molfing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax.numpy as np\n",
    "from jax import value_and_grad, jit\n",
    "import numpy as onp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from scipy import sparse\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = sparse.load_npz('/Users/lmar3213/Documents/GitHub/fp_generalizability/code/raw_data/y.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sparse.load_npz('/Users/lmar3213/Documents/GitHub/binding_site/code/processed_data/fingerprints/morgan.npz')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Softmax that sets distances to self to -np.inf, meaning the probability goes to zero.\n",
    "@jit\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp((x - np.diag(np.ones(x.shape[0])*np.inf)) - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "\n",
    "\n",
    "#E_fn is just a pairwise distance function,\n",
    "#stole from https://github.com/google/jax/issues/787 .\n",
    "@jit\n",
    "def E_fn(conf):\n",
    "    ri = np.expand_dims(conf, 0)\n",
    "    rj = np.expand_dims(conf, 1)\n",
    "    dxdydz = np.power(ri - rj, 2)\n",
    "    #return squared euclidean:\n",
    "    dij = np.sum(dxdydz, axis=-1)\n",
    "    return dij\n",
    "    \n",
    "    \n",
    "@jit\n",
    "def loss(A, X, y_mask, zero_mask):\n",
    "    embedding = X.dot(A.T)\n",
    "    distances = E_fn(embedding)\n",
    "\n",
    "    \n",
    "    p_ij = softmax(-distances)\n",
    "    p_ij_mask = p_ij * y_mask\n",
    "    p_i = p_ij_mask.sum(1)\n",
    "    loss = -p_i.sum()\n",
    "    \n",
    "    logprobs = np.log(p_i[zero_mask])\n",
    "    clf_loss = -np.sum(logprobs)\n",
    "\n",
    "    diff_class_distances = distances * ~y_mask\n",
    "    hinge_loss = (np.clip(1- diff_class_distances, 0, np.inf)**2).sum(1).mean()\n",
    "    return clf_loss + hinge_loss\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing on circles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_circle(r, num_samples):\n",
    "  t = onp.linspace(0, 2*np.pi, num_samples)\n",
    "  xc, yc = 0, 0  # circle center coordinates\n",
    "  x = r*np.cos(t) + 0.2*onp.random.randn(num_samples) + xc\n",
    "  y = r*np.sin(t) + 0.2*onp.random.randn(num_samples) + yc\n",
    "  return x, y\n",
    "\n",
    "\n",
    "def gen_data(num_samples, num_classes, mean, std):\n",
    "  \"\"\"Generates the data.\n",
    "  \"\"\"\n",
    "  num_samples_per = num_samples // num_classes\n",
    "  X = []\n",
    "  y = []\n",
    "  for i, r in enumerate(range(num_classes)):\n",
    "    # first two dimensions are that of a circle\n",
    "    x1, x2 = make_circle(r+1.5, num_samples_per)\n",
    "    # third dimension is Gaussian noise\n",
    "    x3 = std*onp.random.randn(num_samples_per) + mean\n",
    "    X.append(onp.stack([x1, x2, x3]))\n",
    "    y.append(onp.repeat(i, num_samples_per))\n",
    "  X = np.concatenate(X, axis=1)\n",
    "  y = np.concatenate(y)\n",
    "  indices = list(range(X.shape[1]))\n",
    "  onp.random.shuffle(indices)\n",
    "  X = X[:, indices]\n",
    "  y = y[indices]\n",
    "  X = X.T  # make it (N, D)\n",
    "  return X, y\n",
    "\n",
    "a, b = gen_data(1000, 4, 0, 1)\n",
    "Y = onp.zeros([len(a), 4])\n",
    "for count, item in enumerate(b):\n",
    "    Y[count][item]=1\n",
    "    \n",
    "X = a.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14141a190>"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = onp.random.choice(range(X.shape[0]), 100, replace=False)\n",
    "batch = X[idx]\n",
    "labels = Y[idx]\n",
    "y_mask = labels.dot(labels.T).astype(bool)\n",
    "plt.imshow(y_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "579.36945\n",
      "551.8223\n",
      "557.95715\n"
     ]
    }
   ],
   "source": [
    "D = 3\n",
    "d = 2\n",
    "A = onp.random.randn(d,D)*0.1\n",
    "\n",
    "g_sq = np.zeros_like(A)\n",
    "m = np.zeros_like(A)\n",
    "\n",
    "mass = 1\n",
    "ayze = list()\n",
    "g_squares = list()\n",
    "lr = 0.1 #step size\n",
    "momentum=0.9\n",
    "\n",
    "values = list()\n",
    "for _ in range(300):\n",
    "    idx = onp.random.choice(range(X.shape[0]), 100, replace=False)\n",
    "    batch = X[idx]\n",
    "    labels = Y[idx]\n",
    "\n",
    "    #y_mask = (labels[:, None] == labels[None, :])\n",
    "    y_mask = labels.dot(labels.T).astype(bool)\n",
    "    zero_mask = onp.array(y_mask.sum(1)==1)\n",
    "    pos_idx = (~zero_mask).nonzero()[0]\n",
    "\n",
    "    value, g = value_and_grad(loss)(A,batch,y_mask, pos_idx)\n",
    "    values.append(value)\n",
    "        \n",
    "    g_sq += g**2\n",
    "    g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\n",
    "    m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\n",
    "    A = A - lr * m\n",
    "    print(value)\n",
    "    ayze.append(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x140ebf400>]"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3CSE2CSE2NTU1FdTYyaIu6MNwTEOC67ozM4RESr/Wkxy5VwyFiPsJACeEEC8av98PXew7iWguABj/dyn7tyqvnw+gvTTNnRzqw/oC2ve88Ab+tK8rz94MM/3RkrqoayzuFUNecRdCdAA4TkRy8dErALwG4CEA1xrbrgXwoPHzQwC2EJGfiBYDWA5gW0lbPcHUBnVx/9bDe/H9P+wvc2sYZuKRvdRkksW9Uig0z/0GAPcQkQ/AYQAfhf5guI+IPgbgGID3AYAQYg8R3Qf9AaABuF4IMa2qcMnFO5IpgX0dgxiJaQj7eUoAU7kkzMidrchKoSDFEkLsALDJ4U9XZNn/JgA3jaNdZaUu5DV/Tglg14kBXLB0VhlbxDATi1w3mD33yoFryzhgX3Zv+/G+MrWEYSYH6bWz5145sLg7oEbu1X4PHtzejtPDsRyvYJjpTaLMkfvLb/ShazBals+uVFjcHajye+BxEdwuwg/+Yj2Onh7BtXdtQ1xjP5KpTKS4lyv9929+1obbnz1Sls+uVFjcHSAi1IW8aKkL4Ko1c/BvH9iA3ScH8ZM/Hih30yaUbzy4Gzf+ale5m8GUAZkKWa7IfSSmYTSuleWzKxVOAclCXciH2TV+AMBVa+bgwmWz8NjeLrx59WxE4kmct6TyBlj3dQwhzhO3ZiSJMnvuyZRAQmO/v5Rw5J6Fr779DHzuLSvM3xc3hnFqIIJv/24vvv7gnjK2bOJICcHZEiUirqVw99ajZhbK6eEYNv/gSRzsGi5zy5wpZ7aMEAJaSvCM8BLD4p6FzSubcfbCBvP3lrog+kcT2N8xhFMD46v1PhzT8MjujvE2seToNxiLeyn46TOH8Y2H9uC+thMAgDd6R3GkZwSvdw6VuWXOlHOGqvzMxCR/diKZwpGekUn9zMmExb1AWmr15ff6RhMYjGqIxMc+L+s3O9vxif9+Gd1DUysDJ5kSSE7iJJahaAKfvXc7+iuwvHKPkV0VSejXiRyMjyam5ny+uBm5T370LB8siUlOWHhwRzuu+tHTGIpWZpFAFvcCaakLWn7vHEfa1nBUHziaaje6lhSTGrm9enIAv97Rjh3H+yftMycLaTF43XodvZgp7lPTepAzU7Uy9NzMz57kB0vnYBTxZAqj4wjUpjIs7gXSUhew/N4xDnGXoj7VBi+TKTGpN7eMZisxxVQODnrd+i0WM8650wM9kUzhfbduxQuHT09eA22UM1tGfnZ8kh8swzE9yKpUr5/FvUBm1wRASjHj8UTuUU2/wcsRJeUiOckDqlLUY5Uo7mbkboh7jmMdiCTw0tE+7DpRvh6MbG85PPeE2WsozXVw7PQonj+U/0Epe9BaUlSkNcPiXiBetwuzqwNwu3SFzybup4djePXEQM73isTLO2EkG8mUmNSusey5VGTknpKRu92WcY7cgfJ+D1LUyxm5l+p+uOT7f8IHfvpC3v1GjMj9sdc6cc5Nj1fc2A+LexHMrQtgcWMYIZ8bHQPOg6E3P3kIH7nzRce/SWTkPtVsGS2VmtTeRMzwn6fa91AK5OCgDAZixjmX5966rzD2Kd/3INtblmyZMtsyx3pHEU2kzEHwSoHFvQj+9vLl+MKVKzCnJoDOIefIvWMgir7RRM61KGX0NtnZAflITvKAqhT12BQbWC4F9lot8kEWcxhQnQo9mIQZuZchWyaPLfPE3k60He0t+n3zfZ8jxoxYmdFUaQOrLO5FsHlVM65eOxfNNX50DGSxZUb0p/9IjqnUpriz567/X4mRu83mSHvumQIyFcYetBJ57qNxDX/zszac6Bst/LNTuW2Zj93dhj+/9fmi2yJtl2xIz12mNY/EWNxnPGtaavHqiQEMOgzCnB7WfbvhHBeWTIebip77ZLapsrNlrGJp2jIOkbv8zssr7qXx3I/0jOCx1zrxyrHCB4fNwdwSBzu57kH17+nIPfv+3UMxvPmHT+GN09Nn0hOL+xh4+7q5iCdTeGxPZ8bfekcMcY9mv1BkpDDVxF1LTW7kLgVvPKJ29b8+jbuem3rVBDNsmSk+oJooUZ67fH0xVlvac888fiHG3p584i4jdTNyz2HL/HZXOw52DePOaVS5ksV9DGxorcO8uiB++sxh7D6ZzoxJpgR6jRH3oVyRuzZFbRnDcx/PDVUMpYjcD3UP4+gUnEJuL8Rleu4Ox2qOPThYNpNFokSR+1h6IbkmUA3mCJLykdeWsUXukRyRu0xpnU4WIov7GCAifPmtq3CyL4LP/r8d5va+0TikLuaK3KeqLTPZ6XCxcdoRSaMWzliyLP7zqUP49u/2julzC0HaMkmbcDtH7kbkWqbIXShjLeP13OWxFCXuOVIhB0aLzz+X6ae5AiwhRHpAtQDP3SfFfRpVrmRxHyPvWt+Cd6xvQb9y8UlLBsjnuU/NVMikmJjiUVoyha2HejK2FzuQmLT1KsYT+T97sAdP7e8u+nWFYp8UlNOWKfPAstqDHG+2TGIMvZBcA6p9Y8g993vcAHJH7qPxpBmIFeK5ez1ktHVq3bO5YHEfB9UBD4ZjaXFX82RzR+5T03NPjjFyf3DHSVz+gyezpn8+vrcTH/zpizh22ppBIQWvYyCCK/7lSRzqzl4ON66lcN63H8eDO9qV14/9exyNJydUTO1rkuaqLWMKYpnqzqiCpaUEth/rw6fueXlMM0blexVzLOlVoPTvqr0/YiYrjE3cdVnLJe7q3wpJhZS2zFS7Z3PB4j4OqvweRBMpPLm/C7c/c9gSucsu4dGeEdz/8gnL6yJTMM99PF3zA53DONwzgl/vOIlN//RYRiR9ekSOQ1i72HK/fR1DONQ9gv0d2cvh9o7E0TMct5RoHY+9FYknJ9QGycxzl4PHDqmQyakUuQs8sbcLv3u1Az3DxQtrKWyZj9y5DT989HUAMHvGaumPfEhxH8oRYKmWTTReeJ57xdkyRHSUiF4loh1E1GZsO4uIXpDbiOhcZf8bieggEe0noqsmqvHlpsqvL2T1V3e9hH96eK8l9304qmEgksBlP3gSX/jlTstNLSP3qbTSvBqtFxuxyWM72DWMnuF4RsQ0GnMeQJbiKqOzXFUy5T4RZR/5uWMR6UgiOaEDmPb0vlyRe7lTQtWHo5YSaDfWK1CDlWLfayy2jBzM7x2Jm+Ww5Xmv8hW+aJzXjNyzt0G9RkcTyYxtGW0scYmEyaCYZfY2CyFU4/R7AP5BCPF7Inqb8ftlRLQawBYAawC0AHiciFYIISprhgDS4i554fBpEOlduOFYAj9//qj5t+GoBn+VG0II8wafSp57Ulijt2KQwjWaJcVTjj/YxStuE7xc5XBNcY+r4j727zEST05oXrl8b+lhp9M+sw+olitbRrNF7u39uriPpdaK/WHmxLHTo2iu8SPg1b1x1RZKJAViiaTpf8vIPehzF9yGlHEtD8eyD8aqY2Lyes8VucdN62jq3LP5GI8tIwDUGD/XApBm6DUA7hVCxIQQRwAcBHCuw+unPVUBq7g/c6AHs6sDqA16MRzTcLI/vWKTvJjUi95pzcjjvaO4+l+fRtc4qk6OBUvkXqS4R20DUvYbeyRLaVW7mEVyRO7yJldvwPGMXYzGtQmNlO29M3OGqu0Btv1Yn/n9TInIPSnQ3q9fe31jyFSJ5xk/iGspXP3jp3Ff23HLZ5o/p1KWGuvyAVPMJSnfbzhn5J75t1wDqtMxci9U3AWAR4noZSK6ztj2WQDfJ6LjAH4A4EZj+zwAx5XXnjC2WSCi6ww7p627e+KyFiYSe+Qe01J465lzUOX3YCiqWVZaGnJYoMPpQtnTPoh9HUN47dTgBLXaGc1iy2TeSacGIlmrXdojdzXK2X6sz0w5y4jcbcdfiC0TTWRG7mOZLxBN6CJSaE7/n/Z14UO3v5CzZpAklUr3zuRD0+ylKA+0nuEY3nvLVjyw/aRln8lGPfeJZMq0F3vHFbk7n8uolsRoPGnO5JafKYklUkgkhXme5QOmGFGV++bKWHOK6nNNYpK9i1IVN/vdq6csc2QmgkLF/UIhxEYAbwVwPRFdAuCTAP5OCNEK4O8A3GHs6zT0kfGNCCFuE0JsEkJsampqGkPTy489cgeA953diiq/B8MxDd3DcTM/9ljvKP6wp8NiPThdsDKKG0uWwHhI2qInO2/67h/xzp886/haGaVJy0SK1G93teO9t2zFoW59ENQu5nYxyzWrMR25p29Ys6pkkaKoJaWwF95LaXujF88dPF2QlaPuY4/cE8n0wPVAJAEhgC4jCJgIm+iR3adw99ajOfdRx1jk6kQA0D8Gz93MlslyLE71hNRzIAVZBgryPihmHEh+Ri4P3SmqzxW5l3ouwj/8Zg/ueu5oSd4rGwWJuxCi3fi/C8AD0G2WawH8ytjll0hbLycAtCovn4+0ZVNRqJF7a0MQHzh3AVa31KDK78FITEPPUAyLGkMAgDufPYKP//xl7FcWSHbyiuXFfXoMmQrjQb3BnDz3XAGujEbtnnt7fxRCwJxBan+Y2W+UXLZM30j2AdViu8oRh+g/H7IbX4gvrvYu0uUHMu0k+TCUaX8TEbnf//JJ3P380Zz7qNfhsd50uuqYbJk8cxecSi3kEveBiBG5F+HLyPfLlY7sJPy5PPdEiT33iU7FBQoQdyIKE1G1/BnAlQB2QxfsS43dLgdwwPj5IQBbiMhPRIsBLAewrdQNnwqo4v7h8xbiO+89U98eMGyZ4RgWN4YBwEzh+9O+LvM1TvbHsC1yH8+KT8WQEmrXPPuN5GRjyAhaZh3IG1c+oGRkmhGp237PPaCq3+QRi+c+tgFV9T0KFdRs4wmO76+Iu1Ped4aNJQVxAm72mJbM6n+f7I/gqde7LdfhcYu4jyVyz11bxikzSI3KR2wlAQYj2W2ZR3afsnj3koJsGQfhH83h0ZtVM0t0jiLx5ISnQhcSuc8G8CwR7YQu0g8LIR4B8DcA/sXY/m0A1wGAEGIPgPsAvAbgEQDXV2KmDGC1ZWqDXvPnar8H7f0RxLUUFjdWAUjnej+xL11szOmClRdk70gCB7uGcN63n5iUBaRzRe5qO52EVEalEZu3LssfO72Pup8kl+feX8JUyNExiLsZuRcwOSfiGLmnEDSyQ+wD0GpbSl3XJ5ZIZX0g3fnsEXzi5y9bbLh2w29vbQiOSdzzzbZ1FnenyF3/X/rgQmRel7/YdjyjkJcQwgxOcpXdHo5pqLaNmeXaP5stE00kiz5ncS0FbRIqsOZNhRRCHAaw3mH7swDOzvKamwDcNO7WTXHCPmdxrwp4zIJHiw1bRnK8N51B43QDjJjiHsMp40Zr74/grNa60jXcgVyeu5q/H4knzendEhlBmwKYtEbuknyRe05bxhCaUYdUyPHYMgWLuxm5F2fLqAOMdUEfIolkhi2jEk+mMr7f8RDTsufz943GEUkkzWUfVRY0hMZkyyTMyN35e3VKX00o15sU92gihVRKYFSJvhPJFNwut+V3+zVksXjy2DJhvweRRNJ8jV6SQIAcZkzJe0K9dvpG4rjgu0/gpx/ZhIuXFz5uKN+j7LYMkx23ixAy8m9VcW+tTwv6vLqQOahqv2ac7A95QfaNJEyhz3WRlgr7FHQVNaXTyZc0I3fbzNvTtgE5e6ZBZrZM+vfRuIYr/uVJvGSswNPvYMvEzFTI4iInS+SeLKxTOeKQypoNu+cuhEBMS6Em6DH+brVlVErtu0dzRO4yg6s/Yj1PXjdhVthvjnMUg7Qtsj1QnDx3NbCwTy4aTSTNe8z+EE8kUxm9PbUXkOu7HIlrqAp44HGnb8pkSmQVXPm+6jnrGY4hmkjhRF/E8TXZsCceTBQs7uNE+u41irhfujL9FG+s9qHasG82Lay3vNbJc5PTontH42YknKu6XalQPXf7WMDJvnziLsXKsGXMyD23LWP3ZVVB6B6K4VD3CF5r11NCnWaoRsc4s1MVhFw+v4rTgGrXUBT/8aeDGemRaiSsGZUrhUgHAPI9Rh16KoUO8D66pwM7FbvuP/50ED97/qjD+yWz2j3Sz+63RegBjxv1Ie/YbJk8lT6dBlzVwVK1ZEDvsF5ltc743jJmOCdFRm9PXnteN+WMjIeieuTudVklMJvvHleOS9pD0TFma8n7ZKJtGRb3cSJ9dzVyX95cZeaIfXoAACAASURBVP7cVOU391k7rxYLZ+lRfZXf45hymLZl4qYVkK8udSnI5bmrkbuTlWCfYSrFxD59PV+eu9Ps00giiVRKmFkTEcPjXPSVh/Hd3+9zfJ98WCP3IgdUlYfB1x7Yje//YT9ePtZn2VdmRIV9biRTwhRzeY1EzdTRzPNaqFB86+HXcNszhzEc0xCJJ/H9P+zH1x/ck7FfzEFMJVJI5Xcr8XvdqA/7MBTVHAcQR+Mabn7yILRkCm1Hey0Pjny1ZZzq6FgHVJUH/LBuB9aGfBn7AXpwZI/cpWCGfJ6c8xhGYhqq/G5L5A5k993VgEdeC9Ess46f2NuZs9cTGWOPs1hY3MeJHJSpDaXFnYgwry4IAKgP+czoviHkw5uWNuqvC3gcJ0RIz7F/NG7efPlWlCkF6sWbsD102i22TGZb7Bd3IpnCYETLsHecBlTdrvTNpU7wiSnWRc9wDCkBzKkJQIhMbz5RxGQkwNlz/9nzR/Gh21/IeJ9Hdnfgo3dtM/Oi1YwWeXz2sYWtB3uwuDGM1oYQtFTaFqkJ6NdI/2gc77t1K7Y7LEVXTGpmNJ7Exm89hjO+/kjW/aT4Ob2vTMG0lxkIeF1mtNwfyfTdn9rfje89sh+3P3sEf37r89ipTG5LV7jMly2jZhQptoxyfXUP6e0yI/dUpq0XTVjPvbyOwz634yCs+TmxJKr8HngMy1TWgHcKXvQ2pr8/uY9Zulv5bkfjGv76Z20ZxQJVJmslNhb3cRL2e+CizMJGv7nhItz/iQvgcpFpy9SFfdhyTiuuXD0bc2sDjraMFPKUSItqrup2pUK9CZK2h45aHbAQKyGupTIyZYDMaC0lrOmkqkUivfBIXDMj4XXzawFkRpq5bmIn1IhZ3pg3PbwXzx08jUd2d1j2ff5QD/60v9u0KNTIXUbi6rFqyRRePNKLC5bOgttFRuRuiLux/9ZDp/HS0T783vZZanvyMRLTEEnkr2yZa2FueV312SovBrxuVBsPIqfxHjlzVVbxbO+P4Nu/24vhmJa3toyT566KnHqtyxLa8nu23y9OFpAZuRvXVbboeDgmbRkyPkPvHWSbpZqwRO5S3DOPZSSm14lXB+DP/OYf8PMX3sh4PQ+oTnGq/B7UBL1wuazdu4awD5sWNRj76BdnQ8iH9a11uO0jmxD0uZ1TIaOaOYB03PC6J9uWsUfc/aNxzArrF789skmlhGMWjBxMVcVb3U/ekNVKOql1sDRty0gR2bCg3mhPZjRZzI3iZP+ct2QWAODmJw9Z9u01PstpMpI8T+qYxKsnBzAc03Dh0kZ4XGQWwgKs4zLZcBLrm588iDZjYBnQHyAxLeWYXWTveag1be7eehSf/p9XzP2GbJG7TNUMeF3meXHqNUrL4aixWPSzB3tw29OH8eLh02avL5bF53fKllEfzOq1Lst31Bm9YruNKcXeqaRH2Dg32a6L4ZhmidxrjcHu0Sz3mmoJjdoid7U3J68teZx/2NOJoaiGHz32enqfSVrPgcV9nCxrrsKK5uqc+9QYN0p9OH1ze1yurOUHFjTovvwJY0LJZNgy6oCqPQruG41jXr1uM9kHVJ1unngyZQ6mLm0Km9st+fKmuKe/E1U41Yk++zqG0FjlN9vgJO5ORdiyMepgy8ia3odtC4bYvVM1SpQ3sjomcbBLf/3aeTUZkXvac8+eoWOPsKMJ3U//nxePZbQ/1/hH30gcpwYiljz7p1/vxu93dyCmJTEST5rFuOT3Kas0Bjxuc5xIWjcqvSP6tjeMxVc6jVTZkXjSEuE6XhsOg+CJLNkyZuRuiLu9lrq0Na0lPfRtIaMn7fSwFEKYqZDSc5cP3sIid2udKPUzpHDHDLvov5/XI/ZW49oFFFtmgmvDF1Pyl3HgS1evyuv3yhulwYh+Ab0ssOq5H+oexmfu3Y6ReBLz60PY1zGEE4ZoTEoqZI48977RBJY3V2PXiYGMQUCnfOaEJkwBWNwYNj1Z9SaQN351FltGXXP0RF8Eq+ZUm5HlQCRzsKqYyD3qkAopH6AjcT0vXOaa2weFVXGXWUzqmISsqDinNgCP22Xz3D1G+zMF00W6FWcXo2O9oxACOKwsUiIzOiKJpPkAqTZmRfdH4gj6gvi/D7xqeejEtCS6hmJIpgSO9IyY/j+Q9tUDRh30oM+Nan92W0ZaVPK76TBmUY/GrAOwMS0zZz/uYMuorxl2Evdglsg9me7d2beF/c7pk7JdWkqgSsmWkb5+tvoyCafI3eFBZYq7lsR3H9mHbUaP67hDxhlH7tMAp0kPKuqAqsTnIctF/eLhXuw+qaf9ychdXjSTkQppXawj/XMqJdA/GkdLnXPkHnXwcuPJpBnxLZilRu6ZOciy++9zuxxrvgzHkni9cwgr51SbNoiTOBYq7vrCyJmRu3pTqz0DezqgOlAohU+1Zdr7I2iq9sPvccNjCK+M1OqN8+/U/rBxjdhLEMiehLRAgLSfOxTVkEwJfOHKFfjen62ztP1EX8RSJyampcxSFq93Dlu8bXvk7ve4c9oy9geefN/hmJZR4dFOwkEQtZQw/X6ruMsBVf17c8pzB5xtmaDPY/7eORjFMwfSlWflZ1QpkXutKe7ZBlSFOehqznlwityVVZ1ue/ow3rW+BV++ehV6R+KmDRZxeN1EwOI+CSxtqkJjlR/1tshdvVhP9KVvxAUNQcvrJ8dzd/ZAh6IaUgKYWxsAkHnxO93AcS2FoWgCbheZryOyCrAUb7VXk1SmZMv3PdE7imgihUWzQuaCDc62TJbUOy1luYn++u423PHsEcghkrjyEJHerhQvp3RONXKXItExGE0XSxuImA9Ct4ugpdLla+X7O4m77MF85X93YdeJdBaNjNj7RxPp4mm2iolBn8cUJ/nd9I7ELd/TaCxpRsIHOocsdov03P2K5y7Pi9Ngvv2BJ0V41GbLFLqkoJYSZq/M0ZbJkufuFLnL8SLTc9dSuO5nbfjLO7aZ4io/I2zx3L0Zn2//LPlwtpfvVo8lkkhnuwkBnKmkP0sbK2KbDzJRsLhPAu/dOA8v3Hi5ucguIMU9fbGqXehZVX4zSgUm33NXUyHljdwQ9iHgdWUM4jnewJqeClkd8OCKM5rx8UuXYEVztW1AVX9dtc2yiirdWiDd5a8N+UwBcErPy9bFPeemx3H1vz5t/v6EUbhNPr/UCVhyZrE6Ycqe9eEk7imlbG97fwTz6vQHmhm5G8dUFfDA46KckXvnYMys7w4AR7rTEbsUeilA8pSFfG7Tl5aWlf2h1N4fMY/59c4hU+iAtCAGvC7jf7fZ23QcUM0yuWkkplmCBKeMGadJTFoyXXfHErnbBlTVc6zWkLFE7sb7mp57MmVaIjL1NB25u81sGem5Z0uFTCRT5jUqH4xO2TJyApssPxLwusye+IM7TuL0cIwHVCsJIjIjBIl9Bp06hbnK7zGjBEC/Uf/xN6+Z3V8hBO7eetRxosRtTx/Cgc7sC01nw77UmkQV95DPk+FJOq4JmtQzMaoDHjRXB3DjW89AwJvuqSRTAg+8oguYHFCVN07as0wPqAK6Xx3KEblnS70biCRMUXRaaCOmGTVM4knMNwa9+ozxAqfaKurDbCiqmRHfcFSDEPoqRnNrZeTugpZM2zJBrxsBr9tR3NUidAc604O6R3pGMLvGb/4MZPaegl436ozrpX80gUg8mfEQlhZN0OvGgc5hDEYyRTvgSUfuAa8bPrfLErl3DUXxjQd341S/c6XSkbhmGSQ80j2S8Z1nG1ANKOLudRNcpI9/eJQSH/IafXDHSfyyLZ1HHrOk0No9d2H2Htve0CebyYlSVX6vacsEvG74PK6sA6paUqAu5AVRWrid7BW5TZ5jv9dtRu4/feYIbnp4r3n+UkWm8BYLi3uZsNsyqm8bSSQxq8pn2f/O546Y6VSvdw7jGw/twW93Wcvkj8Q0fPt3+/DLHBMosmH33LVkCloyZYp7XciLoNedactkidyHoppl0M7rdpk3wdZDPbjdqOa3zLCs1s7Tc9jljWoX65qg14zuBouI3CX7OgYtq1vJ94prKdPDluK+9VAP7nvpuOPDUxWS4VjCFN6RuKYLayJp2jL2yF0Xd5ejr6umjL7eOQQtmcLXH9yNXScHcPHyJnhchANdQ+ZnqQR9bnNAcCCScIysjxu234rZVeiPJMzIvbFKbz8R4DMGVKXI66Wr09/1rU8ext3Pv5F1gZORWNLS6/vrn7Xhf7Yds+wjkwjU2aNaKmUKeDShD8LKyDvs95g9XnmO73ruKO58Ll0N0mLL2LJlEsmU2dP5tycO4M0/fMoU3rDfbb63160/RLIOqKYEfB43qnzp78TZltG3mWu/GnMG3n7mXADA9uP9lt7BREbvLO5lwut2WQaXOoei+PD5C/C+s+fjspVNZuTuUyL++18+gRN9o2a6nb0wl+yKj6UGvDXPPYW/f3APVnzt9/h/L+n1sutDPoR87oxuq2PXO6mLu5rD7vOkH2aynVetmY23r5uLtq+9GWvn6cvxmraMLfKsCXjTnrtDtozTZBX1gXXNT57Dh25/EQDwbx/YgOe+cjl8bpdlvU45q/ieF4/hS/+7C88d1NeDV0tLqJUoo4kUZtcYYxGxJNoHIsb76NvcboKWSk+RD/jcGdkjUlBVce8aiuEffvMafvb8G7hkeSP+6k2LcN6SBjzwyknEtGRG/ZOg142Qzw2vm9AfSWRYMkA6cm9tCCGWSJrRp4xqW+tD8LjSUSygW2aqTRL0qbZiZhLBiDGgKh+cAHBYsZWAdJSrroKlJYVlAWy/x2X+Hva5FXHX9x+IJCw9Csc8d3/64d05mJ5kdrBrGK8bPdsqv8c8Zo/LhbDPk31ANZmC15iQmPbcMwMRmYklAxD5Xf7Hhzbi829ZgSM9I5b7cyJ9dxb3MuF1u5AwKgaeGohACGD9/Dp8/33rEfJ5TJuiqdpvvsZFhJufPIRDRgaFGln+cV+nuT2buNuzGVTsC2T/cV8nUkKfhAGkxT0jW8YhZzuu6dky1fbI3bg5pbB8691rLbnV+vtli9w9ac/daUDV4bjUqDOmpcyIbW1LDRrCPvg8LsQSKVPA6sM+i8h+x6hdI2sFVQc8Zk9FRoPN1bo4jsQ1c7JVRuSu2DJ+r/WWkxlUDWEf1s+vxfs3tQIAfv7CG3jf2fNx+7XnYO28Wnzi0qXoGorh19tPZkTuIZ8bRITaoBf9o87ifrx3FETAvPogYloKg9EEfB6XeZ2tnVdjroUpvfcqv8eSCqlaOa0N1lLW8jvQksIcPwCA5hq/ZR+1CqcUei2VMq8DQBd32aMI+T3mg0T6+X2jccuAsCXPPWWN3E+P6GWNP3nZUnz80iUAYA5YqwOqXo8rZ+SuJQU8bkJ1wJuO3M3aMplpklK0A8r5lpPwXjicnpA2kQt2sLiXCZ+bENdSWPfNR/GqsVDuPGWig7zpZLe/pTaAvzhnPn7ZdtxM65KzJ4eiCXzs7jb85I8HAQBdg5lT/1MpgbXf+AM+e+8Ox/ao4h5NpHB6OI5Vc9KTs6oDHgQLjNwTSZERuau2jIxqVNtG3tx2z11SE/DC43bB53Y5z1B1aIe6n4zQpMABetQcT6Yj4ZDPY+aiqyyfXQ0i/QEni6LJ6E2en+Gohv986jCWNIWxpkW3mGS2TCSRhNdN8Lpd5kNMIs9z0OvGg5++CJ++fJn5t79/52rz54uWNWLVnGrc8+KxTM9dKTs9EIk72jI9w3HMCvsR9nmgpQQGIxrCPrc5kL6mpdZ8QMpzIRd6l6hBg4z4VUZiSSSSKTNqBhwmvalzHaS4J4Xx/ejnyOdx4ZxFuhD6PS4zco8b4yMDkYSlR/HC4dP4074uROJJc1arzJaRWWir5lTjE5csBZAeWJUD3ADgdRFCfg/6RxN4dE9HxtyVRDIFr1ufuSsfco6pkInMXpVkXWstiGy1jThyrzzkBTsU0/D8odMAgJbaTHGXA2XzG0L4xKVLkRLAS0f1gaFeo6ZJ/6i+0PIeozyuPXL/yztexP+5+yUAwMOvnnJsjyruh7qHoaUEPnrhInOby0X6gGrCNokpW7ZMNGERb79iy8ioUY3WZJffni0DwLJv0Oc8IDkYTWRUDZT7XXfJEtz10XPgdhHm1ARMa8Tv0R84w2ZqXPq9v/Xuteb7fOqypfjX95+FkM+N3+46hcU3/s60OaQt8+hrHdjfOYTPXLHcLIameu4BJc1QRY6teA17Zl5dEO9c34K7/uocy/dHRPiLTa3YdWIA221VKKWANIR96BmOZ11/V8+/1z9nMJJAwOs2j2NNS415fvymLeO1zLGQGUEAcPZCvbRGnVIwbzSuIZEUFivRPp3faQZrIiXgdrngMSYU+T1unG+UgzjcPWIOemopgcGofq2r2vvQznZ846E9+M7v9+Jbv30NQLq2jFwcZ3ZNAPVhH+pDXnQNxbBwVsgMGADA43Yh5HVj66HTuO7nL5v3mNnuVFrch2L2bJn0tWrvyarXeE3AizPm1FjfdwJnqbK4lwk1e2a3IcpSKID0hJdG4+a/dEUT5teH8OYzms19ugZjuOEX201vWEYEI/Gk2XU8PRzDMwd68OR+PdqXN7fKfS8dx6+2pwdh9xkDj2fMrcHlq5rNEgJBR1tGv8DVeVzRRBLDMc0SBXvdlBb3iHWwFUh3o6Ufqg5cqvsGvW5L1PbWtXMAAJ+5dwf+3ei5SGTK5JWrZ+Pi5U04Z1E9VsxO90Z8hribec8+j5ktsaG1Dg9efyG+9vYz0NoQwjVnzTNFDwDuMAaEZeQuM1zOMeoJAUa2jJHnLgXY7rnL8ywf9i4X4d8/sAGbVzXDzjVntcDjIjy+t8uyXX53ixvDONw9kjVVsSHsNc//gCHuMhBY01JrDkbKmaq6555+kHYNRvHejfNw5Dtvw5oWXaTkOAWgR+5aMoX1rXX4znvPRNjnzsg+cY7cdT9bRu5+rwvnLta/R73Xkx5Qdeq1AXpAo2YZhWyRu7y3ljbpFttlK/Q1F2QqpNdNlh6HmtLZNRTVbRkXoSbozZ3nHreLu/V+U9d6sL+21HD5gTKhDkjtPTVo2h4SGbm/+YzZeM+G+TjPuNj/YlOr6YMf6BrGga5hMx9YpXMwhuqA17R8JDEthd6RuKUUwpf+d5dln0PGINjSpircce0mc3vI68ZwVMPPnj+KX71yEje9Z605MaPK5zGjvD5jAofdc3/j9Cgu/O4fMa8uaK5KJFkxuxpvWjoL//zIPpy/ZJbFllH3VfP/t//9WzAQSZjVFY/0WAfvZBQuB0Rv/bB1VUg5oCo9bNUrXtpUhaDPjfXK8obqg/GPRr68HBORg6nq4Kvquctzq/rZQgicMbcGD+1sh89hgNLOrCo/1rTUWErsAunIfXlzNe5rO5ExiClpCPvNB9RgNAG/x4U7rz0Hj+zpQFO13/SrZVtVWyaVEugejmF2TQBEhAuXNeLjlywBKN1jHIlr8Lh0G+UD5y7Afz51CKNxPUX05icP4b0b51nOq/w5mdL97PqwD4NRzfTc/R4X3rp2jlkiIJEUWR9cMS2FA12Z4i6z0JqN87S0qQptb/SZIit7BV63y3xIAgBB3/7gjpP4jGFletwu+L3p9FDpueeyZdTIHdAfKrcoxek4W6YCUbu4cS1lidoBYJGx9mpLXRAXLJ1lVp284ozZ+PnHzrVYJodsxa4APcoCgFcNIXj6i5tx64c3ArAWx8o2aWNeXRBhvwdEZJZXWNdah66hGL7129ew43g/3v5vz+LWp/QLVc3TlgN69mwZQJ+s9cqxvozI3e0i3PKhs1Hl9+CHj71usWWcvHn5nl5FcO1FrkxxN6yDupDPtLnk63/3aod5s1X5PfjJBzfg/ZtaLQ9aiVOvp9ZI0YwmUpacbHlMWlKv3piuuKj/31zjx65vXoW3rNYjdK+7sFtxmUOROtnWZbP1qPTFI70Wu0T2qhpCmZH7m5Y14h+v0S0oaWvJcYHqgMfM3+8bjSORFKZIVvk9uPFtZ1hKashJTDJwCfk8GIkl0TkYw/f/sB8P7zpliVTjSuaRx+0ye1XyWtn7j1fjX7dsgNdDZvucJrBJepSVv+T6xqcGogh63eaDe9OiejRW+Uzbx7RlXNbIXQr3bU8fNrf5jAHVwUgCQgjHSUz2nq1d3DfaV2Njca887L7obFtWwao5Ndj21SvM/G+Vi5c3WQa01AeFHEiSMzt3nRzAkqYwFswKYfVc/b2+/4f9ZglZ+4NBTstvqcscMNtyTiuWNVdBSwnc+uGN+Pt3pAf81CwTOfnHHrlLtJRwLH9bG/LiYxctxh/3dWGHsoSc+pAI2VLmVH9Xivkrx/rwxukRDBhRXm2WUrtS6PYZWS4hvxvvWNeCf/7zdVn21z9788omU6xrAl5TOGqDXkudobTnnlI893R2kNtFZg0Un8ODw4nlhoDLj/G4yHytzOrpGY5ZvN20J+83j0EXd+tnmraMHFAN6IOvMS1lXmP2IEQee2OVD4mkwGgsbaOE/Xr2ibQIh6KaJTtECr2WEvC6CCsNcZdRs0tJUwSkLVPY0n8hf3qAXg083repFS/+3zebUbppy3hcCHrT+8USusUkeyWA/iCoNr6TaCLlWBUyl+cO6PfBE5+/FD/ecpZ5TBMFi3uZ+OJVK3HD5cvMjJTZ1Zli2uywTaLOYFVZNVe/qU8ZZVj3nBzAmcYDQmaJvHikF199YDcAmBNjJPLmV20bidftwk8/sgm3fvhsXL12Lj520WL810fPwQfPW5BxEQNWUbZHvdUOWSkA8J6N8wHo2R1SwNQHgYxSXQQze0YyGEkgpiXx3pu34s9u2YqBSEJPP/Rktg3InFof9uV2KWUaY2OVH898eTO+/+fr0FwTMCM++0PE7SYkUgLRuOq5uyzvNSvsw8JZISxTlmbMxTLDM5YDimo2hjog/5ELFsJF+kNAinZDlS9jQFVFTkCSoi8fzoPRhOnN24MQ+VCXoh9PpkyrI2SMYUi7biiqWSL33+xsR3t/xEgzdGGFcS+opTiAtIWZSIqsnrsd9VyGbb0wdfUvMxXS5bJG7okUXrGtlCVTIfVjSaQjd4dJTJKAw0N7aVOVeW/byxiXkoLEnYiOEtGrRLSDiNqU7TcQ0X4i2kNE31O230hEB42/XTURDZ/uzKkN4PNXrsQcIwJvst00+bDPYJW01gexcnY1HtrRbvqkaiGrt52pD0BqqRT2dwzh969aVwOSouMk7oA+aHfVmjnm75etbMa333OmeQOq0aAqynbbwW7LSBqV45L72AdUgXSkK7vsADAQ0czB5Z5hvXBWtqgd0Gf6qqg3vRPyQdJY7UdjlR/vM3LSZRRo742o2TJpz92a1x/wuvHUFzfj4uXWgbZsyMhdotpH6oIxb1k9G2G/R88IMsSnIeQzz29KICMt0x65y2Jmw1HN9LrtQUXYJu4ATI887HdjNKaZHvVQNIG4ljIfCHc8ewTX3rlNt2WUyF2tgAkY5Ttc+oC8U0kIJ9RgIpTjoS0fRB43WeorRRNJtL3Ra9nX63KZSQKDUc1MhUwkhVlmQbU5ve7MsiMSn3HdTpUB1c1CiB75CxFtBnANgHVCiBgRNRvbVwPYAmANgBYAjxPRCiFE9hUKZjBNxmQNp8g9F/abTNYDbwj78fFLm/C5+3bi97s7kEgKi2Vy84fOxj/+5jXc+9Ix/PmtWzOq/kkBy9YzyIa8iKv8HkQTmZ57hrgHnS89v0cvWjUc01AX8mIgknAcUJXttETu0YT5sDpjbg0GIrnFvVhkl1ueM0nYlyVyd7mQTAmMxjUEvfoYihRX+2SmQplfb508FLJFpQ//7UVIpfTzUeXXZ1zKCLMh7LM8wDJtGWvkrhYPk+9hH4uoN7z9+cocDXmuQ8aMT9WWkeIue02nR+Ig6OK6uFHPynJaHsHj1ucMjMScbRmPMadA4nYRiPT3UiNyO/JB5LXNn4gmkth+rB+LG8No748gpuk9Ehlo7Djej6iWND8jnkwh4HJbInf7w9PyuTIDaIpOYvokgO8KIWIAIISQ+VnXALhXCBETQhwBcBDAueNrZuUiZ/DZvcx8zArrr5MR7KJZYVT5PVjUGMI717cg4HXh0dd0obNPzJlfHzRuOv0GUzN3ZAZDtsg9Gz7lhpaoYmf3lLNF7upny8FPS+Quxd24cVSximsps+pjNJHUxT2U/XPu+/gFuPXDGx0nLjkhB2wbq63iLnOq7ZG79HOHY1pmxJ7jxs+F20X41GVL8ZMPboDHRRnWypqWWpxprDVb5fdYBLwh7LNEtHa7SmbL+JUBVUAXZRml2tt99sJ63PlXm7B5ZTp1U0bDeiqkZs5yHYolMiY51Ye8RuTugs/jwpeuXon//th5GcctJ8Fli9zVhwugR/teh2vSTjpbxlqtM6qlsON4Pza01pkPUJnnDgBf+OVOS+AkI/BIPJlei9ZhUF4i74ep4LkLAI8S0ctEdJ2xbQWAi4noRSJ6iojOMbbPA3Bcee0JY5sFIrqOiNqIqK27u9v+5xmDjALt07TzMb8+iE9vXoa/PH+h+fo/fv5SfODcBfC6XagP+cwVgqocxF3yHx/ciC9etdL83ZyKX3TkLn1W/YJurPKZ664CmbVIcq0nKuvez6nR0+HUqe5y0EuKlH2hFJmpMxhJ5I3cz13cgKvXzsUzX74cL9x4Re4DRLqOd/bI3fo9u41jHo5q5iSt8UbugL761zvWtZg1ZbKh2zLW9Fr1c+2Re8YMVUXc5apD9nYTES5fNdtyjZkPer8Ho7GkYstoiGkpVCkP64awz7IQxqcuW4aLljdmHIvX7cJ/bT2Kh3a2Z/wNgD72Yfsu/MrAbja8ZraMC1+8aiUuM1IkD3UNo3sohg0L6tKDr27C0qYqy8xtGXi090dw/refQMdg1Nxm/36dPncqzFC9UAixEcBbAVxPRJdAt3TqAZwP4IsA7iP9TnMyLjM6WkKI24QQ3BBTEQAAESJJREFUm4QQm5qaCvMbK5GNC+v1AbWmwgbUJC4X4QtXrcQ6I0qrC/rQXBMwL5raoNfM8ZULdEvUrv3KOVWWQTk5U7XYyN3rtnbl182vswivyybCuaJl+VBorPLjmS9txjuMinpAeiarU1qipCHsw0AkgZ7huMXDz0Zt0GuOfeRClk2wv6e8+e0PEjm1fUQZUB1v5K4S8LkdUzYl9si9LuS1iL096peeu4wqzaX2Ypo5qSzb4LRqK6qRezyZQq/h18sBVXVpxdqg1zKDNxsepYd2yYq0XtzyoY34xd+cj2XNVVhqG5SWabI5I3eXLHlAWDgrjP/66LnwuV3YZaQQr26pNb9jj8uF+rAPj3z2EmwyUhplkPLvTxw0M9RkGmqucywfgE4F70pFQf1RIUS78X8XET0A3WY5AeBXQi/CsI2IUgAaje2tysvnA3B+3DJYN78OT31x85hfLwss1dnsh5qA15ztac9MkVkzXrd+Qe84bp0UA8CyalQhyMhLXqpyBqNE9ghkVb1ckbt8sPg9bjTb7KqQQ+rgP7xrDfpHE/jR43pJ5FVzqrH10Gn0DMfM76cU1Id9OHp6NOO7yZot40q30Z7nPp7IXRLyuS3pe3YuW9mE7qGYOXDsdbustoxNUP/m4iX40eOvm72BtC2TQFTT6+NkG3RWe4Memx0iF9AeiiYyxoBGYkkIkVuAgXQO+7euWYO182rx9Ot6b3/DgnrMqQ1gfWstEprAK8f6zNRWeU2qn2fHo0TuEr/Xha4hvc36OgbSlkkf+1mtdWh7o8+c3fzwq6fMSXFyr1wPXhkMfe3Xr6J/NI6/vnhJzuMfC3mvMCIKE1G1/BnAlQB2A/g1gMuN7SsA+AD0AHgIwBYi8hPRYgDLAWwrecsZAGkhtHvLNUGvufKO/eKuDXpRE/BgaVOVMTMv8yJsKNKWkRernCB1xlyruEs/U4r+rBwPD/k3p9zvgC1bBgCufdMiy7TulUq3OdfnFMutHz4bP3r/+owHRr7IHUj7r1Jc80WqhfCWM2bj0hWZFobkry9eghvfdoalvaq4222Dz7x5OY5+9+1Knno6WyaWSOWMRF0uSp83GbkbD73OISnuelVS1cI5bdRHymUvAemVs1a31Fq+O3XCVG3Ii82rmvHJy/QCYT4zcs8lsulsGUnA6za9/Sp/uhqpmvlylVH2Qn2offC8BQD0JRuBfAOq+udFEymzmmipKSRynw3gAaOL7QHwP0KIR4jIB+BOItoNIA7gWiOK30NE9wF4DYAG4HrOlJk4ZEqkXERYomaYOA1enr9kFhY1pmvG2KkPF5dlIgXhouVN+M3OdrPbKpGWxrvWz8PHL1lq5t47IQdSk6lMP9KeLSNRbR7VE7UPfo6H2TUBvGfD/Izt2bNl0oIRskfuBU5aysXXlElkuXji85eaA6JqtJ7PGvJ59Eh/KKYhqiXz9jYWNYZxeiRuRsHyoddhRO7murlKsCHHSHJFuSpnzK221Gf35vge7Q8pJ+bXB1Hl91hmLqsPvSp/uiyImvF1zqIGPP65S3GoexhbjcJ/79kwD0ubwqgN+fC3v9ie8/tSgxOnCpulIK+4CyEOA1jvsD0O4MNZXnMTgJvG3TomL01VfnzjnastueeAVWjsA6oAcNtHrDVj7OTqyjohI5HrNy/FP12zNqMn8enLl+Ho6RG8fd3cvOmJVUbENxzLjAlMcbfd1PI9q/0ezKtLjymU0pbJhpktE8geudtry5Qici+U2qAXML4fa+Sevw16/XI9hTGb3y5Z3BjGy2/0memAZuRuK0HtJO75JpBJQj4PAt50Vov9Ia+SzuDK3u7NK5vxyt+/xXI9BZRMrIDXlY7cbZbUsuYqy4SrefVBrG+tM+vFB3N8v+qDYk5tMOt+44ELh01ziAgfvXBxxnZVaPIJtZPfac9CyYe8WP0et2P64cJZYfzyE28q6L1kpOW0Er2TLQOkB7bm1gUsvZbJEHcZudvHEaw55aWP3MeCx0XmnIhc2RwSuRpTSoi8kbvMU5frAcvryj4TOOR348tXr8JvdrabSx/ms2U+csFCS0lgSa6aPPIayfXgICJzQpFErWdPROaD2SmzRX24zFLGitT3ccIq7hNzjbK4Vyi1SqSWr25JoV3iXHjMySDFPRScyCXuIZ+zOMqxg5a6oKVnYE9bnAg2LKjHpoX1ps0lUX3czPIDkxe5qxAR/B59sk2+SByQA+AJeFyZC43Y+csLFmJP+wD+6k2LAFhFNeB1mROhmozZvcd6R0xxz3cNyuJm8r0AfeJerlnFZp57jlRIJ+yTuOQ151RkT723ZECUHlfJft+p7Z5Tw5E7UwQyisxWw0VFjZp+8sENWdeRzIUsA1BodcNcyDorm5Ta6JJ0+YHMG3ZxYxhrWmrMXovXTVlnwpaSlXOqcf8nM3slaraMjGLLHbkDejaInn6Yvw1yqb2gL3OJQDs1AS9u/lC6rLIqqi11QbMUsayMqEa2+SJ3FfmQyXetec2UzOKuAXUNWb1t+v/2ujGA83n0F2m9lc1zZ6YncoCxEO9cvbHesa5lTJ+nTuMeL0ubqvDslzdbCmFJglkGVAHgV596E9xKieJZYX/R9lIpUT3axcaCJw1hH4gmp0eRjWIydqr8HhwbGYWLqOjc/Nk1ATM9cJ4i7kvkQL5F3AuXIpeL4LMVjXPCW4Dn7oTs0ch7R+bl21f6ApwXOJevz+W5q9jTmEsFi3uFYg4w5pjmLymFLSNvpFLYMkBmDRWJvXCYimozVPs9aKwuXRrkWFC73i1GdNZSF8Tjn7sUi2eFs71swkl7woV47vqAqrqYdqFU+T24ZEUTHt/baVnoXT5wxxq5A3p0nC+QkA+xXNkyTsjvRb7uYxcvxumRuOPYlkQ9vmKtt4kKQMrXN2QmFGnLFBK554uACqEqoK9SX2hd8rEiI7x8tkZN0Dspg6m5MBdfdpPlBl7aVGWp4DjZmOJThOeeL889G2YVUmMm5mXKfITgOMQ94HXnDSTGGrnbyy+EfB58811rHB8S8hq77pL0JKSg142NC+pypvtOBhy5VygycndKg7RTisjhLzbNx4YFdQUJxngIZhlQtbPlnFZLTZpyIAV8SWNxpSUmmmI8YVnBMZLIn+fuxLvWt+Bozwjef+4C/OUFC7G2JS14amGtYnuPhYxZFJLn7oRZz76A1zWEfTjynbdZS224CL/61IUFfVZTCedh2GFxr1CKGVAtBdUBLzYuqM+/4zjJZcuo3HDF8glvSz4GjFmOS5rKZ8E4UZwt40FK6OvijiVy97hd+NyVemE6dTFtAJb88WJ7jwGv26yhng3fWG0Zm+eej7EGR9u+ekXBvvxYYHGvUMI+fRm3QqKP6YTP48Ln3rICbz5jdrmbkhfZZX+rUvhsKlDUgKpSGbIU9XBUpLAFfe6iBTLgdSGRZxUjGbkXK6B2W2aiyLXSWimorDufMSEifP7KFeZCwJXE306BqLwQLlreiKe/uBkLZpXXHrKT9tzzi7Va1qLUs2plhc9iPXFARte5U3Z9bn2Gab4VtjLe25uZATMdmd6tZ3LyqcuWlbsJM56pJuxAYTMoJWqNoVLn5kv7o5g0SElt0At3LLdov3vDPCwYQ1aSPc99ujK9W8+UjF9ff6FjIX6m8vAb0WwhcxLU9MdSR+5yQHUskfs337XGsqyeExsW1GPDGMaBZApjsV79VGN6t54pGWe11pW7Ccwk4fe4ECgwCldLP5c6cpde+FjEfSIzoQIOE5OmI5znzjAzjIWzwlhYoF2hlsItdT2c9IDq1BJRtmUYhpmWfPLSpfjEpUsL2ldNOS155G5E7Pa1T8vN6pYarJpTjcVTbH5CsbC4M8wMY6yzY0vuuSupkFOJpU1VeOSzl5S7GeOGbRmGYXIiyyiUPFvGO/ZUSCY/LO4Mw+QkvYpUaUXY53bB46Jpn5UyVeFvlWGYnIR8bn2GaokjdyLCj7dswLr55S2wVamwuDMMkxN9sYvYhKz7+vZ1U6s0QyXBtgzDMDmRtkwZqxQzY4DFnWGYnMwyCqDFtcyViJipS0HiTkRHiehVItpBRG22v32BiAQRNSrbbiSig0S0n4iuKnWjGYaZPH7wvnX41GVLxzSVnykfxXjum4UQPeoGImoF8BYAx5RtqwFsAbAGQAuAx4lohRCi+FWXGYYpO83VAXzp6lXlbgZTJOO1ZX4E4EsA1Ao+1wC4VwgRE0IcAXAQwLnj/ByGYRimCAoVdwHgUSJ6mYiuAwAieheAk0KInbZ95wE4rvx+wthmgYiuI6I2Imrr7u4eQ9MZhmGYbBRqy1wohGgnomYAjxHRPgBfBXClw75OY+oZtTmFELcBuA0ANm3alLt2J8MwDFMUBUXuQoh24/8uAA8AuBTAYgA7iegogPkAXiGiOdAj9Vbl5fMBtJewzQzDMEwe8oo7EYWJqFr+DD1af0kI0SyEWCSEWARd0DcKIToAPARgCxH5iWgxgOUAtk3YETAMwzAZFGLLzAbwgLGArQfA/wghHsm2sxBiDxHdB+A1ABqA6zlThmEYZnLJK+5CiMMA1ufZZ5Ht95sA3DSuljEMwzBjhmeoMgzDVCAkRPkTVYioG8Ab43iLRgA9efea+lTKcQB8LFMVPpapyViPZaEQosnpD1NC3McLEbUJITaVux3jpVKOA+BjmarwsUxNJuJY2JZhGIapQFjcGYZhKpBKEffbyt2AElEpxwHwsUxV+FimJiU/lorw3BmGYRgrlRK5MwzDMAos7gzDMBXItBZ3IrraWO3pIBF9pdztKRanFa6IqIGIHiOiA8b/U3L5GyK6k4i6iGi3si1r26fy6lxZjuWbRHTSODc7iOhtyt+m5LEQUSsR/YmI9hLRHiL6jLF92p2XHMcyHc9LgIi2EdFO41j+wdg+sedFCDEt/wFwAzgEYAkAH4CdAFaXu11FHsNRAI22bd8D8BXj568A+OdytzNL2y8BsBHA7nxtB7DaOD9+6NVEDwFwl/sY8hzLNwF8wWHfKXssAOZCL+AHANUAXjfaO+3OS45jmY7nhQBUGT97AbwI4PyJPi/TOXI/F8BBIcRhIUQcwL3QV4Ga7lwD4G7j57sBvLuMbcmKEOJpAL22zdnaPqVX58pyLNmYsscihDglhHjF+HkIwF7oC+VMu/OS41iyMZWPRQghho1fvcY/gQk+L9NZ3Ata8WmKk7HCFYDZQohTgH6BA2guW+uKJ1vbp+u5+jQR7TJsG9llnhbHQkSLAGyAHiVO6/NiOxZgGp4XInIT0Q4AXQAeE0JM+HmZzuJe0IpPU5wLhRAbAbwVwPVEdEm5GzRBTMdzdQuApQDOAnAKwL8Y26f8sRBRFYD/BfBZIcRgrl0dtk31Y5mW50UIkRRCnAV98aJziWhtjt1LcizTWdyn/YpPInOFq3MBdBLRXAAw/u8qXwuLJlvbp925EkJ0GjdkCsBPke4WT+ljISIvdDG8RwjxK2PztDwvTscyXc+LRAjRD+BJAFdjgs/LdBb3lwAsJ6LFROQDsAX6KlDTgiwrXO2GfgzXGrtdC+DB8rRwTGRr+7RbnUvedAbvgX5ugCl8LKSvqHMHgL1CiB8qf5p25yXbsUzT89JERHXGz0EAbwawDxN9Xso9kjzOUei3QR9FPwTgq+VuT5FtXwJ9RHwngD2y/QBmAXgCwAHj/4ZytzVL+38BvVucgB5pfCxX26EvqH4IwH4Aby13+ws4lp8DeBXALuNmmzvVjwXARdC777sA7DD+vW06npccxzIdz8s6ANuNNu8G8HVj+4SeFy4/wDAMU4FMZ1uGYRiGyQKLO8MwTAXC4s4wDFOBsLgzDMNUICzuDMMwFQiLO8MwTAXC4s4wDFOB/H+aO8M/nRzAlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x140f480d0>"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "idx = 2\n",
    "\n",
    "fig.set_figwidth(10)\n",
    "emb = X.dot(ayze[0].T)\n",
    "ax[0].scatter(emb[:,0], emb[:,1], c=Y[:,idx], cmap='Spectral')\n",
    "\n",
    "emb = X.dot(ayze[-1].T)\n",
    "ax[1].scatter(emb[:,0], emb[:,1], c=Y[:,idx], cmap='Spectral')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing on sklearn multilabel set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_multilabel_classification\n",
    "X, Y = make_multilabel_classification(n_samples=5000, n_features=4, n_classes =20)\n",
    "\n",
    "empty_rows = Y.sum(1)==0\n",
    "X = X[~empty_rows]\n",
    "Y = Y[~empty_rows]\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14fb6ddc0>"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = 4\n",
    "d = 2\n",
    "A = onp.random.randn(d,D)*0.1\n",
    "idx = onp.random.choice(range(X.shape[0]), 100, replace=False)\n",
    "batch = X[idx]\n",
    "labels = Y[idx]\n",
    "y_mask = labels.dot(labels.T).astype(bool)\n",
    "plt.imshow(y_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-468-483a0567d07b>:15: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for _ in tqdm.tqdm_notebook(range(1000)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1572f8c73484653bab7e7f0787b6f5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1459.4419\n",
      "1455.6921\n",
      "1434.2765\n",
      "1433.399\n",
      "1419.3739\n",
      "1417.4548\n",
      "1406.8793\n",
      "1385.935\n",
      "1395.7771\n",
      "1391.4628\n",
      "1386.3883\n",
      "1390.994\n",
      "1381.635\n",
      "1361.098\n",
      "1369.4056\n",
      "1372.6907\n",
      "1335.9723\n",
      "1356.7136\n",
      "1352.2843\n",
      "1346.393\n",
      "1332.542\n",
      "1342.4857\n",
      "1343.0637\n",
      "1355.5421\n",
      "1346.9138\n",
      "1343.7177\n",
      "1337.0841\n",
      "1343.9432\n",
      "1318.0338\n",
      "1349.9532\n",
      "1331.7981\n",
      "1332.2097\n",
      "1341.5261\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
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      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/api.py\u001b[0m in \u001b[0;36mvalue_and_grad_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    408\u001b[0m     \u001b[0mf_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyn_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_argnums_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m       \u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvjp_py\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdyn_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m       \u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvjp_py\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdyn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/api.py\u001b[0m in \u001b[0;36mvjp\u001b[0;34m(fun, *primals, **kwargs)\u001b[0m\n\u001b[1;32m   1270\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m     \u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten_fun_nokwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1272\u001b[0;31m     \u001b[0mout_primal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_vjp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_fun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprimals_flat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1273\u001b[0m     \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1274\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/interpreters/ad.py\u001b[0m in \u001b[0;36mvjp\u001b[0;34m(traceable, primals, has_aux)\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvjp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m     \u001b[0mout_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0mout_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinearize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_aux\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/interpreters/ad.py\u001b[0m in \u001b[0;36mlinearize\u001b[0;34m(traceable, *primals, **kwargs)\u001b[0m\n\u001b[1;32m     95\u001b[0m   \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_flatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprimals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m   \u001b[0mjvpfun_flat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_tree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjvpfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m   \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_pvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace_to_jaxpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjvpfun_flat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_pvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m   \u001b[0mpval_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpval_tangents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_unflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_pvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0maval_primals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconst_primals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munzip2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpval_primals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/interpreters/partial_eval.py\u001b[0m in \u001b[0;36mtrace_to_jaxpr\u001b[0;34m(fun, pvals, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mnew_master\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJaxprTrace\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmaster\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m     \u001b[0mfun\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrace_to_subjaxpr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstantiate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m     \u001b[0mjaxpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout_pvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_wrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpvals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mmaster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/linear_util.py\u001b[0m in \u001b[0;36mcall_wrapped\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgen_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_store\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m       \u001b[0mgen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m       \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_store\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0mgen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/api.py\u001b[0m in \u001b[0;36m_argnums_partial_\u001b[0;34m(dyn_argnums, fixed_args, *dyn_args, **kwargs)\u001b[0m\n\u001b[1;32m   1389\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_argnums_partial_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdyn_argnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfixed_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mdyn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1390\u001b[0m   \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfixed_args\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1391\u001b[0;31m   \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdyn_argnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdyn_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1392\u001b[0m     \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1393\u001b[0m   \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/util.py\u001b[0m in \u001b[0;36msafe_zip\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0msafe_zip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m   \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "D = 4\n",
    "d = 2\n",
    "A = onp.random.randn(d,D)*0.1\n",
    "\n",
    "g_sq = np.zeros_like(A)\n",
    "m = np.zeros_like(A)\n",
    "\n",
    "mass = 1\n",
    "ayze = list()\n",
    "g_squares = list()\n",
    "lr = 0.1 #step size\n",
    "momentum=0.9\n",
    "\n",
    "values = list()\n",
    "for _ in tqdm.tqdm_notebook(range(1000)):\n",
    "    idx = onp.random.choice(range(X.shape[0]), 200, replace=False)\n",
    "    batch = X[idx]\n",
    "    labels = Y[idx]\n",
    "\n",
    "    #y_mask = (labels[:, None] == labels[None, :])\n",
    "    y_mask = labels.dot(labels.T).astype(bool)\n",
    "    zero_mask = onp.array(y_mask.sum(1)==1)\n",
    "    pos_idx = (~zero_mask).nonzero()[0]\n",
    "\n",
    "    value, g = value_and_grad(loss)(A,batch,y_mask, pos_idx)\n",
    "    values.append(value)\n",
    "        \n",
    "    g_sq += g**2\n",
    "    g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\n",
    "    m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\n",
    "    A = A - lr * m\n",
    "    print(value)\n",
    "    ayze.append(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14f92cfd0>]"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nSSRTuO25pdjcXDwHgnte/gQfrdsVfGIJYO9QW0Vjd1sCX33oXVz6h7kdfu9ypqKEvj6Rm+LCV8KFTTsK7hW0fFNLQfPLli8+8DZuf/5jtCckZlE+vLViK2bMXonrn1pUlPyTKcYvX/0U5/76raLknyvkGmvRn027WjFv9fa8763qbKnbULlRUUK/SjPvFEHmY9qv3sI5BWx0zy1aj1PvfgMvLtlQsDyzRe00VoxOspJQ+sae9kRR8lffJ94J9ouYcu9sfPGBt/POR70TXdkTKkzo6x8/WSQh1rR9b+A5LyzegC0hRgQfr99l/t+cd7lyRWlqpRb6+/rEe7WpcMQTxXmOUn8fL3Ix6+zYU5iV7KqNi8zPpGKFfqkaye62BL77+Af4xsPvBZ9MZSBwzVdW6jmQfT06alXMaGptRQrtrapIucn+UtYbNeiJdOB23KkUl3349ooS+nHtYxRacwybnxJen23ZHXiuqqqlbMeRMhH6pb5/vlRFlKZfHIFQTpp+Ipmy2kMpy5XW9DtO6F8+cx6G3dDhAYSzoqKEvj4ZWWgZ4mYuYpc0JbzCCDGrrpaw4SjzTqmF7r6+t7Eyc8SLpAXaP8+cFVtx98ufFOVeQQy74Xlc9ZcFANLlKkUVVhp3R9r0n1tUuvm3sFSW0NcaXKGFmFvIZpXywuINOOv+N42hnym8wjQCJXDLQYcr1hxI6Pvv45q+Kn4Yoc/MWXtL2TXqi3/3Du579dOs8igkf//QiLuvvltJhL5570hFSblgwmyi8jARbSKixVraz4loIRHNJ6KXiKi/dmwMEc0hoiVEtIiIas308ebv5UR0n7mDVoeiD63dtPB8cNNE1RD3ij99iMVrd6E9mbI6hzBCVL2hUsrbcjHv7Os2ffX+4iH2c5j53hqM+O/nsW5HsFOAolwmuu327FLWG9XWCmHeaY0n0XDtLPx57pq887Jzxwsf4+IZ7xQ8Xy/C9IGPAphqS7uTmccw81gAzwL4KQAQUQzA4wC+y8yjAEwCoKbiHwQwHcBw8589z6JTW5WOt1NozdWtctvTkilOC/0w5h3z/1w8IJZvakGr6W6ZD6pv9irvxTPeKaibqhel7nTyRSkZbSE0+H8uMLaPDjPvoyiX19Nqe758bPqpFGPtjr14fdkmx7Ef/3kBXljsH89HKWL5CP13V27FnvYENu0yvO3uf8179JSra/UDr6/AnJVbc7o2FwKFPjPPBrDNlqYv++uGtAViCoCFzLzAPG8rMyeJqB+Answ8h43a/xiAcwvxANlwwfh6nHl4PwCFbyRumqhV4Tl9Tkf4UbfGkzj17jdwxcwPfc9Lphg3P7MEa7bt8TxHNRevYs9ZuRUL1hR/34BSaPpbWtrQcO0s/PWDprzzSmv6wd9fmSOyEZhe53Z0Z6krGhfNmIN1O3KPZptkxtR7Z+Obj7zvOPa3eU347uP+gfjUs+dq0l+3Yy++POMdXP3XhVa7jfnYiv7zjx9Yf5fLyMuNnK1dRHQrEa0B8FWYmj6AEQCYiF4konlEdI2ZPgCA3nKazDSvvKcT0Vwimrt58+Zci+ggFo3g28cPAVD4j+Jm07cmsZDW7rPZrjFX846au3h7hb/2MH/NDjz69ir/za6VeafUNv0SbHOpNO2Z763OO69sbPoRy1XX+P3covU4/Zdv+pokvYR+oSaOg8yh23e3o+HaWXj8nc+ttHdWbsNvZ+cewiPFjObW3BezKVNarhO5LW3GvZdtaLbeY9i8St1e/MhZ6DPzDcw8EMATAC43k2MAjoPRERwH4DwiOgVwXYvt+VaYeQYzNzJzY11dXa5FdEV9tLBa1Pw1O9Bw7SysChhqu9n07ZNYiVQqKy8UZVrJtvqkfbaDrmSzXN7nqQ+XLLH3TCm8d1T7LoTbobViNoQQJtv6jB/+6UMsXb8rwxHBjlcRCzVCCspGLUp86M3PMtLzGWnk+8nTmn5uQl+9UwLQGleafkih3xk1fY0nAZxv/t0E4A1m3sLMewA8B+BIM71eu6YewLoC3DtrIgE2ajtPzTMGKP9ysSvquOVnFxaJZHaaviJbmaNGMWFnDfzyT9v0sytDoSmFv7dd47aTSjE+3xrO7p4W+sHPEbFGeMa5QfMqev5Aek4AcE6sKr75yHv4yV8XZqS9vmwTdu51Xw3rpUC8u3Ir2hJJyySltGNFPsJP15ZzcbzI16avRuhEwN52w2wVi4bLq1iuuYUgJ6FPRMO1n9MAqGDZLwIYQ0RdzUndEwF8xMzrATQT0UTTa+cSAE/nUe6cURXgyZBDdhWvJ+gjumlUrhO5mvqyeO1O17wumjEHP3/2I6us2U7kJmwjDC8s81GIc0qtuRTSpn/Jw+/hd7NXZqSt2rLbIVis9+/xIh94fTlOvPN1fLoxOExGNsW3Ohuzqigx49dh6Pn/UnPV9Lrm9WWb8X+aJ8rWljZ885H38b0nPnA93638yzY048sz3sGts5Z6mj3y0vS1965nE7YDSORp3klr+mTNVURD+n/an3vtjr1Wx1FqwrhszgQwB8BIImoioksB3E5Ei4loIYzJ2x8CADNvB3A3gPcBzAcwj5lnmVldBuAhAMsBrABQkmVr6pu9vmwzWuNJtLQlfAW6Wj4fpKH52/TNc1Kckc9Z9//bNa93Vm7D7//9Wc42/bANzWoKPjdIm3dKLPQLaNOf/clm3PrcUuv3is0tmHTX6/jVa8szzosEhMF4a7kxZ7JxV3AcpWzmkOxmpTCjUz1/XcaFNYupeukVkdLtHWzdbTz3sg3NiHpo0/mZd9LX6vmEzdIy7+Qr9CkdeDCseceupBx7+2v49mPOCelSEMZ752Jm7sfMVcxcz8y/Z+bzmXm06bZ5NjOv1c5/nJlHmcev0dLnmmlDmflyLrSjfEj0od72Pe0YfdOL+JbmHbCwaQf+8aH1OJamH7RYxs9PXz3q/a99il224bOfW2V6QW62mr65ACxghBCm8VimhVJP5Bax01Ea2DMLMi2Oqqp4yU31nqtjwdpfNuYp0sxKzGwJHL+YLnr+eh03TIop7G5znxDd3NyGZIpzUzA0ofjy0o2up+RTb5IeQj9sXVDKXK7eO3r72dOendB3K+9by7fi3ZVbceztr3l+j46g4taq6Q1i225jR51/L99ipU371Vv40f+lvVmqTRtekHnH1U/fVuH/Pm8tbn8hc9u4FZu9Y30XW9NXQsTXvJNlnrmyfFML3ta+g51iumyq4f+a7Zmuq2TTuO20m9pxGDtvNu9Pt+k/8Hra+yVuBvM67hevOfac1bPXFYl4MoUf/mk+Rt30ouu9jrr1FfxCq5NepXR7ByqFQLjjhWWu1yVtik826M+UzDD1hMtL3dtrFBJEWtOntKYf0qav11f9e9z2/MdYu2MvloUwCRaLChT66b+37w4O4apsePnY9PUjO21hY3ft9e7xcw3DENamH+a8MJOIheDUu9/AVx561/N4rvd/ev5a3Pmi//6sKm/loaFQ78XTHdIc/fkJlecXrcezC9flZNNPpBhvfJJ2WU4kU2huTaBp+17HZix6GVdtTXdeiRRjVsCm5C9/tDFQOOf6+fMxy+nPpLvshlZqCuS9EyGgtT07m74+KsvohM0688C/ljuu6SgqTujrkzrKJumHEva52PRVA9LbkV37V94OKt7KX7TJtXw1fbfL5q/ZgUfe+gxA+tn8zEDlM5GbnTfErtY4/vD2KvzwT/Px63/5+4p7PZtK93p0VSY/E8ZlT8zD5U9+mJV5Jy30UxmTf3odtNc3L2GtmyX9zENBMXJcNX3NvONFPl5XGSYSdv/7wddX4I/a2gAdy3snRymnrtdt+iEV/QwlUF1LlG5zryx1egN2lMW74oS+3utv2Om/WnBrS5sVqfCjdbt8dz3y89P3S2tpMzT/B15fgRH//TyutrnRAYYAa7h2Fp5daNicX126Ec2t3qMUSyC41KFzf/0WfvbPjzLOC1PXOsplsj2RwlqXmDNhO53WeBLJFOPGfyzGTc8scRx3jXzqqd1yxv924lmE1Ah6fwvW7MA5v34LrfGkJUTjCcZurc4lU2yV1T6y9O6Y0gf8/Pz9FAXAvY7oLo1B+eaCPqmsty99gvcXL3yMG/+xGG7k672jf7OEiwLw3mfb8N5n2+yXAch8bt3HX/8eyRRnuLh2lGJVeUI/Q9Nv9zwvlWIs1Fwq31u1DZc/mQ5rsKc9gVtnfWRpYvoHe/NTwzbtNjiwN54Wc8Wh21J/ZVpRC8MefH0F1mzbg0v/MBf/9X8LvMuuRhgBhqEwET/V6+qoMAjXP7UIx97+mqODDXv/Q258AT+YOQ9bW9y/rWu4DI+8Uy4jNR2lRSvhMvnuN3D3S/62bS9u/ucSLFizA0vW7bQUk7hD0095hub26lR07d7LGYE53Zl45ePWWYbZuCWoM/Hj0j+kHSx0nSqscMx3cZYesE0tTtTNvF/67Rx86bdzfK8FoLl7UsY3iCdTGK3NtXSUs0TlCX3t++tbFiaSKczRwha0J1PoVh3LuHbOiq1Ww3vkrVX43Zuf4WHTVBJWmNgrbLPZ04etlqoCrbItCkqlGNc/tQiL1+4MLSDDLBSytksM8SyF4PVlhg3bvvxe2XQXr92JuavctSvFc4s2eHZ4YUZfCpWsBOEVMz/ESXe9jp174liwZoelOavrP93Ugvs0t09dULq15+272/Hn9w1znhXjiNN1NJ5IWV4jgFHH1Le1jyy9rF/6N/bzQPMy7yRTjG27211HEirJV+jnIcj0sl8x80Pc+4ox6g5b7eIBQj+RTOGvHzRhV2scI2543hEwTb0TQrp9h110lanpG9+wKhLJuN6el2j6RUKfdNuiaYO/+tdyXPy7dHjT9mQKtVWZr6dLdRTDb3geF/7mbWtIu8s0s7j76QebEpSm71YvVVIY2/6a7Xvw5Lur8b0n5llaSZAGlnbt9MbPpq9X2kJV2C7Vxju/88Vl+LU22aUa3Vn3/xsX/MZdu9LxFoIuZriAYGUpc77lmQXr8NmW3fjq743IokqLTjJj2q/Say5ufmYJnl24Dou0kaJeF/4+rwnfeWwunlu8Htf8bSE27Gy1RnVAWki1J1MZK2QTyZTV+dlft6emn0pZ5g2/CJ9eE7m/e3Mljvz5y1jrsvczB4wOMvNNX/PXD5rQlshuodJ7q7bh3lc+Dbxfxr1d4uWkUox/f7oFzIw/zPkcV/1lAe575VO0J1MOM5GlPBFZ773dQ1Gyvzc9sKIy70SjlFH/drdlvoOOEvqx4FM6F3rj2tKc1vRXbs7UnOOJlENodjFDM89bvQNnH2FsIdBqmXecDWpPexI3PZ1ZkZw2fSX0nVI/7TJo/M8AvvjA28bftsKt2bbXuiaMx0Q8mbI0Kb8JJFUqt9GDXoEN4RJ1nBOGD1dvt/6ujRl52M1dYRq6Phrx0vTd3o23pm8KtRRw2ePplaqL1xpBZpUQTaZSWNiUFvCPvr0Kj769yvMeV/7ZMM0dM/QAAIbioN7zrIXrrQ1IfvlKZhjfHXvi+Nu8tXDDLRIlYDxvLEJIptjTps/w1vQ/MV0Ln13kHTXF79PYR4gvfbQRV/1lAVZsbsFPph5iHc9mAVX23jvptIff+gy3zFqK313SaM2LrdtptJ1NzZmOHUoZIO1vt+0u31m5FUc17O9ZxlZtYVeb5iG2fU+75zXFpOKEvt7r6+Yde51rT6YcGmCX6rRQU5Ohe+NJvL18C1ranJrL4+987pg3cAj91mDzjm7XVeYglcvWljb07lqNz7cZnVaXqig+Wr8r4xyFLtwTqXBxgFRn9M7KrZhy2IEZjTNh8yapybE26SMs/R3rhDFZ6dqVQxNOMZrbEjj3AWfsf0+hb2bHzHj1Y6e3hTK9hHmPboJR3be5NWFp93pnsdu2bP+mZ5a4TnIDmXVZJ55MGcIGIc07WlpLWwLPzDeEvX1RIRDs0go4R1Fq5KLi0x9/x7+QSKXw7vWneubhVdYgVJ0hIjAzLvjNHKw018VsbWmz9tfwcptW2j1R+p5qdJwZQvodvHLliZn31upEejVvBFvb0/Jgh819W4R+kfCy6ds17fZEyqGl2M09ALBsYwu+8tC7rh4CfhPFCiXE3eyOqr0oIW5n5944xt/yCi49brvOV6cAACAASURBVEjGSkHVITmGnFpFjCdSoSZyFY++vQr1+3XBt48/WMtP1/QzM5m7ahua2xI4aWTfwLyrIhG0wlzdGnW3OIaJ8pnRaGzPFE+l8OKSDfh8q3PvAC+hlfbeyeK+Xue43CNhCf14qEmdzR6C3Y9EihGLRgAk/YW+NWmdLuets5ZaZWyLO69NuHQUjnxt78ZaZW5e5dWJ+ZHt4qwIGR30B59rI8qqKGpNYewVZM7qNLS/P9lobE60y+Y99+anmSHg3TT9DbsyvQV37s2UD5tb2nBA95pQz5YPFWfT1zVVvT7aJw51TwnFlmanEG82K0yuvbSfTd9euVM2oaa0rxcWp4WZXoEZmfut6nbUeEoz74Tw0weAe17+JEMoxLXy6F4ii5p24oLfzLHCW+zY047vPDbXWgFtp0oLY1DlIfSDtOktLW0ZAt3+TIkke07oec3NBXm0WHnn6LKp6ow9MqUX2URu7N+r1rpGKQSe5h121/RXb0ubPPfawoW8unQjvvPYXAD+76fQXl/bdrfn5L1jL38yxbjZVI68hL56LtJs+gDw9ootjtHBepv7t1Ko2hMp/OYN93Ui222a/tR73/R9nkJReULfo+FvtPXCrXGnecfeUwPhtr/zw6/B29uSX6P/3Nz9Svf2YAbuf205Rvz382hpS2SsOFUxWfT7rNqyG4lkClPvnY2H3jSiUOqva3d7MiNkhW7f1Bvi2b/KDCT3xzmf4+WPNuL3/86MbKnQRylVHnFsghp64y2v4PRfphuN27vzUqbtcVKUZsYF1PTdvJ/U9zTMO4FZZLVIr6tpa0sk2RqFerpsgjMmrRX6inV7jKib/5leA+FXLvuxfLfGPv/Bt7PW9PXQyIol69KjZ7vWDhjzTOr76DZ9wHiP9veh2rGa61NKykP/XokVm93Db9vNO4p/LduEnz692JpPKTQVKPTd0+0C/az7/21Vmm8d2+CZn1uFyYYWH/OOvXLrHQwjLZDX7tiLpaYJyK61qAnRLc1tjpgsuha2YWcrJt31Om577mN8vKEZt8wyolCSTVS2aCMi3W1Qaf1uURr9IpWmUpyh3XuteMxWY7SfHU+y5yIiXWj/8E8f4pAbXzDTzbzy1PS7VUddOw4lhFtaE473rAgTzM31nqbQ376n3ZqgVPdzXaDmMpG7Q5totIeo2NOmKxch5jRy8tR38tmW3dgVcjctfZ3Fbtu6D91caJ+veH7Repz3wNv4y1yj7eg2fQB4/J3VeN/mNqwCqCnzpKoTXoIdAHbsdR/5fuuR9/HYnM8x5Z7Z3g+XBxUo9N0b1+Zmp710tak9nzWmv2d+QdE3g2huTeCcX7/lare3Cwpd02fmUFpTjSk02hKpjE4jnkyvBmSkPQnsm1B7dZLLN7VYXixAetLLLR57tU+k0rH/81KGXdfLBJGt+cw5n5HyFvrauc8uNOLUtCWSltAI8jUPmm+Imt4zdtT3aG6Ne5atxsPcFUTPWkPoq84bgGNdgY6bfV43ebZqpsFUKnOlcE4WHHbvLD743H8NBgBc97dFgecAmS63dvdI3TRpL78KgqgUGELmStp/L99izZspLKFvtjfr3j4vxx6Hq6OowInc8MPLG54y3C39lnHnu0NOS1scC9a4T9DZNX37vcLEaK+pUkI/mfHs9736qRVKOMVpE4Bdi/LqWE69+43MsvnsUqQawl8/aMK/lm3Ctt3teOp7x2JY3+6O+3ltNGHXppMp9v0ubh2mlzbt9h43N7dZ6UHvOagKJFPsapJQcyzNbQlvoV8VgYs+Ekh3F1cqawWx7XmYgW88/J75t9PrBMjU9De3tGX8zkaLT0/kOk2jyRTj/AeD12As94lMm5Gfpunb65V/mGpVxrR5KJlKoSpKngsaVaeilCw1CvarOn6jACD3kNBBhNlE5WEi2kREi7W0nxPRQiKaT0QvEVF/2zWDiKiFiK7S0sYT0SIiWk5E91G+xr0csQuKYX27B1/jU9R856nsw2adO1/MXNJv15TtgsTN86XG9Hu3a/p67HhdgOoxff7x4VqHmGTbOXoegL/Qb2lL4POte9DcmsCT77rvXLbX433YtemgzjaMeUcJOF3zVRrypua2dOMPtOkHR2B1yyOUeSdA029ujaNpu9MjyU/o+9nE9UN656A7AehzJ/Zr/PJdvqkZOzRTij2m/JJ17jvJOfMK1+isTtvFvOMlvJn1+Q0jTa19UW3JDZV/V9PleG+7sUGTPhlux2sCWbF/t+J48oQZOz4KYKot7U5zA5WxAJ4F8FPb8Xvg3BnrQQDTAQw3/9nz7BDsvWfDAd2CrykTI5heURnOYXrPLlWOa5Tmsbc9iTaPDVsSSbZEjt4x6PsKWOemGKu2OIVMPJnCxl2trtp3jYtduj2ZdG28Sz3cU+3aadAEurt5J7NsVhwb7Vz1Djftai2Y944eKE0nbd7x0/T9F7yd++u3cNwv/uVI71odddT1nSE8zdQR++hGd9m0e2GFUXx27I3j1Ltn4+fPps0idpPLKhd3Wr8y6oy5+UXHHgP6nIxd0/cyI8aTjGUbms3rjDQCIcXsO7+iOrAetUb9aWlL4Mu/neMaTVOxw0Po9zAVjwkH7+96PF/C7Jw1G8A2W5reMrtB+w5EdC6AlQCWaGn9APRk5jnmjlmPATg3v6Lnhr3hhzGZxspE6rdn2PSBF2yxQnp2cWp3SuDuaU96CspEKuUp2FZuydRUWuNJV81yydpdmHDbqxmhBxRubpiPv7MaP/mbM6KoF0mbZvb/XlqG9kQKf5yzynWobn8cvWOz0lzMN01muIFlG1oK5r2TSLG7Td8UpC1tCU8zWpCm7+UZUh2LODRTZav2G5ioZ7a7MOumnkH7d3W9xg+3+Rxd+04kU9gach2C2+12tSYy5i+AzCipdk3fy7zz+dbdWrtKm3cSKfb9FqoDUwJ7d1syw0NI0UXrxHfucZ/ItRa9FWmxVs42fSK6FcYG5zsBnGSmdQPwEwCTAVylnT4AgL6uvslM88p7OoxRAQYNGpRrEUMRJuxqLnNpxw47wNpD1YvqWCTnieDV2/Y4dityG9Krhr+nPQFmd63RiMMTroK1xZOuqtY8LZSCTsrH9v7nuc7Iol7YNeXH5nyO3W1J/G1ek2tnZu982l0mco34SlHXZ/9g9XYM3L+L8QwF8NN3E/rtlstmHL26VrteV+OyIDAM1bEIqmORDGH9qbmwyK7l6sJF/WkPEaCPEpMpxoDeXawJ+I83ZO9ayMy4/fn05jbtyZRnZNSw2N+xvvbA7qjhZd7RQzHo5p1kyl/TV95/1bEIaqsijk5G0a0mZn2TdR6h3ZWXXbFW6OaswjLzDcw8EMATAC43k38G4B5mts+0uLV6zydi5hnM3MjMjXV1dbkWMRRuGlZXWyiAXEKz2iN0uuEmpPPBbf9O5VK6dP0uxwIVhZcmqtDNMzc+vcTVTdW+zkFx6j1v4E/vudvvs8FtgleNOMKs6ky4LLaL+9i41+3YG9qm7zcpaN3L5Rx9ItdL9/DTLv2euyoacZjVtu9pxyE3voArZn6YkR63vZe7X1rmEPqtWsfa0pZwNSV64dZ8/jF/XcauYO2JVKhNjfxImR3JKx8Z+/UmtZHaOyszFTCvOSG9Q7QWZ4GQSPoLfUWECN1rYlZ4dTtqJOBFQnOlLpbQL4TUeRLALAA3AZgA4AIiugNAbwApImoF8DcA9do19QC8Izh1IG6TtLVV0YxFTrlswhCmgnSviXmuUs2FmIuAeNsMF/27Nz9D/X5dXK9LJtl3yN+rS1WGBmQP6ww4g1UpVm7e7Qhm50ZNLOJrp//t7JXWwheFslGH2WQ6nnR2bIkUY+n6Xfjp087NVnbsiWsTgf6NL8xoze0cZd5pbk14Lhzz0/SPvf01z2PVsYjjWiXo5gQIwPteW+4w6+nvbufeOA7t18Pz3nZOGF6XIeDdaE+kMqLe5sKm5jZr9eu5Y/tbsXWYGZ9szNRD7Yur9HIo2K7phxjyRyNGu/aam7IrlHb0NlCs+Po5afpENFz7OQ3AxwDAzMczcwMzNwC4F8BtzPwrZl4PoJmIJppeO5cAeDq/ohcGN3leaxPYuWj6fkJfaeRemr5duAHAgT2DZ/LrAuJ2NLmExwUMd0u9gh3cpxuunDzC+m03X6zYFF7oh6VbiFGP3YSkhESYfQHcwmq0J1JWqAg7O/e2h57IDbMq223isE3z3vEijKDxus5+rdeiJrfIkWq9ghe9stD0w4wK2hLhbfph+Mf8dVbAungy5Ri5eI16r9F2rtPnKpI+E7l6TK4IkW9dtlelbrZOoNVmRisGYVw2ZwKYA2AkETUR0aUAbieixUS0EMAUAD8Mca/LADwEYDmAFXB695SESIRw14VHZKTV2jwmctL0fRqryt9L6LvdLigK4Y8nj8Axw4xQvQf36ZZVo0ymOMPt8LozDsV+3dI2ZrtQsGuKgPvitmywa0DXnn6I4xx7g1HmgDBrJVraEo5G9NmW3Z7bTsaTbAnjoLYXRui7lVFplXvjSc97+LkJ+uE2kbvDY+LQbxvF3359PE499EBHeu8u7nMQbnQJMS9x/B3/wrzVO0LnGYaPTBfQrS3tjrrj5SqdEbvK5r7qJfTPG5c2YgQJffvmN/YOsfHWV6y/Syb0mfliZu7HzFXMXM/Mv2fm85l5tOm2eTYzO4J8M/PNzHyX9nuuec1QZr6cO2oX4ACiRJgyKrNS293k3GzlQfhp+upYdw/7nn1kccUpw13P0zljTD+rnPX7d8V/HDskbFFNm376d++uVRmjnXgqhf27hW/kuWC3P3/h4AMc5/zy1cz48qoGhTGvtLQlHCOWSx5+zxG+WGebh5C042Uq0HE172i+7177L+c8kRt1mne8ZIjfSGncwN6uZsFeXYOViiF9uuFXXxmHIwb2Djy3GCjPJrfopPZvNviAro5z1ILDNz/dgm2721xdjwFgaF03jKnvBcBQEHv62O3tgQPtypkuFctuIndfZuVtZ+C28w4HYAhYu13fHkI5mw0eFHY3xZNG1mnHjPy6VEcdk1wzvj7ekaabWjzvF4lYrqURAmJeQWxcYM6cjOzdpSqj40umGHNvOBW/vGhs6DzduGB8vecxu1ZqH20BTv9wRRhNv7k1nnUjCmtu8NL0r5k6Et8/aSgAYOl6p4eLfp3dRVKRq3mnysW8kwvdamKuCoyfYFMMPqArzhrTP+fRihtTDnOOOoJwW/lqF/pubsX6aGDNtr2eQj8aIUtRixDQy2cUFLdp+nb3Vx0R+gUkEiHLThuJkMN8U2urpH4rcr2wN5TGhv3Rp7tRGVQFIwDdNS+fMfW9MGXUQZ5zCH4NrSpGlqCPEGU9OtEFUK+uVRmdQDzJiEQI54xNe9kOD7GS2c7A/bwruP6+3rh6kmcDc8NNU7Vff9tzH2NdlrHbg1xuFV6a/lcnDLYatdv6BT0cr6fQzzHgmjGRm7+w7Vodde08wszBqEBmuT4DkN5dTHHk4P1yzkvHbt4J8qoBvNfrRCPp9hYhQm+fUZC9bffuWoWVt53hGtSxrCZyOwOW0Cenzb4Qmr69oUSILPdQ/Zhu4tE3fXDjjguOcD8Ao0JmavreFdSNbz2antDs3aU60E5941mH+R53oyrm/R51wTD4gG5ZmTXcbNJujfi3s91DO9vpXhPDwX26hd7gw2uCPBYhREMu7POyt3vtLxBEVTQSypYeBBFldKCnmabQbjUxX5Pf8cP74L/NOpJNB25nms2poWdtWqAqJSoX7BO5Z43pj5nfmeh7jdfoOUJkyYgIEXp7zKd969gGx/xhNGJca1fSIlS8xVkVK/SH1hma6hH1vV3MO9lP5F45eQR+cf7h1m+7dhOhtDeA3pD1yVz1jb00/amjD/K8f1WUrEVk5KPpdwmh/VXHItjPY7GQIui4a77ac18zdWTGMbtgsI+2dE4YUYfjh/exfruZd8Jool6kmDMmsoNw0+KBTA3QC6VgeM0tZDtiG3FgegQWZq2IF0Pr0uFJ9Lr8lQmD8czlx+LcsQPw9rUne17/x0sn4MhBhlaej9C3yz29M3/82xNyMvcATtNJbVUEXxjqnEfS8foWMe07RyPemv5NZ4/CYJs5J2pdZ6v/HosGC0HFCv1jh/XBy/91Ai5srM/Q5HvWxtDV1ljCmHd+cPIwfPmo9Ophe0WPRtLhWZXGy8jU9FWnkEssulg0Yk0C+dn0w3oinTbqQNx5wRhHumpk2cwZKPTO7rhhfTKOqU5YTRr6afqTRtThsH49rd9uW/nlI/CYww337ehlAoznDXrfbl4w1dEILjpqIAAgqr3nLzV6z4kolBKxN57I6RspXvjRCfj450Z4LF3oE4Ax9b3NlafhzEfZzC2oiU2v1dC6klQVjeQd8FARpoxeI/4MEzHBc3U14BIGxvxt71BqYhGx6ReD4Qf2cHyED26c7AgV69Zwq6KEK04eZv2252MflhORNVxrHGwEUjrniP42TZ/N+2X7JEZ59JFClYdZIWjS85RD+lrlPePwfo7jv/naeKy47QxrMto4N2wZ02Xq070Gd14wBv3Mbf16danCaz8+EU98ewIAf1dFe/A0N8+XfFY7M9gKnJUNE20eRxEK1tTdtMJvHDPY2iBev97PvKfobpZ7d1sylGlpsoemXBVNC3X9W+SyZkXtA33eOM/IKxb7me+ji7awSkd37Y0SWZ3ju9efknW5dMLMf3h9y0MO6mEdY2Yc4DNKtMuSiDZC0KmtiorQ7yiqohGHz7j+Qe75stHwmP0XndjNO1FKD1WH9u2OVbefiSmjDsrQKP3CEweWOxLR5imck9OK9kQqQ2DbuVozu7hNwCmtRp/UCmuC0O8bjRAubBxoDaljEcLBdd0x2Ix66qch79wbz+gYW9qcppGuNVFc6OMt5EeKc+s07HZi0my9XrgJ/WgkYnWQYecEFF+dYIw2jx/ex/c7K77UOND6221tBJBZD3KJ8T5pZB2mn3Awbj57VEb61aeNxGWThmakqY5GCf0UA8/+4DjreMxWh0497ECsuv1MHNizNvuCaYQxQdm/RXU0gveuPwVj6ntb9TWRZPTv7b7yHXBaDdRve32viUVkIrcjuWZqZuXXv4faRSvF7DvJZj8W0XZPqtYqri5c6k3vllyEvu6RRD7mnYS2PeHpow/Cj07NXAOgC3A/YW5vfGHIFB7mNWp0koU02bE3nvGOWtqcLnndamL4zxOHOtIB4O4vBWjM7PSUUgLUK5QFAMs90+06L7rXxBzvLxpJvx/1DdRG50GMG9gbq24/E4MP6Bbqu+jf2D5SUWSYd3KomzWxKK4/41CHb//xw/tkrCf5yoRBlomyxhL6jNEDemHcIOXrn75/NvMuOueMda54Dyf07b8Jfc3ORn2vFDP69/b+VmTLY8RBPay8MssTlYncjqRnbZVldgCMiq4WC1VFI7ji5GH4+/eO9bWZ2rVkIrJ6br1D+OKR9fjJ1EMw4+vjcd9F48xz8yu/4bIZHAbizDH9HItSdGHq18D1Z/C6l/3yjL1w1XDY9jsMO21C3211ZU004vkexwe4/aWYHTZ9VXZ7R6jqxQ9PGY7+vZwdQlBY7miEHPeKRiLW8vy98SRe/q8TMOuK433zcSPsCOwgU3B5dVA1GUI/62J40rU6mtFObjvvcKuNKBu76gTclN5cTXg32UYcQLq9Xn3aSFRHI64OD/a5Pn2+QcmCRMp/sxVd03/qe8dYo1H7t6qtishEbql59D+OwoKfTgEAXDllJMYO7O3bqOwTQ1HNpq8Lv4kHH4DLJg3FlFEHWZqQn6b/zWMaPI/5uaHqpAVYxDFkDSt8M0YEHsJi4pAD8Of//ILjvoD3MDcMpx7aN3A0tKc96XmOfRR2ztj++M7xaY2T4TTdpc0tmY1WLavfr2uV62hFfzdHNzg3xYhFIhmbjAPGu+hrxlra3NyG4Qf2CK3V6nLi8PoQK2EJGGR2/Hs8PIh0wVxIO/N+XasdGrZqI+o9W5ubm8cLsYWgW+emvu/3TxqGT2493bVt21fP6m7Nqh2p9/Obr413vbdef8YN2s9SrETTLwPsmkVNLOoYnvrZW+2VOULpwGVBvtd+FfvmaaOw6vYzXY+pfL3COyjUJGF1jFDlMC1k/r77S0fgORctsyqma/ruBWYwBmjmkIzJX/Ny1hbJheW8cfWBk90tbQn0712LwQd0xQ1nHJpxzN5JnTt2AG44M73ugJnR2+aBoXeUinGD9rPs+F5CWe/87//KOOfxWMSxziAaAep6GEI/20B2uvY57Yj+ePI7EzKO218zAbj3y2Nx0VEDcYRXJ6G1haCQFwP374JJI8OFQ+/dtdqhHClXW+V6quzj/2EuXhrSpxvuuvAIzPi6u1BV+JlrqqIRh7+8XUFwq49+iyNVG1BC/7RR7hPkXrqKQ+iLpl+e+Akeh5++9lGrfRYpAeFs+sqdTuf00f1w5eQRuPb0Q12uSKMmvaIRp0uh/fcXj6zHYf0zXREBZHgH2cv7A82rSRf01b6avm+RHQTZlpvbEqiJRfHG1SfhVJuHimPhnPnMx5oB61IM7G8T+moexv5+lHbsFeBOXyTn5uKol0XFqIlGIuhnmorsT7n4Z6fhpf86wfVeRtkzBcUhB2V+O7tJhIjQv3cX3H7+GM+Vs3qeerwgAHjlysyyzL76JDz6raM9y6ejFibp/Obr4/GTqYfg+jMOxRPfnoDTzbUp54wdgFW3n4neXatxwfh6TBnlXLNyyRcGW3/7uZPGIuQICWL/rm5ROP0cN1QbUIKaiDD9hIMd53mNaO1tqCYWDYzumisi9D0IY23wE86OiVzt3MBVliHu7VapoxHCFacMNxt2ZoXRbZQHmfMVO/a0O4brYc0s1T52Xj1Ymq4Z66MDh00/Sz/VIDPU2WPS7qb2kYi9w1DRNB/5ZlpY2Vebque137fVFPpei9X0e7u6/moKQFfzG0UjhhvgTWcfhjtsayW618Qw4sAeWH7r6Y68Rh7YAwNsniNuroDZolcR+0rtYX17ZLgo5jLRqzOgdxdcNmkoiAjHDuuTVX7/c85o6/ntq+p13L6Dvd6rEc0t54620nr6uPEO6WOYyPR3odxJg+4NODdur6kSP/0OJ0wn6zdJ57YiVxEk9Athy7OXf96Nk62/Jw4xbMs9amPYbgtGFdbMEsb2T8h0HdWfW3WCqpx+nc1L/3UCZl1xXEaa3+2X/s9UXHpc2kZv/xbda2I4fEAvXHy00SgPMTcEUaOSE0fUOYS+FS/Jdt89cX+hr9/b7RljkQieu+J4/N/0iZbZKRqJgIjwrWOHeLoiuoXZ+Nk5oxxC0t7hOdaP2PKYffVJePOak2z3Sp/lFp4jlzAlxcavc3PrSLp4bG4yVosQat+DWh+lXTZpGB66pBEnm+tcAODguu4O06hXJ7bT1g5rY8VbkVvY/foqDD/ZbZ8s8hJ+boTZFETnvRALU3TN52sTB+OQfj3ROHg//OHtVRnn5RJGmnyGJrGM53ZqvWnvHe/8u1ZHHfv7+o2y7A1YL8P9F49DNEL4p+n7fdt5h1sNkYjwxtWT0LdHLez9udc3U9s4eoUa1u+t53nVlBG466VPEI2QZT570Nz1KdeFtG7fLsh8Z3+Ng1xCDJ84vA7D+3bHp5taMMwl0F4uAQntHUuhUHNEfmE87Nz75bGuzwUA/XrV4tRD++KVpZvQpSpTXN48Le0FpNYM2HEzjbphjwRaUxUpv43ROztqVe7jl07wPCebiVwiQo+aGJrbEoG+2/aNFoJw07Ts1UXXMIgIR5meJBcdPQg3//Mj37zyQRcy1RmafuZ5fkI8Fok4Vklns5ZB14rtu5LZNS+1OAwA/vOEg/Hxhma88cnmDNOUzo1nHYafP/sRepi28rOP6I9/LkjvBOrlsaTKpL8Hr4U6YXH7dva8HKauELbESITw8pUnYlNzK/r2cI481D2CXGF1BmoxaH42bZSn0M0WVUv8zDuKv112DHa3JXDCCO+J515dqnDPl8finwvWZ2wR6eVMkSt254KaWAknconoYSLaRESLtbSfE9FCIppPRC8RUX8zfTIRfUBEi8z/T9auGW+mLyei+yhf418H0ddnm0I/7dQulKJEOGecIXCCGpp9o4Ug3DS8sHNAtVXRjAaXi9bmd4nXCIcs8w47zkufnxaCdk1bna9itPiRa1z568441Aoapi+ou+28wy1z09cmDsayW063BO79F2d66AQtYtPryRZzvwDluROGIPORPc2edzY7rLkJfCD9XHaPmLB845gGHGuLxZQravLTLazCNVNH4pCD0oJ7/OD9fAU+YHTOPWqr8JUJg/Ker/DjJ6cfgv8+M+2AUcyJ3DCa/qMAfgXgMS3tTma+EQCI6AoAPwXwXQBbAJzNzOuIaDSAFwGogBsPApgO4B0AzwGYijLZMtEPP40yG20zQsaikJMP6YvDzV12vMjWvOOm4fVzWRV4+UnDXDWqoMnGXGFwhtBx05b9FmdVRyOIJ5OIRpwb3ajT9YUwd114hPvz5RF4TN0nvcm2sXI0LG4dHQBt9XQ6beUmY/Puw/r51w/Fc1ccjwO6V2PCba8CcI8saq8b9ft1xa3nHY6DetXi3ZVbA+tiGFT9KZaQygZVBDeb/vcmDcP3Jg1zpHc0Q+u64WsTB2ek9aytwrePPxi3zFoKwFB4ijWRGyj0mXk2ETXY0vSt3rvBbLvM/KGWvgRALRHVANgfQE9mngMARPQYgHOxDwh931AEPuYdu1YQMbXVkw8JDgUbZicoHTcN78hB++Hp7x+Lc379lpV21WkjHecBmTbwbIT+sz84Dp9uasYdLywDAFx/xiE4oFumJqkLHfuaAADpMAwuz1BTFcXu9iRSzJ57HOhXnTiizlVLzmWeQvEfxw3BlpY2nHLogXjz0y1ZX+8V+C5lxVlKp3130lDc+eIy31APOnZ78UEhQjXEImR1FGZcrgAADVxJREFUjKe47H2bC+pbhBFSr181CRt3tQaelyuWeSePcM7F5tUfTwo8J0JGAEVmLvgII+c3Q0S3EtEaAF+FoenbOR/Ah8zcBkPbb9KONSE9AnDLezoRzSWiuZs3b861iAXBTwj6rbC3X5XNqEDZ9CeNrMPVHoJax6uMYfcmVeEfgOzMO6MH9MJ54+qtZz3j8H44f3y9p8up3wS2220f+eZRuGB8PfbvWu3oYNX71K/zEu75NJpuNTH87JzROcfn9xplKPmof7vvnzQMq24/M+d5Fa+wBI986ygrLlA0j1GPF6rOhDFLNvTphgkeMX4KgZ+mnw09amIZexMo/vLdL+Dv3zsmr7zDEM2iI82WnCdymfkGADcQ0XUALgdwkzpGRKMA/ALAFJXkloVP3jMAzACAxsbGkowZ2aVR2vHT9B0hVLNoa8q8c9FRg3w3Tknn7Z35A189MlBz1CfViul+5+YWZ5+g1TliYG+r46oyi6ViyitBo8+PFLPsuWbtLfRz3zshW04a2RefbzE2CXcdbeWJPWRCKbG8d/LcNWzBTVNc049yCaVRDCyhz1xwb5tC5PckgFkwhT4R1QN4CsAlzLzCPKcJgL4Erh7AOpQRXxw3wFUT9ROofvOD9ft1wY8nj8D/e/kTI58sGpvanzZMaFyjHN7nucXELzRhBZff0vigyW0iwuKfnWYtMnONc1NEoZ+NcP7t18dbT+Np3tE2vMmXKyePwM69zkijOgkrpk3hzR73f2UcZryxEof2C+eeWEzS3jv5afqFVCD+8B9HO1Z4BzFhyP64cvKInCLuBpFTDSAiPR7vNAAfm+m9YXQA1zGzZUxm5vUAmolooum1cwmAp3MudRG4+8tj8QuXnaL83rnfByEi/OCU4ZZ3STYfT2n6XvvcKpTppxD188azDkOf7uG9RnSunDwCAKzrx9T3Rv9etbhqSqZpyk1wZqMc6mGI1TPrWQbNR/jt6RpENq/4tFEHWWECvISHFXOoAI36ilOGB+5ZrIR+PpPaXgyt645fXDCmoE4AuWLfdOVgbdvHUnHiiLqsJ8wbG/bHFacMz3mPZD8CNX0imglgEoA+RNQEQ6M/g4hGAkgB+ByG5w5gmHmGAbiRiG4006Yw8yYAl8HwBOoCYwK3rCdxw8iioLC5+jnZ2MqVTT9oKP79k4bh+ycVxhvh0uOGZKxizYbzx9cbtnyT7jUxvH1duJ2MVBvNVva5LqX3eV/PXXG8r/ttEIVWuPSIqB2Bsg0XczRUDqh222qulJ466iA88PoK7wvKjK9OGITXlxV3HjOM987FLsm/9zj3FgC3eBybC2C027Fyxq+JhBkpqzaWTVtLhNT0OwPKpp+tKHIbNfh1rGFXRnYUKauz6xghrLya1EY9nRXlFaX2WAiKOFtu3Hre4UW/x771RjqQr00YjHte+cR3r9Qwmr7SPrNp3PEiDsXLlaw1fXK+13KMAeNFqoDmnTBcaHpBnXJo3+CT92GUpq+iZPbIY6/kzkrnVyVz5IpThmHFbWd4BmICwm1gribOsrF3xs2gVrmuJN2XyNXhw7LpF64oofDzNsqGbx0zBOMG9caFjbnt45stREZsmH1kIXzumJ9nuLkWYWiBwjt0JqQb9ICIAgNfhfGEUHI7GyX03HH98dH6Xb4bLHc+shNGulY/ekBPLF67y+fs/AkToyYbDupVi6e+d2xB8xTSI6jpJxyMyYcdiHGDwscDqhRE6OdBmMlZ1TFko2F95/iD8Y1jGnz32uws5Ko364uzZn5nIjbuym6HqY7k11/JXCuRjxeR4I+qT7FoRAS+ByL08yDMRK4aLdhdyfwgoooQ+EA+3jvp63rUVvnOvRSCfMw6Z2oburx3/Smo9TEZCvkxekAvvPfZtk7vpZQPIvTzIBuXzWIFT9rXuPjogVi6vllLyc97p9Bml2LT12NTFKEw/O6SRny6sTnvxVmdGRH6eeAm8689/RB8siEt1IoZQ2Nf5H+/6FwAB2TvuphLCGih89OrSxUabaESLps0NHBD90pChH4euGn63z1xaMZvJfSLtSHCvk7u3jvOgGvFZF8bUQhpfjL1kFIXoazo/D6BRSSMthnRAicJTo4bbmye0eCyTZ8fRQgh44sK4OW1kYgg7CuIpp8H2UzkJrPcGKVS+OYxDTjz8H5Z27otTb8YhXJh+IE9cNeFR2BygWLQC0KpEE0/D8JM5J53pLH4ZqS2TZuQhohymtyMukVcKzIXjK/33ABdEPYVRNPPgzCa/rQj+mOabTNuIX9KtSJXEPZ1RNPPgzCavlAsRNwLQi6I1MoDWf9ResRzUxCyQ4R+HnT64FUF4pCizGfIxLgg5EKg0Ceih4loExEt1tJ+TkQLiWg+Eb1ERP21Y9cR0XIiWkZEp2np44lokXnsPupEElOWfPsz64rjsfzW0wuapxW+oaC5CkLnJ4ym/yiAqba0O5l5DDOPBfAsgJ8CABEdBuAiAKPMax4gIrUe+kEA0wEMN//Z89wnuevCI/DCj04odTHKmmiECr4hjNLzO5HuIAgdQmBLZObZALbZ0vQ4tt2QboPnAPgTM7cx82cAlgM4moj6AejJzHPYiDz2GIBzC/EApeaC8fUYJjG7S4aIfEHIjpxdNonoVhgbnO8EcJKZPADAO9ppTWZa3Pzbnu6V93QYowIMGjQo1yIKnRhZ4CwIuZHzmJuZb2DmgQCegLEhOuCueLFPulfeM5i5kZkb6+rqci2iUAGIdUcQsqMQhtYnAZxv/t0EYKB2rB7AOjO93iVdEHJC7U8ggdAEITtyEvpENFz7OQ3Ax+bfzwC4iIhqiGgIjAnb95h5PYBmIppoeu1cAuDpPMotVDgqkF1NlXgdC0I2BNr0iWgmgEkA+hBRE4CbAJxBRCMBpAB8DuC7AMDMS4jozwA+ApAA8H1mTppZXQbDE6gLgOfNf4KQE+MH7YcfnDwMX584uNRFEYR9CspmG79S0NjYyHPnzi11MQRBEPYpiOgDZm60p8vYWBAEoYIQoS8IglBBiNAXBEGoIEToC4IgVBAi9AVBECoIEfqCIAgVhAh9QRCECkKEviAIQgVR9ouziGgzjFW/udAHwJYCFqeYSFmLg5S1OOxLZQX2rfIWqqyDmdkRsbLshX4+ENFctxVp5YiUtThIWYvDvlRWYN8qb7HLKuYdQRCECkKEviAIQgXR2YX+jFIXIAukrMVByloc9qWyAvtWeYta1k5t0xcEQRAy6eyaviAIgqAhQl8QBKGC6JRCn4imEtEyIlpORNeWujwAQEQPE9EmIlqspe1PRC8T0afm//tpx64zy7+MiE7rwHIOJKJ/EdFSIlpCRD8s47LWEtF7RLTALOvPyrWs2v2jRPQhET27D5R1FREtIqL5RDS3nMtLRL2J6K9E9LFZd79QjmUlopHm+1T/dhHRjzq0rMzcqf4BiAJYAeBgANUAFgA4rAzKdQKAIwEs1tLuAHCt+fe1AH5h/n2YWe4aAEPM54l2UDn7ATjS/LsHgE/M8pRjWQlAd/PvKgDvAphYjmXVynwlgCcBPFuudUAr6yoAfWxpZVleAH8A8G3z72oAvcu1rFqZowA2ABjckWXt0IfsoBf5BQAvar+vA3BdqctllqUBmUJ/GYB+5t/9ACxzKzOAFwF8oURlfhrA5HIvK4CuAOYBmFCuZQVQD+BVACdrQr8sy2re003ol115AfQE8BlMx5RyLqutfFMAvNXRZe2M5p0BANZov5vMtHLkQGZeDwDm/33N9LJ4BiJqADAOhgZdlmU1zSXzAWwC8DIzl21ZAdwL4BoAKS2tXMsKAAzgJSL6gIimm2nlWN6DAWwG8IhpOnuIiLqVaVl1LgIw0/y7w8raGYU+uaTta36pJX8GIuoO4G8AfsTMu/xOdUnrsLIyc5KZx8LQoo8motE+p5esrER0FoBNzPxB2Etc0jq6Hh/LzEcCOB3A94noBJ9zS1neGAzT6YPMPA7AbhgmEi9K/m6JqBrANAB/CTrVJS2vsnZGod8EYKD2ux7AuhKVJYiNRNQPAMz/N5npJX0GIqqCIfCfYOa/l3NZFcy8A8DrAKaiPMt6LIBpRLQKwJ8AnExEj5dpWQEAzLzO/H8TgKcAHI3yLG8TgCZzlAcAf4XRCZRjWRWnA5jHzBvN3x1W1s4o9N8HMJyIhpi96UUAnilxmbx4BsA3zL+/AcN+rtIvIqIaIhoCYDiA9zqiQEREAH4PYCkz313mZa0jot7m310AnArg43IsKzNfx8z1zNwAo06+xsxfK8eyAgARdSOiHupvGPbnxeVYXmbeAGANEY00k04B8FE5llXjYqRNO6pMHVPWjp686KAJkjNgeJ2sAHBDqctjlmkmgPUA4jB670sBHABjYu9T8//9tfNvMMu/DMDpHVjO42AMHxcCmG/+O6NMyzoGwIdmWRcD+KmZXnZltZV7EtITuWVZVhh28gXmvyWqHZVxeccCmGvWhX8A2K+My9oVwFYAvbS0DiurhGEQBEGoIDqjeUcQBEHwQIS+IAhCBSFCXxAEoYIQoS8IglBBiNAXBEGoIEToC4IgVBAi9AVBECqI/w9SfHtXumN/kgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14fd8bc10>"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)\n",
    "idx = 0\n",
    "\n",
    "fig.set_figwidth(10)\n",
    "emb = X.dot(ayze[0].T)\n",
    "ax[0].scatter(emb[:,0], emb[:,1], c=Y[:,idx], cmap='Spectral',alpha=0.5)\n",
    "\n",
    "emb = X.dot(ayze[-1].T)\n",
    "ax[1].scatter(emb[:,0], emb[:,1], c=Y[:,idx], cmap='Spectral', alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying on molecules:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = x.copy().toarray()\n",
    "# Y = y.copy().toarray()\n",
    "\n",
    "X = sparse.load_npz('/Users/lmar3213/Documents/GitHub/lbvs_data_blocking/code/raw_data/x_norm_sparse.npz').toarray()\n",
    "Y = sparse.load_npz('/Users/lmar3213/Documents/GitHub/lbvs_data_blocking/code/raw_data/y.npz').toarray()\n",
    "\n",
    "target_idx = onp.arange(Y.shape[1])\n",
    "onp.random.shuffle(target_idx)\n",
    "onp.save('target_idx.npy', target_idx)\n",
    "first_half = target_idx[:target_idx.shape[0]//2]\n",
    "\n",
    "mask = Y[:,first_half].sum(1)!=0\n",
    "Y = Y[mask]\n",
    "X = X[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x122fc4e50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = onp.random.choice(range(X.shape[0]), 1000, replace=False)\n",
    "batch = X[idx]\n",
    "labels = Y[idx]\n",
    "y_mask = labels.dot(labels.T).astype(bool)\n",
    "plt.imshow(y_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def loss(A, X, y_mask, zero_mask):\n",
    "    embedding = X.dot(A.T)\n",
    "    distances = E_fn(embedding)\n",
    "\n",
    "    \n",
    "    p_ij = softmax(-distances)\n",
    "    p_ij_mask = p_ij * y_mask\n",
    "    p_i = p_ij_mask.sum(1)\n",
    "    loss = -p_i.sum()\n",
    "    \n",
    "    logprobs = np.log(p_i[zero_mask])\n",
    "    clf_loss = -np.sum(logprobs)\n",
    "\n",
    "    diff_class_distances = distances * ~y_mask\n",
    "    hinge_loss = (np.clip(1- diff_class_distances, 0, np.inf)**2).sum(1).mean()\n",
    "    return clf_loss + hinge_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-f05f73dabf2c>:15: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for _ in tqdm.tqdm_notebook(range(200)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "929d100ad2f84cf8a011b0640ba3ad45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=200.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10969.845\n",
      "9795.481\n",
      "14347.865\n",
      "16406.086\n",
      "18216.057\n",
      "16329.272\n",
      "14834.76\n",
      "13322.274\n",
      "11927.469\n",
      "11875.628\n",
      "13048.648\n",
      "13863.088\n",
      "12005.894\n",
      "12123.322\n",
      "11292.907\n",
      "11249.376\n",
      "10784.474\n",
      "10888.539\n",
      "11104.424\n",
      "11454.038\n",
      "10624.52\n",
      "10587.802\n",
      "10207.9\n",
      "10260.732\n",
      "10362.842\n",
      "10260.979\n",
      "10165.6875\n",
      "10016.639\n",
      "10032.219\n",
      "9897.149\n",
      "10094.462\n",
      "9632.985\n",
      "9762.501\n",
      "9685.946\n",
      "9727.151\n",
      "9637.207\n",
      "9716.239\n",
      "9727.752\n",
      "9744.014\n",
      "9775.526\n",
      "9690.257\n",
      "9774.472\n",
      "9601.739\n",
      "9584.869\n",
      "9461.167\n",
      "9500.272\n",
      "9528.63\n",
      "9640.923\n",
      "9802.235\n",
      "9656.24\n",
      "9573.991\n",
      "9445.91\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-f05f73dabf2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1.\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mg_sq_inv_sqrt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmomentum\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mayze\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/interpreters/xla.py\u001b[0m in \u001b[0;36m_forward_method\u001b[0;34m(attrname, self, fun, *args)\u001b[0m\n\u001b[1;32m    635\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_forward_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    638\u001b[0m \u001b[0m_forward_to_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_forward_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_value\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/jax/interpreters/xla.py\u001b[0m in \u001b[0;36m_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    658\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_if_deleted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_npy_value\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 660\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_npy_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_py\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    661\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_npy_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_npy_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/numpy/core/_internal.py\u001b[0m in \u001b[0;36m_dtype_from_pep3118\u001b[0;34m(spec)\u001b[0m\n\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m_dtype_from_pep3118\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    599\u001b[0m     \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m     \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__dtype_from_pep3118\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_subdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "D = 256\n",
    "d = 400\n",
    "A = onp.random.randn(d,D)*0.01\n",
    "\n",
    "g_sq = np.zeros_like(A)\n",
    "m = np.zeros_like(A)\n",
    "\n",
    "mass = 1\n",
    "ayze = list()\n",
    "g_squares = list()\n",
    "lr = 0.1 #step size\n",
    "momentum=0.9\n",
    "\n",
    "values = list()\n",
    "for _ in tqdm.tqdm_notebook(range(200)):\n",
    "    idx = onp.random.choice(range(X.shape[0]), 1000, replace=False)\n",
    "    batch = X[idx]\n",
    "    labels = Y[idx]\n",
    "\n",
    "    #y_mask = (labels[:, None] == labels[None, :])\n",
    "    y_mask = labels.dot(labels.T).astype(bool)\n",
    "    zero_mask = onp.array(y_mask.sum(1)==1)\n",
    "    pos_idx = (~zero_mask).nonzero()[0]\n",
    "\n",
    "    value, g = value_and_grad(loss)(A,batch,y_mask, pos_idx)\n",
    "    values.append(value)\n",
    "        \n",
    "    g_sq += g**2\n",
    "    g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\n",
    "    m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\n",
    "    A = A - lr * m\n",
    "    print(value)\n",
    "    ayze.append(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13fc245b0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "#np.save('A.npy', A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = x_full.dot(A.T)\n",
    "embedding = np.save('embedding.npy', embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing a nonlinear contrastive loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sparse.load_npz('/Users/lmar3213/Documents/GitHub/lbvs_data_blocking/code/raw_data/x_norm_sparse.npz').toarray()\n",
    "Y = sparse.load_npz('/Users/lmar3213/Documents/GitHub/lbvs_data_blocking/code/raw_data/y.npz').toarray()\n",
    "\n",
    "target_idx = onp.arange(Y.shape[1])\n",
    "onp.random.shuffle(target_idx)\n",
    "onp.save('target_idx.npy', target_idx)\n",
    "first_half = target_idx[:target_idx.shape[0]//2]\n",
    "\n",
    "mask = Y[:,first_half].sum(1)!=0\n",
    "Y = Y[mask]\n",
    "X = X[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax.config import config\n",
    "from jax import jit, grad, lax, random, vmap\n",
    "from jax.experimental import optimizers, stax\n",
    "from jax.experimental.stax import Dense, Relu, LeakyRelu, Elu\n",
    "from jax.nn.initializers import he_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Softmax that sets distances to self to -np.inf, meaning the probability goes to zero.\n",
    "@jit\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp((x - np.diag(np.ones(x.shape[0])*np.inf)) - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "\n",
    "\n",
    "#E_fn is just a pairwise distance function,\n",
    "#stole from https://github.com/google/jax/issues/787 .\n",
    "@jit\n",
    "def E_fn(conf):\n",
    "    ri = np.expand_dims(conf, 0)\n",
    "    rj = np.expand_dims(conf, 1)\n",
    "    dxdydz = np.power(ri - rj, 2)\n",
    "    #return squared euclidean:\n",
    "    dij = np.sum(dxdydz, axis=-1)\n",
    "    return dij\n",
    "    \n",
    "\n",
    "def plot_loss(losses):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(losses)\n",
    "    fig.savefig('loss.png')\n",
    "    plt.close(fig)\n",
    "    \n",
    "def init_my_network(embed_dims):\n",
    "    test_rng = random.PRNGKey(1)\n",
    "    _, dec_init_rng = random.split(random.PRNGKey(2))\n",
    "    decoder_init, decode = stax.serial(\n",
    "        Dense(2048, W_init=he_uniform()), Relu,\n",
    "         Dense(512, W_init=he_uniform()), Relu,\n",
    "         Dense(256, W_init=he_uniform()), Relu,\n",
    "         Dense(128, W_init=he_uniform()), Relu,\n",
    "         Dense(64, W_init=he_uniform()), Relu,\n",
    "        #worked pretty good\n",
    "         Dense(128, W_init=he_uniform()), Relu, \n",
    "         Dense(256, W_init=he_uniform()), Relu, \n",
    "#         Dense(256, W_init=he_uniform()), Relu, \n",
    "        \n",
    "        Dense(embed_dims, W_init=he_uniform()),)\n",
    "\n",
    "    _, init_decoder_params =decoder_init(dec_init_rng, (-1, 256))\n",
    "    #opt_init, opt_update, get_params = optimizers.adam(step_size=1e-4)\n",
    "    opt_init, opt_update, get_params = optimizers.adagrad(step_size=1e-2)\n",
    "    opt_state = opt_init(init_decoder_params)\n",
    "    return opt_state, opt_update, decode, get_params\n",
    "\n",
    "@jit\n",
    "def loss(p, x_, y_mask, zero_mask):\n",
    "    embedding = decode(p, x_)\n",
    "    distances = E_fn(embedding)\n",
    "\n",
    "    #     eps = 1e-8\n",
    "#     x = embedding\n",
    "#     n = np.linalg.norm(x, axis=1)\n",
    "    \n",
    "#     pairwise_cosine = np.clip(x.dot(x.T) / (n * n.T), a_min=eps)\n",
    "#     distances = np.exp(pairwise_cosine/0.5)\n",
    "    ###Seems like the traditional euclidean distance works best!\n",
    "    #OR: normalized vector dot products\n",
    "    #lengths = np.linalg.norm(embedding, axis=1)\n",
    "    #emb_norm =(emb.T/lengths).T\n",
    "    #emb_norm = emb.copy()\n",
    "    #distances = -1/emb_norm.dot(emb_norm.T)\n",
    "    \n",
    "    p_ij = softmax(-distances)\n",
    "    p_ij_mask = p_ij * y_mask\n",
    "    p_i = p_ij_mask.sum(1)\n",
    "    loss = -p_i.sum()\n",
    "    \n",
    "    logprobs = np.log(p_i[zero_mask])\n",
    "    clf_loss = -np.sum(logprobs)\n",
    "\n",
    "    diff_class_distances = distances * ~y_mask\n",
    "    hinge_loss = (np.clip(1- diff_class_distances, 0, np.inf)**2).sum(1).mean()\n",
    "    return clf_loss + hinge_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-92-2c70798a6791>:33: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for i in tqdm.tqdm_notebook(range(500)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "168f5647ea1d42dd8a213763dfd8e9c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=500.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28824.766\n",
      "12157.514\n",
      "11563.313\n",
      "11179.942\n",
      "11196.879\n",
      "11151.219\n",
      "11091.865\n",
      "10982.591\n",
      "10890.857\n",
      "10979.509\n",
      "10772.135\n",
      "10707.16\n",
      "10579.039\n",
      "10602.233\n",
      "10585.91\n",
      "10510.877\n",
      "10388.383\n",
      "10403.751\n",
      "10190.72\n",
      "10270.39\n",
      "10158.777\n",
      "10255.432\n",
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      "9893.427\n",
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      "\n"
     ]
    }
   ],
   "source": [
    "###Set up the network:\n",
    "embed_dims = 400\n",
    "batch_size = 1000\n",
    "opt_state, opt_update, decode, get_params = init_my_network(embed_dims = embed_dims)\n",
    "\n",
    "\n",
    "\n",
    "loss_list = list()\n",
    "\n",
    "def step(i, opt_state):\n",
    "    ###Selecting a batch of labels and removing bad ones:\n",
    "    idx = onp.random.choice(range(X.shape[0]), batch_size, replace=False)\n",
    "    #use a *10 for the normalized dot product version:\n",
    "    #batch = X[idx]*10\n",
    "    #use a *10 for the normalized dot product version:\n",
    "    batch = X[idx]\n",
    "    labels = Y[idx]\n",
    "    y_mask = labels.dot(labels.T).astype(bool)\n",
    "    zero_mask = onp.array(y_mask.sum(1)==1)\n",
    "    pos_idx = (~zero_mask).nonzero()[0]\n",
    "    \n",
    "    #getting current network parameters:\n",
    "    p = get_params(opt_state)   \n",
    "    \n",
    "    #get parameter gradients:\n",
    "    v, g = value_and_grad(loss)(p, batch, y_mask, pos_idx)\n",
    "    loss_list.append(v)\n",
    "    print(v)\n",
    "    if i%5==0:\n",
    "        plot_loss(loss_list)\n",
    "    return opt_update(i, g, opt_state)\n",
    "\n",
    "for i in tqdm.tqdm_notebook(range(500)):\n",
    "    opt_state= step(i, opt_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(loss_list) - min(loss_list))\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = get_params(opt_state)\n",
    "emb = decode(p, batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.linalg.norm(emb, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps = 1e-8\n",
    "x = emb.copy()\n",
    "pairwise_cosine = np.clip(x.dot(x.T) / (n * n.T), a_min=eps)\n",
    "xexp = np.exp(pairwise_cosine/0.5)\n",
    "out = np.diag(xexp) / (xexp.sum(0) - np.exp(1/0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray([[7.389056 , 2.54829  , 2.0901406, ..., 2.3991342, 2.224106 ,\n",
       "              2.0507271],\n",
       "             [2.6221783, 7.389056 , 2.12776  , ..., 2.3077583, 2.5525453,\n",
       "              2.1887605],\n",
       "             [4.2299633, 4.1924844, 7.389058 , ..., 3.196281 , 3.237155 ,\n",
       "              2.7744029],\n",
       "             ...,\n",
       "             [2.7691913, 2.5715237, 1.996431 , ..., 7.3890553, 1.9138935,\n",
       "              3.0397472],\n",
       "             [3.3846328, 4.0026326, 2.4990695, ..., 2.34124  , 7.3890553,\n",
       "              2.5337434],\n",
       "             [2.4357755, 2.5656772, 1.9090607, ..., 3.26759  , 2.128799 ,\n",
       "              7.3890533]], dtype=float32)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xexp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "out = np.diag(xexp) / (xexp.sum(0) - np.exp(1/0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(3.107316, dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19ec855e0>]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iGGPMMcG1CeGnZ6RRXF7FK19uae2iGGPMMcG1CWFg9w4MToljwYamT181xpi2xLUJAeCs9ARW7Cik8KB3+um+4nL2FVsXkjHGnVydEIamdUIVlmUXADD4wTkMfnBOK5fKGGNah6sTwknJHRCBpdv2tXZRjDGm1bk6IcRGhtErMYbVOftbuyjGGNPqXJ0QABJjImzqqTHGYAnBu2K5pJJqe5ymMcblXJ8Q2rcLY39pBcXlto2FMcbdLCFEhnGgtDJgXyNrLRhj3Mj1CSE20rvrqf84QklFVQN3GGNM2+T6hNC+nff5yv4b3VlCMMa4kSUE5/nKO/Yd9MVKyi0hGGPcx/UJITbS20JYtr3AFztoCcEY40KuTwjt23lbCP9ZluOLHbQZR8YYF3J9QugUHe47HndKN8C6jIwx7uT6hJCeFOs7Ht4zHrAuI2OMO7k+IYR4hI5R3nGEQSlxAFz/RmZrFskYY1pFo89UdoOPbx1B9r4SeiRE+2KlFVVEhoW0YqmMMebocn0LAaBrh3acltaJyLAQHrxsIACFJRWtXCpjjDm6Gk0IIvKqiOSJyCq/WCcRmSMiG52fHf3OTRGRLBFZLyIX+sWHiMhK59wzIiJOPEJE3nXii0UkrXmr+P3EOQvVLCEYY9zmcFoIrwFj6sTuBOaqajow13mPiPQHJgADnHueE5GafpfngclAuvOq+czrgH2q2ht4Cni0qZVpDh0sIRhjXKrRhKCqC4C9dcLjgNed49eBy/zi76hqmapuAbKAoSLSFWivqgtVVYE36txT81nvA6NrWg+twZcQDlpCMMa4S1PHEDqrai6A8zPJiXcHtvtdl+3EujvHdeMB96hqJVAIxAf7UhGZLCKZIpKZn5/fxKI3LM6ZcXT9G5nc8KbNNjLGuEdzDyoH+8teG4g3dE/9oOqLqpqhqhmJiYlNLGLDaloIAJ+s3tUi32GMMceipiaEXU43EM7PPCeeDaT4XZcM5Djx5CDxgHtEJBToQP0uqqOmZm8j8K5RqLJnIxhjXKKpCWEGMMk5ngRM94tPcGYO9cA7eLzE6VY6ICLDnPGBiXXuqfmsK4B5zjhDqwjx1DZYqqqVXnd9TFmlrVw2xrR9hzPtdBqwEOgrItkich3wCHC+iGwEznfeo6qrgfeANcAs4GZVrfltehPwMt6B5k3ATCf+ChAvIlnAHTgzllrTezcM58FxA3zvV2QXtmJpjDHm6Gh0pbKqXn2IU6MPcf1UYGqQeCYwMEi8FBjfWDmOpqE9OtHOb5Xywk17OC2tUyuWyBhjWp6tVD6ErnGRvuO56/IauNIYY9oGSwiHkBATwfxfj+S3Y/qyfHsBWXlFrV0kY4xpUZYQGpCWEM0VQ5KJiQjlvCc/Z/QT8ym15y0bY9ooSwiNSIqN5IFLvQPMm/KLydy6jytfWMjMlbmAdybS2tz9rVlEY4xpFpYQDsOPhiTzxPhBANz27lKWbNnLtK+9C7I/Wb2Ti57+gle/3NKaRTTGmCNmCeEwjRnYBYDdReUAZO89CMA25+ebi75rnYIZY0wzsYRwmKIjQkmMjfC937y7mA+X55C3vwyA8srq1iqaMcY0C0sI38OJXdsDMHH4CcREhPLgf9eQU1ACwN7i8tYsmjHGHDFLCN/Dn8afzNiTuvD/RvZm6g8HknegjFmrdwJQUlFFSbnNQDLGHL8sIXwPSbGRPPeTIXTpEMl5J3YmPjo84PyNb33DH/+7hu3OuIIxxhxPLCE0UXREKI/+6GQGpcRxVYZ3g9fPN+Tz8pdbuOJvX9W7/vMN+Tw2a93RLqYxxhw2SwhH4Lz+nZl+85lceVpyQHxPUTmFJRX88b9rfI/inPTqEp6bv8kezWmMOWZZQmgGMRFhAe8rq5U73l3Gy19u4ZNVOwPOrcwuZE9RGZlbW+2RD8YYE5QlhGbQOymGH5+eGhCr2RBv5/5SissqffHl2QVM+vsSrvjbQiqqbKqqMebYYQmhGYR4hId+eFLQc1l5RXy6tvZRnBt3HWDVDu9WFzsLS49K+Ywx5nBYQmhGs24bwYxbzvS9H5TcgRnLc7j1nWUApMVHsWV3se+pbNn7vGsY3ly4lc835B/18hpjjD9LCM2oX5f2nNS9A788tzfTbz6T03vGB5w/Kz2BzbuLCQ/x/rPvKChBVbl3+momvbqkNYpsjDE+lhCamYhwxwV9GZQSx6WDugWc65UYw4HSSkqcLbRzCkooOFg762jWqtyjWlZjjPHX6CM0TdMN6Nae343pR0xECCclx5EUG8Ff5mWxx9nm4p0l20ju2M53/Y1vfcuWh8dSrVCtSlhIYL5WVcoqq4n0e7ynMcY0F2shtCAR4aaRvbh2eBqnpMTRLa4dN43s5TufU1jKHe8tD7gnp7CUm976hvS7Z7I2dz/Pzc/y7ZP018+y6HfvLIr8Zi0ZY0xzOaKEICK3isgqEVktIrc5sU4iMkdENjo/O/pdP0VEskRkvYhc6BcfIiIrnXPPiIgcSbmOZT8+PZXLTunGrNtG0KdzTL3z7yzZxuw13llJFz39BY/NWs/rX20F4J/fZAMw15m1VF2tTF+2g7LKKnIKSthZWGpTWY0xTdbkhCAiA4FfAEOBQcAlIpIO3AnMVdV0YK7zHhHpD0wABgBjgOdEpKbv43lgMpDuvMY0tVzHuqjwUP48YTD9urTnD+MG1jv/7LyserFP1+5i6kdriA739vDNX++dkfRF1m5ufWcZ989YwxmPzGPYw3NJv3umtSCMMU1yJC2EE4FFqnpQVSuBz4EfAuOA151rXgcuc47HAe+oapmqbgGygKEi0hVor6oLVVWBN/zuadNOSYnjrN4JXD64OwBjT+rCuf2SAq756RlprM7Zz0tfbGGN86jOTflFbMovYsayHADe/XpbwD0D7/uEjbsOHIUaGGPakiMZVF4FTBWReKAEGAtkAp1VNRdAVXNFpOY3XHdgkd/92U6swjmuG69HRCbjbUmQmpoa7JLjSmRYCG9dfzqVVdUM7xXP5acmE+IRZq3K5ca3vgXg0lO68ZrTZVRjRXYho5/43Pe+Wut/9leb9pDeObYli2+MaWOanBBUda2IPArMAYqA5UBDfRXBxgW0gXiw73wReBEgIyMj6DXHo9AQD+OdHVMB4qJqt9UenBLH5YO7Ex0RyoHSCuKiwusliGDeWLiVq05LITIshNzCEuKjIyg4WE5S+8gWqIExpi04okFlVX1FVU9V1bOBvcBGYJfTDYTzM8+5PBtI8bs9Gchx4slB4q4VF1W7WZ6I8ORVp/DgZQP584TBXHqKd23DZad0q3ff/53b23e8Kb+YX7yRSVZeEcMfnkefe2Yy9KG5jH36C775bh8Hy7//OMNdH6zk0TpbeFdXKws25OPt7TPGHM+OdJZRkvMzFbgcmAbMACY5l0wCpjvHM4AJIhIhIj3wDh4vcbqXDojIMGd20US/e1wprl34Ic+dmtqRr+8+j6euOoUFvxnFkrtHc2bveHomRnPjOd4prT87M41z+iTyxcbd9VZAr8ndz4+e/4rfvL8i6OevyC7g8U/WBf0F/4/F23h+/qaA2OsLtzLx1SV8snpXveuNMceXI12Y9i9nDKECuFlV94nII8B7InIdsA0YD6Cqq0XkPWAN3q6lm1W15pmTNwGvAe2Amc7LtfxbCMEkxkYAkBofBcDb1w/zndv6yMWoKtn7Shjx2GfscJ75/MtzezOqXxKlFdVc/dIiPlqRy9NXVRPqt/jtvumreH3hdwD8cHB3eifVjkEUH2Lm0qb8IgB2FpYEPZ93oJSXFmzmd2P6BXyXMebYc0QJQVVHBIntAUYf4vqpwNQg8Uyg/hxMl4oMC6FP5xiuO6tHk+4XEVI6RfHklYN8C99uGtmbduHeWb4PXjaQe/+zit53z2REegLtwkL46Rlp/GNJ7WylBRt20zsplupqxeMRtuwu9p17ccEmJp/tbY3UDGgXlVWiqtRdQnLPB6uYvWYXo/omcUbvhCbVxxhzdNjWFceo2befc8SfcX7/zr7jmmQAcHqPTr7jLzbu9n6fsxju9vP68NHKHN75ehsTh5/Ata8soaKqOmDB20Mfr+PaYWm0Cw/xdS39afYGlm0v4Pz+nfnh4GTCQ72tgZptOiqCTYUyxhxTrA3fhsVGhvGHcQO4/bw+AfE+nWO55+ITfe//ds0Q33FaQhQ3j+rNhl1FnHT/bBZu3kPmd/tYnbM/YGV1Vp63q6jK7xf9p2vz+N2/VvpWVgOUVXp7BfcUlfliK7ILuH/Gaqr97l2bu5+PV9rmfsa0JmshtHETh6cFjQ85wbejCGMGduG//3cWv3l/BWf2TiA+Opzl2wt59X9bAHhlUgZ9u8SycVcRP3vtawD+/OkGxp7UleLyqnqfPfXjtbyXuZ1LTu7Grv3eRLDbLyFM+fdKVufs55KTu5KR1glV5aKnvwBg80Nj8Xja7M4lxhzTLCG4VEqnqID3A7t3YOattUNCk8/uyav/28KVGcmMPtHb9RQbGUbvpBiy8oqYuy6PuevyaB8Z/D+hjXlFPPXpBt/73UXllFdWs7+0wrfl99NzN3JGrwReXFA7c2nXgVK6dmhX7/OMMS1Pjtf54xkZGZqZmdnaxThuqSo97/qY20b34dbz0oNes2V3Mckd29XbhvuJ2euD7rk0fkgye4vLfc+TbooR6Qm8PCmDiFDb4tuYliAi36hqRtBzlhBMU+wrLie/qIwLnlrgi215eCwiws1vf8tHK3O5dtgJ/O6ifvzk5cUs314QcP/ofkmHTBxdO0QyKDmOv13rHdsoLqvkr59l8dn6fK7MSOaaYSdQVa2NPhci2Kynuqqq1fdIU2PcoKGEYF1Gpkk6RofTMTqcZ64ezC+nLQXw/fJ96PKTuGBAZ8ad4t2SanBKnC8hvHXd6cxdt4trhp1wyISQW1hKbuFO1ubu5zfvL6dPUiz/XroDgAc+XMMDH64B4Cenp3LzqN50ig73JYdtew7y0Mdr6d6xHdOX7eB/d557yNbG3LW7uPkf3zLn9nPqdaHV9fDHazm7TyJn2tRZ04ZZQjBH5NJB3YgODyHvQO2gcYd2Yb5kAHD5qd157autPHjZQM5KT+CsdO8v1VF9E/nfpj2UV3qntG55eCw795cy/OF5APzk5cXsLS5n1Q7vLq9n9U6gR0I0by7yLp57e/E23l68jZiIUO4aeyK9EqN59X9bAlZNb9xVxNLtBYzsk0hKpyg+W5/HiV3akxQbwaOz1lFaUc38Dflcc3rqIVsTBQfLeWHBZl5YsJmtj1zcjP96xhxbrMvIHBXb9x6ke1y7gBlElVXVVCvsLCwlMszj23ivtKKKfvfOCri/T+cYZt9+DhVV1fz1syz+/OlGAEI8EjD1FcAjcEJ8NFt2F/Or8/vwxJwNRIR6+OD/ncnYZ74gJiKUy0/tzhvOqmyAi0/qyl9+PDhoUvgqazc/fnkxgCUEc9xrqMvI1iGYoyKlU1S96aShIR7CQz2kxkcF7MIaGRbCSxMz+PvPTmPBb0YxKLkDE07zbnceFuLhllG9OSE+ikd/dBIb/ngRw3vG++791fl92Dh1LHPvOIfo8BCemOOd6VRWWc3YZ7xTW4vKKnlj4Xec0SveN37w0cpcPlm9kxGPzWP26p0A5B8o46UFm5m5aqdTrvr/u5z7xHxue2dpc/0zGdOqrIVgjntPzl7PM/OyeP/G4WSk1a7C/vOnG3wtiRqRYR5KK7xdVOv/OIbN+cXMX59fbxfX+37Qn39mZvseSgTelsdb153Oup0H+PlZPSirrKLvPd6WzOzbz+aVL7YwoHt7fnByNzpGh1NVrWzKL+LxT9bzp/GD6NCu4T2qamzfe5BVOwo5p28iUeHWq2ual80yMm1aRVU1mVv3MbxXfEBcVekx5eOA2PCe8VwwoDMhHvEt2is4WM4pf5hT73MjQj1cO+wEXv5yCyPSE3zbfAAsuWs0v/vXCj5zHmcaHx3u26ZjQLf2vHvDcJ6as4FXvvQu7vvT+EFcMcS7y/sXG/NZuq2AW0b1xuMRPluXR0Soh9jIMBJjIxj28Fzf9/zrpuEMOaE2yfkrdNZzdGhkM0Rj/NksI9OmhYV46iUD8M56+urOc9m5v5QV2wu4/8M1jD25K/zmuQ4AABA8SURBVNcOOyHgupoHEg3s3h6PCCuyCwGYcFoK91zSnzsu6ENRaSVDH6r9RX3LtKUs2bLX974mGQCsztnPwPs+CfiOdbn7WbdzP52iwrn2Fe+W5BcM6Eyox+Nb/R0dHkLvpJiA+2at2nnIhHDGI3Mpqahi88M2rmGahyUE06Z1i2tHt7h2nJrakbPSE+mVGB30uqX3nk9kWAgbdh3guflZ9EqM4Qbn+RJR4aFEhYdyz8UnUlhSwbPzsnzJICk2glF9k1i6fR+3ju7Dtr0H63U/Abz85RZedloLNZ6dl8WiTXt874vLq1juJKMa63YeCFhPcee/VlBeWc3j4wf5tg2prKrdxnxvcTl/+HA19186gLiocLbvPUhOQQmn96yfMCFwrcb6nQfYUXCQc/t1DnqtafssIRjXqPvXt7+O0d5WwqCUOF64NmhrmutH9ATgYHmVrytoyd3noaqogscjFB6s8CWEq4em8MfLTuI/S3fwq38u931Oz8RoNucX89EK72Z+fxg3gPtmrCZY7+0XG3fz/jfZnJWewN/mb+Kdr7cDgY9Z7X33TJ4YP4joiBDfs7gHdOvA9SN6cP5Tn1NaUc0395xHfEyE756tu4u5+qVFXJmRwo6CEiYOP4FL//I/IHA/qXv/s4ryymquPj2VLu0j6dIhkin/XkmPhCjfFuim7bAxBGOaYHN+EQfLqxjYvUO9c+9+vY1TUzuS3rn2AUPf7Smmc/tIZizLYWS/RL79roAb3/oGgDV/uJBrXl7Mt9sK6n1WSqd2RISGkBgTwcLNe+qdP5Srh6YS6hHfmo27xvbjF05CExEem7WO5/yefnfLqN785TPvdiQLfjPK9/CltDs/8l0TFxXG13efR/rd3udXffbrkfRICN7iMscum3ZqTDPrmRgTNBkAXHVaakAyAO+6iMiwEK48LYWk2EjGDOzCzFtH8MK1Q4gKD2Xy2T0ZPySZxNgI0uJrV03/9IweZOUVBSSDv//0NMA7Y+qT287mwgH1u3imLdnGm4u+o1diND0Tolm4aQ9PzN7AyQ/M5vFP1rFt78GA62uSAcDS7fsA72C7v4KDFb4n5AHc/Pa3QetfVlnFXR+s5Mk5G4KeBzhYXklJkJ1yTeuyLiNjWsmJXdtzYtf2AIwZ2JUxA7vy4oJNlFVUc2LX9qTGR5GeFENKx3ZMX5bDry/sy5bdRYzql8SHt5xFt7hI4mMiAnaH7ZkYTUrHKD7fkM8T4wcxtEcn/jIvi3czt/tmRP31s01By1Pj9neX0adzLBNeXFTv3FpnGu7Ivol8uXE3pRVV5B8o44EPV5MYG8ElJ3cj/0AZ/1jsffrebaPT+ffSHXy9ZS+3nZ9O1w7tUFVOf2guHdqF8e4Nw3n60w3cf+mAgCm2B8sreWrOBn45Op3YyKbPotpbXE72voOcnBzX5M9wE0sIxhxDgvXLXzCgCxcM6ALg66I5Kbm2dRIdUbtXU0RoCH/58WBKKqpIivUu9hvRJ4F3M7fToV0YoR7xzYjq1iGSgpIKKquUcueJePf9oD+Pf7Le93yK9KQYNubVtgpuf3c5cVFhjO6XxPz1+fVWlE9bsp2Jw2tncT316Qbe/yab3MJS/rNsB/dc0p97/7MKgAOllTw2ax3Tl+UwrGc8l5/qnZZbXa289tVWXvpiC6HOQsQQjzS6mWEwV/ztKzbnF9tzNg6TdRkZc5y78Zxe/PSMNACuGZZKbGSYLxmAd1uON34+lHcmD+OrKedyw9nesYRhveJZ/cCF9HRmXn38yxH87MwePPTDkxCBKRf1Y84d9R/let2ZPUirM3Zwlt+mf/5bgjw7L4vcwlLO6BVPeKjHlwxqTF+WA8DXW/dSUl7F4s17eHTWOh6btR7wjr2c+8R8JjpTdQF2FJSgqizdto+PVuTS0Djo5nzvs8Dz/R7QdDimL9vB2Ke/qLctSlt3RC0EEbkduB5QYCXwMyAKeBdIA7YCV6rqPuf6KcB1QBXwS1X9xIkPAV4D2gEfA7fq8TrabcxRFhsZxv2XDuDeS/oT7I9gEeHsPom+9/27ebupCg9WICL8afwg3lr0HX27eMc9LhvcnVH9knwrq284pycvfL4ZgMeuOJkrTk0OGIO4+KSuTBnbj8iwEM5/8nP2HazgqowUzu/fmbs+WEnegTIu6N+ZZ68ezJA/fhq0DtOWbGfu2ryATRIBPl7p3TZk1/4yVu0opEO7MM55/DPuvKgfL3y+mT3F5UwafgK/OLsnhSUVDOjWgeKySg6UVvLGwq2+zznn8c9YPOU8wkKFqPBQKqqqeejjtfRMjPGtSyksqeCjFbks3LyHD5d7E9UjM9eSlhDNT04PXLtyKN9u28eMZTnc94P+jW69Dt4nCT42ax33/WAA0RGt32HT5FlGItId+BLor6olIvIe3l/m/YG9qvqIiNwJdFTV34lIf2AaMBToBnwK9FHVKhFZAtwKLHI+4xlVndnQ99ssI2OaZt3O/Yz58xf8/Mwe/P4H/Ru9vuZ3RFW1+tY7VFRVk373TO68qB83nhPYzZVTUEJcVBhR4aEs217ApFeX8OEtZ5EaH+WbtfTYj07m1BM6Eh8dzocrcvj99NVBvzsmIpRfX9CHxz9Zz4BuHdiyp5j8OjvrFpZU+N4P7dGJJVv2EhcV5nsyX43eSTFs3V3Mry7oG7BWZP6vR/L24u946YvAdSL+Vtx/AbERocxbl8fTczcy7RfDOFheRceoMEI8wgMfruHM3glMfjMTVfj67vNYm7ufO/+1gutH9OTnZ/Vg2pJttAsL4bLB3SkqqyQmIpQp/17BtCXbefyKkxmfkcLm/CJ6Jh56enRzaJGtK5yEsAgYBOwH/gM8AzwLjFTVXBHpCsxX1b5O6wBVfdi5/xPgfrytiM9UtZ8Tv9q5/4aGvt8SgjFNt3jzHgalxDWpX/5IvPf1dr75bh+PXnGyL5ZTUMIZj8zjNxf25eKTunLv9FW+bUJqHro09aM19X5hx0aE8vlvRzHqT/MDksLh6ts5lpzCEg6UVvpiXdpHcukp3XhxweaAa9OTYujfrb2vi2twahxLtxUw7pRuDDmhY72E9vefnsaizXt4YcFmBqfG8c8bhtPbma775JWDuOO95XRo500me4vLeXDcAFI6RfHTv3/NT05P5ZZze/Prfy7nD+MG0quZE0SL7WUkIrcCU4ESYLaq/kREClQ1zu+afaraUUT+AixS1bec+CvATLwJ4RFVPc+JjwB+p6qXNPTdlhCMaTsKD1YE7MlU05Ko2W58T1GZr7upT+cYHr9iEF3jIkmKjWTyG5nMXuN9Bsavzu/Dc/M3UVJRO6X1gv6dmb1mF3FRYUwcnsYzczfSKzGaub8aycyVudzkTJ+9emgKd5zfl9zCEi79y//45eh0zk5PYG3ufu49RAumuUx2xnVqElGn6HD2FpczODWOySN60iEqjDN6JVBdrbywYDPjM5JJ8Fto+H20yF5GItIRGAf0AAqAf4rINQ3dEiSmDcSDfedkYDJAamrq9yqvMebYVXeDvn/eOJyOfqux42MiWH7fBQCEeiSgv/2ei/tTUFLBMxMG06VDpG/L84SYCK4Zlspt5/UhK6+I8spqUuOjCPMIE4Z6f39cOKALN5zdk9EndmZoD++eUYmxEcy45Uz6d21PaIiHjLROdIqOYFN+EdeP6MG/v93BPf9ZxT0Xn8gjM9dR2QwDz5vziwLGZfY6M8GWbivwJaxhPTuxaLN3y5SIUA8/P6vHEX9vXUfSZTQeGKOq1znvJwLDgNFYl5ExppX8+dMNvPbVVpbcdR7hoS0zkXL9zgP06RzDki17uerFRYzul8SSrXu58ZxeLNq8hx37Sti8u9h3fZf2kUwZ248+nWNZtr2AalW27T1IQrR3Bfo853Gyz1w9mL1FZdzvPCY2LiqMqirlQJm3W2twahzpSTE8cvnJTZ5G21JjCKcDrwKn4e0yeg3IBFKBPX6Dyp1U9bciMgD4B7WDynOBdGdQ+Wvg/4DFeAeVn1XVj+t+pz9LCMaYQ/HftK+lLd68hyEndPQNuIP34UqnTf2Um0f1IjoilEnD0w45i2jG8hx+OW0pPROjmferkZRWVNH/97O4oH8XnrxqEBGhIbyxcCtp8dGM6pd0xOVtyTGEB4CrgEpgKd4pqDHAe3gTwzZgvKruda6/G/i5c/1tNTOJRCSD2mmnM4H/a2zaqSUEY8yxLP9AGR2jwgISRTCVVdU8/sl6xmck0zvJO/W3pLyKEI+0SAvHHpBjjDEGsM3tjDHGHAZLCMYYYwBLCMYYYxyWEIwxxgCWEIwxxjgsIRhjjAEsIRhjjHFYQjDGGAMcxwvTRCQf+K7RC4NLAHY3Y3GOB1Znd7A6u8OR1PkEVU0MduK4TQhHQkQyD7VSr62yOruD1dkdWqrO1mVkjDEGsIRgjDHG4daE8GJrF6AVWJ3dwersDi1SZ1eOIRhjjKnPrS0EY4wxdbguIYjIGBFZLyJZzhPd2gQReVVE8kRklV+sk4jMEZGNzs+OfuemOP8G60XkwtYpddOJSIqIfCYia0VktYjc6sTbcp0jRWSJiCx36vyAE2+zda4hIiEislRE/uu8d0Odt4rIShFZJiKZTqxl662qrnkBIcAmoCcQDiwH+rd2uZqpbmcDpwKr/GKPAXc6x3cCjzrH/Z26RwA9nH+TkNauw/esb1fgVOc4Ftjg1Kst11mAGOc4DO8jZ4e15Tr71f0OvI/g/a/z3g113gok1Im1aL3d1kIYCmSp6mZVLQfeAca1cpmahaouAPbWCY8DXneOXwcu84u/o6plqroFyML7b3PcUNVcVf3WOT4ArAW607brrKpa5LwNc15KG64zgIgkAxcDL/uF23SdG9Ci9XZbQugObPd7n+3E2qrOqpoL3l+gQM0TutvUv4OIpAGD8f7F3Kbr7HSdLAPygDmq2ubrDPwZ+C1Q7Rdr63UGb7KfLSLfiMhkJ9ai9Q49gsIejyRIzI3TrNrMv4OIxAD/Am5T1f0iwarmvTRI7Lirs6pWAaeISBzwgYgMbODy477OInIJkKeq34jIyMO5JUjsuKqznzNVNUdEkoA5IrKugWubpd5uayFkAyl+75OBnFYqy9GwS0S6Ajg/85x4m/h3EJEwvMngbVX9txNu03WuoaoFwHxgDG27zmcCl4rIVrxdvOeKyFu07ToDoKo5zs884AO8XUAtWm+3JYSvgXQR6SEi4cAEYEYrl6klzQAmOceTgOl+8QkiEiEiPYB0YEkrlK/JxNsUeAVYq6pP+p1qy3VOdFoGiEg74DxgHW24zqo6RVWTVTUN7/+v81T1GtpwnQFEJFpEYmuOgQuAVbR0vVt7JL0VRu7H4p2Rsgm4u7XL04z1mgbkAhV4/1q4DogH5gIbnZ+d/K6/2/k3WA9c1Nrlb0J9z8LbJF4BLHNeY9t4nU8Gljp1XgX83om32TrXqf9IamcZtek6450Judx5ra75XdXS9baVysYYYwD3dRkZY4w5BEsIxhhjAEsIxhhjHJYQjDHGAJYQjDHGOCwhGGOMASwhGGOMcVhCMMYYA8D/BzlIClKs5Ka6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_full = sparse.load_npz('/Users/lmar3213/Documents/GitHub/lbvs_data_blocking/code/raw_data/x_norm_sparse.npz').toarray()\n",
    "p = get_params(opt_state)  \n",
    "embedding = decode(p, x_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('embedding.npy', embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/lmar3213/Documents/GitHub/nca_jax\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = decode(p, batch)\n",
    "distances = E_fn(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "linal = np.linalg.norm(emb, axis=1)\n",
    "emb_norm =(emb.T/linal).T\n",
    "tdistances = emb_norm.dot(emb_norm.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray([[0.99999976, 0.74999887, 0.76123255, ..., 0.7563629 ,\n",
       "              0.66751677, 0.7688525 ],\n",
       "             [0.74999887, 0.9999997 , 0.7036865 , ..., 0.7791331 ,\n",
       "              0.7126986 , 0.76763886],\n",
       "             [0.76123255, 0.7036865 , 0.9999998 , ..., 0.7073661 ,\n",
       "              0.6055904 , 0.81234264],\n",
       "             ...,\n",
       "             [0.7563629 , 0.7791331 , 0.70736617, ..., 1.0000001 ,\n",
       "              0.7966421 , 0.7888763 ],\n",
       "             [0.6675167 , 0.7126986 , 0.6055904 , ..., 0.7966421 ,\n",
       "              1.0000001 , 0.7650386 ],\n",
       "             [0.76885235, 0.7676388 , 0.8123425 , ..., 0.7888763 ,\n",
       "              0.7650386 , 0.99999994]], dtype=float32)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tdistances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(1.0000001, dtype=float32)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax(-emb.dot(emb.T)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_ij = softmax(-tdistances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "embt = emb/100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray([[0.0000000e+00, 1.0048924e-06, 9.7835721e-07, ...,\n",
       "              9.1356327e-07, 1.2406878e-06, 1.0336106e-06],\n",
       "             [1.0048924e-06, 0.0000000e+00, 9.9221188e-07, ...,\n",
       "              9.3361217e-07, 1.2422532e-06, 1.0367572e-06],\n",
       "             [9.7835721e-07, 9.9221188e-07, 0.0000000e+00, ...,\n",
       "              9.1745153e-07, 1.2246680e-06, 1.0466715e-06],\n",
       "             ...,\n",
       "             [9.1356310e-07, 9.3361217e-07, 9.1745153e-07, ...,\n",
       "              0.0000000e+00, 1.1589192e-06, 9.2141823e-07],\n",
       "             [1.2406877e-06, 1.2422532e-06, 1.2246682e-06, ...,\n",
       "              1.1589192e-06, 0.0000000e+00, 1.2148168e-06],\n",
       "             [1.0336106e-06, 1.0367572e-06, 1.0466715e-06, ...,\n",
       "              9.2141823e-07, 1.2148168e-06, 0.0000000e+00]],            dtype=float32)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax(-embt.dot(embt.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD4CAYAAADsKpHdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAUcElEQVR4nO3df6yddX3A8ffHVoHNFSmUprZsxVETWzNR7ro6M+NWNyqoxQRmkylladLAcNFkbikzy7I/WMA5UeIg6cTR4hw0TEOj62ZXZDpXgYuCWGrt5YdwbddWQIQZ0OJnfzyfa8893B/n/ug9p+37lZyc53ye5/s833POfc7n+X6/z/PcyEwkSXpZtysgSeoNJgRJEmBCkCQVE4IkCTAhSJLK7G5XYLLOOOOMXLx4cberIUnHlPvuu++HmTlvpHnHbEJYvHgx/f393a6GJB1TIuL7o82zy0iSBHSYECLisYh4MCLuj4j+is2NiO0RsbeeT2tZ/qqIGIiIPRFxfkv8vFrPQERcHxFR8ZMi4raK3x0Ri6f3bUqSxjORFsLvZua5mdlXrzcAOzJzCbCjXhMRS4E1wDJgFXBDRMyqMjcC64El9VhV8XXA05l5DnAdcO3k35IkaTKm0mW0GthU05uAi1rit2bmC5n5KDAALI+IBcCczNyZzf0yNreVGVrX7cDKodaDJGlmdJoQEvhyRNwXEesrNj8z9wPU85kVXwg80VJ2sGILa7o9PqxMZh4GngFOb69ERKyPiP6I6D906FCHVZckdaLTs4zekpn7IuJMYHtEfHeMZUc6ss8x4mOVGR7I3AhsBOjr6/OufJI0jTpqIWTmvno+CHwBWA4cqG4g6vlgLT4InNVSfBGwr+KLRogPKxMRs4FTgacm/nYkSZM1bkKIiF+OiF8Zmgb+APgOsBVYW4utBe6o6a3Amjpz6GyaweN7qlvp2YhYUeMDl7aVGVrXxcCd6X25JWlGddJlNB/4Qo3xzgY+l5n/HhH3AlsiYh3wOHAJQGbuiogtwEPAYeDKzHyx1nUFcDNwCrCtHgA3AbdExABNy2DNNLw3SdIExLF6IN7X15deqSwdHYs3fGnE+GPXXDjDNdF0i4j7Wi4fGMYrlSVJgAlBklRMCJIk4Bi+26mk6TXauMFoyziecPwxIUgnsE6SgE4cdhlJkgATgiSpmBAkSYAJQZJUTAiSJMCEIEkqnnYqaVK8JuH4YwtBkgSYECRJxYQgSQIcQ5BOON6uQqOxhSBJAkwIkqRiQpAkASYESVJxUFnSlHmR2vHBFoIkCTAhSJKKCUGSBJgQJEnFQWXpBODVyeqELQRJEmBCkCQVE4IkCTAhSJKKg8qSppVXLR+7bCFIkgATgiSpdJwQImJWRHwrIr5Yr+dGxPaI2FvPp7Use1VEDETEnog4vyV+XkQ8WPOuj4io+EkRcVvF746IxdP3FiVJnZhIC+GDwO6W1xuAHZm5BNhRr4mIpcAaYBmwCrghImZVmRuB9cCSeqyq+Drg6cw8B7gOuHZS70aSNGkdJYSIWARcCHy6Jbwa2FTTm4CLWuK3ZuYLmfkoMAAsj4gFwJzM3JmZCWxuKzO0rtuBlUOtB0nSzOi0hfAJ4C+An7fE5mfmfoB6PrPiC4EnWpYbrNjCmm6PDyuTmYeBZ4DT2ysREesjoj8i+g8dOtRh1SVJnRg3IUTEO4GDmXlfh+sc6cg+x4iPVWZ4IHNjZvZlZt+8efM6rI4kqROdXIfwFuDdEXEBcDIwJyI+CxyIiAWZub+6gw7W8oPAWS3lFwH7Kr5ohHhrmcGImA2cCjw1yfckSZqEcVsImXlVZi7KzMU0g8V3Zub7gK3A2lpsLXBHTW8F1tSZQ2fTDB7fU91Kz0bEihofuLStzNC6Lq5tvKSFIKlzizd86RcPqRNTuVL5GmBLRKwDHgcuAcjMXRGxBXgIOAxcmZkvVpkrgJuBU4Bt9QC4CbglIgZoWgZrplAvSdIkTCghZOZdwF01/SSwcpTlrgauHiHeD7x+hPjzVEKRJHWHVypLkgATgiSpeLdTSUeNdz49tthCkCQBJgRJUjEhSJIAE4IkqZgQJEmACUGSVEwIkiTA6xCk44o3stNU2EKQJAEmBElSMSFIkgDHECTNkPbxDe9t1HtsIUiSABOCJKmYECRJgAlBklRMCJIkwIQgSSomBEkSYEKQJBUTgiQJMCFIkooJQZIEmBAkScWb20nHOP8pjqaLCUFSV7QmMu982hvsMpIkASYESVIxIUiSABOCJKmYECRJQAcJISJOjoh7IuKBiNgVEX9T8bkRsT0i9tbzaS1lroqIgYjYExHnt8TPi4gHa971EREVPykibqv43RGxePrfqiRpLJ20EF4Afi8z3wCcC6yKiBXABmBHZi4BdtRrImIpsAZYBqwCboiIWbWuG4H1wJJ6rKr4OuDpzDwHuA64dhremyRpAsZNCNl4rl6+vB4JrAY2VXwTcFFNrwZuzcwXMvNRYABYHhELgDmZuTMzE9jcVmZoXbcDK4daD5KkmdHRGEJEzIqI+4GDwPbMvBuYn5n7Aer5zFp8IfBES/HBii2s6fb4sDKZeRh4Bjh9hHqsj4j+iOg/dOhQZ+9QktSRjhJCZr6YmecCi2iO9l8/xuIjHdnnGPGxyrTXY2Nm9mVm37x588artiRpAiZ0llFm/gi4i6bv/0B1A1HPB2uxQeCslmKLgH0VXzRCfFiZiJgNnAo8NZG6SZKmppOzjOZFxKtq+hTg7cB3ga3A2lpsLXBHTW8F1tSZQ2fTDB7fU91Kz0bEihofuLStzNC6LgburHEGSdIM6eTmdguATXWm0MuALZn5xYjYCWyJiHXA48AlAJm5KyK2AA8Bh4ErM/PFWtcVwM3AKcC2egDcBNwSEQM0LYM10/HmpOPV8XaHU2901xvGTQiZ+W3gjSPEnwRWjlLmauDqEeL9wEvGHzLzeSqhSJK6wyuVJUmACUGSVEwIkiTAhCBJKiYESRJgQpAkFROCJAkwIUiSiglBkgSYECRJxYQgSQJMCJKk0sndTiVpxnjn0+6xhSBJAkwIkqRiQpAkAY4hSMeM4+2/pKn32EKQJAEmBElSMSFIkgATgiSpmBAkSYAJQZJUTAiSJMCEIEkqJgRJEmBCkCQVb10hqWd5K+yZZQtBkgSYECRJxYQgSQIcQ5B6mre81kyyhSBJAjpICBFxVkR8JSJ2R8SuiPhgxedGxPaI2FvPp7WUuSoiBiJiT0Sc3xI/LyIerHnXR0RU/KSIuK3id0fE4ul/q5KksXTSQjgM/Flmvg5YAVwZEUuBDcCOzFwC7KjX1Lw1wDJgFXBDRMyqdd0IrAeW1GNVxdcBT2fmOcB1wLXT8N4kSRMwbkLIzP2Z+c2afhbYDSwEVgObarFNwEU1vRq4NTNfyMxHgQFgeUQsAOZk5s7MTGBzW5mhdd0OrBxqPUiSZsaExhCqK+eNwN3A/MzcD03SAM6sxRYCT7QUG6zYwppujw8rk5mHgWeA00fY/vqI6I+I/kOHDk2k6pKkcXScECLilcC/Ah/KzB+PtegIsRwjPlaZ4YHMjZnZl5l98+bNG6/KkqQJ6CghRMTLaZLBP2fm5yt8oLqBqOeDFR8EzmopvgjYV/FFI8SHlYmI2cCpwFMTfTOSpMnr5CyjAG4Cdmfmx1tmbQXW1vRa4I6W+Jo6c+hsmsHje6pb6dmIWFHrvLStzNC6LgburHEGSQKaazKGHjo6Orkw7S3A+4EHI+L+iv0lcA2wJSLWAY8DlwBk5q6I2AI8RHOG0pWZ+WKVuwK4GTgF2FYPaBLOLRExQNMyWDPF9yVJmqBxE0Jm/jcj9/EDrBylzNXA1SPE+4HXjxB/nkookqTu8EplSRJgQpAkFW9uJ/UYB03VLbYQJEmACUGSVEwIkiTAhCBJKiYESRJgQpAkFROCJAnwOgRJx6DWazUeu+bCLtbk+GILQZIEmBAkScWEIEkCTAiSpGJCkCQBJgRJUvG0U6kHeMtr9QJbCJIkwIQgSSomBEkS4BiCpGOct7GYPrYQJEmACUGSVEwIkiTAhCBJKiYESRJgQpAkFROCJAkwIUiSihemSV3iDe3Ua2whSJIAWwiSjiPexmJqxm0hRMRnIuJgRHynJTY3IrZHxN56Pq1l3lURMRAReyLi/Jb4eRHxYM27PiKi4idFxG0VvzsiFk/vW5QkdaKTLqObgVVtsQ3AjsxcAuyo10TEUmANsKzK3BARs6rMjcB6YEk9hta5Dng6M88BrgOuneybkSRN3rgJITO/CjzVFl4NbKrpTcBFLfFbM/OFzHwUGACWR8QCYE5m7szMBDa3lRla1+3AyqHWgyRp5kx2UHl+Zu4HqOczK74QeKJlucGKLazp9viwMpl5GHgGOH2kjUbE+ojoj4j+Q4cOTbLqkqSRTPdZRiMd2ecY8bHKvDSYuTEz+zKzb968eZOsoiRpJJNNCAeqG4h6PljxQeCsluUWAfsqvmiE+LAyETEbOJWXdlFJko6yySaErcDaml4L3NESX1NnDp1NM3h8T3UrPRsRK2p84NK2MkPruhi4s8YZJEkzaNzrECLiX4C3AWdExCDw18A1wJaIWAc8DlwCkJm7ImIL8BBwGLgyM1+sVV1Bc8bSKcC2egDcBNwSEQM0LYM10/LOJEkTEsfqwXhfX1/29/d3uxrShHi7iu7wIrUjIuK+zOwbaZ63rpAkASYESVIxIUiSABOCJKmYECRJgAlBklRMCJIkwH+QIx11XnvQff7jnM7YQpAkASYESVIxIUiSABOCJKmYECRJgGcZSTrBeMbR6GwhSJIAWwjStPO6Ax2rbCFIkgATgiSpmBAkSYBjCJJOYJ5xNJwtBEkSYAtBmhaeWaTjgS0ESRJgQpAkFbuMJAkHmMGEIE2a4wY63thlJEkCbCFI0kucqN1HthAkSYAtBGlCHDfQ8cyEIEljOJG6j0wI0jhsFehEYUKQpA4d762FEzIhHO9fqqSj73j8HemZhBARq4BPArOAT2fmNV2ukk5gdhPpRNQTCSEiZgH/APw+MAjcGxFbM/Oh7tZMJxKTgCZrtL+dY63l0BMJAVgODGTmIwARcSuwGjAhaNr5w6+Z0snfWi8ljV5JCAuBJ1peDwK/1b5QRKwH1tfL5yJizyS3dwbwQ4C4dpJrOPp+UcceZh2nh3WcHsdkHbvwG/Rro83olYQQI8TyJYHMjcDGKW8soj8z+6a6nqPJOk4P6zg9rOP06PU69sqtKwaBs1peLwL2dakuknRC6pWEcC+wJCLOjohXAGuArV2ukySdUHqiyygzD0fEB4D/oDnt9DOZuesobnLK3U4zwDpOD+s4Pazj9OjpOkbmS7rqJUknoF7pMpIkdZkJQZLUyMyeegCrgD3AALBhhPkBXF/zvw28abyywFxgO7C3nk9rmXdVLb8HOL8lfh7wYM27niPdaycB/wX8FHgeuLalzNraxl7gy71WR+BcYCewq+q1rdfq2FJuDvAD4IFerCPwq/Ud7waeBB7rwTp+tL7r3cD9Xfwc31qxBPa3badX9pkP1+eXwGdblm/dZ34I/G8XP8NvAoeBi0f4XRzaXz41pd/f6fwxn+qDZkD5YeA1wCtofgyWti1zAc0PWQArgLvHK0uzY2yo6Q0c+YFcWsudBJxd5WfVvHuAN9d2tgHvqPiVwDO1nT8CflTrmQs8Us9/CPwEOK3H6vhaYEkt8z6aHeBVvVTHlu/5k8BdwPd77buueXfR3GrlApofsl/qpToCvw18verxTuBp4G1dquNraC48/TzNGYQP0EP7TG3j+zX9WZrkPrSN1wJL6nu+kyahvb0Ln+Fi4DeAzYycED4JfI4pJoRe6zL6xS0sMvOnwNAtLFqtBjZn4xvAqyJiwThlVwObanoTcFFL/NbMfCEzH6XJystrfXMyc2c2n/bmljLvB3Znc5uN24CX13rOB7Zn5lPASuB/gFW9VMfM/F5m7q1lfgc4AMzrpToCRMR5wHyao7Xv9tp3HRFLgdmZub3W+0+Z+ZNeqmN9difT/Ei9hyYhHOhSHefT3IbmxzRHuEPb6ZV9ZjnN39m2qt/Xh7bRss+sBv4ROEiTPGb0M8zMxzLz28DPadOyv3y5fd5E9VpCGOkWFgs7XGassvMzcz9APZ/ZwboGR1nXq2mOasjMw8BzwK+3rWsh8GhLmV6pY6tlND8aD/dSHSPiZcDfA39Oc7T43Cjr7ebn+FrgRxExdMT7rrpBY8/UMTN3Al+hOaK9FPhaZu7uUh072We7uc+0L/9kyzaGLAReSZNgH57h+o2qbX+Zsl5LCJ3cwmK0ZTq6/cU0rKuTMtES76U6NjObI5Fzgb/NzJ+3LdPtOv4J8G+Z+cQk1zsTdZxN08L6MM3R5KuBy3qpjhFxDvA6mqv+dwDnRcRbe6mO9M4+08k2Tgb+Cvjjln1mJj/D0bTuL1PWawmhk1tYjLbMWGUP1I/g0I/hwQ7WtWiUdf2Apr+QiJhNc9TwSNu6Bmn6Bve1le92HYmIOcCXaAbKnh1lvd2s45uBD0TEYzRH4u+IiGvayne7joPAt6qb5gmaAcc39Vgd3wN8IzOfo+ni2E3TN9+NOnayz3Zzn2lf/vSWbQztM28APl/dWTNdv7G07i8fAy5t2V8mbrxBhpl80Bx5PULzhzE0QLOsbZkLGT6ofM94ZYG/Y/jgzkdrehnDB3ce4cjgzr21/qHBnQsq/qc0faFnc2QQbxnNwNijNN0c76UZIJvbY3V8Bc3R4od69XNs+64/zvBB5Z6oI81A4gPAvPocB2kGd3upju8F/rPq8W6aM2Te1aU6Dm3ndo4MKvfMPtO2jc00g8pD2xjaZzbSxf2lZZ+4mREGlWveZRxPZxnVm7oA+B5NP91HKnY5cHlNB80/03mY5vSsvrHKVvz0+lL31vPclnkfqeX3UCP6Fe8DvlPzPsWR079OBr7GkdP8PtZSx800A0QDtZ1eq+NngBdpTkG8HzhEc4TbS3Vs/a4voznFr9c+x8uBT1TdHqQ5+u61Ol4BfLXq9lB9392q42/SJKSf0/z9HejBfeaDwM+qjj+hafVdTrPP/Iwj+8sLtb1ufIaDwP/RjHHsGuG38zKmmBC8dYUkCei9MQRJUpeYECRJgAlBklRMCJIkwIQgSSomBEkSYEKQJJX/B+35Gliqs9GYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ =plt.hist(softmax(-embt.dot(embt.T)).flatten(), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_ij = softmax(-emb.dot(emb.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray([[0., 0., 0., ..., 0., 0., 0.],\n",
       "             [0., 0., 0., ..., 0., 0., 0.],\n",
       "             [0., 0., 0., ..., 0., 0., 0.],\n",
       "             ...,\n",
       "             [0., 0., 0., ..., 0., 0., 0.],\n",
       "             [0., 0., 0., ..., 0., 0., 0.],\n",
       "             [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_ij"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.hist(p_ij[p_ij>0],bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeviceArray(0.99999946, dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_ij.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying the metric-learn approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import metric_learn\n",
    "sdml = metric_learn.SDML_Supervised(sparsity_param=0.1, balance_param=0.0015,\n",
    "                                    prior='covariance')\n",
    "# X_sdml = sdml.fit_transform(X, y)\n",
    "\n",
    "# plot_tsne(X_sdml, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lmar3213/miniconda3/envs/lew_jax/lib/python3.8/site-packages/metric_learn/sdml.py:369: ChangedBehaviorWarning: As of v0.5.0, `SDML_Supervised` now uses the `random_state` given at initialization to sample constraints, not the default `np.random` from the `fit` method, since this argument is now deprecated. This warning will disappear in v0.6.0.\n",
      "  warnings.warn('As of v0.5.0, `SDML_Supervised` now uses the '\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "bad input shape (143466, 243)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d026fb010d00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_sdml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msdml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/metric_learn/sdml.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, random_state)\u001b[0m\n\u001b[1;32m    373\u001b[0m                     \u001b[0;34m'This warning will disappear in v0.6.0.'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m                     ChangedBehaviorWarning)\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mnum_constraints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_constraints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnum_constraints\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/metric_learn/base_metric.py\u001b[0m in \u001b[0;36m_prepare_inputs\u001b[0;34m(self, X, y, type_of_inputs, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \"\"\"\n\u001b[1;32m     95\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_preprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m     return check_input(X, y,\n\u001b[0m\u001b[1;32m     97\u001b[0m                        \u001b[0mtype_of_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype_of_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m                        \u001b[0mpreprocessor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessor_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/metric_learn/_util.py\u001b[0m in \u001b[0;36mcheck_input\u001b[0;34m(input_data, y, preprocessor, type_of_inputs, tuple_size, accept_sparse, dtype, order, copy, force_all_finite, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    124\u001b[0m                              ensure_min_features=0, ensure_min_samples=0)\n\u001b[1;32m    125\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m     input_data, y = check_X_y(input_data, y, ensure_2d=False, allow_nd=True,\n\u001b[0m\u001b[1;32m    127\u001b[0m                               \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m                               \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    742\u001b[0m                         dtype=None)\n\u001b[1;32m    743\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    745\u001b[0m         \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_numeric\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'O'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/lew_jax/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcolumn_or_1d\u001b[0;34m(y, warn)\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bad input shape {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: bad input shape (143466, 243)"
     ]
    }
   ],
   "source": [
    "X_sdml = sdml.fit_transform(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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