1JWgYhmEkLb5RgoqviVy5clGgQAGPrImMdqVKQ1fbqUgoJV0z9QBnCrnG74hoTYCzA6EpzqFIIXv1+j799FPAzVZVrCl8nh64hf1i8eLFaT6nnIp+e2vXrk1XCW7cuDGRJgHBOE56sRypiEAg4DRzl4dG5TtbtmwBoFmzZk4Kv+EN+fPn5/TTTwdSf3bgNjr3ai6kKUHDMAwjafFNdqjQ8MQaNWo4tT19+vRJuB1ZRUW4ihf27t0bCPr1FavwG2pMIOWmGNGUKVOOet/SpUs72ZPh9YGKI23evDlmtmYGFcKrNdpxxx2X5hjtPqtWrQq48ecOHTo4StLvlC5d2mncrtFdav+WEdRibcGCBc4IJiGVqMzL0EzLeBEpq1Pt0+rVqwe4TRsKFy7sHKPzlYrlu3fvDiSm9VasiJQdqu9mdowNqqValy5deO655wD3c9L5RfkTGqYQD7LVZPmFCxcCULZsWadgNzuhBVvdENRpI/zk4ieefvppwC1i1WL9888/O1321QRAxdM6ZsCAAWkWP5WHbN++Pc6WR0eT4ZWgI3dMq1atnGOU/KITpRIo9Hr9TEpKCgDz58935ldmJplHBef6nUX6jmqDk4jFT6h85d9//3VOoh999BEQ/WQWXiyfnRa/aKgZgJpRZAfU9UcT5zt27OjcpnI4JaMlogFDNMwdahiGYSQtvlOCIhAIODvC7IDKOXr06AG4O1aVSPg5+WXMmDGA26tR7qj333/fmewsN7XcUtrpgfta5Yp76KGHAP/03xwxYoTXJsQFpftLBYL7GWp69759+5zb5A6+++67AVcBhn6WclVJVXkxAWTt2rVAMJFMNrZs2TLisa+++qoz8WLdunVA9i4q//XXX4Hg761WrVoeW5N1NBswVAHKnasWjH7BlKBhGIaRtPhWCRYpUoT27dsDbkmCn1GzZSlCpeGrlZifWblyZapL2T5mzBinKFmXkVCruNByECP+LFmyBHA77oNbFiJVFFoaoKQtxUYjIQV4+eWXA96qqnnz5jFv3jzPnt8L5P0KVfCtW7cGskdMsEaNGgBp8jk2btzozA30G6YEDcMwjKTFd0pQu9r9+/c7cajswCuvvAK4o17efPNND63JGmoqrCYFoennalelgn+xe/du2rRpkyALjVAWL14MwLRp05zxXiKa2gtHZRCjRo1yxuKsWrUqRlYaWWH9+vVOJnbo79DvqO2ghg2IZ5991jftL8MxJWgYhmEkLb4rlp82bRoQnIytwbp+3UEYhh8oUKCAE8NTrZ/anIUOp1b2rtBgWWWSKo5oeE/FihWZOnUqEMyABZzmIX5E+QBqYqD4n7Ljn376aed75gXR6kt9twgahmEY2YvHH38ccJtNSLio0N/LBRBsnqBhGIZhRMSUoGEYhnFMqLfsggULgGDvXSDNPEivMCVoGIZhGBEwJWgYhmHkaEwJGoZhGEYEbBE0DMMwkhZbBA3DMIykxRZBwzAMI2mxRdAwDMNIWmwRNAzDMJIWWwQNwzCMpMUWQcMwDCNpsUXQMAzDSFpsETQMwzCSFlsEjZhRuXJlKleuzPTp05k+fToHDhzgwIED1KhRw2vTDMMwImKLoGEYhpG05PXaACP7c9ZZZwEwf/58AHbu3AnAmDFjAPj111+9MczIENWqVQOCk8uvvfZaAHbs2OGlSTGhZcuWACxZsoTcuXOnum7p0qUeWWX4Dc8Wwa5duwLQtm1bAOrVqwdA9erVnWM+/vhjANq1awfA7t27E2liwilUqBAffPABAGXKlAHg7LPPBmDr1q0eWRWdiy++mJkzZwLBkyjAvffeC8C///7rmV05hRNOOIHChQsD7vc/1u+rpn83b96cHj16APDoo48CcOjQoZg+VyLo1q0bALfeeisAR44ccW578sknAZg4cSLgbtSy4+vMiQwaNAiAoUOHAvDEE08A8L///S9uz2nuUMMwDCNpSeg8wRIlSgAwfvx4R93t2rULgJUrV6Y6tkWLFhQqVAiADRs2AFCrVq2Y2pNopO5KliyZ6vq//voLgHPPPZeXX34ZgG+//RaARo0aAfD3338nyswMUbVqVQDWr1/PsmXLAFdRhO68jWNjyJAhzi54wIABADz11FMxfY5mzZoB8P777zvXKZlp06ZNMX2ueCIFKC9T8+bNndvkDg3/blapUgWAbdu2JcDCrFGhQgUA7rzzTgD69OkDQN68eZk2bRoA11xzjTfGxZATTjjBOe+VKlUKgIMHDwLQt29fAF566aUsPbbNEzQMwzCMCCQ0JqjEiYoVKzq+3uHDhwPw559/pjq2Ro0afPLJJ4AbuH/ggQcAePjhhxNib1aoU6cO4MYjtIsD93WUL18+1X0ee+wxIKh0pb5//vlnAPLnzx9fgzNJwYIFARg3bhwAX3zxBZ06dQJyhgIsXrw4AJ07d+aee+4BXAUv7r//fgCGDRuWUNsefPBBADZv3gzA3LlzY/K42nVnJ4oVKwZA/fr1He+JPCwFChRIdeyGDRscJajfYHbghhtuAFzl/9133wFw0003AVCuXDnnO6Fzorxm2Ym8eYPLUO/evdN8F5VUF+4pjCWmBA3DMIykJSExwdatWwOuEnz99de5+uqrj3o/7W7uu+8+wPXbV6pUKSZ2xYPbbrsNiBy32b9/PwAzZswA4PzzzwegdOnSzjF6z6+77joAXnvttfgZmwWk3G+55RYgGBv86aefvDQpJjRt2hRwswcbNWoUNY4AMGnSJLp37x5Xu0JjgmLPnj0AtGnTBoA1a9Zk6bGVdbpw4ULAjT+D63VJtNo9Gu3btwegZ8+eQPA9SC/eJ2688UbnGHkwhN9igvL83HXXXc5noO+kfnvKozjjjDNYvXo14HqX5EHKTigmrcz4UJQ78s477xzTc1hM0DAMwzAikJCYYL58+QA300wZTUdD9WdSgopHFSlSBHB3xH7goYceAtwMPvHqq68CwQLyESNGOP+HYDwDYMGCBUAwe1a36bX7BcVZunTpAri7tuyuApWx/OKLLwJQs2ZNIPgZzZkzB3Bjb1LnV155JQBNmjRxdu4HDhyIi31btmxJc52+/4MHDwaCn4kyjDODMnxDFaBf0fdOv6dQpPLSI9SjdbRjvUaehSFDhnDHHXcAMHr06IjHtmnTht9++w3IngqwYsWKADz99NNpbluyZAmQOmM5XiRkEXzvvfcAOP3004GMF/vKfSgUNFU6sIqz/YDKOY477jjAda+ocDy0A4dcMEq8UED/33//dU5s//33XwKszjh333034LrQ9LqyO1rgtPjJNahyj1C0iWvVqhUAp5xyinO/zz77LC72vfLKK05ijpIghBpNdOjQgfHjx2f6sZV0oESbypUrO7e9/vrrWbI31mjxGzVqFOC6PPX7+PXXXznhhBMAN6lJ6Ji///7b2Tj4NXlLtj/yyCNAcBP8/PPPRzxWyXZqbJBdeeutt4DUpW8SNnL97tu3L+52+HtbZBiGYRhxJCFKMKuqRjvUr7/+GnB3DHLj+Am5Ly+88ELAVRYqf+jTpw9FixYF3ED3xRdfDLjlIUOHDuW5555LnNGZQEkYy5cvB+DTTz/10pyYEb7TzEzZwZ49e/j9999jbVIqDh8+zDPPPAPg9PWUJ0H07duXN954A4A//vgjw48tz0qoAvQT7du3d9yf4Qpu1apVQFCVq0g+POlFnpbZs2c7x/gNlQfodyX3Zu/evdNt5aZkucqVKzNy5MgEWBkfTjvtNCB10orOf4sWLUqYHaYEDcMwjKTF11Mk1DJHl35m/fr1gFvUKSWoMojWrVs7ZRPhxfKKA6YXAPcSpS83adIEgLp166Z7rDr0K7nnq6++iq9xMUBJE7pUgknBggU59dRTAbcd15lnngnAL7/8AgRj04lISFDjbKmFcCVYp04dypUrB6SvBPPnz+8UWQsl+PgNvd+KA4LrTZICVClSKIrLSj2GxtTkqVFphV+SgTp27Ai4RfznnXcekLZ5CLi5EPot7t2710m2y07IE6bfnJTgkiVLnJhoIjElaBiGYSQtvlaCSstXaYTwWzNpcDNZw8s2VAg/a9asNDsfNYNVKr4fURzqm2++Adw4rejWrZsTlzjxxBMB973o378/4I6r8SPhcYl+/foBwWJlKT9x1VVXAd6Vr8jLcP3116e5TcX+8khoxqMuCxcu7JQaRUNtt7JSchEr1JZOGdfgFu1rxFMoH330EQDvvvsuEHl+5d69e4G0Gedeo89SjaNXrFiR5piUlBTAbcChMo/Ro0dnq1mdOg+o4YF+c59//jkQPNd4kRVvStAwDMNIWnytBFVMGTpoF9z2a6Go6FnDebUznjFjhrPLSgQZab+kFkDy5//4449xtelYUBNfxSO0k1aR+IMPPujEmlT0rxo7NTb+/vvvI35mfkAxNNWaNWjQAAjGK7RTVV2rspS9QrWAir2Gth589tlnU12Gkzt37gzVyCmWrd16VkfXZAU1j9BnkTt3bvLkyXPU+2Vm3JO8MX4pmletp1qkheY/qLZx1qxZgHuOU330448/njA7j5VGjRo53ykpW6FGFcolSDS+WwTlAj3llFOcqerh6Euwdu1azjjjDMAtNlWCgFymVapUSUh6tH6sSiSJ1Hd13rx5gNsPz++cdtppTgp3eLq23vf58+encQ9Onz4dgHPOOQcITov26yIod6iSDU455RTAfQ0QTLEH7xdBIfez3LMZ4ciRI0fthRqK3o9ELIK1a9cG3JO93OqxLGxXkwdt3rwumlfCnAgvzWnbti1jx44F3EQ6LfYq/fBTx6yjccMNN6TqkQxuiCVW01Cyij+2Q4ZhGIbhAQlRgmoldvLJJwPBVPPGjRsDbkpw+LHRpshr967ic4AJEyYArtqSmytS78V4oH6oV1xxBRC5a3lmduJ+INRtEe5SVvlDtGQLpah/8cUXcbAutnz88ceAOw8yFL9NUsgKmzZtcr5/+o2o9EKuOK9QM4Dw0qFYolIEv5RGqCheiSDyPMgVXLJkSSf0IK+SEkv0uWUH1P/0xhtvTHP+03Sh7du3J9yuUEwJGoZhGElLXJSg1JwmKygGVqNGjXTvI/+2UpkPHTrkxKOEEgMUE/S6dZcaG3fv3p0OHToArtqTbSrg7d69u6OEsyPh0yIyUqaSHSdMKD6V0UQSv6Ei6x9++AFw44dTp05Nc6wa2nutBNNDTduPlRo1avDEE0+kum7r1q2Ad43q5R25+eabgaBSAvd8MXXqVCfJSfMiFSPMDig3Q02+c+fOzeHDhwG3vZ3XClCYEjQMwzCSlrgoQRV/y+cr37ZiEVu2bHEygnSbdmZSDxs2bHBaCalAW4XMUoteowyvhx9+2LlOMTLt4pQW3L17d99kF2aUXLlyRcxyzSgtWrQA/NncID3UUPvIkSPOzMR4zQrMKt9//z0AEydOBIKNlJVppwbExxqHVcN0ZWp6UTyfmWbgkZDnae7cuZx00kmAG4tTjNDrYnN9hrrU723UqFFOg3N5mfw2Xi0Saun35ptvAqnL21TsP3DgwMQbFgVTgoZhGEbSEhclqF2kMjO1k1m3bl36hvx//E8FoKeccoqza+vUqRPgHwWoYmVltQFceumlACxevBhwMytD4y1Su9mFQCCQpYzWfPnyAW68Y9KkSTG1Kx6oSFyxmZ07dzrZrX773BQ/VyODeFC2bFnArauLJ+kVsL/88suOQsoIqgXUfS677DLnNnmTLrnkEiBttrNfkPfklltuYejQoQCsXr3aS5MyhZRfeIMTcNWh34jLIqgT565du4Dorhn1BZ0xYwbgztjbv3+/UwzsdQJMOHLzqkRj6dKlvP3224C7AOjHpmNy5coV99lzsebrr79mx44dgDvhO71p1+C+dh2jjj+Rel36BX0+KubXyX/gwIGe9QhNFPp9aipGeCcPcMtD1BUovRl3x8KQIUMAt0wgtPTp/fffB9xzisIoWsTuvvtuZxHVgq0yCHX6GTZsmNPwwK+Ln5gyZQoQTBoJT+bJDqhpSTgffPCBb8NB5g41DMMwkpa4KMGNGzcCbi9A9YZTcPqzzz5z3BMDBgwAXPmseWF9+vSJ6j71Eu1KQy+lgpQI8/TTTwNuQsH48eN9OzU+PXbs2OEogfAJ1pMnTwbg1FNPdWYMqp2TAvhyi/tZAWu3LQWopgfZeWJ3RgkPV8yePdtJxhBS8ZrfFw8luGTJklR2qH1a0aJFad68OeC2OVNbwlDkRtUxS5cuBdImnfgZ9axVf9DbbrvNN+GfzJDePMDnn3/e08kk0TAlaBiGYSQtuQJRMh+OJT0e3F2B5spF6tyuYKka9fq10XIoKlpVIejMmTOdHXT4TlXK8K233kqghbGnb9++AAwfPhxwG52DWwKhRCHFePxWWhBKq1atADfGJBWh2KfXTX29oGHDhs73VIpEqBxIKiueSJX36tXLKTmK1rhACXTLli0D3PhldmgvppwIzRFUSUrt2rX5559/PLMrs6iVpcqKFBscPHgwEFwLvGwbGe25TQkahmEYSUtcG2hrQrQucwoqTBYdO3Z0VLPaVqnZrUomsjt6PX6eEp9RKlasmGpUErixr2RUgGL16tVOQwp5b9TgQq27EsHPP/8MBGdVppc7sGHDBiDomVDzgOXLlyfMxljRvXt3wJ2DqjyK7KQCwR29pQbgQs1Q/Dw8wJSgYRiGkbTENSaYU5HfvmfPnkBQ6WqnrBinWgQZ/kGN3YcPH+4U8isTsXPnzp7ZZSQvqp2TYmrYsCEQnyzcRLBt2zYAjj/+eMDNEPc60z+aErVF0Ega+vTpA8Do0aNZuXIl4CZ86CRkGIlEjQrUfzi7lVFlFywxxjAMwzAiYErQyPGojZZaZ7300kvOTLPsOPPQMIzMYUrQMAzDMCJgStAwDMPI0ZgSNAzDMIwI2CJoGIZhJC22CBqGYRhJiy2ChmEYRtJii6BhGIaRtNgiaBiGYSQttggahmEYSYstgoZhGEbSYougYRiGkbTYImgYhmEkLb5dBKdMmcLmzZvZvHkzjRs3pnHjxl6bZBiGYeQwfLsIGoZhGEa88W0D7eXLl9O0aVMANm3aBMBpp50GwMGDBz2zK6N06NABgIIFCwLQoEED7rjjDgDef/99IDjSB+Cbb74B4NNPP020mYZhGDmebDVZvly5ckBw4cuXL1+q2woVKgTAvn37Em5XNI477jhq1KgBwCOPPALAeeedB0CBAgWOev8tW7YA8N577zFw4EAA9uzZA8Dhw4djbq8R/MwA2rZtC8CDDz4IQP369YHIP5obb7wRgL/++su5Thu0L7/8Mn7G/j/t27cH4NZbbwXg3HPPBYK/0/R+xnPmzAHg3XffZeHChQCcdNJJAGzcuBGAvXv3xs9ow/h/atWqBeCIgTJlygBw8cUXAzB37lxWrFiR6j4vvvgiALt27Tqm57YpEoZhGIYRAd8pwTp16gDw2WefOddpN9uxY0cAjhw5knC7Qqlbty4AzZo1A4JqQruZWDF48GAA3njjDQC++OKLmD5+PClfvjwAK1euBILvTyKUUjjVq1cHcNR1KMcffzwAV1555TE9x1dffQW4302pq1giBThx4kTA9Yhklu+++w5wX/vvv/8OwIEDB5xj+vXrB5BmR24Yx8qTTz4JwO23357h+8jrcu+99wIwduzYLD23KUHDMAzDiEBerw0QefMGTYm0a586dSrgvQIUUoDPPPNMusf88MMPQPSYXunSpQE3eSYUxai0W/dKCVarVg2A//77D3BfVzSef/55wFUYf//9d5ysi86iRYsAKFu2bNyeQ8laq1evBuDVV18F4LbbbovZc5QsWRLIugIUVatWTfV3pPdl+vTpAFxxxRWA+7qMzKPPS7/vSy65BHDjzplF5xvlEGQ30vO6rFu3DoCff/45zW3KrbjqqquArCvBaJgSNAzDMJIW3yhB+YuvueYajy3JOIpVtm/fnl9++QWA8ePHAzB8+HAgeuad1MJTTz0VTzOzzOWXX+4oGynTaLaqpKVVq1YAPPbYYwBs27Ytnmami1SN4lyh7N69G3CzeXv16gW4yjezFC5cGICWLVsCrkJUzPBYeO655475MTKKMvY++ugjABYvXgxAly5dgNSZsX4lT548AFSuXDnNbfJk7N+/Py7Pfc0113D22WcDOJfKczhWLrroIsD1RP32228xeVyvUIxaryv09ZQqVQqANWvWAFCvXj0AunXrxrx58wDYuXNnTOwwJWgYhmEkLZ4rwZ49ewJuDVZ2YMqUKQBMmjQJCGYuKWa2devWDD9OtHjLP//8A8Rut5MVrr32WkftZkStXnbZZYAb3501a1b8jMsAUuWKUYZy6NAhwFUGs2fPBmDAgAFA0DPx+uuvA1ChQgUAihcvftTnlJJSHC8WSIVLjYmPP/4YgJdfftm5rkGDBoD7uxJVqlRxFFJG0Gd4wQUXAHDKKacAWVeCl156KQBvvvlmlu4fTpEiRQBo06YNN9xwAwD58+cHcOqLpZhCeeCBBwAYMmRITOwIZ/LkyU7ugi7DPSHLli1zftdqlBGJ2rVrA67HqEqVKkDwdwn+9SBlFJ3jQhXgiSeeCLjfX/2exEsvveScVzp16hQTOzxbBLt37w7A6NGjAfcLrK4pZ5xxhjeGZYDwE4EK24+GfpzDhg0DoqfnK0FoxowZWTExJpx99tnOQp8R9IX1stNQKFoQJk+efNRjdaK65ZZbnOs6d+4MwGuvvQZAo0aN0r2/Gjjox/vBBx9k3uB00PdFl9FYtmwZkPYE2bt3b6c0Qtxzzz0AFCtW7KiPqxKQrCZoKUkps5x88skAtG7dGnDLXlq0aAGkXuh07pg7dy7gJqQ0bNjQOUavOV6L4MaNGx1Xq55Dm6nMUK5cOZo3bx7xNq/CC7FGjVH0Or/88kveffddwP3thnPkyBFnYx4rzB1qGIZhJC1xUYJKElAwU7s37cg6derkyF6hAsp33nkHcIOmOYVzzz2XO++8EyBqYf3mzZsBt0jeC6ToChYsGLXINBz1S/33338Bt6zCKzKiAJXGLsWhZBpwXW7hpQWhKPHp5ptvBrz93KIRySWsllR6D0aNGsWFF14IuL9h0aZNG8B1zWaWrLY6lKJWW0J5GfS9zJUrl+PK7t27N+C615Ry37BhQ+d+ajgQL3SuyyqVKlUCguox3BsmhZtVVe031L5PvZSjoTDTE0884YSjYoUpQcMwDCNpiYsSlK9XUxLC0853797NuHHjABgxYgTgFoAqAJ9TUOxz7NixR01MePjhhx1/t0ouvGD79u1AMPYplaBG4NFSy9WUev369QB8//33zn3jlZKeVfS6lDyTlSD77t276dGjB+Am1mQn1MRACQrvvfdeul6KUaNGJcyuUKT4pCTVTnHo0KFAMI70448/Am4iipKbHnroIedx5GFRO0K/oDityoqkziMlVt1///2Ad80nEoliz0rM+uOPP4D4TBAyJWgYhmEkLXFRgkr7VUwwPKayZ8+eDLXfEsfaLiqRqLm2ygW0e4ukAhUzUxx04sSJvmqJNHPmTO666y7A3ZkOGjQIcHfWkVCRsgqthw0b5rs4hjIijyXNul+/ftlSAYaj2GekuKHKMDISt4kH+v5pZJUuI6EyDKk9ZYdu3brVUVo7duyIm61ZQWpVrzMaY8aMAVI34FAx+SuvvAJkrkQrkVSvXt3xFKXHkSNHnKYViq0f6wiljGBK0DAMw0ha4lonqDhQZsboyN/9yy+/kJKSArg7PO12/Ea+fPmcQlbV9elvcfjw4TT+bBXujhw5MgFWZp5HH33UeR2qaZRyUu3Tf//95yg/ZeBJWagw2m8qENwYk3bS6dUlRWPYsGFOjEpNgLMTyoiN9LtSfE23edWia/78+Uc9RjFA/Z6kABUrbNu2ra88LKGEnyeiEan4X9m8NWvWBNy2k14P41azBQ1+HjduXJqKAJ3rFyxYAATjvJ9//nkCrQziu3mCYvny5U4vSk0ijja1wUvuu+++dAPuKl6ePn16RHeT35EbV4XjKoNQKje4XR7UWUWuYBW+qjuLH9FJ6Mwzz0xzm/qmqslBJLSBufvuu+NgXWzRZ9mtWzfA7dLUuHFj5xhN/njiiSeArJdEJIp27doxc+ZMwP2c5Kq/9dZbAfd76Ec0bV3lApHQZqVr166A2yGoQoUKPP7444DbbERzIM8991xPfnc6L6hpRKSpQEKNKRJxXrR5goZhGIYRgWyhBKVCvGwhFooSdaQiZs2alUoZgZtIoN2b3wLysUSvUcpJcxJ//fVXz2yKBSoLefrpp4G0/TjBdTvJ7bN8+fIEWZd5pPyUhh+JDz/8EHBfj19RKcfUqVOdwn4l26nFWnZvuKEpFCoHue6664DUMz1VUK85e/q7Tp06fP311wmzVZ4UJYplpNStbdu2gJtAF09MCRqGvS5VbwAAIABJREFUYRhGBDyfIhENrd5+m5ulmEqkGKXaPF1++eVAchS2RprblhNQYpfaNCkpqGjRos4xirP5pWl4OEoa6du3LyVKlIh4jBrA16tXz3dNDcKRAlRTiTx58jhNGdTaza9JMJmhSZMmzjxOxZsjlZWpabhaBEoJLlq0iLJlyybCVOrXr+98HuFTH+Qpefvtt51cAb9hStAwDMNIWjxRglWrVk2TLquUdbXHeeqpp5zMJxVq61Kthh555BEnMyxWc8qioTTkSJmAS5YsAdyZbxlRgMqmVIxxyJAhznXhqEB20KBBTgaYHyhQoADt2rUD3FKYjI6Wyi5oBx6pZZNiMZ988klCbUqPs846C3Ab0msmnVoZhqJCeGWCZqaBRaLRd0yKJ7T5hG7LCQpQ9O/f3yku37Bhw1GP1/dP31GVlyWCyZMnp1GAagCitpj169c3JWgYhmEYfiOuSlC1K6eeeiqA02z4pptuSjPgU/VJUjyhU7xVmK1pzHrcokWLOo2m460E69ev79gRKfNJ7ZzUIi48jvnQQw+laZ2mCdHpqb9Q1IjbTyoQgjWCp59+OoCj3LM6Nsdv6LNUTDBSTE3fV31/vUbfTQ3BDUWNslWX9eijjwKJaU2VVaRgNaBWmaD6ffXt25dvv/3WG+PiSIkSJZzf1dSpUwF3qLIyeMFtYiGVFa2mNdbI61WtWjWn6YTa06klpJTpTTfdlDC7MktcFsFSpUoBbud5lThEQqUDSoL56quvALdb/NGI93wwsWnTJt577z3AXdRD0Yes5Ilwl2D58uWPKXnCr9M1QqcOzJo1y0NLgpx33nlOSUM4mvkXWrqxe/duwD15aHN21113OSncGdmk+IWHH3443ds0uWXevHmA9/Mej0aJEiWckhN9/+Wyvf766wFYunSpN8bFmfXr1zsdYlTyoZKJ33//3TlOyS/hG2yVw8QDzUzUdy137txOAkx6YaArrrjC+b82MH5JeDR3qGEYhpG0xEUJqn9degpw3rx5Trsp7fTiMScqluzdu9dJNlCKfKQJBEr4CU/8ySxqV/Xnn38C7mxGvxE6/Xrt2rUeWhKkSJEiTiuqcELdSELuZbk6w2dfRuOVV15x2uJ5jZSsvDCRUPtBXapT/7///pvm2AkTJgBu8ow8NYksoTjttNMcBagWYDq3+C0sEGsGDhzo9EBVkwZ5KcqXL5/u/aT2J02aFDfbdP4LnXmoRBih35EUu3qJgvu986JPaCRMCRqGYRhJS1zaplWsWBGAuXPnAu6kciWWqAFsduWSSy4B3B11ZltMqbv91VdfDRCxvZF86+rm7zc0K3LdunWOmo/U5T7RtG/fPm6xSSWQ6DNp1qxZhtLXE0H79u0BN4lCyWOxQin49957rxMbjzf16tVzvlsLFy4EUseWcjr6DJUMpJh2pGbb+nx0jo3WJuxYUYmYEqvA/W1Ioas0J1S1Kn6r72oiS6msbZphGIZhRMC3DbSzA9qhtWvXzlG/SuUWalYcGo/SqJdVq1YlwMr40LJlSyDYJEDt4+68804PLQpy+umn07dvX8AtQcmqKlLmsTLf+vfvD/i7pEAxGKkGxdTCi5mPhgrPFYeS96Jy5cqpYkHxRkpC73lOa8SQHZHKkzqPFocW+/bt46qrrgKCLdQSjSlBwzAMw4iAKUEjS6jVVrdu3Zy6Sb81C5dyUxF/NDQUef369c51qqfzekr3sdCgQQPAjeGCW9sZqY3V/fffD7g1n2paoSbV7dq1822mspFY6tSpAwQVoQb/hqOp8SNGjEhYLDkS0ZSgLYJGltAieNZZZ3HOOed4bI1hGEb6mDvUMAzDMCJgStAwDMPI0ZgSNAzDMIwI2CJoGIZhJC22CBqGYRhJiy2ChmEYRtJii6BhGIaRtNgiaBiGYSQttggahmEYSYstgoZhGEbSYougYRiGkbTYImgYhmEkLb5dBIcMGUIgECAQCLBt2za2bdtG4cKFnRl+hmEYhnGs+HYRNAzDMIx4k9drA0SePHkAd55Zv379mD9/PuBOYK9cuTIAn3/+uQcWGkb2pm3btgD873//A2DRokUArFmzBnAnhRtGMuGbRfCqq64C4IEHHgDgscce45577vHSJMPIUVxyySUANG/eHIAWLVoAsHTpUgCWL1/OP//8441xMeDCCy8E4O233wZg+/btAPTq1ctZ6Hfu3OmNcQa5cwcdjzrHP/jggwAMGjQICJ7zPbHLk2c1DMMwDD8QiAIQ93+NGjUKNGrUKLBz587Azp07A2vWrAmsWbMmUKBAgYQ8v/07tn8NGzZ0vi+HDx+O+O++++7z3M5k/3fRRRcF9uzZE9izZ4/zuRw5ciRw5MgR5+8yZcp4buex/LvwwgsDF154YeDgwYNp/s2ePTswe/Zsz21M1n+VKlUKTJw4MTBx4sQ054e33nor8NZbb8X1+aNhStAwDMNIWjyPCd58880AnHjiiQCMHDkSgP3793tmk5FxHnzwQY4cOQLgXEY6pmTJkgDMmjULgA8//DAxBsaZcuXKAXDHHXcA0LRp01SXK1eu5KyzzvLGOODUU08FYPLkyRQqVCjiMZMnTwbgt99+S5hdsaJYsWI899xzgBvrjIRigoY3jBw5kssuuyzVdQcPHgTgnXfe8cIkB1OChmEYRtLimRJUZlrXrl0BmD59OuBdhlC8qVOnjqMInn/++VS35cqVC4AFCxbw9NNPA/Duu+8m1sAMUrFiRSBoK0BKSkqG7nfLLbcAsHHjRiB7KsFOnToB0LhxYyCo9qT40uOnn36Ku13RuP322wEoUqRImtuWLFkCwCOPPALAoUOHEmdYjKhbty6NGjUCoHTp0kBkj8TgwYMB+OqrrwCYO3du3G0rWLAggOMFEWeffbbzm69atepRH0eK6bXXXgPcrNfDhw/HzNZ4Ua1aNSD4OYWjrNDw82GiMSVoGIZhJC2eKcHatWsDbpH8zz//7JUpceHKK68EoGPHjkCwRks7w2DirYv+bt26tbNjuvfeewF4+eWXE2JvRsmXLx/gNi7IqZQrV86J8+mzVPwvEitXrgRg1KhRALz++utxtjA6w4cPB6Bz587pHtO6detEmRM3TjzxxHRjnZEYO3Ys4KrFt956Ky52AbzyyiuA60E4VoYOHQrAm2++CcCNN97IH3/8EZPHjjWlSpUC3HhfpUqV0hzjlzitZ4vgxRdfDMCOHTsAmDBhglemxJRrr70WgCeffBKAEiVKADBp0iTHhZg/f34AHn/8cSC1u0RfnlNOOSUxBmcSuZWi0bNnTwAaNGgAwE033RRXm+LByJEjncVPzJgxA4CZM2c613m92KVH/fr1Aff7B+6Jf/To0Z7YFAsULpB7PRQVY0dDv68KFSrE1rAIFChQAIBPP/0UyHqyn4SC3L6XXnopAE2aNGHevHnHamZcKFq0KBB58VuxYgXghka8xtyhhmEYRtLiiRI88cQTOeOMMwB46aWXANiwYYMXpsScbt26Ae4OXH0an3nmGWcnKCVYs2ZNAPr06QNAoUKFnN3633//nTCboxHeiioSctOoHZJQMkbu3LmdXboSAvyKSnSaNGniKL+nnnoKcF2efua0004DoFatWmluk3uuX79+iTQppih0kF45DriKfdmyZUCwdOKKK65Idczll18OwLRp0wD4/fffY26rvEJKbNFlZsmbN3iaViJTs2bNAGjZsqVvlWD4+w2uAlSI6Ndff02oTelhStAwDMNIWjxRgv379+fkk08GvE8hjzUnnHBCqr+Vdn7w4EGngbF2syqZCA3sKw1aCRZ+IdrOO1wBiki79vCkIL+g5AWppJUrV3LXXXcB8OOPP3pmV2bp1asXELl0Zf369Yk255gpW7Ys4BbCd+nSJdXtu3btclTc2rVrAejbty8A+/btA3DONaHo8RS7iocS/Pfff2PyODo/SAEKv8ajwfVyic2bNzsx9vQUYLFixZw8CeWKKDFw27Zt8TLVlKBhGIaRvHiiBDt06OD8X+m+OYXwXY5U0l133cXevXuB9Atk58yZ4+xi/cLRskFVuBuKYp6hmYl+R+UQYuXKlU4hvDJ1s0NMsEmTJl6bEDPq1avneEYU4wz3SEyaNCndGKd+ZzaSLXEo+/a8885Ldf3YsWP55ZdfIt5HWa9jxoxxckWEcixOP/10AP76669YmguYEjQMwzCSmIQqweLFiwNBH/dHH30EkO7uIBrly5cH4IcffoidcTFCmZ7aETVs2BCI3LYqnAULFsQsjhArVNCqnVg4ikGFcuuttwLZYweuuEpow2sINm8YMWIEAB9//DHgxq/9GCOsXr06AGXKlAEiZ+GqOF7fRR2j13ngwIG425kZmjdvTo0aNbJ8f3kpxo4dm26tqjwd4bFGP9G+fXuvTcgwPXr0ANzvoc5nq1atSnOshibcf//9AGlUILgNKlRzGQ8SugjqC122bFkn8SNa/zv1ApS7o1ixYoDbBWP//v2MGTMGgIceeiguNmcWdb5R4F1dYnLlyuXYHd4rb+LEiYD/usOAW+ge7oaSG1vJCKFEWhj9RNOmTZ1SCC1+SoJRev2PP/7oJGXo+6dj/IjchTr5REpAateuXapLLYIq41m7dq2T2h4Pt1NGUSJIzZo1ndIaXX799dcAtGnTBnATKKIRWqIT/jgDBgyIoeXxQYuF0Ob/u+++c65T8xGdbyKhCS7xRL2FxZYtWwC3XCUUNT646KKLnOu0Cf3ss88Ad8pQPDF3qGEYhpG0eNY27WjF8R06dHB269qRq9h3ypQpQFBxKMnGL0pQyLWky5o1a6YJ4C9duhQIloxA1otp48U777yTphWVdp+hyU3hSGGE3lct46TcvSR0+oPUndrcJSNSXs2bN3dUvJpYxKN04GjIRdmzZ880HogXXngByJgClCoOfRwpQBWyZ+RxEolKCwoWLOiEj8LVqhK1QssGChcuDLi/vb1797Ju3ToAZs+eHV+jcZPg5GUQoWo1HIW1xC+//MI111wDBBsBgClBwzAMw4grninB9DryKyYxZcoUtm7dCsC5554L4CTTKPW5V69ezlRsv6Ldzh133OHYLcWnHZ7fOsFr1mP16tXTTI2PVuyudkjawYbu4r2eGRbKk08+mSr2lx7hDbT9jJJAdu3aBbjx81AUZ1GcRq3DQhk2bBgArVq1AnB25jt37oyxxekTKUFCqNm8pplk1nuilPvPP/88a8YdA1LcLVq0cObs6X2Wt0t5ExlJBAkEAk4OwsKFCwF3asPmzZv5/vvvY2h9dPR5hM9O1JzYUM4880zA/ZyVHNmxY0cn3nnjjTfGzdZwTAkahmEYSYtnSlAqT0g9SNlNmTLFmYq9Z88ewN0dqYAWImcn+gn5tnv06OGoKMUvN2/e7JFV0dFMw3CffXpoh6u2cGpFJXr27BnXuW1Z4WhlDv369XO8FYoX+rE0QigFfdOmTYA7xioUZeipIfhjjz0GwLhx4wCoU6eOc6yKnaUWX3zxxXiYHRE1hQ5vEwZwzjnnAJHbnSkzUXGp0GYN/9femQdMNfb//5U2SiKK9pSSrVKyfBN5SJHSIkUqEj15SNpIJU9SHkvaJFvoTou0iAjZIktUHlvpQYmkooVClu7fH/N7X2dm7rmne5mZc+aez+ufqTlnZq5zn+W63p9V0cz6u6TyuaFxqKt9tFrKDV1v0VYzWVwWLlyYqCEmDUWXP/300+7voNJoem5MmTIFCEWGKj5CCfR6Rv7+++9JG6MpQcMwDCNjSakSVEmxXbt2ubwWRQ2qdZBU3rx585wCVCsR2b21mhs+fLiLsAwaDRs2BODRRx8FQvZ7RbZpBZ6OxCpzpy7miriLJtUReCqGXZACw4rg7d+/v1uJB62YeSykkJQ0Hws1olV7LCWIx2tvddVVVwGpVYKx8kxVtKFXr15A7KhVRVbGivTV+PW8SSWKEN+1axcQUoJSQzqOrKwsIDJvWkpJuXPKm46Vc+c3KlYuS8QxxxwDeE0CWrZs6Z7xivEQUnmjR4920doqTKEcQvm6k0FKJ0E5ards2eJqZKrrshLF1ZW8XLly7mbV5CGzwLBhwwAYO3Zsikaed3ShamzqCr1q1aqY3bDTjeiH4ahRowqUUJ9M5IxXPVBNYrEmRaXhRKevzJ07N626SMhs9OmnnwJeEYBY1KlTB/BMqLE6hKjSh0ylqUTXix6k4JnHbr75ZsDrb9m2bVt3/SklJ/p4Jk6c6MvkJ7SY1/V42GGHucT1eGY+Bcxt3boV8J4tQUST1LJlywDv3KmO8IwZM3ItjKKKMeEolSpeikWiMHOoYRiGkbEUy44T856sLuAXX3yxC4CRqVOrHVGyZElXf3PPnj2AF1AyadIkIHi1DsFTtjKHKhz90ksvdduCTr9+/YBQAEX06lpKXSbpPn365Nhn1qxZgH/1GBVmLcuBlJxqgJ5++uk5gg1kclJpu3RQf7GQWnjmmWeA+F0lclNOAAsWLAC8IIxUolqmCoyD+GPNbR+ldXTp0iWQJsS8oqR3PVMUWBO01CrwgumkvJUKklfUdUfmYvVjLSzxUrtMCRqGYRgZiy8pEs8++6yb8ZWYHV1u56effnL+Jyk/hRgHjeLFi3PZZZcBXnqBHMVy0qeLCoTYHeH174ceeihi31j7jBw5MtlDjIvCrOWDkX9M6m/u3LkuWV6KKR16BeYFBSGp6ETfvn0BuOmmm1xprXisWLEC8Lqh+EG8ZPl4yGKkBHIlxsfqYJBOyKeYLMtcIpEVplWrVoCn5tu2bet80UL3oPyIs2fPdsFD8RorJBpTgoZhGEbG4otPsKjRuHFjl7iqJN7hw4cDnopNJ7p37w7A5MmTnXqI54tR1K+ieBXZFbSC4JlMuXLlXES22hDt3bvXbYNQxKWiQf30N8nnNXv2bNeSLJ5PUFHISqGKtlakO7JoyKoUZJ9gbhx99NHu/NSuXRvwComkwl9rPkHDMAzDiIEpwUKgYrdvvfWWK/umyDaVplJx2HSkT58+rqRRPCWo4rmGkUhq1qzpcj6bNm0K5IxS3rx5s8sr9KPtUyooCkoQvNxOWZdUmi/esyVRxFOCNgkWAJk8Va3mpJNOcv/u2bMnkL4h9tHIrKtgF3UrCK/q4WcismEUdbTAVkUcpfqkMngk3TFzqGEYhmHEwJRgPlAi6NKlSwGoVKkSANOnT3dh+UFM4DcMw8hkTAkahmEYRgxMCeaREiVKuI4Q6muoot8K/TUMwzCChylBwzAMw4iBKUHDMAyjSGNK0DAMwzBiYJOgYRiGkbHYJGgYhmFkLDYJGoZhGBmLTYKGYRhGxmKToGEYhpGx2CRoGIZhZCw2CRqGYRgZi02ChmEYRsZik6BhGIaRsdgkaBiGYWQsNgkGiEGDBrF79252797NgAEDGDBggN9DMgzDKNLYJGgYhmFkLCX8HkA0ixYtAqBt27Zcd911ADz44IN+DikhnHDCCZQoEfnn/uWXXwD4+uuv3XsHHXQQAFdccQUA48aNS9EIDcPwmwMOCOmSKVOm0KRJEwC2bt0KwNq1awGYN28eAGvWrGHHjh0+jLJoYUrQMAzDyFgCowRLlSoFQJkyZQDYt28fN954IwBPPfUUAD///LM/gysAhx9+OAATJkwAoGPHjq4jvXpbbd++HYDTTjstx+c3bNiQglEa0dSsWROA6dOn07x5c8A7X+qvuWbNGgBatGjBtm3bUj7GrKwsAL755hsAFixYkK/Pb9y4EcCXsRvx2bdvHwDjx49n9OjRALRp0wbAPT+6desGhFTjGWecAcBXX32V6qEmhdtvvz3i/yNHjtzvZ8455xwA3njjjQL9pilBwzAMI2MJTGf5Qw45BIClS5cCOHs4QJUqVQDYsmVLysZTUM4880zAW9G0aNHCbdPfM/pPfvLJJwMhxXvUUUcBcOyxxwIE1uavY6lcuTKdO3cG4JJLLgGgTp06AJx++umApzyCzHHHHQfgVt8XX3xxjvMV/f9XXnmFCy64INVD5YMPPgCgcePGbjz7G2uxYsXcv7/99lsAfvzxRwB69OgBeAo3HShbtiwA8+fP5/zzzwc8FSX0vJg2bZp775FHHgE8Fe03hx12GOCpGVmQjj/+eG644QYA9u7dC0C7du0Az0r02muvsWfPHrd/uqJnZV5UXzzizVfWWd4wDMMwYhAYn6BWcX///bd774477gCCq4bCufDCCwF4+umnATjwwAMjtj/44INupXLWWWcBcN555wEwZMgQAOrXr+9W50E75mrVqgEhhQTQtWtXAJo1a5Zj319//TXiNcgoCve+++4DPIWxevVqpxrmz58PwCmnnALA4sWLAf+UU9OmTQHo06cPELIayH+ZF3SMUpLyMer4goiipnXvzJ07FwjFEOiZ8cMPPwBQvHhxACpVqgTALbfc4r5HVorLLrssBaOOTcmSJZ3q6du3L+Apwljo2BcuXAhA9+7dAdi1axf16tUDvONKJ9+grGT5VYDy/b355psJGUdgJsEaNWoAcOqpp+Z4748//vBlTPlBD0ZN5h999BEArVu3BkJBCDrpv//+OwCTJk0CvIuhePHiLFmyJFVD3i8NGjRg6NChAHTo0AHwAphkkpk8ebJL/fjnP/8JwMsvvwx45rYgo8lPQSI6hgULFlCxYkUgFNQEXkCCJsUxY8akdKzRPPTQQwX6nFwN77//PgCff/55wsaUaLT4uvvuuwG49NJLI7bv2LGDm266CYAZM2YAnmtFZrZ+/fpF7O8306ZNc4tIpT3cdtttADzzzDMAlCtXzrkXNInr3qtQoQIQWmgrfSydJj/x+uuvR/z/jTfeyDGxacIraNBLXjBzqGEYhpGxBEYJpjtSgHLArly5EoDdu3e7fbSakXKKVn2LFy+mV69eyR5qrsg5r0CCI4880pl1ZRrUalvH9+uvv9KoUSPAU1GffPJJ6gZdQIYNGwbg1J7UXXi6gYJljjjiCABncpQCSwelG4sPP/wQ8K5ZmdmCxsEHH+zGpmtMaUU6X5MnT+bTTz+N+Fzt2rUB6NSpk3tP99qtt96a3EHHoWHDhkDIlSBz7uWXXx5z3y1btnDXXXcBMHXqVACqVq0KwMSJE4GQhUwKOajIytWiRYsc6Q+FTW1IFKYEDcMwjIwlMEpQdv105d577wVg4MCBAE7RKXT5+uuvd4ExTzzxRMRnN2/eDHh+Ab+Q4pE/c8+ePc5HoXJ20WHosfjtt9+SNMLE0b59eyB+6PSyZcsAnF9U+8qPk25I/eoc3nnnnYCnqoLCwQcfDIQUkBSgVLfScd56660cn5PPTAF1Uk6AU1U7d+5M0qj3j1IdsrOz3XHJfxmvEIhiIhREI4tE37593bMjaMjfF54iFq0E/VaAwpSgYRiGkbEERgmWLFkyx3tKBE0Hhg8fDoTSHMArdaSE8SVLljglqIRYreKUdiAF5hfyU+g1r0RHScqHEWSUIP7ZZ58BXui9xr5gwQKuvfZaAJeMrSRzlfFLJ0aPHu0U7apVqwDPtxQ06tatC3hpOAC9e/cGYivAE088EfD81kohCRqyINx///0MHjwYgC+//BKAESNGAPD4448DkRHx48ePB6BVq1aAF6Wc3/s0FYT7AMP597//nfrB5BFTgoZhGEbGEpiyafKTKREUvBVeOpVzKl++POBFoclHCN7fU61RVHLLbwVYWFavXg140W/KY/LT/5JX5POUjzC83Fh06TEdZ1CVRizkBxw1apQ7DpXmC2p0qxLZs7KyXAK8yhFGF5a/+uqrXam7cuXKAV7SfK1atYDQeVMkYni0tl8UL17cRR4rz09FJ5Rje+WVVzofoFrKyaoU5JzA6OlEfj/9/f0inu/fd3NodO08sX379rSoOBLNrl27AHjvvfeAyIWEeoXpJk33yS8ahd6rT2I6oIRkJcSrKED9+vVdZRXVcZW5LR1QoNmoUaOAkClXC8ygTn5CCxLwihhEPx9UV7NatWquoooWzTItahJ85513AjH5ib///tuldSjI5ZprrgHgnnvuAeD77793+6uyVJAnv9yQWfT11193ifCpSIDPD2YONQzDMDIW35WgemSpPJJYvnx5YCq95weVpFJJNMnwbdu2OVVYvXp1wFu5Rif7phPVqlVzph2lUYTXf00XlCYQni6g44hnSgkqUq8ae3Z2tjtPepWbQcowKG4H3TudOnXipJNOAnJX4R9++KHrrqBAOh2fVGQ6BGrp+BT8IssEeGlWr732WuoHlk8UABNdD7RFixY5aoVq3+jUiVRjStAwDMPIWHxXgkUF9QR89tlnAS/4QGkQbdu2dSWSBgwYAIQS6MErN5aOXHzxxS5JWSv4okDHjh2dcpdSevjhh/0cUr6YOXMm4HXyaN68eY5OGbF6DyoZ3c8E+q+//hqAsWPHOj9mtKVI6R0jRoxwClDqSUX45XMPisKNheIEpFblk+7du7frcKJj/euvvyL2DSJSddGvkFMd6v9++whNCRqGYRgZi+8pElJMmzZtinj/ueeei4gSCzrqYp1bIvyqVatcsVspQfkCFfkWhDYv+WXmzJmuvY0KF6dDJ/nckD9pxYoVlClTBvBSWRS+nq6okEPNmjUj/q8Ixfbt26dlGohSI6JTcpRkHp6mFDSU+K6ejiplN2LECHdc7777LuAdZ8+ePYHgRFfmlWifYHRCfTLnG+ssbxiGYRgxMJ9gApg2bZorPq0Vh/x9KlEVC63sorvQpxOVK1fm448/BtJbAQqppDJlyrhzl+4KUKhsl15feuklAL744gvA80elGy1btgS8e08J9UpEDyJ6XkT70cP9zsq3vfrqqwF49dVXAa9AeIcOHQKf8xmOlGt0aTW/FW1gJ8F4VdWDgpz2PXv2dA7uCRMmAF6ATDgKSJDsl+M+qJXgMwmZQZ988kkg9ED1u3N8qgjvqJGOxxzdfUUVZFSXM4ioHu2hhx4KeInwcquE8/777wPQv39/wJsou3XBx+vwAAAgAElEQVTr5p436YAmvbPPPtvfgURh5lDDMAwjY/FdCVaqVCnm++reHWQUMJGdne2qvr/yyisx9z3hhBNcKoTMhjKZpiPqg3baaafx9ttv+zyawqPwenWa37ZtW0SX+aKIumTodc2aNWl3zEcddZRLqNc9qO7zQUadMoSed+HdI6J57rnnIvZRnWK/uf322526UwJ8tImzRYsWrsdgNCqn5hemBA3DMIyMxXcleOWVV/o9hAIjPxJ41en//PNPwLP5q+RRuK9FYejp7Ats27YtEArqSeckeSk/9atTcEU6+sbyio5ZRZt1zPPmzfNtTAXlqquucv+Wj13l+9KJDz74YL/7VKlSBYASJXx/bANeInx4Enx02kM8pBatbJphGIZh+EQwlhRpyueffw7ASSed5KK8XnjhhVz3l91fPd7SGbUgAq/jejqiElQ1atQAvOjedIq6yy865saNGwNeibXoKMsgozZJPXv25LfffgNCBbfTBRVnl39PpeJiUa9ePQCGDBkCeKXW/LYkRZdByytBKZwtTAkahmEYGUvglODKlSsjXoPMuHHjAPjuu+8YNGhQxDY1mFXprXbt2uXoil0U+Pnnn13LmnRC0aDKkVOR5aLsC1RpLh2zimQr3zWdUBTrMccc4wpohzeiDTrKR1VzYFmQnnjiCbePEupvvvlmwCuGrjxIv5s8x2qbpPcULarIz6Covlj4Xju0S5cugGeS0cQyePDgpP+2UXA0aZQuXdrVDE0XypYty4oVKwAvcEkPFnUlT3fUWb5Zs2ZAqErM0KFDAc/Uqwk/HaqOHH300QDMmjUL8Lq2FC9e3AXXzZgxw5exFQYF0On5V6FChRz7aMLX+Vu3bl2KRld0sNqhhmEYhhED382hc+bMiXg1go0q8qsDQToUNYimfv36rvO6zDV+m5YSzbJlywDPlLZt27a07oZxyimnRLyKiRMnpqUCFDoXMn0aqceUoGEYhpGx+K4EjfRC/RLFM88849NICs7KlSsDk3CcLBRYpn6dRQ0FnhUVH67hH6YEDcMwjIzF9+hQwzAMw0gmFh1qGIZhGDGwSdAwDMPIWGwSNAzDMDIWmwQNwzCMjMUmQcMwDCNjsUnQMAzDyFhsEjQMwzAyFpsEDcMwjIzFJkHDMAwjY7FJ0DAMw8hYbBI0DMMwMpaiXUrfKDQHH3wwACeeeCIAl1xyCQA///wzEOrwXblyZQCmTp0KwPTp0wHYt29fSsdqGIaRX0wJGoZhGBmLL10kGjduzOOPPw5AgwYNAJg3bx4AV199NQC7du1Kym+nirPPPhuAJk2aADB8+HDKly8fc98DDgitRapVq8amTZtSM8A41KlTB4A77riD1q1bA3DooYcC8PvvvwPw119/AVC2bFn27t0LwIEHHghAy5YtAXj11VdTN+gEoeuxWbNmPPDAA3H3LVasGDt37gTg//7v/wBYs2ZNcgdoGEa+sS4ShmEYhhGDlCrBRo0aAbB06VIqVKgA4FREyZIlAWjXrh0AL7zwQkJ/O9n06tULgJEjRwI41VeuXDkg/kpEf+fNmzfz999/A/Dkk08CMGPGDADWrVuXhFHHZsmSJUDIp/fll18C8NNPPwHw7rvvArB27VogdJy//fYbAM8991zEtg4dOqRszAWlbt26gDfWf/7znwDUqlUr7jmL5vPPPwc8S8aKFSsSOUzDSHuqV68OwDXXXBPxfs+ePQGoUaOGe69fv34APPzwwwDccMMNQOj5+tlnnwFw1llnAfDHH3/s97dNCRqGYRhGDFKqBDWbT5gwgR07dgAh/yDAkCFDAOjWrRsADRs25Jtvvkno7+eHKlWqAPDjjz8C8Vcb5557rvNpKppS6G+YFyUYa59BgwYBMH78+LwOvdBoRbZx48Z8fW716tUAHHvssQAcddRRgBdJGiQOOeQQAF5++WUAmjZtGrG9WLFi+VKC4uabbwbg3nvvLeQI94+um+uvv36/+2RnZ3PEEUcAIf80wE033eS2ASxatMjXey5RHH/88QDceuutAHTt2pVzzz0XgDfffNO3cYVz8sknA3DRRRcBnvLROcrOznbnacyYMfv9Plmchg4dCoR823feeSfgWW/8QHECXbp0cefjmGOOKdR3/vnnnwAcfvjhAOzZs2e/nzElaBiGYRgx8C1P8NFHHwVwK88tW7YA3gr98ssvZ+zYsf4MDmjTpg3g+bl++OGHXPf94osv+M9//gPA7bffDsCvv/4KeMezc+dONmzYAHh+vrZt2wLQokWLHN/51VdfAbB48eJCHEXByK8CPO200wAvl3D+/PkA/PLLL4kdWCFRhOv48ePdCvywww7L8+flv5YVQ0o3VVSsWBGACy64AIBhw4YBXjRvLGJZGfTv+++/P+L/Q4YMISsrC/CuUfk6g8qRRx5J2bJlAfj6668Bzx9bpkwZAAYMGLBfBdi5c2fmzp2bxJF6ZGVl0aVLFwCKFy8esS08t3bUqFEALF++HIitYnVNv/jiiwCceuqpbpv290MJVq1aFfAsLfXr13fb9FxQhoCei8cdd1wOf2E0y5cvdwo5LwowL1iyfC488sgjed73u+++44knngBg69atAC6gRGaPZcuWOXNwPFOZLgg9pP/3v//la9yppmzZsu7YFTzzr3/9C4hvgvCDTp06AdC9e/cCfX79+vUAjBs3DvCc9qlC16SujURTuXJl55bo2rUr4KUuaXGXqAdPYSlRIvTomjVrllsE1KxZE8BNinoAP/bYY/v9Pk2YyeCUU04BPNfGJZdc4hYnCiLTolvul7p163LmmWcC8Pbbb+f63ffddx8QOflB6NgnTJiQqEPIM7lNfuvWrWPixImAF/QoAVS6dGkAtz0WCr4bO3Ysy5YtS+iYzRxqGIZhZCyBVYLxTDxBZPPmzYC36lSyvBLhly5dut9k+YceesiZn4KuAOXAf/rpp925UvCBVrNBQWXdFIodDwUhfP31165QgErF3XXXXQCUKlUqGcPcL8cdd1yu20aPHg0U7G8v85LOKXiqSsEzS5cuBeCll17K9/cnEv3tZa5t0aKFM/fJrSALhMyJfpvlFSzVsWNHAK677jqeeeYZwDOx7969O+Izq1evdoFmudGtWzcuv/zyiPe2b98OhFJ+8pI6kGgU/CIFKDdXmzZtnLk6GqU69O7dO9fvVcCkzL6JxJSgYRiGkbGkVAnKrguenfzSSy8FvNW2OP/8851ySocSagrX1WrvnnvuASKT5aN9ZB999BEA06ZNA0JKUMnyQUNBID169AA8n1GjRo3cilMJ5w0bNgRg5syZgLc69QsFF2lc4AUgaGxTpkwB4O677wZC1+qzzz4LwIgRIwAvWElpMK1bt3bnOxXo7zt48GAg0repcSjYKj+pDpMnTwZCfqigln076KCDAC8YSM+LVatWuWO/7LLLIj6Tn79BMq9RnRPxzDPPOP95flBg1OzZswE4/fTTnTJWIJ6Kdqi8od/omb9t27Yc26pVqwbggoRiocBEWSKSgSlBwzAMI2NJqRJUi51mzZo5/8w555wDwDvvvAPgwpQ7d+7sVnt5ie7ym0mTJgGeso3F888/D+BSP5SKIH9iEJFvUxGg8hWFo9Vo//79I95XuHO4AkslSt2oXbt2jm1a+R955JG5fl77RKsEhaXn5uNNFvIXX3vttYAXWditWzeXIC51qFSHwia/awXvl59X15asJVINKiPYr18/F2kpH+C3334L5PSzxUOKIxn897//BTzrV+fOnV1ZxHDrWDStWrUCvCL81113HeD5uMO57bbbgOQeR154+umnAe881apVCwg9+/XMV/EMnVPFEoQjP6+u52RGJZsSNAzDMDKWlCpB+Y769OnjlJOQGpIy7Ny5M1dccQXgKUiVywkieVEFWlVrpRpkBSi0AlOkms6FIr3kNwtHEWtKxh4xYgR33HFH0scajUpIyS8bjnyABeG8884DYq9gU4HuAxVomD17touWlHqQEswPffr0yfGeovFWrlxZoLEWlvPPPx/I6TdSQnzVqlVp37494PlqtU1trsJREX8VTlcOXjLvRRU3kG/6gQceYODAgYDXkiwWKl+o0mOxULSuVJXfKEFf0ZzKCWzXrp2zJimSOdZ9qXOmKO1URPb60k8wHnKAz5s3z4Wo61UJmEFEJorom1Xhv+qOAd6J1c0blHqGiUammTPPPDNflVkSxcKFC4GcgQljxoxxN1k8c1RuqFNErGT5VNYOTRQypT733HPO3P3WW28B3jXqV3CaKqqowlRe0ly+//57INI0KLO+6lYq2V7XRioqMykFZcKECS6FQJNyNMuWLXMTgjruKHlebNiwwZlKY034fqLnuApUjBs3zgUP5sbOnTud+TPRXYSsdqhhGIZhxCBwyfJamT/22GP84x//ALyu3UFWgjKnRHd70P9vu+02Z25SusFrr70GeCHvDz/8cL6c+UFHAU3RK9hkIzUWrr7BK2n33nvvFUgBCh1Xs2bNuPLKKyO2+WE9KSxSzApigJDJDvxPT1LKkPo8aqwKMqlatWqOc6D0lwsvvDDH98n8qSAjlTdMBQou6tatm1OFsQLNIBT0JFdEs2bNAHKUC5swYULgFKDQ/SUz/a5du9y5i0a1eHv27OlLH1lTgoZhGEbGEjglKObNm+dK8Fx88cUArj9WkANkcmPUqFEuyEBlqlQ0V4n1c+bMKVJKUJQsWdKtfFMRai/7f7QfYNGiRUDi/D/79u3L8RtBKxoeDxU+UMfv7Oxsp5BU1isoqLyYArH0OnLkSLePgp3i9VfMjVatWqW0JJzug7zcD9EpPira8NRTTyV+YAlGxcyji6GEo7Q4PzrmgClBwzAMI4MJrBIEr1SOfGZSE+mQWhCLDz74APCSyBV2ri72N9xwA7fccos/g0sCOl9//vln4IpqZzIqv6W2SSVLlgRCUZXRkbRBRUUOhg4d6u6rgihAIXUVJBSZGx1nIH9tQUqvpQq1plK6lNLdwItkVXqIWin5hSlBwzAMI2MJtBKMpkGDBkD6KkGhiLdo/5/KKxUVlIuXSho0aOAiCRONVqxqjhzexkb+Xq3Sg4yKUKs1k/yY06dPL3SZtVSh/NtSpUq5xr+FIZVRonlFTXhVpk+l4mbNmuXbmPKKClUorgO8yF5tU+F2NeL1i7SaBFXlwe+eZoVFHRh0PKJdu3a+XuBKIFbfvKFDh+YrCEmJzaoGJHNoKqvFfPzxx0ydOhXwarQmCk1+qtQSjqohFSb1ItkoyV/HoT6W69evB9Ij0EJVYbTQ2bRpk6tEUpQoV66cMymqQ4QC6JTqE0TUcUV1TsXgwYNdwQPVDg0KZg41DMMwMpa0UoJ+I+euKsBrJV2tWjU2bdoUsa/KNKmsUXZ2tluBRyfIqi5nssx4eUVJ7QMGDABC5jJ1FpcpJhYK4VYZMRU5+OSTTwBy1IlNN/r16wd4XQrCUQm8oJtBK1as6LpPyPyppHL14QtqL8FwdAyqMdy3b99AK6OC0r59e9d9Zfny5UBw6oPGQuZpPS9US1ml6x555BF3rygwS6UmVebOL0wJGoZhGBmLL0qwZcuWPPjgg4Bn35ZdP1ZR4iDQpEkTN+bolfRFF13kes6pAKxWRuGd5UV0QvXEiRMB/0tUffjhhwB89913QKjslIory5mtVAepxurVq9O5c2fA89cowEeFz4OSHqGE3ebNm7ugFnX0iKZBgwZO3SmFRYEx6tq9e/duVzD9jTfeSNq4E8EFF1zgrBJCxQOk2IPMiSeeCHg9K5UsL6tMUUHnaNy4ce69RAT+JJOyZcu6a0nPO1nG1E0ivB+gUj70bPErSV6YEjQMwzAyFl+U4HnnnefUkMJ/VfJI4bJZWVkxOyj7xbp165g9ezYAV111VcS2vPqDZBOXjV9Rk++//36ihlkolLKhcm5PPfWUW4HPnDkz5meKFSvmzuWrr74KeEnYW7ZsSep4c0Ph7j/88APgFSyXn6J8+fJO9eZG+HEJnT+lGATdDxiOxhyO+j0q+jDIyF+ua0qtqqTKiwrVqlUDQu2TVCpOxQCCysCBA50C1LWkSORwBajnZocOHQDPF6ioUb/wpZ9g7dq1nRnq2GOPBUKOU4DTTjsNCFUTUMi9TI3Ka/LLvCaz2GeffQbAIYccAkSaN5UDqBOs4JlRo0a5RrSvv/56agZcSI477jiXzqHgEF3kq1atAkINXWXO0CShv4Hf5NZPMC/EmgRvvPFGwMtvSgdUl7Fjx47uPZnX1FFhw4YNgGceDRK659auXQt47hLl0BU19Bzs1auXu6+iu6EEjbvuustV9dKY1SFHdOzYkVNPPRXwKhSpJvTzzz+f9DFaP0HDMAzDiEFgOstLMQ0cOBAIJV0q0EKrvnBnsZ8o/UHKoF27dkyYMAHw+pSp55zhH+re/e677wKecs8LxYoVIysrC8Al38sspZqH6YBUeazbXPe3+nUGxSwfjlwG9erVA7xE/6KYFgFegYchQ4bQqlUrwAu6Cup1F64E84KelfpMKixHpgQNwzAMIwaBUYKGYSSeeEpQyddKOwhagMzhhx/uLCvyTaeLP72gyE82f/58955q8KokWdAoX768i9uIRu9PmTLF9ahUTIVSzFKBKUHDMAzDiIGVTTOMDEMrcpUgCyqPPPIIo0ePBoq+AhSKgp0+fbpLkQh6fMGuXbtcJH86YkrQMAzDyFjMJ2gYhmEUacwnaBiGYRgxsEnQMAzDyFhsEjQMwzAyFpsEDcMwjIzFJkHDMAwjY7FJ0DAMw8hYbBI0DMMwMhabBA3DMIyMxSZBwzAMI2OxSdAwDMPIWGwSNAzDMDIWmwQNI4+ULl2a0qVLs3DhQhYuXMi+ffvYsGEDGzZs8HtoRhGmevXqVK9enXXr1rFu3Tqys7PJzs5m69atnHTSSZx00kl+DzGtsUnQMAzDyFh86yJx+umnA3DyyScDMHDgQABq164NwDnnnMObb76ZtN83Eo86X//73/8G4NNPPwVg7NixzJo1y7dxJYomTZoAsGLFCvfexo0bATj66KN9GZNo2rQpAB988EGhvueSSy4BcPfetm3bCjcwo8DUrFkTgJdeegmAunXr5tjnq6++AqBevXqpG1ghqVixIhDqFwnQtm1bt23kyJEAro9korAuEoZhGIYRg5QqwQoVKgAwefJkzj33XACOOOKImPvu2LGDTZs2xdw2fPhwAJYvX8727dsTOkbDW4XNnTuX//73v/vdv3Tp0gD8+OOPAJQpUyZi+7JlyzjnnHMSPMrUUaJECQBmzpwJQKdOndy2L774AoDjjz8+9QP7/7Rq1YoZM2YAsHXrVgAaNWoEwJ9//pnn7+natStPPPEEAM8++ywAXbp0SeBI/UOWpltvvRWApUuXAsE9vpYtW7JgwQIADjrooIht7777LgBnnHEG69evB+CYY45J7QALQN++fYHQ9QrQpk2bXPft168fAA8++GBCftuUoGEYhmHEoEQqf+wf//gHkLfVV4UKFTjssMNiblu4cCEACxYs4IorrgDg999/T9Aok8Nxxx3HjTfeCECHDh0Azza+Zs0aIORTmz9/fsrHpr9zu3btAG/F1rt3b6pXrw7kTVFEK8CigvwU4QoQ4Msvv3Tn0g9at24NwPTp052VRa8FseLs3buXffv2ASElAvDkk08C0LNnz0KPN9UccEBojT99+nS6du0KeEr5zjvv9G1c8ZBvecKECTkU4EcffQR452bWrFmceOKJqR1gPvm///s/AB5//HGOOuooAA4++GAAd63F4u677wbgwAMPBELWQ8ifZSOvpGQSPOOMMwB4+OGHE/q9HTp0cBPLf/7zn4R+d6IYNmwYALfccoubJCTN9XrssccCoQeO3pMpJBXo96dNm5ZjWzKDo9IFLQ6imTdvnjOHppKyZcsCXgDS4Ycf7h4oU6dOBeCvv/7K9/cuWLCA77//HvAexgpgK1euHAC//PJLIUaeWho3bgzA5Zdf7t7T3+zjjz/2ZUz7IysrC/DuSYAtW7YAcP311wPw22+/AXDVVVdRsmTJFI8wb5x99tkAzJ49G8jd7ZUbelZqMhT3339/AkYXiZlDDcMwjIwlqUrwkEMOAWDx4sUAlC9fPs+fXbx4sTPTSUnG4uabbwbggQceAGD37t0FGmthkWlTynTo0KGAp6TWrFnjQp2l8hRer5D7ihUrutVrKpWgERs57hWqLn7++WfAM9GkmkmTJgFwyimnuPemT58OwA033JDQ31LAhUzBCpzxm2uvvRYIPRsGDx4MeIFZMrfpbwKeWXfOnDmpHGaeUcBO+DmVAlTaigJixI4dO1I0uvwjN0F+FWBu6HybEjQMwzCMBJJUJXj44YcDeVOAcniOGzcOCK2M5ORXeL18ioceeqj7nL5bTnA/qFixIi+88ALg+SEU7NKjRw8A1q5dy6+//gqEgmQArrnmGsD7O82fP5+xY8embuBGXKSq5A9T8FX79u0BnP8sVejauuiiiyLe3759u7OEFHWU+nHfffcBoefGbbfdFrGPUlnq168PhFRg//79Adi1a1eqhpon5G/WMYT7+J5++mkA3nnnndQPrICoYEY8i0RentXR+0jdH3/88Xz++eeFGGGM30rotxmGYRhGGpFUJXjHHXfked+77roLgNtvv929p0T4efPmAd7K+6yzzgJCYduKotLqPNwPkCpGjx7tVulahXbv3j3X/Zs3bw54fgBFhL788stOLQYFJbY+99xzPo8ktYwbN84duyIv3377bYCUl/NTuTYleMvXLq677jpWrVqVkN/SMUaXgVNKgV8+QVl/pOgUIfvSSy/x7bffAl5ZsTPPPBPwVN9NN90UOAUodC6jozyXL1/uIsvTCT2/46U/iPzso/SK3r17M2DAgIIPMAamBA3DMIyMJSlKUNGcimqKhfJ1xo8fD5AnBaToKL2eeeaZTgnKX7hu3ToA3nvvvYIMvUDUr1/fqbl4CjB8f8iZL+hHojx4K+dYaNX1+uuvA170raK/atSokWseXToiK0OXLl3cedmzZw/gXaupRiUGoxXg6tWrAXj++ecT9lsqlKD2PPLBFS9ePGG/URCkMORj/+mnnwAi/IGvvfYa4KlGRWjv3LkzVcPMM9WqVQM8ZRvNqlWr3HWXDlStWjXu9p07d1KqVCnA8+8JxYNMmjTJPceVZ/jQQw9F7NujRw8XOf/WW28VfuAkaRIcMmQIkFPiK9F21apVLjFb4eaFRb+lcOno6h7JpHnz5jlOVixUk1NpFEqfUGi3XlNNdKBFOJoUtLj4+++/AS+YR3VD4xHU6hzhnHrqqQCu24XML+Cl4bz44oupHxje9SJ0ncicruTpRKDv+uOPPyLe1wOsVq1aKe2fqNB4JYpr8rv33nuBUNcMLcL0IFbd03vuuSdl48wvMrWri47QuZ0zZw69e/cG4Morr4z5Hdu2bXPJ5NHpE6mkYcOGrnZtNFqA9OnTx6WRTZkyJWIfpfzoPoPcu2KUL18+4ZWpzBxqGIZhZCxJUYLqCRjNsmXLgNzLUOWXOXPmcOGFF0a8V6dOnYR8d37Izs525sFatWoBOZPdO3TowPnnn+/2D2fMmDHJH2QMpPIU1BOPI488ssC/kyi1n0x69eoFQOXKld17SnORsvALjUnXjUz9L7/8ckK+X6o3/NgVeCKUirRkyRJnzk82nTp1cm4ThcxL8ciKcuedd+ZQyqNGjQK8oIpGjRq5Lh8KXPOb3NTd2rVrgVCnHNWGjYf6SCp4Sgn2qUDKe8aMGbleE//617+ASFfPN998A3jXc37MmosWLWLlypUFGm9umBI0DMMwMpakKEGt2qKLLye6GPOMGTM44YQTgFCB6mT8Rl5YtWqVWwnJ1i/VF142TZ2UlSSv0O6nnnoqpeMVUjoKi9fYE4X8AeHFDYKGlHu3bt2ASJUuv3Wqk+L3h5S7CivES5SXz0ldFGKhe0jBMPGoVKlSnsdZWC699NIcFgj5r2P5sXWvSVlIMZ944omBK7AfnYIiYgWpvfLKK4CXPK9gmhNOOMGpdxWqTmXfTlmSwntpRie5x1JtS5YsyfNvRH9f+/bt3XM0P98T9zcS8i2GYRiGkYYkRQnKFh/t+1LkXSLRb+T2m6mgadOmTglG+yekshYsWOBKwmmM8pH6FRW6bds2wAs7V5/H8Cit3NBqbNu2bW6VHR36rGjga6+9NmH+q0ShFaY6WEdHnE2bNs2V5vIb+ZdVEKJKlSqAF4Wt12Si1Jjzzjsv6b8lBg0a5MoRCil3WVOqVKnCDz/8AHiWjehnwJIlS1zx+nThl19+cSkfipDVc+LDDz8EvBQZSG1neaW+KaozVtK73uvTpw9Q8Gs01ncn+hlvStAwDMPIWFLaWV5+iURFaB1wwAGu87DfKKpLycbRZGVlOb+TFKAUmN9ohSnfUn6LMUsFRxfNVVf69evXF3aICUfJytHKXer40UcfTfmYckM5r7pvpNgThXxo4eXgFC0r1amE/ESVZ8sL3377rWuBJOS3DC+ILXWa6MLKfrJ8+XI6duwYc1us9kTyCSYTJbCrcXOsxgjynyviWPmd4BVIya0IwKGHHupyCWVpEuEJ9YlKkhcpnQQl2evUqcNXX31V6O+rVq1ajodYUAmvKuNXSkSykNkm0b3skkluQSCqz/n++++ncjh5omfPnoBnYtKCSybCcJYvXw54wVdNmjTJdWJXYvzevXvdewrs0iSo4Ay/UNCLJgaZ3ocMGVIkJr/o6y2WUFDqWXj91r/++gvwepImE7kMYk1+ixYtArwuEkr0FyVKlKBGjRqAZ7aOpnv37s5lJHeFzKGxEuoThZlDDcMwjIwlKUrw008/BUKhyeGozufzzz9P27ZtAfjyyy8L/DuxynHpt4OCSqU1btyYCRMmAIlLcg4KzZo183sI+aJ169Y89thjEe+98cYbQLDVrExCelXJrLwgE/z+kELWqkuhui0AAAQiSURBVD0oSAGOHDkS8JLmk9Fp3A+UCiKT43fffee2qTThddddB0TW6ZQKnjt3bkrGGYtFixY564RcK/np9KDjUQBQODK9ypSaDEwJGoZhGBlLUpSgVtNawaikmKhXr56zIc+ZMwfw+gmG+yVyQ+WEWrZs6d5TWZ6grOTVPV6V7LOzs3OUUisq5KWIdpCYOnWqc8ALOfR37Njhx5ACwyeffALAxo0bgchC4n5w0EEHAV4xAxVgGDhwoG9jSgTqzyn1I7+uCoOPGTPGBf2pEMjFF18c8R3r16/n0ksvTcVwI4hOYN+4cWO+0ryig19kFQwnlV1LTAkahmEYGUtSlKA6wl9xxRWAp9LCy3LJP6h+YCp8rTYbsfoBtmjRAvDCgcuVK+e2XX311YD/xZqlUhcvXgx4UW09evRIeGhvEKhUqZIvq9GCoPJg0cWhwVuJa5WqVIlMZevWrYCXmK2o2VQzaNAgwCsUoJY9qewXmgwmT54MeM0EpLg7d+4c8RoLRUwOGjSI//3vf8kcZtzfF2eddZbrtZlbf8RwpADbtGkT8X1vvvmmL9YyU4KGYRhGxpLUPMHff/8d8HyCyn3Rqi4c2fyVGPzXX3/lWHHI96TX7777zkWzqUSX34T7AMFLiC+q/sBSpUoVqs1SKtm1axfg5VaFowTevPikMwFF+5UoEXpEhEcrphI9D3RepDjSnc8++wzw4hpU1k0FsWM1AtAzRX7DhQsXJn2c4ege0X2kfMEGDRrQoEEDANfaLl5pM+WLq+2T8ga7du3qSwnJYtlxRpvojgwVKlQAQkEjcvLKlKbqIuG/ndvQNOH1798/InHUT4YNGwZ4KRGqviETblGlSpUqruu8ghiETGkjR45MWMX3RPDNN9+4ijFCZimZsY1goIdiVlYWADfddJOfw0k6CpQ544wz3MSvggebN28GvO4mfqGFicyZ3bt3d9uik9xjEV23N78VqgpCvEnZzKGGYRhGxpJSJRiLunXrAnDuuecCXn3Rs88+O8dqQiYDldB58cUXkz6+vNCxY0dX4/DXX38F4IILLgBSW2vRL1RiK/p8qHTS448/nvIxGenPQQcdxKZNmwAvlSq32rxG6tGzO7xsXTwlqEIHeibqc0rHSSamBA3DMAwjBiktoB0LhfjqVWVy0olWrVq54rLqH5gJClCoDFwqE1yNok/ZsmVdB5LwdCgjGOiZXbJkSZ9HUjhMCRqGYRgZi+9KsCiQnZ3twnzDI6UMwyg4P/74I02aNPF7GEYRx5SgYRiGkbH4Hh1qGIZhGMnEokMNwzAMIwY2CRqGYRgZi02ChmEYRsZik6BhGIaRscRNkYjnTDQMwzCMdMeUoGEYhpGx2CRoGIZhZCw2CRqGYRgZi02ChmEYRsZik6BhGIaRsdgkaBiGYWQs/w/rVmsjFp4MVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize data\n",
    "# visualize our data\n",
    "\n",
    "grid_images = np.transpose(make_grid(train_mnist.data[:64].unsqueeze(1)), (1,2,0))\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training Images\")\n",
    "plt.imshow(grid_images,cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize data\n",
    "train_mnist.data = (train_mnist.data.float() - train_mnist.data.float().mean()) / train_mnist.data.float().std()\n",
    "eval_mnist.data = (eval_mnist.data.float() - eval_mnist.data.float().mean()) / eval_mnist.data.float().std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_images.shape: torch.Size([64, 1, 28, 28])\n",
      "--------------------------------------------------\n",
      "batch_labels.shape: torch.Size([64])\n"
     ]
    }
   ],
   "source": [
    "# parse data to batches of 128\n",
    "\n",
    "# pin_memory = True if you have CUDA. It will speed up I/O\n",
    "\n",
    "train_dl = DataLoader(train_mnist, batch_size = 64, \n",
    "                      shuffle = True, pin_memory = True)\n",
    "\n",
    "eval_dl = DataLoader(eval_mnist, batch_size = 128, \n",
    "                      shuffle = True, pin_memory = True)\n",
    "\n",
    "\n",
    "batch_images, batch_labels = next(iter(train_dl))\n",
    "print(f\"batch_images.shape: {batch_images.shape}\")\n",
    "print('-'*50)\n",
    "print(f\"batch_labels.shape: {batch_labels.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Neural Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute average accuracy of batch\n",
    "\n",
    "def accuracy(pred, labels):\n",
    "    # predictions.shape = (B, 10)\n",
    "    # labels.shape = (B)\n",
    "    \n",
    "    n_batch = labels.shape[0]\n",
    "    \n",
    "    # extract idx of max value from our batch predictions\n",
    "    # predicted.shape = (B)\n",
    "    _, preds = torch.max(pred, 1)\n",
    "    \n",
    "    \n",
    "    # compute average accuracy of our batch\n",
    "    compare = (preds == labels).sum()\n",
    "    return compare.item() / n_batch\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train(model, iterator, optimizer, criterion):\n",
    "    \n",
    "    # hold avg loss and acc sum of all batches\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    \n",
    "    \n",
    "    for batch in iterator:\n",
    "        \n",
    "        # zero-out all gradients (if any) from our model parameters\n",
    "        model.zero_grad()\n",
    "        \n",
    "        \n",
    "        # extract input and label\n",
    "        \n",
    "        # input.shape = (B, 784), \"flatten\" image\n",
    "        input = batch[0].view(-1,784).cuda().float() # shape: (B, 784), \"flatten\" image\n",
    "        # label.shape = (B)\n",
    "        label = batch[1].cuda()\n",
    "        \n",
    "        \n",
    "        # Start PyTorch's Dynamic Graph\n",
    "        \n",
    "        # predictions.shape = (B, 10)\n",
    "        predictions = model(input)\n",
    "        \n",
    "        # average batch loss \n",
    "        loss = criterion(predictions, label)\n",
    "        \n",
    "        # calculate grad(loss) / grad(parameters)\n",
    "        # \"clears\" PyTorch's dynamic graph\n",
    "        loss.backward()\n",
    "        \n",
    "        \n",
    "        # perform SGD \"step\" operation\n",
    "        optimizer.step()\n",
    "        \n",
    "        \n",
    "        # Given that PyTorch variables are \"contagious\" (they record all operations)\n",
    "        # we need to \".detach()\" to stop them from recording any performance\n",
    "        # statistics\n",
    "        \n",
    "        \n",
    "        # average batch accuracy\n",
    "        acc = accuracy(predictions.detach(), label)\n",
    "        \n",
    "        # record our stats\n",
    "        epoch_loss += loss.detach()\n",
    "        epoch_acc += acc\n",
    "        \n",
    "    # NOTE: tense.item() unpacks Tensor item to a regular python object \n",
    "    # tense.tensor([1]).item() == 1\n",
    "        \n",
    "    # return average loss and acc of epoch\n",
    "    return epoch_loss.item() / len(iterator), epoch_acc / len(iterator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "        \n",
    "    # turn off grad tracking as we are only evaluation performance\n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for batch in iterator:\n",
    "\n",
    "            # extract input and label       \n",
    "            input = batch[0].view(-1,784).cuda()\n",
    "            label = batch[1].cuda()\n",
    "\n",
    "\n",
    "            # predictions.shape = (B, 10)\n",
    "            predictions = model(input)\n",
    "\n",
    "            # average batch loss \n",
    "            loss = criterion(predictions, label)\n",
    "\n",
    "            # average batch accuracy\n",
    "            acc = accuracy(predictions, label)\n",
    "\n",
    "            epoch_loss += loss\n",
    "            epoch_acc += acc\n",
    "        \n",
    "    return epoch_loss.item() / len(iterator), epoch_acc / len(iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# record time it takes to train and evaluate an epoch\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time # total time\n",
    "    elapsed_mins = int(elapsed_time / 60) # minutes\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60)) # seconds\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      "Epoch: 01 | Epoch Time: 0m 12s\n",
      "\tTrain Loss: 2.257 | Train Acc: 19.67%\n",
      "\t Val. Loss: 2.208 |  Val. Acc: 11.47%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 02 | Epoch Time: 0m 15s\n",
      "\tTrain Loss: 1.928 | Train Acc: 35.99%\n",
      "\t Val. Loss: 2.658 |  Val. Acc: 15.09%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 03 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.684 | Train Acc: 45.90%\n",
      "\t Val. Loss: 2.697 |  Val. Acc: 18.07%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 04 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.530 | Train Acc: 50.23%\n",
      "\t Val. Loss: 3.533 |  Val. Acc: 16.05%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 05 | Epoch Time: 0m 16s\n",
      "\tTrain Loss: 1.444 | Train Acc: 51.98%\n",
      "\t Val. Loss: 3.861 |  Val. Acc: 16.42%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 06 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.393 | Train Acc: 52.64%\n",
      "\t Val. Loss: 4.699 |  Val. Acc: 16.97%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 07 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.359 | Train Acc: 53.11%\n",
      "\t Val. Loss: 5.268 |  Val. Acc: 16.34%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 08 | Epoch Time: 0m 16s\n",
      "\tTrain Loss: 1.335 | Train Acc: 53.38%\n",
      "\t Val. Loss: 4.952 |  Val. Acc: 14.97%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 09 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.319 | Train Acc: 53.84%\n",
      "\t Val. Loss: 5.703 |  Val. Acc: 15.13%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 10 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.304 | Train Acc: 54.26%\n",
      "\t Val. Loss: 5.436 |  Val. Acc: 15.64%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 11 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.292 | Train Acc: 54.25%\n",
      "\t Val. Loss: 5.915 |  Val. Acc: 15.70%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 12 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.279 | Train Acc: 54.77%\n",
      "\t Val. Loss: 5.348 |  Val. Acc: 17.54%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 13 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.269 | Train Acc: 55.03%\n",
      "\t Val. Loss: 5.428 |  Val. Acc: 14.49%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 14 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.258 | Train Acc: 55.50%\n",
      "\t Val. Loss: 5.109 |  Val. Acc: 15.97%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 15 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.250 | Train Acc: 55.75%\n",
      "\t Val. Loss: 5.109 |  Val. Acc: 15.11%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 16 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.240 | Train Acc: 56.00%\n",
      "\t Val. Loss: 5.531 |  Val. Acc: 15.63%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 17 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.232 | Train Acc: 56.31%\n",
      "\t Val. Loss: 5.849 |  Val. Acc: 14.88%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 18 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.225 | Train Acc: 56.48%\n",
      "\t Val. Loss: 5.722 |  Val. Acc: 16.13%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 19 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.218 | Train Acc: 56.77%\n",
      "\t Val. Loss: 6.119 |  Val. Acc: 17.38%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 20 | Epoch Time: 0m 20s\n",
      "\tTrain Loss: 1.211 | Train Acc: 57.05%\n",
      "\t Val. Loss: 5.578 |  Val. Acc: 17.43%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 21 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.205 | Train Acc: 57.24%\n",
      "\t Val. Loss: 5.528 |  Val. Acc: 15.08%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 22 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.198 | Train Acc: 57.58%\n",
      "\t Val. Loss: 5.489 |  Val. Acc: 17.37%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 23 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.193 | Train Acc: 57.58%\n",
      "\t Val. Loss: 5.657 |  Val. Acc: 16.93%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 24 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.191 | Train Acc: 57.96%\n",
      "\t Val. Loss: 5.609 |  Val. Acc: 16.39%\n",
      "---------------------------------------------------------------------------\n",
      "Epoch: 25 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.182 | Train Acc: 58.17%\n",
      "\t Val. Loss: 5.460 |  Val. Acc: 15.82%\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 25\n",
    "\n",
    "# track statistics\n",
    "track_stats = {'activation': [],\n",
    "               'epoch': [],\n",
    "               'train_loss': [],\n",
    "              'train_acc': [],\n",
    "              'valid_loss':[],\n",
    "              'valid_acc':[]}\n",
    "\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "\n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss, train_acc = train(model, train_dl, optimizer, criterion)\n",
    "    valid_loss, valid_acc = evaluate(model, eval_dl, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    \n",
    "    # record operations\n",
    "    track_stats['activation'].append('ReLU')\n",
    "    track_stats['epoch'].append(epoch + 1)\n",
    "    track_stats['train_loss'].append(train_loss)\n",
    "    track_stats['train_acc'].append(train_acc)\n",
    "    track_stats['valid_loss'].append(valid_loss)\n",
    "    track_stats['valid_acc'].append(valid_acc)\n",
    "    \n",
    "    \n",
    "\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    # if this was our best performance, record model parameters\n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'best_linear_relu_params.pt')\n",
    "    \n",
    "    # print out stats\n",
    "    print('-'*75)\n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "From the above, we can tell that our model is severely suffering from overfitting by the gap between training and validation accuracy. However, to attain a better understanding, we will graph our recorded statistics and use HiPlot, a new graphing library by Facebook, to understand the overall patterns of our model\n",
    "\n",
    "**NOTE**: If you do not have HiPlot installed, go to their [github repo](https://github.com/facebookresearch/hiplot) to find latest installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save statistics\n",
    "# track_stats = torch.load('ReLU_stats.pt')\n",
    "#torch.save(track_stats, 'ReLU_stats.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activation</th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>1</td>\n",
       "      <td>2.256706</td>\n",
       "      <td>0.196678</td>\n",
       "      <td>2.207836</td>\n",
       "      <td>0.114715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>2</td>\n",
       "      <td>1.928205</td>\n",
       "      <td>0.359941</td>\n",
       "      <td>2.658292</td>\n",
       "      <td>0.150910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>3</td>\n",
       "      <td>1.684190</td>\n",
       "      <td>0.459005</td>\n",
       "      <td>2.697213</td>\n",
       "      <td>0.180676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>4</td>\n",
       "      <td>1.530393</td>\n",
       "      <td>0.502265</td>\n",
       "      <td>3.532650</td>\n",
       "      <td>0.160502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>5</td>\n",
       "      <td>1.443833</td>\n",
       "      <td>0.519773</td>\n",
       "      <td>3.861390</td>\n",
       "      <td>0.164161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>6</td>\n",
       "      <td>1.393192</td>\n",
       "      <td>0.526353</td>\n",
       "      <td>4.698839</td>\n",
       "      <td>0.169699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>7</td>\n",
       "      <td>1.358555</td>\n",
       "      <td>0.531100</td>\n",
       "      <td>5.268126</td>\n",
       "      <td>0.163370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>8</td>\n",
       "      <td>1.335125</td>\n",
       "      <td>0.533765</td>\n",
       "      <td>4.952165</td>\n",
       "      <td>0.149723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>9</td>\n",
       "      <td>1.318727</td>\n",
       "      <td>0.538430</td>\n",
       "      <td>5.703450</td>\n",
       "      <td>0.151305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>10</td>\n",
       "      <td>1.303947</td>\n",
       "      <td>0.542644</td>\n",
       "      <td>5.436379</td>\n",
       "      <td>0.156448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>11</td>\n",
       "      <td>1.291508</td>\n",
       "      <td>0.542494</td>\n",
       "      <td>5.915389</td>\n",
       "      <td>0.157041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>12</td>\n",
       "      <td>1.278763</td>\n",
       "      <td>0.547708</td>\n",
       "      <td>5.347536</td>\n",
       "      <td>0.175435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>13</td>\n",
       "      <td>1.269061</td>\n",
       "      <td>0.550323</td>\n",
       "      <td>5.427631</td>\n",
       "      <td>0.144877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>14</td>\n",
       "      <td>1.257962</td>\n",
       "      <td>0.554971</td>\n",
       "      <td>5.108685</td>\n",
       "      <td>0.159711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>15</td>\n",
       "      <td>1.249527</td>\n",
       "      <td>0.557536</td>\n",
       "      <td>5.108813</td>\n",
       "      <td>0.151108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>16</td>\n",
       "      <td>1.239964</td>\n",
       "      <td>0.559968</td>\n",
       "      <td>5.530833</td>\n",
       "      <td>0.156349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>17</td>\n",
       "      <td>1.232409</td>\n",
       "      <td>0.563083</td>\n",
       "      <td>5.849154</td>\n",
       "      <td>0.148833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>18</td>\n",
       "      <td>1.224834</td>\n",
       "      <td>0.564765</td>\n",
       "      <td>5.722316</td>\n",
       "      <td>0.161294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>19</td>\n",
       "      <td>1.218170</td>\n",
       "      <td>0.567697</td>\n",
       "      <td>6.119403</td>\n",
       "      <td>0.173754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>20</td>\n",
       "      <td>1.210809</td>\n",
       "      <td>0.570496</td>\n",
       "      <td>5.578406</td>\n",
       "      <td>0.174347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>21</td>\n",
       "      <td>1.204878</td>\n",
       "      <td>0.572378</td>\n",
       "      <td>5.527742</td>\n",
       "      <td>0.150811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>22</td>\n",
       "      <td>1.198037</td>\n",
       "      <td>0.575760</td>\n",
       "      <td>5.488918</td>\n",
       "      <td>0.173655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>23</td>\n",
       "      <td>1.193254</td>\n",
       "      <td>0.575776</td>\n",
       "      <td>5.657108</td>\n",
       "      <td>0.169304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>24</td>\n",
       "      <td>1.190567</td>\n",
       "      <td>0.579608</td>\n",
       "      <td>5.608899</td>\n",
       "      <td>0.163865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>25</td>\n",
       "      <td>1.182364</td>\n",
       "      <td>0.581690</td>\n",
       "      <td>5.459799</td>\n",
       "      <td>0.158228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   activation  epoch  train_loss  train_acc  valid_loss  valid_acc\n",
       "0        ReLU      1    2.256706   0.196678    2.207836   0.114715\n",
       "1        ReLU      2    1.928205   0.359941    2.658292   0.150910\n",
       "2        ReLU      3    1.684190   0.459005    2.697213   0.180676\n",
       "3        ReLU      4    1.530393   0.502265    3.532650   0.160502\n",
       "4        ReLU      5    1.443833   0.519773    3.861390   0.164161\n",
       "5        ReLU      6    1.393192   0.526353    4.698839   0.169699\n",
       "6        ReLU      7    1.358555   0.531100    5.268126   0.163370\n",
       "7        ReLU      8    1.335125   0.533765    4.952165   0.149723\n",
       "8        ReLU      9    1.318727   0.538430    5.703450   0.151305\n",
       "9        ReLU     10    1.303947   0.542644    5.436379   0.156448\n",
       "10       ReLU     11    1.291508   0.542494    5.915389   0.157041\n",
       "11       ReLU     12    1.278763   0.547708    5.347536   0.175435\n",
       "12       ReLU     13    1.269061   0.550323    5.427631   0.144877\n",
       "13       ReLU     14    1.257962   0.554971    5.108685   0.159711\n",
       "14       ReLU     15    1.249527   0.557536    5.108813   0.151108\n",
       "15       ReLU     16    1.239964   0.559968    5.530833   0.156349\n",
       "16       ReLU     17    1.232409   0.563083    5.849154   0.148833\n",
       "17       ReLU     18    1.224834   0.564765    5.722316   0.161294\n",
       "18       ReLU     19    1.218170   0.567697    6.119403   0.173754\n",
       "19       ReLU     20    1.210809   0.570496    5.578406   0.174347\n",
       "20       ReLU     21    1.204878   0.572378    5.527742   0.150811\n",
       "21       ReLU     22    1.198037   0.575760    5.488918   0.173655\n",
       "22       ReLU     23    1.193254   0.575776    5.657108   0.169304\n",
       "23       ReLU     24    1.190567   0.579608    5.608899   0.163865\n",
       "24       ReLU     25    1.182364   0.581690    5.459799   0.158228"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# format data \n",
    "import pandas as pd\n",
    "\n",
    "stats = pd.DataFrame(track_stats)\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare training vs validation statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x1296 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols = 2,figsize = (12,4))\n",
    "stats[['epoch','train_loss','valid_loss']].plot(x = 'epoch',ax=axes[0])\n",
    "axes[0].title.set_text('Training and Validation Loss')\n",
    "axes[0].set_ylabel('Loss')\n",
    "stats[['epoch','train_acc','valid_acc']].plot(x = 'epoch',ax = axes[1])\n",
    "axes[1].title.set_text('Training and Validation Accuracy')\n",
    "axes[1].set_ylabel('Accuracy')\n",
    "plt.tight_layout()\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, we can confidently conclude that our model is severely suffering from overfitting due to the large gap in loss and accuracy statistics.\n",
    "\n",
    "Now, let us use HiPlot to attain a more complete picture of our model's performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'activation': 'ReLU',\n",
       "  'epoch': 1,\n",
       "  'train_loss': 2.2567057985740937,\n",
       "  'train_acc': 0.1966784381663113,\n",
       "  'valid_loss': 2.20783610283574,\n",
       "  'valid_acc': 0.11471518987341772},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 2,\n",
       "  'train_loss': 1.9282052176339286,\n",
       "  'train_acc': 0.3599413646055437,\n",
       "  'valid_loss': 2.658291973645174,\n",
       "  'valid_acc': 0.15090981012658228},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 3,\n",
       "  'train_loss': 1.6841899164195762,\n",
       "  'train_acc': 0.459005197228145,\n",
       "  'valid_loss': 2.697213281559039,\n",
       "  'valid_acc': 0.18067642405063292},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 4,\n",
       "  'train_loss': 1.5303927748950559,\n",
       "  'train_acc': 0.5022654584221748,\n",
       "  'valid_loss': 3.5326503319076346,\n",
       "  'valid_acc': 0.16050237341772153},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 5,\n",
       "  'train_loss': 1.4438330806902986,\n",
       "  'train_acc': 0.5197727878464818,\n",
       "  'valid_loss': 3.8613895464547072,\n",
       "  'valid_acc': 0.16416139240506328},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 6,\n",
       "  'train_loss': 1.3931915998967217,\n",
       "  'train_acc': 0.5263526119402985,\n",
       "  'valid_loss': 4.698839404914953,\n",
       "  'valid_acc': 0.16969936708860758},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 7,\n",
       "  'train_loss': 1.358555124766791,\n",
       "  'train_acc': 0.5311000799573561,\n",
       "  'valid_loss': 5.268125509913964,\n",
       "  'valid_acc': 0.16337025316455697},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 8,\n",
       "  'train_loss': 1.3351253029634196,\n",
       "  'train_acc': 0.5337653251599147,\n",
       "  'valid_loss': 4.9521654346321204,\n",
       "  'valid_acc': 0.14972310126582278},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 9,\n",
       "  'train_loss': 1.3187274078824627,\n",
       "  'train_acc': 0.5384295042643923,\n",
       "  'valid_loss': 5.703450263301028,\n",
       "  'valid_acc': 0.15130537974683544},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 10,\n",
       "  'train_loss': 1.3039471396505198,\n",
       "  'train_acc': 0.5426439232409381,\n",
       "  'valid_loss': 5.4363789618769776,\n",
       "  'valid_acc': 0.15644778481012658},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 11,\n",
       "  'train_loss': 1.2915082008345549,\n",
       "  'train_acc': 0.5424940031982942,\n",
       "  'valid_loss': 5.915389435200751,\n",
       "  'valid_acc': 0.15704113924050633},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 12,\n",
       "  'train_loss': 1.2787634355427107,\n",
       "  'train_acc': 0.5477078891257996,\n",
       "  'valid_loss': 5.347536111179786,\n",
       "  'valid_acc': 0.17543512658227847},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 13,\n",
       "  'train_loss': 1.269061058060701,\n",
       "  'train_acc': 0.5503231609808102,\n",
       "  'valid_loss': 5.427630847013449,\n",
       "  'valid_acc': 0.14487737341772153},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 14,\n",
       "  'train_loss': 1.257962289903718,\n",
       "  'train_acc': 0.5549706823027718,\n",
       "  'valid_loss': 5.108685457253758,\n",
       "  'valid_acc': 0.1597112341772152},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 15,\n",
       "  'train_loss': 1.2495273354211087,\n",
       "  'train_acc': 0.5575359808102346,\n",
       "  'valid_loss': 5.108813322043117,\n",
       "  'valid_acc': 0.15110759493670886},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 16,\n",
       "  'train_loss': 1.2399636860341152,\n",
       "  'train_acc': 0.5599680170575693,\n",
       "  'valid_loss': 5.530833183964597,\n",
       "  'valid_acc': 0.15634889240506328},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 17,\n",
       "  'train_loss': 1.2324086008295576,\n",
       "  'train_acc': 0.5630830223880597,\n",
       "  'valid_loss': 5.8491543154173256,\n",
       "  'valid_acc': 0.14883306962025317},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 18,\n",
       "  'train_loss': 1.224833994786114,\n",
       "  'train_acc': 0.5647654584221748,\n",
       "  'valid_loss': 5.7223159210591374,\n",
       "  'valid_acc': 0.16129351265822786},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 19,\n",
       "  'train_loss': 1.2181698406683101,\n",
       "  'train_acc': 0.5676972281449894,\n",
       "  'valid_loss': 6.119402535354035,\n",
       "  'valid_acc': 0.17375395569620253},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 20,\n",
       "  'train_loss': 1.210808662463353,\n",
       "  'train_acc': 0.5704957356076759,\n",
       "  'valid_loss': 5.5784058389784414,\n",
       "  'valid_acc': 0.17434731012658228},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 21,\n",
       "  'train_loss': 1.2048782316098081,\n",
       "  'train_acc': 0.572378065031983,\n",
       "  'valid_loss': 5.527741637410997,\n",
       "  'valid_acc': 0.150810917721519},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 22,\n",
       "  'train_loss': 1.1980372186916977,\n",
       "  'train_acc': 0.5757595948827292,\n",
       "  'valid_loss': 5.488918256156052,\n",
       "  'valid_acc': 0.17365506329113925},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 23,\n",
       "  'train_loss': 1.193253962470016,\n",
       "  'train_acc': 0.5757762526652452,\n",
       "  'valid_loss': 5.657107968873616,\n",
       "  'valid_acc': 0.16930379746835442},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 24,\n",
       "  'train_loss': 1.190566984066831,\n",
       "  'train_acc': 0.5796075426439232,\n",
       "  'valid_loss': 5.608898694002176,\n",
       "  'valid_acc': 0.16386471518987342},\n",
       " {'activation': 'ReLU',\n",
       "  'epoch': 25,\n",
       "  'train_loss': 1.1823643275669642,\n",
       "  'train_acc': 0.5816897654584222,\n",
       "  'valid_loss': 5.459799464744858,\n",
       "  'valid_acc': 0.15822784810126583}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# organize data to hiplot format\n",
    "\n",
    "data = []\n",
    "for row in stats.iterrows():\n",
    "    data.append(row[1].to_dict())\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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e<=r?o=w(0,e):t<=n?((o=w(0,r-2)).push(\"ellipsis\"),o.push(e-1)):t>=e-1-n?((o=w(e-(r-2),e)).splice(0,0,\"ellipsis\"),o.splice(0,0,0)):((o=w(t-n+2,t+n-1)).push(\"ellipsis\"),o.push(e-1),o.splice(0,0,\"ellipsis\"),o.splice(0,0,0)),o.DT_el=\"span\",o}t.extend(Te,{simple:function(t,e){return[\"previous\",\"next\"]},full:function(t,e){return[\"first\",\"previous\",\"next\",\"last\"]},numbers:function(t,e){return[Ae(t,e)]},simple_numbers:function(t,e){return[\"previous\",Ae(t,e),\"next\"]},full_numbers:function(t,e){return[\"first\",\"previous\",Ae(t,e),\"next\",\"last\"]},first_last_numbers:function(t,e){return[\"first\",Ae(t,e),\"last\"]},_numbers:Ae,numbers_length:7}),t.extend(!0,c.ext.renderer,{pageButton:{_:function(e,n,s,a,i,c){var l,u,p,d=e.oClasses,f=e.oLanguage.oPaginate,h=e.oLanguage.oAria.paginate||{},m=0,b=function(o,r){var n,a,p,g,_=d.sPageButtonDisabled,v=function(t){Pt(e,t.data.action,!0)};for(n=0,a=r.length;n<a;n++)if(p=r[n],t.isArray(p)){var y=t(\"<\"+(p.DT_el||\"div\")+\"/>\").appendTo(o);b(y,p)}else{switch(l=null,u=p,g=e.iTabIndex,p){case\"ellipsis\":o.append('<span class=\"ellipsis\">&#x2026;</span>');break;case\"first\":l=f.sFirst,0===i&&(g=-1,u+=\" \"+_);break;case\"previous\":l=f.sPrevious,0===i&&(g=-1,u+=\" \"+_);break;case\"next\":l=f.sNext,i===c-1&&(g=-1,u+=\" \"+_);break;case\"last\":l=f.sLast,i===c-1&&(g=-1,u+=\" \"+_);break;default:l=p+1,u=i===p?d.sPageButtonActive:\"\"}null!==l&&(le(t(\"<a>\",{class:d.sPageButton+\" \"+u,\"aria-controls\":e.sTableId,\"aria-label\":h[p],\"data-dt-idx\":m,tabindex:g,id:0===s&&\"string\"==typeof p?e.sTableId+\"_\"+p:null}).html(l).appendTo(o),{action:p},v),m++)}};try{p=t(n).find(o.activeElement).data(\"dt-idx\")}catch(t){}b(t(n).empty(),a),p!==r&&t(n).find(\"[data-dt-idx=\"+p+\"]\").focus()}}}),t.extend(c.ext.type.detect,[function(t,e){var o=e.oLanguage.sDecimal;return _(t,o)?\"num\"+o:null},function(t,e){if(t&&!(t instanceof Date)&&!d.test(t))return null;var o=Date.parse(t);return null!==o&&!isNaN(o)||m(t)?\"date\":null},function(t,e){var o=e.oLanguage.sDecimal;return _(t,o,!0)?\"num-fmt\"+o:null},function(t,e){var o=e.oLanguage.sDecimal;return v(t,o)?\"html-num\"+o:null},function(t,e){var o=e.oLanguage.sDecimal;return v(t,o,!0)?\"html-num-fmt\"+o:null},function(t,e){return m(t)||\"string\"==typeof t&&-1!==t.indexOf(\"<\")?\"html\":null}]),t.extend(c.ext.type.search,{html:function(t){return m(t)?t:\"string\"==typeof t?t.replace(u,\" \").replace(p,\"\"):\"\"},string:function(t){return m(t)?t:\"string\"==typeof t?t.replace(u,\" \"):t}});var Se=function(t,e,o,r){return 0===t||t&&\"-\"!==t?(e&&(t=g(t,e)),t.replace&&(o&&(t=t.replace(o,\"\")),r&&(t=t.replace(r,\"\"))),1*t):-1/0};function Fe(e){t.each({num:function(t){return Se(t,e)},\"num-fmt\":function(t){return Se(t,e,h)},\"html-num\":function(t){return Se(t,e,p)},\"html-num-fmt\":function(t){return Se(t,e,p,h)}},(function(t,o){n.type.order[t+e+\"-pre\"]=o,t.match(/^html\\-/)&&(n.type.search[t+e]=n.type.search.html)}))}t.extend(n.type.order,{\"date-pre\":function(t){var e=Date.parse(t);return isNaN(e)?-1/0:e},\"html-pre\":function(t){return m(t)?\"\":t.replace?t.replace(/<.*?>/g,\"\").toLowerCase():t+\"\"},\"string-pre\":function(t){return m(t)?\"\":\"string\"==typeof t?t.toLowerCase():t.toString?t.toString():\"\"},\"string-asc\":function(t,e){return t<e?-1:t>e?1:0},\"string-desc\":function(t,e){return t<e?1:t>e?-1:0}}),Fe(\"\"),t.extend(!0,c.ext.renderer,{header:{_:function(e,o,r,n){t(e.nTable).on(\"order.dt.DT\",(function(t,s,a,i){if(e===s){var c=r.idx;o.removeClass(r.sSortingClass+\" \"+n.sSortAsc+\" \"+n.sSortDesc).addClass(\"asc\"==i[c]?n.sSortAsc:\"desc\"==i[c]?n.sSortDesc:r.sSortingClass)}}))},jqueryui:function(e,o,r,n){t(\"<div/>\").addClass(n.sSortJUIWrapper).append(o.contents()).append(t(\"<span/>\").addClass(n.sSortIcon+\" \"+r.sSortingClassJUI)).appendTo(o),t(e.nTable).on(\"order.dt.DT\",(function(t,s,a,i){if(e===s){var c=r.idx;o.removeClass(n.sSortAsc+\" \"+n.sSortDesc).addClass(\"asc\"==i[c]?n.sSortAsc:\"desc\"==i[c]?n.sSortDesc:r.sSortingClass),o.find(\"span.\"+n.sSortIcon).removeClass(n.sSortJUIAsc+\" \"+n.sSortJUIDesc+\" \"+n.sSortJUI+\" \"+n.sSortJUIAscAllowed+\" \"+n.sSortJUIDescAllowed).addClass(\"asc\"==i[c]?n.sSortJUIAsc:\"desc\"==i[c]?n.sSortJUIDesc:r.sSortingClassJUI)}}))}}});var Me=function(t){return\"string\"==typeof t?t.replace(/</g,\"&lt;\").replace(/>/g,\"&gt;\").replace(/\"/g,\"&quot;\"):t};function Ne(t){return function(){var e=[se(this[c.ext.iApiIndex])].concat(Array.prototype.slice.call(arguments));return c.ext.internal[t].apply(this,e)}}return c.render={number:function(t,e,o,r,n){return{display:function(s){if(\"number\"!=typeof s&&\"string\"!=typeof s)return s;var a=s<0?\"-\":\"\",i=parseFloat(s);if(isNaN(i))return Me(s);i=i.toFixed(o),s=Math.abs(i);var c=parseInt(s,10),l=o?e+(s-c).toFixed(o).substring(2):\"\";return a+(r||\"\")+c.toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g,t)+l+(n||\"\")}}},text:function(){return{display:Me,filter:Me}}},t.extend(c.ext.internal,{_fnExternApiFunc:Ne,_fnBuildAjax:dt,_fnAjaxUpdate:ft,_fnAjaxParameters:ht,_fnAjaxUpdateDraw:mt,_fnAjaxDataSrc:bt,_fnAddColumn:L,_fnColumnOptions:I,_fnAdjustColumnSizing:B,_fnVisibleToColumnIndex:P,_fnColumnIndexToVisible:R,_fnVisbleColumns:z,_fnGetColumns:O,_fnColumnTypes:H,_fnApplyColumnDefs:U,_fnHungarianMap:E,_fnCamelToHungarian:T,_fnLanguageCompat:A,_fnBrowserDetect:N,_fnAddData:W,_fnAddTr:q,_fnNodeToDataIndex:function(t,e){return e._DT_RowIndex!==r?e._DT_RowIndex:null},_fnNodeToColumnIndex:function(e,o,r){return t.inArray(r,e.aoData[o].anCells)},_fnGetCellData:$,_fnSetCellData:Y,_fnSplitObjNotation:G,_fnGetObjectDataFn:Q,_fnSetObjectDataFn:K,_fnGetDataMaster:J,_fnClearTable:Z,_fnDeleteIndex:tt,_fnInvalidate:et,_fnGetRowElements:ot,_fnCreateTr:rt,_fnBuildHead:st,_fnDrawHead:at,_fnDraw:it,_fnReDraw:ct,_fnAddOptionsHtml:lt,_fnDetectHeader:ut,_fnGetUniqueThs:pt,_fnFeatureHtmlFilter:gt,_fnFilterComplete:_t,_fnFilterCustom:vt,_fnFilterColumn:yt,_fnFilter:xt,_fnFilterCreateSearch:wt,_fnEscapeRegex:Dt,_fnFilterData:Et,_fnFeatureHtmlInfo:St,_fnUpdateInfo:Ft,_fnInfoMacros:Mt,_fnInitialise:Nt,_fnInitComplete:jt,_fnLengthChange:Lt,_fnFeatureHtmlLength:It,_fnFeatureHtmlPaginate:Bt,_fnPageChange:Pt,_fnFeatureHtmlProcessing:Rt,_fnProcessingDisplay:zt,_fnFeatureHtmlTable:Ot,_fnScrollDraw:Ht,_fnApplyToChildren:Ut,_fnCalculateColumnWidths:qt,_fnThrottle:$t,_fnConvertToWidth:Yt,_fnGetWidestNode:Vt,_fnGetMaxLenString:Xt,_fnStringToCss:Gt,_fnSortFlatten:Qt,_fnSort:Kt,_fnSortAria:Jt,_fnSortListener:Zt,_fnSortAttachListener:te,_fnSortingClasses:ee,_fnSortData:oe,_fnSaveState:re,_fnLoadState:ne,_fnSettingsFromNode:se,_fnLog:ae,_fnMap:ie,_fnBindAction:le,_fnCallbackReg:ue,_fnCallbackFire:pe,_fnLengthOverflow:de,_fnRenderer:fe,_fnDataSource:he,_fnRowAttributes:nt,_fnExtend:ce,_fnCalculateEnd:function(){}}),t.fn.dataTable=c,c.$=t,t.fn.dataTableSettings=c.settings,t.fn.dataTableExt=c.ext,t.fn.DataTable=function(e){return t(this).dataTable(e).api()},t.each(c,(function(e,o){t.fn.DataTable[e]=o})),t.fn.dataTable}))},function(t,e,o){\"use strict\";o.d(e,\"b\",(function(){return r})),o.d(e,\"a\",(function(){return n}));class r{constructor(t){this.params={},this.prefix=\"\"==t?\"\":t+\".\"}get(t,e){return this._get(this.prefix+t,e)}set(t,e){this._set(this.prefix+t,e)}_get(t,e){if(void 0!==this.params[t])return this.params[t];var o=new URLSearchParams(location.search).get(t);return null===o?e:JSON.parse(o)}_set(t,e){const o=new URLSearchParams(location.search);o.set(t,JSON.stringify(e));try{history.replaceState({},\"title\",\"?\"+o.toString())}catch(o){this.params[t]=e}}children(t){return new r(this.prefix+t)}}class n{constructor(t,e){this.params={},this.prefix=\"\"==t?\"\":t+\".\",this.params=e}get(t,e){var o=this.params[this.prefix+t];return void 0!==o?o:e}set(t,e){this.params[this.prefix+t]=e}clear(){Object.keys(this.params).filter(t=>t.startsWith(this.prefix)).forEach(t=>delete this.params[t])}children(t){return new n(this.prefix+t,this.params)}}},function(t,e,o){var r=o(8),n=o(94);\"string\"==typeof(n=n.__esModule?n.default:n)&&(n=[[t.i,n,\"\"]]);var s={insert:\"head\",singleton:!1},a=(r(t.i,n,s),n.locals?n.locals:{});t.exports=a},,,,function(t,e,o){var r;\n",
       "/*! DataTables Bootstrap 4 integration\n",
       " * ©2011-2017 SpryMedia Ltd - datatables.net/license\n",
       " */r=function(t,e,o,r){\"use strict\";var n=t.fn.dataTable;return t.extend(!0,n.defaults,{dom:\"<'row'<'col-sm-12 col-md-6'l><'col-sm-12 col-md-6'f>><'row'<'col-sm-12'tr>><'row'<'col-sm-12 col-md-5'i><'col-sm-12 col-md-7'p>>\",renderer:\"bootstrap\"}),t.extend(n.ext.classes,{sWrapper:\"dataTables_wrapper dt-bootstrap4\",sFilterInput:\"form-control form-control-sm\",sLengthSelect:\"custom-select custom-select-sm form-control form-control-sm\",sProcessing:\"dataTables_processing card\",sPageButton:\"paginate_button page-item\"}),n.ext.renderer.pageButton.bootstrap=function(e,s,a,i,c,l){var u,p,d,f=new n.Api(e),h=e.oClasses,m=e.oLanguage.oPaginate,b=e.oLanguage.oAria.paginate||{},g=0,_=function(o,r){var n,s,i,d,v=function(e){e.preventDefault(),t(e.currentTarget).hasClass(\"disabled\")||f.page()==e.data.action||f.page(e.data.action).draw(\"page\")};for(n=0,s=r.length;n<s;n++)if(d=r[n],t.isArray(d))_(o,d);else{switch(u=\"\",p=\"\",d){case\"ellipsis\":u=\"&#x2026;\",p=\"disabled\";break;case\"first\":u=m.sFirst,p=d+(c>0?\"\":\" disabled\");break;case\"previous\":u=m.sPrevious,p=d+(c>0?\"\":\" disabled\");break;case\"next\":u=m.sNext,p=d+(c<l-1?\"\":\" disabled\");break;case\"last\":u=m.sLast,p=d+(c<l-1?\"\":\" disabled\");break;default:u=d+1,p=c===d?\"active\":\"\"}u&&(i=t(\"<li>\",{class:h.sPageButton+\" \"+p,id:0===a&&\"string\"==typeof d?e.sTableId+\"_\"+d:null}).append(t(\"<a>\",{href:\"#\",\"aria-controls\":e.sTableId,\"aria-label\":b[d],\"data-dt-idx\":g,tabindex:e.iTabIndex,class:\"page-link\"}).html(u)).appendTo(o),e.oApi._fnBindAction(i,{action:d},v),g++)}};try{d=t(s).find(o.activeElement).data(\"dt-idx\")}catch(t){}_(t(s).empty().html('<ul class=\"pagination\"/>').children(\"ul\"),i),d!==r&&t(s).find(\"[data-dt-idx=\"+d+\"]\").focus()},n},t.exports=function(t,e){return t||(t=window),e&&e.fn.dataTable||(e=o(9)(t,e).$),r(e,0,t.document)}},,,,,function(t,e,o){var r;\n",
       "/*! ColReorder 1.5.2\n",
       " * ©2010-2019 SpryMedia Ltd - datatables.net/license\n",
       " */r=function(t,e,o,r){\"use strict\";var n=t.fn.dataTable;function s(t){for(var e=[],o=0,r=t.length;o<r;o++)e[t[o]]=o;return e}function a(t,e,o){var r=t.splice(e,1)[0];t.splice(o,0,r)}function i(t,e,o){for(var r=[],n=0,s=t.childNodes.length;n<s;n++)1==t.childNodes[n].nodeType&&r.push(t.childNodes[n]);var a=r[e];null!==o?t.insertBefore(a,r[o]):t.appendChild(a)}t.fn.dataTableExt.oApi.fnColReorder=function(e,o,n,c,l){var u,p,d,f,h,m,b,g=e.aoColumns.length,_=function(t,e,o){if(t[e]&&\"function\"!=typeof t[e]){var r=t[e].split(\".\"),n=r.shift();isNaN(1*n)||(t[e]=o[1*n]+\".\"+r.join(\".\"))}};if(o!=n)if(o<0||o>=g)this.oApi._fnLog(e,1,\"ColReorder 'from' index is out of bounds: \"+o);else if(n<0||n>=g)this.oApi._fnLog(e,1,\"ColReorder 'to' index is out of bounds: \"+n);else{var v=[];for(u=0,p=g;u<p;u++)v[u]=u;a(v,o,n);var y=s(v);for(u=0,p=e.aaSorting.length;u<p;u++)e.aaSorting[u][0]=y[e.aaSorting[u][0]];if(null!==e.aaSortingFixed)for(u=0,p=e.aaSortingFixed.length;u<p;u++)e.aaSortingFixed[u][0]=y[e.aaSortingFixed[u][0]];for(u=0,p=g;u<p;u++){for(d=0,f=(b=e.aoColumns[u]).aDataSort.length;d<f;d++)b.aDataSort[d]=y[b.aDataSort[d]];b.idx=y[b.idx]}for(t.each(e.aLastSort,(function(t,o){e.aLastSort[t].src=y[o.src]})),u=0,p=g;u<p;u++)\"number\"==typeof(b=e.aoColumns[u]).mData?b.mData=y[b.mData]:t.isPlainObject(b.mData)&&(_(b.mData,\"_\",y),_(b.mData,\"filter\",y),_(b.mData,\"sort\",y),_(b.mData,\"type\",y));if(e.aoColumns[o].bVisible){var x=this.oApi._fnColumnIndexToVisible(e,o),w=null;for(u=n<o?n:n+1;null===w&&u<g;)w=this.oApi._fnColumnIndexToVisible(e,u),u++;for(u=0,p=(m=e.nTHead.getElementsByTagName(\"tr\")).length;u<p;u++)i(m[u],x,w);if(null!==e.nTFoot)for(u=0,p=(m=e.nTFoot.getElementsByTagName(\"tr\")).length;u<p;u++)i(m[u],x,w);for(u=0,p=e.aoData.length;u<p;u++)null!==e.aoData[u].nTr&&i(e.aoData[u].nTr,x,w)}for(a(e.aoColumns,o,n),u=0,p=g;u<p;u++)e.oApi._fnColumnOptions(e,u,{});for(a(e.aoPreSearchCols,o,n),u=0,p=e.aoData.length;u<p;u++){var D=e.aoData[u],C=D.anCells;if(C)for(a(C,o,n),d=0,h=C.length;d<h;d++)C[d]&&C[d]._DT_CellIndex&&(C[d]._DT_CellIndex.column=d);\"dom\"!==D.src&&t.isArray(D._aData)&&a(D._aData,o,n)}for(u=0,p=e.aoHeader.length;u<p;u++)a(e.aoHeader[u],o,n);if(null!==e.aoFooter)for(u=0,p=e.aoFooter.length;u<p;u++)a(e.aoFooter[u],o,n);for((l||l===r)&&t.fn.dataTable.Api(e).rows().invalidate(),u=0,p=g;u<p;u++)t(e.aoColumns[u].nTh).off(\".DT\"),this.oApi._fnSortAttachListener(e,e.aoColumns[u].nTh,u);t(e.oInstance).trigger(\"column-reorder.dt\",[e,{from:o,to:n,mapping:y,drop:c,iFrom:o,iTo:n,aiInvertMapping:y}])}};var c=function(e,o){var r=new t.fn.dataTable.Api(e).settings()[0];if(r._colReorder)return r._colReorder;!0===o&&(o={});var n=t.fn.dataTable.camelToHungarian;return n&&(n(c.defaults,c.defaults,!0),n(c.defaults,o||{})),this.s={dt:null,enable:null,init:t.extend(!0,{},c.defaults,o),fixed:0,fixedRight:0,reorderCallback:null,mouse:{startX:-1,startY:-1,offsetX:-1,offsetY:-1,target:-1,targetIndex:-1,fromIndex:-1},aoTargets:[]},this.dom={drag:null,pointer:null},this.s.enable=this.s.init.bEnable,this.s.dt=r,this.s.dt._colReorder=this,this._fnConstruct(),this};return t.extend(c.prototype,{fnEnable:function(t){if(!1===t)return fnDisable();this.s.enable=!0},fnDisable:function(){this.s.enable=!1},fnReset:function(){return this._fnOrderColumns(this.fnOrder()),this},fnGetCurrentOrder:function(){return this.fnOrder()},fnOrder:function(e,o){var n,a,i=[],c=this.s.dt.aoColumns;if(e===r){for(n=0,a=c.length;n<a;n++)i.push(c[n]._ColReorder_iOrigCol);return i}if(o){var l=this.fnOrder();for(n=0,a=e.length;n<a;n++)i.push(t.inArray(e[n],l));e=i}return this._fnOrderColumns(s(e)),this},fnTranspose:function(e,o){o||(o=\"toCurrent\");var r=this.fnOrder(),n=this.s.dt.aoColumns;return\"toCurrent\"===o?t.isArray(e)?t.map(e,(function(e){return t.inArray(e,r)})):t.inArray(e,r):t.isArray(e)?t.map(e,(function(t){return n[t]._ColReorder_iOrigCol})):n[e]._ColReorder_iOrigCol},_fnConstruct:function(){var e,o=this,r=this.s.dt.aoColumns.length,n=this.s.dt.nTable;for(this.s.init.iFixedColumns&&(this.s.fixed=this.s.init.iFixedColumns),this.s.init.iFixedColumnsLeft&&(this.s.fixed=this.s.init.iFixedColumnsLeft),this.s.fixedRight=this.s.init.iFixedColumnsRight?this.s.init.iFixedColumnsRight:0,this.s.init.fnReorderCallback&&(this.s.reorderCallback=this.s.init.fnReorderCallback),e=0;e<r;e++)e>this.s.fixed-1&&e<r-this.s.fixedRight&&this._fnMouseListener(e,this.s.dt.aoColumns[e].nTh),this.s.dt.aoColumns[e]._ColReorder_iOrigCol=e;this.s.dt.oApi._fnCallbackReg(this.s.dt,\"aoStateSaveParams\",(function(t,e){o._fnStateSave.call(o,e)}),\"ColReorder_State\");var a=null;if(this.s.init.aiOrder&&(a=this.s.init.aiOrder.slice()),this.s.dt.oLoadedState&&void 0!==this.s.dt.oLoadedState.ColReorder&&this.s.dt.oLoadedState.ColReorder.length==this.s.dt.aoColumns.length&&(a=this.s.dt.oLoadedState.ColReorder),a)if(o.s.dt._bInitComplete){var i=s(a);o._fnOrderColumns.call(o,i)}else{var c=!1;t(n).on(\"draw.dt.colReorder\",(function(){if(!o.s.dt._bInitComplete&&!c){c=!0;var t=s(a);o._fnOrderColumns.call(o,t)}}))}else this._fnSetColumnIndexes();t(n).on(\"destroy.dt.colReorder\",(function(){t(n).off(\"destroy.dt.colReorder draw.dt.colReorder\"),t.each(o.s.dt.aoColumns,(function(e,o){t(o.nTh).off(\".ColReorder\"),t(o.nTh).removeAttr(\"data-column-index\")})),o.s.dt._colReorder=null,o.s=null}))},_fnOrderColumns:function(e){var o=!1;if(e.length==this.s.dt.aoColumns.length){for(var r=0,n=e.length;r<n;r++){var s=t.inArray(r,e);r!=s&&(a(e,s,r),this.s.dt.oInstance.fnColReorder(s,r,!0,!1),o=!0)}this._fnSetColumnIndexes(),o&&(t.fn.dataTable.Api(this.s.dt).rows().invalidate(),\"\"===this.s.dt.oScroll.sX&&\"\"===this.s.dt.oScroll.sY||this.s.dt.oInstance.fnAdjustColumnSizing(!1),this.s.dt.oInstance.oApi._fnSaveState(this.s.dt),null!==this.s.reorderCallback&&this.s.reorderCallback.call(this))}else this.s.dt.oInstance.oApi._fnLog(this.s.dt,1,\"ColReorder - array reorder does not match known number of columns. Skipping.\")},_fnStateSave:function(e){var o,r,n,s=this.s.dt.aoColumns;if(e.ColReorder=[],e.aaSorting){for(o=0;o<e.aaSorting.length;o++)e.aaSorting[o][0]=s[e.aaSorting[o][0]]._ColReorder_iOrigCol;var a=t.extend(!0,[],e.aoSearchCols);for(o=0,r=s.length;o<r;o++)n=s[o]._ColReorder_iOrigCol,e.aoSearchCols[n]=a[o],e.abVisCols[n]=s[o].bVisible,e.ColReorder.push(n)}else if(e.order){for(o=0;o<e.order.length;o++)e.order[o][0]=s[e.order[o][0]]._ColReorder_iOrigCol;var i=t.extend(!0,[],e.columns);for(o=0,r=s.length;o<r;o++)n=s[o]._ColReorder_iOrigCol,e.columns[n]=i[o],e.ColReorder.push(n)}},_fnMouseListener:function(e,o){var r=this;t(o).on(\"mousedown.ColReorder\",(function(t){r.s.enable&&1===t.which&&r._fnMouseDown.call(r,t,o)})).on(\"touchstart.ColReorder\",(function(t){r.s.enable&&r._fnMouseDown.call(r,t,o)}))},_fnMouseDown:function(e,n){var s=this,a=t(e.target).closest(\"th, td\").offset(),i=parseInt(t(n).attr(\"data-column-index\"),10);i!==r&&(this.s.mouse.startX=this._fnCursorPosition(e,\"pageX\"),this.s.mouse.startY=this._fnCursorPosition(e,\"pageY\"),this.s.mouse.offsetX=this._fnCursorPosition(e,\"pageX\")-a.left,this.s.mouse.offsetY=this._fnCursorPosition(e,\"pageY\")-a.top,this.s.mouse.target=this.s.dt.aoColumns[i].nTh,this.s.mouse.targetIndex=i,this.s.mouse.fromIndex=i,this._fnRegions(),t(o).on(\"mousemove.ColReorder touchmove.ColReorder\",(function(t){s._fnMouseMove.call(s,t)})).on(\"mouseup.ColReorder touchend.ColReorder\",(function(t){s._fnMouseUp.call(s,t)})))},_fnMouseMove:function(t){var e,o=this;if(null===this.dom.drag){if(Math.pow(Math.pow(this._fnCursorPosition(t,\"pageX\")-this.s.mouse.startX,2)+Math.pow(this._fnCursorPosition(t,\"pageY\")-this.s.mouse.startY,2),.5)<5)return;this._fnCreateDragNode()}this.dom.drag.css({left:this._fnCursorPosition(t,\"pageX\")-this.s.mouse.offsetX,top:this._fnCursorPosition(t,\"pageY\")-this.s.mouse.offsetY});for(var r=this.s.mouse.toIndex,n=this._fnCursorPosition(t,\"pageX\"),s=function(t){for(;t>=0;){if(--t<=0)return null;if(o.s.aoTargets[t+1].x!==o.s.aoTargets[t].x)return o.s.aoTargets[t]}},a=function(){for(var t=0;t<o.s.aoTargets.length-1;t++)if(o.s.aoTargets[t].x!==o.s.aoTargets[t+1].x)return o.s.aoTargets[t]},i=function(){for(var t=o.s.aoTargets.length-1;t>0;t--)if(o.s.aoTargets[t].x!==o.s.aoTargets[t-1].x)return o.s.aoTargets[t]},c=1;c<this.s.aoTargets.length;c++){var l=s(c);l||(l=a());var u=l.x+(this.s.aoTargets[c].x-l.x)/2;if(this._fnIsLtr()){if(n<u){e=l;break}}else if(n>u){e=l;break}}e?(this.dom.pointer.css(\"left\",e.x),this.s.mouse.toIndex=e.to):(this.dom.pointer.css(\"left\",i().x),this.s.mouse.toIndex=i().to),this.s.init.bRealtime&&r!==this.s.mouse.toIndex&&(this.s.dt.oInstance.fnColReorder(this.s.mouse.fromIndex,this.s.mouse.toIndex),this.s.mouse.fromIndex=this.s.mouse.toIndex,\"\"===this.s.dt.oScroll.sX&&\"\"===this.s.dt.oScroll.sY||this.s.dt.oInstance.fnAdjustColumnSizing(!1),this._fnRegions())},_fnMouseUp:function(e){t(o).off(\".ColReorder\"),null!==this.dom.drag&&(this.dom.drag.remove(),this.dom.pointer.remove(),this.dom.drag=null,this.dom.pointer=null,this.s.dt.oInstance.fnColReorder(this.s.mouse.fromIndex,this.s.mouse.toIndex,!0),this._fnSetColumnIndexes(),\"\"===this.s.dt.oScroll.sX&&\"\"===this.s.dt.oScroll.sY||this.s.dt.oInstance.fnAdjustColumnSizing(!1),this.s.dt.oInstance.oApi._fnSaveState(this.s.dt),null!==this.s.reorderCallback&&this.s.reorderCallback.call(this))},_fnRegions:function(){var e=this.s.dt.aoColumns,o=this._fnIsLtr();this.s.aoTargets.splice(0,this.s.aoTargets.length);var r=t(this.s.dt.nTable).offset().left,n=[];t.each(e,(function(e,s){if(s.bVisible&&\"none\"!==s.nTh.style.display){var a=t(s.nTh),i=a.offset().left;o&&(i+=a.outerWidth()),n.push({index:e,bound:i}),r=i}else n.push({index:e,bound:r})}));var s=n[0],a=t(e[s.index].nTh).outerWidth();this.s.aoTargets.push({to:0,x:s.bound-a});for(var i=0;i<n.length;i++){var c=n[i],l=c.index;c.index<this.s.mouse.fromIndex&&l++,this.s.aoTargets.push({to:l,x:c.bound})}0!==this.s.fixedRight&&this.s.aoTargets.splice(this.s.aoTargets.length-this.s.fixedRight),0!==this.s.fixed&&this.s.aoTargets.splice(0,this.s.fixed)},_fnCreateDragNode:function(){var e=\"\"!==this.s.dt.oScroll.sX||\"\"!==this.s.dt.oScroll.sY,o=this.s.dt.aoColumns[this.s.mouse.targetIndex].nTh,r=o.parentNode,n=r.parentNode,s=n.parentNode,a=t(o).clone();this.dom.drag=t(s.cloneNode(!1)).addClass(\"DTCR_clonedTable\").append(t(n.cloneNode(!1)).append(t(r.cloneNode(!1)).append(a[0]))).css({position:\"absolute\",top:0,left:0,width:t(o).outerWidth(),height:t(o).outerHeight()}).appendTo(\"body\"),this.dom.pointer=t(\"<div></div>\").addClass(\"DTCR_pointer\").css({position:\"absolute\",top:e?t(\"div.dataTables_scroll\",this.s.dt.nTableWrapper).offset().top:t(this.s.dt.nTable).offset().top,height:e?t(\"div.dataTables_scroll\",this.s.dt.nTableWrapper).height():t(this.s.dt.nTable).height()}).appendTo(\"body\")},_fnSetColumnIndexes:function(){t.each(this.s.dt.aoColumns,(function(e,o){t(o.nTh).attr(\"data-column-index\",e)}))},_fnCursorPosition:function(t,e){return-1!==t.type.indexOf(\"touch\")?t.originalEvent.touches[0][e]:t[e]},_fnIsLtr:function(){return\"rtl\"!==t(this.s.dt.nTable).css(\"direction\")}}),c.defaults={aiOrder:null,bEnable:!0,bRealtime:!0,iFixedColumnsLeft:0,iFixedColumnsRight:0,fnReorderCallback:null},c.version=\"1.5.2\",t.fn.dataTable.ColReorder=c,t.fn.DataTable.ColReorder=c,\"function\"==typeof t.fn.dataTable&&\"function\"==typeof t.fn.dataTableExt.fnVersionCheck&&t.fn.dataTableExt.fnVersionCheck(\"1.10.8\")?t.fn.dataTableExt.aoFeatures.push({fnInit:function(t){var e=t.oInstance;if(t._colReorder)e.oApi._fnLog(t,1,\"ColReorder attempted to initialise twice. Ignoring second\");else{var o=t.oInit,r=o.colReorder||o.oColReorder||{};new c(t,r)}return null},cFeature:\"R\",sFeature:\"ColReorder\"}):alert(\"Warning: ColReorder requires DataTables 1.10.8 or greater - www.datatables.net/download\"),t(o).on(\"preInit.dt.colReorder\",(function(e,o){if(\"dt\"===e.namespace){var r=o.oInit.colReorder,s=n.defaults.colReorder;if(r||s){var a=t.extend({},r,s);!1!==r&&new c(o,a)}}})),t.fn.dataTable.Api.register(\"colReorder.reset()\",(function(){return this.iterator(\"table\",(function(t){t._colReorder.fnReset()}))})),t.fn.dataTable.Api.register(\"colReorder.order()\",(function(t,e){return t?this.iterator(\"table\",(function(o){o._colReorder.fnOrder(t,e)})):this.context.length?this.context[0]._colReorder.fnOrder():null})),t.fn.dataTable.Api.register(\"colReorder.transpose()\",(function(t,e){return this.context.length&&this.context[0]._colReorder?this.context[0]._colReorder.fnTranspose(t,e):t})),t.fn.dataTable.Api.register(\"colReorder.move()\",(function(t,e,o,r){return this.context.length&&(this.context[0]._colReorder.s.dt.oInstance.fnColReorder(t,e,o,r),this.context[0]._colReorder._fnSetColumnIndexes()),this})),t.fn.dataTable.Api.register(\"colReorder.enable()\",(function(t){return this.iterator(\"table\",(function(e){e._colReorder&&e._colReorder.fnEnable(t)}))})),t.fn.dataTable.Api.register(\"colReorder.disable()\",(function(){return this.iterator(\"table\",(function(t){t._colReorder&&t._colReorder.fnDisable()}))})),c},t.exports=function(t,e){return t||(t=window),e&&e.fn.dataTable||(e=o(9)(t,e).$),r(e,0,t.document)}},function(t,e,o){var r;\n",
       "/*! Buttons for DataTables 1.6.1\n",
       " * ©2016-2019 SpryMedia Ltd - datatables.net/license\n",
       " */r=function(t,e,o,r){\"use strict\";var n,s=t.fn.dataTable,a=0,i=0,c=s.ext.buttons,l=function(e,o){if(!(this instanceof l))return function(t){return new l(t,e).container()};void 0===o&&(o={}),!0===o&&(o={}),t.isArray(o)&&(o={buttons:o}),this.c=t.extend(!0,{},l.defaults,o),o.buttons&&(this.c.buttons=o.buttons),this.s={dt:new s.Api(e),buttons:[],listenKeys:\"\",namespace:\"dtb\"+a++},this.dom={container:t(\"<\"+this.c.dom.container.tag+\"/>\").addClass(this.c.dom.container.className)},this._constructor()};t.extend(l.prototype,{action:function(t,e){var o=this._nodeToButton(t);return e===r?o.conf.action:(o.conf.action=e,this)},active:function(e,o){var n=this._nodeToButton(e),s=this.c.dom.button.active,a=t(n.node);return o===r?a.hasClass(s):(a.toggleClass(s,o===r||o),this)},add:function(t,e){var o=this.s.buttons;if(\"string\"==typeof e){for(var n=e.split(\"-\"),s=this.s,a=0,i=n.length-1;a<i;a++)s=s.buttons[1*n[a]];o=s.buttons,e=1*n[n.length-1]}return this._expandButton(o,t,s!==r,e),this._draw(),this},container:function(){return this.dom.container},disable:function(e){var o=this._nodeToButton(e);return t(o.node).addClass(this.c.dom.button.disabled),this},destroy:function(){t(\"body\").off(\"keyup.\"+this.s.namespace);var e,o,r=this.s.buttons.slice();for(e=0,o=r.length;e<o;e++)this.remove(r[e].node);this.dom.container.remove();var n=this.s.dt.settings()[0];for(e=0,o=n.length;e<o;e++)if(n.inst===this){n.splice(e,1);break}return this},enable:function(e,o){if(!1===o)return this.disable(e);var r=this._nodeToButton(e);return t(r.node).removeClass(this.c.dom.button.disabled),this},name:function(){return this.c.name},node:function(e){if(!e)return this.dom.container;var o=this._nodeToButton(e);return t(o.node)},processing:function(e,o){var n=this.s.dt,s=this._nodeToButton(e);return o===r?t(s.node).hasClass(\"processing\"):(t(s.node).toggleClass(\"processing\",o),t(n.table().node()).triggerHandler(\"buttons-processing.dt\",[o,n.button(e),n,t(e),s.conf]),this)},remove:function(e){var o=this._nodeToButton(e),r=this._nodeToHost(e),n=this.s.dt;if(o.buttons.length)for(var s=o.buttons.length-1;s>=0;s--)this.remove(o.buttons[s].node);o.conf.destroy&&o.conf.destroy.call(n.button(e),n,t(e),o.conf),this._removeKey(o.conf),t(o.node).remove();var a=t.inArray(o,r);return r.splice(a,1),this},text:function(e,o){var n=this._nodeToButton(e),s=this.c.dom.collection.buttonLiner,a=n.inCollection&&s&&s.tag?s.tag:this.c.dom.buttonLiner.tag,i=this.s.dt,c=t(n.node),l=function(t){return\"function\"==typeof t?t(i,c,n.conf):t};return o===r?l(n.conf.text):(n.conf.text=o,a?c.children(a).html(l(o)):c.html(l(o)),this)},_constructor:function(){var e=this,r=this.s.dt,n=r.settings()[0],s=this.c.buttons;n._buttons||(n._buttons=[]),n._buttons.push({inst:this,name:this.c.name});for(var a=0,i=s.length;a<i;a++)this.add(s[a]);r.on(\"destroy\",(function(t,o){o===n&&e.destroy()})),t(\"body\").on(\"keyup.\"+this.s.namespace,(function(t){if(!o.activeElement||o.activeElement===o.body){var r=String.fromCharCode(t.keyCode).toLowerCase();-1!==e.s.listenKeys.toLowerCase().indexOf(r)&&e._keypress(r,t)}}))},_addKey:function(e){e.key&&(this.s.listenKeys+=t.isPlainObject(e.key)?e.key.key:e.key)},_draw:function(t,e){t||(t=this.dom.container,e=this.s.buttons),t.children().detach();for(var o=0,r=e.length;o<r;o++)t.append(e[o].inserter),t.append(\" \"),e[o].buttons&&e[o].buttons.length&&this._draw(e[o].collection,e[o].buttons)},_expandButton:function(e,o,n,s){for(var a=this.s.dt,i=t.isArray(o)?o:[o],c=0,l=i.length;c<l;c++){var u=this._resolveExtends(i[c]);if(u)if(t.isArray(u))this._expandButton(e,u,n,s);else{var p=this._buildButton(u,n);p&&(s!==r?(e.splice(s,0,p),s++):e.push(p),p.conf.buttons&&(p.collection=t(\"<\"+this.c.dom.collection.tag+\"/>\"),p.conf._collection=p.collection,this._expandButton(p.buttons,p.conf.buttons,!0,s)),u.init&&u.init.call(a.button(p.node),a,t(p.node),u))}}},_buildButton:function(e,o){var n=this.c.dom.button,s=this.c.dom.buttonLiner,a=this.c.dom.collection,c=this.s.dt,l=function(t){return\"function\"==typeof t?t(c,f,e):t};if(o&&a.button&&(n=a.button),o&&a.buttonLiner&&(s=a.buttonLiner),e.available&&!e.available(c,e))return!1;var u=function(e,o,r,n){n.action.call(o.button(r),e,o,r,n),t(o.table().node()).triggerHandler(\"buttons-action.dt\",[o.button(r),o,r,n])},p=e.tag||n.tag,d=e.clickBlurs===r||e.clickBlurs,f=t(\"<\"+p+\"/>\").addClass(n.className).attr(\"tabindex\",this.s.dt.settings()[0].iTabIndex).attr(\"aria-controls\",this.s.dt.table().node().id).on(\"click.dtb\",(function(t){t.preventDefault(),!f.hasClass(n.disabled)&&e.action&&u(t,c,f,e),d&&f.blur()})).on(\"keyup.dtb\",(function(t){13===t.keyCode&&!f.hasClass(n.disabled)&&e.action&&u(t,c,f,e)}));if(\"a\"===p.toLowerCase()&&f.attr(\"href\",\"#\"),\"button\"===p.toLowerCase()&&f.attr(\"type\",\"button\"),s.tag){var h=t(\"<\"+s.tag+\"/>\").html(l(e.text)).addClass(s.className);\"a\"===s.tag.toLowerCase()&&h.attr(\"href\",\"#\"),f.append(h)}else f.html(l(e.text));!1===e.enabled&&f.addClass(n.disabled),e.className&&f.addClass(e.className),e.titleAttr&&f.attr(\"title\",l(e.titleAttr)),e.attr&&f.attr(e.attr),e.namespace||(e.namespace=\".dt-button-\"+i++);var m,b=this.c.dom.buttonContainer;return m=b&&b.tag?t(\"<\"+b.tag+\"/>\").addClass(b.className).append(f):f,this._addKey(e),this.c.buttonCreated&&(m=this.c.buttonCreated(e,m)),{conf:e,node:f.get(0),inserter:m,buttons:[],inCollection:o,collection:null}},_nodeToButton:function(t,e){e||(e=this.s.buttons);for(var o=0,r=e.length;o<r;o++){if(e[o].node===t)return e[o];if(e[o].buttons.length){var n=this._nodeToButton(t,e[o].buttons);if(n)return n}}},_nodeToHost:function(t,e){e||(e=this.s.buttons);for(var o=0,r=e.length;o<r;o++){if(e[o].node===t)return e;if(e[o].buttons.length){var n=this._nodeToHost(t,e[o].buttons);if(n)return n}}},_keypress:function(e,o){if(!o._buttonsHandled){var r=function(r,n){if(r.key)if(r.key===e)o._buttonsHandled=!0,t(n).click();else if(t.isPlainObject(r.key)){if(r.key.key!==e)return;if(r.key.shiftKey&&!o.shiftKey)return;if(r.key.altKey&&!o.altKey)return;if(r.key.ctrlKey&&!o.ctrlKey)return;if(r.key.metaKey&&!o.metaKey)return;o._buttonsHandled=!0,t(n).click()}},n=function(t){for(var e=0,o=t.length;e<o;e++)r(t[e].conf,t[e].node),t[e].buttons.length&&n(t[e].buttons)};n(this.s.buttons)}},_removeKey:function(e){if(e.key){var o=t.isPlainObject(e.key)?e.key.key:e.key,r=this.s.listenKeys.split(\"\"),n=t.inArray(o,r);r.splice(n,1),this.s.listenKeys=r.join(\"\")}},_resolveExtends:function(e){var o,n,s=this.s.dt,a=function(o){for(var n=0;!t.isPlainObject(o)&&!t.isArray(o);){if(o===r)return;if(\"function\"==typeof o){if(!(o=o(s,e)))return!1}else if(\"string\"==typeof o){if(!c[o])throw\"Unknown button type: \"+o;o=c[o]}if(++n>30)throw\"Buttons: Too many iterations\"}return t.isArray(o)?o:t.extend({},o)};for(e=a(e);e&&e.extend;){if(!c[e.extend])throw\"Cannot extend unknown button type: \"+e.extend;var i=a(c[e.extend]);if(t.isArray(i))return i;if(!i)return!1;var l=i.className;e=t.extend({},i,e),l&&e.className!==l&&(e.className=l+\" \"+e.className);var u=e.postfixButtons;if(u){for(e.buttons||(e.buttons=[]),o=0,n=u.length;o<n;o++)e.buttons.push(u[o]);e.postfixButtons=null}var p=e.prefixButtons;if(p){for(e.buttons||(e.buttons=[]),o=0,n=p.length;o<n;o++)e.buttons.splice(o,0,p[o]);e.prefixButtons=null}e.extend=i.extend}return e},_popover:function(r,n,s){var a=n,i=this.c,c=t.extend({align:\"button-left\",autoClose:!1,background:!0,backgroundClassName:\"dt-button-background\",contentClassName:i.dom.collection.className,collectionLayout:\"\",collectionTitle:\"\",dropup:!1,fade:400,rightAlignClassName:\"dt-button-right\",tag:i.dom.collection.tag},s),u=n.node(),p=function(){t(\".dt-button-collection\").stop().fadeOut(c.fade,(function(){t(this).detach()})),t(a.buttons('[aria-haspopup=\"true\"][aria-expanded=\"true\"]').nodes()).attr(\"aria-expanded\",\"false\"),t(\"div.dt-button-background\").off(\"click.dtb-collection\"),l.background(!1,c.backgroundClassName,c.fade,u),t(\"body\").off(\".dtb-collection\"),a.off(\"buttons-action.b-internal\")};!1===r&&p();var d=t(a.buttons('[aria-haspopup=\"true\"][aria-expanded=\"true\"]').nodes());d.length&&(u=d.eq(0),p());var 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       " * Copyright JS Foundation and other contributors\n",
       " * Released under the MIT license\n",
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e=t.clientY-this.m_pos;if(this.m_pos=t.clientY,0!=e){var o=this.state.internalHeight+e;this.setState({height:Math.max(this.props.minHeight,o),internalHeight:o,position:t.clientY})}}.bind(this),this.onMouseUp=function(t){null!=this.m_pos&&(this.m_pos=null,document.removeEventListener(\"mousemove\",this.onMouseMove,!1))}.bind(this),this.onWindowResize=function(){this.div_ref.current&&this.setState({width:this.div_ref.current.offsetWidth})}.bind(this),this.state={width:0,height:this.props.initialHeight,internalHeight:this.props.initialHeight},this.onWindowResizeDebounced=c.a.debounce(this.onWindowResize.bind(this),100)}componentDidMount(){var t=a()(this.div_ref.current);t.on(\"mousedown\",function(e){e.offsetY>t.height()-this.props.borderSize&&(this.m_pos=e.clientY,document.addEventListener(\"mousemove\",this.onMouseMove,!1))}.bind(this)),document.addEventListener(\"mouseup\",this.onMouseUp),a()(window).on(\"resize\",this.onWindowResizeDebounced),this.setState({width:this.div_ref.current.parentElement.offsetWidth})}componentDidUpdate(t,e){e.height==this.state.height&&e.width==this.state.width||this.props.onResize(this.state.height,this.state.width)}componentWillUnmount(){document.removeEventListener(\"mousemove\",this.onMouseMove,!1),document.removeEventListener(\"mouseup\",this.onMouseUp),a()(window).off(\"resize\",this.onWindowResizeDebounced)}render(){return u.a.createElement(\"div\",{ref:this.div_ref,style:{height:this.state.height},className:Tl.a.resizableH},this.props.children)}}Al.defaultProps={borderSize:4,minHeight:50};class Sl extends u.a.Component{constructor(t){super(t),this.on_resize=null,this.m=[75,0,10,0],this.dimensions_dom=null,this.render_speed=10,this.animloop=null,this.root_ref=u.a.createRef(),this.foreground_ref=u.a.createRef(),this.highlighted_ref=u.a.createRef(),this.svg_ref=u.a.createRef(),this.svgg_ref=u.a.createRef(),this.yscale={},this.d3brush=An(hn),this.position=function(t){return this.state.dragging&&t==this.state.dragging.col?this.state.dragging.pos:this.xscale(t)}.bind(this),this.update_ticks=function(t,e){var o=Re(this.root_ref.current),r=this;if(t){var n=Re(this.svg_ref.current).selectAll(\".\"+L.a.brush).filter((function(e){return e==t}));this.d3brush.move(n,e)}else Re(this.svg_ref.current).selectAll(\".\"+L.a.brush).each((function(t){Re(this).call(r.d3brush)}));o.selectAll(\".\"+L.a.axis+\" g\").style(\"display\",null),o.selectAll(\".background\").style(\"visibility\",null),o.selectAll(\".\"+L.a.axis).each((function(t,e){Re(this).selectAll(\"line\").style(\"display\",\"none\"),Re(this).transition().duration(720).call(r.axis.scale(r.yscale[t])),Re(this).selectAll(\"line\").transition().delay(800).style(\"display\",null),Re(this).selectAll(\"text\").style(\"font-weight\",null).style(\"font-size\",null).style(\"display\",null)})),r.setState((function(t){return{brush_count:t.brush_count+1}}))}.bind(this),this.paths=function(t,e,o){var r=this,n=t.length,s=0,a=ot([2/Math.pow(n,.3),1]),i=(new Date).getTime(),l=c.a.shuffle(t);this.props.rows.rendered.set([]),e.clearRect(0,0,this.w+1,this.h+1),this.animloop&&this.animloop.stop(),this.animloop=Zo((function(){if(s>=n||o<r.state.brush_count)return!0;var t=ot([s+r.render_speed,n]);!function(t,o,n,s){var a=t.slice(o,n);r.props.rows.rendered.append(a),a.forEach((function(t){r.path(t,e,r.props.get_color_for_row(t,s))}))}(l,s,t,a),s=t,i=function(t){var e=(new Date).getTime()-t;return r.render_speed=Math.max(Math.ceil(30*r.render_speed/e),8),r.render_speed=Math.min(r.render_speed,300),(new Date).getTime()}(i)}))}.bind(this),this.path=function(t,e,o){o&&(e.strokeStyle=o);var r,n,s=!1;this.state.dimensions.map(function(o,a){var i=void 0===t[o];i||\"inf\"!=t[o]&&\"-inf\"!=t[o]||!this.props.params_def[o].numeric||(i=!0);var c=this.xscale(o),l=this.yscale[o](t[o]);if(isNaN(l)&&(i=!0),i)return s&&(e.lineTo(r+15,n),e.stroke()),void(s=!1);s||(r=c-15,n=l,e.moveTo(r,n),e.beginPath(),s=!0);var u=c-.88*(c-r),p=n,d=c-.12*(c-r),f=l;e.bezierCurveTo(u,p,d,f,c,l),r=c,n=l}.bind(this)),s&&(e.lineTo(r+15,n),e.stroke())}.bind(this),this.state={height:t.persistent_state.get(\"height\",t.window_state.height?t.window_state.height:600),width:0,order:t.persistent_state.get(\"order\",t.order?t.order:[]),hide:new Set(t.persistent_state.get(\"hide\",t.hide?t.hide:[])),invert:new Set(t.persistent_state.get(\"invert\",t.invert?t.invert:[])),dimensions:[],brush_count:0,dragging:null},this.onBrushChange_debounced=c.a.throttle(this.onBrushChange.bind(this),75),this.sendBrushExtents_debounced=c.a.debounce(this.sendBrushExtents.bind(this),400)}componentWillUnmount(){Re(this.svgg_ref.current).selectAll(\"*\").remove(),this.props.rows.off(this),this.props.colorby.off(this),this.animloop.stop()}componentDidUpdate(t,e){if(e.height==this.state.height&&e.width==this.state.width||null!=this.on_resize&&this.on_resize(),e.invert!=this.state.invert&&this.props.persistent_state.set(\"invert\",Array.from(this.state.invert)),e.hide!=this.state.hide&&this.props.persistent_state.set(\"hide\",Array.from(this.state.hide)),e.order!=this.state.order&&this.props.persistent_state.set(\"order\",this.state.order),e.dimensions!=this.state.dimensions&&void 0!==this.xscale){var o=Re(this.svgg_ref.current).selectAll(\".dimension\");this.xscale.domain(this.state.dimensions),this.dimensions_dom.filter(function(t){return-1==this.state.dimensions.indexOf(t)}.bind(this)).remove(),this.state.dragging||(o=o.transition()),o.attr(\"transform\",function(t){return\"translate(\"+this.position(t)+\")\"}.bind(this)),this.update_ticks()}e.brush_count!=this.state.brush_count&&this.paths(this.props.rows.selected.get(),this.foreground,this.state.brush_count),this.props.window_state.height=this.state.height}onBrushChange(){this.sendBrushExtents_debounced(),this.brush()}onResize(t,e){this.state.height==t&&this.state.width==e||this.setState({height:t,width:e})}render(){return u.a.createElement(Al,{initialHeight:this.state.height,onResize:this.onResize.bind(this)},u.a.createElement(\"div\",{ref:this.root_ref,className:`${L.a[\"parallel-plot-chart\"]} pplot-root`,style:{height:this.state.height}},u.a.createElement(\"canvas\",{ref:this.foreground_ref,className:L.a[\"background-canvas\"]}),u.a.createElement(\"canvas\",{ref:this.highlighted_ref,className:L.a[\"highlight-canvas\"]}),u.a.createElement(\"svg\",{ref:this.svg_ref,width:this.state.width,height:this.state.height},u.a.createElement(\"g\",{ref:this.svgg_ref,transform:`translate(${this.m[3]}, ${this.m[0]})`}))))}sendBrushExtents(){const t=this.yscale;var e={};this.dimensions_dom.selectAll(\".\"+L.a.brush).each((function(o){const n=En(this);null!==n&&(e[o]=function(t,e){const o=aa().domain(t.range()).range([0,1]),n=[o(e[0]),o(e[1])];switch(t.hip_type){case r.CATEGORICAL:const o=t.domain(),s=o.slice(Math.ceil(Math.min(n[0],n[1])*(o.length-1)),Math.floor(Math.max(n[0],n[1])*(o.length-1)+1));return{type:t.hip_type,brush_extents_normalized:n,values:s};case r.NUMERIC:case r.NUMERICLOG:case r.NUMERICPERCENTILE:return{type:t.hip_type,brush_extents_normalized:n,range:[t.invert(e[0]),t.invert(e[1])]}}}(t[o],n))})),this.props.sendMessage(\"brush_extents\",e)}componentDidMount(){const t=function(t){var e=this.props.params_def[t];return void 0===e||e.special_values.length+e.distinct_values.length<=1||e.type==r.CATEGORICAL&&e.distinct_values.length>this.props.categoricalMaximumValues||this.state.hide.has(t)}.bind(this);var e=On(this.props.params_def).filter(function(e){return!t(e)&&(this.yscale[e]=this.createScale(e),!0)}.bind(this)).sort(function(t,e){var o=this.state.order.findIndex(e=>e==t),r=this.state.order.findIndex(t=>t==e);return(-1==o?this.state.order.length:o)-(-1==r?this.state.order.length:r)}.bind(this));this.setState({width:0==this.state.width?this.root_ref.current.offsetWidth:this.state.width,dimensions:e,order:Array.from(e)},this.initParallelPlot.bind(this))}initParallelPlot(){var t=this,e=this.props,o=this.div=Re(t.root_ref.current),r=Re(t.svg_ref.current);function n(){var e={};return t.dimensions_dom.selectAll(\".\"+L.a.brush).each((function(t){e[t]=En(this)})),e}function s(){void 0!==t.props.context_menu_ref&&t.props.context_menu_ref.current.hide(),t.animloop&&t.animloop.stop();var r=n(),s=t.state.dimensions.filter((function(t){return null!==r[t]}));t.dimensions_dom.each((function(e){if(c.a.include(s,e)){var o=t.yscale[e],n=r[e];Re(this).selectAll(\"text\").style(\"font-weight\",\"bold\").style(\"font-size\",\"13px\").style(\"display\",(function(){if(Re(this).classed(L.a.label))return null;var t=Re(this).data();return n[0]<=o(t)&&o(t)<=n[1]?null:\"none\"}))}else Re(this).selectAll(\"text\").style(\"font-size\",null).style(\"font-weight\",null).style(\"display\",null);Re(this).selectAll(\".\"+L.a.label).style(\"display\",null)})),o.selectAll(\".\"+L.a.label).style(\"font-weight\",(function(t){return c.a.include(s,t)?\"bold\":null}));var a=[];e.rows.all.get().map((function(e){return s.every((function(o){var n=t.yscale[o],s=r[o],a=e[o];return s[0]<=n(a)&&n(a)<=s[1]}))?a.push(e):null})),e.rows.selected.set(a)}function a(o){void 0!==e.params_def[o]&&t.setState((function(t,e){var r=new Set(t.hide);return r.add(o),{hide:r}})),t.setState((function(t,e){return{dimensions:c.a.difference(t.dimensions,[o])}}))}t.foreground=this.foreground_ref.current.getContext(\"2d\"),t.foreground.globalCompositeOperation=\"destination-over\",t.highlighted=this.highlighted_ref.current.getContext(\"2d\"),e.rows.highlighted.on_change((function(o){t.highlighted.clearRect(0,0,t.w,t.h),0!=o.length?(Re(t.foreground_ref.current).style(\"opacity\",\"0.25\"),o.forEach((function(o){t.path(o,t.highlighted,e.get_color_for_row(o,1))}))):Re(t.foreground_ref.current).style(\"opacity\",null)}),t),this.brush=s,e.rows.selected.on_change((function(e){t.setState((function(t){return{brush_count:t.brush_count+1}}))}),t),e.rows.all.on_change((function(e){t.d3brush.on(\"brush\",null).on(\"end\",null),t.d3brush.move(t.dimensions_dom.selectAll(\".\"+L.a.brush),null),t.d3brush.on(\"brush\",t.onBrushChange_debounced).on(\"end\",t.onBrushChange_debounced),t.onBrushChange_debounced();var o=[];t.state.dimensions.forEach((function(e){var r=t.createScale(e);null!==r?t.yscale[e]=r:o.push(e)})),o.forEach((function(t,e){a(t)})),t.update_ticks(),this.animloop&&this.animloop.stop()}),t),this.on_resize=c.a.debounce((function(){t.compute_dimensions(),o.selectAll(\".dimension\").attr(\"transform\",(function(e){return\"translate(\"+t.xscale(e)+\")\"})),r.selectAll(\".\"+L.a.brush).each((function(e){Re(this).call(t.d3brush)})),t.axis=t.axis.ticks(1+t.state.height/50),o.selectAll(\".\"+L.a.axis).each((function(e){Re(this).call(t.axis.scale(t.yscale[e]))})),s()}),100),t.compute_dimensions(),t.xscale.domain(t.state.dimensions),t.dimensions_dom=Re(t.svgg_ref.current).selectAll(\".dimension\").data(t.state.dimensions).enter().append(\"svg:g\").attr(\"class\",\"dimension\").attr(\"transform\",(function(e){return\"translate(\"+t.xscale(e)+\")\"})).call(Vn().on(\"start\",(function(e){t.setState({dragging:{col:e,pos:t.xscale(e),origin:t.xscale(e),dragging:!1}}),Re(t.foreground_ref.current).style(\"opacity\",\"0.35\")})).on(\"drag\",(function(e){const o=Ce.dx;var r=Re(this).select(\".\"+L.a.brush);t.setState((function(r,n){return{dragging:{col:e,pos:Math.min(t.w,Math.max(0,r.dragging.origin+=o)),origin:r.dragging.origin,dragging:!0}}}),(function(){t.state.dragging.pos<12||t.state.dragging.pos>t.w-12?r.style(\"fill\",\"red\"):r.style(\"fill\",null)}));var n=Array.from(t.state.dimensions);n.sort((function(e,o){return t.position(e)-t.position(o)})),n.every((function(e,o){return e==t.state.dimensions[o]}))||t.setState({dimensions:n}),t.dimensions_dom.attr(\"transform\",(function(e){return\"translate(\"+t.position(e)+\")\"}))})).on(\"end\",(function(e){if(t.state.dragging.dragging)Re(this).transition().attr(\"transform\",\"translate(\"+t.xscale(e)+\")\"),r=n()[e];else var r=function(e){var r=n(),s=null!==r[e]?[t.h-r[e][1],t.h-r[e][0]]:null;return t.state.invert.has(e)?(t.setState((function(t,o){var r=new Set(t.invert);return r.delete(e),{invert:r}})),t.setScaleRange(e),o.selectAll(\".\"+L.a.label).filter((function(t){return t==e})).style(\"text-decoration\",null)):(t.setState((function(t,o){var r=new Set(t.invert);return r.add(e),{invert:r}})),t.setScaleRange(e),o.selectAll(\".\"+L.a.label).filter((function(t){return t==e})).style(\"text-decoration\",\"underline\")),s}(e);t.state.dragging.pos<12||t.state.dragging.pos>t.w-12?a(e):t.setState({order:Array.from(t.state.dimensions)}),t.update_ticks(e,r),Re(t.foreground_ref.current).style(\"opacity\",null),t.setState({dragging:null})}))),t.dimensions_dom.append(\"svg:g\").attr(\"class\",L.a.axis).attr(\"transform\",\"translate(0,0)\").each((function(e){Re(this).call(t.axis.scale(t.yscale[e]))})).append(\"svg:text\").attr(\"text-anchor\",\"middle\").attr(\"y\",(function(t,e){return-14-e%3*16})).attr(\"x\",0).classed(L.a.label,!0).classed(\"pplot-label\",!0).text(String).on(\"contextmenu\",(function(e){void 0!==t.props.context_menu_ref&&t.props.context_menu_ref.current.show(Ce.pageX,Ce.pageY,e),Ce.preventDefault(),Ce.stopPropagation()})).append(\"title\").text(\"Click to invert. Drag to reorder. Right click for options.\"),t.dimensions_dom.append(\"svg:g\").classed(L.a.brush,!0).classed(\"pplot-brush\",!0).each((function(e){Re(this).call(t.d3brush)})).selectAll(\"rect\").style(\"visibility\",null).append(\"title\").text(\"Drag up or down to brush along this axis\"),t.dimensions_dom.selectAll(\".extent\").append(\"title\").text(\"Drag or resize this filter\"),s(),t.sendBrushExtents(),t.props.colorby.on_change(s,t),t.setState((function(t){return{brush_count:t.brush_count+1}}))}setScaleRange(t){var e=[this.h,0];this.state.invert.has(t)&&(e=[0,this.h]),this.yscale[t].range(e)}createScale(t){var e=this.props.params_def[t];if(void 0===e)return null;var o=[this.h,0];this.state.invert.has(t)&&(o=[0,this.h]);var r=Dl(e);return r.range(o),r.parallel_plot_axis=t,r}compute_dimensions(){this.w=this.state.width-this.m[1]-this.m[3],this.h=this.state.height-this.m[0]-this.m[2],this.axis=_t(aa()).ticks(1+this.state.height/50),this.d3brush.extent([[-23,0],[15,this.h]]).on(\"brush\",this.onBrushChange_debounced).on(\"end\",this.onBrushChange_debounced),this.div.style(\"height\",this.state.height+\"px\"),this.div.selectAll(\"canvas\").attr(\"width\",this.w).attr(\"height\",this.h).style(\"padding\",this.m.join(\"px \")+\"px\"),this.xscale=Fs().range([40,this.w-40]).domain(this.state.dimensions);var t=this;this.state.dimensions.forEach((function(e){t.setScaleRange(e)})),this.highlighted.lineWidth=4}}Sl.defaultProps={categoricalMaximumValues:80,data:{}};class Fl extends u.a.Component{constructor(t){let e;function o(e){var o=t.persistent_state.get(e,t[e]);return void 0===o&&(o=null),null!=o&&void 0===t.params_def[o]?null:o}super(t),this.on_resize=null,this.root_ref=u.a.createRef(),this.container_ref=u.a.createRef(),this.canvas_lines_ref=u.a.createRef(),this.canvas_highlighted_ref=u.a.createRef(),e=t.window_state.height?t.window_state.height:t.height?t.height:ot([et([document.body.clientHeight-540,240]),500]);const r={...t.experiment._displays[this.props.name],axis_x:o(\"axis_x\"),axis_y:o(\"axis_y\"),width:0,height:e};this.state={...r,enabled:null!==r.axis_x&&null!==r.axis_y}}componentDidMount(){if(this.props.context_menu_ref&&this.props.context_menu_ref.current){const t=this;this.props.context_menu_ref.current.addCallback((function(e,o){var r=a()(o);r.append(a()('<div class=\"dropdown-divider\"></div>')),r.append(a()(`<h6 class=\"dropdown-header\">${t.props.name}</h6>`)),[\"axis_x\",\"axis_y\"].forEach((function(o,n){var s=\"Set as \"+[\"X\",\"Y\"][n]+\" axis\",i=a()('<a class=\"dropdown-item\" href=\"#\">').text(s);t.state[o]==e&&i.addClass(\"disabled\").css(\"pointer-events\",\"none\"),i.click((function(o){0==n?t.setState({axis_x:e}):t.setState({axis_y:e}),o.preventDefault()})),r.append(i)}))}),this)}}mountPlotXY(){var t=this,e=this.props;t.params_def=e.params_def;var o=Re(this.root_ref.current);t.svg=o.select(\"svg\");var r,n=e.dp_lookup,s=[],i=[],l=this.canvas_lines_ref.current.getContext(\"2d\");l.globalCompositeOperation=\"destination-over\";var u=this.canvas_highlighted_ref.current.getContext(\"2d\");u.globalCompositeOperation=\"destination-over\";const p={top:20,right:20,bottom:50,left:60};var d,f,h,m,b,g,_,v;function y(e,o){var r=Dl(t.params_def[e]);return r.range(o),r}function x(){t.svg.selectAll(\".axis_render\").remove(),t.svg.selectAll(\".brush\").remove(),t.svg.attr(\"viewBox\",[0,0,t.state.width,t.state.height]),t.svg.append(\"g\").attr(\"class\",\"axis_render\").call(m),t.svg.append(\"g\").attr(\"class\",\"axis_render\").call(h),t.svg.append(\"g\").attr(\"class\",\"brush\").call(r)}function w(e=!1){(e||t.state.enabled)&&(b=d=y(t.state.axis_x,[p.left,t.state.width-p.right]),g=f=y(t.state.axis_y,[t.state.height-p.bottom,p.top]),r=Tn().extent([[p.left,p.top],[t.state.width-p.right,t.state.height-p.bottom]]).on(\"end\",D),h=e=>e.attr(\"transform\",`translate(${p.left-10},0)`).call(_t(f).ticks(1+t.state.height/40).tickSizeInner(p.left+p.right-t.state.width)).call(t=>t.select(\".domain\").remove()).call(e=>e.select(\".tick:last-of-type text\").clone().attr(\"x\",3).attr(\"text-anchor\",\"start\").attr(\"font-weight\",\"bold\").text(t.state.axis_y)),m=e=>e.attr(\"transform\",`translate(0,${t.state.height-p.bottom})`).call(gt(d).ticks(1+t.state.width/80).tickSizeInner(p.bottom+p.top-t.state.height)).call(e=>e.select(\".tick:last-of-type text\").clone().attr(\"y\",22).attr(\"text-anchor\",\"end\").attr(\"font-weight\",\"bold\").text(t.state.axis_x)),o.selectAll(\"canvas\").attr(\"width\",t.state.width-p.left-p.right).attr(\"height\",t.state.height-p.top-p.bottom),o.selectAll(\"svg\").attr(\"width\",t.state.width).attr(\"height\",t.state.height),o.style(\"height\",t.state.height+\"px\"),o.selectAll(\"canvas\").style(\"margin\",p.top+\"px \"+p.right+\"px \"+p.bottom+\"px \"+p.left+\"px\"),x())}function D(){var e,o=Ce.selection;if(o){if(void 0!==d.invert){var r=[d.invert(o[0][0]),d.invert(o[1][0])];(d=y(t.state.axis_x,[p.left,t.state.width-p.right])).domain(r)}if(void 0!==f.invert){var n=[f.invert(o[1][1]),f.invert(o[0][1])];(f=y(t.state.axis_y,[t.state.height-p.bottom,p.top])).domain(n)}}else d=b,f=g;e=i,x(),t.clear_canvas(),a.a.each(e,(function(t,e){e()})),i=e}function C(e,o,r){r.lines_color&&(o.strokeStyle=r.lines_color),r.dots_color&&(o.fillStyle=r.dots_color),r.lines_width&&(o.lineWidth=r.lines_width);var a=t.params_def[t.state.axis_x],i=t.params_def[t.state.axis_y];function c(t,e,o){return null==t||isNaN(e)||o.numeric&&(\"inf\"==t||\"-inf\"==t)}function l(e){var o=d(e[t.state.axis_x]),n=f(e[t.state.axis_y]);return o-=p.left,n-=p.top,c(e[t.state.axis_x],o,a)||c(e[t.state.axis_y],n,i)?null:(r.remember&&s.push({layerX:o+p.left,layerY:n+p.top,dp:e}),{x:o,y:n})}var u=l(e);if(null!==u){if(e.from_uid&&r.lines_width>0){var h=n[e.from_uid];if(h){var m=l(h);null!==m&&(o.beginPath(),o.moveTo(m.x,m.y),o.lineTo(u.x,u.y),o.stroke())}else console.log(\"DataPoint with id \"+e.from_uid+\" not found 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r=function(){C(o,l,{lines_color:e.get_color_for_row(o,n),lines_width:k.lines_thickness,dots_color:e.get_color_for_row(o,s),dots_thickness:k.dots_thickness,remember:!0})};i.push(r),t.state.enabled&&r()}))}this.clear_canvas=function(){l.clearRect(0,0,t.state.width,t.state.height),u.clearRect(0,0,t.state.width,t.state.height),s=[],i=[]},e.rows.highlighted.on_change((function(o){t.state.enabled&&(u.clearRect(0,0,t.state.width,t.state.height),Re(t.canvas_highlighted_ref.current).style(\"opacity\",\"0\"),Re(t.canvas_lines_ref.current).style(\"opacity\",\"1.0\"),o.length&&(Re(t.canvas_highlighted_ref.current).style(\"opacity\",\"1.0\"),Re(t.canvas_lines_ref.current).style(\"opacity\",\"0.5\"),o.forEach((function(t){for(;void 0!==t;){var o=e.get_color_for_row(t,1).split(\",\");if(C(t,u,{lines_color:[o[0],o[1],o[2],\"1)\"].join(\",\"),lines_width:4,dots_color:[o[0],o[1],o[2],\"0.8)\"].join(\",\"),dots_thickness:5}),null===t.from_uid)break;t=n[t.from_uid]}}))))}),this),this.update_axis=function(){var e=i;w(!0),t.clear_canvas(),a.a.each(e,(function(t,e){e()})),i=e},this.update_axis(),e.rows.all.on_change((function(t){w(),E(e.rows.selected.get())}),this),e.rows.selected.on_change(function(e){t.clear_canvas(),E(e)}.bind(this),this),E(e.rows.selected.get()),this.on_resize=c.a.throttle((function(){w(),t.clear_canvas(),E(e.rows.selected.get())}),75)}onResize(t,e){this.state.height==t&&this.state.width==e||this.setState({height:t,width:e})}render(){return this.state.enabled?u.a.createElement(Al,{initialHeight:this.state.height,onResize:c.a.debounce(this.onResize.bind(this),100)},this.state.width>0&&u.a.createElement(\"div\",{ref:this.root_ref,className:\"checkpoints-graph\",style:{height:this.state.height}},u.a.createElement(\"canvas\",{ref:this.canvas_lines_ref,className:L.a[\"checkpoints-graph-lines\"],style:{position:\"absolute\"}}),u.a.createElement(\"canvas\",{ref:this.canvas_highlighted_ref,className:L.a[\"checkpoints-graph-highlights\"],style:{position:\"absolute\"}}),u.a.createElement(\"svg\",{className:L.a[\"checkpoints-graph-svg\"],style:{position:\"absolute\"}}))):[]}componentWillUnmount(){this.clear_canvas&&(this.clear_canvas(),this.svg.selectAll(\"*\").remove()),this.props.rows.off(this),this.props.context_menu_ref&&this.props.context_menu_ref.current&&this.props.context_menu_ref.current.removeCallbacks(this)}componentDidUpdate(t,e){var o=!1;[\"axis_x\",\"axis_y\"].forEach(function(t){e[t]!=this.state[t]&&(this.props.persistent_state.set(t,this.state[t]),o=!0)}.bind(this)),0==e.width&&this.state.width>0&&this.mountPlotXY(),e.height==this.state.height&&e.width==this.state.width||this.on_resize&&this.on_resize(),null===this.state.axis_x||null===this.state.axis_y?this.state.enabled&&this.setState({enabled:!1}):this.state.enabled?o&&this.update_axis():this.setState({enabled:!0}),this.props.window_state.height=this.state.height}}Fl.defaultProps={axis_x:null,axis_y:null,lines_thickness:1.2,lines_opacity:null,dots_thickness:1.4,dots_opacity:null,data:{}};class Ml extends u.a.Component{constructor(t){super(t),this.btnRef=u.a.createRef(),this.state={btnEnabled:!1}}componentDidMount(){var t=this,e=this.props.rows,o=this.btnRef.current;e.selected.on_change((function(o){t.setState({btnEnabled:0<o.length&&o.length<e.all.get().length})}),this),a()(o).click((function(t){e.all.set(e.selected.get())}))}componentWillUnmount(){this.props.rows.off(this)}render(){return u.a.createElement(\"button\",{title:\"Zoom in on selected data\",ref:this.btnRef,className:L.a.keepData,disabled:!this.state.btnEnabled},\"Keep\")}}class Nl extends u.a.Component{constructor(t){super(t),this.state={btnEnabled:!1}}componentDidMount(){var t=this,e=this.props.rows;e.selected.on_change((function(o){t.setState({btnEnabled:0<o.length&&o.length<e.all.get().length})}),this)}onClick(){var t=i.difference(this.props.rows.all.get(),this.props.rows.selected.get());this.props.rows.all.set(t)}componentWillUnmount(){this.props.rows.off(this)}render(){return u.a.createElement(\"button\",{title:\"Remove selected data\",className:L.a.excludeData,disabled:!this.state.btnEnabled,onClick:this.onClick.bind(this)},\"Exclude\")}}class jl extends u.a.Component{onClick(){var t=this.props.rows.selected.get(),e=as(t),o=new Blob([e],{type:\"text/csv\"});!function(t,e){var o=document.createElement(\"a\");o.setAttribute(\"href\",t),o.setAttribute(\"download\",e),o.style.display=\"none\",document.body.appendChild(o),o.click(),document.body.removeChild(o)}(window.URL.createObjectURL(o),`hiplot-selected-${t.length}.csv`)}render(){return u.a.createElement(\"button\",{title:\"Export data as CSV\",onClick:this.onClick.bind(this)},\"Export\")}}class Ll extends u.a.Component{constructor(t){super(t),this.state={btnEnabled:!1}}componentDidMount(){var t=this,e=this.props.rows;function o(){t.setState({btnEnabled:e.all.get().length!=e.experiment_all.get().length})}e.all.on_change(o,this),e.experiment_all.on_change(o,this)}componentWillUnmount(){this.props.rows.off(this)}onClick(){this.props.rows.all.set(this.props.rows.experiment_all.get())}render(){return u.a.createElement(\"button\",{title:\"Remove all applied filters\",className:L.a.restoreData,disabled:!this.state.btnEnabled,onClick:this.onClick.bind(this)},\"Restore\")}}class Il extends u.a.Component{constructor(){super(...arguments),this.selectedBar=u.a.createRef(),this.renderedBar=u.a.createRef()}componentDidMount(){var t=this.props.rows,e=this.selectedBar.current,o=this.renderedBar.current;t.selected.on_change((function(o){var r=t.all.get().length;e.style.width=100*o.length/r+\"%\"}),this),t.rendered.on_change((function(e){var r=t.selected.get().length;o.style.width=100*e.length/r+\"%\"}),this)}componentWillUnmount(){this.props.rows.off(this)}render(){return u.a.createElement(\"div\",{className:L.a.fillbar},u.a.createElement(\"div\",{ref:this.selectedBar,className:L.a.selectedBar},u.a.createElement(\"div\",{ref:this.renderedBar,className:L.a.renderedBar},\" \")))}}u.a.Component;var Bl=o(11),Pl=o.n(Bl),Rl=\"data:image/svg+xml;base64,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\",zl=o(4),Ol=o.n(zl);class Hl extends u.a.Component{componentDidMount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-root\").addClass(Ol.a.highlightElement)}componentWillUnmount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-root\").removeClass(Ol.a.highlightElement)}render(){return u.a.createElement(\"div\",{className:\"alert alert-info\",role:\"alert\"},u.a.createElement(\"h4\",{className:\"alert-heading\"},\"Step 1/4: The parallel plot\"),u.a.createElement(\"p\",null,\"The first plot you see above is a \",u.a.createElement(\"strong\",null,\"Parallel Plot\"),\". Parallel plots are a convenient way to visualize and filter high-dimensional data. HiPlot will draw one vertical scaled axis for each metric you have in your dataset, and each training/data point is a continuous line that goes through its value on each of the axes.\"),u.a.createElement(\"hr\",null),u.a.createElement(\"p\",{className:\"mb-0\"},\"Learn more about \",u.a.createElement(\"a\",{href:\"https://en.wikipedia.org/wiki/Parallel_coordinates\"},\"Parallel coordinates\"),\" on Wikipedia.\"))}}class Ul extends u.a.Component{componentDidMount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-brush\").addClass(Ol.a.highlightElement)}componentWillUnmount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-brush\").removeClass(Ol.a.highlightElement)}render(){return u.a.createElement(\"div\",{className:\"alert alert-info\",role:\"alert\"},u.a.createElement(\"h4\",{className:\"alert-heading\"},\"Step 2/4: Slicing data\"),u.a.createElement(\"p\",null,\"Slicing along an axis allows to discover patterns in the data. \",u.a.createElement(\"strong\",null,\"Drag vertically along an axis\"),\" to display only a subset of the data. You also can do it on several axis at the same time.\"),u.a.createElement(\"hr\",null),u.a.createElement(\"p\",{className:\"mb-0\"},\"To remove a slicing on an axis, click on the axis.\"))}}class Wl extends u.a.Component{componentDidMount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-label\").addClass(Ol.a.highlightText)}componentWillUnmount(){a()(this.props.rootRef.current.parentElement).find(\".pplot-label\").removeClass(Ol.a.highlightText)}render(){return u.a.createElement(\"div\",{className:\"alert alert-info\",role:\"alert\"},u.a.createElement(\"h4\",{className:\"alert-heading\"},\"Step 3/4: Move and remove axis\"),u.a.createElement(\"p\",null,\"Move an axis by \",u.a.createElement(\"strong\",null,\"dragging its label above\"),\". In parallel plots, we can very easily spot relationships between nearby axis. 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u.a.createElement(\"div\",{className:`row ${Ol.a.tutoAlert}`},u.a.createElement(\"div\",{className:\"col-md-9\"},this.steps[this.state.stepNum]({rootRef:this.props.navbarRoot})),u.a.createElement(\"div\",{className:\"col-md-3\"},this.state.stepNum>0&&u.a.createElement(\"button\",{className:\"btn btn-outline-primary\",onClick:this.onClickPreviousTutorial.bind(this)},\"Previous\"),this.state.stepNum+1<this.steps.length&&u.a.createElement(\"button\",{className:\"btn btn-outline-primary\",onClick:this.onClickNextTutorial.bind(this)},\"Next\"),this.state.stepNum+1==this.steps.length&&u.a.createElement(\"button\",{className:\"btn btn-outline-primary\",onClick:()=>this.props.onTutorialDone()},\"Done\")))}}class Vl extends u.a.Component{constructor(t){super(t),this.textarea=u.a.createRef(),this.state={value:t.initialValue}}onInput(){var t=this.textarea.current;if(this.props.hasFocus||!this.props.minimizeWhenOutOfFocus)return t.style.height=\"auto\",void(t.style.height=t.scrollHeight+\"px\");t.style.height=\"55px\"}onKeyDown(t){13!==t.which||t.shiftKey||(this.props.onSubmit(this.textarea.current.value),this.props.onFocusChange(!1),t.preventDefault())}onFocusChange(t){\"focus\"==t.type?this.props.onFocusChange(!0):\"blur\"==t.type&&this.props.onFocusChange(!1)}componentDidMount(){this.onInput()}componentDidUpdate(){this.onInput()}render(){return u.a.createElement(\"textarea\",{style:{height:\"55px\",flex:1,minWidth:\"100px\"},ref:this.textarea,className:Pl.a.runsSelectionTextarea,disabled:!this.props.enabled,value:this.state.value,onKeyDown:this.onKeyDown.bind(this),onInput:this.onInput.bind(this),onChange:t=>this.setState({value:t.target.value}),onFocus:this.onFocusChange.bind(this),onBlur:this.onFocusChange.bind(this),placeholder:\"Experiments to load\"})}}class Xl extends u.a.Component{constructor(t){super(t),this.selected_count_ref=u.a.createRef(),this.selected_pct_ref=u.a.createRef(),this.total_count_ref=u.a.createRef(),this.controls_root_ref=u.a.createRef(),this.state={isTextareaFocused:!1,hasTutorial:!1}}recomputeMetrics(){if(!this.selected_count_ref.current)return;const t=this.props.rows.selected.get().length,e=this.props.rows.all.get().length;this.selected_count_ref.current.innerText=\"\"+t,this.selected_pct_ref.current.innerText=\"\"+(100*t/e).toPrecision(3),this.total_count_ref.current.innerText=\"\"+e}componentDidMount(){this.props.rows.selected.on_change(function(t){this.recomputeMetrics()}.bind(this),this),this.props.rows.all.on_change(function(t){this.recomputeMetrics()}.bind(this),this),this.recomputeMetrics()}componentWillUnmount(){this.props.rows.off(this)}componentDidUpdate(){this.recomputeMetrics()}onToggleTutorial(){this.setState((function(t,e){return{hasTutorial:!t.hasTutorial}}))}renderControls(){const t=null!=this.props.onRequestLoadExperiment;return u.a.createElement(u.a.Fragment,null,t&&u.a.createElement(Vl,{initialValue:this.props.persistent_state.get(_,\"\"),enabled:this.props.loadStatus!=n.Loading,minimizeWhenOutOfFocus:this.props.loadStatus==n.Loaded,onSubmit:this.props.onRequestLoadExperiment,onFocusChange:t=>this.setState({isTextareaFocused:t}),hasFocus:this.state.isTextareaFocused}),this.props.loadStatus==n.Loaded&&!this.state.isTextareaFocused&&u.a.createElement(u.a.Fragment,null,u.a.createElement(\"div\",{className:Pl.a.controlGroup},u.a.createElement(Ll,{rows:this.props.rows}),u.a.createElement(Ml,{rows:this.props.rows}),u.a.createElement(Nl,{rows:this.props.rows}),null!=this.props.onRequestRefreshExperiment&&u.a.createElement(\"button\",{title:\"Refresh + restore data removed\",onClick:this.props.onRequestRefreshExperiment},\"Refresh\"),u.a.createElement(jl,{rows:this.props.rows}),u.a.createElement(\"button\",{title:\"Start HiPlot tutorial\",onClick:this.onToggleTutorial.bind(this)},\"Help\"),u.a.createElement(\"div\",{style:{clear:\"both\"}})),u.a.createElement(\"div\",{className:Pl.a.controlGroup},u.a.createElement(\"div\",{style:{fontFamily:\"monospace\"}},\"Selected: \",u.a.createElement(\"strong\",{ref:this.selected_count_ref,style:{minWidth:\"4em\",textAlign:\"right\",display:\"inline-block\"}},\"??\"),\"/\",u.a.createElement(\"strong\",{ref:this.total_count_ref,style:{minWidth:\"4em\",textAlign:\"left\",display:\"inline-block\"}},\"??\"),\" (\",u.a.createElement(\"span\",{ref:this.selected_pct_ref},\"??\"),\"%)\"))))}render(){var t=this.state.hasTutorial?u.a.createElement(\"div\",null,u.a.createElement(Yl,{navbarRoot:this.controls_root_ref,onTutorialDone:(()=>this.setState({hasTutorial:!1})).bind(this)})):this.renderControls();return u.a.createElement(\"div\",{ref:this.controls_root_ref,className:\"container-fluid \"+Pl.a.header},u.a.createElement(\"div\",{className:\"d-flex flex-wrap\"},u.a.createElement(\"img\",{style:{height:\"55px\"},src:Rl}),t))}}class Gl extends u.a.Component{render(){return u.a.createElement(\"div\",{className:\"alert alert-danger\",role:\"alert\"},u.a.createElement(\"div\",{className:\"container\"},u.a.createElement(\"h4\",{className:\"alert-heading\"},this.props.error),u.a.createElement(\"p\",{className:\"mb-0\"},\"The error above was returned by the server when trying to load your experiment\")))}}var Ql=\"data:image/svg+xml;base64,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\";class Kl extends u.a.Component{constructor(t){super(t),this.context_menu_div=u.a.createRef(),this.trigger_callbacks=[],this.state={visible:!1,column:\"\",top:0,left:0},this.hide=function(){this.setState({visible:!1})}.bind(this),a()(window).on(\"click\",this.hide)}addCallback(t,e){this.trigger_callbacks.push({cb:t,obj:e})}removeCallbacks(t){this.trigger_callbacks=this.trigger_callbacks.filter(e=>e.obj!=t)}show(t,e,o){var r=a()(this.context_menu_div.current.parentElement).offset();this.setState({top:Math.max(0,e-10-r.top),left:Math.max(0,t-90-r.left),visible:!0,column:o})}componentWillUnmount(){a()(window).off(\"click\",this.hide)}componentDidUpdate(t,e){var o=this.context_menu_div.current;if(o.style.display=this.state.visible?\"block\":\"none\",o.style.top=`${this.state.top}px`,o.style.left=`${this.state.left}px`,o.classList.toggle(\"show\",this.state.visible),this.state.visible&&!e.visible||this.state.column!=e.column){o.innerHTML=\"\";var r=this;this.trigger_callbacks.forEach((function(t){t.cb(r.state.column,o)}))}}render(){return u.a.createElement(\"div\",{ref:this.context_menu_div,className:\"dropdown-menu dropdown-menu-sm context-menu\",style:{fontSize:16}})}}const Jl={top:20,right:20,bottom:50,left:60};class Zl extends u.a.Component{constructor(){super(...arguments),this.axisBottom=u.a.createRef(),this.axisLeft=u.a.createRef(),this.axisRight=u.a.createRef(),this.svgContainer=u.a.createRef(),this.histAll=u.a.createRef(),this.histSelected=u.a.createRef()}isVertical(){return this.props.param_def.type!=r.CATEGORICAL||this.props.param_def.distinct_values.length<3}figureWidth(){return this.props.width-Jl.left-Jl.right}figureHeight(){return this.props.height-Jl.top-Jl.bottom}createDataAxis(t,e){var o;this.isVertical()?(t.range([0,this.figureWidth()]),Re(this.axisBottom.current).call(gt(t).ticks(1+this.props.width/50)),this.axisRight.current.innerHTML=\"\"):(t.range([this.figureHeight(),0]),Re(this.axisRight.current).transition().duration(e?this.props.animateMs:0).call((o=t,bt(it,o)).ticks(1+this.props.height/50)).attr(\"text-anchor\",\"end\").selectAll(\"text\").attr(\"x\",-4),Re(this.axisLeft.current).transition().duration(e?this.props.animateMs:0).call(_t(t).ticks(1+this.props.height/50)))}createDataScaleAndAxis(){this.dataScale=wl(this.props.param_def),this.createDataAxis(this.dataScale,!1)}createHistogram(){const t=this,e=this.dataScale.copy().range([0,1]);var o=[];if(this.props.param_def.type==r.CATEGORICAL)o=(o=this.props.param_def.distinct_values.map(t=>e(t))).map((t,e)=>0==e?t:(t+o[e-1])/2);else for(var n=1;n<this.props.nbins;++n)o.push(n/this.props.nbins);return tt().value((function(o){return e(o[t.props.axis])})).domain([0,1]).thresholds(o)}drawAllHistograms(t){const e=this.createHistogram();var o={selected:{bins:e(this.props.histData.selected),g:this.histAll.current,color:Fc[4],draw_fn:this.drawHistogramRects.bind(this)},all:{bins:e(this.props.histData.all),g:this.histSelected.current,color:\"black\",draw_fn:this.drawHistogramLines.bind(this)}};const r=et(Object.values(o),(function(t){const e=rt(t.bins,t=>t.length);return e?et(t.bins,t=>t.length/e):0}));var n=aa().domain([0,r]),s=o.all.bins.map((t,e)=>e);if(this.isVertical())n=n.range([this.figureHeight(),0]),Re(this.axisLeft.current).transition().duration(t?this.props.animateMs:0).call(_t(n));else{n=n.range([0,this.figureWidth()]),Re(this.axisBottom.current).transition().duration(t?this.props.animateMs:0).call(gt(n));var a=Array.from(s).sort((t,e)=>o.selected.bins[t].length-o.selected.bins[e].length);a.forEach((function(t,e){s[t]=e}));var i=this.dataScale.domain(),c=i.map((function(t,e){return i[a[e]]}));this.createDataAxis(Fs().domain(c).range(this.dataScale.range()),!0)}Object.values(o).forEach(function(e){e.draw_fn(e,n,t,s)}.bind(this))}drawHistogramLines(t,e,o,r){const n=rt(t.bins,t=>t.length),s=t=>e(n?t.length/n:0),a=aa().range(this.dataScale.range());var i=Re(t.g).selectAll(\"line\").data(t.bins);const c=this.isVertical()?\"x\":\"y\",l=this.isVertical()?\"y\":\"x\";i.enter().append(\"line\").merge(i).transition().duration(o?this.props.animateMs:0).attr(`${c}1`,(e,o)=>a(t.bins[r[o]].x0)+1).attr(`${l}1`,(t,e)=>s(t)).attr(`${c}2`,(e,o)=>a(t.bins[r[o]].x1)).attr(`${l}2`,(t,e)=>s(t)).style(\"stroke\",t.color).style(\"stroke-width\",2),i.exit().remove()}drawHistogramRects(t,e,o,r){const n=rt(t.bins,t=>t.length),s=t=>e(n?t.length/n:0),a=aa().range(this.dataScale.range());var i=Re(t.g).selectAll(\"rect\").data(t.bins),c=i.enter().append(\"rect\").merge(i).attr(\"data-value-sample\",function(t,e){return t.length?t[0][this.props.axis]:\"empty\"}.bind(this)).on(\"mouseover\",(function(t,e){Re(this).transition().duration(150).attr(\"opacity\",\".5\")})).on(\"mouseout\",(function(t,e){Re(this).transition().duration(150).attr(\"opacity\",\"1\")})).transition().duration(o?this.props.animateMs:0);this.isVertical()?c.attr(\"x\",1).attr(\"transform\",(function(t){return\"translate(\"+a(t.x0)+\",\"+s(t)+\")\"})).attr(\"width\",(function(t){return a(t.x1)-a(t.x0)-1})).attr(\"height\",function(t){return this.figureHeight()-s(t)}.bind(this)).style(\"fill\",t.color):c.attr(\"transform\",(function(e,o){return\"translate(0,\"+a(t.bins[r[o]].x1)+\")\"})).attr(\"width\",(function(t){return s(t)})).attr(\"height\",function(e,o){const n=Math.abs(a(t.bins[r[o]].x1)-a(t.bins[r[o]].x0));return n>2?n-1:n}.bind(this)).style(\"fill\",t.color),i.exit().remove()}componentDidMount(){this.createDataScaleAndAxis(),this.drawAllHistograms(!1)}componentDidUpdate(t,e){if(t.axis==this.props.axis&&t.param_def==this.props.param_def&&t.width==this.props.width&&t.height==this.props.height||this.createDataScaleAndAxis(),t.axis!=this.props.axis||t.nbins!=this.props.nbins||t.histData!=this.props.histData||t.width!=this.props.width||t.height!=this.props.height){const e=t.nbins!=this.props.nbins||t.histData!=this.props.histData;this.drawAllHistograms(e)}}render(){const t=this.isVertical()?this.props.axis:\"Density\",e=this.isVertical()?\"Density\":this.props.axis;return u.a.createElement(\"div\",null,u.a.createElement(\"svg\",{width:this.props.width,height:this.props.height},u.a.createElement(\"g\",{transform:`translate(${Jl.left}, 15)`,textAnchor:\"start\",fontWeight:\"bold\"},u.a.createElement(\"text\",{style:{stroke:\"white\",strokeWidth:\"0.2em\"}},e),u.a.createElement(\"text\",null,e)),u.a.createElement(\"g\",{ref:this.svgContainer,transform:`translate(${Jl.left}, ${Jl.top})`},u.a.createElement(\"g\",{ref:this.histAll}),u.a.createElement(\"g\",{ref:this.histSelected}),u.a.createElement(\"g\",{className:\"axisLeft\",ref:this.axisLeft}),u.a.createElement(\"g\",{className:\"axisRight\",ref:this.axisRight,transform:`translate(${this.figureWidth()}, 0)`}),u.a.createElement(\"g\",{className:\"axisBottom\",ref:this.axisBottom,transform:`translate(0, ${this.figureHeight()})`}),u.a.createElement(\"g\",{transform:`translate(${this.figureWidth()}, ${this.props.height-Jl.top-20})`,textAnchor:\"end\",fontWeight:\"bold\"},u.a.createElement(\"text\",null,t)))))}}class tu extends u.a.Component{constructor(t){super(t),this.container_ref=u.a.createRef();var e=this.props.persistent_state.get(\"axis\");void 0!==e&&void 0===this.props.params_def[e]&&(e=void 0),void 0===e&&(e=this.props.axis),e&&void 0===this.props.params_def[e]&&(e=void 0),this.state={height:ot([et([document.body.clientHeight-540,240]),500]),width:0,histData:{selected:[],all:t.rows.all.get()},axis:e}}componentDidMount(){if(this.props.context_menu_ref&&this.props.context_menu_ref.current){const t=this;this.props.context_menu_ref.current.addCallback((function(e,o){var r=a()(o);r.append(a()('<div class=\"dropdown-divider\"></div>'));var n=a()('<a class=\"dropdown-item\" href=\"#\">').text(\"View distribution\");t.state.axis==e&&n.addClass(\"disabled\").css(\"pointer-events\",\"none\"),n.click((function(o){t.setState({axis:e}),o.preventDefault()})),r.append(n)}),this)}this.props.rows.selected.on_change(function(t){this.setState((function(e,o){return{histData:{...e.histData,selected:t}}}))}.bind(this),this),this.props.rows.all.on_change(function(t){this.setState((function(e,o){return{histData:{...e.histData,all:t}}}))}.bind(this),this)}componentDidUpdate(t,e){e.axis!=this.state.axis&&this.props.persistent_state&&this.props.persistent_state.set(\"axis\",this.state.axis)}componentWillUnmount(){this.props.rows.off(this),this.props.context_menu_ref&&this.props.context_menu_ref.current&&this.props.context_menu_ref.current.removeCallbacks(this)}onResize(t,e){t==this.state.height&&e==this.state.width||this.setState({height:t,width:e})}render(){return void 0===this.state.axis?[]:u.a.createElement(Al,{initialHeight:this.state.height,onResize:c.a.debounce(this.onResize.bind(this),150)},this.state.width>0&&u.a.createElement(Zl,{axis:this.state.axis,height:this.state.height,width:this.state.width,histData:this.state.histData,param_def:this.props.params_def[this.state.axis],nbins:this.props.nbins,animateMs:this.props.animateMs}))}}function eu(t){return{params_def:{},rows:new g,get_color_for_row:null,render_row_text:function(t){return t.uid},dp_lookup:{},context_menu_ref:u.a.createRef(),colorby:new m(\"colorby\"),experiment:null,name:null,window_state:null,persistent_state:null!=t?t:new kl.a(\"\",{}),sendMessage:null}}tu.defaultProps={nbins:10,animateMs:750},o.d(e,\"HiPlot\",(function(){return ou})),o.d(e,\"defaultPlugins\",(function(){return nu})),o.d(e,\"hiplot_setup\",(function(){return su})),o.d(e,\"PlotXY\",(function(){return Fl})),o.d(e,\"ParallelPlot\",(function(){return Sl})),o.d(e,\"RowsDisplayTable\",(function(){return I})),o.d(e,\"AllDatasets\",(function(){return g})),o.d(e,\"HiPlotLoadStatus\",(function(){return n}));class ou extends u.a.Component{constructor(t){super(t),this.domRoot=u.a.createRef(),this.comm_message_id=0,this.table=null,this.plugins_window_state={},this.state={experiment:t.experiment,version:0,loadStatus:n.None,error:null},this.data=eu(this.props.persistent_state),this.data.sendMessage=this.sendMessage.bind(this),t.plugins.forEach(t=>{this.plugins_window_state[t.name]={}});var e=this.data.rows;e.selected.on_change(i.debounce(this.onSelectedChange.bind(this),200),this),e.all.on_change(this.recomputeParamsDef.bind(this),this)}sendMessage(t,e){null!==this.props.comm&&(this.props.comm.send({type:t,message_id:this.comm_message_id,data:e}),this.comm_message_id+=1)}onSelectedChange(t){this.sendMessage(\"selection\",{selected:t.map(t=>\"\"+t.uid)})}recomputeParamsDef(t){Object.assign(this.data.params_def,Cl(this.data.persistent_state.children(y),t,this.data.params_def))}_loadExperiment(t){var e=this;e.data.experiment=t;var o=this.data.rows;function n(){function t(t){var o=e.data.params_def[t],n=0;return null!==o.colors&&(n+=100),o.type==r.CATEGORICAL&&(n-=20),o.optional&&(n-=40),n}return Object.keys(e.data.params_def).sort((e,o)=>t(o)-t(e))[0]}e.data.dp_lookup={},o.experiment_all.set(t.datapoints.map((function(t){var o=a.a.extend({uid:t.uid,from_uid:t.from_uid},t.values);return e.data.dp_lookup[t.uid]=o,o}))),o.all.set(o.experiment_all.get()),o.selected.set(o.experiment_all.get()),e.data.params_def=Cl(this.data.persistent_state.children(y),o.all.get(),t.parameters_definition),this.data.colorby.set(this.data.persistent_state.get(v,n())),void 0===e.data.params_def[this.data.colorby.get()]&&this.data.colorby.set(n()),this.data.colorby.on_change((function(t){e.data.persistent_state.set(v,t)}),this),this.data.get_color_for_row=function(t,o){return function(t,e,o){if(t.type==r.CATEGORICAL){!function(){if(void 0===t.__val2color){t.__val2color=null!==t.colors?t.colors:{};for(var e=0;e<t.distinct_values.length;++e)if(!t.__val2color[t.distinct_values[e]])if(t.distinct_values.length<10){var o=xl(Fc[e%10]);t.__val2color[t.distinct_values[e]]=\"rgb(\"+o.r+\", \"+o.g+\",\"+o.b+\")\"}else{var r=_l(JSON.stringify(t.distinct_values[e]));o=xl(ml()({seed:Math.abs(r)}));t.__val2color[t.distinct_values[e]]=\"rgb(\"+o.r+\", \"+o.g+\",\"+o.b+\")\"}}}();var n=t.__val2color[e];return void 0===n&&(n=\"rgb(0, 0, 0)\"),console.assert(n.startsWith(\"rgb(\")||n.startsWith(\"hsl(\"),n),n.slice(0,3)+\"a\"+n.slice(3,n.length-1)+\",\"+o+\")\"}if(null==e||yl(e))return\"rgba(0,0,0,\"+o+\")\";var s=wl(t);s.range([0,1]);var a=s(e),i=xl(Mc(a));return`rgba(${i.r}, ${i.g}, ${i.b}, ${o})`}(e.data.params_def[e.data.colorby.get()],t[e.data.colorby.get()],o)}}loadWithPromise(t){var e=this;e.setState({loadStatus:n.Loading}),t.then((function(t){if(void 0===t.experiment)return console.log(\"Experiment loading failed\",t),void e.setState({loadStatus:n.Error,experiment:null,error:void 0!==t.error?t.error:\"Unable to load experiment\"});e._loadExperiment(t.experiment),e.setState((function(e,o){return{experiment:t.experiment,version:e.version+1,loadStatus:n.Loaded}}))})).catch(t=>{throw console.log(\"Error\",t),e.setState({loadStatus:n.Error,experiment:null,error:\"HTTP error, check server logs / javascript 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.navbar{display:none}.scoped_css_bootstrap .badge{border:1px solid #000}.scoped_css_bootstrap .table{border-collapse:collapse !important}.scoped_css_bootstrap .table td,.scoped_css_bootstrap .table th{background-color:#fff !important}.scoped_css_bootstrap .table-bordered th,.scoped_css_bootstrap .table-bordered td{border:1px solid #dee2e6 !important}.scoped_css_bootstrap .table-dark{color:inherit}.scoped_css_bootstrap .table-dark th,.scoped_css_bootstrap .table-dark td,.scoped_css_bootstrap .table-dark thead th,.scoped_css_bootstrap .table-dark tbody+tbody{border-color:#dee2e6}.scoped_css_bootstrap .table .thead-dark th{color:inherit;border-color:#dee2e6}}.scoped_css_bootstrap .dropdown-header{font-size:0.875em;padding:0.5em 1.5em}.scoped_css_bootstrap .dropdown-menu{font-size:1em;min-width:10em;padding:0.5em 0;margin:0.125em 0 0;border-radius:0.25em}.scoped_css_bootstrap .dropdown-item{padding:0.25em 1.5em}.scoped_css_bootstrap .dt-buttons{margin-left:10px}\\n',\"\"]),t.exports=e},function(t,e,o){(e=o(7)(!1)).push([t.i,'table.dataTable {\\n  clear: both;\\n  margin-top: 6px !important;\\n  margin-bottom: 6px !important;\\n  max-width: none !important;\\n  border-collapse: separate !important;\\n  border-spacing: 0;\\n}\\ntable.dataTable td,\\ntable.dataTable th {\\n  -webkit-box-sizing: content-box;\\n  box-sizing: content-box;\\n}\\ntable.dataTable td.dataTables_empty,\\ntable.dataTable th.dataTables_empty {\\n  text-align: center;\\n}\\ntable.dataTable.nowrap th,\\ntable.dataTable.nowrap td {\\n  white-space: nowrap;\\n}\\n\\ndiv.dataTables_wrapper div.dataTables_length label {\\n  font-weight: normal;\\n  text-align: left;\\n  white-space: nowrap;\\n}\\ndiv.dataTables_wrapper div.dataTables_length select {\\n  width: auto;\\n  display: inline-block;\\n}\\ndiv.dataTables_wrapper div.dataTables_filter {\\n  text-align: right;\\n}\\ndiv.dataTables_wrapper div.dataTables_filter label {\\n  font-weight: normal;\\n  white-space: nowrap;\\n  text-align: left;\\n}\\ndiv.dataTables_wrapper div.dataTables_filter input {\\n  margin-left: 0.5em;\\n  display: inline-block;\\n  width: auto;\\n}\\ndiv.dataTables_wrapper div.dataTables_info {\\n  padding-top: 0.85em;\\n  white-space: nowrap;\\n}\\ndiv.dataTables_wrapper div.dataTables_paginate {\\n  margin: 0;\\n  white-space: nowrap;\\n  text-align: right;\\n}\\ndiv.dataTables_wrapper div.dataTables_paginate ul.pagination {\\n  margin: 2px 0;\\n  white-space: nowrap;\\n  justify-content: flex-end;\\n}\\ndiv.dataTables_wrapper div.dataTables_processing {\\n  position: absolute;\\n  top: 50%;\\n  left: 50%;\\n  width: 200px;\\n  margin-left: -100px;\\n  margin-top: -26px;\\n  text-align: center;\\n  padding: 1em 0;\\n}\\n\\ntable.dataTable thead > tr > th.sorting_asc, table.dataTable thead > tr > th.sorting_desc, table.dataTable thead > tr > th.sorting,\\ntable.dataTable thead > tr > td.sorting_asc,\\ntable.dataTable thead > tr > td.sorting_desc,\\ntable.dataTable thead > tr > td.sorting {\\n  padding-right: 30px;\\n}\\ntable.dataTable thead > tr > th:active,\\ntable.dataTable thead > tr > td:active {\\n  outline: none;\\n}\\ntable.dataTable thead .sorting,\\ntable.dataTable thead .sorting_asc,\\ntable.dataTable thead .sorting_desc,\\ntable.dataTable thead .sorting_asc_disabled,\\ntable.dataTable thead .sorting_desc_disabled {\\n  cursor: pointer;\\n  position: relative;\\n}\\ntable.dataTable thead .sorting:before, table.dataTable thead .sorting:after,\\ntable.dataTable thead .sorting_asc:before,\\ntable.dataTable thead .sorting_asc:after,\\ntable.dataTable thead .sorting_desc:before,\\ntable.dataTable thead .sorting_desc:after,\\ntable.dataTable thead .sorting_asc_disabled:before,\\ntable.dataTable thead .sorting_asc_disabled:after,\\ntable.dataTable thead .sorting_desc_disabled:before,\\ntable.dataTable thead .sorting_desc_disabled:after {\\n  position: absolute;\\n  bottom: 0.9em;\\n  display: block;\\n  opacity: 0.3;\\n}\\ntable.dataTable thead .sorting:before,\\ntable.dataTable thead .sorting_asc:before,\\ntable.dataTable thead .sorting_desc:before,\\ntable.dataTable thead .sorting_asc_disabled:before,\\ntable.dataTable thead .sorting_desc_disabled:before {\\n  right: 1em;\\n  content: \"\\\\2191\";\\n}\\ntable.dataTable thead .sorting:after,\\ntable.dataTable thead .sorting_asc:after,\\ntable.dataTable thead .sorting_desc:after,\\ntable.dataTable thead .sorting_asc_disabled:after,\\ntable.dataTable thead .sorting_desc_disabled:after {\\n  right: 0.5em;\\n  content: \"\\\\2193\";\\n}\\ntable.dataTable thead .sorting_asc:before,\\ntable.dataTable thead .sorting_desc:after {\\n  opacity: 1;\\n}\\ntable.dataTable thead .sorting_asc_disabled:before,\\ntable.dataTable thead .sorting_desc_disabled:after {\\n  opacity: 0;\\n}\\n\\ndiv.dataTables_scrollHead table.dataTable {\\n  margin-bottom: 0 !important;\\n}\\n\\ndiv.dataTables_scrollBody table {\\n  border-top: none;\\n  margin-top: 0 !important;\\n  margin-bottom: 0 !important;\\n}\\ndiv.dataTables_scrollBody table thead .sorting:before,\\ndiv.dataTables_scrollBody table thead .sorting_asc:before,\\ndiv.dataTables_scrollBody table thead .sorting_desc:before,\\ndiv.dataTables_scrollBody table thead .sorting:after,\\ndiv.dataTables_scrollBody table thead .sorting_asc:after,\\ndiv.dataTables_scrollBody table thead .sorting_desc:after {\\n  display: none;\\n}\\ndiv.dataTables_scrollBody table tbody tr:first-child th,\\ndiv.dataTables_scrollBody table tbody tr:first-child td {\\n  border-top: none;\\n}\\n\\ndiv.dataTables_scrollFoot > .dataTables_scrollFootInner {\\n  box-sizing: content-box;\\n}\\ndiv.dataTables_scrollFoot > .dataTables_scrollFootInner > table {\\n  margin-top: 0 !important;\\n  border-top: none;\\n}\\n\\n@media screen and (max-width: 767px) {\\n  div.dataTables_wrapper div.dataTables_length,\\n  div.dataTables_wrapper div.dataTables_filter,\\n  div.dataTables_wrapper div.dataTables_info,\\n  div.dataTables_wrapper div.dataTables_paginate {\\n    text-align: center;\\n  }\\n}\\ntable.dataTable.table-sm > thead > tr > th {\\n  padding-right: 20px;\\n}\\ntable.dataTable.table-sm .sorting:before,\\ntable.dataTable.table-sm .sorting_asc:before,\\ntable.dataTable.table-sm .sorting_desc:before {\\n  top: 5px;\\n  right: 0.85em;\\n}\\ntable.dataTable.table-sm .sorting:after,\\ntable.dataTable.table-sm .sorting_asc:after,\\ntable.dataTable.table-sm .sorting_desc:after {\\n  top: 5px;\\n}\\n\\ntable.table-bordered.dataTable th,\\ntable.table-bordered.dataTable td {\\n  border-left-width: 0;\\n}\\ntable.table-bordered.dataTable th:last-child, table.table-bordered.dataTable th:last-child,\\ntable.table-bordered.dataTable td:last-child,\\ntable.table-bordered.dataTable td:last-child {\\n  border-right-width: 0;\\n}\\ntable.table-bordered.dataTable tbody th,\\ntable.table-bordered.dataTable tbody td {\\n  border-bottom-width: 0;\\n}\\n\\ndiv.dataTables_scrollHead table.table-bordered {\\n  border-bottom-width: 0;\\n}\\n\\ndiv.table-responsive > div.dataTables_wrapper > div.row {\\n  margin: 0;\\n}\\ndiv.table-responsive > div.dataTables_wrapper > div.row > div[class^=\"col-\"]:first-child {\\n  padding-left: 0;\\n}\\ndiv.table-responsive > div.dataTables_wrapper > div.row > div[class^=\"col-\"]:last-child {\\n  padding-right: 0;\\n}\\n',\"\"]),t.exports=e},function(t,e,o){(e=o(7)(!1)).push([t.i,\"/*\\n * Copyright (c) Facebook, Inc. and its affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n.hiplot__1tz36 {\\n  margin: 0;\\n  width: 100%;\\n  height: 100%;\\n  padding: 0;\\n}\\n.hiplot__1tz36 {\\n  font-family: Ubuntu, Tahoma, Helvetica, sans-serif;\\n  font-size: 12px;\\n  line-height: 1.4em;\\n  background: #f7f7f7;\\n  color: #404040;\\n}\\n.dark__3Mtlu {\\n  background: #131313;\\n  color: #e3e3e3;\\n}\\n.hiplot__1tz36 a {\\n  text-decoration: none;\\n}\\n.dark__3Mtlu a {\\n  color: #5ae;\\n}\\n.wrap__1QnDj {\\n  padding: 0 3.5%;\\n}\\n.parallel-plot-chart__3Dxj4 svg {\\n  font-family: Ubuntu, Tahoma, Helvetica, sans-serif;\\n}\\n.parallel-plot-chart__3Dxj4 canvas, .parallel-plot-chart__3Dxj4 svg {\\n  position: absolute;\\n  top: 0;\\n  left: 0;\\n}\\n.parallel-plot-chart__3Dxj4 {\\n  position: relative;\\n}\\n.hiplot__1tz36 .brush__3H_ut rect.extent__1yYHW {\\n  fill: rgba(100,100,100,0.15);\\n  stroke: #fff;\\n}\\n.dark__3Mtlu .brush__3H_ut rect.extent__1yYHW {\\n  fill: rgba(100,100,100,0.15);\\n  stroke: #ddd;\\n}\\n.hiplot__1tz36 .brush__3H_ut:hover rect.extent__1yYHW {\\n  stroke: #222;\\n  stroke-dasharray: 5,5;\\n}\\n.hiplot__1tz36 .brush__3H_ut rect.extent__1yYHW:hover {\\n  stroke-dasharray: none;\\n}\\n.resize__xXFNR rect {\\n  fill: none;\\n}\\n.background__1HVDu {\\n  fill: none;\\n}\\n.dark__3Mtlu .background__1HVDu {\\n  fill: none;\\n}\\n.axis__1Rdjm line, .axis__1Rdjm path {\\n  fill: none;\\n  stroke: #777;\\n  stroke-width: 1;\\n}\\n.dark__3Mtlu .axis__1Rdjm line, .dark__3Mtlu .axis__1Rdjm path {\\n  stroke: #777;\\n}\\n.axis__1Rdjm .tick {\\n  width: 200px;\\n}\\n.axis__1Rdjm text {\\n  fill: #111;\\n  text-anchor: right;\\n  font-size: 11px;\\n  text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;\\n}\\n.axis__1Rdjm text.label__1qU4l {\\n  font-size: 12px;\\n  cursor: move;\\n  padding-bottom: 24px;\\n}\\n.dark__3Mtlu .axis__1Rdjm text {\\n  fill: #f2f2f2;\\n  text-shadow: 0 1px 0 #000, 1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;\\n}\\n.dark__3Mtlu .axis__1Rdjm text.label__1qU4l {\\n  fill: #ddd;\\n}\\n.quarter__26Pim, .third__3ZtPv, .half__3OAHv {\\n  float: left;\\n}\\n.quarter__26Pim {\\n  width: 23%;\\n  margin: 0 1%;\\n}\\n.third__3ZtPv {\\n  width: 31.3%;\\n  margin: 0 1%;\\n}\\n.half__3OAHv {\\n  width: 48%;\\n  margin: 0 1%;\\n}\\n.hiplot__1tz36 h3 {\\n  margin: 12px 0 9px;\\n}\\n.hiplot__1tz36 h3 small {\\n  color: #888;\\n  font-weight: normal;\\n}\\n.hiplot__1tz36 p {\\n  margin: 0.6em 0;\\n}\\n.hiplot__1tz36 small {\\n  line-height: 1.2em;\\n}\\n.hiplot__1tz36 button:disabled {\\n  color: #555 !important;\\n  opacity: 0.4;\\n}\\n.settings__3XUvA button:disabled {\\n  display: none;\\n}\\n.dark__3Mtlu button:disabled {\\n  opacity: 0.2;\\n}\\n.hiplot__1tz36 .restoreData__kmQov,\\n.hiplot__1tz36 .keepData__1wGUt,\\n.hiplot__1tz36 .excludeData__1xfsY {\\n  font-weight:bold\\n}\\n.hiplot__1tz36 .keepData__1wGUt:hover:enabled {\\n  color: #080;\\n}\\n.hiplot__1tz36 .excludeData__1xfsY:hover:enabled {\\n  color: #900;\\n}\\n\\n.renderedBar__3fKBS,\\n.selectedBar__1RrPb {\\n  width:0%;\\n  font-weight: bold;\\n  height: 100%;\\n}\\n.renderedBar__3fKBS {\\n  background: #3d9aff;\\n  border-right: 1px solid #666;\\n}\\n.selectedBar__1RrPb {\\n  background: rgba(171, 171, 171, 0.5);\\n  border-right: 1px solid #999;\\n}\\n.fillbar__1Ynhr {\\n  height: 2px;\\n  line-height: 2px;\\n  width: 100%;\\n}\\n.little-box__34AUq {\\n  width: 268px;\\n  float: left;\\n}\\n.controls__3m4MV {\\n  float: right;\\n  height: 24px;\\n  line-height: 24px;\\n}\\n\\n/* Scrollbars */\\n\\n::-webkit-scrollbar {\\n  width: 10px;\\n  height: 10px;\\n}\\n\\n::-webkit-scrollbar-track {\\n  background: #ddd;\\n  border-radius: 12px;\\n}\\n\\n::-webkit-scrollbar-thumb {\\n  background: #b5b5b5;\\n  border-radius: 12px;\\n}\\n.dark__3Mtlu ::-webkit-scrollbar-track {\\n  background: #222;\\n}\\n.dark__3Mtlu ::-webkit-scrollbar-thumb {\\n  background: #444;\\n}\\n\\n.checkpoints-graph-svg__275nK .tick line {\\n  color: #9a9a9a26;\\n}\\n\\ntr.highlight__2gbzS {\\n  background-color: #e2e2e2 !important;\\n}\\n\\n.min-height-100__D7-2J {\\n  min-height: 100vh;\\n}\\n\\n.horizontal-scrollable__1486A {\\n  overflow-x: auto;\\n}\\n\\n.colorBlock__p1XlS {\\n  height: 10px;\\n  width: 10px;\\n  display: inline-block;\\n}\\n\",\"\"]),e.locals={hiplot:\"hiplot__1tz36\",dark:\"dark__3Mtlu\",wrap:\"wrap__1QnDj\",\"parallel-plot-chart\":\"parallel-plot-chart__3Dxj4\",brush:\"brush__3H_ut\",extent:\"extent__1yYHW\",resize:\"resize__xXFNR\",background:\"background__1HVDu\",axis:\"axis__1Rdjm\",label:\"label__1qU4l\",quarter:\"quarter__26Pim\",third:\"third__3ZtPv\",half:\"half__3OAHv\",settings:\"settings__3XUvA\",restoreData:\"restoreData__kmQov\",keepData:\"keepData__1wGUt\",excludeData:\"excludeData__1xfsY\",renderedBar:\"renderedBar__3fKBS\",selectedBar:\"selectedBar__1RrPb\",fillbar:\"fillbar__1Ynhr\",\"little-box\":\"little-box__34AUq\",controls:\"controls__3m4MV\",\"checkpoints-graph-svg\":\"checkpoints-graph-svg__275nK\",highlight:\"highlight__2gbzS\",\"min-height-100\":\"min-height-100__D7-2J\",\"horizontal-scrollable\":\"horizontal-scrollable__1486A\",colorBlock:\"colorBlock__p1XlS\"},t.exports=e},function(t,e,o){(e=o(7)(!1)).push([t.i,'/*\\n * Copyright (c) Facebook, Inc. and its affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n.resizableH__1C5ia {\\n    padding-bottom: 4px;\\n    position: relative;\\n}\\n\\n.resizableH__1C5ia:after {\\n    content: \" \";\\n    background-color: #ccc;\\n    position: absolute;\\n    bottom: 0;\\n    width: 100%;\\n    height: 4px;\\n    cursor: row-resize;\\n}\\n',\"\"]),e.locals={resizableH:\"resizableH__1C5ia\"},t.exports=e},function(t,e,o){(e=o(7)(!1)).push([t.i,\"/*\\n * Copyright (c) Facebook, Inc. and its affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n.runsSelectionTextarea__3aada {\\n    width:  100%;\\n    height:  50px;\\n    font-family:monospace;\\n    font-size: 12pt;\\n    resize: none;\\n    overflow: hidden;\\n}\\n\\n.hasFocus__3kGCD {\\n    height: 25px !important;\\n}\\n\\n.header__F5Vsl {\\n    border-bottom: 1px solid rgba(100,100,100,0.35);\\n    background: #e2e2e2;\\n    padding: 6px 24px 4px;\\n    line-height: 24px;\\n}\\n.dark__1XaRk .header__F5Vsl {\\n    background: #040404;\\n    color: #f3f3f3;\\n}\\n.header__F5Vsl h1 {\\n    display: inline-block;\\n    margin: 0px 14px 0 0;\\n}\\n.header__F5Vsl button {\\n    vertical-align: top;\\n}\\n\\n.controlGroup__1C7Pw {\\n    margin-left: 5px;\\n    margin-right: 5px;\\n}\\n\",\"\"]),e.locals={runsSelectionTextarea:\"runsSelectionTextarea__3aada\",hasFocus:\"hasFocus__3kGCD\",header:\"header__F5Vsl\",dark:\"dark__1XaRk\",controlGroup:\"controlGroup__1C7Pw\"},t.exports=e},function(t,e,o){(e=o(7)(!1)).push([t.i,\"/*\\n * Copyright (c) Facebook, Inc. and its affiliates.\\n *\\n * This source code is licensed under the MIT license found in the\\n * LICENSE file in the root directory of this source tree.\\n */\\n\\n .highlightElement__1eCg- {\\n    animation-duration: 1s;\\n    animation-name: highlightAnimation__2OR5v;\\n    animation-iteration-count: infinite;\\n}\\n\\n@keyframes highlightAnimation__2OR5v {\\n    from {\\n        fill: #00000000;\\n        background-color: #00000000;\\n    }\\n\\n    to {\\n        fill: #e0e0e087;\\n        background-color: #e0e0e087;\\n    }\\n}\\n\\n.highlightText__RS78M {\\n    animation-duration: 1s;\\n    animation-name: highlightTextAnimation__2y7Wz;\\n    animation-iteration-count: infinite;\\n    font-size: 18px !important;\\n}\\n\\n@keyframes highlightTextAnimation__2y7Wz {\\n    from {\\n        fill: black;\\n    }\\n    to {\\n        fill: #00259e;\\n    }\\n}\\n\\n\\n.tutoAlert__3OtBs {\\n    font-size: 16px;\\n}\\n\",\"\"]),e.locals={highlightElement:\"highlightElement__1eCg-\",highlightAnimation:\"highlightAnimation__2OR5v\",highlightText:\"highlightText__RS78M\",highlightTextAnimation:\"highlightTextAnimation__2y7Wz\",tutoAlert:\"tutoAlert__3OtBs\"},t.exports=e}]);\n",
       "//# sourceMappingURL=hiplot.bundle.js.map"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html>\n",
       "<html>\n",
       "  <head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\"/>\n",
       "    <title>HiPlot</title>\n",
       "    <link rel=\"icon\" href=\"static/icon.png\" />\n",
       "  </head>\n",
       "  <body style=\"margin:0px\">\n",
       "    <div id=\"hiplot_a1b0096502c24293a5ecddeabd7567f3\" style=\"background-color: white\"><div style=\"text-align: center\">Loading HiPlot...</div>\n",
       "      <noscript>\n",
       "        HiPlot needs JavaScript to run\n",
       "      </noscript>\n",
       "    </div>\n",
       "\n",
       "    <script  type=\"text/javascript\" ></script>\n",
       "    <script type=\"text/javascript\">\n",
       "    function _hiplot_setup() {\n",
       "      if (typeof hiplot_setup == \"undefined\") {\n",
       "        console.info(\"waiting for HiPlot bundle to be loaded...\");\n",
       "        setTimeout(_hiplot_setup, 1000);\n",
       "        return;\n",
       "      }\n",
       "      var options = {\n",
       "        is_webserver: true,\n",
       "      };\n",
       "      /*ON_LOAD_SCRIPT_INJECT*/\n",
       "const comm_id = \"comm_d09312\";\n",
       "try {\n",
       "    console.log(\"Setting up communication channel with Jupyter: \", comm_id);\n",
       "    var comm = Jupyter.notebook.kernel.comm_manager.new_comm(comm_id, {'type': 'hello'});\n",
       "    Object.assign(options, {\"comm\": comm});\n",
       "}\n",
       "catch(err) {\n",
       "    console.warn('Unable to create Javascript <-> Python communication channel' +\n",
       "        ' (are you in a Jupyter notebook? Jupyter labs is *not* supported!)');\n",
       "}\n",
       "        \n",
       "        Object.assign(options, eval('(' + \"{\\\"is_webserver\\\": false, \\\"experiment\\\": {\\\"datapoints\\\": [{\\\"uid\\\": \\\"0\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 1, \\\"train_loss\\\": 2.2567057985740937, \\\"train_acc\\\": 0.1966784381663113, \\\"valid_loss\\\": 2.20783610283574, \\\"valid_acc\\\": 0.11471518987341772}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"1\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 2, \\\"train_loss\\\": 1.9282052176339286, \\\"train_acc\\\": 0.3599413646055437, \\\"valid_loss\\\": 2.658291973645174, \\\"valid_acc\\\": 0.15090981012658228}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"2\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 3, \\\"train_loss\\\": 1.6841899164195762, \\\"train_acc\\\": 0.459005197228145, \\\"valid_loss\\\": 2.697213281559039, \\\"valid_acc\\\": 0.18067642405063292}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"3\\\", \\\"values\\\": 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\\\"from_uid\\\": null}, {\\\"uid\\\": \\\"21\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 22, \\\"train_loss\\\": 1.1980372186916977, \\\"train_acc\\\": 0.5757595948827292, \\\"valid_loss\\\": 5.488918256156052, \\\"valid_acc\\\": 0.17365506329113925}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"22\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 23, \\\"train_loss\\\": 1.193253962470016, \\\"train_acc\\\": 0.5757762526652452, \\\"valid_loss\\\": 5.657107968873616, \\\"valid_acc\\\": 0.16930379746835442}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"23\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 24, \\\"train_loss\\\": 1.190566984066831, \\\"train_acc\\\": 0.5796075426439232, \\\"valid_loss\\\": 5.608898694002176, \\\"valid_acc\\\": 0.16386471518987342}, \\\"from_uid\\\": null}, {\\\"uid\\\": \\\"24\\\", \\\"values\\\": {\\\"activation\\\": \\\"ReLU\\\", \\\"epoch\\\": 25, \\\"train_loss\\\": 1.1823643275669642, \\\"train_acc\\\": 0.5816897654584222, \\\"valid_loss\\\": 5.459799464744858, \\\"valid_acc\\\": 0.15822784810126583}, \\\"from_uid\\\": null}], \\\"parameters_definition\\\": {}, \\\"_displays\\\": {\\\"PARALLEL_PLOT\\\": {}, \\\"TABLE\\\": {}, \\\"XY\\\": {}}}, \\\"persistent_state\\\": null}\" + ')'));\n",
       "        \n",
       "      var hiplot_instance = hiplot_setup(document.getElementById(\"hiplot_a1b0096502c24293a5ecddeabd7567f3\"), options);\n",
       "      /*AFTER_SETUP_SCRIPT_INJECT*/\n",
       "    }\n",
       "    if (document.readyState == \"complete\") {\n",
       "      _hiplot_setup();\n",
       "    }\n",
       "    else {\n",
       "      document.addEventListener(\"DOMContentLoaded\", _hiplot_setup);\n",
       "    }\n",
       "    </script>\n",
       "  </body>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<hiplot.ipython.IPythonExperimentDisplayed at 0x2cc1dda6f28>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hiplot as hip\n",
    "hip.Experiment.from_iterable(data).display(force_full_width = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are numerous insights that we can gather from the above graph:\n",
    "\n",
    "* As number of epochs increase, train loss correspondingly decreases while training accuracy increases\n",
    "* However, a higher training accuracy corresponded with a high validation loss, which means that on such instances, our model's prediction on the validation set was far from the truth. \n",
    "* Further, we reached our best validation accuracy of 18% on epoch 3! This gives us insight that our model was very quick to overfit the data and hence, a lower learning rate plus an added regularization term might produce better results.\n",
    "\n",
    "With this knowledge, we can make more informed decision on how we can \"tweak\" our architecture to possibly produce better results. We will perform these tweaks on our [lr and regularization]() tutorial.\n",
    "\n",
    "# Comparison\n",
    "\n",
    "Now, we will compare the performance of our ReLU activation function with two other activation functions:\n",
    "\n",
    "* Tanh and\n",
    "* Leaky ReLU\n",
    "\n",
    "As was previously alluded, distinct activation functions inherit distinct properties.\n",
    "\n",
    "As for the case of ReLU, we described its \"gate\" like properties. However, ReLU suffers from the \"dying ReLU\" problem: the input and gradient of negative inputs turn to zero, thereby making it impossible for weights that correspond to the negative input to update.\n",
    "\n",
    "In a similar fashion, Tanh suffers from the \"vanishing gradient\" problem. Let us look at the graph of tanh:\n",
    "\n",
    "$$\n",
    "f(x)=\\frac{ e^x-e^{-x}}{e^x+e^{-x}} \\\\f'(x) = 1-f(x)^2\n",
    "$$\n",
    "\n",
    "\n",
    "![Hyper-parameters in Action! Part I — Activation Functions](https://miro.medium.com/max/848/1*SeCBB7lfA7KPJ-T1Mi7GRg.png)\n",
    "\n",
    "\n",
    "\n",
    "As the input infinitely diverges from zero, its gradient infinitely approximates zero. In such case, while these neurons do not become \"dead,\" their chances of \"recovering\" towards a non-zero gradient to make meaningful weight changes are diminished as the input diverges from zero.\n",
    "\n",
    "Leaky ReLU attempts to solve the \"dying ReLU\" problem by giving a chance for the gradients of negative inputs to \"recover.\" Look at its below graph\n",
    "\n",
    "$$\n",
    "f(x) =\\begin{cases}.01x & x < 0 \\\\x & x \\ge 0\\end{cases} \\\\f'(x) =\\begin{cases}.01 & x < 0 \\\\1 & x \\ge 0\\end{cases}\n",
    "$$\n",
    "\n",
    "\n",
    "![Activation Functions | Code Odysseys](https://codeodysseys.com/posts/activation-functions/graph-activation-6.png)\n",
    "\n",
    "As we can see, negative inputs are no longer \"cut\" to zero. They are just \"down-weighted\" by a negative slope. Usually, this is left as a parameter for the user to choose, but it is standard to have a negative slope of .01. This gives negative inputs a chance to \"recover\" or still remain liable to make meaningful changes to our corresponding parameters.\n",
    "\n",
    "Figuring the optimal activation function for a model is often a matter of trial and error. Hence, we will apply the equivalent architecture as we did above but this time, we will use our newly defined activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "################## Tanh Layer #######################\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "\n",
    "class tanh_layer(torch.autograd.Function):\n",
    "    \n",
    "    def __init__(self):\n",
    "        ''\n",
    "    \n",
    "    def tanh(self,x):\n",
    "        tanh = (x.exp() - (-x).exp()) / (x.exp() + (-x).exp())\n",
    "        return tanh\n",
    "    \n",
    "    # forward pass\n",
    "    def forward(self, input):\n",
    "        # save input for backward() pass \n",
    "        self.save_for_backward(input) \n",
    "        activated_input = self.tanh(input)\n",
    "        return activated_input\n",
    "\n",
    "    # integrate backward pass with incoming_grad\n",
    "    def backward(self, incoming_grad):\n",
    "\n",
    "        input, = self.saved_tensors\n",
    "        chained_grad = (1 - self.tanh(input)^2)  * incoming_grad\n",
    "        return chained_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tanh(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.tanh = tanh_layer()\n",
    "        \n",
    "    def forward(self, input):\n",
    "        output = self.tanh.forward(input)\n",
    "        return output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "################## Leaky ReLU Layer #######################\n",
    "\n",
    "class Leaky_ReLU_layer(torch.autograd.Function):\n",
    "    \n",
    "    @staticmethod\n",
    "    def forward(self, input):\n",
    "        # making operation in-place to tensors that hold \"history\" will mess up\n",
    "        # the dynamic graph. Thus, clone it.\n",
    "        activated_input = input.clone()\n",
    "        self.save_for_backward(activated_input)\n",
    "        activated_input[activated_input < 0] *= .01\n",
    "        return activated_input\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(self, incoming_grad):\n",
    "\n",
    "        input, = self.saved_tensors\n",
    "        output_grad = incoming_grad.clone()\n",
    "        output_grad[input < 0] *= .01\n",
    "        return output_grad "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeakyReLU(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        ''\n",
    "        \n",
    "        \n",
    "    def forward(self, input):\n",
    "        output = Leaky_ReLU_layer.apply(input)\n",
    "        return output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate models\n",
    "tanh_model = NeuralNet(activation = Tanh()).to(device)\n",
    "leaky_model = NeuralNet(activation = LeakyReLU()).to(device)\n",
    "\n",
    "model = {'tanh': tanh_model,\n",
    "        'leaky_relu': leaky_model}\n",
    "\n",
    "\n",
    "optimizer = {'tanh': optim.SGD(tanh_model.parameters(), lr = .01),\n",
    "        'leaky_relu': optim.SGD(leaky_model.parameters(), lr = .01)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 01 | Epoch Time: 0m 23s\n",
      "\tTrain Loss: 2.135 | Train Acc: 25.89%\n",
      "\t Val. Loss: 2.144 |  Val. Acc: 16.62%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 02 | Epoch Time: 0m 38s\n",
      "\tTrain Loss: 1.789 | Train Acc: 43.35%\n",
      "\t Val. Loss: 2.489 |  Val. Acc: 15.23%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 03 | Epoch Time: 0m 39s\n",
      "\tTrain Loss: 1.603 | Train Acc: 49.43%\n",
      "\t Val. Loss: 3.176 |  Val. Acc: 16.60%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 04 | Epoch Time: 0m 39s\n",
      "\tTrain Loss: 1.510 | Train Acc: 51.15%\n",
      "\t Val. Loss: 3.610 |  Val. Acc: 16.60%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 05 | Epoch Time: 0m 33s\n",
      "\tTrain Loss: 1.454 | Train Acc: 52.14%\n",
      "\t Val. Loss: 3.997 |  Val. Acc: 17.10%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 06 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.411 | Train Acc: 52.73%\n",
      "\t Val. Loss: 4.143 |  Val. Acc: 17.32%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 07 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.378 | Train Acc: 53.21%\n",
      "\t Val. Loss: 4.446 |  Val. Acc: 16.69%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 08 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.351 | Train Acc: 53.54%\n",
      "\t Val. Loss: 4.687 |  Val. Acc: 16.29%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 09 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.332 | Train Acc: 53.74%\n",
      "\t Val. Loss: 4.419 |  Val. Acc: 16.83%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 10 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.313 | Train Acc: 54.02%\n",
      "\t Val. Loss: 4.609 |  Val. Acc: 16.77%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 11 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.300 | Train Acc: 54.30%\n",
      "\t Val. Loss: 4.505 |  Val. Acc: 16.28%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 12 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.288 | Train Acc: 54.45%\n",
      "\t Val. Loss: 4.570 |  Val. Acc: 15.96%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 13 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.276 | Train Acc: 54.94%\n",
      "\t Val. Loss: 4.893 |  Val. Acc: 15.96%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 14 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.266 | Train Acc: 54.99%\n",
      "\t Val. Loss: 4.865 |  Val. Acc: 17.41%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 15 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.259 | Train Acc: 55.17%\n",
      "\t Val. Loss: 4.627 |  Val. Acc: 11.21%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 16 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.249 | Train Acc: 55.60%\n",
      "\t Val. Loss: 4.820 |  Val. Acc: 18.63%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 17 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.241 | Train Acc: 56.04%\n",
      "\t Val. Loss: 4.849 |  Val. Acc: 16.69%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 18 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.233 | Train Acc: 56.27%\n",
      "\t Val. Loss: 4.656 |  Val. Acc: 17.83%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 19 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.228 | Train Acc: 56.35%\n",
      "\t Val. Loss: 4.678 |  Val. Acc: 17.43%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 20 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.219 | Train Acc: 56.68%\n",
      "\t Val. Loss: 4.487 |  Val. Acc: 18.07%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 21 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.213 | Train Acc: 56.96%\n",
      "\t Val. Loss: 4.870 |  Val. Acc: 17.18%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 22 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.207 | Train Acc: 57.20%\n",
      "\t Val. Loss: 4.697 |  Val. Acc: 18.75%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 23 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.202 | Train Acc: 57.41%\n",
      "\t Val. Loss: 4.862 |  Val. Acc: 16.95%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 24 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.194 | Train Acc: 57.54%\n",
      "\t Val. Loss: 4.773 |  Val. Acc: 17.14%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: tanh\n",
      "Epoch: 25 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.189 | Train Acc: 57.83%\n",
      "\t Val. Loss: 4.729 |  Val. Acc: 17.24%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 01 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 2.229 | Train Acc: 19.80%\n",
      "\t Val. Loss: 2.163 |  Val. Acc: 14.59%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 02 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.891 | Train Acc: 35.63%\n",
      "\t Val. Loss: 2.367 |  Val. Acc: 17.35%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 03 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.675 | Train Acc: 45.36%\n",
      "\t Val. Loss: 2.842 |  Val. Acc: 15.40%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 04 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.546 | Train Acc: 50.31%\n",
      "\t Val. Loss: 3.885 |  Val. Acc: 17.01%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 05 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.461 | Train Acc: 51.76%\n",
      "\t Val. Loss: 3.837 |  Val. Acc: 17.00%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 06 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.407 | Train Acc: 52.52%\n",
      "\t Val. Loss: 4.455 |  Val. Acc: 14.65%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 07 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.371 | Train Acc: 53.03%\n",
      "\t Val. Loss: 5.038 |  Val. Acc: 17.06%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 08 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.346 | Train Acc: 53.43%\n",
      "\t Val. Loss: 4.564 |  Val. Acc: 16.89%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 09 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.324 | Train Acc: 53.94%\n",
      "\t Val. Loss: 4.592 |  Val. Acc: 15.30%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 10 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.308 | Train Acc: 54.31%\n",
      "\t Val. Loss: 4.754 |  Val. Acc: 13.86%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 11 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.294 | Train Acc: 54.36%\n",
      "\t Val. Loss: 4.803 |  Val. Acc: 13.49%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 12 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.282 | Train Acc: 54.94%\n",
      "\t Val. Loss: 5.465 |  Val. Acc: 15.55%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 13 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.270 | Train Acc: 55.12%\n",
      "\t Val. Loss: 5.038 |  Val. Acc: 16.33%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 14 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.260 | Train Acc: 55.39%\n",
      "\t Val. Loss: 5.293 |  Val. Acc: 16.86%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 15 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.251 | Train Acc: 55.74%\n",
      "\t Val. Loss: 4.618 |  Val. Acc: 15.69%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 16 | Epoch Time: 0m 17s\n",
      "\tTrain Loss: 1.238 | Train Acc: 56.18%\n",
      "\t Val. Loss: 4.864 |  Val. Acc: 17.60%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 17 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.228 | Train Acc: 56.52%\n",
      "\t Val. Loss: 5.425 |  Val. Acc: 15.45%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 18 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.219 | Train Acc: 56.80%\n",
      "\t Val. Loss: 5.255 |  Val. Acc: 16.88%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 19 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.211 | Train Acc: 57.14%\n",
      "\t Val. Loss: 5.215 |  Val. Acc: 16.84%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 20 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.204 | Train Acc: 57.29%\n",
      "\t Val. Loss: 5.489 |  Val. Acc: 16.86%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 21 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.197 | Train Acc: 57.66%\n",
      "\t Val. Loss: 5.389 |  Val. Acc: 17.53%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 22 | Epoch Time: 0m 18s\n",
      "\tTrain Loss: 1.190 | Train Acc: 57.75%\n",
      "\t Val. Loss: 4.996 |  Val. Acc: 16.31%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 23 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.185 | Train Acc: 58.14%\n",
      "\t Val. Loss: 5.293 |  Val. Acc: 18.03%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 24 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.180 | Train Acc: 58.35%\n",
      "\t Val. Loss: 5.186 |  Val. Acc: 17.22%\n",
      "---------------------------------------------------------------------------\n",
      "Activation: leaky_relu\n",
      "Epoch: 25 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 1.178 | Train Acc: 58.29%\n",
      "\t Val. Loss: 5.750 |  Val. Acc: 18.29%\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 25\n",
    "\n",
    "# track statistics\n",
    "track_stats = {'activation': [],\n",
    "               'epoch': [],\n",
    "               'train_loss': [],\n",
    "              'train_acc': [],\n",
    "              'valid_loss':[],\n",
    "              'valid_acc':[]}\n",
    "\n",
    "track_stats = {'tanh': track_stats.copy(),\n",
    "              'leaky_relu': track_stats.copy()} \n",
    "\n",
    "\n",
    "for activation in ['tanh','leaky_relu']:\n",
    "    \n",
    "    # re-initiate per activation\n",
    "    best_valid_loss = float('inf')\n",
    "\n",
    "\n",
    "    for epoch in range(N_EPOCHS):\n",
    "\n",
    "        start_time = time.time()\n",
    "\n",
    "        train_loss, train_acc = train(model[activation], train_dl, optimizer[activation], criterion)\n",
    "        valid_loss, valid_acc = evaluate(model[activation], eval_dl, criterion)\n",
    "\n",
    "        end_time = time.time()\n",
    "\n",
    "        # record operations\n",
    "        track_stats[activation]['activation'].append(activation)\n",
    "        track_stats[activation]['epoch'].append(epoch + 1)\n",
    "        track_stats[activation]['train_loss'].append(train_loss)\n",
    "        track_stats[activation]['train_acc'].append(train_acc)\n",
    "        track_stats[activation]['valid_loss'].append(valid_loss)\n",
    "        track_stats[activation]['valid_acc'].append(valid_acc)\n",
    "\n",
    "\n",
    "\n",
    "        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "\n",
    "        # if this was our best performance, record model parameters\n",
    "        if valid_loss < best_valid_loss:\n",
    "            best_valid_loss = valid_loss\n",
    "            torch.save(model[activation].state_dict(), 'best_'+activation+'_params.pt')\n",
    "\n",
    "        # print out stats\n",
    "        print('-'*75)\n",
    "        print(f'Activation: {activation}')\n",
    "        print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "        print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "        print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')\n",
    "        \n",
    "        # if this is the last loop, save statistics\n",
    "        if epoch + 1 == N_EPOCHS:\n",
    "            torch.save(track_stats[activation],activation + '_stats.pt')\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activation</th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_acc</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>1</td>\n",
       "      <td>2.256706</td>\n",
       "      <td>0.196678</td>\n",
       "      <td>2.207836</td>\n",
       "      <td>0.114715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>2</td>\n",
       "      <td>1.928205</td>\n",
       "      <td>0.359941</td>\n",
       "      <td>2.658292</td>\n",
       "      <td>0.150910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>3</td>\n",
       "      <td>1.684190</td>\n",
       "      <td>0.459005</td>\n",
       "      <td>2.697213</td>\n",
       "      <td>0.180676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>4</td>\n",
       "      <td>1.530393</td>\n",
       "      <td>0.502265</td>\n",
       "      <td>3.532650</td>\n",
       "      <td>0.160502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ReLU</td>\n",
       "      <td>5</td>\n",
       "      <td>1.443833</td>\n",
       "      <td>0.519773</td>\n",
       "      <td>3.861390</td>\n",
       "      <td>0.164161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activation  epoch  train_loss  train_acc  valid_loss  valid_acc\n",
       "0       ReLU      1    2.256706   0.196678    2.207836   0.114715\n",
       "1       ReLU      2    1.928205   0.359941    2.658292   0.150910\n",
       "2       ReLU      3    1.684190   0.459005    2.697213   0.180676\n",
       "3       ReLU      4    1.530393   0.502265    3.532650   0.160502\n",
       "4       ReLU      5    1.443833   0.519773    3.861390   0.164161"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "relu_stats = pd.DataFrame(torch.load('ReLU_stats.pt'))\n",
    "tanh_stats = pd.DataFrame(track_stats['tanh'])\n",
    "leaky_stats = pd.DataFrame(track_stats['leaky_relu'])\n",
    "all_stats = pd.concat([relu_stats, tanh_stats, leaky_stats])\n",
    "all_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for row in all_stats.iterrows():\n",
    "    data.append(row[1].to_dict())        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<div id=\"wrap_html_934ae1\"><!DOCTYPE html>\n",
       "<html>\n",
       "  <head>\n",
       "    <meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\"/>\n",
       "    <title>HiPlot</title>\n",
       "    <link rel=\"icon\" href=\"static/icon.png\" />\n",
       "  </head>\n",
       "  <body style=\"margin:0px\">\n",
       "    <div id=\"hiplot_a3dd82cda6b94c52a4792397922c829c\" style=\"background-color: white\"><div style=\"text-align: center\">Loading HiPlot...</div>\n",
       "      <noscript>\n",
       "        HiPlot needs JavaScript to run\n",
       "      </noscript>\n",
       "    </div>\n",
       "\n",
       "    <script  type=\"text/javascript\" ></script>\n",
       "    <script type=\"text/javascript\">\n",
       "    function _hiplot_setup() {\n",
       "      if (typeof hiplot_setup == \"undefined\") {\n",
       "        console.info(\"waiting for HiPlot bundle to be loaded...\");\n",
       "        setTimeout(_hiplot_setup, 1000);\n",
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       "      var options = {\n",
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       "      /*ON_LOAD_SCRIPT_INJECT*/\n",
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       "    console.log(\"Setting up communication channel with Jupyter: \", comm_id);\n",
       "    var comm = Jupyter.notebook.kernel.comm_manager.new_comm(comm_id, {'type': 'hello'});\n",
       "    Object.assign(options, {\"comm\": comm});\n",
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       "    console.warn('Unable to create Javascript <-> Python communication channel' +\n",
       "        ' (are you in a Jupyter notebook? Jupyter labs is *not* supported!)');\n",
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       "        \n",
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       "      /*AFTER_SETUP_SCRIPT_INJECT*/\n",
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       "    scale_to_100pct_screen();\n",
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       "<IPython.core.display.HTML object>"
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       "<hiplot.ipython.IPythonExperimentDisplayed at 0x1cb929e8c18>"
      ]
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     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import hiplot as hip\n",
    "hip.Experiment.from_iterable(data).display(force_full_width = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "There are many insights our graph sheds light to:\n",
    "\n",
    "* tanh and leaky ReLU attained the best validation accuracies \n",
    "* ReLU reached its best accuracy early on the process at epoch 3 while tanh and leaky ReLU reached their best performance along epochs 16 to 25. \n",
    "\n",
    "Just the above two insights let us know that ReLU very quickly overfits our data while tanh and leaky ReLU take more time, which is a desirable property.\n",
    "\n",
    "Overall, if we had to choose one activation operation, we would go with tanh as it received the top two best validation accuracies.\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "Activation functions are fundamental to any DL architecture and understanding their theory along with their implementation will make one more \"aware\" of the care and importance that goes on choosing the right activation function. Hopefully, it may also bring you to create your own, never before seen, activation function.\n",
    "\n",
    "That is it for this tutorial. Thank you guys!\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Where to Next?\n",
    "\n",
    "**Logistic Regression Tutorial:**\n",
    "https://nbviewer.jupyter.org/github/Erick7451/DL-with-PyTorch/blob/master/Jupyter_Notebooks/logistic_regression.ipynb"
   ]
  }
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