Neural_Networks.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks for Regression \n",
    "\n",
    "In this notebook the main aim of the project is performing regression using different architectures of Neural Networks which are Full MLPs, Full RBFs, Extreme MLP, Extreme RBF, Block decomposition for RBF nws.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set \n",
    "\n",
    "The dataset is generated using Franke's Function which has two Gaussian peaks of different heights, and a smaller dip. It is used as a test function in interpolation problems. \n",
    "\n",
    "https://www.sfu.ca/~ssurjano/franke2d.html\n",
    "\n",
    "![title](franke_function.png)\n",
    "\n",
    "\n",
    "### Generating Dataset \n",
    "\n",
    "\n",
    "The data set is obtained by sampling on 100 random points x_i from the function and adding a uniform noise, \n",
    "i.e. yi = f(xi) + εi and εi is a random number in [−10−1,10−1].\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import math\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
    "from random import sample\n",
    "\n",
    "\n",
    "\n",
    "'''Generate 100 random points in R2'''\n",
    "def generate_dependent_variables(seed=1772576):\n",
    "    np.random.seed(seed)    \n",
    "    X = np.random.uniform(low=0.0,high=1.0,size=(100,2))\n",
    "    return X\n",
    "\n",
    "'''Generate random Noise'''\n",
    "def generate_random_noise(seed=1772576):\n",
    "    np.random.seed(seed)\n",
    "    noise = np.random.uniform(low=-0.1,high=0.1,size=100)\n",
    "    return noise\n",
    "\n",
    "'''Generate Data Set'''\n",
    "def generate_data():\n",
    "    X = generate_dependent_variables(seed=1772576)\n",
    "    noise = generate_random_noise(seed=1772576)\n",
    "    Y = np.zeros(shape=len(X))\n",
    "    counter = 0\n",
    "    for vector in X:\n",
    "        x1 = vector[0]\n",
    "        x2 = vector[1]\n",
    "        term1 = 0.75 * (math.exp(-(np.square((9*x1)-2)/4)-(np.square((9*x2)-2)/4)))\n",
    "        term2 = 0.75 * (math.exp(-(np.square((9*x1)+1)/49)-((9*x2)+1)/10))\n",
    "        term3 = 0.5 * (math.exp(-(np.square((9*x1)-7)/4)-(np.square((9*x2)-3)/4)))\n",
    "        term4 = 0.2 * (math.exp(-(np.square((9*x1)-4))-(np.square((9*x2)-7))))\n",
    "        Y[counter] = term1+term2+term3-term4\n",
    "        counter+=1\n",
    "    return X,Y+noise\n",
    "\n",
    "def divide_dataset(X,y):\n",
    "    random.seed(1772576)\n",
    "    indices = sample(range(len(X)),int(0.70*len(X)))\n",
    "    X_train = X[indices]\n",
    "    mask = np.ones(len(X), dtype=bool)\n",
    "    mask[indices] = False\n",
    "    X_test = X[mask]\n",
    "    y_train = y[indices]\n",
    "    y_test = y[mask]\n",
    "    return X_train,y_train,X_test,y_test\n",
    "\n",
    "X, y = generate_data()\n",
    "\n",
    "X_train,y_train,X_test,y_test = divide_dataset(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full MLP Network\n",
    "Consider a shallow Feedforward Neural Network (FNN) (only one hidden layer) that approximates the franke's function. We will select a MLP network,  ω are the parameters to be found out by the optimization procedure and π the hyper-parameters of the network to be found by means of an heuristic procedure using gridsearch etc. \n",
    "\n",
    "![title](function.png)\n",
    "\n",
    "ρ in the range [10−5 ÷10−3] is one of the hyper parameters.\n",
    "\n",
    "![title](function2.png)\n",
    "![title](function3.png)\n",
    "\n",
    "![title](mlp.png)\n",
    "\n",
    "\n",
    "### Initializing parameters \n",
    "The input data is of the shape p x n where there are p number of data points with n columns (in our dataset p = 100, n = 2).\n",
    "\n",
    "w which is the matrix of weights between the input layer and the hidden layer will be of dimensions N x n,where N is number of hidden units in the hidden layer. N will be selected through gridsearch eventually. \n",
    "\n",
    "v is the vector of weights between the hidden layer and one output unit of the dimension N x 1.\n",
    "\n",
    "b is vector of bias units for every unit of hidden layer. it is of the dimension N x 1. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#header \n",
    "import numpy as np\n",
    "import OMML_gen_data as data   #script to generate data\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def init_parameters(Num_nuerons = 10, X = data.X_train):\n",
    "\n",
    "    No_nueron_units = Num_nuerons\n",
    "    np.random.seed(1772576)\n",
    "    v = np.random.rand(No_nueron_units,1)                 #vector of length equal to no of neurons N x 1\n",
    "    w = np.random.rand(No_nueron_units,X.shape[1])        #matrix of dimension N x n\n",
    "    b = np.random.rand(No_nueron_units,1)                 # N x 1\n",
    "    shapes = (v.shape,w.shape,b.shape)\n",
    "    vctr = toVector(v,w,b)\n",
    "    \n",
    "    return(vctr,shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the function f(x) (for neural network) and the activation function. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#this is the activation function g() as written in the equations\n",
    "def g(t,sigma):\n",
    "    val = (1-np.exp(-sigma*t))/(1+np.exp(-sigma*t))\n",
    "    return val\n",
    "\n",
    "#this is the approximating function f(x) of the Neural network \n",
    "def fx(X,v,w,b,sig):\n",
    "    \n",
    "    #dimensions of X  = p x n\n",
    "    \n",
    "    #(v,w,b) = params\n",
    "    #v                  #vector of length equal to no of neurons N x 1\n",
    "    #w                  #matrix of dimension N x n\n",
    "    #b                  # N x 1\n",
    "    \n",
    "    #np.tanh(np.dot(w,X.T) - b)                        # N x p \n",
    "    \n",
    "    \n",
    "    f_xp = np.dot(v.T,g((np.dot(w,X.T) - b),sig))           #1xN x Nxp = 1xp vector\n",
    "    return f_xp.T\n",
    "\n",
    "#cost function or loss function which will optimize the least squares between f(x) and true labels y. \n",
    "\n",
    "def cost_function(vctr,X,y,roh,shapes,sigma):\n",
    "    \n",
    "#     No_nueron_units = 3\n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "#     params = [v,w,b]\n",
    "    \n",
    "    v,w,b = toWZ(vctr,shapes)\n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(w)**2) + np.sum(abs(b)**2) + np.sum(abs(v)**2))\n",
    "    cost = np.sum((fx(X,v,w,b,sigma).reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Supporting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def toVector(v, w, b):\n",
    "    assert v.shape == (v.shape[0], v.shape[1])\n",
    "    assert w.shape == (w.shape[0], w.shape[1])\n",
    "    assert b.shape == (b.shape[0], b.shape[1])\n",
    "    return np.hstack([v.flatten(), w.flatten(),b.flatten()])\n",
    "\n",
    "def toWZ(vec,shapes):\n",
    "    \n",
    "    vshape,wshape,bshape = shapes\n",
    "    v1 = vec[:vshape[0]*vshape[1]].reshape(vshape[0],vshape[1])\n",
    "    v2 = vec[vshape[0]*vshape[1]:vshape[0]*vshape[1]+wshape[0]*wshape[1]].reshape(wshape[0],wshape[1])\n",
    "    v3 = vec[-bshape[0]*bshape[1]:].reshape(bshape[0],bshape[1])\n",
    "    return v1,v2,v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizing the parameters\n",
    "To define an optimization problem:\n",
    "1. choose a measure of closeness, loss function l \n",
    "typical loss functions can be : quadratic squared loss |f(x) - y|^2 , hinge loss, logistic loss....etc. \n",
    "2. choose a class of functions f \n",
    "3. choose regularization term R\n",
    "Regularization term R(W,λ) = λ*|W|^2 (l2 norm ridge and l1 norm for lasso)\n",
    "\n",
    "\n",
    "The minimization problem of finding the optimal parameters w,v,b is an unconstrained optimization problem (not really sure about the mathematics ...) but it can be optimized using BFGS,CG routines ( available in the scipy library https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html). \n",
    "\n",
    "The hyperparameters N,λ,ρ are found out using gridsearchCV.There are even better methods like gaussian processes for hyperparameter tuning but it is computationally expensive ( https://arxiv.org/pdf/1609.08703.pdf , https://thuijskens.github.io/2016/12/29/bayesian-optimisation/ ). \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#function to implement optimization and gridsearch. This returns the predicted value y,optimal v,w,b and hyperparameters\n",
    "# N,roh,sigma.\n",
    "def gridsearch(X_train,y_train,X_test,y_test,N_vec=[4,10,20],roh_vec=[0.0001,0.01,0.05,0.1,3,0.0003],sigma_vec = [0.001,0.1,0.05]):\n",
    "    X = X_train\n",
    "    yy = y_train.reshape(len(y_train),1)\n",
    "\n",
    "    dumpdct={}\n",
    "    cnt = 0\n",
    "    for N in N_vec:\n",
    "        vctr,shapes = init_parameters(N,X)\n",
    "        for roh in roh_vec:\n",
    "            for sigma in sigma_vec:\n",
    "                cnt = cnt+1\n",
    "                res = minimize(cost_function, vctr,args=(X,yy,roh,shapes,sigma),method='BFGS',options={'disp': True,'maxiter':30000})\n",
    "                v_hat,w_hat,b_hat = toWZ(res.x,shapes)\n",
    "                #res.fun                                        #train error\n",
    "                err = cost_function(res.x,X_test,y_test,roh,shapes,sigma)    #test error\n",
    "                dumpdct[cnt] = {'N':N,'roh':roh,'sigma':sigma,'train_error':res.fun,'test_error':err}\n",
    "\n",
    "                \n",
    "                \n",
    "    #select parameters with lowest test and train errors\n",
    "    param_dct = select_parameters_mlp(dumpdct)\n",
    "    \n",
    "    #test set predictions on these parameters\n",
    "    vctr,shapes = init_parameters(param_dct['N'],X_train)\n",
    "\n",
    "    res_test = minimize(cost_function, vctr,args=(X,yy,param_dct['roh'],shapes,param_dct['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "    v_hat,w_hat,b_hat = toWZ(res_test.x,shapes)\n",
    "    y_hat = fx(X_test,v_hat,w_hat,b_hat,param_dct['sigma'])\n",
    "    \n",
    "    return y_hat,v_hat,w_hat,b_hat,param_dct,dumpdct\n",
    "\n",
    "#supporting function\n",
    "def select_parameters_mlp(dct_object):\n",
    "    # Find parameters having lowest test and train error.\n",
    "    tst_error_list = []\n",
    "    train_error_list = []\n",
    "    for i in dct_object:\n",
    "        tst_error_list.append(dct_object[i]['test_error'])\n",
    "        train_error_list.append(dct_object[i]['train_error'])\n",
    "\n",
    "    a,b = tst_error_list.index(min(tst_error_list)),train_error_list.index(min(train_error_list))\n",
    "    return dct_object[a+1]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = data.X_train\n",
    "X_test = data.X_test\n",
    "y_train = data.y_train\n",
    "y_test = data.y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.021514\n",
      "         Iterations: 117\n",
      "         Function evaluations: 2394\n",
      "         Gradient evaluations: 133\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009205\n",
      "         Iterations: 943\n",
      "         Function evaluations: 18900\n",
      "         Gradient evaluations: 1050\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.015340\n",
      "         Iterations: 152\n",
      "         Function evaluations: 3150\n",
      "         Gradient evaluations: 175\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 53\n",
      "         Function evaluations: 3323\n",
      "         Gradient evaluations: 184\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.081515\n",
      "         Iterations: 65\n",
      "         Function evaluations: 1296\n",
      "         Gradient evaluations: 72\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105501\n",
      "         Iterations: 61\n",
      "         Function evaluations: 1188\n",
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      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
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      "         Function evaluations: 3630\n",
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      "         Current function value: 0.158753\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.021489\n",
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      "         Function evaluations: 4662\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.009189\n",
      "         Iterations: 800\n",
      "         Function evaluations: 36834\n",
      "         Gradient evaluations: 877\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.015334\n",
      "         Iterations: 127\n",
      "         Function evaluations: 6510\n",
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      "         Current function value: 0.158753\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.081493\n",
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      "         Function evaluations: 3444\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.105479\n",
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      "         Current function value: 0.158760\n",
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      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158754\n",
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      "         Function evaluations: 10932\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.016492\n",
      "         Iterations: 135\n",
      "         Function evaluations: 6300\n",
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      "Optimization terminated successfully.\n",
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      "Optimization terminated successfully.\n",
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      "         Function evaluations: 11480\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.015033\n",
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      "         Function evaluations: 10250\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.015344\n",
      "         Iterations: 115\n",
      "         Function evaluations: 11234\n",
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      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 46\n",
      "         Function evaluations: 15510\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.081436\n",
      "         Iterations: 70\n",
      "         Function evaluations: 5904\n",
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      "Optimization terminated successfully.\n",
      "         Current function value: 0.105451\n",
      "         Iterations: 61\n",
      "         Function evaluations: 5330\n",
      "         Gradient evaluations: 65\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 61\n",
      "         Function evaluations: 17724\n",
      "         Gradient evaluations: 216\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 92\n",
      "         Function evaluations: 20266\n",
      "         Gradient evaluations: 247\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 83\n",
      "         Function evaluations: 19937\n",
      "         Gradient evaluations: 243\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 57\n",
      "         Function evaluations: 19692\n",
      "         Gradient evaluations: 240\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 87\n",
      "         Function evaluations: 21003\n",
      "         Gradient evaluations: 256\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 101\n",
      "         Function evaluations: 28302\n",
      "         Gradient evaluations: 345\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158753\n",
      "         Iterations: 51\n",
      "         Function evaluations: 21004\n",
      "         Gradient evaluations: 256\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158755\n",
      "         Iterations: 62\n",
      "         Function evaluations: 19856\n",
      "         Gradient evaluations: 242\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.158754\n",
      "         Iterations: 61\n",
      "         Function evaluations: 21168\n",
      "         Gradient evaluations: 258\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.035639\n",
      "         Iterations: 101\n",
      "         Function evaluations: 9840\n",
      "         Gradient evaluations: 120\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.016478\n",
      "         Iterations: 96\n",
      "         Function evaluations: 9266\n",
      "         Gradient evaluations: 113\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.017365\n",
      "         Iterations: 114\n",
      "         Function evaluations: 10660\n",
      "         Gradient evaluations: 130\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.014315\n",
      "         Iterations: 28641\n",
      "         Function evaluations: 33426\n",
      "112.19912552833557\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start = time.time()\n",
    "y_hat,v_hat,w_hat,b_hat,dct_mlp,d = gridsearch(X_train,y_train,X_test,y_test)\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### Uncomment to view to predictions #########################\n",
    "\n",
    "#pd.DataFrame({'x':y_hat.reshape(len(y_hat)),'y':y_test})\n",
    "#pd.reset_option('^display.', silent=True)\n",
    "#pd.set_option('display.float_format', lambda x: '%.5f' % x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots for approximating function found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UmegOnujKFRE6Ojo4efIkJpMJESEvL4+CggIKCgqw2+2nuX5F5DSRjN6rjT5ORPD7/Qti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\nl18OlTGhyLrwdfrEoNvBfvrpp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uGRRx7h+uuvp7Gxkb1795Kbmxs04/Du6vbt+eKLL1ix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\nHZ0QoAdYHR0dHR2dEPD/AbGDoO1Q0sdbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a600e3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsAEgr3FCtiev1+u1BLeqqmqa9Vap97eaYrDFUKxrWlVVkskkf/u3f1u2dom9\nvb3WSECA1tZWent7KyqwaBqOvlDlzr9GsQU2h5kGoM+n89JUhoaG6OjoYOPGjWzbto1kMlmOLZdV\nYE3T5I9//CM+n69ipTdLzUJuHgo1TsiOPhscHKSjowMhhBXvCwaDFYuFrjULttgSHcj09l71Hhq3\nC7ZuqfQuKsvvbRdxRZhrAPpCBDaRSHDkyBFUVeWSSy7B7XYzNDRUtuYQ5RBYKSVdXV1EIhF27dpl\nWWjlpJiLezkTtoqh3NZkdqi53++3rBVd1y0rd2hoiGg0ihACp9NpWbqrdbABrAyBLeeEK4CWlhZO\nnToz1aynp8eK7VcMu9NERVjzAjvVHVyu3sG59aLnnntuXqeYcrt1F7JWtvSmrq6OhoaGvHIWm4Wj\nqmpeos3IyAgjIyN4PB5rqDlkBhtkrdzFGmxQCZZjB6lcpJRlv9G67rrreOihh7jxxht58cUXCQaD\nlXUPA6Q1GAhVdg9rkDUrsKXUtJYqsOPj4xw9epTGxsaC9aLlcDlnma/AFiq9ee211yoaA13OMdhy\n4nQ6WbdundVa0jAMa6h5Z2cn8Xjcmq+atXLtwQalkR19WCyl3ATcdNNNPPvss4yMjNDa2sp9991n\neb/e9773ceDAAX784x+zbds2fD4fX/3qV0vef9lxu2CtN5r4te0iXnSKnXiTS7GCqGka7e3tJJNJ\n9u7dO2PSRFnjpok4qV/8ghc/8xlSdXVc8tGP4pvDHZVtarFhw4a80pty7ms+lstaEdipn4vD4aCm\npoaamhrgTKeiSCRiNcHPtnzMlgmVOthgrVGsBVuKKznL448/PuvzQgj+/d//vaQ1lwTbRbzkrCmB\nLWXiTS5zCayUkr6+PkKhEFu3bmXdunWzXvzKJWTSNOn7zPsY+NpvYH0rYnSU5+66iwvuuIPGK66Y\ndrymaRw7dgxN07jwwgvxer15z5dL4Ap1hKqUq3C5UcznkNupKLcJftbKzR1skGvlLsektEp956VM\n0lkLw9bRNRgKVXoXa441IbALbXE4W61pNBrlyJEj+P3+ojNvy+EiTo2P84e7bqX3N79FbKvG4a/D\nTBnEwmFe+vjH2Xb99Wy77TZL7AYGBjhx4sSsNwDlEv75CvVasGDne6OhKIplvWZLQAr1481audlR\nb2v1pqaURv9rQmBdLti0pdK7qDC2i7iszMcdXCyGYXDixAlGRkbYtWtX3kSXuViIkEkp6frOdzj6\nn/9OvPMI7vVViB21+NwK4aMm0jAx3G46nniCSHs72z/4QTq6u3G5XHNO5im3BWsznXJ+LrlNE+DM\nYIOJiQm6u7uJxWKoqkowGET8DqygAAAgAElEQVTXdTRNm/X7LzeV/DdQigW70lt/Fo3tIl5yVq3A\nztcdXAzDw8N0dHSwYcOGkhrzZ5mvBRs+fJhDX/wi4ePtpHvaUatcVO/fxGQiitOTpupsDxMdSbTT\n47y6n3+erptvZt/HP86mPXvmXH+xLNjsTc1c1ttaEObFdJXnDjZobW0FMmGBiYkJ+vv7OXz4MOl0\nGr/fn2flLlYZTaVKdCAjsMV4k9aMBatrMBKq9C7WHKtOYKWURKNRDMPA7XaX1WpNJpMcPXoUgIsu\numjexemlCll6cpKj//EfnPrFLzANA30ohFAkwSvOQnE6IJE5zlOVRN/kZbJzktGxMXyqilfXOXrf\nfah///dsuOaaWc+zmBZsMWsvtcBWSsyX0m3rcrmor6/H4/Fw4YUXWi0fI5EIPT09RKNRa65uNp5b\nrsEGlRbYYl3Eq32SDpBxEW/cUuldVBjbRTxvcgeg9/f3I4Rg06ZNZVtb0zRefvlltm/fbrnk5kux\nAiulpOfJJzn2yCOkJiYAMMP9yGSC4OWbUavc045XfGNofgUvHjx+PyKVQk+lePWznyXc1saOO+5A\nmeHCU+kYbCVY6hhlJZK9crtH5bZ8zB1skG2A39fXh6Zp1mCDbMvH+QjlShHYNeEiFkBlvoo1zaoQ\nWCkl6XQawzCsLjmpVKosa0ciEY4cOYKUsmztA4u5wE50dHDoC19gvL3desxMTKCPj+Df0Yi7JT/m\nm06niSfiuF1uNu72Eu50ooWTeNxu0DQAQj/+MRMnTnDBvffiyWl8kbuvcluwUkpOnTpFKBTKGwEX\nDAaneQBWkjDPl0oI7FzndDqd1NfXW81Qsi0fsz2W29vb89zPWSt3rvexVKPqZjq3bcFOwY7BLjmr\nQmA1TcvrxKSqKrFYbEFrptNpOjo6iEajnHfeeZbILjbpeJz2//N/6Hr6aWROnFYaafThU7jXBfCf\nt+7M41JiGibJZJKqQNbSMKneKgkfdaKldFyKAqct07GjR3n+/e/nwo9+lNq9e/POXW4LNh6Pc/jw\nYQKBAPv27bOScCKRCAMDAySTSSseGAwGV724QuUs2FKELrflY7Ztpq7rTExMMDExwcDAgDXYICu4\nhQYbVHJUXSlJTmtCYA0NxkKV3sWaY1UI7NRfpIWUwUgpGRwcpLOzky1btrBz506EEDgcjkXvk9v3\n9NMcefhhkuPj0/akD4VweBxUX7bZukBrmkYikUAIMc3NpSoaVef4mGhLYigqjtNWLEByfJwXP/Qh\ndt52G5uvv956vJwWZE9PD8PDw+zcuZPa2lo0TUNRlLy2gbmWUm9vL+FwGNM0kVJa7smlzHpdrZRj\nko6qqtMGGyQSCSKRCIODgxw/fhzItHzMfncrwYKdnJysfJ/gpcDpgg1bKr2LCmPHYMuCqqpWzWsp\nxONx2tra8Hg800paskPSF4NoKMShL3yB0cOHCz5vhvtBTxF8/TYUZ0boY/GYJazRaLSgZeR2xvGf\nU0X0yCQOl8tyFQOYus7hL3+Z8NGjnPehD+FwucrSbD8ajTI+Po7L5WL//v2zXuSmWkqjo6OMjo4S\nDAaJRCJ0d3fn1XYGg0H8fv+Kru2sxNi4xZjgI4TA5/Ph8/msPruGYVhW7vHjx4nFYkgpCYVClpW7\nVC0fixXYeDy+NrKIwXYRV4BVKbClWrCmaXLy5EmGhobYsWNHwcHqi2HBGqkUHf/1X5x88knMGcTb\nTExghEeovmwzatBDMpkklUrh8/mKigd7fVGMrVUkQ7E8V3GW3l//mslQiIs//nFrHmwenU/B1msy\nE5tnwTRNQqEQg4ODVFdXs2XLlpLdg9lyqtxB57lu5VAoZHUwygruSuvTuxxjsOXC4XDkeShGR0cZ\nHh7G6/Vagw2klHlW7mK1fCzWel4zSU6GBuFQpXex5lg5V6ZZmPoLWooFm+3Lu27dullrWsvZoB9A\nb3uV57/070QHhzFncMtm466+cxtxrg8wMTFhJQrlvWcx80VUIPHXxTETXowxHUeB5K+J7m6eu/12\nWt79bjzZelkpMX/+HzijP8Co3wQ1u2Z8L5OTkxw+fJj6+nr279/PoUOHytbJKTe5JtvBKJlMEolE\nGBkZ4cSJE8AZ12Q2eWq5WrkrIQZbzvO6XC6am5utlo+GYVg3TLktH7OCW66Wj8W+5zUjsE4XrN9S\n6V1UGNtFXBaKEUNN0zh69CjpdJoLLrgAn8836/EFrbt5YqQTNDv+B69DQWmSSMBEYEjl9P/BkIJU\nzwlcOwN439BIIqXR2loLiophSAwddENiGJA2VVxuB6YBpjld2BRp4G/RmUgKkPmuYuvziEZp//zn\nabr2WjZ/8G74zqdw6M+jBE1k3y8xCwisaZpWN6vsRB5Y/GzgqX16s67J7MzVRCJhlZlkXZOVigUu\nB5bTyLhszW1u57NkMsnExARjY2OEQiFrsEE2gWq+YYFiXrNmkpzsMp2KsGoFdiYLVkpJT08P3d3d\nbNu2jaampqJ+EVVVLZvAJk88ibs5hryiCfO3GkpKw4HEIU6vL8AI91EVSFL3Z+vx+HXcHg8CA5i+\nh8mogd+XcYmZp8VZR8GQGbHWDQVDShJnBxh7TYdJFV0zME0wTIluZP6fSJiM/fRJXnv+f9h1ZRD3\nzsy5xPDvpp0zEonQ1tZGc3Mzl156aZ6ALXUv4qmuydzkqb6+PqLRaF4v32AwWLHkqcWIhxZzzkpZ\nsMWcN3vD1NTUZL0ua+V2dXURi8XyBtQHg8GyDTZYM60SDQ0mQpXexZpjVQjs1AvWTL/Uk5OTtLW1\nEQwG2b9/f0mxu3JZsFJKtJGnEAJ82+KEwwHcx0BJ5iQgJSLokWGa/qKFqtpqHMrssUwhBBk7GBQB\nipA4c4XYepujNF1aTardRMSmu4q1VAplqAtfaxXqxjN39cpkL0asB/ytGIZBZ2cn4+Pj7Nmzp+Dd\n/3w7OZWLQmUmuc0Uenp6SKfTeDyZmHa2Xd5SCV8lYrDLWWCnkhsWyJIdbDA1+S3Xyp16k1cMa8ZF\nrLqgeUuld1FhbBfxoqDrOp2dnYTDYXbu3Jn3i1ss5YrBagMvI81eBAIUk+rdaUaTbgKnBEo8hZaM\nY/SHWPfGDdRtbCh+4SK1y+OYxDirCuO4CTmijqHhnOxD3dOAt6UBI57G8Jg41Ey3KKXv54w2XMeR\nI0emzZGdynKcpjO1mYJpmtas1ayVtBTJU5Xu5LTU5y3XZ1hosEG25eOpU6eIRqNWfkJ1dXXRCYlr\nxoK1XcQVYdUL7NDQEB0dHWzcuJHt27fP+0LjcDhIp9ML3k/i1P9k/nJaTJxVGoEtTiZMFXlsEs9w\nF40XNVC3o/h2jLkW7JzHSonPEyO20YfZqWeyitNJnOoE6qWt6DJzE+HASSo2gSPoRkpJ5NjTdIzv\nLCpevRCBXSoURcHv9+Pz+di9ezdAwSHnud2LvF7vgve4mrOIp5LtB74YKIpCVVVVnjim02kikQjh\ncJhUKsXBgwfx+/3W91dosIGmaYu2x2WFoUE0VOldrDlWhcAWuniYpskrr7yCqqpccsklC/4lcjgc\nJJPJBa2hx4YxU78HTle9yIzh6WgeI3HCgbMpjidQRcPlG0pfvAQ9U6SBtzZNstWH2TOBt0XibGwi\nradBO2Oli7hJwj2JljLxedLs27sN4ZldXDPvbf6WaCW7ObndbpqamqxYoGEYTE5OEolEOH78OIlE\nAq/XSzAYpKamhkAgUHIpUiXe33KPwZYLp9NJQ0MD1dXVRKNRzj//fGKxGBMTE/T29lqx+GwW/ny6\nTD311FPceeedGIbBe9/7Xj784Q/nPd/d3c273vUuwuEwhmHwqU99igMHDpTrLc4fpwsat1R6FxXG\ndhEvGNM06erqIpFIcO655y64MX+WcriIEyf+X84kKQkMwyCeiKM6VDbu0Rl+KYHyzxeSbEvhPTVR\n9LqC4i3YLKqRxLnOjwg4cQrlzDo5PYS1pANzbJSqdZsyMej+X2Ke9fa597MMXcTzweFwUFNTQ01N\nDZDfvai/v5/JyUnrgp11LRdzI7dWLNhKCXu2yUTuYIOpsfiDBw/yuc99ju7ubm644QYuv/xy/vRP\n/5QLLrhg1nVvv/12nnnmGVpbW9m3bx/XXXcdu3adybB/4IEHuOGGG3j/+99PW1sbBw4cIBQKLfZb\nLg7bRTwrQohrgS+SacnxX1LKT015fhPw30DN6WM+LKX88WxrriqBHR8f5+jRozQ2NlJTUzOvWOtM\nLFRgTVMnHf4lABKJYejo6TT+gB+HkOjh42z8v/aS8KsMX6xS7xT4T0SKW/y0NVwqbiNG1O9BxGI4\nxZnG++l0mng8jsfjQZoudDOJS/EhBp+HRRTY5U6h7kW6rhOJRKx2j7nzVrNuyVxxW2t1sJUU2EJk\nY/FvfvObufbaa3n961/PAw88wAsvvEBbW9usAnvw4EG2bdvG1q1bAbjxxhv5/ve/nyewQggmTk++\nikQilrBXHFODeKjSu1i2CCEcwL8Dfw70AL8TQvxAStmWc9hHgW9JKb8khNgF/BjYMtu6q0JgDcPg\n0KFDJJNJ9u7di9/v5/e//31ZG0MsVGBTXT8HwqR1nXg8hiIUPF4PqkNFHzxO1fnrcDelMQ2TlENh\n9AIfUhUE2sNzrj0fCzaLX6YIO6E6nQYEaT2NKU2rdlQ3g6THRqHJhxg/AnoCVO/s+1klFmwxqKo6\nbRJNNvmmu7s7r8QkGAxaE5+WkkrGYJebwOaSTqdxuVyce+65nHvuuXMe39vbazU7AWhtbeXFF1/M\nO+bee+/lTW96E//2b/9GLBbjZz/7WelvYDFQXVC/pdK7qDCzuogvBY5LKU8ACCGeAP4SyBVYCWSt\ntiDQN9cZV4XAOhwOmpubaWhosC4k5axbzZ5jIesl+n5ILB7DMDJF9JqWzliy4X7cLT7cmzI1nNVE\nGJG1SAFj53mRqkJV29jsi8/TgoVM0lOV6iacHMIRc6EIB1WBM4kjDsWHHh9AS0dxEUAM/BrZujSD\n21ciheatZktMxsfHGR4eZmxsjNraWkt0y5E8NRuVtGArMU2nlFF15e5D/Pjjj3Prrbdy991388IL\nL3DzzTdz6NChyjc6Edi9iGenBTiV83MPsH/KMfcCPxVC/D3gB94416KrQmCFEDQ1NeVd1MvdnH8h\nAtvf/gLJ8VfweN34fG4EIEQaUlEUZ4LA+WdZxwqRptpMEHFkrMTxHW5MtZ7qP44iZtCshViwpjRJ\nxuNIBPh1lGT+PwmBQHEFMUZHkev8KIPPYSySwK5WYc4tMTFNk4aGBhwOB5FIhM7OTuLxuJU8NdPo\nt4Ww0upgF8piDVtvaWnh1Kkz1+Cenp5pk3gefvhhnnrqKQAuv/xykskkIyMjVuJcxTA1SIQqu4fK\n0yCEeCnn5y9LKb9cwutvAh6RUj4ohLgc+LoQ4jwp5Yw1YatCYAuxHAQ2mUxy5MgR/P1PUFcdQBE5\nhfCmgUiMUP1n28jcXp7BLSZxm25Spy9OkW0uTGcDNa+MFBbZeVqwmqaRSCbwerz4nF4i8QE0xQSm\njL5zBDFjo2haFNfIa2AaMEvzi9UqlOVASjmtXaCU0uqvPDg4SEdHB0KIvM5TC8mCL2c9aqnnXe4C\nW0qbxH379tHR0cHJkydpaWnhiSee4LHHHss7ZtOmTfz85z/n1ltv5ciRIySTybIlWi4Ihwtqt1R6\nFxWma0RKeckMT/YCG3N+bj39WC7vAa4FkFK+IITwAA3A0ExnXLUCW0kXcW47xnO2bECNhpiWwjfR\nj+/iZhRX4QtBtQwzKuswT2vv5GYnptpI3e9GEFP6DZdqwZrSJB6Lg8g0yc8Kv1f1MmFE0cwYLuWM\n60wRToTThxwdBacfhl+E5tfNuP5MnZzmYi0Ic6F4qBACr9eL1+tl3bp1wJkB59l2j5qm5SVPTe1c\nVOo5l4LlLrCxWKwkF7Gqqjz00ENcc801GIbBbbfdxu7du7nnnnu45JJLuO6663jwwQf5m7/5Gz7/\n+c8jhOCRRx5ZHoMnbBfxXPwOOEcIcRYZYb0R+F9TjukG/gx4RAixE/AAw7MtumoEdurFudwWrKIo\nRXWHiUajtLW1UVVVxf79+0kd/xYp8tsSGpF+fOdW46ie2SoRQqfaSBDOSSiKtahIRyN1L46gGDl7\nEUCRk/RSWopkMonPO33cncsZxJ0YY5JJgtKJKnLm4apB9EQ/WmoSdeA3mCUKbDEstcBWQsyLFbtC\nA84LdS7KtXJn6s9bSaGr1HmL6VU8nzaJBw4cmFbXev/991t/37VrF88991xJay4ZtsDOiJRSF0J8\nAHiazCf1FSnlYSHE/cBLUsofAHcD/ymE+L/J+AxvlXNcRFaNwE6l3BbsXBfF3Jmyu3btIhgMZmpJ\nR5/OPy45iate4tlUm2nsMAsuZRKP4SKZczceX+fAfF0jDS8Mo+gZVRUIzDkUNjukXVEUqquqC74f\nIRSkUoXbjBFRFGqpRxGZczscVehiCHN0DBF8CaSccUbsSrJEV0pGb6HkKU3TrM5F3d3dGIaRN5w+\nO2u1UhZspWK/hmHg9c6e6Q6lu4hXNFKDVKjSu1jWnK5p/fGUx+7J+XsbcEUpa65qgU0VmH26GITD\nYY4cOUJzc3PeTNlU7wtIc9A6TpppFBHGv7cVw9CLiptWEUYz6zCVMxfIZKPC8OsbaXx+BCVlzBmD\nTaVSJFPJzJB2dZY7ewEKHhxKCqcZZ0JRqBH1ZCRcIFwBSEVIhU+ghg9D7XmFl1khFuxKx+VyTevP\nmztrNRaL4fF40HXduuFc6qze5VwetKYE1uGC4JZK76LC2J2c5s3UX+RyD0gvhK7rtLe3E4vFrPrb\nXJK9P8j7WcYHqb58/em9Fhc3FcIgaCYYV/JbFKZqFYaubKDx+VFEOl1wLcM0iMfiOByOGa3WvHNl\n8ptR3Q3IeC+aTDBphqlSMiVEqlpDOhWBkXHoebpkgZ3LkloWsapFZjGtyanD6aWUpFIpjh49Sjgc\nZmgok4uR23nK4/HMserKwzCMopK61kyj/yx2J6clZ9UI7FRUVS1rDHYq2SECmzdvZufOndMumumJ\nPgztNSs/2IwNU7W3DsWV+chLsdacShSf4SLuyP+6tKDC4OsbqHt2EML57uZkKkkqlcLv85eUQSqR\nOB0+dNWHS4+TcDhImE68SgCH8JB2uBHpFOkTP8K55+6Cawghip5mMu38q9yCXUp3rRACj8djJU8F\ng0F0Xbf6Kw8MDJBKpfKG0xdqiL/SKMWCbWgoYWLVikYDPVTpTaw5Vq3ALoYFK4QgkUhw7NgxgFmH\nCCRPfhdxOi5qpibxbVFRa305a5V27oAIkzQbMKdcN/SAYPAN9dT8XAMzY7XGYjFrdJdgfhdzp7se\nTU/gNeJEVQVVqjiFB4erGiMxjOg7iTH2Go66vdNeO/XmIRqNcvjwYXRdty7kNTU10z4724Jd/HOq\nqjrjcPpsQ/zcMqLq6uqKDaefL8U2uCg1i3hFo7igakuld1FhbBfxvJl60Sq3BSulRNd1Xn75ZbZv\n3z5r4bhpaKQnfpV5nZnGFUzh3jT1TrnUeKNJjYwyxvSYkeFVGLiqDvHCJAzH8Pl9qI6FfbWq4iHt\nDCDTUbxGnLBDoU6qqI4ghhhBpA30176A4+qvTHttVmCllIRCIQYGBjj33HNxu91Eo1HC4TD9/f1W\n6UlNTY2VBbsWLNilZrYs4pmG02f7K2eHm1dVVU1LnlqulFKmU85+5csau0ynIqwagZ1KOS3YWCxG\nW1sbpmly8cUXz5kYkQw9BUQBUEQE3676aceIORKTCqGKOH7DQ2yKeJqmSVLRGbwyyObfe1HHEqUt\nPANudz1JPYYiTdxZkaUO4fQjtSii8yD6/n5U7/q81wkhrHmctbW17N+/H9M00XV9mvWUTcoJhULE\nYjE0TSMUClFTU1P2jkbLheWeuZwd+5Z1n05NnorH47jdbiuWW2g4fSVvlIoV2MnJybWT5CQ1MEKV\n3sWaY9UKbDks2NzSm507dxIKhYq6UKUGTmd6GxEC59fM8Jr5tTf0EyZh1mMqAikz3aI0TUN1OHBX\n+Ri80kfzbxVcQ7GS156KIpwoziCmFkaVOqZMMEEEv1qDrkVxxCZItT2MevFHrddIKRkZGWFkZISL\nLrrI6lZUKCYrhLCGZre2tqJpGq+99hoej8fqaKQoiuVSnq3Wc6WwEqfpTE2eAqzOUyMjI5w4cQIg\nz8p1Op0Vi+WWYsFWUmCz/xZ+//vf09jYSGtrK0IIh5TSEELUAnEpZXlKIRQX+LeUZamVi+0injdT\nL1rFNoaYiWzpTVNTk1V6U4xVnBo+jDRDSCNBYLsLxV34I553SYowqTGjDJteYvE4TqeLQCBAPBEH\nQKoweHkVjb8TePqipa8/BberlkR6AqSJy0gSdzhICgeq4gQzjWh/hvS5/wtnYCvxeJzDhw/jcDho\naWmxxDX7fudCURQURWHdunVWR6NC7srcLNjFbpRfbiodgy0XHo8Hj8dDc3MzkBG1bOepoaEhEokE\nmqbR3d1t9VdeKsEt9v1Go9GKuoize/zGN77BW9/6VlpbW5FSZi8w/wT8CvihEELM1dBg7pNhu4gr\nwKoR2KnM94Ki6zodHR1MTk5OK70pRmCTXd9Fmjq+jaDWzF3sXjoSPTUGKT/+YC0OhwPTlJnGD9kj\nHIKhfVU0vqKUNLi9EEI4cLhrMZKjgMB3Oukp4PSgpNI4oiNoxx6jv/EWenp62LlzpyWK5aCQuzKb\nBXv8+HESiQQ+n8+ycEvJgl3OnZzKfc7FFjeHw5Hn/s/24XY6nfT399Pe3m55I7J/Kp08VWkL9sSJ\nE/zgBz/g+9//PrFYjLGxMd7+9re/HegE9gDPlO9sGshQ+ZazKYpVK7DzYXh4mPb2djZt2sSOHTtK\nrq01kmGMxO9w1em41pdfXPXTs2TdLjfrg5IxoaCTiedOkwoHDF/sL21w+wy4nDUktQjSzLjcfUac\nqMNLFRIlEWXiyG+Ii71ceukBVFVleHh40RpN5F6kN23alJcF29PTQzQateav1tTUFIwPTj3nUlKp\nJKdKuKWdTifr16+3htOn02nLyu3p6SGdTud1nvL7/Uu6z0oLLMD4+Dgej4fjx4/T3t4O8I9AgEzL\nvpcBFmy9QsZF7N2y4GVWNraLeN4s5BczlUpx5MgRYPbSm4y1OLPbOdH5PRy+BN6t8598UhhJIp5A\nN/TTFpoDkATlJKOyGhB5FqyFwunB7QqB9vF5n10gcLjr0BODgECRJh4zRdIhcKY0AsSoTv8OVb0u\nc/wSdnIqlAWraRrhcJjR0VErPph1KxcqD1pqVloMtlzndDqdecPpTdO0+it3dXURi8VwuVx5JULz\nmQJU7OdbbM/ixWLr1q3cd999XH311fzJn/xJ9uHLyuISLoTtIl5yVo3AzsRsLjkpJb29vXR1dXHO\nOefMObNxtgECpjTRYz/Ff65S1guorqetrM0qX368yCGSVBkeJlXXrOlSY+d5MJ11VB+eY3D7LLjU\nKgxHBGmkQEqEkUQqDnCAKzWBPnSM1MhB3A2XVrzlocvloqmpyfo+s/HBqeVBXq+XdDq9pG7b1RKD\nnYtiRF1RlLwkN8jc7EYiEUZHRzl58iSmaU7rPDXbeynWWl8O5WDZz+i1115j06ZNbN26FUVRPgLs\nFkJ8G/jBbLNGS0NDilB5lrIpmlUtsNlM4kJ3qdnSG7/fz/79+4u6U57NgtV6n8W3dRzFWR5LQUpJ\nIhHHNMwcq3U6XiVC0py7G034XDdSraf6tZkHt8OZ0XfTG1QIVHcdWrQXwzRxOBQ8gCFAJMYw0TGO\nfQvZsG/Z9SKeGh/MlgcNDQ0Ri8U4ePAgHo/HsnAXszzIFtjZcbvd026OsjH3jo4Okslk3nD6QCCQ\n912V2m+5kgly2XM/9NBDvOc97+HQoUMA1wBfBP4ZeI45xqEVi1RcGO4t5VhqBWO7iOdNoV+UbMw0\nV2BN07SaH+zcudO66BaDw+EgnS48AUetCyHUpky9mfVnfmVCWavV4/bg8s3lzpRUywlGi9ClyNku\nTHWWwe0wdfa7hSlNUgmJKdyoqmYdKFSBTOuk44MoqGiDv0S4LlwWFsJMZMuDsvW6O3funDbwPDfW\nW1NTUzZXYqU+l6UWknKNqnM4HNTU1FBTUwNkbzwTRCIR+vv7mZyctMqIshZuMQK7HP59Zr8Tv99P\nZ2cnn/rUpwC+IKX8nhDiI0D5ppUIMGwX8ZKzagS2EFNrYSORCG1tbTQ2NnLZZZeVfAFwOBwzTuhx\n+G7BFG6kyLlLkuYUwc38Eaf/b5JGCieCNMgcq9U0CQSKL2twiBT+tASCcx472+D2M/smT2g1TSOR\nTOD1enE41pGKnbKeE4qKkAlkfJiUrw5x5H/g/PPnbcFWgkIDz7MJOeFwmFOnTmEYRl6d50LKg1ZS\nWdF8KbZdYakIIfD5fPh8Pit5Stf1vFKubLlYrpU79TNPpVLLZtDBDTfcwNe+9jUmJiYAfimEcJER\n14UXs59GopF2nJr7QJuysqoEttDQdcMw8kpv9uzZM+/MwdmyiAUqirwek28ixel/yEIB4SEz+P4M\n2R1GZS0Bz3ociiAeCxMODxGs9uH3qSDTBcWZGUIyPnUSaTShOea+eM84uJ18F7EpTeLxTH1tVVUV\nisgIvuKswkxPZo4XDqRDwR+bpLdhgoaJHuTwL5By55z7WM4USsgpV3nQWhHYpUqsUlXV+q4aGhro\n6elh06ZNecPps5nl2ZrcaDRach/ip556ijvvvBPDMHjve9/Lhz/84WnHfOtb3+Lee+9FCMH555/P\nY489Nue6H/nIR3j22Wf5+7//e7Zs2RIWQrQA/5RTE7tgpPBgOLeVa7kVyoklP+OqEtipqKrK6Ogo\nhw4dYuPGjQVLb0pBUZRZu0MJXCjyBkweQ4r+udcTCoauMxIeR5qS5vXbcKhn7vqn2YASQJ8uuqaG\nLvuocSiMCi+ZmrfZ3YwKmmgAACAASURBVNPxdQ7MKxpo+O0oipbze3y6haOmayQSCbwe77R6RZe7\njqQetTKXperEkUrhS0QY9yrUnPgJ5vqz53z/K4lylwetdiqRuQxnRtXNNJx+fHycL3zhC/zoRz9C\nURT++7//m9e97nVs27Zt1muDYRjcfvvtPPPMM7S2trJv3z6uu+46du3aZR3T0dHBJz/5SZ577jlq\na2ut8YBz0d7eTltbGy+//DJ33303QALoXsDHUBDTnle35KzaK4CmaYyMjKCqKhdffHFZ3EGqqs7Z\nHUrgRpE3YvIoUsz+C2YYBgMDA9TV1xVnVQsAFYQK5M+HjZlO6nwbCchGJhQFMCzxFQUtYY1ko8bw\nFY00Pj+cGdx+mngiDjLfas1FEU4cziCGFs5sS6ggUgSiYVIeHxOpLryRXwAXndn6KrPaVmJ50FJS\nSYEt5JrOHU7/8Y9/nL/8y7/kX/7lXxgZGeHDH/4w73znO7nhhhtmXPfgwYNs27aNrVu3AnDjjTfy\n/e9/P09g//M//5Pbb7/dyuuYqyoBMmGrBx54AMMwcgV2J/BZ4PJylexIBIZdp7PkrCqBzc4h7evr\nIxQKUV1dTUNDQ9liLYqiFDVAQODNEdnRac8busHwyDCGYdDU3ITXW76mFF5GSMl1pAQgvODwzljC\nI4GETzJwrU7jb/pIjw0RTvZTU+0j4HeCTCNlCmG5q8/cXLhctSTTE0iZKYuQqoozkcKrx5lUFRxj\nv8DU34+iFnbDGSaEU4KxBIwlBeOn//yht5U/jDu4dKPO3p0rS5SLKQ9KJpP09vZWpLHCUlKpGtNi\nY7+6rrNlyxbuvvvurKjNSm9vr9WHGaC1tZUXX3wx75jTjSK44oorMAyDe++9l2uvvXbWdQcGBjh1\n6hS//OUvufLKK7MPn+RMFsQ8xoJMRyLQWdl9vFciq0pg4/E4hw4dwuv1cumll9Lf31/WmbClTOgR\nBFDkTRh8A0TG0pMSotFJwuNh6urrMr13C1iI80GcHn8nBFTJMJqoLvK3UpAMODi020ftL7043S04\nA/XIHLewtP6TdU9nRFcKP+nYyczcW1XBkZhE0XX8xAgr0Nv5JTwbP8h4QjAaVxicVAlrCuNJwURK\nFNxfVHcSjyg82emm/YTGG68wqalZHEtosTNJC5UHvfjii9YYv2x981KUBy01i5XkNBe6rldskk42\n1+PZZ5+lp6eHq666ij/+8Y9WBnQhpJQ0NTXx05/+NPeG5Epg+p35AjFsF/GSs6oEtq+vj7PPPtu6\noKmqOmPW73wodQSeoBqHvAmDR9GNMYaHh3E4HGxoacHhUEjEE5hlqiPPJngJIXCIBNVmgEgRLrpE\nIs7IyAjBuhocN56H8d1jhe+Xp7inJeD2N5DQE5hGAonEk9ZBcxL3NmFoafqP/Yxnx99BSj8bKSWG\n4SjKYtMTmWPau130D+lcfr7GxRcW99pSWUoLUgiBoii0trZajRWSySThcLhgedBy6Nc7XyrlIi5l\n2HopAtvS0sKpU2eycHt6eqz4bpbW1lb279+P0+nkrLPOYvv27XR0dLBv374Z1928eTPveMc7+NrX\nvkY6nUYI8WHgMjK1sFAG6zWziLBjsBVgVQnsOeeckyeA5ZwJO+/1ZA2DfW8gkf4KTc2ZYdVZytlY\nYepaHjFMUm4gJQrvV0qT0dFR0lqa9evXo6pODGDkDbXUHBK4koXrffPPqeD2biMR/SMCge7044lN\nMhFsRhUChw7rlc9zyPwMflGsm16ixc/8NJlU+flBBydCCf7k9YLGxtVh4WXxeDzTpgdNLQ8KBAJW\ntnKp5UGVqvesZAy2GNd0LBajqqqq6HX37dtHR0cHJ0+epKWlhSeeeGJahvBf/dVf8fjjj/Pud7+b\nkZER2tvbrZjtTEgpueiii/B4PLz00ks899xzDuAeKeVrp58vyxdokibBQDmWsimBVSWwUynHTNhc\nShXYRCLB4cOH8fl8nLP9HxHOb5JJEMywmAILUC1HGRG10+bOJpMJRoZHqDodo84tepUewcAVVWx+\n1cQ5Gp7zvD5XCym1C1OfwFADeGJ9OAydNFDjUtjac5LfnP8jOkJvY7cCc1URqWnJ1PJcQwqOD/oY\nfjLJ+dtTXH6ZC0cR5UjLkbnEsdzlQUsxSacQyy3JaSqTk5MljapTVZWHHnqIa665BsMwuO2229i9\nezf33HMPl1xyCddddx3XXHMNP/3pT9m1axcOh4PPfOYz1vc4E6+++irPP/88H/zgB3nrW9/Kfffd\n9wkhxF4hxN9JKf+jXElOAhcKWxa6zArn/1vyM65qgS23BVus5SCl5NSpU/T09LBjxw7q6upOP34j\nBo+DSGbWUwRypmYP89jb1N9DRaQImmnCinr6/CZjY2Okkima160reKcvBBhOycDr/DQfFLgG5xgS\nIMDnO5foxO9AcWI43XhjYZLeIAKod+i8ru85vrNxM092X8olUqdlluufmp7ZZR5JevjtYZUToTh/\ndrVKS8uq/ucLzF4e1Nvby+Tk5KzlQZWYpAOltyxc6vPGYrFpLt65OHDgAAcOHMh77P7777f+LoTg\nc5/7HJ/73OfmXCuZTPLiiy/y8MMPMzQ0xBve8AYikQh//ud/fjbw/7P35sFxXefZ5+/crW+vWIgd\n4CKSoiiS2rjKtiTLtmRJdKI4zmankpmJx1PJN/6mPOWaxE6q4nzlmVR9qeTLVGqcSlUiL3FkW1k8\nXyxvSix7JEdeJMuyRRHgAhLgBhJsAL0vt+925o9GNxtAA+gGGoQE4qlSiei+ffrc7r7nue973vd5\nfgPonD10tiVgdShXEW+miG82NtQKNX8haXUE2wjy+TzDw8PEYjGOHTs252IX9KPKX8fjGRB2a7V3\nRf10YEDMYMoB0naeqfgUkWiUgcEtLKqJOFu0KDXJ9fuD9LyqEJhYut4ioG+haHTh2dPlKLaQgWBZ\nVcpQ4d6pUX7YM0Gu6wwvzNzBoCM5onqE61zvir3052F7GhPpKF/7dp7bt1u8850BDOOtGc2uBIu1\nB1UE8ue3B61XpfKbPYLN5XJNpYhbDdd1OXfuHCdPnsR1XT772c9WPJT/G2VlmqdmD21JkYaPQ4Hr\nrRhqE01gQxHsfLQ6gl0KUkouXrzI1atX2bdv36KVg4IhVPmrePwTilBaWuRUrxxCSok9c4YZK0hv\nXy+6vnTRTC3pSwXiRwJ0aT0ELy7d0xsO3kHWnsFTQwQKSYR/43OPGQ7vvfY94tsGyBbjTBR7ibuC\n97guW+a28yJuyBwvCikEKSfCyKUS439f4D3vDrBr14b+KS+J2h5PmNseVIlyh4eHq2nlm0G6b3aC\nXW8v2FAoxO/8zu+wbds2bNvmne98J4lEgmeeeeZ/AyZqXHR0IYSz2jSxwEBl++onvommsKFXpZsV\nweZyOYaHh+no6GhI41iwA0V+AMTnkf7yxUSNoF40XCqVylFrJMK+wR5SyvLFH2KetawUMH2fxhat\nl9D5xe+AdS2Kag4grQk8LUiomMUxb7Dn3ZlRBrOTeJ0G1mQYLxnmZFrhnbvm3mDIkpivLLko8naA\nkqLzzecy7Njq8MgjAUKhzTRYbXtQf38/Z8+erUoHVnxX17o9aJNgl0bls3n00Uerj0UiEaSUlwGE\nEJqU0gX+L+Ay8P+s5v02q4jXBxuKYOffla9VBFtph/F9n/HxceLxOPv372+qaEJhN671BGjPtmRO\ncyJPKUkmkxQKBXp6e2ZbPRIE5QDFRaqK641TgRQwfbdKp9ZH5MzilYiR4G7SpUk8PUqomCHd0QuA\n73t4don3T3+Vv+j53+kJj3P5/G4msjqJtKSz7cb35lqNEyyA6yvklHYuxws89VSeRx4x2bdvs6G+\nggrRzfddXev2oFa56azkfd8KKeIKPM+rtm/NQ+UiDAK51b6Pj0OOxqQbN9E6bCiCnQ9FUVreplAh\n7UKhUHXmOXbs2MoWE38PhczDdHa8zpImrQ2gQox2ySY+FSccCjM4ODjnpiMip7BFF96S2zqLC8ck\n9in42gCx4at1n1eVILq5jVJxDM1yQHo4roeUEiOgs6sQ553GKD8y9hMxLFJC4wenihzalsIMmJim\niVMUsHhf/qJIOyGCQZ3n/i3LyZMOjz9uEott3rEv5gW71u1B6yU08VYj2Mpcl4j4I0B2te8jMNDY\nuvyBm2gpNjTBrgUUReHcuXOkUin279+/qotUVVWswk4U2Y8vvr2qeQkE2WwW27bp7umuq3urCIc2\n3yKpBBa07lTHWabwKrUHpD5I7OdXqafFFA7uwrGu4AkDJZNACXeg6uVFREqP9019k9f79xGJlMhn\n2iloMQJhk6Bews0VsEseVyeuEggECJgBTNNsWDS/6OqowQ7iiSx/93c5Hn7Y5OBBfVFieDN4gq41\nGk3VrkV70HoVVzXyvs32wa4Vzp49y+7du5f6LGNAZrXvU04Rb6we8rcCNhTBrvUFXfGcjMViHD16\ndNUpMEVRygsg94Hv4CvPr2gcx3bI5rKYpsng0OCSn4MukoRkP/lFIub5e7D1kL5N4muDtP90AjHv\nYIGK4/fikiJqF0m3dc8+IxFANBnnl7f+hM9teTfqGZBBeOOSzsN3KYRVk6xeor+/n5JdwrIspqen\n8TwPwzAwzXKUq2v6ooVQnhRkiBFrt3j+uzlOnXJ44gmTLVvqLy4bVQu4gpX2wdZrDyoWi3MKp2ot\n4Nra2hbcCK3HZyuEaOh9C4XCHNGXm43KDci3vvUt4vE4DzzwAIcOHaK3t7dySG2aadURrI9DlqnV\nDrOJJrGhCHYxrPZu2vO8atTa2dnJwMBAS/aXVFWtuvMoHAXfxVdeaPj1UkI6nSKbzRIOhwmajaXx\nQnKKEl24dUi20dah7FYfXxuk85WriNlzcGyHeDxOMNiPKy6juWnmu7dLPO6+8O/ctesIF56TKGHB\n1YwgmYXY7GFCEVUyLZ+nxLEdLMsimUjiOA66rlePMQxjAeFmbBOjXWM6meWzn83zwAMB7r/fQFHW\nj1DXI2JuVR9srdH5/PagRCLBhQsXkFJW24OWc51aKzT6GUsp11X3ufKdvO1tb+Mf/uEfePrppzlx\n4gR/+Id/+HZgXEpZ8bv8OKy+v0ZgoDO02mE2NIQQj1OWqFSBp6SU/7XOMb8O/BfKC9vrUsrfXGrM\nDU+wlShxpRdTMpnk1KlTDA4OcvToUUZGRlpWODXfnUfh7SAdfPGDZV/rOGUyMwMmg4NDZDLphhcX\nRbjEZJGkMOskeQWNtt7l+33k2wbo/PE1sokEmUyGnu4eAmaASxMDIIoYVh7bvFGtKYBAJsUTvMir\n/bu5ng0iNHj9gsqOwfoV30IIjICBETCItcVAls/fsizS6TS2Y6OpWjWlHAgEEEJgexqO2k60Pc+L\nL1qcOuVw/HiQ/v71WVjXg2DXMlW7VHuQZVm88sorhMPhm9oe1AjeTFsDx44d49ixY6RSKf7oj/4I\n4PvAs0KILwLflVJebMX7SDb9YJeCEEIF/hp4FLgC/EQI8ayUcqTmmNuBPwTeIaVMCiGW9SPcUARb\n7+KtFCU1S7AVZ4xcLse9995bTSe1sjK5Qv5zHpPvBBx88Urd10gJmUyaTCZDd1c3ZrAc5TUrWqGL\nNGE/RE6Z+/7l6ujGx8l02lze4TEw7TM0OISYjRCF14mI5AhY03MItgyPobMv8OShX+LvvrEVrQOu\nZgQzjWbsBOiGjm7oRGPlfTTXdbEsi1wuR2ImcSMKDph4Zohoj04ilePv/z7PkSMGDz20Pt6sN5tg\nbma7TG170PT0NIcPHyaXy9209qBmbybeDGR/+vRpXnnlFXK5HP39/QDPAKeA3wP+ByHEb0sp86t9\nHw+PNInVDrORcRQ4J6UcAxBCPAP8EjBSc8z/Avy1lDIJIKVctix7QxFsPVR6YZtpO5iZmeH06dNs\n27aNvXv3zrkQW0mwi42lyEeQOEjxszmPu45LfCqOoRsMDg7NSXcK0bzsYog4JdmLU9O608gebAW5\nXI5kMsGW3V2wbQj5wziiZM8OBKHQXrzsfyx4nQCMfI6DHc9yT9d/4kRGQWqCV66pK1ZL1TSNSCRS\n7W30PI+SVaJYLJJMJZkEwkGdds3mhz90OXvW4cEHfRqsn2oJ1qPwZ72KjaD8m7yZ7UGNtgatl3xk\nPXzhC1/g6tWrdHd38/73vx8p5W9VnhNCvNaq91HQMRlo1XBvVXQJIV6t+ftvpZR/O/vvQcr9xhVc\nAY7Ne/0eACHEDyinkf+LlPK5pd5wwxHs/EiuGUJ0XZczZ85gWRYHDx6sa4TeaoJdbK9KkY/j4yLF\nG0gJ2WyGdCpNV3dX3XkJIZpWhRLCIyazzIgglU3M8sKzNMP6vsfU1DRSSgYGBlFVlVLIJ/5gN90/\nmEEtlrWWA/oWLKUHzbZwjcCc3ViJx9D173Kg47e5mo8y40oulhS6/NZENKqqEgqHCIVDs3P2KZVK\nJCwL1Zvi+kiR06c1du926esr0t7eOtP7xbAeZLcegg9LZVLWsj2o0a2gSkX0mwEf/vCH2bNnT/Vv\nMfdkH29F9ArlK3pTi5hpKeXhVbxeA24HHgaGgO8LIe6SUi7qirLhCHY+GlVzmpqa4uzZs+zYsYOB\ngYFFL+pWp4gXG0sgUOT7KDkFpqZfQtM0BocGF10sV6prrIksUT9MVqm8dulxKv6x7e0dC9oc7Kjk\n+kNb6H3phkFAKHoXWfsV3Hl1SAJQnQJ3h/6RXP/v8Pw5lbSEUS/Knrl1US2BoigEg8HZm5MOegZs\nMteucuaMy5/92SUOHsyzb1+A9vZ22tvbCYVCLSfD9dqDvdkE2wypL9UedP78eYrFIsFgsKH2oGac\ndNZTxakWteQKC+zpWqYM4eGRYhnjjlsbEzCnUXho9rFaXAFellI6wLgQ4ixlwv3JYoNueIJdjhAd\nx+H06dO4rsvhw4fr9o82M14zWIoUpZRcuzbJ+Hg/+++5l0hs6ULC1RgHBIlTkn3YwptNES8cp+wf\nm8C2S/T3Dyzam+qGJNcf6sC+Uo4QdDVKUHRRZOGNuMRjj/fvDOsf4J7ONn58UWHKMEk70LbGPuOW\nZ6C09dIpXEytjdFRsG2XQ4cKJBJj1TaORvs+G8V6RLBvpai5NmUMNNUe9FaRSVwPKOgE6VvvabyZ\n8RPgdiHEbZSJ9YPA/ArhfwU+BHxeCNFFOWU8ttSgG45g5xPNUhHs9evXOXfuHDt37qSvr6+hhUhV\nVWzbbtlc66FUKjEyMoKmaRw9+jY0/e348p+Q4sKSY62UYIXwicoMMyI8O87C+UxNxYlEonR1LeXE\nU4ZrSuIPtbP9cgwtlaFNv520/zrevLYgARiKxQH1y9g9/4lz51wu+zYjWZO3LW2j2RJ4KORFBz29\nYfIzOS5e1IjH23j3u3s5elSvLuxXrlwhl8thGEaVcGOxWNMFOrfKHmwrreqaaQ9qxGgdyrUDtxrB\nAptCE0tASukKIf4z8G+U91c/J6UcFkJ8GnhVSvns7HPvFUKMULYQ/H0p5ZJWYxuOYOejXsRp2zYj\nIyMIIThy5EhThRVr7dAzOTnJ+fPnuf322+npuVEFrshfw+cZpLhc93Wrtb7TRI6YH6Y4RypRkkym\nKOTz9PT0NvU5yQBMPhih94cK+gx4/jYc9SLzl0Bh2OzI/AenAh/gvv09XPkPl3HF42jYIhYxyLtr\nrytc6ZkNlLIUsi7f/GaR4WGHJ54IMjAwUF3YS6USqVSKeDzOuXPnUFW1WhFbT2hhPm6VPdi1fs/F\n2oOuXbtGKpVatj3ozZQivlnY9INdHlLKbwHfmvfYp2r+LSn3JX+80TE3PMHWRrBSSiYnJxkbG2P3\n7t21qikNY60I1rZtTp06BVCX9AU6ivx1fL6CFAu1gFvhLRskjiE7yEmJ4zhMxeOYQZOBwaXVoRaD\nr8HkO0L0vqJw21XJS16ODjVJqLbPVvMRvsfd4ksU2z5OX9RjypX88KrKgx1FwtESIhgg52i0fGO2\n5uOq9MzGuvJkZywuXHB56qkcDz0U4MiRskBFIBCgt7e3+rtxHIdUKkUymZwTSVX2ced/h7dKBHuz\nSb3SHuS6LsFgkB07dpDP50mlUgvagzKZDMlksmmCfe655/jYxz6G53l85CMf4ZOf/GTd47761a/y\nq7/6q/zkJz/h8OHV1NO0Fi4eCdLrPY1bDhuOYOs56ti2jWVZnDp1ajbterThdNJ8LFX5u1LE43FG\nR0fZtWtXtbqyHgQBFPkb+HwJKebWP7TEvF1IYiSZtByuT07S1d2Faa6uulaqcP2YSfdPutg6lmfc\ntGgTFm14VbrUgzaDxZ8QDV7ijh0G2dEwVzSdjONA1odsETOkoEcMcraOXCPCkEKQ8SKEe3TsVA6n\n5PPd794QqOjpmZti03V9QSSVTqer+4WO4xCNRquEu15KThstgl0MldS0EKLasjW/Pehzn/scX//6\n13EcB9/3eeCBB3jkkUcIh8NLjvvRj36U73znOwwNDXHkyBGefPJJ9u3bN+e4bDbLX/3VX3Hs2Pzu\njvWHgkaY5gOKTawOG45g50NVVZLJJNeuXWPPnj3VxXA147XKY9ZxHIrFIhMTEw2nqgVBFPkhfJ5G\nihvp/1YQrOd6pBOTGFYbW3bd1rJFsmzcbtDDVibGCmSDl3EQbMFFAVTDh6LkbvFFzhr/M7cPKpxM\n+JzI6TzQUd7vtgo+VsHCCJQw2wxyroEv14Zo83YALaITMTPk0g5Xr3p8/vN57r/f4B3vCKBpi1eY\nd3Z20tnZCdyoiE2lUpw9e5ZisYjneUxMTNw0ZaP12oN9s1nVVdqDPv3pT7Nr1y6y2Sz33XcfL730\nEgcPHlySYF955RV2797Nzp07AfjgBz/I1772tQUE+8d//Md84hOf4M///M9bd1Itw6Yf7HpgQxNs\nsVjk/PnzeJ7H/fff37Ary1JoVQRbaQvSNI277767qaIQQRhFfgiPp0GUW7BWS7D5fJ7ETIJYWwxV\nLWAKgxItNKtXYPqgzqCym9HxIsKIcx2dLbgYuoeiOvTl36DXGKP79l4uvgSXDZ2s4xDVb5yXXZLY\n8RKabhNp0yn4Adw1INqKz2xbT4HsdAHPk/zgByVOn3Y5ftxk69blf0u1FbHbt28nn88zOjqKlJIL\nFy6Qz+erLSjt7e0tq1SuxXpFsOtlVdfITWoul6Onp4fHH3+cxx9/fNnjJyYm2Lr1RgfH0NAQL7/8\n8pxjXnvtNS5fvsz73ve+NyXBunhMr96UZxNNYsMRbIVorly5wqVLl9i2bRupVKol5ApL9642Atd1\nOX36NLZtc+jQIV5//fUVEaMghip/c5ZkMysmWN/3mZ6exvd8BgYH8H2fYqFIVM5gi/ZFbe0aw7yG\nVgWUg0GE2It90cJQM0yh0a75iFnzu/tD/8KPxds4sEvlx+M+r6s3othauI4kM22jqjaxDh1LGtgt\nEqmoRdoJEdyiI3NZrKLHzIzH008XOHhQ5+GHTQKB5shd13WGhoYYGhpatAWlQrgrqVSej1thD7aC\nZtp0KtFoK+D7Ph//+Mf5whe+0LIxWw0FjQiry95tonlsOIItFAq8/vrrRCIRjh07RqlUYmZmyUrq\nptCocEU9VCQYa8UsKkVTK7kBELSjyg/h8SWEaFzsvwKraDE1NVWOnqLRcg+sL5FIVGER8z3SK10o\nZyUX663tg/eE+al+J4NjP0cVJZKKQiDsEcpLeovj9IReQxk6zPnLgsueTs51iGj1z83zIDPtoCgO\n0XYNRw1gua0l2orPbCyYJZMoIaXkpz+1GR11efxxk927G9vPn0929VpQKpXKU1NTnDt3rhoFVypi\nm60duBX3YJdDs206g4ODXL58o3r/ypUrDA4OVv/OZrOcPHmShx9+GCh3Ajz55JM8++yzb6JCp00/\n2PXAhiNY27bZvXs3HR0dQPmia9WeKdQX6F8Oruty9uxZCoUChw4dqtqwrXS8Wgi2oMoP4fMPTVl1\nJRIJLMuir79vzqJdGwmbYoqSHMISzgrmVWn3WciwYUWlZ0+ES8ZdbD/zGgo+lilxFIlhCe71v8Tz\n4hD37lX47gmX11Wdd9SJYmvh+5BNuAjhEm3T8AyDgtO6n3fVZ7bXIj+Tw3MlmYzPP/1Tgf37dR59\n1CQUWppUGvl+6lUqVwqnLl68iO/7cwh3OWGUzQh2IZoVmjhy5Aijo6OMj48zODjIM888w5e//OXq\n821tbUxPT1f/fvjhh/mLv/iLNxG5llPEcXLrPY1bDhuOYDs6OnCcG4TQ6rYaRVGaihQrdndbt27l\nzjvvrFvlvNr5CbpR/N/A85bf+7FLNvF4nEgkwsDA4MIIc54UcUROYYtO/AYt7GrHWSyCBditm0xu\nL3ExcIDtJ99AUSSOJnG7fWLZKwyZL0Hng2yNKVxwde5xbSIN/FqlhGzKBVzCUQXMQEt7aef3zAIM\nDzuMj3s88kiAAweW3gNslux0Xaerq4uuri5griVcvUpl0zTnvMd6SCW+GYucapHP5xfIfC4FTdP4\nzGc+w2OPPYbneXz4wx9m//79fOpTn+Lw4cM8+eSTq5n2TYGCRoyboN6yiTnYcAQ7H2stDLEYPM9j\ndHSUbDY7x+5uPlYbwVagKgPMxN/Ftm2jIBZGexVz9ly2XOBhBOoTwQKzBFEiJkukRHMktZxpgCYE\ntylBzvT7XArsYduro+CCr0KyzWeH/DJXCu/gnr0KV1/xOKEbvL29OQWt/GyLTyhcQg23rsVnfs+s\nlJJCwefZZ4sMD5fTxm1tCwmmFdFkrSUclKPFXC5HKpVidHSUYrFYFVlob2/H87x1iWBX44qzUjRK\n7LlcrimCBTh+/DjHjx+f89inP/3puse+8MILTY19c7BZRbwe2PAEux62VKlUipGREQYHB7njjjuW\nnEOrbgCEEDilblR5Dx7/BOJGWtx1XOLxOIFAgMGh5UQjxILCpgAJTDmIJZpLtS8X6G/TDa6USuS2\ndHLl/q30/eAq0enIRwAAIABJREFUiuOjKD6+nMK/7VsELxzn9l7BqUw5ig2v4BdbyPuQtwgESwQi\nsy0+ldT1Cn8e9XpmAc6fd/i7v3N5+GGTQ4f0BdFkq3+PiqIQi8WIxWJs27YNKeUckYWZmRksy2LL\nli1VD9a1ji7XK0XcaPXyrahF7OBzncJ6T+OWw4Yj2PX0efR9n3PnzpFMJrnnnnuW7K2roFURbAWC\nHSjyA/h8FYlHLpcllUwtanO34PVC1A08y6niLQ2nihuxvRMI9mhBfua5WG09XD/m0/3ja+ieBCSH\n8v/C/330IQ4pES68ACeyBm9bZi92KZSKklKxhG6UW3ymxeo/9/k9swC2Lfn3fy8yMuJw/LhJV1d5\n0b8Z+6HzRRbeeOMNBgcHKZVKXL16dU6lcqWFqNUtNetFsK7rNlzkFIvFbsKM3jzQUGmnY72nccth\nwxHszcL8xTKTyTA8PExfXx9Hjx5teCFdixS2wm4c+31MJZ5CUVjS5m4+yvy6kBhVYdPul0gqRsOt\nO/Mj2HLquVz4VPl8ulWdTs9gBsh5g3hHPfpfnUFzXCKFHIdKX+f7D/4yQ3mD0Z9r3L3CKLYWji1x\npmyEaxFRLXS1A8dbOSHM75n1Z43vr1xx+dzn8rz97QHe9jZj3ezqQqEQnZ2d9Pf3A+VCwFQqxfT0\nNGNjYwghVlWpPB9v9gjWsqxli8M2GspaxJtVxDcbtwzBtjJ6qESdFdGJsbExpqenueuuu5pOPbU6\ngoWKiEWaPXs/QEfXD0C0ZmHXRQJTDlAUy98QzI1g5SzpCKT0AVklXyEEd6gmP/YchBYkle1BHFPo\n/fEMquPynsvf4j+2vJcrvyhRVZUTJ1cXxdbCcyGf9giqOWIdGpYIYHsrX4Tm98wCuK7k+9+3OH3a\n4R3v8BdVgVor1PvdG4ZBT09P1UzCdd0Flcq1msrNktF6CU00c42vxw3AesLBZxJrvadxy2HDEWy9\nC2w1vab1UBmvUCgwPDxMd3c3R48eXdFF20qClVIyMjJCsVisetv6MobPt1tGshE5hSO6cVmOZMvF\nUlL61chNVRVAmX38xn9hIeiTBpMRm9Sr/QSPFrn+Duj5QYJAscR741/j6/0fxH+vy6lelf2vSmJK\n64jK9yEzM9vi067hagGKK+ylnd8zW0E87vGVr7js3q2yY4dE128O0TYSTWqaNsf0vLZS+dq1a9i2\nTSQSqRJuMBhcksjWq4q4EXJdj7alNwM0VDpoW+9p3HLYcARbDxVxiFaqOY2Pj5NIJDhw4EDTFYm1\naFWKOJVKkc/n2bZt25x2IIV7QTr44jurfg8ARTjE/AIJxWC5CiHfl7PkKua06wghFhT/7BVBJj0L\nxfCYGdlJ332nuP5AJ50vZXho/Hm+t+U4GDGKe12+2aPwiy/5RHKtXSilhGxytsUnpkJgZXZ59Xpm\nK+f5xhsKuVyOJ54Icttta3/5rYRQlqpUPnfu3JxK5YoZfe17rFeKuJk+8FuNZOVmFfG6YEMS7IJW\nkxYK9OdyOdLpNKZpcuzYsVUvJKuVXvR9n/Pnz5NIJAiFQgwNDS1YPBSOgO/iK//fquZagS5ShPx+\nCkr9yFvKcptG/Pp1jECAYDBIMGgueYOTT6fpcl2KA4LchEni3A667zhP4qEYbS/lOT76Vb66/XcI\nByHZBt99Hxx5CQYmWnJKC+eT8YCyXZ4SDJBdgV3e/J7Zym8ylfL5ylfy3H23wXveYxIMrt1i3wqy\nq1epXCgUSKVSXLp0qWoHV9sa9GZNwbbSDP6tBAefCVqztbKJxrEhCXY+NE1bdZQopeTixYtcu3aN\n9vZ2hoaGWrKIVOz0VoJcLsfJkyfp7u7myJEjvPzyy4venSu8bTaSfWm1UwYgQhxb9uLO2Y+9kfYt\nL7ZtlEolirOSjJ7rzRKuiWkG0XVt1nd2imAoxMHuPtJFm9wE2NMdpNt6aeu/TvKhKAde/in/PvV+\nMqFOgqZPShW8/C6fPSfgztcFyhpFJPkauzwjYpB1dGQTRFvbM1u4VKCWpE+csBkbc3n0UZM771wb\nY/m1iNaEEITDYcLhcFUysFgskk6nuXbtGslkkpGRETo6Oqqayq3KHi2GRm8kcrlcQ9X9Gw0aKl3c\nWpXTbwbcEgS72jRsoVDg5MmTtLW1cezYMc6cOdOyyt+V7MFKKbl8+TJXrlxh//79tLW1zRlrsYVG\nkQ8BNr54ZbXTRgiPmMyTEAEq8k/lQqbalLDANM1ZaciyH2qpZGNZRaanp3DssidnrC1WLQ67s01n\nUpH4viA7NogRyROM5kgca+M9Y8/y/5Y+TMgSJIWP1+Vz5l5IbpEcedFDcwSKIqitUm4Vau3ygrN2\neV6DLj6VntlQl40/lZjzXC7n89//e4HhYZ3HHjOJRlsf+d2MdGg5SxGkr6+PQqHA3r17yeVyzMzM\nMDY2BlCtVG5vb191pfJ8rJUO8UZBuYr4zZlV2MjYkAQ7P0W8UoH+WiLbt28f7e3tQGtba5ody7Is\nTp48STgc5tixY3MWlUbIWpGPIHGR4rUVz7kCXaQJe/3klBvpz6UWcyEEphnAMHRs20YxVWKxaNmQ\nYXoa13UxAgbH9kZ4dTSC4ygkT+9Ev+cUquHRf/sY1nQBfcYkmFXJX4GOPo+prfDCkyrHvucTSwH4\nc6qUW0m4tXZ5be06ea9xu7y8G0BGO4hE1WrPbAVnzzpcuuTyrneZ3Huv/pbeI/R9H9M0CYVCdSuV\nL1++jOd5xGKxKunW6nOvBGulQ7yRsCn2f/OxIQl2PlaSIi4Wi5w8ebLqylN78baSYJuJYCcnJzl/\n/jx79+6tVnyuZCxFPoaPixQnmp5vLaSEIJMUvV5cxVtUd7gWlmUxPT1NW1s70Wh5oQsGg9BeHs+2\nSxiGxZFdk7xyNkYhZzA9upWevWO40iCkZ4nv0AnkfJwxBfuaghHzKbbDS7+gcO+PBFsvlMeqpKuX\nItyVEpnrSNJTNXZ5GMu3+EjwpFq3Z7b82Ui+/e2yQMUTTwTp7HxrRhz10tL1KpUrZvQV+8ZKpXJb\nWxuhUKip72Yzgl0aNpLLNG/asYnV4ZYg2GaKnKSUTExMcOnSJfbu3UtnZ2fd8W5mBOs4DiMjIwAc\nPXp00fRaowQrECjyOD4OUpyqe8xSQv1QWyEMbWRIsPS+lpTlSudioUBvb2/dcxCi7CZTLpiBrUPw\n4mtwZSpG8mo3WncO3SsiXbAiEg54JBPQEVfQCuB3+rz6oCTZJbj7pwJldr+zTLQLCdf3/epjKyXa\nWru8WLuGvYRdnkTOugzV75mt4OJFl89+NseDDwY4etSYTXu/tbDc56mqajVdDOXvplKpPDY2RqFQ\nIBQKzTGjX641aJNgF4eGSg+33nmvNzYkwda7e26kkMiyLIaHhwkGgxw9enTRwoybGcFWPGR37txZ\nVeJZ6Vi1ECgo8snZSHZ03nOLW83VElVln1UjT0RGyC3Sa+u6LlPxKQJmgP6B/obJTNfgPUfg56MK\nwxe2EghfxfA9DNujpAgQArcDEm0SMy0JX1fRTcnYHkmqE+5/EQK2mG0Lono+Ukosq0QymaSjo2O2\nV7fm7EXzKWXfh8xydnnzPtLFemYBHEfyve9ZjIw4vO99QXp7N3Z6TwhBNBolGo2ydevWaqVyOp3m\n8uXL5HI5DMOYY0ZfW2uwSbDLY3MP9uZjQxLsfCwXwUopuXbtGuPj49xxxx1Va7ClxiuVSkse08zc\n6pGi53mcPXuWfD6/wEN2MQghmiqYEqgo8pfx+WekGK99om4EWyZXv+a5GwcEiVPy+3GUuZ9zPp8n\nmUiypWtLQ1rIC88J7tvj0xEVfHd4J3K7SkzkmfHawfBBgIek2AFWm0tgRhK8Ikh0wHePK9z/fehM\n1PbcQiaTJZfL0ddXjqTnR7irIdw5dnkxBWEGyDk1frvzbloW65mtYHLS4/Ofz3PsmMGDDwZuuhLU\neqG2UrliRm9ZFqlUiuvXrzM6OoqqqtU93GZ0iFfTt/5WhY3k0rLiMJtoNW4Zgl0s4iyVSoyMjKDr\nOseOHWuonaDVEez8sTKZDCdPnmRwcJC9e/c2Jf/WbEWyQEORv4rPPyLFpfJjYm4EOze9Wj/9J5DE\nZJoZGQFRriiemZnG83z6B/pX3Xu4o1/yeEDja6/vILjtMtLZgtA8FEUgZwlQKFDqkdhbJMa0jzFd\n4rtvhztf9bn9oo6mqUxNTaNpGgM1kXS9CLdyzuXWI5/Kx1pLtst9L/mMD5kiwXAJPWKQw1+0lbae\nz2wFvi/50Y9KnDnjcvy4ybZtt8RluwCmadLX10dfXx9Q1lROp9MkEgmmp6fxfR/P86r7uPUs827V\nCFZDoZf6lpmbWDtsyCu1Xoq4XgRbKRq6/fbbq9WOjWCtUsRSSsbGxpiamuLuu+++abrGAh1F/ho+\nX0GKqwhuVGHfIFe/WiC0GFSRJyrDTNs201NTRGNRotFYQ8VPjaCrHR67M8T3J9sxYxYl2wDTQ1HK\nKVpVzH73msDuU3C7VLwZyan7POLtRbZ9O4GpG5imiW3bGEag7twaIdzKZ9NIwVQx71PMW+hOFiOi\noxC9YZdXg3o+s7VIJDy+9KUC996r8+53mwQCt0Y0uxgMw6C7u5vu7m7C4XDVfL62Unm+GX0+n6/2\n7jaC5557jo997GN4nsdHPvIRPvnJT855/i//8i956qmn0DSN7u5uPve5z7F9+/ZWn2oLsCn2vx7Y\nkAQ7H/MJ0bZtRkZGUBSFI0eONG0OvRZFToVCgTfeeIPOzs5V6Rqv1LFFEECRv4HPlxHiGlLKOYVM\ny5FrGRI7fY5kKkz3YPcamG4LggY8MtTDt9IJLrsqmuuDJstp7Xkz9DVBsVdg2VAwTfwtWzn6Yx+S\nJdLpDKVSCU3TCAaDmKZJIGDUj87rEC5Q7fttlHBLRYltOXRuyRFt18l5ATx/7jGL+cxWn5eSn/3M\n5vx5l8ceM7n99sX7SdfLvWc94Hkeuq7T2dlZLUz0fb9aqXzmzBk+8YlP4Hke99xzD8eOHVs2O+R5\nHh/96Ef5zne+w9DQEEeOHOHJJ59k37591WPuu+8+Xn31VUKhEH/zN3/DH/zBH/CP//iPa36+zaIE\nXGzQBWsTrcMtQbC1EWw8Hmd0dJTdu3fT29u7ovFaSbBCCCzL4uc///mcXtuVYLXGAYIgivwgnnsB\n3/cRQlmw17oYfM8jPhVH13Xu3NpNSg00bGvXKCqjqYrg0Y52XkhKJi6r0Gmj6ODio2gSqTJLuBLp\nS9DB6tcZ74JUr8ID39PoS5X34RzHxbIsMpnmCBdAVRcSbiXaLz8OtaIbFTi2JB230TSbtnaDAsYC\nu7x6PrO1yGR8/vmfC+zbp/Pooybh8MIbsvXQ3F0vHWLP8xbc0CmKUvW83b59O9/4xjf4+Mc/jqqq\n/Mmf/AljY2P8+Mc/XnRb6JVXXmH37t3s3LkTgA9+8IN87Wtfm0Ow73rXu6r/vv/++3n66afX4OxW\nDx1Bn1xdr/EmmseGJNj5i4qqqjiOw4kTJ/B9f0VR6/zxWkGwpVKJ4eFhPM9bsmq5UbTEmUeGyGd+\ngUn5r0RjeYLB4OxntfhCXSwWmJmZobOzk1AoDJSI+j4Zde0WdxOVxztgwhO88qqOvcXD8kB1PYSQ\n+IrEVhzUoAoBgdQlUpekDfi390ju/iHcdgVimkY0Gqn25NYSrm3bqKqKaZoEgyaBQKAhwgXmZAAq\nqeVKpWuZgAWuK0hP2yiKTaxDoyQClGp6aRfzma3FyIjD+LjLe95jcvfdc3/TUsqbTnbrpUPcSBWx\npmkoisJv/dZv8fa3v33ZMScmJti6dWv176GhIV5++eVFj//sZz/LE0880fikbyoEvr9ZRXyzsSEJ\ndj5SqRTpdJpt27Yt2+rSCFpBsNevX+fcuXPs2bMHy7JaotW6GoItE4KP7/vcvvs+SvYglvt5Uqkp\nbNtG13WCZhAzaFYJV0pJMpHAtm36+/tR1RvnYIopSv4gJaWVze0LyW2wS/L4O+C7JwVSk9i+ArZd\nVhMyDIQrIHvjeNUHbMkZVeVkxEB1fUxXEnYkYd8j4kkiWpC29hg9mo/iuxSLFtlsjunpGRRFqUa4\nplmfcKFMujciXEin0xSLRXp6umv2cpl9XpCedlAUl1i7hjPPLm+pnlmAYlHyjW8UGR4uC1S0t5cX\n0nIW4uZHsOshpt/o+65VkdPTTz/Nq6++yosvvtjysVuBkoQx59bes18PbGiCdV23qhITCoVaQq6w\nOoJ1XZdTp07hum41kh4dHV3+hQ1gpQRbIddK9KEoCqFgP0F+D6l+CUkOx3GwiuU2iUpk5zgOkXCE\n3r6+ugt5lBls2Y4UrTWUn49gSPDrR+Ebwz6XSi6CcgFMlY8lqFKCDZ4zq+RklNnN0xTyGuRNqFwO\nwpcIBxQbAq6PqUhMUxIxPcLCpeBaGNksYnoGVVVmI9wggUBggSiE7/vE41Nomsbg4ED1c6pEuBWD\nhHKEC6kZDyFsIm3anBafpXpmKxgfd3nqqRwPPRTgyBFjXSLY9UwRNyqV2GibzuDgIJcvX67+feXK\nlboFUs8//zx/+qd/yosvvti0Of3Ngi4VBp0359xuFkbW4T03JMEKIaoCDTt27GBgYIAf/ehHLRt/\npQSbSCQ4depUdU6tji5WQrCe51VfoyjKnDkJOsH7IKhfRteL6LpBNBYlm8mSSqeIRCK4jsvExBUM\n3cAMmgTNILqhU5aysIj5Lmm1NQuuXELzd6ZY4q6uq4TNAcaSOtKVZWL1Z4nVnffaJbTmpSKQAfAD\n4KKSL785iqOhOAY4IaTeiRKAgO9j+A5G2sJwCkSEy5aAQndYR1UE09PTs0pEc5WuaiPc8vBzTeiz\nKQdwCEYUtLBJztHxUJbsmQWwbcnzz1ucOuXy7ne33vRgOWwkgj1y5Aijo6OMj48zODjIM888w5e/\n/OU5x/zsZz/jd3/3d3nuueea6kS46ZDgeZsp4puNDUmwpVKJixcvNizQ0CzmmwksB9/3GR0dJZPJ\ncPDgwRUJLjSCZrxla1PCSwkpCLrB+w1Qv4LnF5iemkYogqHBIUR1IZU4tkPRKpJIJnAcB0M3yqnU\noE2AHZTU1vjx1juPZCpJqVRiZ+82+rZLuiZgeEziFMCxBbKOKJVUyhbUslF1GyHwDfCNGwN5gOsK\nio6KcExw2vFciev4+FkPzbUJKYNEC9BmK2wxFdo0n3bNx1DmD7/QhF5KiZWXyFwBM6igRw1yjk7a\nChBoVwmUcgt6ZiuYmHD54hddtm412L9fziHztcR6pYjXQslJ0zQ+85nP8Nhjj+F5Hh/+8IfZv38/\nn/rUpzh8+DBPPvkkv//7v08ul+PXfu3XANi2bRvPPvvsqs5lLVDyYay0SbBLQQjxOPBXgAo8JaX8\nr4sc9yvAvwBHpJSvLjXmhiTYQCDAwYMH12z8ZqKCbDbLyZMn6e/v5/Dhw4u+thUVn4qi4DjL73lK\nWS64qbzncu8r6COdeIxU7m/p7IwQXrBACXTDQDcMYrE2qoRbLJJIJLBKCXKBAQJhY9YHVl9hb+zc\nF7muS3wqTjAYoq+vnyIQ8x32DPrsGSwf60uJZZX3KYsWlEqSUglKtsT1IF9UsW2J7QgcR+J44Drg\nS25IWVWqgKutQIJqMK0KpArSLFuCSQR+yUdxQCOM7UjitmTC8fFzZSdZRVEwFUFYgYgiiQifqOrT\npnrENI+wspBwHVtiT9voho0Z1ck5BiURJdKRJzebMp7/Pbqu5Gc/07CsPO97n8ng4Npf7m/mIico\n63o3k8Y9fvw4x48fn/PYpz/96eq/n3/++cYnuY4wUNjut7pt7q2FC0s8J4RQgb8GHgWuAD8RQjwr\npRyZd1wU+BiweLVbDTYkwS5W6Xkz01dSSi5cuMDk5CQHDhxYMi1VkUtc7Z3/cini2qi1cvxy8H2f\n8fFxkskC++/6XwmEnkWyXDRaQ7htZcLNWEGuOy6JxAyu46IbxqyHqImmNUa4tUbn+UKBRCJBV1cX\nwZosRcnVEIZTbRFShCAUhFBw4RuEhzQymYWXgETiOGVStooSy5JYFWIuSUolWSZlu5ySdZzK/30c\nx0FRVTRNxRcewgAlIAgAigvC9pG2xLV9Eq5kBgVFVVBVFUUttwWpUhJEEsQnLDwiQhL1XToMiAlB\nIemhqUXC7TpZN0Koy8BOZ3Fsf86+q5wN3aenPb74xQKHDuk8/LCJYaxdNPtmTxG/lW0AVwUJbKaI\nl8JR4JyUcgxACPEM8Ess3Lr9P4E/A36/kUE3JMHC4p6wrRc/WIhiscgbb7xRNWhfbsGppHbXkmDn\nFzI1stAUi0WGh4fp6Ojg4MGDZSEL7wOgfhXZlK6pIGbmEYFBim3OrC2dTbFYZGamTLhGoBzdBoNB\ndH3xn6WUkplEOQ090L9QgrHkC9o9hay6/PyEVj/NLxAYOhi6oC3W2Bnm83lmZlJEo114vl4mZesG\nIVfI2bbFLBmrOI7ELfh4BRe/VMLJ+0hPgFApCgXNEih58PPgWwZIMHRJNOjTFvJpi5TY0lbitl0G\ngc4OgqU82ZRd/d1Xer8r6eZXX7UZHXV5/PEgu3atzaW/XgTbyPuu1jnprQxbwoXirXfe89AlhKhN\n6f6tlPJvZ/89CFyuee4KcKz2xUKIg8BWKeU3hRC3NsHOx1oQ7PyLVUrJ1atXuXDhAvv27aOjo6Oh\ncVrSv7rEOEsVMi2GyclJxsfHufPOO+eIXwh2gv9LoPwrkubmHBZxSrILX3gEAgaBgAG0VX1gi8Vi\njfF6gGDQnE0pl3+mtu1w9dpVwuEIW7ZsWbQzN+OqGIqPs4i7T/VcWvDrLxN++SZhcKgfVWnuJklS\njoQr0XIp71G6WiR7skTOdrENkKqBG9JxXBXHUUkXFGayEiZBUUF93SdklGiPaXS2CUzFxtQKeF6K\nnp4tNbKXkmTS55lnchw4oPPoo0FCodaS4XoR7CaWhgHsWO9JrDMuwrSU8vBKXiuEUIC/BP6nZl53\nyxBsK9WX4EaEXCEr27YZHh5uyjSg1XObT7CNFjLVwnVdzpw5g+d5HD58uL5vq9wD/i+A8vWm1JoU\nHGLSIok2t1pZ3PCBLRuvS0olG8u6QbiKqpAsZGhv7yAcWlq03JcCw9NwtKX3o6W6OqUp13WJx+OE\nQqFZwm8+QhAIAgaYBkSDkNd0vKhB+A5J/lx5W8OyLIpWkZKVRVU8ouEAwYAJfpBiQSGTleSyVNPY\nUxmYinvgDhK5AGET2kI+7VGfLVsEwaDkjTdszp93eOSRAPv3l1PTrSDG9dqDbeS3XdafvkX3ISVs\nmuksiQlga83fQ7OPVRAFDgAvzP7W+oBnhRBPLlXotGEJdrEUcatQIUVFUZiamuLs2bNNmwZUsBYR\nbLOFTFB28RkZGakKciz1GiH3ge+C8u2mSNYgSVj2UxCLX+1CCEwzgGkG8GNlVx7HcYjF2shMp0km\nEwQCgXJK2TTr3sxkXEFEUSgqi3+u3ip+/YVZ9aryHvDqqsIjnsTJQsa+QUx5BOEtPvkZhVAoRGj2\npsKXZcJN5iyK1hRSSsywyUB3gGjIIJWYImAYdHb0cuZMntd+muZSwkFJqQgUVCQBVRIOlFPNP/95\ngXvuMfjQh0J0d5dvpipkuxKiXK8q4kaq+nO5HOFweNnjNiJsHy7k13sWb2r8BLhdCHEbZWL9IPCb\nlSellGmg6mMqhHgB+D9uySriemh1BKuqKrZtc/bsWUqlEocPH15xk3mrI9j5KeHlIKXk4sWLxONx\n7rrrroYXISHvBt9BKt9pap4hrlOSfXhi6Rse23aYmooTiUTp6uqm6G+hX+ufjXBLFC2LqekpPM+7\nQbjBIJpaFiP2XRWMsohDPbhK8xGsRJJKpihaRfr7+9HUlV9CppRoecgVFqks7xAwM7fHSBEKoWCI\nUPAG4ZasErl8juvXp1FVlVBIwU4V6dse5hd2Rpm4nOeN12dIJQp4PhSkIFcQTOZVBJKfXyjw9Ney\n7N8T4MBelV07VPbdGWD3bqO6pdAo4fq+3xJVsmbQ6L7qrWpVB7Mp4ls8c39xieeklK4Q4j8D/0a5\nTedzUsphIcSngVellCvqvbplCLbVEaznebz22mvcdtttDA4OrqpwolURrBCCYrFIsVjENM2G5lTR\nQ45EIhw+fLjpqEXIQyi+g6+80PBrFHxifp6EUt8uDiCbzZFOp+ju7pndq71RRVyOcM3ZHuf2GsIt\nEo9n8X2PQKCsH9wX0SguYuvmCBDCR8rGztnzPOLxOIFAoBzhryAlDGVVqUgJMlmwlhDPKCCI9kqy\n1xcfSxEKnu9RskoMDg6i6Rolq/xZZNIZfN8nEAzwwMOdTE3ByHCGZGq2rQeJkD5IiS8VTow7nI8r\ndJwQqF8vEdQKDPTAjiHBru0qB/abDA7qVbKt91tZjz3YRtPSzYhMbEhspoiXhJTyW8C35j32qUWO\nfbiRMTcswdYT/G9FlOj7PufPnyeXy3HgwIGWqLe0Ym4VN5H29naGh4dxXZf29nY6Ozvp6Oiou5c6\nNTVV1UPesmXLit9byPsRvoNUftDwa3SRJiIHyIuF5uLT09NIKRkYGGho4awl3I7ZPVyrVMKyioxN\npJG6hR4syxkGzSBqVVlKoutg28vP15qNljs7OwmHVpZmFEgijqSYEaS9xsjZjoKIy7oqVlJKErMV\n1f0DNwqsyu1PQeiofBYWVtHCNIvcc69PMmFyfswhm3URQgUxm9JVIWP75KZseroNFF3lYhIuJuCF\nn0nkV/OEDclQP+zcqnD7To39dwbo6jKq39N6EWwjUfOtnCLe3INdH2xYgp0PTdNWTWK5XI6TJ0/S\n09NDb29vXdJaCVol0q+qKrt27QLKi046nSaRSHDx4kWklFXCjUajjI+PUywWOXToUEsKPxT5IL7v\nIJVXGn5NiElKfj/urCGAbdtMxaeIxmJEo9E60W1jpCSEIGia5f7Y9g5iisO0X6BYLJLJlKO6KiEL\nhXICrT7A8vVlAAAgAElEQVQkkkw6Qy6fo6+vD11b2Xde2WfN2s2RTwlBrE+SuTb3cc/zuB6/TtAM\n0tvXu2g0Xf4syjcWHXQgpaS7q8TWbUUuXMgxOmqRy/nViFQIgS8lk/ESQVMrk6dKOWmmQhpJbtLn\n7KTPcy874Nt0RiX93YLd2zUiZpFjR8JNbVGsFo1GsLdyitj24EJ2+eM20VrcMgRb2TNdCaSUXLp0\niYmJCQ4cOEAsFqtW2rZqbisZa6lCJlVV55hPu65LKpVicnKSEydOYBgGvb295HI52traWlKYosh3\n40sHKX7W0PECn6jMkpAm2WyWbCZDd8/iRu1yhSnZrK/RFghWBSn8Skq5WCQ+PUEmo2MGzFlpR7Ma\nCfq+z9TUFKqqMtC/Mu1oU0rUJfZZG0ExAooi8WfN2a2SxfTU9Kw94NIV1fMxJ9q/t4N775GMj6d5\n/USKRMKqClUoikKhKLlyxWXLFpNQuPz7kghcOcu2ovy/yQJcuyB5+UwBzwny5X+T9HRMsW1AsGu7\nxr7bA9y5J4Cqrqxwajk046Rzq6aIDQE7bpnVvj6W2oNdK9wyH/lKI1jLsjh58iSRSIRjx45VL+RW\nFk01G8GuRJFJVVUsyyKfz3PkyBFM0ySZTFYN6DVNo6Ojg87OTmKx2IoXQuG/FxQPKU40dLwmU2Qn\nFCxD0D8wsMCNphWQCFRXA70cKSs1EW5UV0lMlT8bq2iRSqeQUqLrOiWrRHtHO7Fog2oTNVCRRKzy\nPutSJgWNwJGC2IBP5oogk82QzWRblkERQrBzZzs7d7YzMVHk9RNJpqYK+H5ZlUpKybVJBzOg0dMb\nxNAF81MLUkoK+TxCUQhFOigJweWk5HISXjopAYuAWqC/G3b3e/ze/9iBaWotbQ1aT6u6tww2U8Q3\nHRuWYOvtwTZb5HTt2jXGxsbYu3fvgj3K9SLYlSgy2bbNyMgIgUCAw4cPVxejnp6e6h5yqVQimUxy\n9epVTp8+jWEY1f3bWCzWcPQmEOA/AYqDFKeWnlepxNTUFD1t7XjRO3CW845dBVHlPIWYqpCf37aj\nLazMzWQypNIpgsHgbGSdJWAGqj6wS4lJCCRRR1JoYp+1ofmbMJO8jucI+gf6UUTrI8HBwSCDg0Gu\nXi3y+okU09NFAKT0KdkeFy+miURUolEVXdfQZ/c98/k8RqWPuQJxoyBNIBFuiaunc1x41WHvdoX3\nvrcswuJ5XjX7slLCbcZJ51YlWNuDC+n1nsWthw1LsPPRTATrOA4jIyMIITh69GjdSKGVBKuqKqVS\nfY/PWqxEkSmRSHDmzBl27dq1ZEFWIBCgr6+Pvr4+oBy5JxIJrly5QiaTIRgMVgk3Eoks3SOLAP8X\nQXGRop7XrSSTyZDL5ujp7UHXDTyZYkZGYBn1pdVgvk4xgF8jNuFLn5npGXzpMzQ0VCWxWrGHVDIF\nUE6zztrzVUgh7Pt4GTGnn7UVcF2X6/Hr9G0LomQ7Vly93CgGBoIMDAS5ds2ajWiLaJoCaNg2pJLQ\n3i6xS3ncmgIjz/NQFaVKrgo+hlPASeYplcq/WyEUXnwxz5NP9ldvFCs3jZUxgDntQcuhmQj2TW0p\nt4YwBOy4RTU2KthMEa8hGo1gp6enq4RUIZvFxmvEuaYRNCPS36hohO/7jI2NkU6nue+++5q27TNN\nk4GBAQYGBpBSVp1xLly4UK3GrKSUQ6HQgjkJlFlJxX9Bigs18/KYmppCUzX6B/oRsySmijxhGSG/\nxKmtlnrr6RRXemEdxyEejxONRonGonNITFHmiT3MI1zD84h4JgURwTTNlqa5i8Ui0zPTdHV1oZsm\nig3u8vdiLUF/v0l/fz+Tkxavv54iPlUAwPPhetwmaEJvbwyQuK5LsVjE9zx0BUzPwss6WCjML047\ndcpietqeU30MLCDc2v7WpQh3M4JtAJtVxOuCDUuw8xf85SJYz/M4e/Ys+Xy+IR/Zyp5mK7BUNLwS\nRaZCocDw8DBdXV0cPHhw1eLmQogqwQwNDSGlJJ/Pk0wmOXfuHIVCgWg0WiXcit+tQAP/V0D5Z6S4\nRGm2OKe9o51weOFCFyaO7Q8snypeBebrFLsK1XPp7u5uSCykQriRUJCoBam0T6FoYRWLJJNJEOUb\nlEpKeSXpXIkknU6Tz+fp7+tH0zR8INovSV9oerhVoa/PpK+vj+vXLX7+epKrVzNICY6jc23Spqfb\nJBBQCQcUlHwOayaP7bi4rlslP03T0DQdVVXwPMG3vz3Nb//2wJz3md9bW7mprK03qBfhbhLs8rA9\nuJBc71ncetiwBDsfS0Ww6XSa4eFhhoaG2Lt3b0OEtNZ7sCspZILyvvHFixe58847aWtra8n85kMI\nQSQSIRKJsHXrVqSU5HI5EokEp0+fxrIsYrFYNaVsBH6FmdRfU7In6O3rRVu01UUSI8GMjC2SKl59\nZOhLgeFqOHq5gCeeTGDnmdNHuhwEkpgjyc/uswqhEg6Fq/2xnu9hWRaFWUu9SquMGTQbIlxflquX\nFUVZUL2c0cAISexVVCWvFF1dOnfdJdm1cwtj4y7x6wU8TxK/nCAibWKGRFEEiqIRCGiU71Uknufj\nug6WVZytHVD55jdt3v/+6JLbDY0SbqlUwjTNZXtwb/kq4uaSWBsOmyniNUQ9Qqx4nU5PT3PPPfc0\n1YTe6j3Y2rFWUsjkui6nT58G4PDhwzdVrk4IUU6tRqNs374d3/fJZrMkEglOnjxJJpMhFD7EHfsU\nFLG0IKpKkYgf5f9v78zDo6rv/f86s2Uj64SQQGQLOwGEsFnrhl4XaqktLcVWKFVvrUrRSq21/Gxx\nqVr1alWsFsWicAGBakXLIohwa1UUQUjCkkAgJIFsM8lkm/Wc7++PcA6TkGWSzGQ9r+fxaZMMM9+Z\nTOZzPtv7XdNErGvvmk5jqmSJcGTyy+uF+gdfkojLHVhwjZIV5GoJRwt9VqOhiYDrvDjgRkREEBYe\n1iDger1eSkpLiI2JbTIYCCTCkhU8eZ0bYN0eN2Wl9UIbA1MiGTlSUFtWxb5d+RQVVuMQUG2QSEwM\nIyJC4sLFkITRaMRoNDYIuMXFPvbsySUx0R1wf7+pgFtRUUFZWRljx45FlmWt2mM0Gi8qKetTxF19\ngL5Hrw2wF/UEG31dW1tLVlYWVquVadOmtXl6MVQZbHsGmRwOB0ePHmXIkCGkpKQE5UwdwWAwEBsb\ni6IoFBcXM378eCwWCxVliTgcGzCYKhqWUBtljpFSKR4lFY+hfXvLrVHndFFWXkp8ajwREeGYfQJX\nK33NMCEw10JNXdvLvUaDkaioKO0CTpZlbWXKZrdhkAyER4QjIVFTW0NS/6QWS9XVBomIaIGzunOC\nbE1tDZUVlSQNSCIyzEQUddSVVhPpkrnm24mUjY7h0KFKiotrKS11ERlpwmq10PSf1IWAe+JELDff\nnKr19/Pz86muriYiIoL4+Hji4+PPC440/TxLSkooKChgypQpWgartlT8/57UgNuXpRI9Pjht6+pT\n9D16bYBtDiEEBQUFFBYWMn78+HaXUYMdYNWr77YMMgkhOH36NOXl5UycOLHNogOhQgjBqVOnsNvt\nDQas4uPjEfwWWVqLy30Op9NvKjfiQsCVJAPRlGMT8dDAdadjAUUAlZUVOJ0ukgZegtUiqEHG0MJf\ngQFB9Pl9VncH91lVjMaGAdcn+ygvL8ftcmMwGrDZbBdej7CmNKUljEkCQqzMIxBUVFTgdrsZekkS\nkbKbmrM2HL6G5fv+/S1cd10S5eUeDh2q5Ny5Wk2gIirKP5ttyOefV7Nkibiov+8838s+c+YM1dXV\n9aIYjQKuKlc6ZcoUrVqjXiSr/Vj/gFtQUMBXX33VJ83WASwGGNo9Ph66DL1EHGIUReHAgQNEREQ0\nEI1oD8EuEdfU1FBWVkZ8fHxA5V2Xy0V2djaxsbFkZGR0G5Nr1TwgJiaGKVOmXHQuiWiM4idERvwv\nERFVQP1k8UU9y4gIzBYT7qiIoEzlyrJMaVkZYWEWUpKTkSSJWp/AYFSQTBf3e0O1z3rRuc5PVavK\nWhISsizjdDqpqanBVm7DYDRc6OGeD7g1kkRknEJdZWh+74qiUFpWSkyYYHB8BLWFNqpasYRLTLRw\n7bVJ2Gz1gfbs2Vqqq4307x9GU39qDofCf/7j4Mor47Xv+Q/UDRo0CEALuOrKmMfjITIykpEjR7b4\nvld/dvDgQRYvXsw///lPLrnkkmZv36vRp4i7hF4bYBtfqRYXF1NXV8fo0aPp379/h+8/GAFW7bWa\nTCbS0tKw2+3k5eU1kDlsSlWptLSUkydPMnr0aE0KsTtgs9nIyclp1TxAIhbkW8H4vwhqMBiMREZG\nEXm+Z6nIMk6XE5fzNCVl8RBen+G6TdaArcn8cbndlJeXER+f0MCs3SckYn1GhLHhgFmUUt9nrXKH\n9qLF4/FQWlpKXHwc/fymqo1GozZEBvUZrsvpoqbaL+BGRGCICkNURAQ9K/P6PFSX5NPfYMZYY6Gm\npq5N/95qtTBrVhJ2u4dDhxwUFdUQHx9Gv37Gi/Sld+6saBBgm0I1L7BarRw6dIghQ4YQFhZGUVGR\nJoqiZrj+fy9CCP75z3/ywgsv8O677zJixIg2PY/ehMcHp0u7+hR9j14bYKE+yHo8Ho4ePYqiKNoq\nSTDoaIBtPMjUWFXJbrdTVFTE0aNHCQ8PJyEhgbi4OIqKivB6vUET6Q8G/ju3U6ZMCWjVRSIe5Plg\nXIeg4Qe4wWgkKqofUVH9iE+0UCLHUueqpayqiiJ7EUajiYjzIg+WsLBmC8cCqKpyUFNTw4AByZry\nkD9VPgNxUn2A7Uifta3U1NRQWVlJUlJSq79Hk9HUMOD6fDhdTkpqajE4S/E4wjTRi7CwsHYHXIMk\nMLjslBw7TVy/BIxhHXt/JSRYuOaa/tjtsRw65KCkxEliogWT6cL5vvmmjpoaH/36tfxRVFVVRXZ2\ndoOLSnXewOVyNVAhO3HiBJmZmXi9XnJzc9m5c2fQ/u57KhYDDO2b7WcNvUQcZGw2G0ePHmXYsGGk\npKRw4MABfD5fUCZsO+KA09ogk+o3qn6A1NXVcfbsWQ4cOIDRaCQuLo7S0lKsVqu2c9pVqFrNCQkJ\nbd65lUgE+cdgXI+g6Z1iAx4SjArG6H7IEQNIjUjF6/PhcjlxVDlwuz2YzSbNis5ssSBxXqi/vAyD\noWWhfkmREG4D8S4FR7UUtD5rc2gWcz5vwHZ8jTGZTET3iya6XzThSYnUHPXhdLmoqq7CU+7BaDRq\nxgVhltYDrsmgEKk4OXu8kKrKerUjk6nj5g8qaqCtqPBy6FAlVVVeoqPrs1mPB3bssDF37oBm/31p\naSl5eXlMmjSpyTmD8PDwBn8vAwcOZOvWrZw6dQqj0cjcuXP57W9/y4033hi059Qj0UvEnU6vDbBC\nCMrKypgyZYoWhILZN21PltAeRSYh6v1R7XY706ZNIyoqqtmd04SEhICyx2Ch+smOGTOm3RmCxACQ\nfwTGdxA0PTUcho0wOVXTIjabTJjPBxhB/WqLy+WkorICj8eLyVTvnBQbHUtiVCzmWjC6weAR4AbF\nIyG7wOMG2SvhNBoxS/W2qBajwGwAg1HUqzkZQBYCnyLhFfV7tO1Fs5iLiGCAtXmLubbgEhLxg0yY\nSupfDwCvz4vL6aKqqgq3243JZNLWgixhFu1xw00+LO46qotqOFFShiRJpKQkh2wQKD7ezNVX96ey\n0kt2dhVutw+TSfDJJ1VNBlghBPn5+dhsNjIyMgIyN7Db7dxzzz3cdNNNvPvuuxgMBkpKSoKmutZT\n8XjhdHFXn6Lv0WsDrCRJjB07tkGWaTKZ2iz4Hyzao8jk8XjIzs4mMjKSjIwMbSir8c5pVVUVdrud\nzMxMZFluYLQein1YRVE4ceKEpnrV0VK1xCCQfwjGjQia/v1ESyWU0FC60qCA2S3o5zVhdEcjeWKo\nKK3EVlJJP1M/6mpdnPLUEGYJ0zK6pvxcjQZBTa2BcItAhEOtr/Hk64X/bzIILCYwGMBoqJ+0VZCQ\nBbiV5gOwZjFnTdBMBYKFOwakMoFQ1AsQM+Zos7aSogZch8OB2+Mm2iwTpSi46wwYjQZKSkrp16/f\neVOHoB6tSeLizFx+uRWHw8epUzWcPOmmsNBFauoFJQRFUTh69CiSJDF58uSAMv0TJ06waNEi/t//\n+3/84Ac/0L4/YEDz2XFfwWKEoaHRnekx6CXiEBPMDDZQGmetgZYE1YGhESNGtDiUZTAYiIuLIy4u\njuHDhyPLMpWVldjtdk6dOoUkSZqEYTB8X51OJ1lZWfTv35+RI0cGLduRGAzyXDD+o8kga8BLolJC\nv1yB7JTwusHnlXAj4UYV6i9HEQr9E4dgMBiIjqoPgB63p17Tt6wcn+xr4P1qMpowng+gLo+ESRZE\n9VOolZv+PfkUCV+DRLuRa5NBYDGC0XghAFc4HNQ4qhmUnIxkCn7f3CMkYpIVqs42/bswm8xYYkwM\njDfjtddQbavF6fRSW1uL0+kkLCwMIQQejweLxRLSICtJEhaLRESEREqKmcmTI3G7fZSWerQA6/F4\nOHz4MElJSVxyySUBvcc+/fRTfvOb37Bq1SqmTZsWuifQU9GniLuEPhVgOzuDbY8ik5odqjt+bS35\nGo1GrFarNsXr9Xo139ecnBzMZnODCeW2BMiSkhLy8vIYO3YscXFxbTpXIEgMO28Q8B6Ci/vbCeYC\nMhL3s/fo9AbfV4X6+0Wfz8L8gp6ERNh5K7W4uDiEn9l6VXUViqxgjTIh0+98K8GIXAUxMQpVzQTZ\nlpAVCacCeNXyvg0hBImJqUiKAYNXYDaC2Vhvol5/VAkfAq8i4VXqv24rzigJg0mg+BoHfIV+uHCW\n1VDtrH/vm81mXC43sixzySWp9f/e6aSyshKPx4PZbCYiIpzw8IgWA67FIhEebiAysv5/IyIkIiIu\n/G94uERkZP3X4eEXftbS2lVtbS2ZmZmkpaUFNO0vhGD9+vW8/vrr/Otf/+q7aziBoAfYTqdXB9im\nPGGDmcFKktSk/mlTOsKBBLLa2lqys7MZMGBA0LJDs9ncYEJZnbhsbEOXkJBAVFRUk48pyzK5ubm4\n3W6mTp0aFKPv5pDESFBuBsMHDWzlVKYP2c3xc8MprkwE2i7UL0lSvdVceDjxxKMIhTCfkxKbG4fD\ngRCCiPAIamrDSUoMw2kworQj4Pl8PkpKSvxKr/X3oQgJtw/cPmiqDC0hMJuEFoANkoSQzveBhYRH\nadrA3atmsYX1P7MYZcJ9TmrO1uDwXbhYEQLsdhter4+BA1MwGAyYTBIJCeFaIDQavfh8tXg8doRw\nEhsbTlJSDAMGxJGQEEVUlJGICAmjMbiprs1mIzc3l/HjxwekuKQoCn/605/Iyspi165dfValKRA8\nXjh9tqtP0ffo1QG2McHOYNVJYv8A295BprNnz1JQUMC4ceOIiYkJ2hkb4z9xKYTQxB1OnjxJXV0d\n/fr10wJuRESEFvSTk5MZPXp0pyjhSGIcKDIYtl4UZI0GL9+79AP+tmcBNpsDj9fTJqH+xhikemec\nBHF+B9fPii4nt5Jwi4w5NhJTeOR5WcfWs1qn00l5eTn9+/dvs02gQMLjq99bbLoPLDAb6wOw0VAv\nrq/1gaMgIcZHtHCh1LqIiJKIGG/RskyzWaGg4ARWaz9Gj047n10aMJub+p3WZ4/+75GKijPk5dVq\nVoXx8fHNXpS1lYKCAoqLi5kyZUpAPX2n08ndd99NSkoK7733Xqdqb/dELEYY2rc3lfQebKgJpocr\nXAjY6h93ewaZvF4vR48exWg0dolIvyrZp7riqCL9R48epba2FlmWGTJkCMnJoZsubfJsYgIoXoTh\no4t+FmM5zZjYzXxeeQPJ1uQOT+MqQqAGsMber7IiI3vr8DhrOGu3YzAYGugo+78mQtRbzNXV1ZGS\nkhKi36WEVwavDGYDpMQIBsUIBsUoDIoR9JsJEHb+vwvUl16z+Pa3h7fJdLyp94haNVAvylrzBm4J\nRVHIycnB6/UyZcqUgGYESktLWbBgAfPnz+eee+7ps/KHbULvwXYJvTrANuUJ63Q6g3b/asm5vYNM\nlZWV2p5uS+bunYUkScTExBAVFUVdXR1Go5FBgwbhcDg4dOgQiqKEfEK5wXnEFAyKF8XwifY9p7MO\nm83Gf01WqDXOosAeBBlFpfn7MBqMGMOiCZf6MTAJKjzKBRlDmw2DwaAFW4fDgclkIiUlJSQf+vER\n9cE0NVYwMEZhQBTNCOo3pKysjJMnT5Kent5hN5mmrApra2ux2+2aN3C/fv20DLelgOvz+cjMzCQ2\nNjbg6siRI0e48847efLJJ5k9e3aHnktfwuOF04VdfYq+R68OsI0Jdg9WFelvzyDTqVOnqKio4NJL\nL+1ysQh/ampqyM7OZtCgQQwaNAhJkkhMTCQtLQ2fz3fRhLJaTo6NjQ2JHrIkZiApXoTh31RWVOJ0\nOUlJScFoNHHzpPf5296foygdexsHoheiCIkqB8RHGzBGRTVQVaqpqaGkpARJkrQsNiJCHRBqX6C1\nGIVfdlr/X1QbB5BVMwi73R5w6bWt+AfcwYMHa97AFRUV5Obm4nQ6tbZDfHw8ERH10o5Op5PDhw9r\n1ZFA2L17N8uWLWPNmjVMnDgx6M+lN2MxwdDm1Uv7BHqJOMQEswcrhMBgMFBRUUFYWJjmP9kaTqeT\n7Oxs4uPjmxTD7yrUPnBhYSHjxo1rcmDEZDKRmJhIYmL9gJHH46GiooLi4mKOHz+OxWLRAm5LNmNt\nxeeeTn7hUeKsdpKTL2SHsRHF3JT+H/51+KqO3X8brrmqqiX6RQrqDKJ+YMntpqamhpSUFMLCwvB6\nvU1M5NaLPJjN5mZfk4QIwaBYQWpMfXaaFGB22hyyLHPkyBHMZnPAe6TBwN8b2D/g2u12cnJytLWg\nmpoaxowZE1C5WgjBm2++yTvvvMOOHTu6RbWnx6GXiLsESbTikNGINt24q1EUpUHPtbq6mlOnTnX4\n6lctCVdVVVFYWIjD4SA8PByr1driNG5JSQmnTp1izJgxIVlzaS+qWbskSYwZM6bdu7Iulwu73Y7d\nbqe6urp+eOh8wG1rb05F9bpNS0vDOuAgQjrQ4OdCGNm0fxF5ZQPbdWYQ1AtIte1sYWYFW10F1S4P\nSUlJTb5mQggt4DqdTrxeLxaLhdh+YQxPCmeo1URqLAyKEUQGMbl0uVwcPnxYq0J0J86ePUt+fj79\n+/enuroal8tFdHR0gwzXH1mWeeSRRygqKuKtt97qNpaMISQkDeWw2Kki5bL9objrHkP+DulrIcTU\nznzMPpfBBsMBR+27RkdHM27cOABt0jIvL08zdlaDi9ls5tixY8iyHLDkW2dRXV1NdnY2gwcPZuDA\n9gapesLDwxk4cCADBw5sMAyj9ub8X5PWpmtV397i4mImTZpEREQEQvkvMPgQ0mHtdpIkc9PE91m5\n5068cttfV6MB5DZ+psmKzOmCUiLDLYwcPIAapensUJIkwixmUuLMDIqJZlCMQpyxBoOrvN6XttSJ\nxxVDpSceQwCvSSCoff1Q7Sq3FyGE5uE6bdo0rX+vKArV1dVUVFQ0kP785ptvmDRpEk888QTjx49n\nw4YNHRZJ6ctYTDC04yZiPZquKBH3qQzW4/Fw6NChdim9tGX9xn8at6SkhJqaGuLi4rjkkktISEjo\nFisFQggKCws5e/Ys6enpmvl3KB9PlXS02+14vV5iY2MbXISo+Hw+rbw5evTohmtQCIThA4R0pMH9\nHz03k/cP/lebzxVuErjqAg+wbo+bstIybUUFBDExgqrzQdZirC/1+vdOI5qJ+2pwqV+BqcDj8TTQ\nlW5rz7SwsJBz584xYcKEoATrYCHLMtnZ2YSHh7e6362+Jk899RRbt27F4/Fw/fXXc80113Drrbd2\nuNR9++238+GHH5KUlERWVtZFPxdCcN9997F161YiIyNZvXo1U6ZM6dBjtpGQZLDR1qki44a+ncHu\nXa9nsEGlqSni9vRg27p+o/ahVPPw6dOn4/F4sNvtnD59ulOGg1pCXQ0ym81MnTq1UzIDSZKIjY0l\nNjaWYcOGoSiKNjB15swZhBBaibCgoIChQ4dq7igN7gfpvBCFFyHlat8fk/wlx5JHcbx4SJvOZZAu\nrOi0RnVNNQ6Hg6QBSVjMavCTqK6Ga8bJjEhS6B9FwFKDBoPhotfE4XBoQiCyLGsXIfHx8c1WPtqz\n6tJZuN1uDh06FHC52mAwcPr0afbs2cPKlSu54oor+Prrr/nqq6+C8neyaNEiFi9ezMKFC5v8+bZt\n28jNzSU3N5d9+/Zx9913s2/fvg4/blfjccPp0119ir5Hrw6wjTEYDLQlY2+syBTobqvb7SY7O5t+\n/foxdepU7YPBX77Qbrdz7tw5jh8/TlhYGAkJCVit1qAt7jeH2tMcOnRolw6LGAwG7SID6rPWkydP\ncvLkScxmM0VFRTidziZN5yUMoNwChs0I6VT99ySF69M/5LTtTtzewOUljQEKgdjsNnw+HwNTGlrM\nGQ2Cmy+VSR/U8eKOwWDQ1ltUXWmHw4Hdbic/Px8hhLYmFRcXh8lkwuPxkJmZidVq7TQhkEBpysO1\nNbZu3cqTTz7Jxo0bGTNmDAAzZ85k5syZQTnTlVdeyekWIs3777/PwoULkSSJmTNnUllZyblz55q8\n2OtJWMwwtI97HrRWIpYk6UbgRcAIvCGEeLrRzx8A7gR8QBlwuxCixbvtUwG2LbRHkQkuWLiNGjVK\nC6iNMZvNDBgwQHP5cDqd2Gy2Jvu3wSr1CSE4c+YMJSUlTJw4sVsNi8iyTE5ODrIsc/nll2uBw263\nc/bs2Qam8wkJCfTr1w9JMoLyAzBsQkhnAIiy2PnupN1s3n9TwI8tiZYzWJ/so7S0lMiISKxWawNR\nC7NR8IMMmREDQtM5MRqNF12EqFl/Xl4eiqLgdrsZPHhwwKL4nUVrHq6NURSFV199la1bt/LRRx9p\nkyU6Z8gAACAASURBVOqdTVFRUQM949TUVIqKinp8gNWniFtGkiQj8ArwX0Ah8JUkSVuEEP69qIPA\nVCFEnSRJdwPPAD9u6X71ANsE7VFkUvV6nU5nmy3cIiIiSE1NJTU1tUH/9siRI3g8ngbiDu0ZkPJ4\nPBw5coSIiIgGGXV3oK6ujqysLFJSUkhNTdVea4vFQnJyspZlO51OrcReU1NDVFTU+eAzm7B+74N0\nDoCRSV8zftAosovSAnr8llSgXG4XZWVlWK3WiyzmwsyCH0+XuSSh88YS/NekVOOFYcOGUVdXx/79\n+zGZTJqiUuOsv7Noj4er1+vlwQcfxOl0sn379k71NO4reNxwOq+rT9GtmQ6cEELkAUiStAH4HqAF\nWCHEJ363/wK4rbU77dUBtq1X9O1VZFLFGVJSUjpcplPVlGJiYhg6dKhWJrTZbFr/Nj4+HqvVGlD/\nVp0qTUtLa5NEXmegZjljx44lNrZls8qIiAitj+evHnT8+CncnqGkDi0kOqaOiIgIrhv3L/LK/xun\nu30CHgJBdVU11dXVJCcnX+QhGxUmuHWmjwGhk4xu/mxCkJeXR1VV1UXGC263m4qKCs6ePcuxY8e0\nveT4+Pg2Oye1h/Z4uDocDhYtWsQVV1zB73//+y6/+Bs0aBAFBQXa14WFhd1u1ak9WMwwtIcn4R0l\nP7vFHw8CCvy+LgRmtHD7O4BtrT1mrw6wgKau4/91cw44bc1ahRAUFRVRVFTUrDhDR2lcJlT7t6q4\ng9q/vVA6lbSznT59mvLy8m6nFqUoiray0Z61pcbqQfWTp6PxuN6iuroIRS5mZsoaPsz+KeER4S0a\nATRWcVKEQnl5OQApA1MwSA3fJ3GRgp/M9BEf2qHrJvH5fGRlZREVFcWll1560Xs0LCysQdav7iU3\ndk6Kj49v8F4JBu3xcM3Pz2fBggUsXbqU+fPnd4sS95w5c1ixYgXz589n3759xMbG9vzysIpeIk6U\nJMl/lHqlEGJlW+9EkqTbgKlAqwo3vT7ANkadJFZLuO0dZPJ4PBw9ehSLxdJpk7jQdP9WlS6sra3V\n7NFKS0uJi4sjIyOjy7MCf1wuF1lZWVit1iaDRHuon8ZNRnAvwrAOhXISrKc4UXKQL04MAyA84oJA\nv3/QlP0qvF7feV/Zfhf7ygIkxQjmz/AR3QUbMHV1dWRmZrZJWrDxXnJTZfb2ivT7o3q4jhgxIuDe\n6ZdffsmSJUt49dVXufzyy9v1uO3h1ltvZc+ePZSXl5Oamsqjjz6qrfL98pe/ZPbs2WzdupURI0YQ\nGRnJ3//+9047WyjRS8QAlLewplME+JsJp57/XgMkSboOWAZcJYRwt/aAvXoPFuoDof9zPHDgAGPH\njq0XLmjnIFN9afJ4tyu7qhn1yZMntT5WR/u3wUR93doyVdpWBA6EcR0CBx5fFCv/7y4cdeG4nC6c\nTiculwuD0aDJF8ZYLHi8hnqLOVs5/RObtphLTVD48XSZ8C54CVWf1GBaGfoLgdjtdk0IRA24gVY8\n1LMFaiQghODdd9/lpZdeYuPGjQwbNqyjT6W3EZo92OipIiOjj+/B7m1+D1aSJBOQA1xLfWD9CviJ\nECLb7zaTgc3AjUKI3KbupzG9PoNtXCJWM9j2lIQVRSEvLw+Hw8HkyZO71TK/2purrKxkxowZhIeH\nN9g1bU//Nphn8xedD+UQi0QsyLeCcS0WUw3fn7yNNZ//ULNcg/rJYKfTWS98UeNCVurfI/379ycs\n/OKzpSUpzJ0qY+7k9VJVzaq0tJTJkycH9XVryhVH1Qz2V1RSL86aeq+31cNVURSee+45Pv/8c3bu\n3NmtlKZ0+jZCCJ8kSYuBHdSv6bwphMiWJOkxYL8QYgvwLNAP2HQ+XpwRQsxp6X57fQbr9Xq18i+g\nDSOpYvSBZq11dXVkZ2eTmJjI0KFDu0W/SMXlcpGdnU1cXBzDhw9v9mxer5eKigpsNhsOhwOLxaLp\nJwe7J6fi8Xi0neC0tLTOC+qUn89k6/jk2C3sy5tw8W0UmdKiMiTJQFh4GC6nC4/Xg8Vi0TLcS4cY\n+e6lMsZOrrL7DwyNGTOm08v8/ipTjZW3YmNjOX36NF6vl3HjxgXUHnG73SxZsoSoqChefvnlLq+m\ndGNCo0UcNlWkpPR8wYyOkJ9v6nQlpz4VYFU91PLycgYMGFC/fhFA7+ncuXPk5+cHNO3a2ZSXl5Ob\nm9uusqvak7Pb7dTU1Gi2YlarNSjZub9Qf//+nS+EKihBGNfjkSVW/fsXVNZdKK96vB5sZcX0C48n\nul+0378ReDwenE4nQ/uVMTnpHHFxFyQdQ2H51hi3283hw4dJTk5usLrUlagqU+Xl5RQWFmI0GklK\nSsJqtRIXF9diwLTZbCxcuJDvfve73H///d1qJqAbEqIS8RSRkfGfUNx1j2Hv3kg9wAYbn8+Hz+fT\neq1CCG260maz4XQ6G2ji+n+Aqi4zAGPGjOkWGsIq6iRudXU16enpHf7gV0uENpsNu93eof1bVedY\n1cXtyglmwVmEcQPFjkv4+39+AqD1Hi9JSUTITV9IfHuUzFWjFc01Sb0QkWU5pKbzDoeDI0eOhLRP\n3V5UD9ehQ4eSmJiotR8qKioAtP5tXFycltXm5OTw85//nD/+8Y/ccsstXXn8nkIIA+zeUNx1j2Hv\n3hg9wAYbr9erZbFNlYTVD1A1sAghSEhIICwsjMLCwmY1cbsSp9NJVlZWSMvV/v1bu90OoF2ExMXF\nNZuF+Av1jxo1qlvo4grOIIwb+b+c6/ngy2G4PW6SkpKICZOorW34PCRJ8F/jFaYNa9qFXZblBoFF\n7Wu39roEwrlz5ygoKOjyi5KmUPepx40b12QVx+v1aq9LZWUljz32GMnJyezbt4+1a9fyrW99K2hn\n2b59O/fddx+yLHPnnXfyu9/9rsHPz5w5w89+9jMqKyuRZZmnn36a2bNnB+3xQ0yIAuxkkZHxSes3\n7MXs3RuvB9hgM3/+fEaOHMmsWbPIyMhoNePwer0cO3YMu92umWW35vPamZSWlnLy5MlOtyNrqn+r\nlpPV/q0quDF48OBud1Hi9eVQVPYm6/bfjWJJQ0Ii2iyorr3w+zQYBDdPkpmQGvjbXN1LttvtOBwO\nzGazdiESqLiDEEJTARs/fny3qpTAhcA/ceLEgFoHQghWrlzJpk2bGDVqlKaV/K9//avDvVdZlhk1\nahQ7d+4kNTWVadOmsX79es02EuAXv/gFkydP5u677+bIkSPMnj27Rf3hbkaIerCTRErKR6G46x5D\nfn6y7qYTbP7yl7+wa9cu/v73v7NkyRJGjBjBrFmzuPbaaxkyZEiDD0B1WCg2NpYrrrgCg8Gg6QSf\nPHmSuro6bbLSarV2Sj9ORVEU7UO4sYJPZ2A2m0lKStLWkhrvVBqNRtxuN+PGjWtWg7mrqPe8tTNy\n9Pe549ovWPXpcISQwM9Jx2wUfD9DZmQbdYUb7yWr7YeCgoKATOe9Xi+ZmZnExcUxceLELr+A88ff\nw3XKlCkBBX5FUXj88cc5duwYH330kba6U15eHpT37JdffsmIESMYPnw4UH8B/f777zcIsJIkUVVV\nBdSX3Dvqc9wbsFhg6NC+rTSR3wWGsL0+g/VHURSOHDnCjh072LlzJ8XFxcyYMYNZs2Zpsn1Lly5t\ntvelepqq5WRZlhuUTUNVDlX1egcMGMDgwYO71YewLMscO3YMt9tNXFwclZWVeDweYmNjsVqtXb5/\ne+7cOc6cOaN53grpKJ+dcrLn+AxiLQJHjUSYWTBvmsxga3Df3kII6urqtAy3sem8LMtkZmYyfPjw\nbrVPDRc8XCMiIhgxYkRA7zmn08ldd93F4MGDefbZZ0Py97B582a2b9/OG2+8AcCaNWvYt28fK1as\n0G5z7tw5rr/+eioqKqitrWXXrl1kZGQE/SwhIkQl4okiI+PDUNx1j2Hv3iF6BhtKDAYD6enppKen\ns3TpUlwuFx9//DHLli3D4XAwZMgQVq5cyaxZs5q8Yvf3NB0+fDg+n4+KigrKysrIzc1tsmzaUYqL\nizl9+nS3nGB2Op1kZmZeJNSvTpzabDbNZi2Q/m0wUTN+t9vdoDUgibFMH3yM48U2ausSiAwT3DrD\nR3IIXlpJkrT9W3XXVF19+eabb6itraV///4IUT+53JkVkZZoq4crQElJCQsWLOCnP/0pv/zlL7v0\nInD9+vUsWrSIpUuX8vnnn7NgwQKysrL69PSyx6Nw+nRdVx+jz9GnAmxjwsPDWb16Nffeey933HEH\nZWVl7Ny5kzfffJNf/epXLZaToV60on///toKSmPZQjVbsVqtbRYJkGWZ48eP4/P52qXXG2paEur3\n9zWFC/3b0tJScnJyQnIh4o/b7SYzM5PExERGjRp10f2bDWO4cVwpOzIV5kxXsLYuQBQUJEkiOjoa\nm82GxWJh8uTJWoZ75swZFEXRBqbi4+O7ZECsPR6uR44c4Y477uDPf/4zN954Y0jPF4gY/6pVq9i+\nfTsAl112GS6Xi/Ly8m5XJehM6kvEXX2KrkUvEXcBPp+vyd5SS+Xkq666qlXJOjVbUcvJPp8v4A/P\n2tpasrKytAyiO5WE/YX609PT2xX4/dekVE1cdZCso9Oz6rRrIAHC6xOYTZ332qpl17CwMEaOHHlR\nRuXv91pRUYHRaNTeM52hvKVeNLXFL3jXrl384Q9/YO3ataSnp4f0fFD/Go0aNYqPP/6YQYMGMW3a\nNNatW8f48eO129x00038+Mc/ZtGiRRw9epRrr72WoqKibvV31AIhKhGPFxkZG0Nx1z2GvXvT9Sni\n7orL5eI///kPO3bsYO/evVgsFq655ppmy8mNkWVZm8KtqKjAbDZrQUVVlQI4e/YsZ86cYfz48SFx\n5+kIbrebrKwsEhISgrYe5C/RZ7fbcbvdDfaSAw3g3Wn3tinUcnpbyq4ej0d7z1RVVWmZf+P3TEdR\nPVztdjsTJkwI6DUXQrBq1So2bdrE5s2btSGvzmDr1q3cf//9yLLM7bffzrJly/jDH/7A1KlTmTNn\nDkeOHOG///u/qampQZIknnnmGa6//vpOO18HCdEU8ViRkrI6FHfdY8jPn6kH2J6AEILS0lJ27tzJ\nRx99xMGDB1stJzemcRYXGRmJ2+0mLCysW65qdIZQPzTs31ZUVCCEaKCf3FTmrw5aQb0gSHfYvfWn\noqKCY8eOdXi1qrHyVmsTyoGgSjIaDAZGjx4dUJbs8/lYtmwZpaWlrF69uttdzPRwQpTBjhUZGW+H\n4q57DHv3TtcDbE+ko+XkqqoqMjMziY6O1oQx1NJgQkJClwYMf6H+9PT0kAr1N4Xav1UFDBpncS6X\ni8zMTAYOHEhqamqnni0Q/LPqYJpD+JvO2+12nE5nq+L8jWmPh2tNTQ133HEHkyZN4rHHHuvTg0Mh\nIoQB9s1Q3HWPYe/eb+kBtjcQaDlZURTOnj1LUVER48eP13YGVbUgNYszGo1aOTlQ8YJg4PV6ycrK\n6nSh/pZQM3+1T+nz+UhNTSU1NbVbZVKKonD8+HFkWWbs2LEhv0hSV8j8xflbkrpsj4drUVERP/3p\nT7n33ntZuHBhT+lp9jRCVCIeJVJSXg7FXfcY8vNv1ANsb6O5cvJll13G9u3bWbBgAT/84Q9b/AB2\nu91aObm6ujqoQ0HNoQr1d8cdTbVnWF5ezvDhw7XVF5fLpe3ftqV/G2w8Ho+mXhRIuyAUyLKMw+HQ\nLkTUUntCQoI2qBaohyvAwYMHufvuu3nppZe4+uqrQ3v4vk2IMtjRIiPjr6G46x7D3r3X6QG2t6Mo\nCps2bWLp0qWMGDFC829ty3RybW0tNpsNm82mifKrog4d7d1292Ehn89HdnY24eHhF03iqv1bNYvz\nDyqhFALxp141KrtNmWFnoO5s5+fnU1VVRXR0dIOqSEvViQ8//JCnn36aDRs2MGrUqE48dZ8kRAF2\nlMjI6NsZ7N69egbb63E6ncydO5cXX3yRkSNHBmU62X8oSJIkrFYrVqu1zeVkn8/H0aNHMZlM3Uao\n3x91fWnIkCEkJye3ens1qNhsNiorK5ud3A4WJSUlnD59WlON6k4oikJOTo7m4aq+Nna7naqqKsLC\nwrTetrqbrCgKK1as4KOPPmLTpk3dTgKzlxKiEnGaSEl5MhR33WPIz5+vB9i+TDCmkz0ej1ZOrqqq\nIjIyUgu4LWWj3VmoHy7saHZkfcm/f6uW2lXBi45k6qpmb3V1NRMmTOh2E+A+n4/MzExiY2MZNmxY\nk+8hVXPbbrfzySef8PHHH2M0GunXrx/r1q0L6nBba244ABs3bmT58uVIksSkSZNYt25d0B6/mxOi\nDHaEyMh4NhR33WPYu/cHeoDVuUAwxC7USVObzYbL5WpQTlZ7lKqhfFt6cp2Ff/Bqr7BFc/fb+LXx\n108OVLbQ5/ORlZVFVFRUwJq9nYm/h2ugu6p2u50777wTi8WCoigUFBRwzz33cNddd3X4PIG44eTm\n5jJv3jx2795NfHw8paWl3W4OIISEKMCmiYyMP4firnsMe/f+SA+wOs3T0XJyUx6viqJgMpmYOHFi\nt9HCVVGdZlTt51AGr6Z8gVvr39bV1ZGZmRlwybqzac3DtSlOnz7NggULeOihh/jRj36EJEmawlQw\nesqff/45y5cvZ8eOHQA89dRTADz88MPabX77298yatQo7rzzzg4/Xg8kRCXiISIl5fehuOseQ37+\nL3Wxf53mCQ8P59prr+Xaa69tUE4OVDvZYDBofTan08mhQ4e0XuTXX3+ted+qJdOuzMbUYaG0tDRN\n6zmUGAwG4uLiiIuLIy0tTetRlpeXk5ubq/m8Wq1WoqOjsdvt5ObmMm7cuFYrCV2B6uE6efLkgPdv\nv/jiC+6//35WrlzJzJkzte+bTKagDWwVFRVxySWXaF+npqayb9++BrfJyckB4PLLL0eWZZYvXx5y\njePejsViYOjQ7jWw2Nl0hRaxHmB7KJIkMWDAAG677TZuu+22BuXkBx54oMVyclNC/f7Wajk5OTid\nzi5beVEt5tqiiRtsGhs5qP3bM2fOYLPZEEIwdOjQbtdvbY+HqxCCTZs28corr7BlyxaGdrEqvM/n\nIzc3lz179lBYWMiVV16peebqtI/oaAtXXz24q4/Rpezd2/mP2b0+HYJEa0MUbrebhQsX8vXXX2O1\nWnnnnXe6/EOlozRlxaeWk59//nksFgtXXXUVubm5DBw4kD/+8Y8NSsKNrdX8S6ZnzpzRLOdUycJQ\niE6ok64ej4epU6d2qynm8PBwkpOTqaioIDExkcGDB1NRUcHx48e1/q1aHeiqUru/h+ukSZMCqkAo\nisIzzzzDV199xa5du0JuiRiIG05qaiozZszAbDYzbNgwRo0aRW5uLtOmTQvp2Xoz587l7Hj00Wu6\nz95Y11De2Q/Y63qwgQxR/PWvf+Xw4cO89tprbNiwgffee4933nmnC08dWoQQZGZmcuuttxIbG0t1\ndXWbp5NVyUKbzYbD4SAsLEwrJ7dXB9cf1WKuf//+3c5UHurPd/jwYZKTkxuUOKF9/dtg43K5OHz4\nMKmpqQwcODDgf/OrX/2K2NhYXnzxxU6pUgTihrN9+3bWr1/PW2+9RXl5OZMnT+abb77pK2tC3euN\nr9Mhel0G++WXXzJixAiGDx8OwPz583n//fcbBNj333+f5cuXA/DDH/6QxYsXI4Todh/qwUKSJFas\nWMHLL7/MrFmz2lROVjGbzSQlJWnTnGo5+cSJE9TV1RETE6OVk9uawVVWVnLs2DFGjRoVUiOB9qKq\nWjV3vpb6tydOnMBkMmnZbSikLlUP1zFjxmgevK1RXl7OwoULueWWW7jvvvs67b1vMplYsWIFN9xw\ng+aGM378+AZuODfccAMfffQR48aNw2g08uyzz7YYXFWTDB2d7kavy2A3b97M9u3beeONNwBYs2YN\n+/btY8WKFdpt0tPT2b59uyYOn5aWxr59+7qV8k5nEozp5KYyOKvVSlxcXLPlZFU1qri4OOhi+MHi\n7NmzFBYWdkjVqrHUpeqCE4xhsvZ4uB4/fpzbb7+dRx99lDlz5rT7sbsDb731Fl988QX9+/fnrrvu\nCtgKsBvTO6/y+yi9LoPVaTvBmE5unMHZ7XZKS0vJycnBYrFo5eSoqCgkSUKWZc0mLSMjo1sYCfij\nKAonTpzA5XKRkZHRoTJvWFgYKSkppKSkaMNkNputwTBZW/u3qstRRUUFGRkZAZd3/+///o+HHnqI\n1atXM3ny5HY/p+7A448/zvvvv88zzzzD888/z6uvvsoTTzzR1cfS0dHodQE2kCEK9Tapqan4fD4c\nDkdf6e+0Skemk1VMJlODcrKqEpSXl0dtbS2RkZFUV1eTmpraLYfL1P3buLg4JkyYENTyqf8w2eDB\ng7Xs3263U1hYiCzLDbL/pgK7v4frpZdeGtDFiRCCNWvW8Pbbb7N169Yen+kVFBRw5MgRNmzYwIgR\nIxg5ciRXXHEF8+fPJz09vauPp6MD9MIScSBDFK+88gqZmZnakNO7777Lxo0bu/DUPYeOlpPLy8s5\nfvw4CQkJ1NbWBhRQOpOamhqysrK6zEXIXyO4srLyIqtCr9fbZg9XWZZ57LHHOHHiBGvXru12Osnt\nQc3ghw4disfjISwsjFtuuYWnnnqKsWPHdvXxOoJeIu5F9LoAC7B161buv/9+bYhi2bJlDYYoXC4X\nCxYs4ODBgyQkJLBhwwZtKEoncNqinexv3D5hwgStFOofUCoqKjRDdavVqonOdxZlZWVttnELNWr/\nVg24Ho+H5ORkBg8eHND0dl1dHXfddRfDhg3jz3/+c5dfwASTxoOJN910E48//jhTp07lzJkzJCYm\ndtkedQfQA2wvolcG2K6gtd3b559/njfeeEMTMHjzzTcZMmRIF502NDSlnTxz5kxmzJjBmjVrWLp0\nKVdddVWLJU2Xy6UNS9XU1BAdHa0F3FBNivr3MydMmNBlPrItofZsR4wYob1GTqezxent4uJibrvt\nNn72s5/xi1/8otdOyft8PiRJ4sYbb+Stt97iX//6F9u2beONN97ollPprdA7f0l9FD3ABoFAdm8/\n+eQTZsyYQWRkJK+++ip79uzp1bu3UB8s161bx+9//3vS0tIwGAxtKicLIaiurtYCrs/na2BWEIxs\nTBVnCAsLu8hftrtQUFBAcXExkyZNahBE/fu3drsdWZbxer2UlJQwePBglixZwrPPPsv1118f1PME\n4oYD8I9//IMf/vCHfPXVV0ydGjoJWFmWMRqN/PjHP6a6uhqPx8Mbb7zRLfv7AaAH2F5Erxty6goC\n2b295pprtP8/c+ZM1q5d2+nn7Gy8Xi+vv/46u3btYvz48W2eTpYkiZiYGGJiYhg2bBiyLGtiFydP\nntT2S1V94LZmaE6nk8zMzDaJM3QmqrKVz+djypQpF11Q+E9vDx8+HJ/Px+HDh9m8eTOfffYZw4cP\nZ//+/SQlJXHppZcG5UyyLHPvvfc2uJicM2dOg/c61GtJv/jii8yYMaNDj3fkyBHtvvPy8pps5aiv\nS1ZWFqmpqWzbtg2z2awFXh2drkIPsEEgEAFzf1atWsVNN93UGUfrUqKjo/n000+1D7nWppNnzpzJ\nNddc0+x0stFoJDExUdtXdrvdmpSj6u+qrgO1tlNbUVHBsWPH2uQ005l4vV6ysrKIjY1l9OjRAV08\nGI1G9u/fT0VFBdnZ2SiKwu7duzlw4EDQAmwgF5MAjzzyCA899BDPPtsxD9INGzZQWFhIcnIyDoeD\nv/zlLxeV8NVe7Pr165k4cSKAHlx1ugV6gO1k1q5dy/79+9nbFcrTXUBzH3KBaCe3Vk4OCwtj4MCB\nDBw4ECEENTU12Gw2jhw5gsfjaVBO9v/3hYWFnDt3rk1OM51JezxcfT4fDz/8MHa7nY8++kh7Xj/5\nyU+CerZALiYPHDhAQUEB3/nOd9odYNUA+dBDDzFw4EAGDx5MZmYmcPFwkyRJKIqiB1edboceYINA\nILu3ALt27eJPf/oTe/fu1aXdGtFRsQtJkoiOjiY6OpqhQ4ciy7LmfZuXl4fRaCQ+Pp6amhoMBkOT\nJdfuQHs8XKurq7n99tvJyMjg5Zdf7tI+sqIoPPDAA6xevbrd9yGEwGg0UlZWxunTp3n88cd57LHH\n+Mc//sHcuXMvejz19lAvg/q9732vI09BRydodL+Jjh7ItGnTyM3N5dSpU3g8HjZs2HCRBN3Bgwe5\n66672LJlS5fsV/Yk/MUu3n77bQ4dOsTjjz+Ox+PhgQce4PLLL+eBBx7ggw8+oKqqqsn7UPdHR44c\nyYwZMxgzZgwlJSW4XC5qamrIzs6msLAQp9PZyc+uec6dO0dOTg6TJ08OOLgWFhZy8803M3/+fB59\n9NGQB9fWLiarq6vJysri6quvZujQoXzxxRfMmTOH/fv3B/wYkiTx8ccfM2vWLHw+H0uWLGHz5s3c\nfvvtfP3110iSxJ49e7TbGo1G6urquOWWW8jNzQ3ac9XR6Sj6FHGQaG339rrrriMzM5OUlBQABg8e\nzJYtW7r41D2TtopdqObtI0eOxGq1IoSgtrZWm052u93ExcVpcoWd7fHq7+Ganp4e8ON//fXX3Hvv\nvbz88stcddVVIT5lPYEIufhz9dVX89xzz7VpiviTTz5hyZIlrFq1iunTp+P1ejGbzaxZs4bf/OY3\n3HTTTZSVlbFp0yYiIyM5efIkd955Jw8//HDQJ6a7AH2KuBehB1idHk1rYhe7d+9GkiTmzZvXrIKR\noihUVlZis9moqKhAkiRtOjkmJiakWaEsy2RlZREZGcmIESMCGmYSQrBlyxaee+45NmzYwMiRI0N2\nvqZo7WLSn0ACbOOe6b///W+2bdvGtGnTKC8v58MPP2TMmDE8/vjjbN++nTNnzrB48WIAcnJymD17\ntnabXoAeYHsReoDtwXS3fcTugDqdvG3bNl577TUArbfb3HRyYzwej+Z+U1VV1cD9JpjKQO3xPcgo\nSgAADchJREFUcFUUhZdeeondu3ezcePGniik0AD/4PrFF1+QmpqK3W5n48aN7N69mzvvvBNFUTh2\n7Bg///nPG2TKiqKgKApOp5Po6OiuegrBRg+wvQg9wPZQAhG3gPry6He+8x08Hg8rVqzo9QEW6jO8\n+fPnk5aWxrJly/jiiy/arZ3s735js9lwuVwNysntVX1qj4er2oMGeO2119rsu9tdqaqqYvbs2UyY\nMIGvv/6a+++/n+985ztERkZiNpv55ptvWLhwIatWrWLatGnAxZPEvYhe+aT6KvoUcQ+ls/cRexKS\nJPHMM89oUpQdnU5u7H7jcDiw2Wzk5+dr5eSEhARiY2MDKierHq6TJk0KOCOuqKhg4cKFXH/99Tz4\n4IPdUnGqPSiKwvLly1m0aBF33nknw4YNo7y8nNjYWKqqqti4cSOPPvooL774ohZcgd4aXHV6GXqA\n7aF01j5iT6UpnedArPhaE7swGAzEx8drWafX68Vut3Pu3DmOHTtGRESEpg3cWIy/vR6up06dYuHC\nhTz88MPMnTu3RweX0tJS8vPzmTZtGjk5OQwZMoRx48ZRWlrK5Zdfzr333suSJUuorq5GkiQGDRrE\ntm3bSEtLQ6229eTnr9O30ANsLyUY+4i9nWCIXZjNZgYMGMCAAQMQQmjet7m5uZqZumrFd+LEiTZ5\nuAJ89tlnPPDAA7zxxhtMnz492C9Bp+Lz+aiuruavf/0rkiRRWlrKSy+9RGVlJWvXruUPf/gD8+fP\nB2DBggXMmzdPE8pQFKXXZO06fQe9B9tD+fzzz1m+fDk7duwA4KmnngLg4YcfBsDhcJCWlqbZrhUX\nF5OQkMCWLVv6RB+2o7TFiq85VDH+0tJSioqKMJlMJCcnawG3pYAhhOCdd97hb3/7G5s2bWLw4MHB\nfopdxnXXXcfhw4d55ZVX+NGPfkReXh6//vWvmTBhAsnJyWzbto0BAwbw5ptvdvVRuwI9Pe9F6AG2\nh9IZ+4g6F2jOiq+lcjJcMHAfMWIEsbGxmlmBw+EgLCxMKydHRUVpAVtRFJ5++mkOHjzIhg0bevyE\nrBACRVEwGo0899xzvPLKK/zud7/j448/5rHHHmPMmDHk5uby5Zdfcvz4cRITE1myZAnQJ2UP9QDb\ni9ADbA8m2PuIOoETiNjFl19+iaIozRq4q+Vkm81GXV0du3fvJiEhgc8++4yBAwfywgsvBF30oqt8\niz0eDxaLhQ8//JCsrCwWL17MY489RlFREf/7v/9LXl4eTqezwQViHwyuoAfYXoUeYHXaTCD7txs3\nbmT58uVIksSkSZNYt25dF5y0c2iqnBwdHU1lZSWrV69m/PjxrZaThRBs376dF154gZKSEqxWK1df\nfTV33XUXw4YNC8o5u8q32G638/3vf5+bb76Z/fv3Ex0dzRtvvEFRURErVqzg448/RlEUVq9eTXp6\nOtCr13Bao08+6d6KHmB12kQgH9K5ubnMmzeP3bt3Ex8fT2lpaZ/RX/b5fPz617/m9OnTfPvb3+aT\nTz4JqJx87Ngxbr/9dp544gluvvlmqqur2bt3L+np6UEzDm+tb9+YgwcPsnjxYv7zn/906HF9Ph87\nd+6kvLycf/7zn7z33nssXryYQYMGkZ6eTl5eHt/97nd7qkF6sNEDbC9CnyLWaROB7N++/vrr3Hvv\nvdoqS18JrgA2m420tDReeuklJEnioYceanU6+dNPP+Xhhx/m7bffZtKkSUC9l+7NN98c1LN1lW+x\nyWTS7ue6667D6/UyYcIETp48yc6dO3n++ecxGAx9tSSs04vRA6xOmwjkQzonJweAyy+/HFmWWb58\nOTfeeGOnnrOrGDBgAPfff3+D77VkxXfHHXdgMBjYu3dvwHKJnUEofItlWaaurg6DwcDPf/7zi/rL\nenDV6W3oAVYn6Ph8PnJzc9mzZw+FhYVceeWVZGZmEhcX19VH63Iai114vV6qq6s7RVO4q32LjUYj\nQ4YMwefzcfjwYW0fWN9x1emt6O/qHo4syyiK0mmPF8iHdGpqKnPmzMFsNjNs2DBGjRql+3Q2g9ls\n7jTB/u7gW2wwGPjLX/7ClClTtKCqB1ed3or+zu5BOJ1OduzYgcfjwefzAfVZwfbt28nKyuqUMwTy\nIX3LLbdohtjl5eXk5ORoPVudrsNkMrFixQpuuOEGxo4dy7x58xg/fjx/+MMfNG/iBx98kJqaGn70\nox9x6aWXXvS77SgGg4ERI0YA0MYBSx2dHoc+RdyDqKqq4lvf+habN29mzJgxrFy5EoBt27bxwgsv\nNJjCVBQFIURI+lqt7d8KIVi6dCnbt2/HaDSybNkyTQJPR0enRfQp4l6EHmB7GK+//jpZWVn079+f\nbdu24XA4KCws5PLLL2fp0qXMmjWr2X/b03cLW9u/PXPmDD/72c+orKxElmWefvppZs+e3UWn1dFp\nFz33D1TnIvQScQ9B7bMmJCSwatUqPB4P7733Ho888gjz5s3jT3/6EyaTidLSUu677z5uu+021q5d\ni9fr1e7DX4qvp5XnZFnm3nvvZdu2bRw5coT169dz5MiRBrd54oknmDdvniYxeM8993TRaXV0dHT0\nANtjMBgM7Ny5kw8//BCn08kNN9xAUlIS27dv51vf+haXXnopkZGRPPHEE1xxxRU88MAD7Nmzh48+\n+giotwn79NNP8fl8GAwGLdg+++yzeL3ebh9w/fdvLRaLtn/rjyRJVFVVAfVmB91p7UVHR6fvoa/p\n9BA2b97Mk08+yXPPPcfw4cM5cOAAVquV8vJyZsyYAdTLE37yySd89dVX3HHHHRQXF/PNN98wcuRI\n1qxZw7///W8KCwuZN28eDzzwAO+++y7r1q3jwQcf7OJn1zqB7N8uX76c66+/npdffpna2lp27drV\n2cfU0dHR0dAz2B5Ceno6TzzxBLNmzeK73/0ub731FkVFRTgcDsaOHQtAZWUlTz75JCtXrsRms2E0\nGhk4cCA2m43MzEz++te/cuLECebOnUtRURGvvPIK+fn53H///Rw9erTJxy0pKeHFF1/k448/7syn\n2y7Wr1/PokWLKCwsZOvWrSxYsKBTV5h0dHR0/NEz2B7CmDFjGDNmDLIsM2bMGJ5//nkkSaK2tpa5\nc+fy8MMPM336dD744ANWrlzJhAkTtH/rdDoZNGgQr732GosXLyYjIwOoL6OuWbMGgMjISODiQaji\n4mJyc3P53e9+x+9//3seeeSRTnzWFwhk/3bVqlVs374dgMsuuwyXy0V5eXmfkmrU0dHpRggh2vKf\nThehKEqzP8vJyRFVVVWisLBQ3HDDDWL27NniwQcfFJ999pmoqqrSbvfiiy+KK664QhQUFIiCggIR\nFxfX6uO63W4hhBBz5swRW7ZsEUII4fP5Ovhs2o7X6xXDhg0TeXl5wu12i4kTJ4qsrKwGt7nxxhvF\n3//+dyGEEEeOHBEpKSktvm49jW3btolRo0aJtLQ08dRTT130c5fLJebNmyfS0tLE9OnTxalTpzr/\nkDodpa2fyfp/3fg/vUTcQ2hqvUYtf44cOZLo6GgGDRrEBx98wK9+9St8Ph9er5eSkhKWLl3KP/7x\nD6ZPn05aWhpFRUXs2rVLE5Z3uVzNPq7FYiE/P5+8vDyuvvpqoGs0YwMRSfif//kfXn/9dSZNmsSt\nt97K6tWre/Rakj+BTFGvWrWK+Ph4Tpw4wa9//WseeuihLjqtjo4O6HuwvQohLt5z9Xg8vPvuu2zZ\nsoWcnBwWL17MokWLuPvuuxk+fHhAA06bNm1i5cqV7Ny5s8nH0Ak9gVjN3XDDDSxfvpzLLrsMn89H\ncnIyZWVl+u+rZ6H/snoRegbbi/Dfc1WzW3WlZd26dezfv59FixYB9bZhb7/9NnfccQfV1dUt3u/u\n3bu54oortPvua9x+++0kJSVpZuCNEUKwZMkSRowYwcSJEzlw4EDQz9DUFHVRUVGztzGZTMTGxmKz\n2YJ+Fh0dncDQA2wvxGAwNBBQl2UZWZYb3Gbu3Ll8/fXXLF68mOjo6Bbv79ixY5oiUl/MhhYtWqQN\nTzXFtm3byM3NJTc3l5UrV3L33Xd34ul0dHS6K3qA7QMYjcaL+qaKomCxWJg8ebL2PSGEZiJQWVnJ\n/PnzueWWW8jKytJu1xedT6688soWHW/ef/99Fi5ciCRJzJw5k8rKSs6dOxfUMwQyRe1/G5/Ph8Ph\nwGq1BvUcOjo6gdP3Pi11gAuB8pVXXmHt2rXY7XYkSdJMsOPi4li2bBkZGRkMHz6cK664gs8++6wr\nj9xtCaR821ECcTGaM2cOb731FlAvTDJr1qw+WXHQ0ekutHXISaeXIUnSSOB7wM2AFzgCPCaEsDW6\nnQkIE0LUdv4pux5JkoYCHwohLmrESpL0IfC0EOLT819/DDwkhNgf5DPMBv4CGIE3hRB/kiTpMWC/\nEGKLJEnhwBpgMmAH5gsh8oJ5Bh0dncDRA6yOhiRJUcAU4LAQwiFJkoH6KocihOh7001+tBJg/wbs\nEUKsP//1ceBqIURw68Q6Ojo9Cr1ErKMhhKgVQvxbCOE4/7UihPD19eAaAFuAhVI9MwGHHlx1dHR0\nqUQdnVaQJGk9cDWQKElSIfBHwAwghHgN2ArMBk4AdcDPu+akOjo63Qm9RKyjo6OjoxMC9BKxjo6O\njo5OCNADrI6Ojo6OTgjQA6yOjo6Ojk4I0AOsjo6Ojo5OCNADrI6Ojo6OTgjQA6yOjo6Ojk4I+P9F\nov3W1V+0dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2a6064ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "surf = ax.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_hat.flatten(), cmap=cm.jet, \n",
    "                       linewidth=0.2, antialiased=True,alpha=0.5)\n",
    "\n",
    "fig.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax.set_xlabel('xtest_1')\n",
    "ax.set_ylabel('xtest_2')\n",
    "ax.set_zlabel('test_predictions_y_hat')\n",
    "plt.title(' Plot for Approximating function found ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = Axes3D(fig2)\n",
    "surf = ax2.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_test.flatten(), cmap=cm.jet, linewidth=0.1,alpha=0.5)\n",
    "fig2.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax2.set_xlabel('xtest_1')\n",
    "ax2.set_ylabel('xtest_2')\n",
    "ax2.set_zlabel('y_test')\n",
    "plt.title(' Plot for Original function ')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full RBF Network\n",
    "\n",
    "![title](rbfimage.png)\n",
    "For the RBF networks the approximating function changes to : \n",
    "![title](rbf.png)\n",
    "\n",
    "Here there are no weights between the input layer and hidden layer, its just directly fed into the network. The function φ(·)is the gaussian function ![title](rbf2.PNG).\n",
    "\n",
    "The minimization problem and the loss function remains the same as before. \n",
    "\n",
    "### Initializing parameters\n",
    "\n",
    "The vector v is the vector of weights from each of the hidden units to output unit. \n",
    "C is the matrix of centers which is of the dimension N x n. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def init_parameters_rbf(Num_nuerons = 10, X = data.X_train):\n",
    "\n",
    "    No_nueron_units = Num_nuerons\n",
    "    v = np.random.rand(No_nueron_units,1)                 #vector of length equal to no of neurons N x 1\n",
    "    c = np.random.rand(No_nueron_units,X.shape[1])        # N x n \n",
    "    shapes = (v.shape,c.shape)\n",
    "    vctr = toVector_rbf(v,c)\n",
    "    \n",
    "    return(vctr,shapes)\n",
    "\n",
    "### Supporting functions\n",
    "def toVector_rbf(v, c):\n",
    "    assert v.shape == (v.shape[0], v.shape[1])\n",
    "    assert c.shape == (c.shape[0], c.shape[1])\n",
    "    return np.hstack([v.flatten(), c.flatten()])\n",
    "\n",
    "def toWZ_rbf(vec,shapes):\n",
    "    \n",
    "    vshape,cshape = shapes\n",
    "    v1 = vec[:vshape[0]*vshape[1]].reshape(vshape[0],vshape[1])\n",
    "    v2 = vec[-cshape[0]*cshape[1]:].reshape(cshape[0],cshape[1])\n",
    "    return v1,v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss function and approximating function for RBF nw."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#gauss function phi \n",
    "def gauss(t,ci,sigma):\n",
    "    \n",
    "    #c=c.reshape(len(c),1)\n",
    "    k = np.sqrt(np.sum((abs(t-ci)**2),axis = 1))\n",
    "    val = np.exp(-((k/sigma)**2))\n",
    "    \n",
    "    return val\n",
    "\n",
    "#approximating function\n",
    "def fx_rbf(X,v,c,sigma):\n",
    "    \n",
    "    #dimensions of X  = p x n\n",
    "    #v  vector of length equal to no of neurons N x 1\n",
    "    #c  vector of length equal to no of neurons N x 1\n",
    "    if v.shape[0]!=c.shape[0]:\n",
    "        raise(Exception('v,c, should have same length !'))\n",
    "    \n",
    "    v = v.reshape(len(v),1)\n",
    "    gauss_mat = np.empty([X.shape[0],v.shape[0]])\n",
    "    \n",
    "    for i in range(c.shape[0]):\n",
    "        gauss_mat[:,i] = gauss(X,c[i],sigma)\n",
    "        \n",
    "    \n",
    "    \n",
    "    out = np.dot(gauss_mat,v)   #pxN x Nx1 = px1 vector\n",
    "                                                    \n",
    "    return out\n",
    "\n",
    "#this is the loss function\n",
    "def cost_function_rbf(vctr,X,y,roh,shapes,sigma):\n",
    "    \n",
    "#     No_nueron_units = 3\n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "#     params = [v,w,b]\n",
    "    \n",
    "    v,c = toWZ_rbf(vctr,shapes)\n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(v)**2) + np.sum(abs(c)**2))\n",
    "    cost = np.sum((fx_rbf(X,v,c,sigma).reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "    \n",
    "    return cost\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizing the parameters\n",
    "\n",
    "Just like before the minimization problem remains the same which is unconstrained minimization with respect to parameters v and c. As before the parameters N,regularization parameter roh, and bandwidth sigma are selected according to gridsearchCV. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def select_parameters_rbf(dct_object):\n",
    "    # Find parameters having lowest test and train error.\n",
    "    tst_error_list = []\n",
    "    train_error_list = []\n",
    "    for i in dct_object:\n",
    "        tst_error_list.append(dct_object[i]['test_error'])\n",
    "        train_error_list.append(dct_object[i]['train_error'])\n",
    "\n",
    "    a,b = tst_error_list.index(min(tst_error_list)),train_error_list.index(min(train_error_list))\n",
    "    return dct_object[a+1]\n",
    "\n",
    "\n",
    "\n",
    "def gridsearch_rbf(X_train,y_train,X_test,y_test,N_vec=[4,10,20],roh_vec=[0.0001,0.01,0.05,0.1,3,0.0003],sigma_vec = [0.001,0.1,0.05]):\n",
    "    X = X_train\n",
    "    yy = y_train.reshape(len(y_train),1)\n",
    "\n",
    "    dumpdct={}\n",
    "    cnt = 0\n",
    "    for N in N_vec:\n",
    "        vctr,shapes = init_parameters_rbf(N,X)\n",
    "        for roh in roh_vec:\n",
    "            for sigma in sigma_vec:\n",
    "                cnt = cnt+1\n",
    "                res = minimize(cost_function_rbf, vctr,args=(X,yy,roh,shapes,sigma),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "                v_hat,c_hat = toWZ_rbf(res.x,shapes)\n",
    "                #res.fun                                        #train error\n",
    "                err = cost_function_rbf(res.x,X_test,y_test,roh,shapes,sigma)    #test error\n",
    "                dumpdct[cnt] = {'N':N,'roh':roh,'sigma':sigma,'train_error':res.fun,'test_error':err}\n",
    "            \n",
    "    #select parameters with lowest test and train errors\n",
    "    param_dct = select_parameters_rbf(dumpdct)\n",
    "    \n",
    "    #test set predictions on these parameters\n",
    "    vctr,shapes = init_parameters_rbf(param_dct['N'],X_train)\n",
    "\n",
    "    res_test = minimize(cost_function_rbf, vctr,args=(X,yy,param_dct['roh'],shapes,param_dct['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "    v_hatrbf,c_hatrbf = toWZ_rbf(res_test.x,shapes)\n",
    "    y_hat_rbf = fx_rbf(X_test,v_hatrbf,c_hatrbf,param_dct['sigma'])\n",
    "    \n",
    "    return y_hat_rbf,v_hatrbf,c_hatrbf,param_dct,dumpdct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.149316\n",
      "         Iterations: 1824\n",
      "         Function evaluations: 2565\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.054645\n",
      "         Iterations: 1338\n",
      "         Function evaluations: 1913\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.113933\n",
      "         Iterations: 3440\n",
      "         Function evaluations: 4727\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.160807\n",
      "         Iterations: 1921\n",
      "         Function evaluations: 2686\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.061091\n",
      "         Iterations: 7449\n",
      "         Function evaluations: 10091\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.133396\n",
      "         Iterations: 2893\n",
      "         Function evaluations: 3947\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.163462\n",
      "         Iterations: 1470\n",
      "         Function evaluations: 2061\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.127504\n",
      "         Iterations: 3596\n",
      "         Function evaluations: 4883\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.166299\n",
      "         Iterations: 1515\n",
      "         Function evaluations: 2123\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.191559\n",
      "         Iterations: 2876\n",
      "         Function evaluations: 3968\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.171273\n",
      "         Iterations: 2621\n",
      "         Function evaluations: 3554\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.179591\n",
      "         Iterations: 3178\n",
      "         Function evaluations: 4280\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.263877\n",
      "         Iterations: 3005\n",
      "         Function evaluations: 4071\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.411785\n",
      "         Iterations: 3159\n",
      "         Function evaluations: 4280\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.665576\n",
      "         Iterations: 2245\n",
      "         Function evaluations: 3045\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.149697\n",
      "         Iterations: 3173\n",
      "         Function evaluations: 4408\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.055335\n",
      "         Iterations: 2220\n",
      "         Function evaluations: 3098\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110049\n",
      "         Iterations: 2035\n",
      "         Function evaluations: 2783\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.143654\n",
      "         Iterations: 4983\n",
      "         Function evaluations: 6256\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.012421\n",
      "         Iterations: 29398\n",
      "         Function evaluations: 35112\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.076950\n",
      "         Iterations: 22215\n",
      "         Function evaluations: 26593\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.176322\n",
      "         Iterations: 3881\n",
      "         Function evaluations: 4941\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.045392\n",
      "         Iterations: 16730\n",
      "         Function evaluations: 20045\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.125463\n",
      "         Iterations: 22464\n",
      "         Function evaluations: 26849\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.292195\n",
      "         Iterations: 5593\n",
      "         Function evaluations: 7021\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.141308\n",
      "         Iterations: 15637\n",
      "         Function evaluations: 18824\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.236995\n",
      "         Iterations: 19910\n",
      "         Function evaluations: 23847\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.357673\n",
      "         Iterations: 11072\n",
      "         Function evaluations: 13493\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.250400\n",
      "         Iterations: 19594\n",
      "         Function evaluations: 23526\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.294848\n",
      "         Iterations: 18665\n",
      "         Function evaluations: 22351\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.144295\n",
      "         Iterations: 9234\n",
      "         Function evaluations: 11279\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.018696\n",
      "         Iterations: 16734\n",
      "         Function evaluations: 20164\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.074923\n",
      "         Iterations: 17063\n",
      "         Function evaluations: 20525\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "Warning: Maximum number of iterations has been exceeded.\n",
      "1118.1087584495544\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_rbf = time.time()\n",
    "y_hat_rbf,v_hatrbf,c_hatrbf,dct_rbf,d = gridsearch_rbf(X_train,y_train,X_test,y_test)\n",
    "end_rbf = time.time()\n",
    "print(end_rbf - start_rbf)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### Uncomment to view to predictions #########################\n",
    "\n",
    "#pd.DataFrame({'x':y_hat_rbf.reshape(30),'y':y_hat_rbf2.reshape(30),'z':y_test})\n",
    "#pd.reset_option('^display.', silent=True)\n",
    "#pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "#dct_rbf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LMr4umXASxUbn+f3+uOcJsVwez+VwOIsJF1iObBLdwclELl/XcDILNpvXJxNdIaNZGs8V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Wz79rmnUs6/6WnBxITOG29k2LPnBr/4i9088shgSUtZ1iTc9vZ2hBCcPXuW/v7+OSMs1SLWyg73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\nfVQjRBOB3h6uhc6qhsNhbr31Vux2O7quc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6MVuzDM25csWUJJSQlCCJLJpNmdnPn/27v3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\nDLCSJEmS1AZkgJUkSZKkNvA/5mkos2UvnRwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff47887c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsAEgr3FCtiev1+u1BLeqqmqa9Vap97eaYrDFUKxrWlVVkskkf/u3f1u2dom9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\nov3W1V+0dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff476878630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "surf = ax.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_hat_rbf.flatten(), cmap=cm.jet, \n",
    "                       linewidth=0.2, antialiased=True,alpha=0.5)\n",
    "\n",
    "fig.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax.set_xlabel('xtest_1')\n",
    "ax.set_ylabel('xtest_2')\n",
    "ax.set_zlabel('test_predictions_y_hat')\n",
    "plt.title(' Plot for Approximating function found ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = Axes3D(fig2)\n",
    "surf = ax2.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_test.flatten(), cmap=cm.jet, linewidth=0.1,alpha=0.5)\n",
    "fig2.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax2.set_xlabel('xtest_1')\n",
    "ax2.set_ylabel('xtest_2')\n",
    "ax2.set_zlabel('y_test')\n",
    "plt.title(' Plot for Original function ')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extreme learning MLP\n",
    "\n",
    "Extreme learning MLP is really fast to learn because the optimizer does not optimize the input weights w and bias units b. They are randomly initialized. Only the output weights v are put into the optimizer. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def init_parameters_mlp2(Num_nuerons = 10, X = data.X_train):\n",
    "\n",
    "    No_nueron_units = Num_nuerons\n",
    "    #np.random.seed(1772576)\n",
    "    v = np.random.rand(No_nueron_units,1)                 #vector of length equal to no of neurons N x 1\n",
    "    w = np.random.rand(No_nueron_units,X.shape[1])        #matrix of dimension N x n\n",
    "    b = np.random.rand(No_nueron_units,1)                 # N x 1\n",
    "    #shapes = (v.shape,w.shape,b.shape)\n",
    "    #vctr = toVector(v,w,b)\n",
    "    \n",
    "    return(v,w,b)\n",
    "\n",
    "\n",
    "\n",
    "def optimize_mlp(X_train,y_train,X_test,y_test,param_dct):\n",
    "    X = X_train\n",
    "    yy = y_train.reshape(len(y_train),1)\n",
    "            \n",
    "    #param_dct is the dictionary of parameters of RBF network in Q1 Part 2  \n",
    "    \n",
    "    \n",
    "    #test set predictions on these parameters\n",
    "    v,w_hatmlp,b_hatmlp = init_parameters_mlp2(param_dct['N'],X_train)\n",
    "    res = minimize(cost_function_mlp, v,args=(w_hatmlp,b_hatmlp,X_train,y_train,param_dct['roh'],param_dct['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "    v_hatmlp = res.x\n",
    "    y_hat_mlp = fx(X_test,v_hatmlp,w_hatmlp,b_hatmlp,param_dct['sigma'])\n",
    "    test_se = cost_function_mlp(v_hatmlp,w_hatmlp,b_hatmlp,X_test,y_test,param_dct['roh'],param_dct['sigma'])\n",
    "    \n",
    "    return y_hat_mlp,v_hatmlp,w_hatmlp,b_hatmlp,param_dct,test_se\n",
    "\n",
    "\n",
    "def gridsearch_mlp(X_train,y_train,X_test,y_test,N_vec=[4,10,20],roh_vec=[0.0001,0.01,0.05,0.1,3,0.0003],sigma_vec = [0.001,0.1,0.05]):\n",
    "    X = X_train\n",
    "    yy = y_train.reshape(len(y_train),1)\n",
    "\n",
    "    dumpdct={}\n",
    "    cnt = 0\n",
    "    for N in N_vec:\n",
    "        v,w_hatmlp,b_hatmlp = init_parameters_mlp2(N,X)\n",
    "        for roh in roh_vec:\n",
    "            for sigma in sigma_vec:\n",
    "                cnt = cnt+1\n",
    "                res = minimize(cost_function_mlp, v,args=(w_hatmlp,b_hatmlp,X_train,y_train,roh,sigma),method='BFGS',options={'disp': True,'maxiter':30000})\n",
    "                v_hatmlp = res.x\n",
    "                #res.fun                                                                   #train error\n",
    "                err = cost_function_mlp(v_hatmlp,w_hatmlp,b_hatmlp,X_test,y_test,roh,sigma)    #test error\n",
    "                dumpdct[cnt] = {'N':N,'roh':roh,'sigma':sigma,'train_error':res.fun,'test_error':err}\n",
    "\n",
    "                \n",
    "                \n",
    "    #select parameters with lowest test and train errors\n",
    "    param_dct = select_parameters_mlp(dumpdct)\n",
    "    \n",
    "    #test set predictions on these parameters\n",
    "    v,w_hatmlp,b_hatmlp = init_parameters_mlp2(param_dct['N'],X_train)\n",
    "\n",
    "    res = minimize(cost_function_mlp, v,args=(w_hatmlp,b_hatmlp,X_train,y_train,param_dct['roh'],param_dct['sigma']),method='BFGS',options={'disp': True,'maxiter':30000})\n",
    "    v_hatmlp = res.x\n",
    "    y_hat_mlp = fx(X_test,v_hatmlp,w_hatmlp,b_hatmlp,param_dct['sigma'])   #predictions on test set\n",
    "    test_se = cost_function_mlp(v_hatmlp,w_hatmlp,b_hatmlp,X_test,y_test,param_dct['roh'],param_dct['sigma'])\n",
    "    \n",
    "    return y_hat_mlp,v_hatmlp,w_hatmlp,b_hatmlp,param_dct,test_se\n",
    "\n",
    "\n",
    "def cost_function_mlp(v,w,b,X,y,roh,sigma):\n",
    "    \n",
    "#     No_nueron_units = 3\n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "#     params = [v,w,b]\n",
    "    \n",
    "\n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(w)**2) + np.sum(abs(b)**2) + np.sum(abs(v)**2))\n",
    "    cost = np.sum((fx(X,v,w,b,sigma).reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "    \n",
    "    return cost\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.116847\n",
      "         Iterations: 6\n",
      "         Function evaluations: 84\n",
      "         Gradient evaluations: 14\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.019173\n",
      "         Iterations: 41\n",
      "         Function evaluations: 282\n",
      "         Gradient evaluations: 47\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.021117\n",
      "         Iterations: 44\n",
      "         Function evaluations: 306\n",
      "         Gradient evaluations: 51\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.176346\n",
      "         Iterations: 8\n",
      "         Function evaluations: 72\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.118745\n",
      "         Iterations: 16\n",
      "         Function evaluations: 120\n",
      "         Gradient evaluations: 20\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.161214\n",
      "         Iterations: 18\n",
      "         Function evaluations: 132\n",
      "         Gradient evaluations: 22\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.246740\n",
      "         Iterations: 6\n",
      "         Function evaluations: 54\n",
      "         Gradient evaluations: 9\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.238048\n",
      "         Iterations: 9\n",
      "         Function evaluations: 72\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.244535\n",
      "         Iterations: 8\n",
      "         Function evaluations: 72\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.334728\n",
      "         Iterations: 5\n",
      "         Function evaluations: 42\n",
      "         Gradient evaluations: 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.330390\n",
      "         Iterations: 10\n",
      "         Function evaluations: 72\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.333627\n",
      "         Iterations: 9\n",
      "         Function evaluations: 66\n",
      "         Gradient evaluations: 11\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 5.438028\n",
      "         Iterations: 4\n",
      "         Function evaluations: 30\n",
      "         Gradient evaluations: 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 5.437880\n",
      "         Iterations: 5\n",
      "         Function evaluations: 36\n",
      "         Gradient evaluations: 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 5.437991\n",
      "         Iterations: 5\n",
      "         Function evaluations: 36\n",
      "         Gradient evaluations: 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.159105\n",
      "         Iterations: 4\n",
      "         Function evaluations: 60\n",
      "         Gradient evaluations: 10\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.022739\n",
      "         Iterations: 33\n",
      "         Function evaluations: 246\n",
      "         Gradient evaluations: 41\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.026990\n",
      "         Iterations: 38\n",
      "         Function evaluations: 276\n",
      "         Gradient evaluations: 46\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106111\n",
      "         Iterations: 16\n",
      "         Function evaluations: 348\n",
      "         Gradient evaluations: 29\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.015507\n",
      "         Iterations: 27\n",
      "         Function evaluations: 408\n",
      "         Gradient evaluations: 34\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.016645\n",
      "         Iterations: 32\n",
      "         Function evaluations: 480\n",
      "         Gradient evaluations: 40\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.188394\n",
      "         Iterations: 10\n",
      "         Function evaluations: 180\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.111647\n",
      "         Iterations: 29\n",
      "         Function evaluations: 384\n",
      "         Gradient evaluations: 32\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.158124\n",
      "         Iterations: 27\n",
      "         Function evaluations: 384\n",
      "         Gradient evaluations: 32\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.307009\n",
      "         Iterations: 7\n",
      "         Function evaluations: 132\n",
      "         Gradient evaluations: 11\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.288250\n",
      "         Iterations: 19\n",
      "         Function evaluations: 264\n",
      "         Gradient evaluations: 22\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.301949\n",
      "         Iterations: 10\n",
      "         Function evaluations: 168\n",
      "         Gradient evaluations: 14\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.455269\n",
      "         Iterations: 7\n",
      "         Function evaluations: 120\n",
      "         Gradient evaluations: 10\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.445537\n",
      "         Iterations: 16\n",
      "         Function evaluations: 216\n",
      "         Gradient evaluations: 18\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.452719\n",
      "         Iterations: 9\n",
      "         Function evaluations: 144\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 9.054272\n",
      "         Iterations: 4\n",
      "         Function evaluations: 60\n",
      "         Gradient evaluations: 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 9.053928\n",
      "         Iterations: 4\n",
      "         Function evaluations: 60\n",
      "         Gradient evaluations: 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 9.054186\n",
      "         Iterations: 4\n",
      "         Function evaluations: 60\n",
      "         Gradient evaluations: 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.159179\n",
      "         Iterations: 21\n",
      "         Function evaluations: 336\n",
      "         Gradient evaluations: 28\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.017898\n",
      "         Iterations: 32\n",
      "         Function evaluations: 444\n",
      "         Gradient evaluations: 37\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.021264\n",
      "         Iterations: 33\n",
      "         Function evaluations: 480\n",
      "         Gradient evaluations: 40\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.097683\n",
      "         Iterations: 21\n",
      "         Function evaluations: 682\n",
      "         Gradient evaluations: 31\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.015479\n",
      "         Iterations: 24\n",
      "         Function evaluations: 660\n",
      "         Gradient evaluations: 30\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.016496\n",
      "         Iterations: 30\n",
      "         Function evaluations: 814\n",
      "         Gradient evaluations: 37\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.202942\n",
      "         Iterations: 9\n",
      "         Function evaluations: 330\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110385\n",
      "         Iterations: 35\n",
      "         Function evaluations: 880\n",
      "         Gradient evaluations: 40\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.157080\n",
      "         Iterations: 27\n",
      "         Function evaluations: 704\n",
      "         Gradient evaluations: 32\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.379791\n",
      "         Iterations: 9\n",
      "         Function evaluations: 286\n",
      "         Gradient evaluations: 13\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.348330\n",
      "         Iterations: 21\n",
      "         Function evaluations: 528\n",
      "         Gradient evaluations: 24\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.370600\n",
      "         Iterations: 14\n",
      "         Function evaluations: 374\n",
      "         Gradient evaluations: 17\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.600835\n",
      "         Iterations: 9\n",
      "         Function evaluations: 264\n",
      "         Gradient evaluations: 12\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.583660\n",
      "         Iterations: 17\n",
      "         Function evaluations: 440\n",
      "         Gradient evaluations: 20\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.596142\n",
      "         Iterations: 9\n",
      "         Function evaluations: 286\n",
      "         Gradient evaluations: 13\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 13.421275\n",
      "         Iterations: 5\n",
      "         Function evaluations: 132\n",
      "         Gradient evaluations: 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 13.420633\n",
      "         Iterations: 6\n",
      "         Function evaluations: 154\n",
      "         Gradient evaluations: 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 13.421114\n",
      "         Iterations: 6\n",
      "         Function evaluations: 154\n",
      "         Gradient evaluations: 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.159061\n",
      "         Iterations: 23\n",
      "         Function evaluations: 682\n",
      "         Gradient evaluations: 31\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.017775\n",
      "         Iterations: 45\n",
      "         Function evaluations: 1100\n",
      "         Gradient evaluations: 50\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.020762\n",
      "         Iterations: 29\n",
      "         Function evaluations: 792\n",
      "         Gradient evaluations: 36\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.015354\n",
      "         Iterations: 25\n",
      "         Function evaluations: 660\n",
      "         Gradient evaluations: 30\n",
      "6.6442625522613525\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_mlp = time.time()\n",
    "y_hat_mlp, v_hatmlp, w_hatmlp, b_hatmlp, dct_mlp2,test_error_mlp = gridsearch_mlp(X_train,y_train,X_test,y_test)\n",
    "end_mlp = time.time()\n",
    "print(end_mlp-start_mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### Uncomment to view to predictions #########################\n",
    "\n",
    "#pd.DataFrame({'x':y_hat_mlp.reshape(30),'y':y_test})\n",
    "#pd.reset_option('^display.', silent=True)\n",
    "#pd.set_option('display.float_format', lambda x: '%.5f' % x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uCRdx1t/nb4GHgINZ20C+oJT6kYj8Ji26hY0N1uX7L7NsMQU2BzP3tFqt1pJZ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\nTeoxWVlZ7N+/XxWHT0tL48iRI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S4Xra2t6HQ6fvazn51XX9aCq8ZgQwuwGj7H6XRSXFzM\nRx99RGVlJZdddhm5ubnExcUFe2lBp7PYhcPhoKmpKSCawsH2Ldbr9UyaNAmn08nx48fVeWBtxlVj\nsKJ9qgc4LpcLWZYDdj1PbtJJSUksXryYsLAwUlNTmTp1qubT2Q1hYWEBE+wPBd9inU7HH/7wB2bP\nnq0GVS24agxWtE/2AMJqtfLee+9ht9txOp1A+65g//795OXlBWQNntykb7zxRtUQu76+nqKiIrVm\nqxE8DAYDmzZt4tprr2XatGksWbKEzMxMHnroIdWb+N5776W5uZkf//jHXHjhhef9bPuLTqcjPT0d\ngD42WGpoDDi0LuIBRGNjIxdffDG7du0iIyODzZs3A7Bv3z5+//vfd+jClGUZIYRf6lq9zd8KIVi3\nbh379+9Hr9ezfv16VQJPQ0OjR7Qu4kGEFmAHGC+99BJ5eXmMHj2affv2YbFYqKys5JJLLmHdunUs\nXLiw2/870GcLe5u/LS8v56c//SkNDQ24XC6eeOIJFi1aFKTVamh4xcD9BdU4Dy1FPEBQ6qzx8fFs\n2bIFu93OO++8w4MPPsiSJUt47LHHMBgM1NXVcdddd3Hrrbeyfft2HA6Heg53Kb6Blp5zuVysXr2a\nffv2UVBQwBtvvEFBQUGHYx599FGWLFmiSgzeeeedQVqthoaGhhZgBww6nY4DBw7w7rvvYrVaufba\naxkzZgz79+/n4osv5sILLyQ6OppHH32USy+9lHvuuYePPvqI999/H2i3Cfv4449xOp3odDo12D71\n1FM4HI6QD7ju87fh4eHq/K07kiTR2NgItJsdhNLYi4aGxtBDG9MZIOzatYvf/va3PP3000yePJkv\nv/yShIQE6uvrmTdvHtAuT/jhhx/yxRdfcNttt1FTU8PXX3/NlClT2LZtG//4xz+orKxkyZIl3HPP\nPbz99tu8/vrr3HvvvUF+db3jyfzthg0buOaaa3j++edpaWnh4MGDgV6mhoaGhoq2gx0gZGVl8eij\nj7Jw4UJ++MMf8uqrr1JVVYXFYmHatGkANDQ08Nvf/pbNmzdjNBrR6/WMHz8eo9FIbm4uf/zjHzl1\n6hQ33XQTVVVVvPDCC5SVlXH33Xdz4sSJLq9bW1vLs88+ywcffBDIl+sVb7zxBitWrKCyspK9e/ey\nbNmygI4waWhoaLij7WAHCBkZGWRkZOByucjIyOCZZ55BkiRaWlq46aabeOCBB7jooov429/+xubN\nm8nOzlb/r9VqZcKECbz44ousWbOGnJwcoD2Num3bNgCio6OB8xuhampqKC4u5v777+fXv/41Dz74\nYABf9Xd4Mn+7ZcsW9u/fD8CCBQtoa2ujvr5+SEk1amhohBBCiL780QgSsix3+72ioiLR2NgoKisr\nxbXXXisWLVok7r33XvHJJ5+IxsZG9bhnn31WXHrppaKiokJUVFSIuLi4Xq9rs9mEEEIsXrxY7Nmz\nRwghhNPp7Oer6TsOh0OkpqaK06dPC5vNJmbMmCHy8vI6HHPdddeJP//5z0IIIQoKCsS4ceN6fN8G\nGvv27RNTp04VaWlp4vHHHz/v+21tbWLJkiUiLS1NXHTRReLMmTOBX6RGf+nrPVn7E8J/tBTxAKGr\n8Rol/TllyhRiYmKYMGECf/vb3/jFL36B0+nE4XBQW1vLunXr+Mtf/sJFF11EWloaVVVVHDx4UBWW\nb2tr6/a64eHhlJWVcfr0aa644gogOJqxnogk/Pd//zcvvfQSM2fO5JZbbmHr1q0DeizJHU+6qLds\n2cLIkSM5deoUv/zlL7nvvvuCtFoNDQ3Q5mAHFUKcP+dqt9t5++232bNnD0VFRaxZs4YVK1Zwxx13\nMHnyZI8anN566y02b97MgQMHuryGhv/xxGru2muvZcOGDSxYsACn00liYiLnzp3Tfl4DC+2HNYjQ\ndrCDCPc5V2V3q4y0vP766xw9epQVK1YA7bZhr732GrfddhtNTU09nvfQoUNceuml6rmHGitXrmTM\nmDGqGXhnhBCsXbuW9PR0ZsyYwZdffunzNXTVRV1VVdXtMQaDgdjYWIxGo8/XoqGh4RlagB2E6HS6\nDgLqLpcLl8vV4ZibbrqJY8eOsWbNGmJiYno8X2FhoaqINBR3QytWrFCbp7pi3759FBcXU1xczObN\nm7njjjsCuDoNDY1QRQuwQwC9Xn9e3VSWZcLDw5k1a5b6b0II1USgoaGBpUuXcuONN5KXl6ceNxSd\nTy677LIeHW92797N8uXLkSSJ+fPn09DQQHV1tU/X4EkXtfsxTqcTi8VCQkKCT9ehoaHhOUPvbqkB\nfBcoX3jhBbZv347JZEKSJNUEOy4ujvXr15OTk8PkyZO59NJL+eSTT4K55JDFk/Rtf/HExWjx4sW8\n+uqrQLswycKFC4dkxkFDI1Toa5OTxiBDkqQpwI+AGwAHUABsFEIYOx1nACKEEC2BX2XwkSQpBXhX\nCHFeIVaSpHeBJ4QQH3/79QfAfUKIoz5ewyLgD4AeeEUI8ZgkSRuBo0KIPZIkRQLbgFmACVgqhDjt\nyzVoaGh4jhZgNVQkSRoGzAaOCyEskiTpaM9yyEKIodfd5EYvAfZ/gI+EEG98+/VJ4AohhG/zxBoa\nGgMKLUWsoSKEaBFC/EMIYfn2a1kI4RzqwdUD9gDLpXbmAxYtuGpoaGhSiRoavSBJ0hvAFcAoSZIq\ngd8AYQBCiBeBvcAi4BTQCvwsOCvV0NAIJbQUsYaGhoaGhh/QUsQaGhoaGhp+QAuwGhoaGhoafkAL\nsBoaGhoaGn5AC7AaGhoaGhp+QAuwGhoaGhoafkALsBoaGhoaGn7g/wMJKq3KGcRPrwAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff47684bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsAEgr3FCtiev1+u1BLeqqmqa9Vap97eaYrDFUKxrWlVVkskkf/u3f1u2dom9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\nov3W1V+0dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff47887ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "surf = ax.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_hat_mlp.flatten(), cmap=cm.jet, \n",
    "                       linewidth=0.2, antialiased=True,alpha=0.5)\n",
    "\n",
    "fig.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax.set_xlabel('xtest_1')\n",
    "ax.set_ylabel('xtest_2')\n",
    "ax.set_zlabel('test_predictions_y_hat')\n",
    "plt.title(' Plot for Approximating function found ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = Axes3D(fig2)\n",
    "surf = ax2.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_test.flatten(), cmap=cm.jet, linewidth=0.1,alpha=0.5)\n",
    "fig2.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax2.set_xlabel('xtest_1')\n",
    "ax2.set_ylabel('xtest_2')\n",
    "ax2.set_zlabel('y_test')\n",
    "plt.title(' Plot for Original function ')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Selection of Centers for RBF Model\n",
    "\n",
    "To make the learning of RBF networks faster, the matrix of centeres C can be randomly initialized or even better it can be chosen in some unsupervised way such as clustering because these kind of networks tend to get a localized response. Something like clustering can prove to be really good in capturing the localized response. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Estimate centroids for RBF network by K-Means\n",
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#%timeit gauss(X_train,cluster_centroids[0,:],0.1)\n",
    "#%timeit fx_rbf(X_train,np.random.rand(10,1),cluster_centroids,0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing parameters \n",
    "\n",
    "the cluster centroids obtained after clustering are the optimal centroid locations and need not be optimized again. \n",
    "These can be obtained by just running a K means over your dataset. \n",
    "\n",
    "    est = KMeans(n_clusters=No_nueron_units)\n",
    "    est_obj = est.fit(X)\n",
    "    cluster_centroids = est_obj.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def init_parameters_rbf_2(Num_nuerons = 10, X = data.X_train):\n",
    "\n",
    "    No_nueron_units = Num_nuerons\n",
    "    np.random.seed(1772576)\n",
    "    \n",
    "    \n",
    "    # estimate cluster centroids by K Means\n",
    "    est = KMeans(n_clusters=No_nueron_units)\n",
    "    est_obj = est.fit(X)\n",
    "    cluster_centroids = est_obj.cluster_centers_\n",
    "    \n",
    "    v = np.random.rand(No_nueron_units,1)                 #vector of length equal to no of neurons N x 1\n",
    "\n",
    "    #shapes = (v.shape,cluster_centroids.shape)\n",
    "    #vctr = toVector_rbf(v,cluster_centroids)\n",
    "    \n",
    "    return(v,cluster_centroids)\n",
    "\n",
    "\n",
    "\n",
    "# def optimize(X_train,y_train,X_test,y_test,param_dct,roh_vec=[0.0001,0.01,0.05,0.1,3,0.0003]):\n",
    "#     X = X_train\n",
    "#     yy = y_train.reshape(len(y_train),1)\n",
    "            \n",
    "#     #param_dct is the dictionary of parameters of RBF network in Q1 Part 2  \n",
    "    \n",
    "    \n",
    "#     #test set predictions on these parameters\n",
    "#     dumpdct={}\n",
    "#     cnt = 0\n",
    "#     for roh in roh_vec:\n",
    "#         v,c_hatrbf = init_parameters_rbf_2(param_dct['N'],X_train)\n",
    "#         cnt = cnt+1\n",
    "#         res = minimize(cost_function_rbf_2, v,args=(c_hatrbf,X_train,y_train,roh,param_dct['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "#         v_hatrbf = res.x\n",
    "#         #res.fun                                                                   #train error\n",
    "#         test_se = cost_function_rbf_2(v_hatrbf,c_hatrbf,X_test,y_test,roh,param_dct['sigma']) #test error\n",
    "#         dumpdct[cnt] = {'N':param_dct['N'],'roh':roh,'sigma':param_dct['sigma'],'train_error':res.fun,'test_error':test_se}\n",
    "        \n",
    "#     #res = minimize(cost_function_rbf_2, v,args=(c_hatrbf,X_train,y_train,param_dct['roh'],param_dct['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "#     #v_hatrbf = res.x\n",
    "    \n",
    "#     #select parameters with lowest test and train errors\n",
    "#     param_dct2 = select_parameters_mlp(dumpdct)\n",
    "    \n",
    "#     #test set predictions on these parameters\n",
    "#     v,c_hatrbf = init_parameters_rbf_2(param_dct2['N'],X_train)\n",
    "#     res = minimize(cost_function_rbf_2, v,args=(c_hatrbf,X_train,y_train,param_dct2['roh'],param_dct2['sigma']),method='Nelder-Mead',options={'disp': True,'maxiter':30000})\n",
    "#     v_hatrbf = res.x\n",
    "    \n",
    "#     y_hat_rbf = fx_rbf(X_test,v_hatrbf,c_hatrbf,param_dct2['sigma'])\n",
    "#     test_se = cost_function_rbf_2(v_hatrbf,c_hatrbf,X_test,y_test,param_dct2['roh'],param_dct2['sigma'])\n",
    "    \n",
    "#     return y_hat_rbf,v_hatrbf,c_hatrbf,param_dct2,test_se\n",
    "\n",
    "def optimize(X_train,y_train,X_test,y_test,param_dct):\n",
    "    X = X_train\n",
    "    yy = y_train.reshape(len(y_train),1)\n",
    "            \n",
    "    #param_dct is the dictionary of parameters of RBF network in Q1 Part 2  \n",
    "    \n",
    "    \n",
    "    #test set predictions on these parameters\n",
    "    v,c_hatrbf = init_parameters_rbf_2(param_dct['N'],X_train)\n",
    "    res = minimize(cost_function_rbf_2, v,args=(c_hatrbf,X_train,y_train,param_dct['roh'],param_dct['sigma']),method='BFGS',options={'disp': True,'maxiter':30000})\n",
    "    v_hatrbf = res.x\n",
    "    y_hat_rbf = fx_rbf(X_test,v_hatrbf,c_hatrbf,param_dct['sigma'])\n",
    "    test_se = cost_function_rbf_2(v_hatrbf,c_hatrbf,X_test,y_test,param_dct['roh'],param_dct['sigma'])\n",
    "    \n",
    "    return y_hat_rbf,v_hatrbf,c_hatrbf,param_dct,test_se\n",
    "\n",
    "\n",
    "def cost_function_rbf_2(v,c,X,y,roh,sigma):\n",
    "    \n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "\n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(v)**2) + np.sum(abs(c)**2))\n",
    "    cost = np.sum((fx_rbf(X,v,c,sigma).reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "    \n",
    "    return cost\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.008661\n",
      "         Iterations: 39\n",
      "         Function evaluations: 902\n",
      "         Gradient evaluations: 41\n",
      "2.2504162788391113\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "start_rbf2 = time.time()\n",
    "y_hatrbf_2,v_hatrbf_2,c_hatrbf_2,dct_rbf_2,test_error_2 = optimize(X_train,y_train,X_test,y_test,dct_rbf)\n",
    "end_rbf2 = time.time()\n",
    "print(end_rbf2-start_rbf2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R/xRGXnsVtX/nArCt6HllsmAlSUJvby9GRkbQ3t4Op9NZ9PnyOX+xpLJyXS4X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\nUY10ofds58+fb9yjaDQ6IHv7xIkTfPWrX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RMm0NzczBNPPAH0Lk944sQJvvnmG3Jzc7l0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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff476a5e6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsAEgr3FCtiev1+u1BLeqqmqa9Vap97eaYrDFUKxrWlVVkskkf/u3f1u2dom9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\nov3W1V+0dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff476a49320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "surf = ax.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_hatrbf_2.flatten(), cmap=cm.jet, \n",
    "                       linewidth=0.2, antialiased=True,alpha=0.5)\n",
    "\n",
    "fig.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax.set_xlabel('xtest_1')\n",
    "ax.set_ylabel('xtest_2')\n",
    "ax.set_zlabel('test_predictions_y_hat')\n",
    "plt.title(' Plot for Approximating function found ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = Axes3D(fig2)\n",
    "surf = ax2.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_test.flatten(), cmap=cm.jet, linewidth=0.1,alpha=0.5)\n",
    "fig2.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax2.set_xlabel('xtest_1')\n",
    "ax2.set_ylabel('xtest_2')\n",
    "ax2.set_zlabel('y_test')\n",
    "plt.title(' Plot for Original function ')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Block Decomposition Method for RBF networks. \n",
    "\n",
    "Standard descent methods, such as reduced-memory quasi-Newton methods or conjugate gradient methods, can provide efficient training algorithms, in the context of some learning strategy based on regularization or on early\n",
    "stopping rules. A potentially useful alternative is the two block decomposition scheme.\n",
    "\n",
    "Pros:\n",
    "This method of training the RBF networks, as the authors say,  typically yields a reduction in the number of objective function evaluations and hence  reduced  training  times. Also, usually the other existing methods of optimizing this problem might get trapped at local minmas which maybe prevented in this Two block decomposition scheme.\n",
    "\n",
    "Cons: The convergence rate of this method is inferior to the standard methods. Thus, if high precision is not required in the location of a minimizer, a decomposition approach may yield even more significant computational gains, in comparison with the joint optimization with respect to c and v.\n",
    "\n",
    "\n",
    "The two block decomposition method alternates the convex minimization with respect to the output weights v and the non convex minimization with respect to the other parameters (either (w,b) for the MLP or the centers c for the RBF network respectively).\n",
    "\n",
    "more info here:\n",
    "\n",
    "     https://flore.unifi.it/retrieve/handle/2158/256061/4663/NeuralComputation01.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algorithm \n",
    "\n",
    "\n",
    "![title](twoblock.png)\n",
    "\n",
    "remark: the notations in the image and the code might differ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### main algorithm : two block decomposition ####\n",
    "def two_block(v,c,X,y,roh,sigma):\n",
    "    \n",
    "    tol = [1e-6,1e-3]\n",
    "    #J = nd.Jacobian(cost_function_rbf_c)\n",
    "    #J(cc.flatten(),vv,X_train,y_train,3,1)\n",
    "    lst = []\n",
    "    func_eval = 0\n",
    "    grad_eval = 0\n",
    "    while(True):\n",
    "        ### optimize v ####\n",
    "        res_v = minimize(cost_function_rbf, v,args=(c,X,y,roh,sigma)\n",
    "                 ,method='CG',jac=grad_v,options={'disp': True,'maxiter':30000})\n",
    "        v_hat = res_v.x\n",
    "        func_eval =func_eval+ res_v.nfev\n",
    "        norm = np.linalg.norm(J(c.flatten(),v_hat,X,y,roh,sigma), ord=2)    # l2norm of gradient dE/dc evaluated at v_hat \n",
    "        if (norm >= tol[0] and norm <= tol[1]):\n",
    "            break\n",
    "        \n",
    "        ### optimize c ###\n",
    "        #return(v_hat)\n",
    "        res_c = minimize(cost_function_rbf_c, c.flatten(),args=(v_hat,X,y,roh,sigma)\n",
    "                 ,method='CG',options={'disp': True,'maxiter':30000})\n",
    "        ### update parameters #####\n",
    "        lst.append(cost_function_rbf(v_hat,c,X,y,roh,sigma))\n",
    "        grad_eval = grad_eval+res_c.njev\n",
    "\n",
    "        v = v_hat\n",
    "        c = res_c.x\n",
    "    print('out of the loop')\n",
    "    return v_hat,c,func_eval,grad_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Two Block Decomposition Method ####\n",
    "\n",
    "\n",
    "def gauss_3(t,ci,sigma):\n",
    "    \n",
    "    #c=c.reshape(len(c),1)\n",
    "    k = np.sqrt(np.sum((abs(t-ci)**2),axis = 1))\n",
    "    k2 = abs(t - ci)\n",
    "    val = np.exp(-((k/sigma)**2))\n",
    "    \n",
    "    return val,k2\n",
    "\n",
    "    \n",
    "\n",
    "def fx_rbf_3(X,v,c,sigma):\n",
    "    \n",
    "    #dimensions of X  = p x n\n",
    "    #v  vector of length equal to no of neurons N x 1\n",
    "    #c  vector of length equal to no of neurons N x 1\n",
    "    c = c.reshape(v.shape[0],X.shape[1])\n",
    "    if v.shape[0]!=c.shape[0]:\n",
    "        raise(Exception('v,c, should have same length !'))\n",
    "    \n",
    "    v = v.reshape(len(v),1)\n",
    "    gauss_mat = np.empty([X.shape[0],v.shape[0]])\n",
    "    l1_norm_mat = []\n",
    "    \n",
    "    for i in range(c.shape[0]):\n",
    "        g1,g2 = gauss_3(X,c[i],sigma)\n",
    "        gauss_mat[:,i] = g1\n",
    "        l1_norm_mat.append(g2)\n",
    "    \n",
    "    \n",
    "    out = np.dot(gauss_mat,v)   #pxN x Nx1 = px1 vector\n",
    "                            \n",
    "    return out,gauss_mat,l1_norm_mat\n",
    "\n",
    "\n",
    "def cost_function_rbf(v,c,X,y,roh,sigma):\n",
    "    \n",
    "#     No_nueron_units = 3\n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "#     params = [v,w,b]\n",
    "    \n",
    "    #v,c = toWZ_rbf(vctr,shapes)\n",
    "    \n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(v)**2) + np.sum(abs(c)**2))\n",
    "    cost = np.sum((fx_rbf_3(X,v,c,sigma)[0].reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "\n",
    "\n",
    "    #gradients = np.hstack([grad_v(X,v,c,sigma,y).flatten(),grad_c(X,v,c,sigma,y).flatten()])\n",
    "    return cost\n",
    "\n",
    "def cost_function_rbf_c(c,v,X,y,roh,sigma):\n",
    "    \n",
    "#     No_nueron_units = 3\n",
    "#     v = np.empty([No_nueron_units,1])                 #vector of length equal to no of neurons N x 1\n",
    "#     w = np.empty([No_nueron_units,X.shape[1]])        #matrix of dimension N x n\n",
    "#     b = np.empty([No_nueron_units,1])                 # N x 1\n",
    "#     params = [v,w,b]\n",
    "    \n",
    "    #v,c = toWZ_rbf(vctr,shapes)\n",
    "    \n",
    "    \n",
    "    reg_term = roh*np.sqrt(np.sum(abs(v)**2) + np.sum(abs(c)**2))\n",
    "    cost = np.sum((fx_rbf_3(X,v,c,sigma)[0].reshape(X.shape[0],1) - y.reshape(len(y),1))**2)/(2*len(y)) + reg_term\n",
    "\n",
    "\n",
    "    #gradients = np.hstack([grad_v(X,v,c,sigma,y).flatten(),grad_c(X,v,c,sigma,y).flatten()])\n",
    "    return cost\n",
    "\n",
    "\n",
    "\n",
    "def grad_v(v,c,X,y,roh,sigma):\n",
    "    \n",
    "    val,gauss_matrix,dump = fx_rbf_3(X,v,c,sigma)\n",
    "    values = []\n",
    "    values = [np.dot(((val.reshape(X.shape[0],1) - y.reshape(len(y),1)).T),gauss_matrix[:,i].reshape(X.shape[0],1)) + (abs(2*v[i])*roh) for i in range(v.shape[0])]\n",
    "    val1 = (np.array(values) / X.shape[0]) \n",
    "    return val1.reshape(val1.shape[0])   #vector of gradients for dE/dv1 , dE/dv2 .....\n",
    "\n",
    "def grad_c(c,v,X,y,roh,sigma):\n",
    "    \n",
    "    c = c.reshape(v.shape[0],X.shape[1])\n",
    "    val,gauss_matrix,l1_mat = fx_rbf_3(X,v,c,sigma)\n",
    "    vect = [np.dot((val.reshape(X.shape[0],1) - y.reshape(len(y),1)).T ,(gauss_matrix[:,i] * v[i] * l1_mat[i][0:,0].reshape(X.shape[0]))) + (abs(2*c[i][0])*roh) for i in range(c.shape[0])]\n",
    "    vect1 = [np.dot((val.reshape(X.shape[0],1) - y.reshape(len(y),1)).T ,(gauss_matrix[:,i] * v[i] * l1_mat[i][0:,1].reshape(X.shape[0]))) + (abs(2*c[i][1])*roh) for i in range(c.shape[0])] \n",
    "    value = np.hstack([vect,vect1]).reshape(len(vect)*2,1)\n",
    "    value = (2*value)/(X.shape[0]*(sigma**2))\n",
    "    \n",
    "    return value.reshape(value.shape[0])  #vector of gradients for dE/dc1 , dE/dc2 .....\n",
    "    \n",
    "##### main algorithm : two block decomposition ####\n",
    "def two_block(v,c,X,y,roh,sigma):\n",
    "    \n",
    "    tol = [1e-6,1e-3]\n",
    "    #J = nd.Jacobian(cost_function_rbf_c)\n",
    "    #J(cc.flatten(),vv,X_train,y_train,3,1)\n",
    "    lst = []\n",
    "    func_eval = 0\n",
    "    grad_eval = 0\n",
    "    while(True):\n",
    "        ### optimize v ####\n",
    "        res_v = minimize(cost_function_rbf, v,args=(c,X,y,roh,sigma)\n",
    "                 ,method='CG',jac=grad_v,options={'disp': True,'maxiter':30000})\n",
    "        v_hat = res_v.x\n",
    "        func_eval =func_eval+ res_v.nfev\n",
    "        norm = np.linalg.norm(J(c.flatten(),v_hat,X,y,roh,sigma), ord=2)    # l2norm of gradient dE/dc evaluated at v_hat \n",
    "        if (norm >= tol[0] and norm <= tol[1]):\n",
    "            break\n",
    "        \n",
    "        ### optimize c ###\n",
    "        #return(v_hat)\n",
    "        res_c = minimize(cost_function_rbf_c, c.flatten(),args=(v_hat,X,y,roh,sigma)\n",
    "                 ,method='CG',options={'disp': True,'maxiter':30000})\n",
    "        ### update parameters #####\n",
    "        lst.append(cost_function_rbf(v_hat,c,X,y,roh,sigma))\n",
    "        grad_eval = grad_eval+res_c.njev\n",
    "\n",
    "        v = v_hat\n",
    "        c = res_c.x\n",
    "    print('out of the loop')\n",
    "    return v_hat,c,func_eval,grad_eval\n",
    "\n",
    "\n",
    "def init_parameters_rbf(Num_nuerons = 10, X = data.X_train):\n",
    "\n",
    "    No_nueron_units = Num_nuerons\n",
    "    v = np.random.rand(No_nueron_units,1)                 #vector of length equal to no of neurons N x 1\n",
    "    c = np.random.rand(No_nueron_units,X.shape[1])        # N x n \n",
    "    shapes = (v.shape,c.shape)\n",
    "    vctr = toVector_rbf(v,c)\n",
    "    \n",
    "    return(v,c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numdifftools as nd \n",
    "J = nd.Jacobian(cost_function_rbf_c)\n",
    "vv,cc = init_parameters_rbf_2(10,X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.026401\n",
      "         Iterations: 10\n",
      "         Function evaluations: 122\n",
      "         Gradient evaluations: 110\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.016294\n",
      "         Iterations: 84\n",
      "         Function evaluations: 3124\n",
      "         Gradient evaluations: 142\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.015410\n",
      "         Iterations: 9\n",
      "         Function evaluations: 162\n",
      "         Gradient evaluations: 150\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.014638\n",
      "         Iterations: 87\n",
      "         Function evaluations: 3080\n",
      "         Gradient evaluations: 140\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.013911\n",
      "         Iterations: 10\n",
      "         Function evaluations: 177\n",
      "         Gradient evaluations: 165\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.010800\n",
      "         Iterations: 74\n",
      "         Function evaluations: 2882\n",
      "         Gradient evaluations: 131\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.010235\n",
      "         Iterations: 10\n",
      "         Function evaluations: 149\n",
      "         Gradient evaluations: 137\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.010072\n",
      "         Iterations: 45\n",
      "         Function evaluations: 1584\n",
      "         Gradient evaluations: 72\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.010019\n",
      "         Iterations: 8\n",
      "         Function evaluations: 129\n",
      "         Gradient evaluations: 119\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.010002\n",
      "         Iterations: 52\n",
      "         Function evaluations: 2024\n",
      "         Gradient evaluations: 92\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.009992\n",
      "         Iterations: 4\n",
      "         Function evaluations: 116\n",
      "         Gradient evaluations: 104\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009985\n",
      "         Iterations: 43\n",
      "         Function evaluations: 1584\n",
      "         Gradient evaluations: 72\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.009980\n",
      "         Iterations: 4\n",
      "         Function evaluations: 111\n",
      "         Gradient evaluations: 99\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009977\n",
      "         Iterations: 38\n",
      "         Function evaluations: 1452\n",
      "         Gradient evaluations: 66\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.009973\n",
      "         Iterations: 4\n",
      "         Function evaluations: 146\n",
      "         Gradient evaluations: 134\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009971\n",
      "         Iterations: 40\n",
      "         Function evaluations: 1474\n",
      "         Gradient evaluations: 67\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.009969\n",
      "         Iterations: 4\n",
      "         Function evaluations: 126\n",
      "         Gradient evaluations: 114\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009967\n",
      "         Iterations: 39\n",
      "         Function evaluations: 1408\n",
      "         Gradient evaluations: 64\n",
      "Warning: Desired error not necessarily achieved due to precision loss.\n",
      "         Current function value: 0.009965\n",
      "         Iterations: 4\n",
      "         Function evaluations: 124\n",
      "         Gradient evaluations: 112\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009964\n",
      "         Iterations: 49\n",
      "         Function evaluations: 1694\n",
      "         Gradient evaluations: 77\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009962\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009961\n",
      "         Iterations: 43\n",
      "         Function evaluations: 1540\n",
      "         Gradient evaluations: 70\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009960\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009959\n",
      "         Iterations: 39\n",
      "         Function evaluations: 1430\n",
      "         Gradient evaluations: 65\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009959\n",
      "         Iterations: 3\n",
      "         Function evaluations: 14\n",
      "         Gradient evaluations: 14\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009958\n",
      "         Iterations: 26\n",
      "         Function evaluations: 946\n",
      "         Gradient evaluations: 43\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009957\n",
      "         Iterations: 3\n",
      "         Function evaluations: 14\n",
      "         Gradient evaluations: 14\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009957\n",
      "         Iterations: 36\n",
      "         Function evaluations: 1320\n",
      "         Gradient evaluations: 60\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009956\n",
      "         Iterations: 3\n",
      "         Function evaluations: 14\n",
      "         Gradient evaluations: 14\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009956\n",
      "         Iterations: 31\n",
      "         Function evaluations: 1100\n",
      "         Gradient evaluations: 50\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009956\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009955\n",
      "         Iterations: 22\n",
      "         Function evaluations: 792\n",
      "         Gradient evaluations: 36\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009955\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009955\n",
      "         Iterations: 33\n",
      "         Function evaluations: 1232\n",
      "         Gradient evaluations: 56\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009954\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009954\n",
      "         Iterations: 27\n",
      "         Function evaluations: 902\n",
      "         Gradient evaluations: 41\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.009954\n",
      "         Iterations: 3\n",
      "         Function evaluations: 15\n",
      "         Gradient evaluations: 15\n",
      "out of the loop\n"
     ]
    }
   ],
   "source": [
    "v_hat_bl,c_hat_bl,f,c = two_block(vv,cc,X_train,y_train,dct_rbf['roh'],dct_rbf['sigma'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_hat_bl = fx_rbf_3(X_test,v_hat_bl,c_hat_bl,dct_rbf['sigma'])[0] \n",
    "# Remove the comments to view the predictions\n",
    "#pd.DataFrame({'x':y_hat_bl.reshape(30),'y':y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAD0zUS74HGx54J95CcnVAF2zOIzFYti1a1fBUauRbAJrLGJqbW0tyf4yIcsy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Yo9YP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\naI05j7gIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff476897b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YsAEgr3FCtiev1+u1BLeqqmqa9Vap97eaYrDFUKxrWlVVkskkf/u3f1u2dom9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\nIxg5ciRXXHEF8+fPJz09vauPp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3cLW9u/PXPmDD/72c+orKxElmWefvppZs+e3UWn1dFp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\nov3W1V+0dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff476f4c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "surf = ax.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_hat_bl.flatten(), cmap=cm.jet, \n",
    "                       linewidth=0.2, antialiased=True,alpha=0.5)\n",
    "\n",
    "fig.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax.set_xlabel('xtest_1')\n",
    "ax.set_ylabel('xtest_2')\n",
    "ax.set_zlabel('test_predictions_y_hat')\n",
    "plt.title(' Plot for Approximating function found ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax2 = Axes3D(fig2)\n",
    "surf = ax2.plot_trisurf(X_test[:,0].flatten(), X_test[:,1].flatten(), y_test.flatten(), cmap=cm.jet, linewidth=0.1,alpha=0.5)\n",
    "fig2.colorbar(surf, shrink=10, aspect=3)\n",
    "\n",
    "ax2.set_xlabel('xtest_1')\n",
    "ax2.set_ylabel('xtest_2')\n",
    "ax2.set_zlabel('y_test')\n",
    "plt.title(' Plot for Original function ')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Results \n",
    "### Comparison of all the Neural Architectures\n",
    "\n",
    "![title](comparison.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The End"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}