Deep Learning with Python.ipynb

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   "source": [
    "# Deep Learning with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "This guide was written in Python 3.6.\n",
    "\n",
    "### Python and Pip\n",
    "\n",
    "If you haven't already, please download [Python](https://www.python.org/downloads/) and [Pip](https://pip.pypa.io/en/stable/installing/).\n",
    "\n",
    "### Libraries\n",
    "\n",
    "Let's install the modules we'll need for this tutorial. Open up your terminal and enter the following commands to install the needed python modules: \n",
    "\n",
    "```\n",
    "pip3 install sklearn==0.0\n",
    "pip3 install lasagne==0.1\n",
    "pip3 install theano==0.7.0\n",
    "pip3 install keras==2.0.6\n",
    "pip3 install tensorflow==1.1.0\n",
    "pip3 install imutils=0.4.3\n",
    "```\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this tutorial, we'll begin with the fundamental concepts that encompass deep learning and build up to actually creating neural networks with the Python programming language.\n",
    "\n",
    "### What is Deep Learning? \n",
    "\n",
    "Deep Learning is a branch of machine learning that involves pattern recognition on unlabeled or unstructured data. It uses a model of computing inspired by the structure of the brain, which we call this model a **neural network**.\n",
    "\n",
    "### Neural Networks\n",
    "\n",
    "#### What is a Neural Network? \n",
    "\n",
    "Neural networks get their representations from using layers of learning. The general structure of a neural network looks like:\n",
    "\n",
    "![This](https://www.analyticsvidhya.com/wp-content/uploads/2016/03/2.-ann-structure.jpg)\n",
    "\n",
    "Meanwhile, a typical singular neuron looks like:\n",
    "\n",
    "![This](https://www.analyticsvidhya.com/wp-content/uploads/2016/03/1.jpg)\n",
    "\n",
    "- The x<sub>1</sub>, x<sub>2</sub>, ... , x<sub>N</sub> variables are the inputs. These can either be the actual observations from the input layer or an intermediate value from one of the hidden layers.\n",
    "- x<sub>0</sub> is the bias unit. This is a constant value added to the input of the activation function.\n",
    "- w<sub>0</sub>,w<sub>1</sub>, w<sub>2</sub>, ... ,w<sub>N</sub> are the weights on each input - note that even the bias unit has a weight.\n",
    "- a is the output of the neuron, which we calculate from:\n",
    "\n",
    "$$a = f(\\Sigma_{i=0}^N w_i x_i)$$\n",
    "\n",
    "formula, where f is the activation function (you can find more on this in section 1.2.5)\n",
    "\n",
    "##### Input Layer\n",
    "\n",
    "The first layer is a type of visible layer called an input layer. This layer contains an input node for each of the entries in our feature vector. From there, these nodes connect to a series of hidden layers.\n",
    "\n",
    "##### Hidden Layers \n",
    "\n",
    "These are the intermediate layers between input and output which help the Neural Network learn the complicated relationships involved in data. The final hidden layer then connects to an output layer.\n",
    "\n",
    "##### Output Layer\n",
    "\n",
    "The final output is extracted from the previous two layers. This layer contains the output probabilities for each class label. For example, in the case of a classification problem with 5 classes, the output later will have 5 neurons, each with its respective probability. The output node that produces the largest probability is chosen as the overall classification.\n",
    "\n",
    "##### Activation Function\n",
    "\n",
    "The activation function allows the neural network to be flexible and have the capability to estimate complex non-linear relationships in data. It can be a gaussian function, logistic function, hyperbolic function or even a linear function in simple cases.\n"
   ]
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    "### Types of Neural Networks\n",
    "\n",
    "#### Feedforward Neural Networks\n",
    "\n",
    "Feedforward Neural Networks are the simplest form of Artificial Neural Networks. These networks have the three types of layers we just discussed: Input layer, hidden layer and output layer. \n",
    "\n",
    "#### Convolutional Neural Networks\n",
    "\n",
    "Convolutional neural networks are a type of feed-forward network. What distinguishes ConVets are that the architectures make the explicit assumption that the inputs are images, allowing us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. \n",
    "\n",
    "#### Recurrent Neural Networks\n",
    "\n",
    "A Recurrent Neural Network is a class of ANNs where connections between units form a directed cycle, as shown \n",
    "\n",
    "![Here](https://camo.githubusercontent.com/a60c701dea057ea0ac5634b09713b3d3e4867ec8/687474703a2f2f75706c6f61642e77696b696d656469612e6f72672f77696b6970656469612f636f6d6d6f6e732f642f64642f526563757272656e744c617965724e657572616c4e6574776f726b2e706e67)\n"
   ]
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   "source": [
    "### BackPropagation\n",
    "\n",
    "Back-propagation algorithms work by determining the loss (or error) at the output and then propagating it back into the network. The weights are updated to minimize the error resulting from each neuron, since the goal of learning is to assign correct weights for these edges.\n",
    "\n",
    "In other words, backprop is one of the several ways in which an artificial neural network can be trained. It is a supervised training scheme, which means, it learns from labeled training data. To put in simple terms, BackProp learns from its mistakes by having the supervisor correct the ANN whenever it makes mistakes. \n",
    "\n",
    "Note: To forward propagate is to get the output and compare it with the real value to get the error. To back propagate is to minimize the error by propagating backwards by finding the derivative of error with respect to each weight and the subtracting this value from the weight value.\n",
    "\n",
    "\n",
    "#### BackProp Algorithm\n",
    "\n",
    "Initially all the edge weights are randomly assigned. For every input in the training dataset, the ANN is activated and its output is observed. This output is compared with the desired output that we already know, and the error is \"propagated\" back to the previous layer. This error is noted and the weights are \"adjusted\" accordingly. This process is repeated until the output error is below a predetermined threshold. \n",
    "\n",
    "Once the above algorithm terminates, we have a \"learned\" ANN which we consider ready to work with \"new\" inputs. This ANN is said to have learned from several examples (labeled data) and from its mistakes (error propagation).\n",
    "\n",
    "\n",
    "### Gradient Descent\n",
    "\n",
    "Gradient descent is a standard tool for interactively optimizing complex functions. Given some arbitrary function, gradient descent's goal is to find a <b> minimum</b>. For some small subset of functions - those that are convex - there's just a single minimum which also happens to be global. But for most realistic functions, there may be many minima, so most minima are local. \n",
    "\n",
    "The main premise of gradient descent is: given some current location x in the search space (the domain of the optimized function) we update x for the next step in the direction opposite to the gradient of the function computed at x. \n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hardware\n",
    "\n",
    "\n",
    "#### GPU & CPU \n",
    "\n",
    "The GPU's advanced capabilities were originally used primarily for 3D game rendering, but are now being used more broadly to accelerate computational workloads in deep learning. \n",
    "\n",
    "Architecturally, the CPU is composed of just a few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. \n",
    "\n",
    "A simple way to understand the difference between a GPU and a CPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Neural Net\n",
    "\n",
    "### Generating a Dataset\n",
    "\n",
    "First, we import all the needed modules. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following just displays plots inline and changes default figure size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by generating a dataset to use. Fortunately, scikit-learn has some useful dataset generators, so we don't need to write the code ourselves and will go with the `make_moons` function instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "X, y = sklearn.datasets.make_moons(200, noise=0.20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using built-in scatter plot functions, we'll do a quick visualization of our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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RfLp98hR8W0RA4XO5eau2snnroBXTb6rr/O7HPoW13FiVvF3Z43HXw7MgCvZH1IDK709C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oJL3gWRQMBH9575u+6nmh1FRHLMu29vw+c3iLbcH487Fi3cnLNTTuzZdZiX560n2nypKNc44//D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\nIh5S8iUiIiLiof8FtFOeMVOAKxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11545fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.Spectral)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the dataset we generated has two classes, plotted as red and blue points. Our goal is to train a Machine Learning classifier that predicts the correct class given the x and y coordinates. \n",
    "\n",
    "Because the data is not linearly separable, we can't draw a straight line that separates the two classes, which means that linear classifiers, such as Logistic Regression, won't be able to fit the data. But that's one of the major advantages of Neural Networks - we won't need to worry about feature engineering because the hidden layer of a neural network will learn features for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "To demonstrate this, let's train a Logistic Regression classifier. Its input will be the x and y values and the output the predicted class (0 or 1). Since scikit-learn has a logistic regression class built in, we'll go ahead and use that. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=None, dual=False,\n",
       "           fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n",
       "           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = sklearn.linear_model.LogisticRegressionCV()\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a helper function to plot a decision boundary. If you don't fully understand this function don't worry, it just generates the contour plot below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(pred_func):\n",
    "    # Set min and max values and give it some padding\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    h = 0.01\n",
    "    # Generate a grid of points with distance h between them\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    # Predict the function value for the whole gid\n",
    "    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    # Plot the contour and training examples\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot the decision boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b8SJnE45TNqKbgkCbLo31U5X3Vgl8xZ2XKqhWNdxJWWvZc+VqLHIZ+TADSRMX\nLjtYXnRRrYS7KUfGLGRPsDPzbg0OWSgVqw2vV0OAeILBF1Erfej9b8d7nxns9jB2MPiinhBPGHe9\npObEDIxPWlha8MIDtXLp5y7afVMhfbs4a1QA2ellsV6wshQGSTt5UQHgB8D87MlnAZMp81ilKVql\nUg6wsuSiVApg24LhUQvprInc5m5NMwFw4TL7QBLdrf05XF/8cO8EXgCDLzplBodtpDIWCvkwzyyZ\nMvuqd+J2lfeNtcY8uKHRs/fnun8DxbZSMaxwb/RJUnrYZqqy81h8TzE/42L8nI3hEQvFQgDTBFJ7\nNqMQ0dHtzd8COp/DdVxn792cTj3Lam/Zg3Ybm7Dh+9ipVaYKDA6bGBru38fUFn1UyXZ5MTqPb3nR\nxY2XxRGLN//dqipcV2Ga0lcfJIjaZX/u1t9fvdHV/K2T4Ls5AWC7lV4iIjh3wcH4hML1wo0KZ+Gk\n67mK9TU3bDMVFwwNW0hnTGxs+A2BViIhMProOWmWr6cB4HmA3WR/yca6W7fDM5U2MXnB5uwYnVmP\nPv1AWOy0zzH4ImysuVhZ8uD7gGGGvfSGRvqn3Uq1ElY/tyzBQLK/q8XvZVrSN7lqd2unD6gCUKBY\nCAvkXrzsoFgM4FZrHw6MMCF98mLvF6YtlwIsL7ool4KmJSWA5rl8hbyPpfn6DST5nI+F2bCOHNFZ\n8dpn342f+PQ8APRU0vzdYPB1xm1ueHWFUAMfWFnywhyjY9RP6gZVxcKsi9yWH2YqI9xZeXnKOXAn\nXKUSoFQIYJqCZNrgLEIPWFyI6AMaAMsLLqaux5DPBaiUA9iO1BXp7VWVcoA7tyoHBl1h+6vmj2W7\n7dFeYf9TH76vZ2I2lM6eB5/wMPC+n6k7Fi4pno6gaxuDrzNudSn6DX512ev54Gtj3UNuq5aQXXsM\nXqCYna5i6nq84fqqioU5t353mQCXpmKIn+Hq8b2gVIheliuXFaoK31cU8kEYY2sYtPTyDGdUG6O9\ntgsJT9Tq2VUrAapVRSwmOx8cXLfJDUiYsM/gi06Lhv6JfZa/dRIMvs64Zm/wvo9j91bstM216J1w\n1UqYoGzbAlVFtaowDUGx6CO3d/dc2FEIs7cruHZvvKcf62knBqB+9GWz0y5Khd2lu3I5QC7n48Il\np2d/Z+Vy9N+VCHDhso1EwoRhCoJAMXO7glIh2NlckUobOHfRQWLA2P2gsPc2gGMVliXqZaclh+u4\nGHydcU6pHYB1AAAgAElEQVRMUK00nigsuzU9DtspOGBmQQPF1qYfVouvTao0q5/lB0ClrCxs2UWD\nQybWVxvLawwkjVqT9d3jqmFD9nIpQGKgN4uf2Y7Aa/LBJl4LvICwa8F2YLn9GPO5ACtLHkbHLBRy\nft1y7HY1/l5fdiVq5kPvf3vd96clh+u4GHydcWMTNuamG9utjI33/ksjnTEaTthAuGnA88N8sP0n\n7ShywGXUGaNjNqqV2tJiLUhODBiIxQ0U8o1LkqpAsdC7wdfomIWZYuPfVWZwt+6cqjb0NQ2PA5vr\nHsYmbFy5FsPKsodSIYBlAyOjNlKZ3nzMRFEeffoByMNvBAD8xKfne67Yabf0/hmW2iqVNnHhUq3d\nSlVhO4LRcRvpE77BF/I+1lc9+J4ilTExOGy1LTdleNRGbiuA5+5WQBcBzl1wsBERlDXFdi5dJ4bg\nwuUYqtUA1YrCcQROzMD6mhc5YymCnt4JOpAMS0IsL7jwvHC82SGzoWdps9fo9myXEzNwvg92dhJt\n21+D6/FnXg88s53DxcBrG4MvQjJtItmC3odrK2HJiu0TSqXiYWPdx9T1WFsCMNMUTF2PIbfpo5AP\nYDthvzzbMbCy5Db9uZ2Tea2dy/kLvZs7dNY4jgFnT6yRyZhYXnQbC6oKTvwBoVMy2bBOWRCE/Sj3\nv8ZEBLG41DWV35ZIcgMI9Z/4x9+Cx5+a7PYw+gKDL2oJ39e6wAsIAxzfU2yseRgZa8/OScMQZIcs\nZIfqjw8kTVTK0U22J8/bKBUDmJYgO2TCtnmi61WmJbh42cHcdHUnx88wgAuX2hPQt5qIHNiTc/K8\n01CSwjDQMENG1GseffoB/KR7f92xLz7Fma2jYvBFLVEpB5HLQ2FdogAjY50dz9CIhc31sHDsNhFg\neNRCZtBChu8RfWMgaeL6y+I7M0SxeO9vBjmqeMLA1I0YNlY9lMuKREIwOGLD5m5G6kF7+yf2Wzuf\nXsPgi1rCNKVp/orVhVeZZQmmrsexuuKikAtgWsDwiI1UhrNc/UhETm1enuMYGD/HvC7qLQ8+4SHx\n1lfvfC8Pv5EBVwsx+KKWcGICxxFU9pWtCCvld+dlZtmCiXMOcO54P6cAFuKjqBgOJssriAfVtozv\ntFJVFAsBSsUAtl2rSN8HS4TU/yqGjbIRQ8orwkR04V463INPeHjS+HHgmT0Hn2Hg1UoMvqglRAQX\nrsQwe7uCalV3liDHJi0MJHs7MXqvdTuDPzz/BriGDSgQiOA1q8/iga2vd3tofSEIFNMvVVCpKDQI\ng++lBReXrrKLALWPJyY+MfYQbiUvQRBAVPHI6pdwX+7Fbg+tb9T1T2Q5iLZj8EUtY9uCqRtxVCoB\nfF8Rj/dX30QF8NHzb0DRTIRRQ81nR16F8eoaJssr3Rtcn1hf9VAu7c5+bhcPnZ+u4uo9jS2fiFrh\nk2PfhFvJi/ANE0D4Ye8zow8i6ZcwVZzr7uB61GuffTe+sHJr5/vT2D+xlzH4opaLxfpzhmMxPoqq\nYdcFXgDgiYHnMtcZfB1CVbG64kVe5roKtxoc2PCc6CSqYuFm8nIt8NrlGRb+fugVDL7QWHvrJ937\n8d6fLQFgWYhuYfBFVFM1rOiql2KgYsQ6P6A+s7rs7bRyIuqUshmDIMD2jNdeBXOg8wPqMa999t0N\nifLiB0jmK4AqSikHgcUPRZ3G4IuoZqK8ikAa34SswMO1wnQXRtQ/VBXrq9GzXkDY65CzXtQOSa8I\nI+JDk2iAyfJyF0bUfXv7J753X+CV2KpgdD6/e2CxgPXxAeSHEp0aHoHBF9GOWODim1e/iL8d+QZ4\nYgBiwAo8DFU3cU/udreH1/OCA2a9tlvkaKDI58OWUPGEIJ4w+q5ml6r23ZhPMxOKb179Ij49+o3w\njPCUJhrAUh8PrX+5y6Nrv0effgB/f/VG3bFmxU4NP8DofB7Gvlh1aKmI8oADL9Y/m6P6HYMvoj3u\n33oBY5U1/GPmBsqmg6uFWdyTu81t64cQEdiOwK02zkDE4oJY3EC1GuDOrQqCIFzdFYTNsy9cdvpi\nY8bGetg+y/fC2nWjEzayg3wL7QWvyN1E0i/hC4OvQMEawER5BQ+tfxmDbv7wH+5De3O4Hn/m9Uf+\nuUQuumyOKJDcKmNzLHnXY6Oj4TsH9bwgUKwsudhc9xEEQCIhGD/vtK10wURlDRPLf9eW2+5XqorA\nBwyzsUfhtvFJG3PT1bq0OZHdVjnzM1X4e1YmFUCpGGB9tX3tp1plY93F0vxuuyrPAxbnXAiATA8H\nYEGgqFYUliWwTnnV/MvFeVwuznd7GG3XkMOlCiNQBIY0bBbaT5oUwgYA4efLjurddw3qWb6v8D2F\nZUtHZizmZ6oo5IOdE1+ppLhzq4Kr12PMI2ozVcXaioe1lTDwEAFGxiwMjVgNQVgqbeLiFQeryx6q\nlQCxuIHRcRvxhAHP08gG0qrA5rrf88HX/r6lQDj2lSWvZ4Ov7Ub32zX3EkkD5y86fdETk0IPPuHh\nPW9+Z92xnRwuVQwuF5FeL0MUCEzB+vgACtnmJV1KKRtDS43HVYBihl0WOqk33zWoJwWBYmHWRT7n\nAxIuG42OWxgaad+J060GdYHXNg3CmlJsy9JeG2teuIux9vxvBxyGAQwON/7eB5JmdFHdAz5xN2tL\n1StUtW7Gbi/X7c3B53P+TsC4/fwWCwHmZ6q4eIU7d3vZ3vpbTz41CXw4+nqDS0WkN8o7+Vumrxhe\nKCAwDZRS0e+Lvm1iYzSBwZXSziyYClDIxlCNMxzoJD7bdGSLc2HgpQpAw/Pp8qIHyzaQzrQnUXNv\ntfz9yhEzKdRaewOvbarh8ajgqxnLDnPCqhHtp9LZ3p69FBFYVrjUuJ/t9OYs0vZMZR0NAzDPC5ch\nqTfsr8H12FHqbwVaF3htMxTILhebBl8AkBsZQDnpILlZgQAoph1UEtahS5bUWgy+6Eh8X5Hb8iNP\nxGsrbtuCL8dp3rA7HuebRTupKnw/+rKoQOQw4xfjmL1VBlTDJUwj7IrQ60uOADA6bmNx3m3IZxsd\n7823UK/ZjJwgTBlg8NUTPvT+t+O9zxy/qrwZNP/gaXmHJ2+5cQsbnOnqKj77dCS+3/yPvZ1LL7Zj\nIJkyGpYexehew+6z4qAdjM4xZnwCCP5m5EF8JXMN1vUKJmZu4sraS7hk5ZDK9EepieyQBQiwuuTB\ndRW2Ixgdt5DJ9uZrcCBlYHO9MXIWHO93R62zt3fiti+esIeibwo0YklAAVRjvfmapHr8LdGR2LY0\nXf4bGGhvbZhzF53G3Y7nHCbbd8DYhIX5mcYZn7HJo89WfWb4AXw1cw2+YcGPW7hz41WYC16B2NLf\nIl2YacOo2yM7aPVNaYmRMRu5LR/Bnvgr/L1ZkD4o63FaxD/+lp3/t7R3ogg2xhIYWirWLT2qABtj\nrOrfD/rjnYS6TkQwNmnVbbcHAMNo/9KLYQjGJx2Msw1Zx6UzFozLguVFF25V4cQEo+M2kqmjBdw+\nDHwle2On+OU2z7Dw+aH7cL2Pgq9+YtuCqetxrC27KBYCWLZgeNQ68u+Njm9/7tbfX72Bn3qqfW9a\n+aEEAtNAdqUEywtQiZnYGB9ANcHTej/gb4mObHDIhm0bWF0Ol14SAwZGxyw4tUbaqopSMVweTAwY\nfVE48yzLWQNYjg0h6ZUxXllFs99WMmWe+KRdNWxok1suWPyE3k62LZg4z93AnfDo0w8cq9hpqxQz\nMRQz3L3ajxh80Y4gUHiuwrSkaS2gZifiUjHA7J0KglrlcgUwed7u2ZyYs0wBfGr0m/D19FUYGkAF\nSHtFfNfcXyHpl1t6X/GgAjtw4Rv7XjOqGKustfS+iDop/vG34Le/HtbUOknSPJ1tPDMSAGB1xcXq\ncm0LmwLprInJc/aR8kOCQDFzu7LT2297VXJh1kU8YcBhblZP+Wr6Kp5PT8E3TPgIg6INO40/n3gU\n3zP38ZbelwB4dPUf8Kmxh3aXHjWApQEeWf1SS+/rLPA8RbUSLiPy76pzHnzCw1d/+nvrjrVzSZFO\nPwZfhK1ND6v7KnjnNn2IAJNHWLbI5/zIGpqqwNaGj9Hx9p0kVMPZOsMQmC3ePh8EYXmNfM6HZQkG\nhyzE2tTSqJO+nL2nIQdLxcBybARFM46BFs9+3Zu/jXhQxeeH7kPOGsBoZR2vWXsWo9WNlt7Paaaq\nWFoIN53sVKwfMHD+EivWt0tD/8SnujgYOnUYfFHTQppbGz7GJ/XQ3K3AR9MK5r7XvjIUhbyPhdnq\nTi2qxICBcxedltQwCoKwhVG1ojvPzea6j8kL/b+U6hrROxUFClfa89jOSt+9dtlc97C57tdVrC8V\nAyzMVXHhEnN+Wq2hfyJRi/X3WYRawjsgQAqCcEfjQQaS0VcQA0im27O7qlIJMHunvolzsRBg5nYF\nU9eb9zY7qo01ry7wAsKT3uKci1Ta7OvNBFOFGTyXuQfBvjwsJ6gi4+W7NKqzZ3sJ0XGMQ5ter61G\nFzgu5AIEvsLg7Ndd+dD73173/XsZeFGbMfgiJBJhEdP9DAMwjxA7OTED2SFz55M5ENYUSiTCAqnt\nsL4a0T4FQLWiKJcDxO9yeTCqmv+2SjlAos21zdrp1etfwa3kJZQRg2dYEA1gaoDHlj7bdMcjtY6q\nYmHO3VnaVw2bkp+70DzHMjigyHEQAPv3M1Bze3snAsBvfz1+4mKnRCfF4IswNmGjWKhEFtI8avXx\n8cmw9tPmehgUpbMmMlmzbdXLo6quA+G4PVeBu5z8Cme2Gu9DFX096wUA8aCKt07/Mb6WuYrZxATS\nbh73bb2AQZezXp2wuuwht1m/hJjP+VheAsYno3MsB1ImcpuNFetNEzDb8C6+3UxcDJyKnLLt/C15\n+I1H651I1GYMvnqQWw2wueEjCBTJlImBZHtbsMTiBq5ci2F12UWppLV+e8cryCgiSKVNpNq0zLjf\nQNLYqSm2lypakhQ/OGyhVKw23L5lC5xY/5+MHPXwqs3n8arN57s9lDNnYy06x3JjzcfYhEb+rY+N\nWyjkGyvWT5x3Wv7eUMj7WJhzd/I1B5IGzl1wWr6hpVPq+ic+w+XEBqqIFT2IKioDNrTPP1z2CwZf\nPWZr08PC7G47l401H8lUuKup3QHY+T5K3B0csrC+5sHf0+BZBMgMmrAPyZ85ilTawNCwifU1H9tr\ncaYBXLx8tN+D6wZYWfJQyPswTcHQsInskNUXfQypvZo1K2+2zA2EPU6vXo9jbdVFqRjAdgwMj1p3\nvby+XzUil7KQDzBzp4Ir1+4+l7LdHn36Afyke3/dMS4pNueUXIxP52pvcQoosDqZRDHb+7/rfteS\n4EtEvhPArwIwAfymqv7yvst/CMC/ATBbO/Trqvqbrbjv0yTwtS7wAmpJtfkA+VyAdIaJHdtMSzB1\nLY7lpSoKuQCGGTbaHhxqzeeJsJ2Sg8ERRakYBlBHnYH0PMXtFys7J1nfUywteKhWtemy0lEEEGw4\nGdiBi7RXPPHtnGVBoFhb8bC54QOqSGdNjIzZHV1aSwyEs7b7xeJy4OvLsuWuXj9H0SyXslJWVMpB\nT5Za2ds/8XHW3joyCRQT0zkYQf0vfGShgGrChufwfNNOd32mEhETwL8D8EYAMwA+KyIfVtV/3HfV\nD6nqj93t/Z1mxWIQ2bw6LPvgMfjaQ1WxvuoitxmexIIAdbNgrWLbAvuYpSXWV134+86t28tKw6N6\nolIYtwfO4a/GH4EvBgIIhqpb+I6Fv0bK5zLKUakqZm5XUS4FdTPLhXyAqeuxjs1Kjk/auPNSBbrn\nNSICTJw7erPydqkekEvpuopYlydEHnzCQ+Ktr975fjeHi6KYrg8jULiOGf4S90jkq5HTraJAcrOM\nzbFkp4Z5JrVimuA1AF5Q1ZsAICK/B+B7AOwPvugQB733c7mq3tqKh/W1+h2JayseTBMYGunuSaxY\n0Mi6ZyLhTknrmH0Sw+rzr60rjLoaG8RHzj+Ot01/jDsUj6hUCuoCLyA897iuIr8VIJ3tzIebeMLA\n1PUY1lY8lEvhbNLwqIVYrPuzSgMD7c2lPIkHn9j9VPWk8ePAM3suZA5XJMMLMDabg1MOnzsVwdpk\nsq4P5P4Zr20CwDhgdy21RiuCrwsApvd8PwPgkYjr/bci8i0Avg7gX6jqdMR1zrTEQJN6WQJkhzjr\ntdfaSnTS8uqK1/Xgy3EE5YhzgipOlI/2XOYGfKl/bagYKFlxLMZHMVleOelQz5RySaM7MQRAqeR3\nLPgCAMcxjtQ9otMGh2u5lPsS+9PZ1uRSHsd278T3Mmfr2Mant+BU/N0PZqoYmc/DdUy48fC0Xx6I\nfp8MBCileu+1edp0KuH+IwA+qKoVEflnAH4bwLfuv5KIvAvAuwBgwk50aGi9wzAEFy47mLlTDQ/U\nzhSDw2bTQqZnkaru9JHcb3vpUVVRLATh9nwBsoNmx2pzDY1akXXC4gkDzglmN3LWAFQifk6BosnE\n2KOybYlc1hc5WVB8GpmW4Mr1OFaWXBTyPgwj3CwyONx4qvA8he8pHEeO1AP2KPbmb7F34snYZQ92\n1W+YERcFMmslrJ5PAwA8x0RuKI70ehmi4YxXIEBlwEY52f0l8NOuFcHXLIBLe76/iN3EegCAqq7u\n+fY3Abwv6oZU9QMAPgAAL08Mnsl5z4GkiRv3xpHLhdvKk6mTnbBPMxGB7Uhkra9YXKCqWJxzsbW5\nGwBtbfgYGrEwNtH+N5V4PNydujBX3SkNMJAMWx+dxKXSAmYHJhv6MQZiYLy82uSnaL9UyoBhoCEf\nL9wl2/mN38WCj+VFF5VymAc4MmYh26INI3fDtgXnLjR/rfq+Ym6milIhACQ8aY9P2sce+97eiQDw\nk+79+OJTnOW6W6YXQCM+ZQgA061/8W+MJ1FO2khtlCEBUMjGUEw7B+fAUEu04i/9swDuEZGrCIOu\ntwGo69UgIudUdbux2z8B8JUW3O+pZZiCbBdOBv1kfNLG3HT9lniR8Hi5pHWBFxC+D62vesgOmh0J\nZlNpE9fvjcPzak2/72I33b25l/Cl7MtQsAbg10qZW4GLl23dYsL9MYghuHw1hvmZKsplhQCwHcG5\ni51vTl0q+pi5vfv6dV3F4ryLwFcMjfb2rMPcdBXFQu0kXktvXJx3YTuCgeTBs8vb+VsD7/sZJsq3\nSTVuQSIS6QNB5IxWOemgnOQyY6fd9RleVT0R+TEAf4Kw1MTTqvqciPwSgM+p6ocB/LiI/BMAHoA1\nAD90t/dLZ1sqbeLiFQcrSx6q1QCxmIHRcRuJAQPLi43FUbcV8kHHZhJFpCXLWbb6eMvsn+FL2Xtx\nM3UJju/i/q3ncSN/pwWjPFscx8CVa3H4Xpj/1Yom7CexvBids7iy7GFwpHfrwbluEFkmQzXMwzwo\n+PrQ+9++m7/FwKttAsvYWU40aq+xAEBgCnJDTFPoFS2ZXlHVjwH42L5jP7/n/+8B8J5W3Bf1Pq2d\nVdpxAgl8xcpSuKQIAJmsiYuX43WNhQ9q/xOVOnXk+w7CWkemKR1fCo4FLh5efw4Prz/X0fs9rbpd\nrb1SiU5aVA3zFq0enfzyPUTmzQHh7N22R59+AH9/9Ubd5VxS7JyNsQFU4xYya2UYfoBi2sHWcAJq\nMoWlV3Bti1rGdRWLc9WdJt3JVLijy2pRMrOq4s6tCqpV3a3TtO6jWAhwZU+dpnTWxOpydLHI9Anb\nH62vuVhe8HZOPE5McPFyrGWPjc4WxxaUm2znP0oz+25xYtJ0Vvn6m8fxyE9NAQAef+b1nRsUNRJB\nMROrKy1BvYXBF7VEECju3CzD21PotJAPcPtWBdfuaU0By0I+QNXVhlyuqqso5IOdvpKOY2DinI3F\neXenNRAUOH/pZP3pigUfywteXSPkSlkxc6eCqeucxqfjGx23MRuRszg0YrVs5+BxqCqqFYUY4d9P\nM4YhGBmvlaNww2NiAMZgDL84/GaUnkl1aMRE/Y3BFx3JYUuJ+S0/smed72vLWiOVS0FdVfCdsQVh\n8dK9Tb2zQxZSGROFvA+BIJky6pYmjyOqphgAVCuKaqVzOWR0eiTTJiYv2FhecOF5gGGEgdfIWOff\nkgt5H/Mz1Z3yLY4jOH/ZaRqEfdsPCN518824/+8+i0S+gLmpK3j20UdQSjPwIjoqBl9nRBAoAh8w\nrePlYlUqARbnwma+ECCdMTFxrrEXXqXSWBUbCAOjaiVAuBfj7tiOQAw0BGDN6jSZpiBzzNZAUXyv\necsVz1M4ETP7paK/U2Msk7UQTzBAo3qZrIV0xoRq+FrqRpJ9tdrYSLtSUUy/VMG1e+IQkbraWwDw\n5FOTwA1g5sb1Do+W9hI/wEDehQSKcpK9GPsNg69TTlWxtOBicz2clhIDGJuwMDh0eEav7ynu3Kzs\nFjRVILfpo1oJcOVa/VJiPG5CpLGwqBita0uSzphYXnCxf4LNMIBUG/teJtMmKpXo3WnxiMe2OF/F\n5vruc7Gx5mNkzMLIWI9mUVPXiEhXSyptrEXP6opt4OqPTmHhrd/EYqc9KF5wMTazVXdsaziBzbGB\nLo2Ijosfx0+57cBrO18p8IGleQ/5rYg1wn02Npovt5VL9dNPqbQRuW3ftsIlv1YwDMHlazHEE3uC\nvkR47KAdjndraNiCua8vrQgwNm41LGWWSkFd4AXU2h4te3CrTcryE3WJ50YfL7gm/uXHrjHw6kES\nKMZmt2Ao6r4yayXEik1+odRzGHydYkGgDYEAsF1P6PA/0mpZm+5sqlb2VU+uBUbprFlbQgEygyYu\nX21Nsv227TpNN14efl25Fj8wQbgVTEswdT2O4RELsXgYTF647EQWw8xvRQesAJDPMfii3vIN//YJ\nuHbj69jQAMsXznVhRHSYeMEFGpoHhe2DUpuVzg+IToTLjqdYcMDk1t6aPM3E4gLZiq7pE7WUaFmC\n8ydsoXNcna5IblqC0Qkbo4e0Jzoo0OzRupl0xuzN4fpnvzyK706nkdrchFXbMePaNm694uXID7Iu\nV29SRLWIFyD6zZp6EoOvU8y0wpwrjQjCjpIAnh2ysLbi1e1iFAmX+phAHi2TNZvujmxnXhpRlEP7\nJ1rAx37g7XjF576Aqa9+DZ5t46uvfhA373tlh0dKR1UesCER7y+BgHW9+giDr1NMRDA2bmFpwWuo\nJzQ2Xj+DUyoGWFlyUSkHsB3ByJiNVNrElWsxLC24KOSDnaXETjSn7ldOzMDYpIXlBa/u+OQFu2ut\nbOhsefAJL6y7Z1zAr7zpRZiej1uvfDmee/gheE7jzLQbi+FLr3sUX3rdo10YLR2XmgbWJpMYXijs\nBGEqQCnloBTRu5F6E4OvU25w2IZpGVhdduG6injCwNi4XTdzVSr6mH5pd7u5X1LMTVcxed5GZtDC\nhcv8NHUcQ8M20hkLhVxYaiKVNju+TEpnU/zjb8GTT03isQ//F5y/+RKGa1WP7//M3+Hy15/HR9/5\n/Qh6uYQ+HUkhG0clYSO5WYERKIopB5UBi7kNfYTB1xmQzpgHFjldWnAjk/KXFt1aAj3/oI/LsgTZ\nIf550cm51QBrKx5KxXA2eng0bBy/7cEnPHz1p7+37md+6qlJDC0t4cLNl2DtaTdh+T7SG5u49PwL\nuP3yl2FweQWv/OznkFlfx+LFi/jKQ69GOZns2GOju+c5JktL9DGeHQiVSnSSpu8BQdDbveaITqNq\nNcDtF3dr7FUqipLr4g2/dB2X3zAMoNY/8anGnx2bm4+8Tdt1MXlnGm4shsf/83+B4fswVDG6sIh7\nv/gl/OEP/gAK2Uy7HhIR7cGsaWqaiyRGWMCUiDprZbFx04ZfDvCHvziHx3//dQc2ri6mUggi/nA9\n00QhncKjf/ynsDwPRu0OTN+HU6ngGz/11y19DETUHE+thJExqyFVQCQsLsolR+pVOWsALyQvYT4+\nGrHxvr8VC431+QDAqZSRyBcO/NnZq1PwbBv7q8qpYWDm+nXEi8WGnzFUcf6l2ycfMBEdC5cdCdlB\nC4GnWFne/bQ9NGxidJwvD+o9CuATow/hhfQUjFqIMeCV8aa5jyPll7o7uBN67bPvxhdWbu18n3/N\nR5F4aaPheqKAGzu4lp6aJv747f89HvuDjyCzvgaFoJJI4JPf/V3ID2YjynOGqnFurCHqFJ5dCQAw\nNGpjcMSC7wGGiba26yG6G19NX8WL6SvwDRN+rWH7lmXizyZfi/9m9i+6PLqj267BJQ+/EY/9bAnA\nbiuf6694BI/M/gVsdzdp3jNN3Ln3nshyEfvlhobwkR9+J5JbWzA8H7mhwZ2dcNPXr+Hiizd3iqoC\ngGtZeO6hb2rRIyOiwzD4oh0iAquPysT4vmJ1ycXWZngSSWdNjI7bLOtwF+biY/h6+goUghv5aVws\nLTSdKemW57L3wDPq37rUMLDqDKFgxpH0y10aWXMPPlFf9+09b34n3vtMrdjpM42zdS/efx/S6xu4\n73OfQ2CaMDwf81NX8Dff8e3Hut9CJoPk5hYe+su/wujCItbHR/GlR78ZsVIZY/PzCEwDhufj+Qfu\nx/Pf8MDhN0hELcHgi/qSqmL6VgXV6m7/yc11H8VCgKnrR+8nWa0EcF1FLGbAsnstzOiszww/EAY2\nYgAQ3ExdxrX8HTy2/NmeCsBcI/ptS6BhUHZ4z/iOevTpBxoT5D98yA+J4B++5fV47pGHkV1dRTGd\nRjGdPvZ9Dy6v4Inf/SBMz4MZBBidn8P1L/8j/uRt34tqLIbU1hbWx8ZQTrJkAVEnMfjqEUEQBhGc\ntTmaQj5A1a1v/K0a9qws5AOk0gfXxwh8xex0FaViWLlfNazeP3HOPpObDDbsFL6cvQf+nsDGEws3\nU5fxyq2bmKisdnF09a7mZ/Bs9l4ERv3v2AlcZNx8l0a1a2/vRAB4/KnJJtc8nBuLYeX8+cjLTNfF\ntef+ERdu3kIxncbXvvFBbI6O1F3nNX/xl7Cr1Z3g2QwUZuDikT/7C3zsne9AbnjoxGMjopNj8NVl\nnvrBmMIAACAASURBVKeYn62imA8Th2NxweR5h70TD1EpB9D927kAaACUS4cHXwtzLkrFAKq7vWi3\nNnw4tWKWp1XFsDE9MAlRxaXiAhwNl8OmB84BEfNbnpi4PXC+p4Kvb9z4Cm6mLqKEBDzDgqFhvarH\nl/62KzN0e3O3fuLT8/W9E+/ChRdv4tWf/BQy6xvIDWbxhW/5rzBz4zoAwK5U8F3/8XcxsJWD7XkI\nRHDPs1/GJ9/0JKbvvWfnNsZnZiOfk9GFBUgQQFlLpjtUkVovI71ZAVRRyMSQG05AmWt7ZjD46qK9\nS2fbKmXF9EsVXL0nzl6AB7Bt2Zmx2ksMwHYOft6CQJHPNW7lVwXW1/xTG3w9n7qMT4w9vFPfSUXw\nbYt/g6niHKzAhyAAUB+0GhrAVrcLo20uFrh46/Sf4OvpKcwmJpB2C3jl1gvIegeXYGiV/flbO0uK\nz5QAtCbwuvjCi3jDh/9wp0r90Moq3vDhP8SnnvxO3Hn5y/Dyz38Bya3czuWGKgzPw+v+6E/woevX\noLXKyJ7jwCw35sD5lgU9gzO8vWJsNod4wYVRew+yVksYyFWxMJVli6AzgsFXF5WKAVyvsZiPKrC5\n7mFk7HQGAa2QypgwFlz4+/J7DMGBrZSAcHYsqoYSEC5HnkY5awCfGHsYvmHVpUT9+cSjeMftj+Bq\nYQafHv3Ghp8TKG7k73RuoEdkq4/7tl7EfVsvdvR+t3sntttDH/9EXXsgALA8Dw994lO48/KXYepr\nzzdcDgCiiqHlFaxNTgAAvvrgN+C+z32+7rqeZeLrD7yKJ/kuccpeXeAFAIYCdtVHIl9FKX3ykh8S\nKOKF8MNSOWlzJq2Hcc65i9yqIqo6pCrqZsOokWEILl+L1fW6SyTCY4eVyTDMcOYsykCqf/8kCmYc\nG3Y6suDoC6nL0IgFKAFwK3kR8aCKNy5+GlbgwvbDLzPw8IblzyLtNRblPCvu+1b3/2fvzmJjPc87\nwf+fb629ijt5js4mHUleJEV2vEjyqsROLHUndtydTOL02NMzjdxMww7aQMdxA4PBXGUCA30zubAx\nI4zRGCDuHk3a6thZHFuJE1teZMmy5EW7zsLDnay96lvfufiKRRar6hwutbL+P0A2WSSr3kPW8q93\neR5svf8iXt0EXt0G/tNvvAzd6/9MYDrfXuMLAFKFAqAU3C61vrQwhLevFMVz73kQVy/fAV/X4dgW\nfEPHjYsX8aMPvr8v46Zbs2ud7z+aAmLV9kB9WPGyi9te3sbsShmzKyXc9vI24kXn2NdH/cWZryGy\nY51f6EWAWJzvWG7FsjScv2Q3Z6u0Qx5WEBEsnjVx/YrbMgOmacDcwvjNNlb0OL6x8BA27SkIQpih\njw9u/ADnq6vN7/FFR9hhpiOENMs2nK+u4JNvfBXXE4tQEJytrcEOR2vJsd8eev6zzY9DP8R/PPMl\nZHdegt6YYn3L+o+wdPUqvv6vPtHXmaNaKoVkqdR+eTIJiOAXb38bZtbWYe4LgqEIilO5lk30Stfx\nj7/5z5EoFpHd3kZxagqVbLZv46ZbC3Qtetdz4F1SKIBvdH/zFy+5yG1UYXgBfFNHfj6BWioK2pof\nYna5FM2m7XtSm10pYzlhIjQ0QEWzYoYXwo3pcGMGZz+HiOFriGJxDfGE1tz4vUvXo6rzdDiHDV37\nJZI6LtxhY2fTh+sqxOOCqRlz7MpNKAB/eeaDKJgpKNEA6PA1E99YeA/+xfW/Rc6LXsAvVFfwk9yb\nGmUk9ggUzldvND83VYBLleUB/guGb3cPV+JP/6hR7DRy7uVX8L5CoRm8AMAIAuQ2t7B49SpWL1zo\n25iefe9DePc3vglz33KhZxh47qEHAABX7r4LczdW8KZnf4xA1wEoOPEEnvytj3W8vmomg2qGTbNH\nQTVlYVoEqsNcdCXbeckxUXQws1JuLlVaboDZ5RI2z6RRS1tIlNyut5csOqimLSxcLUL3Q0A1sp8A\nlbSFwmwCgXXzrRrUe+O7xnJKnD1vYWrGgG5Ey2HZnI4Ld8RYYX4AbFvD4lkL5y/ZmFu0xi54AcC6\nPYOKEW8Erz2BaPhp5nLz83lnG5dLV2CEfvTOWIUwQh/3Fl5CbgTKMwzLV774CTyqfRqPap9uCV4A\nMLO62jKztEsLfMysrvV1XK/eew+efvgDqMfjCDUN9VgMz3zgfXjp/l+KvkEETz/8ATz34LsRahoM\nP4AbszvOltGI0QSr57PwLB2h7M14rZ3PRDNUHeQ2qi17xIBomXJqPTpkooUK0mG/gahoH9jMjTIM\nL4Smohd9afx8quhi6Y0CdHfEiuNNAE6vDJmmCeYWzLFc7qLhqxixzvsGRUPJTLZc9v7Np3G5chWv\npM5DlMJdpTewOEIlJPrtYP/EL78Uw3NPdD+dWM5m4ZlmWwALdAPlASzdvfS2+/HS/b8Ew/Pgm2bb\nEtG9T30P937vB83ZsZm1dXzovzyOv/nd38HmmaW+j4+Oz7d1rNyeg+4GEAC+qXVfAlQKhtehrg7Q\nvLyWNJHdRFsAUwI4CRO5zVrHkiOCKLhlt2rYXkod+99DR8fwRTTG5uvbCKXDu2Wl4IqBEAKtkc4E\nwNnaOs7W1gc7yCHZv38LQFv/xFt5401345f//tvQPa+5RBCKwDdNXGvU2+o7kY69HDXfx73f/2HL\nsiQA6L6P+//pO/i73/mXgxkfncihlvtEEOgCo8NJ7KAxU+bFDFSyNpIFpxnAlACVjA3PvvltCIBY\ndbL2do4Chi+iMZYKari79Bp+nr4dan/FdxGsx2bwo6m34p07Lxz6+kIIXk2dx4vpiwCAu0uv43L5\n6ki1F+pmf/2tP/7YJ/H5z7X3TDwK37LwV7//u3jf1/4KU+sbAICtxQX84z9/FKEx3KfOeKVzTTMB\nMLWxOdjBUN8VZuOYWm9degwFyM/Gm59vLyRRTVtIFqITjpWMjXoymjH1bB2mE3R9HAc32ehP/cHw\nRTTm3rv5DF5NnYdzoEBqoBl4PncX3rHzwqHCkwLwjYUHcT2xCF+LlsHXYrN4I3kWH1p7aqQD2P2P\n+HhU+/TeBbfqnXhIxZkZfO2T/wpWrQaIwI3FenPFJ1RPJCBditUVZqYHPBrqt3Iuut/lNmvQAoVQ\nF+Rn46jk9t0fRVBPWqgn22dKN5dSWLxaBML2Tf6hAIWZeNvPUH8xfBGNOUHUh7ETXwyE0KCj856R\n/dbsGVxPLDVLTwCArxm4mjiDdXsaC852r4YMIGp1FEIQD7uf1OrmYP/Efhc+deOj9eIUmCZ+/va3\n4U3PPgvT219A1cBz73loiCOjvhBBeSqOci4GUdGS4lHKRHgxA8t35JDM15HeqcPwVbQZQQN25hKo\npzrXjaP+YfgiGlEKQFWPwQz9Zg/GbmbcHazHZtsuT/uVQwUvALgRX2grRQFEJydvxBd6Fr4qehzf\nWng3VhvjzbklPLz+fcy6nQuLAtHMVuJP/wgA8Mzm6/h3A6gyP+qe+cD74FkW3vrDp2E5Dkq5HH7w\nqw9j7dxtwx4a9YtIFLyOIdQ1lGYSKM0koPkh9CCEZ+pRWxAaOIYvohF0Lb6Af5h7J+p6DEqAc9UV\nPLz+g65FTx/c/DG+duaDUXhqBCgj9PHQ5jOHvk07dKGrEMGBAKarEHbQm0rZIQRfPfsrKBuJZnmM\nbSuLJ848jN+7+rXmLNjB/omPap8Gmnu4GLwAACJ4/qEH8PyD74YoxSbZdGihoXUta0GDwfBFNGJ2\nzAz+dvG9Lct/1+JL+OvF9+KjN57s+DOLzhZ+c/lbeHr6HmzZOWTdEt6x8wKW6offfH1H+Sq+N/NL\nHb6icEfl2lH/GR0txxdQ1+3WumQiCEXDi+lLuL/wIh56/rNtNbfoJkSO1STbrlZh12oo5XLNRtwt\nVxuGMDwvalfESuhEPcXwRTRifpK9C8GB+sehpmPDnsaOmcaU17mQ5py7g0dW//HYtxsLXTyy8m38\n7eJ7EDZuX0eID69+p2dthkpmEmGHrfuBZqD8W/ci9qm3Mnj1mek4eN9ffh1n3riCUNMQahp++Csf\nxKv33gMgCl1v+/Y/4U3P/hh6EKCWTOAHv/Iwrt5913AHTnSKMHwRjZiCle64hKSrEGUj2TV89cKZ\n+gY++cZXsW5HJ+bmne1mnbBemKtvQ4+bCJzW6wwF+IdXMvj6F0bjNOFp9oGv/iUWrl2DHgTN1kkP\nfOObKGezWDt/Du/41t/jzp8836whliyV8b6v/RW+GYth9cL5YQ6d6NRg+CIaMUu1dazbMwi0A6Uj\nRMfMTTal76cAXI8vYsOeQsav4GLlOgx1uI33GlRPK9/v378V/+1z+H/+owFbvGbNIoWozlAl3bmv\nHfVOolTCwrVrMILWdjK67+Oe7/8QW4uLuOsnz8M4ULzV8H380nefYvgi6hGGL6IRc0/hFfwsexmh\nkubeKCP0cXfpdSSC+i1/3hMDT5x5GHkrjUB0GCrAd2feho8u/x2yfufinP3ylS9+Ap/f38LncQC3\nKWS2a0jnHUApVNMWCnMJnroagFililDXgQPhSwAkS0XEqpWu+8fSO4cL/kR0awxfRCMmHjr4F9f+\nFj+cvgfXEkuwQg/35V/Em0uvHernfzT1FuxYGQSNDfueaPBFx7cWHsBvLX+zn0PHg4/dh2cvRQ29\nu/ZO1ATF2QSKs4lj3YYECumdGpIlF6EmKE3FUE1zU/hhFKenoYXtM6CBpmHl/HlUU537+4UAthfm\n+zy60WO4AZJFBxIq1FIWnLjB+xn1BMMX0QhKBTU8vPHDY/3sy+kLzeC1S4mGTXsKjmb2bPM80K1/\nYv9IqLB4pQDDC5rLlla9DLtmY2eBjYFvxbdMPPveh3D/d55qNgwPG/0jf/rudyI0DDz30AP4pe88\n1dI3MjAM/Pi941W8VUJ15GKk+yXzdUyvVZq9EtM7dVQzNrYWkwxgdGIMX0SnTv9eGPbv3/rFv/+d\ngZ9MTBadluAFAJoCUnkHxek4AvMQjYon3M/e9U6UpqZwz/d/gHilihsXz+MnDz6AajoNAPjpu96J\neiKB+576PuKVCrYWF/CjD74f2wsLQx754dhVD9OrZZhuCAhQztjYWUhCHWFZWwtCTK9VWu5nooBE\n0dnrmUh0AgxfRKfM5dIVvJC9E+H+DfsqxIyTP9GsV1v/xC+cYJDHFKu4LS+I+9k1H1WGr0O5dudl\nXLvzcucviuDVe+9plp4YJ4YTYP5ace8+ohqB3Q+xfi5z6OuJVTo/TnYD2ESFL6WiUzHck9lTDF9j\nol4P4TohbFuDHTteZeIgUNhY81AqRpttM1kds/MmdJ0PqtPkl3d+iuXEAopmCp4YMJQPQwX4lfXv\nHel6Btk/0az7iFU9BIaGWsrqOksRGDoUvPa5PQECnRW7J11mp9ZcJtylqWg2zHADhJogt1lFouRC\naYJS1kZxJt55GVGATlVWjtveZ9xIqDC1VkGq6AAKcG0d24tJuPEJCp59xPA14sJA4fpVB/Waaj4Z\nxBMazp63oB3hnYhSCldec+C5e88mhZ0AtUqIC3fYEO5hODUs5ePj17+Ba4lFbNjTSPsV3F6+BlMF\nXX9mf+/EXQNZUlQKMzfKSJTd6IVOACWCtfMZeLH2p6dSLoZUvt7yAqsAhJoGJ8Gns0lnOkHHRXcl\nAsMJMLNWge43yvwGCtmtGuy6j43bWmfFakmra/CqZCejJMrc9RLsmtd8rNlOgIWrRaxcysG3OMN8\nUny2GnHrqx7qNQWl0HwyqFVDbK55mF86fCf6cimE77c+mygFuJ5CtRwimeaD6TTRoHChuoIL1ZWu\n37N//1Zr78TBSRYdJMpuyzKRUgrz10tYviPXNiPh2zo2z6Qxs1KGIFoO8U0dG7eluQma4MQN2DW/\nLYCJUjAdD1rQ2l9BU9ESo+n48Oy9l0OlCzbOpjG33FrQuDgdn4iZH8MNYNe8tiV+UUB6u4adRR5u\nOSmGrxGmlEKxEETBq+VyoJAPML90+Ouq1wJ0qrGpQqDuMHxNmv39E7UgRKzsItQ1uDH98CFGKaR2\n6sht1aAFCp6lYWc+iXrq8G8KUnmn/Qm+MSbTCTrOftXSFq6npmA6AZQmfBdOTaWpWFQ/LlTNkBUK\nUMnYsNyw635Bq94avgCgnrKwfHkK8ZILUUA9aU7Mfc1wg+h54MCLjyCaXaSTY/gacQeD160u78ay\nNYi0BznRAMvkXpnTLvbkx/Hll/Za93y+Ebwym1Vkt2pQIhCl4Jsa1s9lDnVqML1Vi4LXbskHN8Tc\ncgkbt2UOvSFZbnJHvmkEFOkYzGiyBaaOlYtZTK1XEKt4CPWoDlxxOo7MVg2hoGMA87vc30NdQyU3\neS2vPFvv+CITAnDjfNz1An+LI0xEEI8LarX2B0EiebTAlM7o2Fj1Dha2hq4BqTTD12nz4GP3Qd75\nYQDAZ767gue+0F7sNFZ2kd0NT40nWtMNMX+9hJVLHYqj7qcUctu1thcyTQHZjSrqyeyhxlnO2DCd\natv1KE3g2pMxy0C95Vt62x4uAKjkYshutS6tKwCBqUXFU6kpMHVUU1bLlgAFAJqgNBUf5tBODd7j\nRtzCGQtXX3ei074qmgkWDZhfPNq+A00TnL/dxuqyh1o1Wn9MJDUsnjUhPEI89vbv3wKAhx9/L/D4\n7gtN5yCV2al3XPIz3ACGE8C/SfjRAtVxQzIAmO7hlyXKUzEkSy6sug9NRUtEALBxhnu4qLcCQ8Pa\n+SxmVkowvRBQQD1hYvNMive1DrbOpOBt1pDJ1yGhQj1hYmc+iYArJT3B8DXi7JiGS5djyO/4qNdD\nxOMaslMGDOPoTxaWpeH8JRthGL1qHuW0JI2u2JMfP1YZCC3o0mhb0GhB0z18hbp0PYrvHWVfTONk\nY6ziIVbxEBiCSjaG0OATPPWeGzewcvsUND+EEkCxPEl3IijOJVCcO14bMLo5hq8xYJiC2fnenbBh\n6Bpv9z/i448/9snm552WFA+jmrJgOu1Lh1FNn1s8NYggPx1v2fMFRDNXhaM+WYugnrKOtFGf6CQY\n7mnYGL5oInieglsPYVoCyx6vJ96O/ROfOPn1lqZiSBUcwI9OgSlEdYy2F5KHqmZdmolDadJ+2nGS\nqn8TER0Dwxcdi+8rbKx6KJWioobprI65hdGrlq+UwuoND6VC0Dw5HY83itSO2FiB9r1biT/9o74V\nO1W6hpVLOaTydcTLLgJDQ2kqfvjTTCIoT8dRnuYGXCKio2D4oiNTocLV1xx4XrTepNColl8NcXHE\nquVvb/koNWql7Z6crtVCrN7wcObcaC1ztfVOBPpe+FRpgtJ0HCUGKKJTxXACJEoOAKCatm96gKbX\ntCBEbr2KZPP2LezMJxFyj10TwxcdWakUtFXLB6KlvUo5RGqECrbmt/yORWrLpQBhqIa+/+2h5z+L\nz3w3qkL/+SeOt3eLJpPhuDj/yiswXRfLly6inBvu/UfCEIlSCW4sBs+ejBY8oyq9VUVuc6/PZXar\nhvxMHKXZAWyeVwoLVwow3b1uAsmCC7vq48bt7V0rJhXDFx2ZUw87FnlVYfS1UQpfYZcDfftnwgap\n4/6tLqUgJppSSBacaE+aAipZC+VcjE/cDQtXr+FXH/8LAFGh2nc8+ff42S//Mp79wPuGMp47nn8B\n73zyH6D5PkQpXLn7Ljz16x9GYHL/36AZboDcZutBGFFAbquGWsbuWqVfGqefT3oCNF72YHitbZwE\ngO6HiJdd1NIM5gDDFx2DZWsQrb1dkWgYuc3siaSGcqk9gVmWDGR/2v49XH/8sU82K8vTTSiFueUS\nYpW93nKW4yNR8rB+jvW/NN/Hr/x//xWm57Vc/uZnnsGNSxexdv7cQMez9MYbeOAb34Th793XL7z4\nEkQp/ONv/LOBjoWARKMd0kGioq8VZ1q3GBhugNkbJVj1qD6fEzOwdSZ17FZKpuN3vX3LCVBLH+tq\nT52evFKKyEdE5EUReUVEPtfh67aIfKXx9e+LyMVe3C4NRzqjQ+twz9H10auWP7doRmPd93otAiyc\n6f878gcfuw+Pap9u/vcclxVvTSkki05L8AKiyvl2zYNd9bv/7IRYunK14+W65+Py8y8MeDTAfU99\nvyV4AYARBLjw0suwanyzMWgKnesfd7pcQoXFKwVY9ejglACw6z4WrxSA8HhLA76lQ3V4GVByxBqA\np9yJZ75ERAfwZwA+DOA6gB+KyBNKqZ/t+7b/CcCOUuqyiPwugP8dwH930tum4dA0wYVLNlZveKhW\nolmlZErD4hlrpDbbA1Fh2UuXY9jZ9lCrKli2YHrG6MsM3Ve++ImWzz//OMPWUehegIWrRegHlix2\niQJiVRfOhJey0LqspWsA9IP9wwYgWSx2vDzUNMSqVbhxHuYYpFraQm6z2iFpRV/bL1FyIPuakDe+\nDRIqJMouqpmjLxFW0xam1jVIuPc4VogKM1dZy6+pF8uO7wLwilLqNQAQkT8H8FEA+8PXRwH8r42P\n/18A/4eIiFLD2HVDvWBaGs5dtLH7Jxy10LWfYQrmFnr/oL//ER+JP/0jAMAzm68fu9gpAVAK89eK\nbXtFWr5FgMDYe+csoYLhBggM7dQXzZQggB4E8C0Lq+fPdQxgnmni9Te/aeBjWz97FsliCVqHp/Ny\n9nA9Pql3fEvHznwCU+vVlst35hNtS4mGF3ZdIjS8Lhtmb0UEqxcymF6tIF6JlsbrSRNbi4erHzgp\nehG+zgK4tu/z6wDe3e17lFK+iBQAzADY3P9NIvIHAP4AABZMvlsaB6McunrtYA2uR7VP7ysFcfT2\nPkOlFKx69O9xY0Zf91HpboBYzUegS1SAtcNtmW5w0+C1q5qxAKWQ2apFTZJFIEqhmrKwtZSCOmVP\n7obr4t3f+CYu/eJFSBiiOD2Fp379w/juR34ND/3130azC2GIwDSxfOkirl2+Y+BjfO49D+LcK6/C\n8LxmAPMMA8+8770IDW4rPpLdAHvCx2N5Ko5aykKi5AKIZqMCs33Jz7UNKEFbAFOCEzW2D0wdG+cy\nPfv3nEYj9chQSn0JwJcA4E3xHGfFaGQct3/iKLKrHuaWS5DGE6MSwcbZNJxEj5fzlMLUegWpvLN3\nkSZYPZ+Bf6B9kRY0usZ3mD3ZXbLYOJtGqGtIFBxkd9saNb4/XnYxvVrB1plUb/8NAAzHR3arBssJ\n4NoGCjPxgdVMevgvnsD89evN5cTc1jY+9F8ex19+6r/HE//6U7j0s5/Brju4dvkOrJ4/N5QXudLU\nFL72yd/H/f/0HSxcX0Y1lcLzD7wbV++6c+BjGVdaEGJ6tdIMS07MwPZSEt6t2nzdRGDqt6zfV0uZ\n8C0dhhs091iGjb1ZPelUwdDVVS/C1zKA/cdrbmtc1ul7rouIASALYKsHt03Ucwd7JwLH7584arQg\nxPy14oF+jtGS3/LlqZ4WQYyXXaTyTsttqUBh/nqprd6PGzM6Bq8QQClnI7+QbH5/dqu9H6Wmov0r\n22Gyp7NfVs3DwtUiREV7YcxG4cq189nDdwI4pvTODuaXl2Ec2Mel+wHe/PQz+P6vfQg/ec9DfR3D\nYRWnp/Ht3/yNYQ9jPCmFhatFmE7QnPm16z4WrhRx445cfwuTSvRmKLtVQ7IQvUmqZG0UZhMMTn3W\ni2ePHwK4U0QuIQpZvwvgEwe+5wkAnwLwFIB/CeBb3O9Fo2R//a1e9U4cRbvvrDt+reiiPBXr2W2l\nd+ptIWm33o/pBPBie08/ShNsLyQxvVZpBp1QgMDUUJhLtrwQ6EH3vShaoBD0MHxNr1Va6yUhWqKZ\nXqtg9WJ/9zOl84XohffAAU9NKeQ2Nzv/EI0du+bDcIP2Te9KIZmvozTT38KoSteQn08iP5/s6+1Q\nqxOHr8Yern8L4G8A6AAeU0r9VET+NwBPK6WeAPB/AfhPIvIKgG1EAY1oKAbZP3HUaIHqusH2ZqHm\nWLfV7eoEzSXP/Sq5GDxbR3qnDt0PUUtFhVUPzmTV4wYSZa9tf5jSBIHRw3frSjVrHx20u1+un/Kz\nM9D89tsPNA0bZ8/0/fZpMAy3831Ma9TFotOpJ/PmSqmvA/j6gcv+l30f1wH8di9ui+ikBt0/cZTU\nE9032NZ7vOerkrFgOn7b7Bcg0TJjB27cxFb85uPIzyURr+aBMJohUIjGv72Q7O1SiQiUJpAO9Y7C\nAWzsr6bTeP3Nb8KlX7zYrKMVisA3Tfz87W/v++3TYHTb1xVKtPeLTif+ZenUe/Cx+/CH3j17F5zS\nJcXDcGMGaikL8bLbssG2njTh3GIPk+aHMLwAvqUfah9KORdDsujAdKLNvLshaXMpdaKQ5Ns6Vi7m\nkN2swq758C0dhZl47w8MINpvdnD5NBSg1MPl2Zt56iO/hsL0NN78zLMwXRcr58/hRx/8AGrp3h8s\noOFw4wbcmAGrvvdGRSEK+JUsW/GcVgxfdOp07p9IAAARbJ5JIVl0kSzUAQCVbAyVjNU9ECmFmZUy\nkiUXqnEisZK1bznTpDTB6oUsEkUX8YqLwNBQzsWO3bZkP9/SsXWm/31K8nMJ6L5CsuRANcpaVDI2\nCrODKYWjNA0/feBd+OkD7xrI7dFwrJ/LILtRRargQJRCLWVhZz5x4j6LNLoYvmisHdy/9Yt//zsM\nW7ci0Tvqw76rzm1Um/3idvdqJQsOfENDcfYWm4FFUM3aqI7rO3gRbJ1JYcdPRLN+pn7qC7rS4ClN\nkF9IRqd6aSIwfNHYevCx+/Dw4+9tvfALwxnLqaVUx1OLmgIyO/Vbh69TIjQ0uD0KXZofwnQDeNb4\nBrnptTXMX19GLZnEtct3sJgq0RHxEUNjJfbkx/Hll6L9NuydOBidTkcCjcKodHhKYWal0rqEmY4q\n849LTSUJQ3zgq/8NZ19/A1AKoa4h1A38ze/9DvKzs8Me3liQUEH3QgSGcFlxgjF80cja3ztxF5cU\nB0wEnq13PPJ+qw361Cq7WYsaGe9bvk2UXARGdWxqLF3+yfM48/obzdOXCAKE8PDBv3gC//Xf8Dmp\nPQAAIABJREFU/OuxCZFDsb8tFqKTuuWMje3FHp/SbdyW6QaAAjxb599lBPHZk0bK/Y/4iP92dIz+\n4cffO1FlIEbV9kIS89f2qrzvnlrcGZPAMAqseh13PvcC9DDE1sJt8Oxow76mgHTeGZvwdddzP4Hp\nt+6z1AAkSyWkd/IoTU8d+rokCKN6baFCPWn25CDGKEvub4u1e1nRiR5Li707vWrWfcwtl6D7UaE9\npQk2zqTh9KJdEPUMwxeNjIee/2w0s/X4sEdC+zkJE6sXs8g0+xvqKMwkBtbfcNyd/8WLeN/X/zpK\nrQDu/Mn38Mo978LKxbsBRMtQUGosZie0LoV4lQi08PBFeu2Kh/nrxZbLSlOxgYVQLQgRL3sAov6G\nfW3h05Dd7twWK1VwsNOLGnVKQQuiVkVaqPaKEAcK89eLuHHHFIIx3WN4GjF80dB85YutXag+z1mu\nkeXZxkBKO5w2sUoV7/v6X+8t0zVcfuEH2JlbQj2ZgRsbn2Wh1976FmS+8922f49r2yjMTB/uSkKF\n+eVSWxBJ79RRT1q9aeh8E8lCHdOrlZbLthaTqGb7W7tN9zvvkRQAWqgQ6se8DzQOxWQ3a9AaBYE7\nXVOyUEexz62K6PAYvmhgHnzsPsg7PwwA+Mx3V/DcE9wwT6fb+ZdfRqeXXFEKc8tv4Mrd90X10sbE\nL95+Py689BJym1swPQ++rkNpGr79m//s0AEyVvWADr8VUVFA6Gf40r0A06uVtuA3s1qBk7AQmP2b\nGXLiBmKV9rZYgS4n6piQ2qkjt1Ht0Elij6a6hz8aDoYv6psHH7uv5fOHH38v8Pju7BaDF51+WhB0\n7GMpKkRgKKxczI3V8m1gmvir3/893Pbqa1i4eg3VdBqvvfUtqCcPP6PS7fSs3ORrvZIoup1yX/S1\nkoPSdP+K5+7MJbBYLQBqb2Yq3N07edyZT6WQ22pfzjwo7EP7MDoZhi/qi6988RMsBUET7/odt+OX\n/+Ef2y4PDAMvvv2tYxW8dilNw7U7L+PanZeP9fP1hNkxAIUCVDInLMarFHQ/hBLpWENNlOq4JBed\nQD3ZTd+KFzOwejGL7GYNVt2Hb2oozCZO3BbrViVfQgFc20AtxfA1Shi+6MTaeicCXFI85SQIYboh\nfFMb20Khg1DO5fD8u9+Fe7//A2iNfVKBaeDl++7F9uLCkEc3HEoXbC0mMbNaaQYeJUAtZZ0oIFh1\nH7M3StC9EALAtXVsnkm3nKKspSxkt2odG8tXBxBOPNvA5tke7p0UQWBoMPz2ww6hRGUmKhkbpVxs\nbPYVTgqGLzqW/f0TWXtrgiiF3HoV6Xy9WXeilrKiZtkn2Ldymv3kPQ/i+h2349LPfg5NKbzxpruw\ncfbssIc1VNVsDG7cRKLoQAuiXoZOwjh2QND8EAtXC9D2ZRCrHmDhagHLd0w1r9eLGSjlYkjn6y3B\nrzQVg2+P58vhzlwcMwf2sYUCbJxNo56yhjcwuqnxvLfRQO2vvQUAz166zMA1oVI7daTzjXZDjSf7\neNnF9HoF2z2sVXTabC8ujO5M15AKcvqWfsv2VBKEMLwQgandtBxEsuC0LWXuniKMlz3U0nshJL+Q\nRDVjIVl0AQCVjAU3Pr5LctVsDErTkNuowvCitlX5uQSD14hj+KKbuv8RH49qn2btLQIQ9XPsVKso\nWXCiU3tc2hgrpuNj7nprQc7NM+m+l3u4pQMzrKKAStrG1lLn+5jhh503nSs0/237uXFzrAPXQbW0\n1RIwafQxfFGbh57/LD7z3RUAwOe5d4v20bts7hUVFQtVx61VRIMXNgpyBq0FOeeuF3Hj9hwCc3iH\nAdIdZlgTJQeBLsh3KM3hxA2EeXQMYG6ML3M0enivnHDd+ycydFG7brWKfFOD4p6vsRKvuF1P/yUL\nzi2XBPsps915hjWdryM/n2ib/aqmLWQ3dRhe0Py5UAAnbsJlD1IaQbxXTqD99bfYP5GOYmc+gcUr\nBSBs7fPIJcfxo/uqY8kHrctS3SB1a2O0WxJCHbyriWD1fBq5zRoSJRdKE5RzNop9rNtFdBIMXxPm\nwcfuiwIX0TF4toGVizlktmqwaz48S0NxNjGeSztKwa76SBYdAEAla8OJH//E3bhxEp3/ZqFg6Hu+\nnJiBeM1vu7zbDKtV8zC7UobhhYAC6raBcpblFWh0jeEzJh1FW/9EFj6lE/ItHdtL43+ycWqtglTB\naZYcSBYdlHM2dhbG/992GJ5toJq2kCi5LUt1nq2jNuSTcjsLSdhXCtFMF/bNsC627/fSvSDau7Zv\nFi9W9bBwtYCVSzkGMBpJDF+nyP7eiQDwzObreO4LDFtEB5l1H6mC0/KCLQpI5R2UszF44ziTdwxb\nSynUkw5SOw5Eqagg59TwZ4y8WDTDmt2qwqr78BplKTrNsKbynctMGF4Iu+afuII8UT9MxjPMKda2\nf+vx/fu3Fgc/IKIxEC+7HdvJiALiFW9iwhdEUMnGUMnGhj2SNr6tY+vMravBm26AbhXADC+E09th\nEfXEhDzDnE6xJz+Oh7/AgEV0VEoTKGnv56ekw2ZuGmn1uIF42e1YZsKJjV/vTJoMDF9j4v5HfPzx\nxz7ZchmXFImOp5q2kduodvzaiZs700BVcjFkt+sQP2yWzQgFqCXNkWsZJKGCXfWgRE7UTonG32jd\nM6lFW//EJ4Y4GKJDklAhUXJhOj7cmIFqyjpR30ctCJEouZBQ9ewFNTA1bC2lMLNSRvMVWwGbS6mh\nNgo36z6yW7Xm764wEx+5ADFqlCZYuZhFdrM60mUmEoU6ZlYrUCLRIQIB1m/LsA7ZhOJffUQc7J8o\n7/ww+yfS2NG9AItvFKCFCpqKZiByhobVC9ljhZpY2cXccin6RAE5AUo5G/n5k9cVq2Zs1JIm4hUP\nQDRTom7SP7Df7KqH+WvF5gk/03WRKLlYO5/lC/QthIaGncUUdkZ0F4bhBHvNr9Xe+uj8tSKuX55i\nU/oJxEf0iGjrn/g4gxeNn5nVCvR97Wo0BYgXYmq9cqjN0/tJqDC3XGrdy6OAdN5BPWX3pBaV0jVU\nDy4zKoVE0UWy6DRmUWIDqXs1vVZpPX2JaE/a1FoFaxezfb996p9Uod75gAeiAx7syzh5GL6GYH/v\nxCYuKdK4U6pj6yEBkCi52Dri1cUqXjS7pVpftUQByUK9P4FIKcxfK8Ku+dAaBeDjZRfF6TgKc31s\nt6MUTCfo+CW73l5slMZLS//M/ZSCFg63mwANB8PXgLTt32LvRKJDaJ8u6OcCTbzsNYPX7m2JAjLb\nNZRzdl+bTSsNkA6vwyGblY+9WspCsuh0PJFZZx2yicTw1Qf7a28BwB969+Dz3L9Fp50IaikT8XLr\n7JfC8U4Q1hNmx96DofTvRGK3kgVAVDW9ku1T+BJBKRdDeqe1oXQoQHHq8DW4tCBEaqeOWMVDYGoo\nTscnp2ZZn8RLLnIbVZheAN/QsDOfQC19tPtfLWXCiRstM6pKgOJ0vK+BnkYXH5U9xt6JNMm2FlNY\nvFKAHoSQMJrN8Q0d+fkEJFRRljrk5mKly96JxN02MwJU01bf9mCFukChw+yaCEKtv5vx83MJaIGK\nek02eupUsjaKM3un9iQIkSw4sNwArq2jkolBNWbGND/E0ht5aEF02EHVGsu9S6n2fW10KPGSi9kb\ne/sOTS/E7I0ythYVqkcpTCuC9XMZJIsuEgPeS0ijieGrB2JPfhxffil6ILJ3Ik0cpaAFCqEmCA0N\nN27PIV72YDYCgm9qmLtegt1olFxPmthaSiE4xOnHaiZqdp0supAwRC1lRS1m+lQfqZy1kd5p3xyt\nEJ2GPJTG3je77sM3og39nZpBtxHB9lIK+bkEDC+Eb2kI952+NNwAi1cKkP0nSTdrWLmYRWDqyGzV\noPmqWe19d8l0erWCatpiTaljmFqvtM2EagrIbdSOFr6ARjcBG5UsgzAxfB3Zg4/dh2cvXW657N+x\nyjxNqEShjun1KiSMXqHKWRs7C0nU0hZqiGZqzr6ahxbubTiOVTwsXCngxu2Ha3ocmHrL7E8/+baB\n7cUkplcrzbHt1mM6zIydhArzVwuwnACiop+dWq9i7UIG3iHrdYWGBrdDMJ1eq7Rs3NYUoAKF6bUK\nNm7LIFF2O7bZEaVgusGhb5/2GF7nzfCGH0YHQU4SaJWC5QTQAgUnZjRnMGky8NF4CG39E4kIsYq7\nV7uoIVVwIAC2F1MAEM1YqdaTXgJA98PoiH1q9I7YV7IxVNP2sSqRp7dr0Qvq7oZ9BSilMLtcxsrt\nJ5gVv8lJ0ng5qlMW6hrQJSz0e8n0tPINDabf/jsNdDlR8DLcAPPXitD9sLnEvDOfQHlqtArDUv8w\nfN3CQ89/lsVOiTrIbtY6LskkCw525pNQmkRNjzttYFfRC9CoUpqgfoxgmCq0n2gTAIYXQPeCk22u\nbrxIH7Tbi7I4HcPMSrnl9hUAN2YgMBm+jqMwG2+rvxYKUJg9QdmRRjkTw2u0Q2pc99R6FZ5twOHp\nx4nA8LVPp/6JPKVI1JnhdQ9Puh/Ct3S4MQOhoD2ACeBO3DLYCZaVRFBJW0gUW5cWQ+yd/KymLVj1\nGDI7dYQiEKXgWTo2zh6tuC3tqeSifV25jSr0QCHUBfnZOMq54+3bMhwfU+vVveC1jyggna8zfE2I\nSXv2a/HQ85/FM5uvNz9/9AuLLHZKdEhuzIBebl8KgwB+Y6alkraQ3dQgXmvTY8/Wo+W8U6actZHd\nqrXNPvmWfuLZp52FJCwnaJkx9CwdO/ONWRgR5OeTKE7HYTk+AkPjPq8T0hrNugszcdSSFgJLO/Zy\no1X3sXCl0GwfdZA0bo8mw8Q9Mnf3b+31TuRmeaLjyM8mEKsUmmUggChY5WcTey9QmmD1Qha5jajp\nMRo1uvJziVN5+q40HUe87MFy/OaGeyWCzTOpE193qGtYuZiFXfUbG+h1ODEddj2AVXfgmzpqKROh\noaFujN5eumGxqx6SBQeiFCqZRluqQ9z3dhth75raqCI/l0DpmA27c2vtJyf3CwUjuQeS+mOiwtdX\nvviJvVIQ7J1IdCJezMDqhSymNqqwatFMS2E23lZTKjQ0bC+lsL00pIEOSLzsYmqtAsMLEWpRSY1q\nyjp8qYnDEIGTNOEkTUiosHCtCKvuY7c4WahrWL2QYeHOhtxaBel8vTnblCi5qKYtbC2lkCi5yG7W\noPsh3JiO/Fyy2cBc88O2wyRAtPxYS1rw7aP/fm/WJioE4Js6yrkjlq+gsXVqw9eDj92HP/Tuabns\nuSdYg4uol7yYgfVzmWEPY+hiZRez+5qA62FUUqOeMHsXvA7IbFZh1fdaIUEB4kdFQNcusBG34QRI\n51s7BmgqCmCBXkE6v3c4Ilb1sXC1gLULWbgxI5ql7UAUkCw6x+rzGepax2VFBSA/F0d5Kt63+wqN\nnlMVvmJPfrz58cOsvUVEA5LbqHY8+ZndrKE0FevLEmuqQ69AAWDXfEgQQumTfcIxXukeoNI7TsvB\nhd1Th7mN6t6bia5LhDdZO7yJ4nSs7X4SClDKxVCa6WPTdhpJYxu+DvZPfPbSZRY7JaKhMLuc/NRC\nFbVV6kcBzZtkgC5VKQ4vDJHKO0iWXAS6oDAbhxcbr1N4R51FEkSb4oGojle3n64esa/jrtJUDLof\nIr1T32sflbGRn2fwmkRjGb7uf8RnsdNeUgrzy8tIFkvYXFpEaWpq2CMiGiueqcN22gNYqEnflpKq\naRupfL1lBkcBcG29pS3RkQUhzr6Whx7sxbdE2UN+JobiXPL41ztg1bSFqbVK2+XqJsl095RuquB0\nvd5j/24bp1ELM3EYXojA1E72d6KxNjbh66HnP4vPfHcFAPB57t3qmXipjF//yn9GolSGEoEWBrhy\n1134zqMfgWJVbKJDyc8lMLdvzxcQbaIuzMb7dqqzMBdHvOoCXtjs9ahEsHXCk5XTaxXogWqb+clt\n1VGeiiM8RE/OURDqGjbOpjG3XAKkcSZBAVuLSVh1v2XPF9BaPLVTHS4gahSv++GJyoYoXYPH0DXx\nRjZ8aXfnEPu7vT1cUVkIhq5ee/9/+xrSO3loau9Z6PxLL2PjzBJefPvbhjgyovFRT1nYXEphZrUM\nbXdPtUQv6P0S6hpuXMohUXJh1Xz4lo5KxjrxXq9k2e265JberqEwPz6zX/WUhet3TketmZRCLWlC\n6Rqqmeh4aDpfhyCaodyZSzRLPdQTBkw3aP89qKhGHdFJjWz4urZmcg9Xn9nVKuZWVlqCFwCYvo83\nPfNjhi+iIzD8sKWApihger0KpQmq2T6VEBBBNWO3lfc4kZtsFkuU3LEKX0C096uWPlA/SwT5hSTy\n8wlojcr1+2coizNxJItuS0P4UKJiqzyRSL3Auc8JZvg+VJclEcP3BjwaojGmVFtleyA68ZjbHK+a\ngpWU1TF/RT0qT1kFdpFoGfXA82Bg6li9mEUlY8E3BI6tY2spheJJejoS7TOyM1/Uf5V0GvVEHKli\nqeXyQNNw5c47hzQqogFRComSi2Rjc3Ula6Oato69R0sLOk8ZGX1sGSNhdJu9nI3ZWUwi1aXO1bjs\n9+oF39KxdYZ9Mak/JueRRO1E8E+PPgLPNBA0Ntd7hoFaKoXnH3pgyIMj6iOlMHujjJmVMhIVD4mK\nh5mV6PPjCozOAcjrQ7V53Qswf7WIcy9t49xL21h8owCjw2nL41C6hnLWwsHIGEpUq4qITo4zXxNu\n7fw5fPV//B9w94+fQzpfwOq5c3j1nrfAt9hjjE4vq+4jXnY7Vj8v1fxmm5lDk2jD9sGWNKFgr/F1\nryiFhavFlhN5Vt3H4pUClu/I9aS46vZCClpYRrzsRjOBSqGcs6OCsaeA5ocw/BCeqfenBhvRLTB8\nESrZLJ75wPuHPQyigYlXPEiHVUJRQKzqHT18AahmY1CaILdRheGF8Cwd+bkE6j1ulhyveND91lII\nAkCUQrLgoHzMxs8tNMHm2TR0P4TuBfCtE9YOGxESKszcKCFe8ZqFTovT8b6WBCHqhOGLiCZOqAuU\noC2AKYnKDhxXLW2jdswK6IdluF2q6SvA7PGG+MDQEJyifV7Tq2XEK140O9n422e2a/BNDZVJaGqt\nFFI7dWR26pBQoZayUJiNsxH7EJyeRxUR0SFVbhKQqpnRXnJ3Y53fM4fS/WsUzXolS27HE6mZ7fE6\nkXpc06sVTG1UYXohjEAhVXCw9EYBWnDKTrGOAYYvIpo4oaFh47Y0Qk2a/wWaYP22zMgvrzlxI2oh\ntG+CTjX+R52so+NI0b0QdtXrWTDYPRna8ba6nFQ9TXQvaGvGLoh+L6md+tDGNan4NomIJlI9aeHa\nnVOwqz4gUagZi30/Ilg/n8XUWqXZg1Aa/82sVmB44XjXowoVZm+UkKh4UCIQpVDK2thZSJ7o7xPq\nUcA2DgQtBaB+jD1+48Zygo5L7Vpjn2NxOMOaWKP9Fo+IqJ9E4CRNOAlzPIJXg9KiPWsAWjbeawrI\nbtVuOssz6qbXK80DEVqoICpqdJ0+6eyMCLYXklEPzMZFCtHvMj9GDcOPyze0jt0LFADP4p6vQTtR\n+BKRaRH5hoi83Pj/qS7fF4jIjxv/PXGS2yQiomi2omNcFMDssil/aEKFRMFBdrOKWNkFVJdw2Dix\n2Xlf1smXxmoZG2vnM6ilTLiWhnLWxsrFLPwJ6NfoxQx4tt6Wv5QApakenJClIznpXOvnAHxTKfUn\nIvK5xud/1OH7akqp+094W0RE1OCbOkw3bAtgojBSJxQNN8DilQKkMYulJKoev3o+01aTTFT7stiu\nXu39cuMmNm4ze3Jd42b9XAYzN8qIVz0oRM3Zt5ZSExE+R81Jw9dHAXyw8fGXAfw9OocvIiLqoeJM\nPJr9OlDUtZ40Ryp8zayUoQWqpeG46QRYeqOAjbNpePtOaCpN4JtaW8kMBURLw3Qioa5h41wGEoTQ\nFBAcaChOg3PSR+iCUmql8fEqgIUu3xcTkadF5Hsi8rET3iYR0cRzEia2FpMIdIn2MQlQS5nYHKF+\nhBIq2DW/fXYOUZPuxSsFWHW/5Wvbi6kO+7L60Clggim9Ub+NwWtobjnzJSJ/B2Cxw5f+w/5PlFJK\npNuEMS4opZZF5HYA3xKR55VSr3a4rT8A8AcAYGfmbjl4IqJh0oIQufUqEiUXEKCSsZGfS/S00fXN\nVLMxVDM2DC9EoEtPWgv11E32/UdV+YHcWgXrF7LQ/BDp7RpiVQ/1hAlAQfcVnLiB0nQcPjeF0yly\ny/CllPpQt6+JyJqILCmlVkRkCcB6l+tYbvz/ayLy9wDeBqAtfCmlvgTgSwCQXrpzfI/rENHpp1TU\n0Hpfj8VUvg676mH1YnZwswoiIxtMlC5w4kbH2a9ddt2H7kXLkBIoaAAUorIIm2fSqKVHu+gt0XGc\n9G3SEwA+1fj4UwC+evAbRGRKROzGx7MA3gPgZye8XSKioUqU3LYei5qKThrGqt7QxjVqtpZSCDtX\nOQAQHQ7IbtagNYIXEM2KaQqYWS13PxlJNMZOGr7+BMCHReRlAB9qfA4ReYeI/J+N73kzgKdF5DkA\nTwL4E6UUwxcRjTWr7reVRAAaG8oP7GOaZL6lY/nyNGoJsy2AhQIUp2NRXa8OPyuhgtHjfpVEo+BE\npx2VUlsAfrXD5U8D+DeNj78L4N6T3A4R0ajxrajFT6cAlt5xUM3GRurU4TApTbBxLo3pRlX+3V9Z\ncTqOci6GVN6B0SGvCqLK9ESnzenvqUBE1AeVjI3cRhVqXxkFoHGSzw8xd70U7f0aIboXIlmow/BC\nOAkTlbQFDOhwAESwvZjCzlwCuh8iMPXmwYTSdBzmarklyCoAtYQ58r02iY6D92oiomNQmmD1QrbZ\n5mc/AWA60UbyUWFXPZx5bQfZrRrSBQfTq2WceSMP6VHx0sNSugbfNlpOhFYyFkpTMYQChFq0HOnE\nDWydSQ10bESDwpkvIqJj8i0dvqnD6tLORwsUglGoDaoUZm+0zixpCoAXIrtVQ35+yL0NRZCfT6I4\nE4dZDxCY2sie4CTqBc58ERGdQC1lIuww+6VE4I1I2xbDCzu259FUdGpzVIS6BidpMnjRqceZLyJq\n12hwnCo4UAKUczFU0xYrYndQnIkjWXSBRssWhaja/PZCYmR+Xzcr+qpGZIxEk4Thi4haKYX5ayXY\nNa+5TGXXyoiXLWyNUOuaURHqGm5cyiKdryNW9hCYGkrTcbix0Xl6DQwNnq3DqgcthwNCAco5e2jj\nIppUo/PsQEQjIVb1WoIXsLc8Vaz7LY2QKaJ0DcWZBIozwx5Jdxtn01i8UoQWhs2Kp7VktNGdiAaL\nz6JE1CJW8dCxS6uKghnD13gKTB3Ld+QQq3gw/BBO3IBn829JNAx85BFRi0AXKEF7ABOw5tK4E0E9\nxV6JPaEUtEBFRWC5b46OiOGLiFpUszHkNmvtXxCJNt0TTTKlkN6qIbddi5ZvRZCfiaM0HWMIo0Pj\n21giahEYGjZuSyPQJCp4qUWzYWvn0jc9NUc0CVI7deS2atDCaC+kFirkNqtI5evDHhqNEc58EVGb\netLC9TunYNV9AAI3pvNdPR2PUkjv1JHdqkELFDxLx85CEvXkKFSfPbrcVq2tn6emgOxWDeWp+HAG\nRWOHM19E1JkI3LgJN24weNGxZTdryG1UoTd6YFpugLnrRdhVb9hDO7rGPq9OdL/z5USdMHwREVF/\nhAqZ7faZIlFAdrM6nDGdhAh8s/PLpseq/HQEDF9ERNQXut+5aXfUeHx0mo4fxc5Csq2dVCjAznxi\nOAOiscTwRUREfREY3V9iRqXv5VHVUhY2bkvDiRkINEE9ZmDjtgxLeNCRcMM9ERH1hyYoTsfblh5D\nAQqz4ztTVE9aWE02wpZSSJRczF8tQAsVKhkb5VyMJ4Ppphi+iIiOq3GSL5V3IFAoZ2yUpuN84d2n\nMBtHqAmy2/tPOybgJMbztONB02sVJAtOM1yaThXJooPVC1keVKGuGL6IiG5FNV5Z97+YKoW56yXE\nqnt9MLNbNSTK7si+8Jp1H6YbwLP0wbWJEkFpJo7STDz6PY7g7+XIGrNd8aKLZNltaVauqWg/W6Lk\nopph03LqjOGLiKgLww0wvVpBrOoBAlTSFrYXklC6BqvutwQvYO+FN172UBuhbgASKsxfKzbqtkWc\neLRXaaCzdKcgeEmosHClANMNoKlmj/IWmgLiZY/hi7rihnsiog4kCLH4RgGxqgdBVB4hUXSxcLUI\nKAW75ndsQK4pwK6NVg2rqfUKrLofVWRXu2P0kVuvDHtoYye9U2sGLwDoFCcVAN8Y/6BJ/cPwRUTU\nQargQJRqXVICYLoB7JqPwNCgOry+hgIEXWpBDYVSLXuSdmkKSBWd4YxpjCWLbtvv8iAlQCUXG8yA\naCyN0DMEEdHoMJ2g64us6QaopiwoTdqXnQSojNhyU6cZOgCQzmW46CZUl6VThUbw1gSbZ9PwWXSV\nboLhi4ioA9fW24pp7vIsHdAEq+ez8Kzo+0IBPFPD2rksQn2EnlpFUI8bbSFRAaifkhOHg1TK2W33\ni91lxrVzGVy/cwo11vyiW+CGeyKiDipZG7mtGlSwt/QYSlQc1IlHT52+rWPl9hx0L4AoRK1nRnBT\n+fZiEktXilBKQVPRv0OJYHsxOeyhjZ1K1kas6iFRcqMLGr/L9XNZ+GNaOJYGj+GLiKgDpWtYuZjF\n9FoF8YoHBaCctZGfS7YFrMAc7Rdd3zawfHsOqXwddt2HEzNQzsUQ3qQCPXUhgq0zaRQcH7Gaj0DX\nUEuZIxm6aXQxfBERdRGYOjZuywx7GD0RGhqKY1xVftT4toGyzZdQOh6+7SEiIiIaIIYvIiIiogFi\n+CIiIiIaIIYvIiIiogFi+CIiIiIaIB7VICKaNEohVvFgugE8S0c9yVIJRIPE8EVENEG0IMTClSKM\nRmFY1ehFuXo+y7pfRAPCRxoR0QSZWqvAdKO+lYKowbbhhpheqwx7aEQTg+GLiGiCJEpi+jjCAAAG\nsUlEQVQuDi4wSuNyqC4duImopxi+iIiIiAaI4YuIaILUkhYOzm8pADVuuicaGIYvIqIJsrOYRGBo\nCBs5KxQg0AXbi8nhDoxogvC0IxHRBAkMDTduzyFRcmE6PjxLRzVjQ2mc9SIaFIYvIqIJozRBJWsD\nsIc9FKKJxGVHIiIiogFi+CIiIiIaIIYvIiIiogFi+CIiIiIaIIYvIiIiogFi+CIiIiIaIIYvIiIi\nogFi+CIiovGkFMy6D90Lhj0SoiNhkVUiIho7iUId02tVCBSgADdmYONsGqHBOQUafbyXEhHRWLFq\nHmZWK9BDBS0ENAXYNR/z14vDHhrRoTB8ERHRWMls1yGq9TIBYDoBDIdLkDT6GL6IiGis6F6Ajm3A\nBdD9cNDDIToyhi8iIhor9aSFsFP6UoAb0wc+HqKjYvgiIqKxUpqOIdQE+1ceQwGKM3EonS9rNPp4\n2pGIiMZKqGtYuZRDZquGRNlFYGgoTsdRS1vDHhrRoTB8ERHR2AkNDfmFJPILyWEPhejIOD9LRERE\nNEAnCl8i8tsi8lMRCUXkHTf5vo+IyIsi8oqIfO4kt0lEREQ0zk468/UCgI8D+Ha3bxARHcCfAXgE\nwFsA/J6IvOWEt0tEREQ0lk6050sp9XMAEOlYcWXXuwC8opR6rfG9fw7gowB+dpLbJiIiIhpHg9jz\ndRbAtX2fX29c1kZE/kBEnhaRp71qYQBDIyIiIhqsW858icjfAVjs8KX/oJT6ai8Ho5T6EoAvAUB6\n6U51i28nIiIiGju3DF9KqQ+d8DaWAZzb9/ltjcuIiIiIJs4glh1/COBOEbkkIhaA3wXwxABul4iI\niGjknLTUxG+JyHUADwL4moj8TePyMyLydQBQSvkA/i2AvwHwcwD/WSn105MNm4iIiGg8nfS0418A\n+IsOl98A8Oi+z78O4OsnuS0iIiKi04AV7omIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiI\niIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAY\nvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiI\naIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGL\niIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgG\niOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiI\niIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAYvoiIiIgGiOGLiIiIaIAY\nvoiIiIgGiOGLiIiIaIBEKTXsMXQkIhsArgx7HNTVLIDNYQ+C+op/48nAv/Ppx7/xYFxQSs0d5htH\nNnzRaBORp5VS7xj2OKh/+DeeDPw7n378G48eLjsSERERDRDDFxEREdEAMXzRcX1p2AOgvuPfeDLw\n73z68W88Yrjni4iIiGiAOPNFRERENEAMX3RsIvLbIvJTEQlFhCdpThER+YiIvCgir4jI54Y9Huo9\nEXlMRNZF5IVhj4X6Q0TOiciTIvKzxnP1Z4Y9JoowfNFJvADg4wC+PeyBUO+IiA7gzwA8AuAtAH5P\nRN4y3FFRH/zfAD4y7EFQX/kAPquUeguABwD8z3wsjwaGLzo2pdTPlVIvDnsc1HPvAvCKUuo1pZQL\n4M8BfHTIY6IeU0p9G8D2sMdB/aOUWlFKPdP4uATg5wDODndUBDB8EVG7swCu7fv8OviETTTWROQi\ngLfh/2/nDlEiCOMwjD8vqMkDGDQYxDOITUw2m8VkNdi9gzcwiiAoKBgW7CIWi3iB9QJm4W/YBUUE\nWV2+T+H5wYSZKU98mRkG7vqWCGCmd4D+tiQ3wMIXtw6r6rJ1jyRpMknmgXPgoKpeevfI8aVvVNVm\n7wY19wwsfThfHF+T9M8kmWU0vE6q6qJ3j0Z87Sjps3tgJclykjlgB7jq3CRpQkkCHANPVXXUu0fv\nHF/6sSTbSYbAGnCdZNC7Sb9XVa/APjBg9IHuWVU99q3StCU5BW6B1STDJHu9mzR168AusJHkYXxs\n9Y6Sf7iXJElqyidfkiRJDTm+JEmSGnJ8SZIkNeT4kiRJasjxJUmS1JDjS5IkqSHHlyRJUkOOL0mS\npIbeAPaupnw08EKOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120c4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: clf.predict(x))\n",
    "plt.title(\"Logistic Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this logistic regression function isn't quite so good! It fails to capture the true form of the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Descent & Loss Function\n",
    "\n",
    "In section 1, we briefly covered gradient descent and loss functions. Here, we'll actually implement these functions and use them to train our neural net. So let's start by defining some useful variables and parameters for gradient descent:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_examples = len(X) # training set size\n",
    "nn_input_dim = 2 # input layer dimensionality\n",
    "nn_output_dim = 2 # output layer dimensionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to define the parameters for gradient descent, specifically the learning rate and the regularization strength. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Rate\n",
    "\n",
    "The learning rate determines how fast or slow we will move towards the optimal weights. If the learning rate is too large the optimal solution will be skipped. But if it's too small we'll need too many iterations to converge to the best values. So using a good learning rate is crucial. Typically, a good technique is to adapt the value of learning rate in each iteration, but for this example, we'll just hand-pick one. \n",
    "\n",
    "#### Regularization Strength\n",
    "\n",
    "The regularization parameter, &lambda; reduces overfitting, which reduces the variance of your estimated regression parameters. However, it does this at the expense of adding bias to your estimate - a common tradeoff you'll encounter in the field of machine learning.\n",
    "\n",
    "One approach you can take is to randomly subsample your data a number of times and look at the variation in your estimate. Then repeat the process for a slightly larger value of lambda to see how it affects the variability of your estimate. Keep in mind that whatever value of lambda you decide is appropriate for your subsampled data, you can likely use a smaller value to achieve comparable regularization on the full data set.\n",
    "\n",
    "With that said, in this exercise, I've just hand-picked a value. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code \n",
    "\n",
    "Here are our hand-chosen rates: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epsilon = 0.01 # learning rate for gradient descent\n",
    "reg_lambda = 0.01 # regularization strength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a helper function to evaluate the total loss on the dataset. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calculate_loss(model):\n",
    "    W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']\n",
    "    # Forward propagation to calculate our predictions\n",
    "    z1 = X.dot(W1) + b1\n",
    "    a1 = np.tanh(z1)\n",
    "    z2 = a1.dot(W2) + b2\n",
    "    exp_scores = np.exp(z2)\n",
    "    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
    "    # Calculating the loss\n",
    "    corect_logprobs = -np.log(probs[range(num_examples), y])\n",
    "    data_loss = np.sum(corect_logprobs)\n",
    "    # Add regulatization term to loss \n",
    "    data_loss += reg_lambda/2 * (np.sum(np.square(W1)) + np.sum(np.square(W2)))\n",
    "    return (1./num_examples * data_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also implement a helper function to calculate the output of the network. It does forward propagation as defined above and returns the class with the highest probability.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(model, x):\n",
    "    W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']\n",
    "    # Forward propagation\n",
    "    z1 = x.dot(W1) + b1\n",
    "    a1 = np.tanh(z1)\n",
    "    z2 = a1.dot(W2) + b2\n",
    "    exp_scores = np.exp(z2)\n",
    "    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
    "    return (np.argmax(probs, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Neural Network\n",
    "\n",
    "Finally, we train our Neural Network. Here, we implement batch gradient descent using the backpropagation derivates we found above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model(nn_hdim, num_passes=20000, print_loss=False):\n",
    "    np.random.seed(0)\n",
    "    W1 = np.random.randn(nn_input_dim, nn_hdim) / np.sqrt(nn_input_dim)\n",
    "    b1 = np.zeros((1, nn_hdim))\n",
    "    W2 = np.random.randn(nn_hdim, nn_output_dim) / np.sqrt(nn_hdim)\n",
    "    b2 = np.zeros((1, nn_output_dim))\n",
    "    model = {}\n",
    "    for i in range(0, num_passes):\n",
    "        z1 = X.dot(W1) + b1\n",
    "        a1 = np.tanh(z1)\n",
    "        z2 = a1.dot(W2) + b2\n",
    "        exp_scores = np.exp(z2)\n",
    "        probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)\n",
    "        delta3 = probs\n",
    "        delta3[range(num_examples), y] -= 1\n",
    "        dW2 = (a1.T).dot(delta3)\n",
    "        db2 = np.sum(delta3, axis=0, keepdims=True)\n",
    "        delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))\n",
    "        dW1 = np.dot(X.T, delta2)\n",
    "        db1 = np.sum(delta2, axis=0)\n",
    "        dW2 += reg_lambda * W2\n",
    "        dW1 += reg_lambda * W1\n",
    "        W1 += -epsilon * dW1\n",
    "        b1 += -epsilon * db1\n",
    "        W2 += -epsilon * dW2\n",
    "        b2 += -epsilon * db2\n",
    "        model = { 'W1': W1, 'b1': b1, 'W2': W2, 'b2': b2}\n",
    "        if print_loss and i % 1000 == 0:\n",
    "            print(\"Loss after iteration %i: %f\" %(i, calculate_loss(model)))\n",
    "    return (model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss after iteration 0: 0.432387\n",
      "Loss after iteration 1000: 0.068947\n",
      "Loss after iteration 2000: 0.068934\n",
      "Loss after iteration 3000: 0.071218\n",
      "Loss after iteration 4000: 0.071253\n",
      "Loss after iteration 5000: 0.071278\n",
      "Loss after iteration 6000: 0.071293\n",
      "Loss after iteration 7000: 0.071303\n",
      "Loss after iteration 8000: 0.071308\n",
      "Loss after iteration 9000: 0.071312\n",
      "Loss after iteration 10000: 0.071314\n",
      "Loss after iteration 11000: 0.071315\n",
      "Loss after iteration 12000: 0.071315\n",
      "Loss after iteration 13000: 0.071316\n",
      "Loss after iteration 14000: 0.071316\n",
      "Loss after iteration 15000: 0.071316\n",
      "Loss after iteration 16000: 0.071316\n",
      "Loss after iteration 17000: 0.071316\n",
      "Loss after iteration 18000: 0.071316\n",
      "Loss after iteration 19000: 0.071316\n"
     ]
    }
   ],
   "source": [
    "model = build_model(3, print_loss=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we're just plotting the decision boundary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+tTkcq90C37C+6rN8q0wuG9x1vcStTZ/rV6IySr4fJWm98nKJYqH/AzDbFmbm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115b3518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: predict(model, x))\n",
    "plt.title(\"Decision Boundary for hidden layer size 3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what happens if we train a network with a hidden layer size of 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GuCyYBhGLVjo1RjI6drbqZ3QtS6CAyJleACh1YTa3ImZpiFlc0EBERHenkYyOnW2/rni1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Tb801DLWrgTN5AqNKC7HmqpGGppc6Kd/5Ll4KN4RPlHAYzy50ZE0ceCdTKyu7XjgrXl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\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QGmfAQo/d2mKIiIgI+d5Y1XOzACikYy2vh6J1oNAqIu8QkW+JSCAib9jhdm8Rke+IyIsi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\nAIBp/QWpIwDaiO5ZTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11574b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_dimensions = [1, 2, 3, 4, 5, 20, 50]\n",
    "for i, nn_hdim in enumerate(hidden_layer_dimensions):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer size %d' % nn_hdim)\n",
    "    model = build_model(nn_hdim)\n",
    "    plot_decision_boundary(lambda x: predict(model, x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that while a hidden layer of low dimensionality nicely captures the general trend of our data, but higher dimensionalities are prone to overfitting. They are \"memorizing\" the data as opposed to fitting the general shape. If we were to evaluate our model on a separate test set (and you should!) the model with a smaller hidden layer size would likely perform better because it generalizes better. We could counteract overfitting with stronger regularization, but picking the correct size for a hidden layer is a much more efficient solution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Modules\n",
    "\n",
    "### Theano\n",
    "\n",
    "Theano is a Python library that is used to define, optimize, and evaluate mathematical expressions with multi-dimensional arrays. Theano accomplishes this through its own data structures integrated with NumPy and the transparent use of the GPU. More specifically, Theano figures out which computational portions should be moved to the GPU.\n",
    "\n",
    "Theano isn't actually a machine learning library since it doesn't provide you with pre-built models to train on your data. Instead, it's a mathematical library that provides you with tools to build your own machine learning models. \n",
    "\n",
    "\n",
    "#### Why Theano? \n",
    "\n",
    "Simply put, Theano's strong suit is efficiency. Its primary purpose is to increase the speed of computation. \n",
    "\n",
    "How does it accomplish this? Identifying 'small' changes like `(x+y) + (x+y)` to `2*(x+y)`, over time, make a substantial difference. Moreover, because it defines different mathematical expressions in C, it makes for much faster implementations. And because of this, Theano works well in high dimensionality problems. Lastly, it allows GPU implementation. \n",
    "\n",
    "#### Symbolic Variables\n",
    "\n",
    "In Theano, all algorithms are defined symbolically, meaning that they don't have an explicit value.\n",
    "\n",
    "#### Symbolic Functions \n",
    "\n",
    "To actually perform computations with Theano, you use symbolic functions, which can later be called with actual values. Symbolic functions allow us to automatically derive gradient expressions.\n",
    "\n",
    "First, we import the needed libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "===============================\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-tbm'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-fma4'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-pcommit'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-clwb'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-pku'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-hle'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-sse4a'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-clflushopt'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-rtm'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-mwaitx'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-prefetchwt1'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-sgx'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-sha'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: unknown argument: '-target-feature'\n",
      "clang: error: no such file or directory: '+sse2'\n",
      "clang: error: no such file or directory: '+cx16'\n",
      "clang: error: no such file or directory: '+prfchw'\n",
      "clang: error: no such file or directory: '+bmi2'\n",
      "clang: error: language not recognized: 'savec'\n",
      "clang: error: no such file or directory: '+fsgsbase'\n",
      "clang: error: no such file or directory: '+popcnt'\n",
      "clang: error: no such file or directory: '+aes'\n",
      "clang: error: language not recognized: 'saves'\n",
      "clang: error: no such file or directory: '+smap'\n",
      "clang: error: no such file or directory: '+mmx'\n",
      "clang: error: language not recognized: 'op'\n",
      "clang: error: no such file or directory: '+rdseed'\n",
      "clang: error: no such file or directory: '+xsave'\n",
      "clang: error: no such file or directory: '+invpcid'\n",
      "clang: error: no such file or directory: '+avx'\n",
      "clang: error: no such file or directory: '+fma'\n",
      "clang: error: no such file or directory: '+bmi'\n",
      "clang: error: no such file or directory: '+rdrnd'\n",
      "clang: error: no such file or directory: '+sse4.1'\n",
      "clang: error: no such file or directory: '+sse4.2'\n",
      "clang: error: no such file or directory: '+avx2'\n",
      "clang: error: no such file or directory: '+sse'\n",
      "clang: error: no such file or directory: '+lzcnt'\n",
      "clang: error: no such file or directory: '+pclmul'\n",
      "clang: error: no such file or directory: '+f16c'\n",
      "clang: error: no such file or directory: '+ssse3'\n",
      "clang: error: no such file or directory: '+cmov'\n",
      "clang: error: no such file or directory: '+movbe'\n",
      "clang: error: no such file or directory: '+xsaveopt'\n",
      "clang: error: no such file or directory: '+adx'\n",
      "clang: error: no such file or directory: '+sse3'\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "00001\t#include <Python.h>\n",
      "00002\t#include \"theano_mod_helper.h\"\n",
      "00003\t#include \"structmember.h\"\n",
      "00004\t#include <sys/time.h>\n",
      "00005\t\n",
      "00006\t#if PY_VERSION_HEX >= 0x03000000\n",
      "00007\t#include \"numpy/npy_3kcompat.h\"\n",
      "00008\t#define PyCObject_AsVoidPtr  NpyCapsule_AsVoidPtr\n",
      "00009\t#define PyCObject_GetDesc  NpyCapsule_GetDesc\n",
      "00010\t#define PyCObject_Check NpyCapsule_Check\n",
      "00011\t#endif\n",
      "00012\t\n",
      "00013\t#ifndef Py_TYPE\n",
      "00014\t#define Py_TYPE(obj) obj->ob_type\n",
      "00015\t#endif\n",
      "00016\t\n",
      "00017\t/**\n",
      "00018\t\n",
      "00019\tTODO: \n",
      "00020\t- Check max supported depth of recursion\n",
      "00021\t- CLazyLinker should add context information to errors caught during evaluation. Say what node we were on, add the traceback attached to the node.\n",
      "00022\t- Clear containers of fully-useed intermediate results if allow_gc is 1\n",
      "00023\t- Add timers for profiling\n",
      "00024\t- Add support for profiling space used.\n",
      "00025\t\n",
      "00026\t\n",
      "00027\t  */\n",
      "00028\tstatic double pytime(const struct timeval * tv)\n",
      "00029\t{\n",
      "00030\t  struct timeval t;\n",
      "00031\t  if (!tv)\n",
      "00032\t    {\n",
      "00033\t      tv = &t;\n",
      "00034\t      gettimeofday(&t, NULL);\n",
      "00035\t    }\n",
      "00036\t  return (double) tv->tv_sec + (double) tv->tv_usec / 1000000.0;\n",
      "00037\t}\n",
      "00038\t\n",
      "00039\t/**\n",
      "00040\t  Helper routine to convert a PyList of integers to a c array of integers.\n",
      "00041\t  */\n",
      "00042\tstatic int unpack_list_of_ssize_t(PyObject * pylist, Py_ssize_t **dst, Py_ssize_t *len,\n",
      "00043\t                                  const char* kwname)\n",
      "00044\t{\n",
      "00045\t  Py_ssize_t buflen, *buf;\n",
      "00046\t  if (!PyList_Check(pylist))\n",
      "00047\t    {\n",
      "00048\t      PyErr_Format(PyExc_TypeError, \"%s must be list\", kwname);\n",
      "00049\t      return -1;\n",
      "00050\t    }\n",
      "00051\t  assert (NULL == *dst);\n",
      "00052\t  *len = buflen = PyList_Size(pylist);\n",
      "00053\t  *dst = buf = (Py_ssize_t*)calloc(buflen, sizeof(Py_ssize_t));\n",
      "00054\t  assert(buf);\n",
      "00055\t  for (int ii = 0; ii < buflen; ++ii)\n",
      "00056\t    {\n",
      "00057\t      PyObject * el_i = PyList_GetItem(pylist, ii);\n",
      "00058\t      Py_ssize_t n_i = PyNumber_AsSsize_t(el_i, PyExc_IndexError);\n",
      "00059\t      if (PyErr_Occurred())\n",
      "00060\t        {\n",
      "00061\t          free(buf);\n",
      "00062\t          *dst = NULL;\n",
      "00063\t          return -1;\n",
      "00064\t        }\n",
      "00065\t      buf[ii] = n_i;\n",
      "00066\t    }\n",
      "00067\t  return 0;\n",
      "00068\t}\n",
      "00069\t\n",
      "00070\t/**\n",
      "00071\t\n",
      "00072\t  CLazyLinker\n",
      "00073\t\n",
      "00074\t\n",
      "00075\t  */\n",
      "00076\ttypedef struct {\n",
      "00077\t    PyObject_HEAD\n",
      "00078\t    /* Type-specific fields go here. */\n",
      "00079\t    PyObject * nodes; // the python list of nodes\n",
      "00080\t    PyObject * thunks; // python list of thunks\n",
      "00081\t    PyObject * pre_call_clear; //list of cells to clear on call.\n",
      "00082\t    int allow_gc;\n",
      "00083\t    Py_ssize_t n_applies;\n",
      "00084\t    int n_vars;    // number of variables in the graph\n",
      "00085\t    int * var_computed; // 1 or 0 for every variable\n",
      "00086\t    PyObject ** var_computed_cells;\n",
      "00087\t    PyObject ** var_value_cells;\n",
      "00088\t    Py_ssize_t **dependencies; // list of vars dependencies for GC\n",
      "00089\t    Py_ssize_t *n_dependencies;\n",
      "00090\t\n",
      "00091\t    Py_ssize_t n_output_vars;\n",
      "00092\t    Py_ssize_t * output_vars; // variables that *must* be evaluated by call\n",
      "00093\t\n",
      "00094\t    int * is_lazy; // 1 or 0 for every thunk\n",
      "00095\t\n",
      "00096\t    Py_ssize_t * var_owner; // nodes[[var_owner[var_idx]]] is var[var_idx]->owner\n",
      "00097\t    int * var_has_owner; //  1 or 0\n",
      "00098\t\n",
      "00099\t    Py_ssize_t * node_n_inputs;\n",
      "00100\t    Py_ssize_t * node_n_outputs;\n",
      "00101\t    Py_ssize_t ** node_inputs;\n",
      "00102\t    Py_ssize_t ** node_outputs;\n",
      "00103\t    Py_ssize_t * node_inputs_outputs_base; // node_inputs and node_outputs point into this\n",
      "00104\t    Py_ssize_t * node_n_prereqs;\n",
      "00105\t    Py_ssize_t ** node_prereqs;\n",
      "00106\t\n",
      "00107\t    Py_ssize_t * update_storage; // input cells to update with the last outputs in output_vars\n",
      "00108\t    Py_ssize_t n_updates;\n",
      "00109\t\n",
      "00110\t    void ** thunk_cptr_fn;\n",
      "00111\t    void ** thunk_cptr_data;\n",
      "00112\t    PyObject * call_times;\n",
      "00113\t    PyObject * call_counts;\n",
      "00114\t    int do_timing;\n",
      "00115\t    int need_update_inputs;\n",
      "00116\t    int position_of_error; // -1 for no error, otw the index into `thunks` that failed.\n",
      "00117\t} CLazyLinker;\n",
      "00118\t\n",
      "00119\t\n",
      "00120\tstatic void\n",
      "00121\tCLazyLinker_dealloc(PyObject* _self)\n",
      "00122\t{\n",
      "00123\t  CLazyLinker* self = (CLazyLinker *) _self;\n",
      "00124\t  free(self->thunk_cptr_fn);\n",
      "00125\t  free(self->thunk_cptr_data);\n",
      "00126\t\n",
      "00127\t  free(self->is_lazy);\n",
      "00128\t\n",
      "00129\t  free(self->update_storage);\n",
      "00130\t\n",
      "00131\t  if (self->node_n_prereqs)\n",
      "00132\t    {\n",
      "00133\t      for (int i = 0; i < self->n_applies; ++i)\n",
      "00134\t        {\n",
      "00135\t          free(self->node_prereqs[i]);\n",
      "00136\t        }\n",
      "00137\t    }\n",
      "00138\t  free(self->node_n_prereqs);\n",
      "00139\t  free(self->node_prereqs);\n",
      "00140\t  free(self->node_inputs_outputs_base);\n",
      "00141\t  free(self->node_n_inputs);\n",
      "00142\t  free(self->node_n_outputs);\n",
      "00143\t  free(self->node_inputs);\n",
      "00144\t  free(self->node_outputs);\n",
      "00145\t\n",
      "00146\t  if (self->dependencies)\n",
      "00147\t    {\n",
      "00148\t      for (int i = 0; i < self->n_vars; ++i)\n",
      "00149\t        {\n",
      "00150\t          free(self->dependencies[i]);\n",
      "00151\t        }\n",
      "00152\t      free(self->dependencies);\n",
      "00153\t      free(self->n_dependencies);\n",
      "00154\t    }\n",
      "00155\t\n",
      "00156\t  free(self->var_owner);\n",
      "00157\t  free(self->var_has_owner);\n",
      "00158\t  free(self->var_computed);\n",
      "00159\t  if (self->var_computed_cells)\n",
      "00160\t    {\n",
      "00161\t      for (int i = 0; i < self->n_vars; ++i)\n",
      "00162\t        {\n",
      "00163\t          Py_DECREF(self->var_computed_cells[i]);\n",
      "00164\t          Py_DECREF(self->var_value_cells[i]);\n",
      "00165\t        }\n",
      "00166\t    }\n",
      "00167\t  free(self->var_computed_cells);\n",
      "00168\t  free(self->var_value_cells);\n",
      "00169\t  free(self->output_vars);\n",
      "00170\t\n",
      "00171\t  Py_XDECREF(self->nodes);\n",
      "00172\t  Py_XDECREF(self->thunks);\n",
      "00173\t  Py_XDECREF(self->call_times);\n",
      "00174\t  Py_XDECREF(self->call_counts);\n",
      "00175\t  Py_XDECREF(self->pre_call_clear);\n",
      "00176\t  Py_TYPE(self)->tp_free((PyObject*)self);\n",
      "00177\t}\n",
      "00178\tstatic PyObject *\n",
      "00179\tCLazyLinker_new(PyTypeObject *type, PyObject *args, PyObject *kwds)\n",
      "00180\t{\n",
      "00181\t    CLazyLinker *self;\n",
      "00182\t\n",
      "00183\t    self = (CLazyLinker *)type->tp_alloc(type, 0);\n",
      "00184\t    if (self != NULL) {\n",
      "00185\t      self->nodes = NULL;\n",
      "00186\t      self->thunks = NULL;\n",
      "00187\t      self->pre_call_clear = NULL;\n",
      "00188\t\n",
      "00189\t      self->allow_gc = 1;\n",
      "00190\t      self->n_applies = 0;\n",
      "00191\t      self->n_vars = 0;\n",
      "00192\t      self->var_computed = NULL;\n",
      "00193\t      self->var_computed_cells = NULL;\n",
      "00194\t      self->var_value_cells = NULL;\n",
      "00195\t      self->dependencies = NULL;\n",
      "00196\t      self->n_dependencies = NULL;\n",
      "00197\t\n",
      "00198\t      self->n_output_vars = 0;\n",
      "00199\t      self->output_vars = NULL;\n",
      "00200\t\n",
      "00201\t      self->is_lazy = NULL;\n",
      "00202\t\n",
      "00203\t      self->var_owner = NULL;\n",
      "00204\t      self->var_has_owner = NULL;\n",
      "00205\t\n",
      "00206\t      self->node_n_inputs = NULL;\n",
      "00207\t      self->node_n_outputs = NULL;\n",
      "00208\t      self->node_inputs = NULL;\n",
      "00209\t      self->node_outputs = NULL;\n",
      "00210\t      self->node_inputs_outputs_base = NULL;\n",
      "00211\t      self->node_prereqs = NULL;\n",
      "00212\t      self->node_n_prereqs = NULL;\n",
      "00213\t\n",
      "00214\t      self->update_storage = NULL;\n",
      "00215\t      self->n_updates = 0;\n",
      "00216\t\n",
      "00217\t      self->thunk_cptr_data = NULL;\n",
      "00218\t      self->thunk_cptr_fn = NULL;\n",
      "00219\t      self->call_times = NULL;\n",
      "00220\t      self->call_counts = NULL;\n",
      "00221\t      self->do_timing = 0;\n",
      "00222\t\n",
      "00223\t      self->need_update_inputs = 0;\n",
      "00224\t      self->position_of_error = -1;\n",
      "00225\t    }\n",
      "00226\t    return (PyObject *)self;\n",
      "00227\t}\n",
      "00228\t\n",
      "00229\tstatic int\n",
      "00230\tCLazyLinker_init(CLazyLinker *self, PyObject *args, PyObject *kwds)\n",
      "00231\t{\n",
      "00232\t    static char *kwlist[] = {\n",
      "00233\t      (char*)\"nodes\",\n",
      "00234\t      (char*)\"thunks\",\n",
      "00235\t      (char*)\"pre_call_clear\",\n",
      "00236\t      (char*)\"allow_gc\",\n",
      "00237\t      (char*)\"call_counts\",\n",
      "00238\t      (char*)\"call_times\",\n",
      "00239\t      (char*)\"compute_map_list\",\n",
      "00240\t      (char*)\"storage_map_list\",\n",
      "00241\t      (char*)\"base_input_output_list\",\n",
      "00242\t      (char*)\"node_n_inputs\",\n",
      "00243\t      (char*)\"node_n_outputs\",\n",
      "00244\t      (char*)\"node_input_offset\",\n",
      "00245\t      (char*)\"node_output_offset\",\n",
      "00246\t      (char*)\"var_owner\",\n",
      "00247\t      (char*)\"is_lazy_list\",\n",
      "00248\t      (char*)\"output_vars\",\n",
      "00249\t      (char*)\"node_prereqs\",\n",
      "00250\t      (char*)\"node_output_size\",\n",
      "00251\t      (char*)\"update_storage\",\n",
      "00252\t      (char*)\"dependencies\",\n",
      "00253\t      NULL};\n",
      "00254\t\n",
      "00255\t    PyObject *compute_map_list=NULL,\n",
      "00256\t             *storage_map_list=NULL,\n",
      "00257\t             *base_input_output_list=NULL,\n",
      "00258\t             *node_n_inputs=NULL,\n",
      "00259\t             *node_n_outputs=NULL,\n",
      "00260\t             *node_input_offset=NULL,\n",
      "00261\t             *node_output_offset=NULL,\n",
      "00262\t             *var_owner=NULL,\n",
      "00263\t             *is_lazy=NULL,\n",
      "00264\t             *output_vars=NULL,\n",
      "00265\t             *node_prereqs=NULL,\n",
      "00266\t             *node_output_size=NULL,\n",
      "00267\t             *update_storage=NULL,\n",
      "00268\t             *dependencies=NULL;\n",
      "00269\t\n",
      "00270\t    assert(!self->nodes);\n",
      "00271\t    if (! PyArg_ParseTupleAndKeywords(args, kwds, \"OOOiOOOOOOOOOOOOOOOO\", kwlist,\n",
      "00272\t                                      &self->nodes,\n",
      "00273\t                                      &self->thunks,\n",
      "00274\t                                      &self->pre_call_clear,\n",
      "00275\t                                      &self->allow_gc,\n",
      "00276\t                                      &self->call_counts,\n",
      "00277\t                                      &self->call_times,\n",
      "00278\t                                      &compute_map_list,\n",
      "00279\t                                      &storage_map_list,\n",
      "00280\t                                      &base_input_output_list,\n",
      "00281\t                                      &node_n_inputs,\n",
      "00282\t                                      &node_n_outputs,\n",
      "00283\t                                      &node_input_offset,\n",
      "00284\t                                      &node_output_offset,\n",
      "00285\t                                      &var_owner,\n",
      "00286\t                                      &is_lazy,\n",
      "00287\t                                      &output_vars,\n",
      "00288\t                                      &node_prereqs,\n",
      "00289\t                                      &node_output_size,\n",
      "00290\t                                      &update_storage,\n",
      "00291\t                                      &dependencies\n",
      "00292\t                                      ))\n",
      "00293\t        return -1;\n",
      "00294\t    Py_INCREF(self->nodes);\n",
      "00295\t    Py_INCREF(self->thunks);\n",
      "00296\t    Py_INCREF(self->pre_call_clear);\n",
      "00297\t    Py_INCREF(self->call_counts);\n",
      "00298\t    Py_INCREF(self->call_times);\n",
      "00299\t\n",
      "00300\t    Py_ssize_t n_applies = PyList_Size(self->nodes);\n",
      "00301\t\n",
      "00302\t    self->n_applies = n_applies;\n",
      "00303\t    self->n_vars = PyList_Size(var_owner);\n",
      "00304\t\n",
      "00305\t    if (PyList_Size(self->thunks) != n_applies) return -1;\n",
      "00306\t    if (PyList_Size(self->call_counts) != n_applies) return -1;\n",
      "00307\t    if (PyList_Size(self->call_times) != n_applies) return -1;\n",
      "00308\t\n",
      "00309\t    // allocated and initialize thunk_cptr_data and thunk_cptr_fn\n",
      "00310\t    if (n_applies)\n",
      "00311\t      {\n",
      "00312\t        self->thunk_cptr_data = (void**)calloc(n_applies, sizeof(void*));\n",
      "00313\t        self->thunk_cptr_fn = (void**)calloc(n_applies, sizeof(void*));\n",
      "00314\t        self->is_lazy = (int*)calloc(n_applies, sizeof(int));\n",
      "00315\t        self->node_prereqs = (Py_ssize_t**)calloc(n_applies, sizeof(Py_ssize_t*));\n",
      "00316\t        self->node_n_prereqs = (Py_ssize_t*)calloc(n_applies, sizeof(Py_ssize_t));\n",
      "00317\t        assert(self->node_prereqs);\n",
      "00318\t        assert(self->node_n_prereqs);\n",
      "00319\t        assert(self->is_lazy);\n",
      "00320\t        assert(self->thunk_cptr_fn);\n",
      "00321\t        assert(self->thunk_cptr_data);\n",
      "00322\t\n",
      "00323\t        for (int i = 0; i < n_applies; ++i)\n",
      "00324\t          {\n",
      "00325\t            PyObject * thunk = PyList_GetItem(self->thunks, i);\n",
      "00326\t            //thunk is borrowed\n",
      "00327\t            if (PyObject_HasAttrString(thunk, \"cthunk\"))\n",
      "00328\t              {\n",
      "00329\t                PyObject * cthunk = PyObject_GetAttrString(thunk, \"cthunk\");\n",
      "00330\t                //new reference\n",
      "00331\t                assert (cthunk && PyCObject_Check(cthunk));\n",
      "00332\t                self->thunk_cptr_fn[i] = PyCObject_AsVoidPtr(cthunk);\n",
      "00333\t                self->thunk_cptr_data[i] = PyCObject_GetDesc(cthunk);\n",
      "00334\t                Py_DECREF(cthunk);\n",
      "00335\t                // cthunk is kept alive by membership in self->thunks\n",
      "00336\t              }\n",
      "00337\t\n",
      "00338\t            PyObject * el_i = PyList_GetItem(is_lazy, i);\n",
      "00339\t            self->is_lazy[i] = PyNumber_AsSsize_t(el_i, NULL);\n",
      "00340\t\n",
      "00341\t            /* now get the prereqs */\n",
      "00342\t            el_i = PyList_GetItem(node_prereqs, i);\n",
      "00343\t            assert (PyList_Check(el_i));\n",
      "00344\t            self->node_n_prereqs[i] = PyList_Size(el_i);\n",
      "00345\t            if (self->node_n_prereqs[i])\n",
      "00346\t              {\n",
      "00347\t                self->node_prereqs[i] = (Py_ssize_t*)malloc(\n",
      "00348\t                              PyList_Size(el_i)*sizeof(Py_ssize_t));\n",
      "00349\t                for (int j = 0; j < PyList_Size(el_i); ++j)\n",
      "00350\t                  {\n",
      "00351\t                    PyObject * el_ij = PyList_GetItem(el_i, j);\n",
      "00352\t                    Py_ssize_t N = PyNumber_AsSsize_t(el_ij, PyExc_IndexError);\n",
      "00353\t                    if (PyErr_Occurred())\n",
      "00354\t                      return -1;\n",
      "00355\t                    // N < n. variables\n",
      "00356\t                    assert(N < PyList_Size(var_owner));\n",
      "00357\t                    self->node_prereqs[i][j] = N;\n",
      "00358\t                  }\n",
      "00359\t              }\n",
      "00360\t          }\n",
      "00361\t      }\n",
      "00362\t    if (PyList_Check(base_input_output_list))\n",
      "00363\t      {\n",
      "00364\t        Py_ssize_t n_inputs_outputs_base = PyList_Size(base_input_output_list);\n",
      "00365\t        self->node_inputs_outputs_base = (Py_ssize_t*)calloc(n_inputs_outputs_base,sizeof(Py_ssize_t));\n",
      "00366\t        assert(self->node_inputs_outputs_base);\n",
      "00367\t        for (int i = 0; i < n_inputs_outputs_base; ++i)\n",
      "00368\t          {\n",
      "00369\t            PyObject *el_i = PyList_GetItem(base_input_output_list, i);\n",
      "00370\t            Py_ssize_t idx = PyNumber_AsSsize_t(el_i, PyExc_IndexError);\n",
      "00371\t            if (PyErr_Occurred()) return -1;\n",
      "00372\t            self->node_inputs_outputs_base[i] = idx;\n",
      "00373\t          }\n",
      "00374\t        self->node_n_inputs = (Py_ssize_t*)calloc(n_applies,sizeof(Py_ssize_t));\n",
      "00375\t        assert(self->node_n_inputs);\n",
      "00376\t        self->node_n_outputs = (Py_ssize_t*)calloc(n_applies,sizeof(Py_ssize_t));\n",
      "00377\t        assert(self->node_n_outputs);\n",
      "00378\t        self->node_inputs = (Py_ssize_t**)calloc(n_applies,sizeof(Py_ssize_t*));\n",
      "00379\t        assert(self->node_inputs);\n",
      "00380\t        self->node_outputs = (Py_ssize_t**)calloc(n_applies,sizeof(Py_ssize_t*));\n",
      "00381\t        assert(self->node_outputs);\n",
      "00382\t        for (int i = 0; i < n_applies; ++i)\n",
      "00383\t          {\n",
      "00384\t            Py_ssize_t N;\n",
      "00385\t            N = PyNumber_AsSsize_t(PyList_GetItem(node_n_inputs, i),PyExc_IndexError);\n",
      "00386\t            if (PyErr_Occurred()) return -1;\n",
      "00387\t            assert (N <= n_inputs_outputs_base);\n",
      "00388\t            self->node_n_inputs[i] = N;\n",
      "00389\t            N = PyNumber_AsSsize_t(PyList_GetItem(node_n_outputs, i),PyExc_IndexError);\n",
      "00390\t            if (PyErr_Occurred()) return -1;\n",
      "00391\t            assert (N <= n_inputs_outputs_base);\n",
      "00392\t            self->node_n_outputs[i] = N;\n",
      "00393\t            N = PyNumber_AsSsize_t(PyList_GetItem(node_input_offset, i),PyExc_IndexError);\n",
      "00394\t            if (PyErr_Occurred()) return -1;\n",
      "00395\t            assert (N <= n_inputs_outputs_base);\n",
      "00396\t            self->node_inputs[i] = &self->node_inputs_outputs_base[N];\n",
      "00397\t            N = PyNumber_AsSsize_t(PyList_GetItem(node_output_offset, i),PyExc_IndexError);\n",
      "00398\t            if (PyErr_Occurred()) return -1;\n",
      "00399\t            assert (N <= n_inputs_outputs_base);\n",
      "00400\t            self->node_outputs[i] = &self->node_inputs_outputs_base[N];\n",
      "00401\t          }\n",
      "00402\t      }\n",
      "00403\t    else\n",
      "00404\t      {\n",
      "00405\t        PyErr_SetString(PyExc_TypeError, \"base_input_output_list must be list\");\n",
      "00406\t        return -1;\n",
      "00407\t      }\n",
      "00408\t\n",
      "00409\t    // allocation for var_owner\n",
      "00410\t    if (PyList_Check(var_owner))\n",
      "00411\t      {\n",
      "00412\t        self->var_owner = (Py_ssize_t*)calloc(self->n_vars,sizeof(Py_ssize_t));\n",
      "00413\t        self->var_has_owner = (int*)calloc(self->n_vars,sizeof(int));\n",
      "00414\t        self->var_computed = (int*)calloc(self->n_vars,sizeof(int));\n",
      "00415\t        self->var_computed_cells = (PyObject**)calloc(self->n_vars,sizeof(PyObject*));\n",
      "00416\t        self->var_value_cells = (PyObject**)calloc(self->n_vars,sizeof(PyObject*));\n",
      "00417\t        for (int i = 0; i < self->n_vars; ++i)\n",
      "00418\t          {\n",
      "00419\t            PyObject * el_i = PyList_GetItem(var_owner, i);\n",
      "00420\t            if (el_i == Py_None)\n",
      "00421\t              {\n",
      "00422\t                self->var_has_owner[i] = 0;\n",
      "00423\t              }\n",
      "00424\t            else\n",
      "00425\t              {\n",
      "00426\t                Py_ssize_t N = PyNumber_AsSsize_t(el_i, PyExc_IndexError);\n",
      "00427\t                if (PyErr_Occurred()) return -1;\n",
      "00428\t                assert (N <= n_applies);\n",
      "00429\t                self->var_owner[i] = N;\n",
      "00430\t                self->var_has_owner[i] = 1;\n",
      "00431\t              }\n",
      "00432\t            self->var_computed_cells[i] = PyList_GetItem(compute_map_list, i);\n",
      "00433\t            Py_INCREF(self->var_computed_cells[i]);\n",
      "00434\t            self->var_value_cells[i] = PyList_GetItem(storage_map_list, i);\n",
      "00435\t            Py_INCREF(self->var_value_cells[i]);\n",
      "00436\t          }\n",
      "00437\t      }\n",
      "00438\t    else\n",
      "00439\t      {\n",
      "00440\t        PyErr_SetString(PyExc_TypeError, \"var_owner must be list\");\n",
      "00441\t        return -1;\n",
      "00442\t      }\n",
      "00443\t\n",
      "00444\t    if (dependencies != Py_None)\n",
      "00445\t      {\n",
      "00446\t        self->dependencies = (Py_ssize_t**)calloc(self->n_vars, sizeof(Py_ssize_t *));\n",
      "00447\t        self->n_dependencies = (Py_ssize_t*)calloc(self->n_vars, sizeof(Py_ssize_t));\n",
      "00448\t        assert(self->dependencies);\n",
      "00449\t        assert(self->n_dependencies);\n",
      "00450\t\n",
      "00451\t        for (int i = 0; i < self->n_vars; ++i)\n",
      "00452\t          {\n",
      "00453\t            PyObject *tmp = PyList_GetItem(dependencies, i);\n",
      "00454\t            // refcounting - tmp is borrowed\n",
      "00455\t            if (unpack_list_of_ssize_t(tmp, &self->dependencies[i], &self->n_dependencies[i],\n",
      "00456\t                                       \"dependencies\"))\n",
      "00457\t              return -1;\n",
      "00458\t          }\n",
      "00459\t      }\n",
      "00460\t\n",
      "00461\t    if (unpack_list_of_ssize_t(output_vars, &self->output_vars, &self->n_output_vars,\n",
      "00462\t                               \"output_vars\"))\n",
      "00463\t      return -1;\n",
      "00464\t    for (int i = 0; i < self->n_output_vars; ++i)\n",
      "00465\t      {\n",
      "00466\t        assert(self->output_vars[i] < self->n_vars);\n",
      "00467\t      }\n",
      "00468\t    if (unpack_list_of_ssize_t(update_storage, &self->update_storage, &self->n_updates,\n",
      "00469\t                               \"updates_storage\"))\n",
      "00470\t      return -1;\n",
      "00471\t    return 0;\n",
      "00472\t}\n",
      "00473\tstatic void set_position_of_error(CLazyLinker * self, int owner_idx)\n",
      "00474\t{\n",
      "00475\t  if (self->position_of_error == -1)\n",
      "00476\t    {\n",
      "00477\t      self->position_of_error = owner_idx;\n",
      "00478\t    }\n",
      "00479\t}\n",
      "00480\tstatic PyObject * pycall(CLazyLinker * self, Py_ssize_t node_idx, int verbose)\n",
      "00481\t{\n",
      "00482\t  // call thunk to see which inputs it wants\n",
      "00483\t  PyObject * thunk = PyList_GetItem(self->thunks, node_idx);\n",
      "00484\t  // refcounting - thunk is borrowed\n",
      "00485\t  PyObject * rval = NULL;\n",
      "00486\t  if (self->do_timing)\n",
      "00487\t    {\n",
      "00488\t      double t0 = pytime(NULL);\n",
      "00489\t      if (verbose) fprintf(stderr, \"calling via Python (node %i)\\n\", (int)node_idx);\n",
      "00490\t      rval = PyObject_CallObject(thunk, NULL);\n",
      "00491\t      if (rval)\n",
      "00492\t        {\n",
      "00493\t          double t1 = pytime(NULL);\n",
      "00494\t          double ti = PyFloat_AsDouble(\n",
      "00495\t                         PyList_GetItem(self->call_times, node_idx));\n",
      "00496\t          PyList_SetItem(self->call_times, node_idx,\n",
      "00497\t                         PyFloat_FromDouble(t1 - t0 + ti));\n",
      "00498\t          PyObject * count = PyList_GetItem(self->call_counts, node_idx);\n",
      "00499\t          long icount = PyInt_AsLong(count);\n",
      "00500\t          PyList_SetItem(self->call_counts, node_idx,\n",
      "00501\t                         PyInt_FromLong(icount + 1));\n",
      "00502\t      }\n",
      "00503\t    }\n",
      "00504\t  else\n",
      "00505\t    {\n",
      "00506\t      if (verbose)\n",
      "00507\t        {\n",
      "00508\t          fprintf(stderr, \"calling via Python (node %i)\\n\", (int)node_idx);\n",
      "00509\t        }\n",
      "00510\t      rval = PyObject_CallObject(thunk, NULL);\n",
      "00511\t    }\n",
      "00512\t  return rval;\n",
      "00513\t}\n",
      "00514\tstatic int c_call(CLazyLinker * self, Py_ssize_t node_idx, int verbose)\n",
      "00515\t{\n",
      "00516\t  void * ptr_addr = self->thunk_cptr_fn[node_idx];\n",
      "00517\t  int (*fn)(void*) = (int (*)(void*))(ptr_addr);\n",
      "00518\t  if (verbose) fprintf(stderr, \"calling non-lazy shortcut (node %i)\\n\", (int)node_idx);\n",
      "00519\t  int err = 0;\n",
      "00520\t  if (self->do_timing)\n",
      "00521\t    {\n",
      "00522\t      double t0 = pytime(NULL);\n",
      "00523\t      err = fn(self->thunk_cptr_data[node_idx]);\n",
      "00524\t      double t1 = pytime(NULL);\n",
      "00525\t      double ti = PyFloat_AsDouble(PyList_GetItem(self->call_times, node_idx));\n",
      "00526\t      PyList_SetItem(self->call_times, node_idx, PyFloat_FromDouble(t1 - t0 + ti));\n",
      "00527\t      PyObject * count = PyList_GetItem(self->call_counts, node_idx);\n",
      "00528\t      long icount = PyInt_AsLong(count);\n",
      "00529\t      PyList_SetItem(self->call_counts, node_idx, PyInt_FromLong(icount+1));\n",
      "00530\t    }\n",
      "00531\t  else\n",
      "00532\t    {\n",
      "00533\t      err = fn(self->thunk_cptr_data[node_idx]);\n",
      "00534\t    }\n",
      "00535\t\n",
      "00536\t  if (err)\n",
      "00537\t    {\n",
      "00538\t      // cast the argument to a PyList (as described near line 226 of cc.py)\n",
      "00539\t      PyObject * __ERROR = ((PyObject**)self->thunk_cptr_data[node_idx])[0];\n",
      "00540\t      assert (PyList_Check(__ERROR));\n",
      "00541\t      assert (PyList_Size(__ERROR) == 3);\n",
      "00542\t      PyObject * err_type = PyList_GetItem(__ERROR, 0); //stolen ref\n",
      "00543\t      PyObject * err_msg = PyList_GetItem(__ERROR, 1); //stolen ref\n",
      "00544\t      PyObject * err_trace = PyList_GetItem(__ERROR, 2); //stolen ref\n",
      "00545\t      PyList_SET_ITEM(__ERROR, 0, Py_None); Py_INCREF(Py_None); //clobbers old ref\n",
      "00546\t      PyList_SET_ITEM(__ERROR, 1, Py_None); Py_INCREF(Py_None); //clobbers old ref\n",
      "00547\t      PyList_SET_ITEM(__ERROR, 2, Py_None); Py_INCREF(Py_None); //clobbers old ref\n",
      "00548\t\n",
      "00549\t      assert(!PyErr_Occurred()); // because CLinker hid the exception in __ERROR aka data\n",
      "00550\t      PyErr_Restore(err_type, err_msg, err_trace); //steals refs to args\n",
      "00551\t    }\n",
      "00552\t  if (err) set_position_of_error(self, node_idx);\n",
      "00553\t  return err;\n",
      "00554\t}\n",
      "00555\tstatic\n",
      "00556\tint lazy_rec_eval(CLazyLinker * self, Py_ssize_t var_idx, PyObject*one, PyObject*zero)\n",
      "00557\t{\n",
      "00558\t  PyObject *rval = NULL;\n",
      "00559\t  int verbose = 0;\n",
      "00560\t  int err = 0;\n",
      "00561\t\n",
      "00562\t  if (verbose) fprintf(stderr, \"lazy_rec computing %i\\n\", (int)var_idx);\n",
      "00563\t\n",
      "00564\t  if (self->var_computed[var_idx] || !self->var_has_owner[var_idx])\n",
      "00565\t    return 0;\n",
      "00566\t\n",
      "00567\t  Py_ssize_t owner_idx = self->var_owner[var_idx];\n",
      "00568\t\n",
      "00569\t  // STEP 1: compute the pre-requirements of the node\n",
      "00570\t  // Includes input nodes for non-lazy ops.\n",
      "00571\t  for (int i = 0; i < self->node_n_prereqs[owner_idx]; ++i)\n",
      "00572\t    {\n",
      "00573\t      Py_ssize_t prereq_idx = self->node_prereqs[owner_idx][i];\n",
      "00574\t      if (!self->var_computed[prereq_idx])\n",
      "00575\t        {\n",
      "00576\t          err = lazy_rec_eval(self, prereq_idx, one, zero);\n",
      "00577\t          if (err) return err;\n",
      "00578\t        }\n",
      "00579\t      assert (self->var_computed[prereq_idx]);\n",
      "00580\t    }\n",
      "00581\t\n",
      "00582\t  // STEP 2: compute the node itself\n",
      "00583\t  if (self->is_lazy[owner_idx])\n",
      "00584\t    {\n",
      "00585\t      // update the compute_map cells corresponding to the inputs of this thunk\n",
      "00586\t      for (int i = 0; i < self->node_n_inputs[owner_idx]; ++i)\n",
      "00587\t        {\n",
      "00588\t          int in_idx = self->node_inputs[owner_idx][i];\n",
      "00589\t          if (self->var_computed[in_idx])\n",
      "00590\t            {\n",
      "00591\t              Py_INCREF(one);\n",
      "00592\t              err = PyList_SetItem(self->var_computed_cells[in_idx], 0, one);\n",
      "00593\t            }\n",
      "00594\t          else\n",
      "00595\t            {\n",
      "00596\t              Py_INCREF(zero);\n",
      "00597\t              err = PyList_SetItem(self->var_computed_cells[in_idx], 0, zero);\n",
      "00598\t            }\n",
      "00599\t          if (err) goto fail;\n",
      "00600\t        }\n",
      "00601\t\n",
      "00602\t      rval = pycall(self, owner_idx, verbose);\n",
      "00603\t      // refcounting - rval is new ref\n",
      "00604\t      //TODO: to prevent infinite loops\n",
      "00605\t      // - consider check that a thunk does not ask for an input that is already computed\n",
      "00606\t      if (rval == NULL)\n",
      "00607\t        {\n",
      "00608\t          assert (PyErr_Occurred());\n",
      "00609\t          err = 1;\n",
      "00610\t          goto fail;\n",
      "00611\t        }\n",
      "00612\t\n",
      "00613\t      //update the computed-ness of any output cells\n",
      "00614\t      for (int i = 0; i < self->node_n_outputs[owner_idx]; ++i)\n",
      "00615\t        {\n",
      "00616\t          int out_idx = self->node_outputs[owner_idx][i];\n",
      "00617\t          PyObject * el_i = PyList_GetItem(self->var_computed_cells[out_idx], 0);\n",
      "00618\t          Py_ssize_t N = PyNumber_AsSsize_t(el_i, PyExc_IndexError);\n",
      "00619\t          if (PyErr_Occurred())\n",
      "00620\t            {\n",
      "00621\t              err = -1;\n",
      "00622\t              goto pyfail;\n",
      "00623\t            }\n",
      "00624\t          assert (N==0 || N==1);\n",
      "00625\t          self->var_computed[out_idx] = N;\n",
      "00626\t        }\n",
      "00627\t      if (!self->var_computed[var_idx])\n",
      "00628\t        {\n",
      "00629\t          /*\n",
      "00630\t           * If self is not computed after the call, this means that some\n",
      "00631\t           * inputs are needed.  Compute the ones on the returned list\n",
      "00632\t           * and try to compute the current node again (with recursive call).\n",
      "00633\t           * This allows a node to request more nodes more than once before\n",
      "00634\t           * finally yielding a result.\n",
      "00635\t           */\n",
      "00636\t          if (!PyList_Check(rval))\n",
      "00637\t            {\n",
      "00638\t              //TODO: More helpful error to help find *which node* made this\n",
      "00639\t              // bad thunk\n",
      "00640\t              PyErr_SetString(PyExc_TypeError,\n",
      "00641\t                              \"lazy thunk should return a list\");\n",
      "00642\t              err = 1;\n",
      "00643\t              goto pyfail;\n",
      "00644\t            }\n",
      "00645\t\n",
      "00646\t          if (!PyList_Size(rval))\n",
      "00647\t            {\n",
      "00648\t              PyErr_SetString(PyExc_ValueError,\n",
      "00649\t                              \"lazy thunk returned empty list without computing output\");\n",
      "00650\t              err = 1;\n",
      "00651\t              goto pyfail;\n",
      "00652\t            }\n",
      "00653\t\n",
      "00654\t          for (int i = 0; i < PyList_Size(rval); ++i)\n",
      "00655\t            {\n",
      "00656\t              PyObject * el_i = PyList_GetItem(rval, i);\n",
      "00657\t              Py_ssize_t N = PyNumber_AsSsize_t(el_i, PyExc_IndexError);\n",
      "00658\t              if (PyErr_Occurred())\n",
      "00659\t                {\n",
      "00660\t                  err = 1;\n",
      "00661\t                  goto pyfail;\n",
      "00662\t                }\n",
      "00663\t              assert (N <= self->node_n_inputs[owner_idx]);\n",
      "00664\t              Py_ssize_t input_idx = self->node_inputs[owner_idx][N];\n",
      "00665\t              err = lazy_rec_eval(self, input_idx, one, zero);\n",
      "00666\t              if (err) goto pyfail;\n",
      "00667\t            }\n",
      "00668\t\n",
      "00669\t          Py_DECREF(rval);\n",
      "00670\t          /*\n",
      "00671\t           * We intentionally skip all the end-of-function processing\n",
      "00672\t           * (mark outputs, GC) as it will be performed by the call\n",
      "00673\t           * that actually manages to compute the result.\n",
      "00674\t           */\n",
      "00675\t          return lazy_rec_eval(self, var_idx, one, zero);\n",
      "00676\t        }\n",
      "00677\t\n",
      "00678\t      Py_DECREF(rval);\n",
      "00679\t    }\n",
      "00680\t  else //owner is not a lazy op. Ensure all intputs are evaluated.\n",
      "00681\t    {\n",
      "00682\t      // loop over inputs to owner\n",
      "00683\t      // call lazy_rec_eval on each one that is not computed.\n",
      "00684\t      // if there's an error, pass it up the stack\n",
      "00685\t      for (int i = 0; i < self->node_n_inputs[owner_idx]; ++i)\n",
      "00686\t        {\n",
      "00687\t          Py_ssize_t input_idx = self->node_inputs[owner_idx][i];\n",
      "00688\t          if (!self->var_computed[input_idx])\n",
      "00689\t            {\n",
      "00690\t              err = lazy_rec_eval(self, input_idx, one, zero);\n",
      "00691\t              if (err) return err;\n",
      "00692\t            }\n",
      "00693\t          assert (self->var_computed[input_idx]);\n",
      "00694\t        }\n",
      "00695\t\n",
      "00696\t      // call the thunk for this owner.\n",
      "00697\t      if (self->thunk_cptr_fn[owner_idx])\n",
      "00698\t        {\n",
      "00699\t          err = c_call(self, owner_idx, verbose);\n",
      "00700\t          if (err) goto fail;\n",
      "00701\t        }\n",
      "00702\t      else\n",
      "00703\t        {\n",
      "00704\t          rval = pycall(self, owner_idx, verbose);\n",
      "00705\t          //rval is new ref\n",
      "00706\t          if (rval) //pycall returned normally (no exception)\n",
      "00707\t            {\n",
      "00708\t              if (rval == Py_None)\n",
      "00709\t                {\n",
      "00710\t                  Py_DECREF(rval); //ignore a return of None\n",
      "00711\t                }\n",
      "00712\t              else if (PyList_Check(rval))\n",
      "00713\t                {\n",
      "00714\t                  PyErr_SetString(PyExc_TypeError,\n",
      "00715\t                                  \"non-lazy thunk should return None, not list\");\n",
      "00716\t                  err = 1;\n",
      "00717\t                  goto pyfail;\n",
      "00718\t                }\n",
      "00719\t              else // don't know what it returned, but it wasn't right.\n",
      "00720\t                {\n",
      "00721\t                  PyErr_SetObject(PyExc_TypeError, rval);\n",
      "00722\t                  err = 1;\n",
      "00723\t                  // We don't release rval since we put it in the error above\n",
      "00724\t                  goto fail;\n",
      "00725\t                }\n",
      "00726\t            }\n",
      "00727\t          else // pycall returned NULL (internal error)\n",
      "00728\t            {\n",
      "00729\t              err = 1;\n",
      "00730\t              goto fail;\n",
      "00731\t            }\n",
      "00732\t        }\n",
      "00733\t    }\n",
      "00734\t\n",
      "00735\t  // loop over all outputs and mark them as computed\n",
      "00736\t  for (int i = 0; i < self->node_n_outputs[owner_idx]; ++i)\n",
      "00737\t    {\n",
      "00738\t      self->var_computed[self->node_outputs[owner_idx][i]] = 1;\n",
      "00739\t    }\n",
      "00740\t\n",
      "00741\t  // Free vars that are not needed anymore\n",
      "00742\t  if (self->allow_gc)\n",
      "00743\t    {\n",
      "00744\t      for (int i = 0; i < self->node_n_inputs[owner_idx]; ++i)\n",
      "00745\t        {\n",
      "00746\t          int cleanup = 1;\n",
      "00747\t          Py_ssize_t i_idx = self->node_inputs[owner_idx][i];\n",
      "00748\t          if (!self->var_has_owner[i_idx])\n",
      "00749\t            continue;\n",
      "00750\t\n",
      "00751\t          for (int j = 0; j < self->n_output_vars; ++j)\n",
      "00752\t            {\n",
      "00753\t              if (i_idx == self->output_vars[j])\n",
      "00754\t                {\n",
      "00755\t                  cleanup = 0;\n",
      "00756\t                  break;\n",
      "00757\t                }\n",
      "00758\t            }\n",
      "00759\t          if (!cleanup) continue;\n",
      "00760\t\n",
      "00761\t          for (int j = 0; j < self->n_dependencies[i_idx]; ++j)\n",
      "00762\t            {\n",
      "00763\t              if (!self->var_computed[self->dependencies[i_idx][j]])\n",
      "00764\t                {\n",
      "00765\t                  cleanup = 0;\n",
      "00766\t                  break;\n",
      "00767\t                }\n",
      "00768\t            }\n",
      "00769\t          if (!cleanup) continue;\n",
      "00770\t\n",
      "00771\t          Py_INCREF(Py_None);\n",
      "00772\t          err = PyList_SetItem(self->var_value_cells[i_idx], 0, Py_None);\n",
      "00773\t//See the Stack gc implementation for why we change it to 2 and not 0.\n",
      "00774\t          self->var_computed[i_idx] = 2;\n",
      "00775\t          if (err) goto fail;\n",
      "00776\t        }\n",
      "00777\t    }\n",
      "00778\t\n",
      "00779\t  return 0;\n",
      "00780\t pyfail:\n",
      "00781\t  Py_DECREF(rval);\n",
      "00782\t fail:\n",
      "00783\t  set_position_of_error(self, owner_idx);\n",
      "00784\t  return err;\n",
      "00785\t}\n",
      "00786\t\n",
      "00787\tstatic PyObject *\n",
      "00788\tCLazyLinker_call(PyObject *_self, PyObject *args, PyObject *kwds)\n",
      "00789\t{\n",
      "00790\t  CLazyLinker * self = (CLazyLinker*)_self;\n",
      "00791\t  static char *kwlist[] = {\n",
      "00792\t    (char*)\"time_thunks\",\n",
      "00793\t    (char *)\"n_calls\",\n",
      "00794\t    NULL};\n",
      "00795\t  int n_calls=1;\n",
      "00796\t  if (! PyArg_ParseTupleAndKeywords(args, kwds, \"|ii\", kwlist,\n",
      "00797\t                                    &self->do_timing,\n",
      "00798\t                                    &n_calls))\n",
      "00799\t    return NULL;\n",
      "00800\t  int err = 0;\n",
      "00801\t  self->position_of_error = -1;\n",
      "00802\t  // create constants used to fill the var_compute_cells\n",
      "00803\t  PyObject * one = PyInt_FromLong(1);\n",
      "00804\t  PyObject * zero = PyInt_FromLong(0);\n",
      "00805\t\n",
      "00806\t  // pre-allocate our return value\n",
      "00807\t  Py_INCREF(Py_None);\n",
      "00808\t  PyObject * rval = Py_None;\n",
      "00809\t  //clear storage of pre_call_clear elements\n",
      "00810\t  for (int call_i = 0; call_i < n_calls && (!err); ++call_i)\n",
      "00811\t    {\n",
      "00812\t      Py_ssize_t n_pre_call_clear = PyList_Size(self->pre_call_clear);\n",
      "00813\t      assert(PyList_Check(self->pre_call_clear));\n",
      "00814\t      for (int i = 0; i < n_pre_call_clear; ++i)\n",
      "00815\t        {\n",
      "00816\t          PyObject * el_i = PyList_GetItem(self->pre_call_clear, i);\n",
      "00817\t          Py_INCREF(Py_None);\n",
      "00818\t          PyList_SetItem(el_i, 0, Py_None);\n",
      "00819\t        }\n",
      "00820\t      //clear the computed flag out of all non-input vars\n",
      "00821\t      for (int i = 0; i < self->n_vars; ++i)\n",
      "00822\t        {\n",
      "00823\t          self->var_computed[i] = !self->var_has_owner[i];\n",
      "00824\t          if (self->var_computed[i])\n",
      "00825\t            {\n",
      "00826\t              Py_INCREF(one);\n",
      "00827\t              PyList_SetItem(self->var_computed_cells[i], 0, one);\n",
      "00828\t            }\n",
      "00829\t          else\n",
      "00830\t            {\n",
      "00831\t              Py_INCREF(zero);\n",
      "00832\t              PyList_SetItem(self->var_computed_cells[i], 0, zero);\n",
      "00833\t            }\n",
      "00834\t        }\n",
      "00835\t\n",
      "00836\t      for (int i = 0; i < self->n_output_vars && (!err); ++i)\n",
      "00837\t        {\n",
      "00838\t          err = lazy_rec_eval(self, self->output_vars[i], one, zero);\n",
      "00839\t        }\n",
      "00840\t\n",
      "00841\t      if (!err)\n",
      "00842\t        {\n",
      "00843\t          // save references to outputs prior to updating storage containers\n",
      "00844\t          assert (self->n_output_vars >= self->n_updates);\n",
      "00845\t          Py_DECREF(rval);\n",
      "00846\t          rval = PyList_New(self->n_output_vars);\n",
      "00847\t          for (int i = 0; i < (self->n_output_vars); ++i)\n",
      "00848\t            {\n",
      "00849\t              Py_ssize_t src = self->output_vars[i];\n",
      "00850\t              PyObject * item = PyList_GetItem(self->var_value_cells[src], 0);\n",
      "00851\t              if (self->var_computed[src] != 1)\n",
      "00852\t                {\n",
      "00853\t                  err = 1;\n",
      "00854\t                  PyErr_Format(PyExc_AssertionError,\n",
      "00855\t                               \"The compute map of output %d should contain \"\n",
      "00856\t                               \"1 at the end of execution, not %d.\",\n",
      "00857\t                               i, self->var_computed[src]);\n",
      "00858\t                  break;\n",
      "00859\t                }\n",
      "00860\t              Py_INCREF(item);\n",
      "00861\t              PyList_SetItem(rval, i, item);\n",
      "00862\t            }\n",
      "00863\t        }\n",
      "00864\t\n",
      "00865\t      if (!err)\n",
      "00866\t        {\n",
      "00867\t          // Update the inputs that have an update rule\n",
      "00868\t          for (int i = 0; i < self->n_updates; ++i)\n",
      "00869\t            {\n",
      "00870\t              PyObject* tmp = PyList_GetItem(rval, self->n_output_vars - self->n_updates + i);\n",
      "00871\t              Py_INCREF(tmp);\n",
      "00872\t              Py_ssize_t dst = self->update_storage[i];\n",
      "00873\t              PyList_SetItem(self->var_value_cells[dst], 0, tmp);\n",
      "00874\t            }\n",
      "00875\t        }\n",
      "00876\t    }\n",
      "00877\t\n",
      "00878\t  /*\n",
      "00879\t    Clear everything that is left and not an output.  This is needed\n",
      "00880\t    for lazy evaluation since the current GC algo is too conservative\n",
      "00881\t    with lazy graphs.\n",
      "00882\t  */\n",
      "00883\t  if (self->allow_gc && !err)\n",
      "00884\t    {\n",
      "00885\t      for (Py_ssize_t i = 0; i < self->n_vars; ++i)\n",
      "00886\t        {\n",
      "00887\t          int do_cleanup = 1;\n",
      "00888\t          if (!self->var_has_owner[i] || !self->var_computed[i])\n",
      "00889\t            continue;\n",
      "00890\t          for (int j = 0; j < self->n_output_vars; ++j)\n",
      "00891\t            {\n",
      "00892\t              if (i == self->output_vars[j])\n",
      "00893\t                {\n",
      "00894\t                  do_cleanup = 0;\n",
      "00895\t                  break;\n",
      "00896\t                }\n",
      "00897\t            }\n",
      "00898\t          if (!do_cleanup)\n",
      "00899\t            continue;\n",
      "00900\t          Py_INCREF(Py_None);\n",
      "00901\t          PyList_SetItem(self->var_value_cells[i], 0, Py_None);\n",
      "00902\t        }\n",
      "00903\t    }\n",
      "00904\t  Py_DECREF(one);\n",
      "00905\t  Py_DECREF(zero);\n",
      "00906\t  if (err)\n",
      "00907\t    {\n",
      "00908\t      Py_DECREF(rval);\n",
      "00909\t      return NULL;\n",
      "00910\t    }\n",
      "00911\t  return rval;\n",
      "00912\t}\n",
      "00913\t\n",
      "00914\t#if 0\n",
      "00915\tstatic PyMethodDef CLazyLinker_methods[] = {\n",
      "00916\t    {\n",
      "00917\t      //\"name\", (PyCFunction)CLazyLinker_accept, METH_VARARGS, \"Return the name, combining the first and last name\"\n",
      "00918\t    },\n",
      "00919\t    {NULL}  /* Sentinel */\n",
      "00920\t};\n",
      "00921\t#endif\n",
      "00922\t\n",
      "00923\t\n",
      "00924\tstatic PyObject *\n",
      "00925\tCLazyLinker_get_allow_gc(CLazyLinker *self, void *closure)\n",
      "00926\t{\n",
      "00927\t    return PyBool_FromLong(self->allow_gc);\n",
      "00928\t}\n",
      "00929\t\n",
      "00930\tstatic int\n",
      "00931\tCLazyLinker_set_allow_gc(CLazyLinker *self, PyObject *value, void *closure)\n",
      "00932\t{\n",
      "00933\t  if(!PyBool_Check(value))\n",
      "00934\t    return -1;\n",
      "00935\t\n",
      "00936\t  if (value == Py_True)\n",
      "00937\t    self->allow_gc = true;\n",
      "00938\t  else\n",
      "00939\t    self->allow_gc = false;\n",
      "00940\t  return 0;\n",
      "00941\t}\n",
      "00942\t\n",
      "00943\tstatic PyGetSetDef CLazyLinker_getset[] = {\n",
      "00944\t  {(char*)\"allow_gc\",\n",
      "00945\t   (getter)CLazyLinker_get_allow_gc,\n",
      "00946\t   (setter)CLazyLinker_set_allow_gc,\n",
      "00947\t   (char*)\"do this function support allow_gc\",\n",
      "00948\t   NULL},\n",
      "00949\t  {NULL, NULL, NULL, NULL}  /* Sentinel */\n",
      "00950\t};\n",
      "00951\tstatic PyMemberDef CLazyLinker_members[] = {\n",
      "00952\t    {(char*)\"nodes\", T_OBJECT_EX, offsetof(CLazyLinker, nodes), 0,\n",
      "00953\t     (char*)\"list of nodes\"},\n",
      "00954\t    {(char*)\"thunks\", T_OBJECT_EX, offsetof(CLazyLinker, thunks), 0,\n",
      "00955\t     (char*)\"list of thunks in program\"},\n",
      "00956\t    {(char*)\"call_counts\", T_OBJECT_EX, offsetof(CLazyLinker, call_counts), 0,\n",
      "00957\t     (char*)\"number of calls of each thunk\"},\n",
      "00958\t    {(char*)\"call_times\", T_OBJECT_EX, offsetof(CLazyLinker, call_times), 0,\n",
      "00959\t     (char*)\"total runtime in each thunk\"},\n",
      "00960\t    {(char*)\"position_of_error\", T_INT, offsetof(CLazyLinker, position_of_error), 0,\n",
      "00961\t     (char*)\"position of failed thunk\"},\n",
      "00962\t    {(char*)\"time_thunks\", T_INT, offsetof(CLazyLinker, do_timing), 0,\n",
      "00963\t     (char*)\"bool: nonzero means call will time thunks\"},\n",
      "00964\t    {(char*)\"need_update_inputs\", T_INT, offsetof(CLazyLinker, need_update_inputs), 0,\n",
      "00965\t     (char*)\"bool: nonzero means Function.__call__ must implement update mechanism\"},\n",
      "00966\t    {NULL}  /* Sentinel */\n",
      "00967\t};\n",
      "00968\t\n",
      "00969\tstatic PyTypeObject lazylinker_ext_CLazyLinkerType = {\n",
      "00970\t#if defined(NPY_PY3K)\n",
      "00971\t    PyVarObject_HEAD_INIT(NULL, 0)\n",
      "00972\t#else\n",
      "00973\t    PyObject_HEAD_INIT(NULL)\n",
      "00974\t    0,                         /*ob_size*/\n",
      "00975\t#endif\n",
      "00976\t    \"lazylinker_ext.CLazyLinker\",             /*tp_name*/\n",
      "00977\t    sizeof(CLazyLinker), /*tp_basicsize*/\n",
      "00978\t    0,                         /*tp_itemsize*/\n",
      "00979\t    CLazyLinker_dealloc,       /*tp_dealloc*/\n",
      "00980\t    0,                         /*tp_print*/\n",
      "00981\t    0,                         /*tp_getattr*/\n",
      "00982\t    0,                         /*tp_setattr*/\n",
      "00983\t    0,                         /*tp_compare*/\n",
      "00984\t    0,                         /*tp_repr*/\n",
      "00985\t    0,                         /*tp_as_number*/\n",
      "00986\t    0,                         /*tp_as_sequence*/\n",
      "00987\t    0,                         /*tp_as_mapping*/\n",
      "00988\t    0,                         /*tp_hash */\n",
      "00989\t    CLazyLinker_call,          /*tp_call*/\n",
      "00990\t    0,                         /*tp_str*/\n",
      "00991\t    0,                         /*tp_getattro*/\n",
      "00992\t    0,                         /*tp_setattro*/\n",
      "00993\t    0,                         /*tp_as_buffer*/\n",
      "00994\t    Py_TPFLAGS_DEFAULT|Py_TPFLAGS_BASETYPE,        /*tp_flags*/\n",
      "00995\t    \"CLazyLinker object\",      /* tp_doc */\n",
      "00996\t    0,                         /* tp_traverse */\n",
      "00997\t    0,                         /* tp_clear */\n",
      "00998\t    0,                         /* tp_richcompare */\n",
      "00999\t    0,                         /* tp_weaklistoffset */\n",
      "01000\t    0,                         /* tp_iter */\n",
      "01001\t    0,                         /* tp_iternext */\n",
      "01002\t    0,//CLazyLinker_methods,       /* tp_methods */\n",
      "01003\t    CLazyLinker_members,       /* tp_members */\n",
      "01004\t    CLazyLinker_getset,        /* tp_getset */\n",
      "01005\t    0,                         /* tp_base */\n",
      "01006\t    0,                         /* tp_dict */\n",
      "01007\t    0,                         /* tp_descr_get */\n",
      "01008\t    0,                         /* tp_descr_set */\n",
      "01009\t    0,                         /* tp_dictoffset */\n",
      "01010\t    (initproc)CLazyLinker_init,/* tp_init */\n",
      "01011\t    0,                         /* tp_alloc */\n",
      "01012\t    CLazyLinker_new,           /* tp_new */\n",
      "01013\t};\n",
      "01014\t\n",
      "01015\tstatic PyObject * get_version(PyObject *dummy, PyObject *args)\n",
      "01016\t{\n",
      "01017\t  PyObject *result = PyFloat_FromDouble(0.21);\n",
      "01018\t  return result;\n",
      "01019\t}\n",
      "01020\t\n",
      "01021\tstatic PyMethodDef lazylinker_ext_methods[] = {\n",
      "01022\t  {\"get_version\",  get_version, METH_VARARGS, \"Get extension version.\"},\n",
      "01023\t  {NULL, NULL, 0, NULL}        /* Sentinel */\n",
      "01024\t};\n",
      "01025\t\n",
      "01026\t#if defined(NPY_PY3K)\n",
      "01027\tstatic struct PyModuleDef moduledef = {\n",
      "01028\t        PyModuleDef_HEAD_INIT,\n",
      "01029\t        \"lazylinker_ext\",\n",
      "01030\t        NULL,\n",
      "01031\t        -1,\n",
      "01032\t        lazylinker_ext_methods,\n",
      "01033\t        NULL,\n",
      "01034\t        NULL,\n",
      "01035\t        NULL,\n",
      "01036\t        NULL\n",
      "01037\t};\n",
      "01038\t#endif\n",
      "01039\t#if defined(NPY_PY3K)\n",
      "01040\t#define RETVAL m\n",
      "01041\tPyMODINIT_FUNC\n",
      "01042\tPyInit_lazylinker_ext(void) {\n",
      "01043\t#else\n",
      "01044\t#define RETVAL\n",
      "01045\tPyMODINIT_FUNC\n",
      "01046\tinitlazylinker_ext(void) \n",
      "01047\t{\n",
      "01048\t#endif\n",
      "01049\t    PyObject* m;\n",
      "01050\t\n",
      "01051\t    lazylinker_ext_CLazyLinkerType.tp_new = PyType_GenericNew;\n",
      "01052\t    if (PyType_Ready(&lazylinker_ext_CLazyLinkerType) < 0)\n",
      "01053\t        return RETVAL;\n",
      "01054\t#if defined(NPY_PY3K)\n",
      "01055\t    m = PyModule_Create(&moduledef);\n",
      "01056\t#else\n",
      "01057\t    m = Py_InitModule3(\"lazylinker_ext\", lazylinker_ext_methods,\n",
      "01058\t                       \"Example module that creates an extension type.\");\n",
      "01059\t#endif\n",
      "01060\t    Py_INCREF(&lazylinker_ext_CLazyLinkerType);\n",
      "01061\t    PyModule_AddObject(m, \"CLazyLinker\", (PyObject *)&lazylinker_ext_CLazyLinkerType);\n",
      "01062\t\n",
      "01063\t    return RETVAL;\n",
      "01064\t}\n",
      "01065\t\n",
      "Problem occurred during compilation with the command line below:\n",
      "/usr/bin/clang++ -dynamiclib -g -march=broadwell -target-feature +sse2 -target-feature +cx16 -target-feature -tbm -target-feature -avx512ifma -target-feature -avx512dq -target-feature -fma4 -target-feature +prfchw -target-feature +bmi2 -target-feature -xsavec -target-feature +fsgsbase -target-feature +popcnt -target-feature +aes -target-feature -pcommit -target-feature -xsaves -target-feature -avx512er -target-feature -clwb -target-feature -avx512f -target-feature -pku -target-feature +smap -target-feature +mmx -target-feature -xop -target-feature +rdseed -target-feature -hle -target-feature -sse4a -target-feature -avx512bw -target-feature -clflushopt -target-feature +xsave -target-feature -avx512vl -target-feature +invpcid -target-feature -avx512cd -target-feature +avx -target-feature -rtm -target-feature +fma -target-feature +bmi -target-feature -mwaitx -target-feature +rdrnd -target-feature +sse4.1 -target-feature +sse4.2 -target-feature +avx2 -target-feature +sse -target-feature +lzcnt -target-feature +pclmul -target-feature -prefetchwt1 -target-feature +f16c -target-feature +ssse3 -target-feature -sgx -target-feature +cmov -target-feature -avx512vbmi -target-feature +movbe -target-feature +xsaveopt -target-feature -sha -target-feature +adx -target-feature -avx512pf -target-feature +sse3 -D NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -m64 -fPIC -undefined dynamic_lookup -I/usr/local/lib/python3.6/site-packages/numpy/core/include -I/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/include/python3.6m -I/usr/local/lib/python3.6/site-packages/theano/gof -fvisibility=hidden -o /Users/lesleycordero/.theano/compiledir_Darwin-16.7.0-x86_64-i386-64bit-i386-3.6.1-64/lazylinker_ext/lazylinker_ext.so /Users/lesleycordero/.theano/compiledir_Darwin-16.7.0-x86_64-i386-64bit-i386-3.6.1-64/lazylinker_ext/mod.cpp -L/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "Compilation failed (return status=1): clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-tbm'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-fma4'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-pcommit'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-clwb'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-pku'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-hle'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sse4a'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-clflushopt'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-rtm'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-mwaitx'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-prefetchwt1'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sgx'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sha'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: no such file or directory: '+sse2'. clang: error: no such file or directory: '+cx16'. clang: error: no such file or directory: '+prfchw'. clang: error: no such file or directory: '+bmi2'. clang: error: language not recognized: 'savec'. clang: error: no such file or directory: '+fsgsbase'. clang: error: no such file or directory: '+popcnt'. clang: error: no such file or directory: '+aes'. clang: error: language not recognized: 'saves'. clang: error: no such file or directory: '+smap'. clang: error: no such file or directory: '+mmx'. clang: error: language not recognized: 'op'. clang: error: no such file or directory: '+rdseed'. clang: error: no such file or directory: '+xsave'. clang: error: no such file or directory: '+invpcid'. clang: error: no such file or directory: '+avx'. clang: error: no such file or directory: '+fma'. clang: error: no such file or directory: '+bmi'. clang: error: no such file or directory: '+rdrnd'. clang: error: no such file or directory: '+sse4.1'. clang: error: no such file or directory: '+sse4.2'. clang: error: no such file or directory: '+avx2'. clang: error: no such file or directory: '+sse'. clang: error: no such file or directory: '+lzcnt'. clang: error: no such file or directory: '+pclmul'. clang: error: no such file or directory: '+f16c'. clang: error: no such file or directory: '+ssse3'. clang: error: no such file or directory: '+cmov'. clang: error: no such file or directory: '+movbe'. clang: error: no such file or directory: '+xsaveopt'. clang: error: no such file or directory: '+adx'. clang: error: no such file or directory: '+sse3'. ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/gof/lazylinker_c.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazylinker_ext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_version'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/gof/lazylinker_c.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazylinker_ext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_version'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-ced23fa49d86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     61\u001b[0m     object2, utils)\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m from theano.compile import (\n\u001b[0m\u001b[1;32m     64\u001b[0m     \u001b[0mSymbolicInput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0mSymbolicOutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOut\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/compile/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m         SpecifyShape, specify_shape, register_specify_shape_c_code)\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_module\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfunctools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mizip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m from theano.compile.io import (\n\u001b[1;32m     21\u001b[0m     In, SymbolicInput, SymbolicInputKit, SymbolicOutput)\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/compile/mode.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgof\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfigparser\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAddConfigVar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStrParam\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_output_guard\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/gof/vm.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 666\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlazylinker_c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m     \u001b[0;32mclass\u001b[0m \u001b[0mCVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazylinker_c\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCLazyLinker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/gof/lazylinker_c.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGCC_compiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             cmodule.GCC_compiler.compile_str(dirname, code, location=loc,\n\u001b[0;32m--> 126\u001b[0;31m                                              preargs=args)\n\u001b[0m\u001b[1;32m    127\u001b[0m             \u001b[0;31m# Save version into the __init__.py file.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0minit_py\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__init__.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/theano/gof/cmodule.py\u001b[0m in \u001b[0;36mcompile_str\u001b[0;34m(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)\u001b[0m\n\u001b[1;32m   2057\u001b[0m             \u001b[0;31m# difficult to read.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2058\u001b[0m             raise Exception('Compilation failed (return status=%s): %s' %\n\u001b[0;32m-> 2059\u001b[0;31m                             (status, compile_stderr.replace('\\n', '. ')))\n\u001b[0m\u001b[1;32m   2060\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompilation_warning\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcompile_stderr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m             \u001b[0;31m# Print errors just below the command line.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Compilation failed (return status=1): clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-tbm'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-fma4'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-pcommit'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-clwb'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-pku'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-hle'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sse4a'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-clflushopt'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-rtm'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-mwaitx'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-prefetchwt1'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sgx'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-sha'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: unknown argument: '-target-feature'. clang: error: no such file or directory: '+sse2'. clang: error: no such file or directory: '+cx16'. clang: error: no such file or directory: '+prfchw'. clang: error: no such file or directory: '+bmi2'. clang: error: language not recognized: 'savec'. clang: error: no such file or directory: '+fsgsbase'. clang: error: no such file or directory: '+popcnt'. clang: error: no such file or directory: '+aes'. clang: error: language not recognized: 'saves'. clang: error: no such file or directory: '+smap'. clang: error: no such file or directory: '+mmx'. clang: error: language not recognized: 'op'. clang: error: no such file or directory: '+rdseed'. clang: error: no such file or directory: '+xsave'. clang: error: no such file or directory: '+invpcid'. clang: error: no such file or directory: '+avx'. clang: error: no such file or directory: '+fma'. clang: error: no such file or directory: '+bmi'. clang: error: no such file or directory: '+rdrnd'. clang: error: no such file or directory: '+sse4.1'. clang: error: no such file or directory: '+sse4.2'. clang: error: no such file or directory: '+avx2'. clang: error: no such file or directory: '+sse'. clang: error: no such file or directory: '+lzcnt'. clang: error: no such file or directory: '+pclmul'. clang: error: no such file or directory: '+f16c'. clang: error: no such file or directory: '+ssse3'. clang: error: no such file or directory: '+cmov'. clang: error: no such file or directory: '+movbe'. clang: error: no such file or directory: '+xsaveopt'. clang: error: no such file or directory: '+adx'. clang: error: no such file or directory: '+sse3'. "
     ]
    }
   ],
   "source": [
    "import theano\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the building blocks of our function. Here, x is a vector, W is an array we set up with numpy, and y is the function we'll use to compute the result. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = theano.tensor.fvector('x')\n",
    "W = theano.shared(numpy.asarray([0.2, 0.7]), 'W')\n",
    "y = (x * W).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we actually perform the computation with Theano.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = theano.function([x], y)\n",
    "output = f([1.0, 1.0])\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we printed output, we get about `(0.2*1.0) + (0.7*1.0)` = `0.9`. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3.2 TensorFlow\n",
    "\n",
    "TensorFlow is an open source library for numerical computation using data flow graphs. Unlike Theano, however, TensorFlow handles distributed computing through the use of multiple-GPUs.\n",
    "\n",
    "We'll go through a classic deep learning problem involving hand-written digit recognition, using the MNIST dataset. First, we'll implement the single layer version and follow up with a multi-layer model in section 5 of this workshop. \n",
    "\n",
    "#### 3.2.1 Single Layer Neural Network\n",
    "\n",
    "As always, we'll need to input the needed modules. `input_data.py` is available on the github link [here](https://github.com/lesley2958/deep-learning-python/blob/master/input_data.py) - make sure to download it and include it in the same directory as your workspace. This will allow you to download the needed data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need to create two variables to keep track of the weights and bias. Since we don't know those values yet, we initialize them to zeros. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we also create a tensor of two dimensions to keep the information of the x points with the following line of code:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", [None, 784])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we multiply the image vector x and the weight matrix W, adding b:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = tf.nn.softmax(tf.matmul(x,W) + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create another placeholder for the correct labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_ = tf.placeholder(\"float\", [None,10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we figure out our cost function. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the backpropogation algorithm, we minimize the cross-entropy using the gradient descent algorithm and a learning rate of 0.01:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start the computation by instantiating `tf.Session()`. This is in charge of executing the TensorFlow operations in the available CPUs or GPUs. Then, we can execute the operation initializing all the variables:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can start training our model! \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(1000):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
    "    \n",
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Lasagne\n",
    "\n",
    "Lasagne is a lightweight library used to construct and train networks in Theano. Lasagne does this by providing an API for constructing layers of a network and getting theano expressions that represent output, loss, etc. \n",
    "\n",
    "As always, we import the needed modules first: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import lasagne\n",
    "import theano\n",
    "import theano.tensor as T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we begin by creating Theano variables for input and target minibatch:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_var = T.tensor4('X')\n",
    "target_var = T.ivector('y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we create a small convolutional neural network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lasagne.nonlinearities import leaky_rectify, softmax\n",
    "network = lasagne.layers.InputLayer((None, 3, 32, 32), input_var)\n",
    "network = lasagne.layers.Conv2DLayer(network, 64, (3, 3),\n",
    "                                     nonlinearity=leaky_rectify)\n",
    "network = lasagne.layers.Conv2DLayer(network, 32, (3, 3),\n",
    "                                     nonlinearity=leaky_rectify)\n",
    "network = lasagne.layers.Pool2DLayer(network, (3, 3), stride=2, mode='max')\n",
    "network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, 0.5),\n",
    "                                    128, nonlinearity=leaky_rectify,\n",
    "                                    W=lasagne.init.Orthogonal())\n",
    "network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, 0.5),\n",
    "                                    10, nonlinearity=softmax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course, our loss function: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction = lasagne.layers.get_output(network)\n",
    "loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)\n",
    "loss = loss.mean() + 1e-4 * lasagne.regularization.regularize_network_params(\n",
    "        network, lasagne.regularization.l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create parameter updating expressions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = lasagne.layers.get_all_params(network, trainable=True)\n",
    "updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01,\n",
    "                                            momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to compile a training function that updates parameters and returns training loss:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_fn = theano.function([input_var, target_var], loss, updates=updates)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, we train the network: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for epoch in range(100):\n",
    "    loss = 0\n",
    "    for input_batch, target_batch in training_data:\n",
    "        loss += train_fn(input_batch, target_batch)\n",
    "    print(\"Epoch %d: Loss %g\" % (epoch + 1, loss / len(training_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the trained network for predictions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_prediction = lasagne.layers.get_output(network, deterministic=True)\n",
    "predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))\n",
    "print(\"Predicted class for first test input: %r\" % predict_fn(test_data[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Keras\n",
    "\n",
    "Keras is a super powerful, easy to use Python library for building neural networks and deep learning networks.\n",
    "\n",
    "\n",
    "\n",
    "### 3.5 Caffe\n",
    "\n",
    "Caffe is a deep learning framework written in C++. We won't be going through an implementation of caffe, but it's important to note its existence in deep learning, as well as the advantages and disadvantages of using the module. \n",
    "\n",
    "Let's begin with why we won't be going into details of caffe: it requires thorough knowledge of architecture, since it's written in C++. This makes it a lot harder to work with, especially as someone just entering the field of Deep Learning.\n",
    "\n",
    "On the other hand, because caffe is written in C++, it's incredibly fast, even faster than theano, and provides access via the command line.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.0 Feedforward Neural Networks \n",
    "\n",
    "Feedforward Neural Networks are the simplest form of Artificial Neural Networks. These networks have the three types of layers we just discussed: Input layer, hidden layer and output layer. There are no backwards or inter-layer connections. Furthermore, the nodes in the layer are fully connected to the nodes in the next layer. \n",
    "\n",
    "First, we import the needed modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Dense\n",
    "from keras.utils import np_utils\n",
    "from imutils import paths\n",
    "import numpy as np\n",
    "import argparse\n",
    "import cv2\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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