research/old/is.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qreservoirpy import QReservoir, Layers, utilities\n",
    "\n",
    "from qiskit.extensions import HamiltonianGate\n",
    "import tikzplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_qubits = 4\n",
    "SHOTS = 10000\n",
    "timeseries = [0, 1, 1, 2, 1, 1, 2] * 10\n",
    "encoder = {\n",
    "    0: '00',\n",
    "    1: '01',\n",
    "    2: '10',\n",
    "    3: '11'\n",
    "} \n",
    "\n",
    "T = 10#s\n",
    "H = utilities.random_ising_H(num_qubits=n_qubits, num_terms=80)\n",
    "UT = HamiltonianGate(H, T, label=\"UT\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "models = np.array([\n",
    "    SVC(kernel=ker) for ker in ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "] + [\n",
    "    RidgeClassifier(alpha=alph) for alph in np.linspace(0, 1e-3, 9)\n",
    "] + [\n",
    "    KNeighborsClassifier(n_neighbors=n) for n in range(1, 10)\n",
    "], dtype=object)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def build_method(circuit, timestep, encoding, reservoir):\n",
    "    circuit.measure(range(2))\n",
    "    circuit.initialize(encoding[timestep], range(2))\n",
    "    circuit.unitary(reservoir, circuit.qubits)\n",
    "\n",
    "qres = QReservoir(qubits=n_qubits, layers=[\n",
    "    Layers.Reset(),\n",
    "    Layers.Timeseries(build_method=build_method, \n",
    "                      encoding = encoder,\n",
    "                      reservoir = UT)\n",
    "])\n",
    "fig = qres.circuit.draw('mpl')\n",
    "tikzplotlib.save(\"data/circuit_ising.tex\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Simulating: 100%|██████████| 1/1 [00:06<00:00,  6.39s/it]\n"
     ]
    }
   ],
   "source": [
    "states = qres.run(\n",
    "    timeseries=timeseries, \n",
    "    shots=SHOTS\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qres.circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WARMUP = 0.1 # 10%\n",
    "warmup_idx = int(len(states) * WARMUP)\n",
    "\n",
    "xstates = states[:-1][warmup_idx:]\n",
    "target = timeseries[1:][warmup_idx:]\n",
    "fig, ax = utilities.result_plotter(xstates, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii in range(xstates.shape[-1] // 2):\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot()\n",
    "\n",
    "    cmap = plt.get_cmap('jet', len(np.unique(timeseries)))\n",
    "    for i, x, y in zip(target, xstates[:, 2 * ii], xstates[:, 2 * ii + 1]):\n",
    "        ax.scatter(x, y,  color=cmap(i))\n",
    "    \n",
    "    ax.set_xlabel(f'state {2 * ii + 1}')\n",
    "    ax.set_ylabel(f'state {2*(ii+1)}')\n",
    "\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "cmap = plt.get_cmap('jet', len(np.unique(timeseries)))\n",
    "\n",
    "plt.scatter(xstates[:, 0], xstates[:, 1], cmap=cmap, c=target)\n",
    "# for i, x, y in zip(target, xstates[:, 2 * ii], xstates[:, 2 * ii + 1]):\n",
    "#     ax.scatter(x, y,  color=cmap(i))\n",
    "\n",
    "# ax.set_xlabel(f'state {2 * ii + 1}')\n",
    "# ax.set_ylabel(f'state {2*(ii+1)}')\n",
    "plt.xlabel(\"state 1\")\n",
    "plt.ylabel(\"state 2\")\n",
    "tikzplotlib.save(\"data/phase_ising.tex\")\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if xstates.shape[-1] >= 3:\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "    cmap = plt.get_cmap('jet', len(np.unique(timeseries)))\n",
    "    for i, x, y, z in zip(target, xstates[:, 0], xstates[:, 1], xstates[:, 2]):\n",
    "        ax.scatter(x, y, z, color=cmap(i))\n",
    "    ax.set_xlabel('state 1')\n",
    "    ax.set_ylabel('state 2')\n",
    "    ax.set_zlabel('state 3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of SVC(kernel='poly'): 0.919999999999999\n",
      "Score of KNeighborsClassifier(): 0.9109523809523798\n",
      "Score of KNeighborsClassifier(n_neighbors=6): 0.9095238095238085\n",
      "Score of KNeighborsClassifier(n_neighbors=4): 0.9038095238095231\n",
      "Score of KNeighborsClassifier(n_neighbors=3): 0.9004761904761897\n",
      "Score of KNeighborsClassifier(n_neighbors=7): 0.8995238095238085\n",
      "Score of KNeighborsClassifier(n_neighbors=2): 0.894761904761904\n",
      "Score of KNeighborsClassifier(n_neighbors=8): 0.8871428571428559\n",
      "Score of KNeighborsClassifier(n_neighbors=1): 0.8766666666666667\n",
      "Score of KNeighborsClassifier(n_neighbors=9): 0.8699999999999994\n",
      "Score of SVC(): 0.8266666666666663\n",
      "Score of RidgeClassifier(alpha=0.001): 0.7657142857142851\n",
      "Score of RidgeClassifier(alpha=0.000875): 0.7657142857142851\n",
      "Score of RidgeClassifier(alpha=0.00075): 0.7652380952380946\n",
      "Score of RidgeClassifier(alpha=0.000625): 0.7652380952380946\n",
      "Score of RidgeClassifier(alpha=0.000375): 0.7647619047619041\n",
      "Score of RidgeClassifier(alpha=0.00025): 0.7647619047619041\n",
      "Score of RidgeClassifier(alpha=0.000125): 0.7647619047619041\n",
      "Score of RidgeClassifier(alpha=0.0): 0.7647619047619041\n",
      "Score of RidgeClassifier(alpha=0.0005): 0.7647619047619041\n",
      "Score of SVC(kernel='sigmoid'): 0.5919047619047618\n",
      "Score of SVC(kernel='linear'): 0.5919047619047618\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "results = utilities.stress_test_models(xstates, target, models, test_to_train_ratio=1/3)\n",
    "sorting = np.flip(np.argsort(results))\n",
    "for model, res in zip(models[sorting], results[sorting]):\n",
    "    print(f'Score of {model}: {res}')\n",
    "    \n",
    "\n",
    "model = models[sorting][0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predicting: 100%|██████████| 43/43 [00:06<00:00,  6.72it/s]\n"
     ]
    }
   ],
   "source": [
    "predict_from = timeseries[-20:]\n",
    "num_preds = 2\n",
    "\n",
    "\n",
    "pred_states, preds = qres.predict(\n",
    "    num_pred=num_preds,\n",
    "    model=model,\n",
    "    shots=SHOTS,\n",
    "    from_series=predict_from\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d37baef640>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_values = timeseries\n",
    "\n",
    "pred_plot = np.append(predict_from[-num_preds:], preds)\n",
    "true_plot = np.append(predict_from[-num_preds:], true_values)\n",
    "\n",
    "minlen = min(len(pred_plot), len(true_plot))\n",
    "\n",
    "plt.plot(pred_plot[:minlen], label=\"Predictions\")\n",
    "plt.vlines(num_preds, ymin=np.min(target), ymax=np.max(target), colors='k', linestyles='dashed')\n",
    "plt.plot(true_plot[:minlen], label=\"True timeseries\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Experiment: 100%|██████████| 1000/1000 [5:35:43<00:00, 20.14s/it] \n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "NUM_EXP = 1000\n",
    "exp_res = np.zeros((NUM_EXP, len(models)))\n",
    "\n",
    "for i in tqdm(range(NUM_EXP), desc=\"Experiment\"):\n",
    "    H = utilities.random_ising_H(num_qubits=n_qubits, num_terms=80)\n",
    "    UT = HamiltonianGate(H, T, label=\"UT\")\n",
    "    qres = QReservoir(qubits=n_qubits, layers=[\n",
    "    Layers.Reset(),\n",
    "    Layers.Timeseries(build_method=build_method, \n",
    "                      encoding = encoder,\n",
    "                      reservoir = UT)\n",
    "    ])\n",
    "\n",
    "    states = qres.run(\n",
    "        timeseries=timeseries, \n",
    "        shots=SHOTS,\n",
    "        disable_status_bar=True\n",
    "    )\n",
    "\n",
    "    xstates = states[:-1][warmup_idx:]\n",
    "    target = timeseries[1:][warmup_idx:]\n",
    "    exp_res[i] = utilities.stress_test_models(xstates, target, models, test_to_train_ratio=1/3)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(\"data/LONGSIM\", exp_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(ncols=3, nrows=3, sharex='col', sharey='row')\n",
    "fig.suptitle(\"KNeighborsClassifier\")\n",
    "ii = 0\n",
    "jj = 0\n",
    "\n",
    "mod = KNeighborsClassifier(n_neighbors=2)\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier:\n",
    "        axes[ii][jj].hist(exp_res[:, idx], density=True)\n",
    "        axes[ii][jj].legend([model.n_neighbors])\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "# temp_res = exp_res[:, 0]\n",
    "# _ = axes.hist(temp_res)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "plt.title(\"KNeighborsClassifier\")\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier and model.n_neighbors in range(1, 4):\n",
    "        plt.xlim([0.5, 1])\n",
    "        plt.hist(exp_res[:, idx], density=True, label=model.n_neighbors, alpha=0.7, bins=50)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "plt.legend()\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Probability density\")\n",
    "tikzplotlib.save(\"data/KNeighbors.tex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9fbFt2zaMGTNG03Z3BgICAlBZWak1XWVlJXx8fPTuBQGA5ORkJCUlaZ7X1NSgW7duzIEMWOvzwKZFSPPuNh8fHwZORiy9m5Q5kCdL5oAZsG9eXl4610vzeouIiMD+/fu1/paTk4OIiIhW5+vu7g53d/cW7cyBfFj684AHphIRObm6ujoUFRWhqKgIwO1TcIuKinD+/HkAwOLFi7X6v/zyyzh79izmzZuHU6dO4b333sPOnTsxZ84cG4+c5I5FCBGRkzt69ChCQkIQEhICAEhKSkJISAjefPNNAGjx00v37t2xb98+5OTkYPDgwVi9ejXef/99xMTE2HzsJG82/TmGTBM8f5/O9nPLY208EpKKvgwA0udg+fLlSE5OxqxZs7B27VpJx+LorLUtGDFiBFq7ZNSGDRuwffv2FtMcO3bMrNclcpoiRAiBW7duQaVSST0Uoz3grdTZ3tDQYPa8XV1doVTqnr8jkmsO9GUAMD8HSqUSbdq0Mem33iNHjmDTpk0YNGiQWWOwJblmALDetsCcDBCZw6wiRC7fgJqamlBeXo7r169LPRSTLB7ZSWd7SUmJ2fNWKBTo2rUr7r//fpOml0sGAHnnQF8GAMvkwMvLC507d4abm5vB09TV1WHy5MnYsmUL3nrrLbPHYAtyzgBg3W2BKRkgMpfJRYhcvgGp1WqUlJRAqVSiS5cucHNzk1213+RZq7O9e4B5R5MLIXD58mVcuHABvXr1MnqPiFwyAMg/B/oyAJiXAyEEmpqacPnyZZSUlKBXr14GX4goMTERsbGxiI6OvmcRcvdFqu68EJatyD0DgHW2BeZkgMhcJhUhcvoG1NTUBLVajcDAQHh5eUk9HJMo2uje1erh4WH2vDt27Ihz587h5s2bRhUhcsoAIP8c6MsAYH4OPD094erqitLSUjQ1NRk0v6ysLBQWFuLIkSMGvYY9XKRK7hkArLctMCUDRJZgUrl75zeg1jQ2NqK2tlbrIRVW9rqZ+k3Q0AwAzIEcGPO+lJWVYdasWdi2bZvBH1bJycmoqanRPMrKykwdqtmYAd34vpAUjN4TYsw3IHv49kOWJ8dvwWQ5BQUFqKqqwtChQzVtKpUKBw8exPr169HY2Nhir5q+i1QRkXMzqvQ19huQPX37IcuQ+7dgMt+TTz6J48ePay5uVVRUhGHDhmHy5MkoKipyqrOtiMg8Ru0JMfYbkL1/+2nt2gvWYOq5/FmZW/DBpnW4crkKD/UdgPlLV2BgSKje/rt27cLChQtx7tw59OrVCytWrMDo0aNNHbYWR/sWbO8Z2LBhAzZs2ICzJecAAD0e6oMZs+fi0ZFP6Z1m7dq12LBhA86fP48OHTrgd7/7HVJSUiz2O7+3tzcGDBig1Xbfffehffv2Ldrlwt5zEBwcjNLS0hbtE6f+Ea//ZVWL9szMTMTHx2u1ubu7W+S0fiJLMqoIaf4GdKf4+Hj06dMHr732Gr8BWcGOHTuwatkCLHg7FQNDQrHtfzfif6Y8h715R4Cufi36Hz58GJMmTUJKSgrGjBmD7du3Y/z48SgsLLTIBwQzYFtdu3bF8uXLofL2hxAC/9j1EWb9cTJ2/PNr9Ozdt0X/7du3Y/78+UhPT0dkZCR++OEHTJs2DQqFAqmpqRIsAVnCkSNHoFKpcOJSDQDgzOmTmPH73+CpMeP1TuPj44PTp09rnsvtTCByDkYVIY74Dcjepaam4reTpmL8xNu31l6QkoqDuV/g0x1bMXJIy+Ms3n33XTzzzDOYO3cuAGDZsmXIycnB+vXrsXHjRrPHwwzY1tixYwEA/7lQDQB45bWF2PlhOv5z7KjOIuTw4cMYPnw4fv/73wO4/Q160qRJ+Pbbb606zry8PKvO39l17NgRAFB16/berPT31iIwqDuGPTJc7zQKhQIBAQE2GR+RqXg4tB1rampCQUEBHnl0hKbNxcUFjzwWhf8U6D4oND8/v8UZKzExMcjPz7fmUMkGVCoV/rn3E9y4cR2Dhz6ss09kZCQKCgrw3XffAQDOnj2L/fv3W+znOJLezaYm7Nu9E+MnTm5170ZdXR2CgoIQGBiIcePGobi42IajJDKM2Zdt5zcg67ly5QpUKhXa//dbULP2HTqi5MyPOqepqKiAv7+/Vpu/vz8qKiqsNk5mwLqOHz+ORx6JQFNjA7zuuw9rtnyIHg/10dn397//Pa5cuYJHH31Uc3nyl19+Ga+//rqNR03W8tXn+3CttgbPPv97vX169+6N9PR0DBo0CDU1NVi1ahUiIyNRXFyMrl272nC0RK3jnhAiO9e7d2/szD6IrX//Es9P+QMWzvl/+OmHUzr75uXl4e2338Z7772HwsJC7N69G/v27cOyZctsPGqylj1ZWzF8ZDQ6BXTW2yciIgJTp07FkCFDEBUVhd27d6Njx47YtGmTDUdKdG9OcwM7OerQoQOUSiWuXr6s1X71ymV06Kj7HhIBAQEtbrtdWVnJ34ZlzM3NDd26PwgA6DdoCIr/7xi2pW/Em8vXtui7cOFCTJkyBdOnTwcADBw4EPX19XjppZfwxhtv8IJUMnfpwnl8++88pG7+0KjpXF1dERISgjNnzlhpZESm4RbJjrm5uSE0NBTfHvpa06ZWq/Htvw9iUKjuYwIiIiKQm5ur1ZaTk4OIiAirjpVsR61W42Zjk86/Xb9+vUWh0XzGUmu3aid52LtzO9p16IjHnnzaqOlUKhWOHz+Ozp317z0hkgL3hNi5pKQkTI2LQ/9BIRgwZCi2/u8G3LhRj/ETbp8tM3XqVDzwwANISUkBAMyaNQtRUVFYvXo1YmNjkZWVhaNHj2Lz5s1SLgaZKDk5GaNGjUK9qx+u113D/r0f42j+v7Fh6ycAgDdmv4wBvbpr1v/YsWORmpqKkJAQhIeH48yZM1i4cCHGjh3L06dlTq1WY+/ObRj7uxfQpo32pvvu7cDSpUvxyCOPoGfPnqiursbKlStRWlqq2UNGZC9YhNi5iRMnoujHUry3+m1cuVyF3v0G4r0PP0b7//4cc/78ea1vvpGRkdi+fTsWLFiA119/Hb169cKnn37K02dlqqqqClOnTsWl8nLc7+2Dh/r2x4atnyDi8ZEAgIqLF9D+/l8vQrZgwQIoFAosWLAAFy9eRMeOHTF27Fj85S9/kWoRyEK+/PJLlF+8gPETX2zxt7u3A7/88gsSEhJQUVGBtm3bIjQ0FIcPH0a/fv1sOWSie1IIG+6jra2tha+vL2pqauDjY95t6A3V0NCAkpISdO/eXbZ3hmy+RsTdBum4WJmxWnt/rLW+mAPj6csAIM8cMAOm4baApGKtdcVjQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEk5ThPBCTbo52/vibMtrKGd6X5xpWY3B94Wk4PBFiKurK4DbV5Kklpqabl9509EvZMUctK75fWl+nxwRM9A6Z8gA2R+Hv1iZUqmEn58fqqqqAABeXl6t3v7aHolbui/R3dDQYNZ81Wo1Ll++DC8vrxZXYHQ0cs+BvgwA5uVACIHr16+jqqoKfn5+Dl2Myj0DgHW2Bc6UAbI/jv3J81/NN29r3vjITdUvN3S2u93wNHveLi4u6Natm+w2xqaQcw70ZQCwTA78/Pyc4iaHcs4AYN1tgbNkgOyLUxQhCoUCnTt3RqdOnXDz5k2ph2O06bvzdLbn/mmE2fN2c3NzmjuryjkH+jIAmJ8DV1dXp/n2K+cMANbbFjhTBsi+OEUR0kypVMryP9rFayqd7XK99LTU5JgDfRkAmANTyDEDALcF5Hic4yswERER2R0WIURERCQJp/o5hojImQTP36f3b+eWx9pwJES6cU8IERERSYJ7QoiI7ExrezCIHAmLEAfEXbAE6M8BM0BE9oJFiB3htx8iInImLEKI7AgLUSJyJjwwlYiIiCTBPSE2xm+6RARwW0AEsAghkgQ/gIiI+HMMERERSYRFCBEREUmCRQgRERFJgkUIERERSYJFCBERAQDS0tIQHBwMDw8PhIeH47vvvtPbNzMzEwqFQuvh4eFhw9GSI2ARQkRE2LFjB5KSkrBo0SIUFhZi8ODBiImJQVVVld5pfHx8UF5ernmUlpbacMTkCFiEEBERUlNTkZCQgPj4ePTr1w8bN26El5cX0tPT9U6jUCgQEBCgefj7+9twxOQIWIQQETm5pqYmFBQUIDo6WtPm4uKC6Oho5Ofn652urq4OQUFBCAwMxLhx41BcXNzq6zQ2NqK2tlbrQc6NRQgRkZO7cuUKVCpViz0Z/v7+qKio0DlN7969kZ6ejr1792Lr1q1Qq9WIjIzEhQsX9L5OSkoKfH19NY/AwECLLgfJD4sQIjJaSkoKHn74YXh7e6NTp04YP348Tp8+LfWwyIYiIiIwdepUDBkyBFFRUdi9ezc6duyITZs26Z0mOTkZNTU1mkdZWZkNR0z2yKgihBseYgYIAL7++mskJibim2++QU5ODm7evImnn34a9fX1Ug+NTNChQwcolUpUVlZqtVdWViIgIMCgebi6uiIkJARnzpzR28fd3R0+Pj5aD3JuRhUh3PAQM0AAkJ2djWnTpqF///4YPHgwMjMzcf78eRQUFEg9NDKBm5sbQkNDkZubq2lTq9XIzc1FRESEQfNQqVQ4fvw4OnfubK1hkgMy6gZ22dnZWs8zMzPRqVMnFBQU4PHHH7fowMg+MQOkS01NDQCgXbt2Ov/e2NiIxsZGzXMekGh/kpKSEBcXh2HDhiEsLAxr165FfX094uPjNX0WL16M1NRUAMDSpUvxyCOPoGfPnqiursbKlStRWlqK6dOnS7UIJENm3UWXGx66VwYA5sDRqdVqzJ49G8OHD8eAAQN09klJScGSJUtsPDIyxsSJE3H58mW8+eabqKiowJAhQ5CdnQ1/f3/N/9k7f6755ZdfkJCQgIqKCrRt2xahoaE4fPgw+vXrJ9UikAyZfGCqoRseHgntuAzJAMAcOLrExER8//33yMrK0tuHByTKw8yZM1FaWorGxkZ8++23CA8P1/r7hg0bNP9es2aNpm9FRQX27duHkJAQWw+ZZM7kIoQbHjIkAwBz4MhmzpyJzz77DAcOHEDXrl319uMBiUSki0k/xzRveA4ePHjPDY+7u7vJgyP7ZWgGAObAEQkh8Morr2DPnj3Iy8tD9+7dpR4SEcmQUUUINzzEDBBwey/Y9u3bsXfvXnh7e2suaOXr6wtPT0+JR2dfgufvc4jXILIGo4oQbnjsixQbHmaAgF+PDRgxYoRWe0ZGBqZNm2b7ARGRLBlVhHDDQ8yA/ZGiGBVC2Pw1icjxGP1zDDk3ZsA43E1ORKQf7x1DREREkjDrYmWkH78BExERtY57QoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBJtpB4AEZEjCJ6/T+ohEMkOixAzccNDzAARkWn4cwwRERFJgkUIERERSYJFCBEREUmCRQgRERFJgkUIERERSYJFCBEREUmCRQgRERFJgkUIERERSYJFCBEREUmCRQgRERFJgkUIERERSYL3jnEy+u5zcm55rI1HQlJhBghgDsg+mLQnJC0tDcHBwfDw8EB4eDi+++47S4+L7BwzQABz4GiuFX5m1PrctWsX+vTpAw8PDwwcOBD79++30UjJURi9J2THjh1ISkrCxo0bER4ejrVr1yImJganT59Gp06drDFGu8A7pf6KGSDAOXPgyBmoP3kQP3/1PlZt2dxifXp4eLTof/jwYUyaNAkpKSkYM2YMtm/fjvHjx6OwsBADBgyQYAlIjozeE5KamoqEhATEx8ejX79+2LhxI7y8vJCenm6N8ZEdYgYIYA4cTe2RT+E9OMbg9fnuu+/imWeewdy5c9G3b18sW7YMQ4cOxfr16208cpIzo/aENDU1oaCgAMnJyZo2FxcXREdHIz8/v0X/xsZGNDY2ap7X1NQAAGpra00dr2TUjdelHoJVdZuzq0Vb8zILITRtxmYAcJwcOGMGAMvkgBmwb0J1C00VZ+Ad+qxWDqp9e2LJlk+w/nzn2/3uyEB+fj6SkpK05hMTE4NPP/1U7+s4Sg6cUfM6ujMDlmBUEXLlyhWoVCr4+/trtfv7++PUqVMt+qekpGDJkiUt2gMDA40cJknp6tWr8PX1BWB8BgDmwFGYkwNmQB6ufrYKV3W0X9ww7fbf78hARUWFzvVfUVGhd/7MgfzdmQFLsOrZMcnJyVqVcnV1NYKCgnD+/HmLLoTUamtrERgYiLKyMvj4+Eg9HIupqalBt27d0K5dO7PmwxzImyVywAzYt/LycvTp0wc5OTkICwvTtC9cuBCHDh3Cnj17uC0wglxz0BpLfR7czagipEOHDlAqlaisrNRqr6ysREBAQIv+7u7ucHd3b9Hu6+vrMCvmTj4+Pg65XC4uvx46ZGwGAObAUZiTA2bAvnl4eECpVKKurk5r3NXV1XjggQc0BcKdGQgICDBqOwAwB47gzgxYZH7GdHZzc0NoaChyc3M1bWq1Grm5uYiIiLDowMg+MQMEMAeOxpT1GRERodUfAHJycrj+yShG/xyTlJSEuLg4DBs2DGFhYVi7di3q6+sRHx9vjfGRHWIGCGAOHI0h63Px4sVITU0FAMyaNQtRUVFYvXo1YmNjkZWVhaNHj2Lz5s1SLQLJkTDBunXrRLdu3YSbm5sICwsT33zzjUHTNTQ0iEWLFomGhgZTXtZuOeNymZqBe81Xzpxxubgt0Cb35dK3PhsaGkRQUJB48cUXtfrv3LlTPPTQQ8LNzU30799f7Nu3z6jXk/v7pY8jLpe1lkkhhIXPtyEiIiIyAG9gR0RERJJgEUJERESSYBFCREREkmARQkRERJKweBFi7K295XIraGOWKzMzEwqFQuuh6y6UUjt48CDGjh2LLl26QKFQtHrPh2Z5eXkYOnQo3N3d0bNnT2RmZursxxzIIwfMgPEcLQMAc2AsZuA2QzPQKkueapOVlSXc3NxEenq6KC4uFgkJCcLPz09UVlbq7H/o0CGhVCrFO++8I06cOCEWLFggXF1dxfHjxy05LLMZu1wZGRnCx8dHlJeXax4VFRU2HvW97d+/X7zxxhti9+7dAoDYs2dPq/3Pnj0rvLy8RFJSkjhx4oRYt26dUCqVIjs7W6sfc3CbHHLADBjHETMgBHNgDGbgNkMzcC8WLULCwsJEYmKi5rlKpRJdunQRKSkpOvtPmDBBxMbGarWFh4eLGTNmWHJYZjN2uTIyMoSvr6+NRmcZhoRu3rx5on///lptEydOFDExMVptzMFtcssBM3Bvjp4BIZiDe2EGbjM0A/disZ9jmm/tHR0drWm71y3e8/PztfoDt28Fra+/FExZLgCoq6tDUFAQAgMDMW7cOBQXF9tiuFZlyPpiDrQ5Wg6YAWYAcN4cMAO/stS6slgR0tqtvfXd2tmUW0HbminL1bt3b6Snp2Pv3r3YunUr1Go1IiMjceHCBVsM2Wr0ra/a2lrcuHEDAHNwJ0fMATPADADOmwNm4FeGZMAQRt87hu4tIiJC6yZOkZGR6Nu3LzZt2oRly5ZJODKyJeaAmAFiBlpnsT0hptzi3ZRbQduaKct1N1dXV4SEhODMmTPWGKLN6FtfPj4+8PT0BMActMYRcsAMMAOA8+aAGfiVIRkwhMWKEEe9FbQlblmuUqlw/PhxdO7c2VrDtAlD1hdzoJ8j5IAZcMwM3Hl6JgB8++23rfaPiIjA3r17tU7P3LRpk8PnwJEzYCyLrStjj5ptTVZWlnB3dxeZmZnixIkT4qWXXhJ+fn6a05GmTJki5s+fr+l/6NAh0aZNG7Fq1Spx8uRJsWjRIrs7HUsI45dryZIl4vPPPxc//fSTKCgoEC+88ILw8PAQxcXFUi2CTteuXRPHjh0Tx44dEwBEamqqOHbsmCgtLRVCCDF//nwxZcoUTf/mU7Lmzp0rTp48KdLS0vSelsccyCMHzIBxHDEDQgjxySefiOnTp4vVq1cLACI+Pr7VHHz99dcCgBg2bJj47LPPxPPPPy8AiLfeektrvo6YA0fNgLW2Bfdi0SJEiNZv7R0VFSXi4uK0+pt7K2hbMWa5Zs+erenr7+8vRo8eLQoLCyUYdesOHDggALR4NC9LXFyciIqKajHNkCFDhJubm3jwwQdFRkaGznkzB/LIATNgPEfLgBDG52DevHkiODhYKwdhYWE6T890xBwwA79OY8i2oDUKIYQwej+MidRqNS5dugRvb28oFApbvSyZSAiBa9euoUuXLnBxsdzFdZkDebFGDpgB++Xr64tt27ZhzJgxmra7M/D4449j6NChWLt2raZPRkYGZs+ejZqaGr3zbmxsRGNjo+a5Wq3Gzz//jPbt2zMHds5anwc2PTvm0qVLCAwMtOVLkgWUlZWha9euFpsfcyBPlswBM2DfJk+erLO9OQP3Oj1T34GJKSkpWLJkicXHS7Zj6c8DmxYh3t7eAG4vhI+Pjy1fmkxQW1uLwMBAzXqzFOZAXqyRA2bAfunaE2KpDCQnJyMpKUnzvKamBt26dWMOZMBanwc2LUKad7f5+PgwcDJi6d2kzIE8WTIHzIB98/Ly0rlemtebqadnuru7w93dvUU7cyAflv48sPhddImIyLHZ+6m0JB8sQoiInFxdXR2KiopQVFQEACgpKUFRURHOnz8PAFi8eLFW/5dffhlnz57FvHnzcOrUKbz33nvYuXMn5syZY+ORk9zxsu0yEDx/n872c8tjbTwSkoq+DADS52D58uVITk7GrFmztM6WIMuz1rbg6NGjGDlypOZ583EbcXFxyMzMbPHTS/fu3bFv3z7MmTMH7777Lrp27Yr3338fMTExZo2DnI9TFSEqlQo3b96UehhGe8BbqbO9oaHB7Hm7ublZ9HQrOZBjDvRlADA/B66urlAq9c+/NUeOHMGmTZswaNAgs8Zga3LMAGC9bcFjjz2G1q7WsGHDBmzfvl2rbcSIETh27JhZr0tkVhEil29AQghUVFSgurpa6qGYZPHITjrbS0pKzJ63i4sLunfvDjc3N5Oml0sGAHnnQF8GAMvkwM/PDwEBAUYddFZXV4fJkydjy5YteOutt8wegy3IOQOAdbcFpmSAyFwmFyFy+gbUvNHp1KkTvLy8ZPefrMmzVmd79wDzjiZvvmBUeXk5unXrZvT7IqcMAPLOgb4MAOblQAiB69evo6qqCgCMup9FYmIiYmNjER0dfc8i5O6LVNXW6l8ea5JzBgDrbAvMyQCRuUwqQuT0DUilUmk2Ou3bt5d6OCZRtNG9q9XDw8PseXfs2BGXLl3CrVu34OrqavB0csoAIP8c6MsAYH4Omk+prKqqQqdOnQz6aSYrKwuFhYU4cuSIQa9hDxepknsGAOttC0zJAJElmHQwwJ3fgFrT2NiI2tparYetNf/u6+XlZfPXloPmn2FUKpVR0xmaAYA5kIPm98WQ4yTKysowa9YsbNu2zeAPv+TkZNTU1GgeZWVlZo3XFMxA64zJAJGlGL0nxJhvQPbw7aeZ3Ha72oop74scvwU3Yw50M+Z9KSgoQFVVFYYOHappU6lUOHjwINavX4/GxsYW36T1XaRKCsyAbnxfSApG7Qkx9huQPXz7IcuS67dgspwnn3wSx48f11xXoqioCMOGDcPkyZNRVFTEXflEZDCj9oQY+w3Inr79kGXI/Vswmc/b2xsDBgzQarvvvvvQvn37Fu1ERK0xqghp/gZ0p/j4ePTp0wevvfaa7L4BtXYBKGsw9oJCBw8exMqVK/Htd0dxuaoCa7ZsxRPP6J/H7t27sWHDBhQVFaGxsRH9+/fH4sWLLXoBIWbAPMZmYPHixS1+ygru0Qt7877TO82uXbuwcOFCnDt3Dr169cKKFSswevRok8brLGyZA2MzoFKpsHjxYmzduhXl5RXo6B+AZ5//PV6a9edWf0LZtm0b3nnnHfz444/w9fXFqFGjsHLlStkelEuOyagihN+AbKu+vh6DBw/GE89ORNJLU+7Z/+DBg3jqqafw9ttvw8/PDxkZGRg7diy+/fZbhISEWGRMzIDt9e/fH3/92yea58o2+v/bHj58GJMmTUJKSgrGjBmD7du3Y/z48SgsLLTq+snLy7PavJ3dihUrsGHDBnzwwQdwaReIE/85hjf/NBP3+/hg8h9m6Jzm0KFDmDp1KtasWYOxY8fi4sWLePnll5GQkIDdu3fbeAmI9HOqK6bKzahRozBq1Cj850K1Qf3vvljY22+/jb179+If//iHxYoQsr02bdqgQyd/g/q+++67eOaZZzB37lwAwLJly5CTk4P169dj48aN1hwmWcnhw4cxbtw4xMbG4j8XqvFAYDf8c+8n+L6oQO80+fn5CA4Oxquvvgrg9mXWZ8yYgRUrVthq2EQGMft63Xl5eXZ/pUxnpVarce3aNbRr186qr8MMWNePP/6I6NC+GD18CJJfSUD5Rf0H9ubn57c4bTomJgb5+fnWHiZZSWRkJHJzc/HDDz8AAE6fOI5jR77BoyP1nx4fERGBsrIy7N+/H0IIVFZW4uOPP+bPcmR3uCfEga1atQp1dXWYMGGC1EMhE4WHhyMzMxMKv864XFmJTWtXIP650fjky8O4737vFv0rKirg76+918Tf3x8VFRW2GjJZ2Pz581FbW4s+ffpAqVRCpVLhlXkLEPsb/f+vhw8fjm3btmHixIloaGjArVu3MHbsWKSlpdlw5ET35lx3LnMi27dvx5IlS7Bz50506qT/viNk30aNGoXnn38eD/UdgOEjnsT6D3bhWm0NPv/sU6mHRjayc+dObNu2Ddu3b0fW/jwsW/MePti0Hn/f9ZHeaU6cOIFZs2bhzTffREFBAbKzs3Hu3Dm8/PLLNhw50b1xT4gDysrKwvTp07Fr1y6DrmhK8uHj64ug7j1Rdu6szr8HBAS0uO16ZWUlAgICbDE8soK5c+di/vz5eOGFF/CfC9Xo1bc/yi9cwP+mrcGzz0/SOU1KSgqGDx+uOTZo0KBBuO+++/DYY4/hrbfe4v1hyG5wT4iD+eijjxAfH4+PPvoIsbHGnQpI9u96fR3KSkvQoZPuoiIiIgK5ublabTk5OYiIiLDF8MgKrl+/DhcX7U21UukCtVpt5DS3T58XQlh+kEQm4p4QO1ZXV4czZ87gh8prAICLZaU4VXwcvn5+6PxAIJKTk3Hx4kX87W9/A3D7J5i4uDi8++67CA8P1xwH4OnpCV9fX8mWg0z35z//GWPHjsV1t7a4XFmODanLoVQqMWrccwCAN2a/jAG9uiMlJQUAMGvWLERFRWH16tWIjY1FVlYWjh49is2bN0u5GGSGsWPH4i9/+Qu6desGl3aBOPX9f/DhlvcwbuJkTZ+7twVjx45FQkICNmzYgJiYGJSXl2P27NkICwtDly5dpFoUohZYhNixo0ePYuTIkZrnq5a+AQB49neTsGzNeygvL8f58+c1f9+8eTNu3bqFxMREJCYmatrj4uKQmZlps3GT5Vy4cAGTJk3ClatX0bZdB4Q8HI4P9+agXfsOAICKixfQ/v5fL58fGRmJ7du3Y8GCBXj99dfRq1cvfPrpp7yGi4ytW7cOCxcuxP/7f/8PlZVV6OgfgN9NnoYZs+dp+ty9LZg2bRquXbuG9evX409/+hP8/PzwxBNP8BRdsjsKYcN9c7W1tfD19UVNTQ18fHxs8poNDQ0oKSlB9+7dzb7dtVT0XSdkUFc/s+fd2vtjrfXFHBivtWvFyDEHzIBpuC0gqVhrXfGYECIiIpIEixAiIiKSBIsQIiIikgSLECIiIpKE0xQhPDdeN2d7X5xteQ3lTO+LMy2rMfi+kBQcvghxdXUFcPviPdRSU1MTgF8vZOSomIPWNb8vze+TI2IGWucMGSD74/DXCVEqlfDz80NVVRUAwMvLCwqFQuJRGUfcatLZ3tDQYNZ81Wo1Ll++DC8vL7Rp49hRkHsO9GUAMC8HQghcv34dVVVV8PPzc+hiVO4ZAKyzLXCmDJD9cexPnv9qvm9G88ZHbqp+uaGz3e2Gp9nzdnFxQbdu3WS3MTaFnHOgLwOAZXLg5+fnFPeXkXMGAOtuC5wlA2RfnKIIUSgU6Ny5Mzp16oSbN29KPRyjTd+dp7M9908jzJ63m5tbi3tMOCo550BfBgDzc+Dq6uo0337lnAHAetsCZ8oA2RenKEKaKZVKWf5Hu3hNpbNdrld9lJocc6AvAwBzYAo5ZgDgtoAcj3N8BSYiIiK7wyKEiIiIJOFUP8cQETmT4Pn79P7t3PJYG46ESDfuCSEiIiJJcE+IA+K3HwL054AZsH+t/R8mciQsQuwINzxERORM+HMMERERSYJ7QojsCPeGEZEz4Z4QIiIikgT3hNgYv+kSwBwQM0AEcE8IERERSYRFCBEREUmCRQgRERFJgkUIERERSYJFCBEREUmCRQgREQEA0tLSEBwcDA8PD4SHh+O7777T2zczMxMKhULr4eHhYcPRkiNgEUJERNixYweSkpKwaNEiFBYWYvDgwYiJiUFVVZXeaXx8fFBeXq55lJaW2nDE5AhYhBAREVJTU5GQkID4+Hj069cPGzduhJeXF9LT0/VOo1AoEBAQoHn4+/vbcMTkCFiEEBE5uaamJhQUFCA6OlrT5uLigujoaOTn5+udrq6uDkFBQQgMDMS4ceNQXFzc6us0NjaitrZW60HOjUUIEZGTu3LlClQqVYs9Gf7+/qioqNA5Te/evZGeno69e/di69atUKvViIyMxIULF/S+TkpKCnx9fTWPwMBAiy4HyQ+LECIyWkpKCh5++GF4e3ujU6dOGD9+PE6fPi31sMiGIiIiMHXqVAwZMgRRUVHYvXs3OnbsiE2bNumdJjk5GTU1NZpHWVmZDUdM9sioIoQbHmIGCAC+/vprJCYm4ptvvkFOTg5u3ryJp59+GvX19VIPjUzQoUMHKJVKVFZWarVXVlYiICDAoHm4uroiJCQEZ86c0dvH3d0dPj4+Wg9ybkYVIdzwEDNAAJCdnY1p06ahf//+GDx4MDIzM3H+/HkUFBRIPTQygZubG0JDQ5Gbm6tpU6vVyM3NRUREhEHzUKlUOH78ODp37mytYZIDMuouutnZ2VrPMzMz0alTJxQUFODxxx+36MDIPjEDpEtNTQ0AoF27djr/3tjYiMbGRs1zHpBof5KSkhAXF4dhw4YhLCwMa9euRX19PeLj4zV9Fi9ejNTUVADA0qVL8cgjj6Bnz56orq7GypUrUVpaiunTp0u1CCRDRhUhd+OGh+6VAYA5cHRqtRqzZ8/G8OHDMWDAAJ19UlJSsGTJEhuPjIwxceJEXL58GW+++SYqKiowZMgQZGdnw9/fX/N/9s6fa3755RckJCSgoqICbdu2RWhoKA4fPox+/fpJtQgkQyYfmGrohodHQjsuQzIAMAeOLjExEd9//z2ysrL09uEBifIwc+ZMlJaWorGxEd9++y3Cw8O1/r5hwwbNv9esWaPpW1FRgX379iEkJMTWQyaZM7kI4YaHDMkAwBw4spkzZ+Kzzz7DgQMH0LVrV739eEAiEeli0s8xzRuegwcP3nPD4+7ubvLgyH4ZmgGAOXBEQgi88sor2LNnD/Ly8tC9e3eph0REMmRUEcINDzEDBNzeC7Z9+3bs3bsX3t7emgta+fr6wtPTU+LREZFcGFWEcMNDzID9CZ6/z+av2XxswIgRI7TaMzIyMG3aNJuPx57ZYv1IkQEiSzCqCOGGx77ww8f+OeqHgxBC6iEQkQMw+ucYcm7MABERWQrvHUNERESSMOtiZaSfo+6GJyIishTuCSEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJtJF6AERyFzx/n9RDIDvAHBAZj0WImbjhISIiMg1/jiEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJ8LLtTkbfZebPLY+18UhIKswAAcwB2QfuCSEiIiJJmLQnJC0tDStXrkRFRQUGDx6MdevWISwszNJjsyu8UZ02ZoAA58uBo2fgWuFnCA5ONHh97tq1CwsXLsS5c+fQq1cvrFixAqNHj7bhiEnujN4TsmPHDiQlJWHRokUoLCzE4MGDERMTg6qqKmuMj+wQM0AAc+Bo6k8exM9fvW/w+jx8+DAmTZqEP/7xjzh27BjGjx+P8ePH4/vvv7fxyEnOFEIIYcwE4eHhePjhh7F+/XoAgFqtRmBgIF555RXMnz+/1Wlra2vh6+uLmpoa+Pj4mD5qCTj6NyBd1I3XUbZ2Qov1ZU4GAPnmwBkzAFgnB8yA/Sn/WxLcO/dCu6f+BwAghBoX35sG79Cx8A6JbZGBiRMnor6+Hp999plmHo888giGDBmCjRs3GvSacs2BM7LWujLq55impiYUFBQgOTlZ0+bi4oLo6Gjk5+e36N/Y2IjGxkbN85qaGgC3F0Zu1I3XpR6CzTUv8511qrEZABwnB86YAcAyOWAG7JtQ3UJTxRl4hz6rtYzugQPQUFaM+/qNvN3vjgzk5+cjKSlJaz4xMTH49NNP9b6Oo+TAGTWvIyP3W9yTUUXIlStXoFKp4O/vr9Xu7++PU6dOteifkpKCJUuWtGgPDAw0cpgkpatXr8LX1xeA8RkAmANHYU4OmAF5uPrZKlzV0X5xw7Tbf78jAxUVFTrXf0VFhd75Mwfyd2cGLMGqp+gmJydrVcrV1dUICgrC+fPnLboQUqutrUVgYCDKysocapdiTU0NunXrhnbt2pk1H+ZA3iyRA2bAvpWXl6NPnz7IycnROhB14cKFOHToEPbs2cNtgRHkmoPWWOrz4G5GFSEdOnSAUqlEZWWlVntlZSUCAgJa9Hd3d4e7u3uLdl9fX4dZMXfy8fFxyOVycfn1+GVjMwAwB47CnBwwA/bNw8MDSqUSdXV1WuOurq7GAw88oCkQ7sxAQECAUdsBgDlwBHdmwCLzM6azm5sbQkNDkZubq2lTq9XIzc1FRESERQdG9okZIIA5cDSmrM+IiAit/gCQk5PD9U9GMfrnmKSkJMTFxWHYsGEICwvD2rVrUV9fj/j4eGuMj+wQM0AAc+BoDFmfixcvRmpqKgBg1qxZiIqKwurVqxEbG4usrCwcPXoUmzdvlmoRSI6ECdatWye6desm3NzcRFhYmPjmm28Mmq6hoUEsWrRINDQ0mPKydssZl8vUDNxrvnLmjMvFbYE2uS+XvvXZ0NAggoKCxIsvvqjVf+fOneKhhx4Sbm5uon///mLfvn1GvZ7c3y99HHG5rLVMRl8nhIiIiMgSeO8YIiIikgSLECIiIpIEixAiIiKSBIsQIiIikoTFi5C0tDQEBwfDw8MD4eHh+O6771rtv2vXLvTp0wceHh4YOHAg9u/fb+khWYQxy5WZmQmFQqH18PDwsOFoDXPw4EGMHTsWXbp0gUKhaPWeD83y8vIwdOhQuLu7o2fPnsjMzNTZjzmQRw6YAeM5WgYA5sBYzMBthmagVZY81SYrK0u4ubmJ9PR0UVxcLBISEoSfn5+orKzU2f/QoUNCqVSKd955R5w4cUIsWLBAuLq6iuPHj1tyWGYzdrkyMjKEj4+PKC8v1zwqKipsPOp7279/v3jjjTfE7t27BQCxZ8+eVvufPXtWeHl5iaSkJHHixAmxbt06oVQqRXZ2tlY/5uA2OeSAGTCOI2ZACObAGMzAbYZm4F4sWoSEhYWJxMREzXOVSiW6dOkiUlJSdPafMGGCiI2N1WoLDw8XM2bMsOSwzGbscmVkZAhfX18bjc4yDAndvHnzRP/+/bXaJk6cKGJiYrTamIPb5JYDZuDeHD0DQjAH98IM3GZoBu7FYj/HNN/aOzo6WtN2r1u85+fna/UHbt8KWl9/KZiyXABQV1eHoKAgBAYGYty4cSguLrbFcK3KkPXFHGhztBwwA8wA4Lw5YAZ+Zal1ZbEipLVbe+u7tbMpt4K2NVOWq3fv3khPT8fevXuxdetWqNVqREZG4sKFC7YYstXoW1+1tbW4ceMGAObgTo6YA2aAGQCcNwfMwK8MyYAhjL53DN1bRESE1k2cIiMj0bdvX2zatAnLli2TcGRkS8wBMQPEDLTOYntCTLnFuym3grY1U5brbq6urggJCcGZM2esMUSb0be+fHx84OnpCYA5aI0j5IAZcMwM3HlmBAB8++23rfYPCAhAQUGB1pkRn3zyicPnwJEzYCxDtgWGsFgR4qi3grbELctVKhWOHz+Ozp07W2uYNmHI+mIO9HOEHDADjpmB+vp6DB48GGlpaQb179u3L3bv3o2RI0eiqKgIs2fPRkZGBnr16qXp44g5cOQMGMti68rYo2Zbk5WVJdzd3UVmZqY4ceKEeOmll4Sfn5/mdKQpU6aI+fPna/ofOnRItGnTRqxatUqcPHlSLFq0yO5OxxLC+OVasmSJ+Pzzz8VPP/0kCgoKxAsvvCA8PDxEcXGxVIug07Vr18SxY8fEsWPHBACRmpoqjh07JkpLS4UQQsyfP19MmTJF07/5lKy5c+eKkydPirS0NL2n5TEH8sgBM2AcR8yAEC1zEB8f32oOXnrpJaFQKLRyoFAoRGhoqNZ8HTEHzpIBS20L7sWiRYgQrd/aOyoqSsTFxWn1N/dW0LZizHLNnj1b09ff31+MHj1aFBYWSjDq1h04cEAAaPFoXpa4uDgRFRXVYpohQ4YINzc38eCDD4qMjAyd82YO5JEDZsB4jpYBIYzPwWOPPSaee+45rRz84Q9/ED4+Pi3m7Yg5YAZ+ncaQbUFrFEIIYcKeGJOo1WpcunQJ3t7eUCgUtnpZMpEQAteuXUOXLl3g4mK5i+syB/JijRwwA/bL19cX27Ztw5gxYzRtd2fgoYceQnx8PJKTkzV99u/fj9jYWFy/fl3vMQGNjY1obGzUPFer1fj555/Rvn175sDOWevzwKZnx1y6dAmBgYG2fEmygLKyMnTt2tVi82MO5MmSOWAG7NvkyZN1tpubgZSUFCxZssTk6Ul6lv48sGkR4u3tDeD2Qvj4+NjypckEtbW1CAwM1Kw3S2EO5MUaOWAG7JeuPSF3Z8DUMyOSk5ORlJSkeV5TU4Nu3boxBzJgrc8DmxYhzbvbfHx8GDgZsfRuUuZAniyZA2bAvnl5eelcL83rLSIiosWN5Qw5M8Ld3R3u7u4t2pkD+bD054HF76JLRETyUldXh6KiIhQVFQEASkpKUFRUhPPnzwMAFi9erNX/5ZdfxtmzZzFv3jycOnUK7733Hnbu3Ik5c+bYeOQkdyxCiMgsy5cvh0KhwOzZs6UeCpno6NGjCAkJQUhICAAgKSkJISEhePPNNwGgxU8v3bt3x759+5CTk4PBgwdj9erVeP/99xETE2PzsZO88bLtMhA8f5/O9nPLY208EpKKvgwA0ubgyJEj2LRpEwYNGiTZGJyJtbYFI0aMQGsnSm7YsAHbt29vMc2xY8fMel0ipylChBC4desWVCqV1EMx2gPeSp3tDQ0NZs/b1dUVSqXu+Rti+fLlSE5OxqxZs7B27Vqzx2Ntcs2BvgwA5udAqVSiTZs2Rv/WW1dXh8mTJ2PLli146623zBqDpch1/RrKWtsCUzNAZC6zihC5fAA1NTWhvLwc169fl3ooJlk8spPO9pKSErPnrVAo0LVrV9x///1GTyu3b8FyzoG+DACWyYGXlxc6d+4MNzc3g6dJTExEbGwsoqOj7aIIkfP6NZQ1twWmZIDIXCYXIXL5AFKr1SgpKYFSqUSXLl3g5uYmu2q/ybNWZ3v3APOOJhdC4PLly7hw4QJ69epl1B4Re/wW3Bq550BfBgDzciCEQFNTEy5fvoySkhL06tXLoAsRZWVlobCwEEeOHDHode6+SFVtrf7lMYXc16+hrLEtMDUDRJZgUhEipw+gpqYmqNVqBAYGwsvLS+rhmETRRveuVg8PD7Pn3bFjR5w7dw43b940qggx5luwtT+ADCH3HOjLAGB+Djw9PeHq6orS0lI0NTXdc35lZWWYNWsWcnJyDH5ta1+kSu7r11DW2hYYmwEiSzGp3L3zA6g1jY2NqK2t1XpIhZW9bqZ8W2z+FpySkmJQ/5SUFPj6+moeUl4pkznQzZj3paCgAFVVVRg6dCjatGmDNm3a4Ouvv8Zf//pXtGnTRufxGMnJyaipqdE8ysrKLDl8Da5f0/G9IykYvSfEmN2wvESv4zHlW/DdV0lsvvIeydOTTz6J48ePa7XFx8ejT58+eO2113TuUdN3kSoicm5GFSHGfgDxw8fx3PktuJlKpcLBgwexfv16NDY2tvgQ4geQY/H29saAAQO02u677z60b9++RTsRUWuMKkKM/QCy9w+f1q69YA3GnsufkpKC3bt348TJU3D38MCQ0DDMfn0xgnv00jtNZmYm4uPjtdrc3d0tcjovYNq3YHtm7xkAgIsXLyL51Tk4dOBLNNy4gcDg7li6Og39B4fo7L97925s2LABRUVFaGxsRP/+/bF48WKnvZCUva/jgwcPYuXKlSgoKEB5eTn27NmD8ePHa/4uhMCiRYuwZcsW/PJLNYY8HI433l6NoO49Wp1vWloaVq5ciYqKCgwePBjr1q1DWFiYKYtEZDVGFSGO9gFk777++mskJibCJ7A3VKpbWLdiGV6e/Fvs/uobeHndp3c6Hx8fnD59WvPckmcJ8Fuwbf3yyy8YPnw4BocNR9rfdqFt+w44X/ITfHz99E5z8OBBPPXUU3j77bfh5+eHjIwMjB07Ft9++63mipiWlpeXZ5X5OoP6+noMHjwYf/jDH/Db3/62xd/feecd/PWvf8UHH3yAm17tkbbybfzPi89hT+43cNezR3rHjh1ISkrCxo0bER4ejrVr1yImJganT59Gp076T/cmsjWjihB+ANlWdnY2AOA/F6oBAEtT38PIIb1w8j9FCH1kuN7pFAoFAgICbDFEsrIVK1YgMDAQy1LTNG1duwW1Os3d1+x5++23sXfvXvzjH/+wWhFCphs1ahRGjRql829CCKxduxYLFizAuHHj8J8L1Xhr7QY8MbQ3vvp8H0aNe07ndKmpqUhISNDsFd24cSP27duH9PR0zJ8/32rLQmQsHg4tI3X/PbvIx69t6/3q6hAUFITAwECMGzcOxcXFVh1XXl6eXV+sTs7+/ve/Y9iwYfjzy9MwYkgvTHjmcXyy/QOj5qFWq3Ht2jW0a9fOSqMkaykpKUFFRYXWmYjePr4YOCQU/ynUfXJAU1MTCgoKtKZxcXFBdHQ08vPzrT5mImOYfdl27oa1DbVajXeWJGPIw+Ho1aef3n69e/dGeno6Bg0ahJqaGqxatQqRkZEoLi5G165dbThisoSzZ89iw4YNeHH6/8MfZyah+P8KseLN+XB1dcOzz08yaB6rVq1CXV0dJkyYYOXRkqVVVFQAAPz9/bXa23fshCtVVTqnuXLlClQqVYtp/P39cerUKesMlMhETnPvGLl7+40/46fTJ5G5+5+t9ouIiEBERITmeWRkJPr27YtNmzZh2bJl1h4mWZharcawYcPw6vzbdzPtO2AQzpw+iV1bMwwqQrZv344lS5Zg7969PBaAiOwOf46RgbcXzMXB3M+xZcc/4N/5AaOmdXV1RUhICM6cOWOl0ZE1de7cGf36ae/5erDnQyi/eOGe02ZlZWH69OnYuXPnPS8sSPap+diuyspKrfarl6vQQU9R2aFDByiVyhbTVFZW8lgxsjssQuyYEAIzZ87EV9n7sGXH3+95QKIuKpUKx48fR+fOna0wQrK24cOHa53pBAClZ39Cl3v8tPbRRx8hPj4eH330EWJjzbvNO0mne/fuCAgIQG5urqat7lotjhcVYNDQh3VO4+bmhtDQUK1p1Go1cnNztfaSEtkD/hxjxxITE7F9+3akbtmG++67H1eqbn+zud/bBx6engCAqVOn4oEHHtBcQn3p0qV45JFH0LNnT1RXV2PlypUoLS3F9OnTJVsOMt2cOXMQGRmJ99etxtNjfoPviwrw8fYP8OaKNZo+ycnJuHjxIv72t78BuP0TTFxcHN59912Eh4drjivw9PSEr6+vJMtB+tXV1WntqSwpKUFRURHatWuHbt26Yfbs2XjrrbfQq1cv3PRsj7RVb6OjfwCeiPm1uHzyySfxm9/8BjNnzgQAJCUlIS4uDsOGDUNYWBjWrl2L+vr6FtcQIpIaixA7tmHDBgDAHyeM0WpfujoN4yb8HgBw/vx5rXs+/PLLL0hISEBFRQXatm2L0NBQHD58uMUufZKHhx9+GHv27MGcP8/DpndX4oHAIMxb/DZif/PrQabl5eU4f/685vnmzZtx69YtJCYmIjExUdMeFxeHzMxMWw6fDHD06FGMHDlS87z5KtPN62vevHmor6/HSy+9hF+qqxHy8CN478OPta4R8tNPP+HKlSua5xMnTsTly5fx5ptvoqKiAkOGDEF2dnaLg1WJpKYQQghbvVhtbS18fX1RU1MDHx/zbkNvqIaGBpSUlKB79+6yvTNk83VC7jaoq5/Z827t/bHW+mIOjKcvA4A8c2Dpecp9/RqK2wKSirXWFY8JISIiIkmwCCEiIiJJsAghIiIiSbAIISIiIkmwCCEiIiJJOE0RYsOTgGTF2d4XZ1teQznK++IoyyEFvnckBYcvQlxdXQEA169fl3gk9qmpqQkAoFQqJR6JdTEHrWt+X5rfJ7nh+jWf3DNA8uTwFytTKpXw8/ND1X/vOOnl5QWFQiHxqIwjbjXpbG9oaDBrvmq1GpcvX4aXlxfatHHsKMg9B/oyAJiXAyEErl+/jqqqKvj5+cm2GJX7+jWUNbYFjpIBkifH/uT5r+abNlXpufW1vav65YbOdrcbnmbP28XFBd26dXPIDfbd5JwDfRkALJMDPz8/2d/cTM7r11DW3BY4QgZIfpyiCFEoFOjcuTM6deqEmzdvSj0co03fnaezPfdPI8yet5ubm9Zl3x2ZnHOgLwOA+TlwdXV1iG+/cl6/hrLWtsBRMkDy4xRFSDOlUinL/2gXr6l0tjvy5amtSY450JcBgDm4mxzXr6G4LSBH4xxfgYmIiMjusAghIiIiSTjVzzFERM4keP4+vX87tzzWhiMh0o1FiAPihocA/TlgBuxfa/+HiRwJf44hIiIiSXBPiB3htx9iBojImXBPCBEREUmCRQgRERFJgj/H2Bh3txMRwG0BEcAihEgS/AAiIuLPMURERCQRFiFEREQkCRYhREREJAkWIURERCQJFiFEREQkCRYhREQEAEhLS0NwcDA8PDwQHh6O7777Tm/fzMxMKBQKrYeHh4cNR0uOgEUIERFhx44dSEpKwqJFi1BYWIjBgwcjJiYGVVVVeqfx8fFBeXm55lFaWmrDEZMjYBFCRERITU1FQkIC4uPj0a9fP2zcuBFeXl5IT0/XO41CoUBAQIDm4e/vb8MRkyNgEUJE5OSamppQUFCA6OhoTZuLiwuio6ORn5+vd7q6ujoEBQUhMDAQ48aNQ3Fxcauv09jYiNraWq0HOTcWIURktJSUFDz88MPw9vZGp06dMH78eJw+fVrqYZGJrly5ApVK1WJPhr+/PyoqKnRO07t3b6Snp2Pv3r3YunUr1Go1IiMjceHCBb2vk5KSAl9fX80jMDDQostB8mNUEcINDzEDBABff/01EhMT8c033yAnJwc3b97E008/jfr6eqmHRjYSERGBqVOnYsiQIYiKisLu3bvRsWNHbNq0Se80ycnJqKmp0TzKyspsOGKyR0YVIdzwEDNAAJCdnY1p06ahf//+GDx4MDIzM3H+/HkUFBRIPTQyQYcOHaBUKlFZWanVXllZiYCAAIPm4erqipCQEJw5c0ZvH3d3d/j4+Gg9yLkZdQO77OxsreeZmZno1KkTCgoK8Pjjj1t0YGSfmAHSpaamBgDQrl07nX9vbGxEY2Oj5jmPBbAvbm5uCA0NRW5uLsaPHw8AUKvVyM3NxcyZMw2ah0qlwvHjxzF69GgrjpQcjVnHhNxrw0OOz5AM8GA0x6ZWqzF79mwMHz4cAwYM0NmHxwLYv6SkJGzZsgUffPABTp48if/5n/9BfX094uPjNX0WL16s+ffSpUvxxRdf4OzZsygsLMSLL76I0tJSTJ8+XYLRk1wZtSfkToZsePjtx7EZkgHg9gfQkiVLbDgysqXExER8//33+Pe//623T3JyMpKSkjTPa2trWYjYmYkTJ+Ly5ct48803UVFRgSFDhiA7Oxv+/v6abfedP9f88ssvSEhIQEVFBdq2bYvQ0FAcPnwY/fr1k2oRSIZMLkIM2fDww8exGZIBgB9AjmzmzJn47LPPcPDgQXTt2lVvP3d3d7i7u9twZGSKmTNntvrzy4YNGzT/XrNmDdasWWOLYZEDM+nnmOYNz4EDB1rd8PBIaMdlaAYAHozmiIQQmDlzJvbs2YOvvvoK3bt3l3pIRCRDRu0JEULglVdewZ49e5CXl3fPDQ+//TgeYzNAjikxMRHbt2/H3r174e3trbmWhK+vLzw9PSUeHRHJhVFFCDc8xAzYn+D5+2z+ms275UeMGKHVnpGRgWnTptl8PPbMFutHigwQWYJRRQg3PMQMGMdRPxyEEFIPgYgcgNE/x5D9kOIDjhkgIiJL4b1jiIiISBIsQoiIiEgSJl8nhFrnqMcCEBERWQr3hBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSTaSD0AIrkLnr9P6iEQEckSixAz8QOIiABuC4hMwZ9jiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBIsQoiIiEgSLEKIiIhIEixCiIiISBK8bLuT0Xdp6XPLY208EiIicnYsQoicDAtRApgDsg8m/RyTlpaG4OBgeHh4IDw8HN99952lx0V2zhkzEDx/n86HM3O2HOjLgKPk4FrhZ0atz127dqFPnz7w8PDAwIEDsX//fhuNlByF0UXIjh07kJSUhEWLFqGwsBCDBw9GTEwMqqqqrDE+u+HIGx5jOWsGSBtz4FjqTx7Ez1+9b/D6PHz4MCZNmoQ//vGPOHbsGMaPH4/x48fj+++/t/HISc6MLkJSU1ORkJCA+Ph49OvXDxs3boSXlxfS09OtMT6yQ8wAAcyBo6k98im8B8cYvD7fffddPPPMM5g7dy769u2LZcuWYejQoVi/fr2NR05yZtQxIU1NTSgoKEBycrKmzcXFBdHR0cjPz2/Rv7GxEY2NjZrnNTU1AIDa2lpTxysZdeN1qYdgVd3m7GrR1rzMQghNm7EZABwnB86YAcAyOWAG7JtQ3UJTxRl4hz6rlYNq355YsuUTrD/f+Xa/OzKQn5+PpKQkrfnExMTg008/1fs6jpIDZ9S8ju7MgCUYVYRcuXIFKpUK/v7+Wu3+/v44depUi/4pKSlYsmRJi/bAwEAjh0lSunr1Knx9fQEYnwGAOXAU5uSAGZCHq5+twlUd7Rc3TLv99zsyUFFRoXP9V1RU6J0/cyB/d2bAEqx6dkxycrJWpVxdXY2goCCcP3/eogshtdraWgQGBqKsrAw+Pj5SD8diampq0K1bN7Rr186s+TAH8maJHDAD9q28vBx9+vRBTk4OwsLCNO0LFy7EoUOHsGfPHm4LjCDXHLTGUp8HdzOqCOnQoQOUSiUqKyu12isrKxEQENCiv7u7O9zd3Vu0+/r6OsyKuZOPj49DLpeLy6+HDhmbAYA5cBTm5IAZsG8eHh5QKpWoq6vTGnd1dTUeeOABTYFwZwYCAgKM2g4AzIEjuDMDFpmfMZ3d3NwQGhqK3NxcTZtarUZubi4iIiIsOjCyT8wAAcyBozFlfUZERGj1B4CcnByufzKK0T/HJCUlIS4uDsOGDUNYWBjWrl2L+vp6xMfHW2N8ZIeYAQKYA0djyPpcvHgxUlNTAQCzZs1CVFQUVq9ejdjYWGRlZeHo0aPYvHmzVItAciRMsG7dOtGtWzfh5uYmwsLCxDfffGPQdA0NDWLRokWioaHBlJe1W864XKZm4F7zlTNnXC5uC7TJfbn0rc+GhgYRFBQkXnzxRa3+O3fuFA899JBwc3MT/fv3F/v27TPq9eT+funjiMtlrWVSCGHh822IiIiIDMC76BIREZEkWIQQERGRJFiEEBERkSRYhBAREZEkLF6EGHtrb7ncCtqY5crMzIRCodB6eHh42HC0hjl48CDGjh2LLl26QKFQtHrPh2Z5eXkYOnQo3N3d0bNnT2RmZursxxzIIwfMgPEcLQMAc2AsZuA2QzPQKkueapOVlSXc3NxEenq6KC4uFgkJCcLPz09UVlbq7H/o0CGhVCrFO++8I06cOCEWLFggXF1dxfHjxy05LLMZu1wZGRnCx8dHlJeXax4VFRU2HvW97d+/X7zxxhti9+7dAoDYs2dPq/3Pnj0rvLy8RFJSkjhx4oRYt26dUCqVIjs7W6sfc3CbHHLADBjHETMgBHNgDGbgNkMzcC8WLULCwsJEYmKi5rlKpRJdunQRKSkpOvtPmDBBxMbGarWFh4eLGTNmWHJYZjN2uTIyMoSvr6+NRmcZhoRu3rx5on///lptEydOFDExMVptzMFtcssBM3Bvjp4BIZiDe2EGbjM0A/disZ9jmm/tHR0drWm71y3e8/PztfoDt28Fra+/FExZLgCoq6tDUFAQAgMDMW7cOBQXF9tiuFZlyPpiDrQ5Wg6YAWYAcN4cMAO/stS6slgR0tqtvfXd2tmUW0HbminL1bt3b6Snp2Pv3r3YunUr1Go1IiMjceHCBVsM2Wr0ra/a2lrcuHEDAHNwJ0fMATPADADOmwNm4FeGZMAQRt87hu4tIiJC6yZOkZGR6Nu3LzZt2oRly5ZJODKyJeaAmAFiBlpnsT0hptzi3ZRbQduaKct1N1dXV4SEhODMmTPWGKLN6FtfPj4+8PT0BMActMYRcsAMMAOA8+aAGfiVIRkwhMWKEEe9FbQlblmuUqlw/PhxdO7c2VrDtAlD1hdzoJ8j5IAZYAYA580BM/Ari60rY4+abU1WVpZwd3cXmZmZ4sSJE+Kll14Sfn5+mtORpkyZIubPn6/pf+jQIdGmTRuxatUqcfLkSbFo0SK7Ox1LCOOXa8mSJeLzzz8XP/30kygoKBAvvPCC8PDwEMXFxVItgk7Xrl0Tx44dE8eOHRMARGpqqjh27JgoLS0VQggxf/58MWXKFE3/5lOy5s6dK06ePCnS0tL0npbHHMgjB8yAcRwxA0IwB8ZgBm4zNAP3YtEiRIjWb+0dFRUl4uLitPqbeytoWzFmuWbPnq3p6+/vL0aPHi0KCwslGHXrDhw4IAC0eDQvS1xcnIiKimoxzZAhQ4Sbm5t48MEHRUZGhs55MwfyyAEzYDxHy4AQzIGxmIFfpzEkA61RCCGECXtiiIiIiMzCe8cQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEkWIQQERGRJFiEEBERkSRYhBAREZEk/j8pYTJUBMyGyQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=4, nrows=3)\n",
    "fig.suptitle(\"RidgeClassifier\")\n",
    "ii = 0\n",
    "jj = 0\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == RidgeClassifier:\n",
    "        axes[ii][jj].set_xlim([0, 1])\n",
    "        axes[ii][jj].hist(exp_res[:, idx], density=True)\n",
    "        axes[ii][jj].legend([round(model.alpha * 1e4, 1)])\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, axes = plt.subplots(ncols=2, nrows=2)\n",
    "# fig.suptitle(\"SVC\")\n",
    "# ii = 0\n",
    "# jj = 0\n",
    "plt.figure()\n",
    "plt.title(\"SVC\")\n",
    "plt.xlim([0, 1])\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == SVC:\n",
    "        plt.xlim([0.5, 1])\n",
    "        plt.hist(exp_res[:, idx], density=True, label=model.kernel, alpha=0.7, bins=50)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "plt.legend()\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Probability density\")\n",
    "tikzplotlib.save(\"data/SVC.tex\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=1, sharey=True)\n",
    "\n",
    "ax[0].set_title(\"KNeighborsClassifier\")\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == KNeighborsClassifier and model.n_neighbors in [1, 2, 3]:\n",
    "        ax[0].set_xlim([0.5, 1])\n",
    "        ax[0].hist(exp_res[:, idx], density=True, label=model.n_neighbors, alpha=0.7, bins=50)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel(\"Score\")\n",
    "ax[0].set_ylabel(\"Probability density\")\n",
    "\n",
    "ax[1].set_title(\"SVC\")\n",
    "\n",
    "for idx, model in enumerate(models):\n",
    "    if type(model) == SVC:\n",
    "        ax[1].set_xlim([0.5, 1])\n",
    "        ax[1].hist(exp_res[:, idx], density=True, label=model.kernel, alpha=0.7, bins=50)\n",
    "        ii += 1\n",
    "        if ii >= len(axes):\n",
    "            ii = 0\n",
    "            jj += 1\n",
    "ax[1].legend()\n",
    "ax[1].set_xlabel(\"Score\")\n",
    "\n",
    "tikzplotlib.save(\"data/BOTH.tex\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quantum",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}