{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GgZvzYp0_ltO"
   },
   "source": [
    "# Quantile Regression with ε-tolerance\n",
    "[![Slides](https://img.shields.io/badge/🦌-ReHLine-blueviolet)](https://rehline-python.readthedocs.io/en/latest/)\n",
    "\n",
    "Quantile Regression with ε-tolerance solves the following optimization problem:\n",
    "\n",
    "$$\n",
    "\\min_{\\beta \\in \\mathbb{R}^d}\n",
    "\\sum_{i=1}^n\n",
    "\\left(\\rho_{\\kappa}\\!\\left(y_i - \\mathbf{x}_i^\\top \\beta\\right)-\\epsilon\\right)_+\n",
    "+ \\frac{\\lambda}{2}\\|\\beta\\|^2\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $\\rho_{\\kappa}(r)=r\\cdot\\big(\\kappa-\\mathbf{1}(r<0)\\big)$ is the check loss (quantile loss),\n",
    "- $\\mathbf{x}_i\\in\\mathbb{R}^d$ is a feature vector,\n",
    "- $y_i\\in\\mathbb{R}$ is a continuous response variable,\n",
    "- $\\kappa\\in(0,1)$ is the quantile level,\n",
    "- $\\epsilon\\ge 0$ is the tolerance parameter.\n",
    "\n",
    "> **Note.** Since the check loss is a plq function, we can optimize it using `rehline.plq_Ridge_Regressor`. \n",
    "> Moreover, this wrapper adapts the `plqERM_Ridge` into a regressor, compatible with the scikit-learn API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "OYf6ieCxG5Is"
   },
   "outputs": [],
   "source": [
    "## install rehline\n",
    "%pip install rehline -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "54zIQq6-Bqm8"
   },
   "outputs": [],
   "source": [
    "# Simulate Data\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "n = 2000\n",
    "x = np.random.randn(n)\n",
    "noise = np.random.randn(n) * (0.3 + 0.5 * np.abs(x))\n",
    "y = 2 * x + noise\n",
    "X = x.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 97
    },
    "id": "LpdGRLz3CKFM",
    "outputId": "1ebabd56-fe21-4cfc-9a09-109e5cf577e6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={&#x27;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;check_eps&#x27;, &#x27;qt&#x27;: 0.9})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={&#x27;epsilon&#x27;: 0.1, &#x27;name&#x27;: &#x27;check_eps&#x27;, &#x27;qt&#x27;: 0.9})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={'epsilon': 0.1, 'name': 'check_eps', 'qt': 0.9})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "# Check Loss with epsilon-tolerance\n",
    "clf = plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.9, \"epsilon\": 0.1}, C=10.0 / n)\n",
    "clf.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "id": "fdFQpu9gCOlT",
    "outputId": "8803bcd5-b411-4a57-ad07-dce4ab446aa5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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njCvg4v6G+1m0ehHz/zifP3z8h6gQMjt/Nv/v4v9H2w/beOibD3HetPNSKoSAjIikHE3TWLZsGUajkeeff56MjAz++7//myVLlvD5559js9kGr62pqeHf//3fB/+cljZ8WhZCCJE4IyndPnTnzFDeQPioO2c2tm2kvqGeR7c+ijPgjGqzGqxcPedqaqtq+dKUL6Vc8BhKgkgSuN1uvvOd7/DMM8+Qnp7OP/3TP/Hiiy+yYMEC/vEf/5EPP/yQbdu2MXv2bADuu+8+ioqKeOyxx7j11lsHXyctLY2ioqJkvQ0hhBDHMNLS7UfunLGbDVGhQdM02h0+5pZmUpplZcA/wGNbH6OuoY6G9oaYe84vnE9tdS3Xz72eTEtmIt/uSZlwQeSMujPocHUk/L5F9iI+qf1kRNf+6Ec/4t133+X555+noKCAn/zkJzQ0NLBgwQL8fj9A1Am3Op0Os9nM+++/HxVEHnnkER5++GGKioq4/PLL+dnPfiajIkIIkWSjLd2+dE4hbQ4vu7oiVVatJj3eQJh2h4/sNCMFefuoefFuHv/scTxBT9S9bEYb1829jpqqGs4oOSPlRz+OZsIFkQ5XB63O1mR345hcLhf3338/Dz/8MBdeeCEAa9euZcqUKQDMmjWLqVOnctddd/GnP/0Jm83G7373O/bv3097e/vg61x33XVMmzaNkpISPv30U/7lX/6FHTt28MwzzyTlfQkhhIgYTen2spw0KgvSufmc6YPTOJ0DPjTFwwH+wjttz3D31m0x9zij5Axqq2q5Zs41pJvH96F4Ey6IFNmTM1Ux0vvu3r2bQCDAokWLBj+Xk5PDqaeeCoDRaOSZZ55h1apV5OTkoNfrWbJkCZdccgmadniRU21t7eD/nzt3LsXFxVx44YXs3r2bGTNmjNG7EkIIMVqHS7cfvRCZ1aSnc8AXtQC1siCd8vNtPP/F26zbej+v7XkOXyj6jJkMcwbXz72emqoaFhYvjOt7SKQJF0RGOj2Syqqrq9m8eTMOh4NAIEB+fj6LFi3ijDPOOObXHAo2jY2NEkSEECKJRrsA9YDnAOu2rGP1ptV83v15zPVfnvJlaqpquGr2VdhMtpj28W7CBZFUN2PGDIxGIx999BFTp04FoK+vj507d3L++edHXZuZGVlstGvXLj755BN+9atfHfN1N2/eDEBxcXF8Oi6EEGJERrIAdU5JBrv6P+Jf3q7n6S+eJhAORL1GliWLFfNWUFNdw5yCOYl+CwklQSTB7HY7q1at4kc/+hG5ubkUFBTw05/+FJ3ucEmXP//5z+Tn5zN16lS2bt3K97//fZYtW8ZFF10ERKZ3Hn30US699FJyc3P59NNP+cEPfsB5553HvHnzkvXWhBBCwLALUPccaKPF/yqvffoiTX9tjPnac6eeS01VDd8+/dtYjSd/xsx4IEEkCe655x5cLheXX3456enp/PCHP8ThOFyApr29nTvvvJPOzk6Ki4tZsWIFP/vZzwbbTSYTf/nLX7j33ntxu92UlZVx5ZVX8q//+q/JeDtCCCGGOHIBamPXAJ90fMh253Pscb1DWAtGXZtrzeWm+Tdxa9WtnJZ/WpJ6nDyKduQKyBQzMDBAZmYmDoeDjIyMqDafz0dTUxPl5eVRW13Hq8WLF7NgwQLuvffeZHflqCba37cQQsRbu7OdBzY9QN3G+9k30BTTXmo9k/nZV/K18su4bN7UEz7YLhUN9/weSkZEhBBCiDESVsO8vvt16hrqeHHHi4S1cFR7miGHU+yXc17J1ZRlVOAJhNjR4aPb2TxY5GyykSAihBBCnKT9A/t5YNMD3L/pfvY59kW1KSgsqfgaU02XoQucyazC7OMWOZtMJIikiHfeeSfZXRBCCDEKITXEK7teob6hnld2vYKqqVHtxfZibll4C6sWrsKgFfK7N3aSlWUcUZGzyUSCiBBCCDEKe/v3cv+m+7l/0/20Odui2nSKjksqL6Gmqoavn/J1DLrIY3Z7x8Coi5xNFuM+iKTwWtsJRf6ehRCTWTAc5MWdL1K3sY7Xd7+ORvTPxCkZU7h14a3csvAWyjLLYr7+RE/ZjSdV1Wjt9+IOhLCZDJRmWZMyLTRug4jRGPlGejwerNbJsdc6mTyeyEFLh/7ehRBiMtjdu5vVDatZs3kNne7OqDa9oueyUy6jtrqWpTOWotfpj/k6ozllNxEau5yDZ9v4QmEsBj0z8u0snVOY8AWz4zaI6PV6srKy6OrqAiAtLW1cnjqY6jRNw+Px0NXVRVZWFnr9sf9DE0KIicAf8vPc9ueob6jnzaY3Y9qnZ03n1oW3cvPCmylJLxnRax7vlN0cm4mLZhcmZESiscvJmg+a6XUHKM60kGay4gmE2NbmoM3hTfjunXEbRACKiiIHzR0KIyJ+srKyBv++hRBiItrRs4P6hnrWbllLj6cnqs2gM7Bs1jJqqmpYUrEEnaI7xqsc29FO2TUb9MwtzeSi2YkZiVBVjde2ddLrDjCzwJ4Su3fGdRBRFIXi4mIKCgoIBoPH/wJxQoxGo4yECCFGLVXWIAzHF/Lx9OdPU9dQx3t734tpr8yppKaqhpvm30ShvfCk71dZkE7FYnvS/l5a+73s7o6MyKTK7p1xHUQO0ev18qAUQogUkkprEI7ms67PqG+oZ92WdfT5+qLaTHoT3zrtW9RU1bB4+uITGv0Yjk6nJG2LrjsQSrndOxMiiAghhEgdqbYG4RBP0MOTnz1JfUM9f2v5W0z7qbmnUltdy4r5K8hLy0t4/xIhFXfvSBARQggxZlJxDcKWji3Ubazjka2P4PA7otrMejPLZy+ntqqWr0z9yoTf9JBqu3dAgogQQogxlCprEFwBF49ve5y6jXV83PZxTPucgjnUVNVww7wbyLHmxK0fqSaVdu8cIkFECCHEmEnmGgRN09jYvpH6jfU8uu1RXAFX9L0NVq6Zcw01VTV8acqXJvzox7Gkwu6dI0kQEUIIMWaSsQbB4XPw6NZHqW+oZ1PHppj2BUULqKmq4fq515NpyRyz+45nyd69cyQJIkIIIcbMWK1BON7WX03T+Kj1I+o21vHEZ0/gCXqivt5mtHHd3Ouora6lurh60o5+DCeZu3eOJEFECCHEmBmLNQjDbf3NTQ/xyNZHqNtYx9aurTFfe0bJGdRW1XLNnGtIN8dnimE81EcZTxQthU8zGxgYIDMzE4fDQUZGRrK7I4QQYoSODBP+UGQ6prLAftw1CIe2/h5w+Um3GNDrdITCYXY5NrLH8zy7nH/BH/ZFfY3dlME3T72a7y+6jerSqoS9r1Ssj5IqRvP8lhERIYQQY+5E1iAc2vq774CHkKqys7ud/f71tIVexq3ujbm+PH0hM21XMDXtQtKDdj7cYSfT6IxbIEjV+ijjnQQRIYQQcTHaNQit/V4a9vWyvW8DLYEX6Q79FZXo4ztMugwur7yabC7GEJ52MBAY4h4IUrE+ykQhQUQIIUTSdbm7+H9//xOPt9ThUffHtOcZF1Cgu5Qi43mcnlZKt9Of0ECQKvVRJqK4BpG7776bZ555hu3bt2O1Wjn77LP5z//8T0499dR43lYIIcQ4oGoqb+55k7qGOp7f/jxBdcjoh5LJdOslVKR9gwzDdJy+IH3uAM09biqPCCGHxDMQpOIZLRNFXIPIu+++y+23386ZZ55JKBTiJz/5CRdddBGff/45NpstnrcWQgiRotqd7azZvIbVDatp6m+Kac/WLeTU9GWUWs5Hr5iAyHZdfzCM2aBD1TTSjlGHJF6BIBXPaJko4vo3tn79+qg/P/jggxQUFLBx40bOO++8eN5aCCFECgmrYV7b/Rr1DfW8uONFwlo4qr3AVsCVp95Ad+fZOF15hMIaobAeRa8RCIXp9wRRgFy7eXBNSCIDQSqe0TJRJDS6ORyRw4Zyco5e19/v9+P3+wf/PDAwkJB+CSGEiI/9A/t5YNMDrG5YTctAS1SbgsJFMy6ipqqGy0+9HINi5L53dvNh0wFCIZU+b2Qqxh2IhBZNgzRTCE9Az64uFwvLshIWCFLxjJaJImFBRFVV7rjjDs455xzmzJlz1GvuvvtufvnLXyaqS0IIMeElo/hWSA3xyq5XqG+o55Vdr6BqalR7sb2YVQtXsapqFdOzpke1HXrYH3AFyLAaaex2YTGAXgd2i4FTC9PpdgZod/iAfmYW2BMWCFLtjJaJImEFzb7zne/w6quv8v777zNlypSjXnO0EZGysjIpaCaEECcg0cW3mvubub/hfh7Y/ABtzraoNp2i45LKS6itruXSmZdi0B379+DGLifrt3bwyrYOet1+Mq1Gcu1mZuTbyLGZ0TSNTfv6QYE8m4lAWB1xwbSxIJVVjy/lCpp997vf5aWXXuK99947ZggBMJvNmM3mRHRJCCEmtEQV3wqGg7y480XqNtbx+u7X0Yj+3dZmKGRu5jK+PuN6rqleMKJ7Vhak840FOra2OTi10E5Wmol0y+F1GYqiMLPQTp87wLWLppJhNSY0EKTKGS0TRVyDiKZpfO973+PZZ5/lnXfeoby8PJ63E0IIQWKKb+3u3c3qhtWs2byGTndnVJuCnmlpX+Gc4quZl3c+vqBG6wEfaz5oHnEA8gTD6HUKpdlp6I/Sx8juGJUMq5FZRTJiPp7FNYjcfvvtPProozz//POkp6fT0dEBQGZmJlarrCwWQoh4iFfxLX/Iz3Pbn6O+oZ43m96MaZ+eNZ05md8km4tYUFI+eG+jnlEHINkuO3nE9Tt43333AbB48eKoz69Zs4aVK1fG89ZCCDFpjXXxrR09O6hvqGftlrX0eHqi2gw6A8tmLaOmqoZTs87m//2lkaw040kHINkuO3nEfWpGCCFEYo3FaIIv5OPpz5+mrqGO9/a+F9NemVNJTVUNN82/iUJ7IQDbOwbGLADJdtnJQ8a0hBBigjnWaIKmaQx4gzR2u5hdkklxhiXmaz/r+oz6hnrWbVlHn68vqs2kN3HlaVdSU1XD4umLY0Y9xno6RbbLTg4SRIQQYoI52miCNxhmZ4eTdocPg07BYtTzp/f2sHROISXZev782Z+pa6jjby1/i3m9WXmzqKmqYcX8FeSl5R3zvvGYTqksSKdisV22y05gEkSEEGICOnI0YVNLHzs7nYTCGsVZFk4tTMdi1PNO08c88Pnz7HKuxxlwRH29xWDh26d/m3+o/gfOKTsnZvTjaOI1nSLbZSc2CSJCCDFBVRakM/08G//12nZ8wTCV+XbMpgCbD7zIhs4naHFtjfmaOQVzqK2q5YZ5N5BtzT6he8p0ihgNCSJCCDGBtQ/46Hb6sdn38lr7s2zqfhG/6om6xqBY+Nas5fzg7H9kUemiEY1+DEemU8RoSBARQogJyuFzsHrT/TzcXEePf0dMe6ntNM7KX06O8lX+6byFY1oYTKZTDpOS8MOTICKEEBOIpml81PoRdRvreOKzJ/AEo0c/zLo0FuZfxpcLr2KKbQ4uf4h+TzBphcEm+kM60ef9jEcSRIQQIoWN9EHd5+3j4U8fpq6hjm1d22La882ns7j0Ghbmfx2L3g7EvzDY8fo+0R/SiTrvZ7yTICKEECnqeA9qTdN4f9/71DfU8+fP/4wv5Iv6+gxzBjfMvYGl5dfx0Y4Met0BgkEzBkWNe2Gw4/V9oj+kE3Hez0QhQUQIIVLQ0Ae11Wihy+ljw54etrTtI7fgQ57b9RDbe7bHfO3ZZWdTW1XL8tnLSTNG1mmcnudM2E6W44WMm86exhufdU3oh3S8zvuZiCSICCFEihn623SfJ8jnbX3sdn5Ei/9FukJ/RdsdjPqabEs2K+avoKaqhtkFs2NeM1E7WUYyEvD0xla6BnwT+iE91uf9TGQSRIQQIsUc+dt0i6Od9U1PsNf3Il6tNebaM4vP4ftf+g7fOu1bWI3Dr/NIxE6WkY4EhDWN0uyj92UiPKTl9OCRk78BIYRIMU5/gF0DH7C360W29f4FjXBUu0nJosjwNeZlf4tLZp7BtXNmpMwUxkhGAlRNRa8oUQ9pTdNw+kIEwiqBUBiTXjeuH9JyevDIjd/vshBCTDBtzjbWbFrDnzaupmWgOaa9wFRNhfUb5Bm+QjBkYFZeZspNYYxkJCDLaiI/3UxLnxe72UCfJ0hjl4s+T4BQWMUTCDOjwI43ED7KHcYHOT145CSICCFEEoXVMK/tfo26jXW8tPMlwtrQ0Y9sppovocx8ORmGKZgMOnrdAQoyIg/zvQfcKTWFMdKRgCWnFbJ2QzOb9vXT5fQRCmuYjTo0TSPDGnk0rd3QPK53z0i5+5GRICKEEEnQ4mjhgU0PcP+m+2kZaIlqU1A4p+wCjN6v0dU1B73XSJ8f+vCgALl2MzPybfiCqbfOYKQjAZUF6dx09jR+9eIXOH0h0kw6VA0KM63MyLeRnWaaELtnUrrcva8H9j4OYTec/i9J60bq/OsVQogJLqSGeHnny9Q11PHqrlfR0KLaS9JLuGXBLayqWkUokMvv3tiJo7cXXzCMUVFQFODg8yuV1xmMdCTAajSQZzdRnJmH0aDDpNeRbjk8ijIRds9AipW7D/uh7WVoWgetL4MWAmMGnPJ/wJCcf0cSRIQQIs6a+5tZ3bCaNZvX0OZsi2rTKTounXkpNVU1XDrzUgw6A6qqcd87u1E1uHh2IZ/s7cMTCJNuNpBm1tPt9PP3pj7OnJ6TsusMRjIS4A6E8IdVSrPT0B/lPUyE3TMpQdPgwEeR8LH3cQj0RbcHB6DzTSi9LCndkyAihBBxEAwHeWHHC9Q11PHG7jdiRj/KMspYtXAVq6pWMSVjSlTbkVtg0y1GzirPHVzM2e8NYtDrMOh1XDy3KKXXGRxvJEC2uMaZqxmaH44EEOeu2HZrMUy/HqbfCNnzEt69Q+S7K4QQY6ixt5HVDat5cPODdLo7o9r0ip7LT72cmqoals5Yil6nP+prOH1Bej0BTAYdmgbZaUbOnJ49uL1Vp8ABl5/8dPOY9Xu0h8+NxWF1ssU1DoIDsO9paFoLXe/GtuutUPYtKF8BhRfCMf4NJpIEESGEOEn+kJ/ntj9HXUMdbzW9FdNenlXOrVW3snLBSkrSS4Z9rcYuJ89tamN3l4vmHhcWg540s4GSLCv5djO5NhMufwiL0TBmIwWjPXxurA6rky2uY0QNQcebkZGP/c9C2Bt7TcFiqLgpEkKMGQnv4nAUTdO041+WHAMDA2RmZuJwOMjISK2/OCGE2NGzg/qGetZuWUuPpyeqzagzcsWsK6itquXCigvRKbrjvt6hM1oOuPx0O/10Of0EQ2FcgTA6RSHXbqIo3YLBoOPLFbncdv7JFzKLPRfGgCcQGgwCQ7fPHu16tz/Inh43NrOB686aytkz8kbVryODjT8UmY6pLLAfdYvrWIzETBj9WyPho/kR8LbHtqefEgkf068H27SEdm00z28ZERFCiFHwhXw8/fnT1DXU8d7e92LaK3Mqqa2q5aYFN1FgKxjx6x55RssphemYDHqaejz4Q2GsRj2hcBi3P8Qev5sMq5FTi9JP+gE82hNij3Z9rzsQWb/i9tPnDbK3x80lc4u5eM7I16+MdIvrWI3EjGveDtj7WCSA9G2ObTflwLRrI1MvuWeCkvohTYKIEEKMwLaubdRvrOehTx+izxe968CkN3HlaVdSW13L+dPOjzljZST293n4tLWfNKOeAW+QbqcPu1lPmkmHN6iiKQq+oEplvg2b2ciODicXnFpwUmFktCfEDr2+1x1gc0s/3kAIu8VIoVGH2x/m4+Ze2h2+URUjO97C1uOd6DueC58dV8gLrS9Ewkf7azCk6B06I5RcFgkfJZeC3pScfp4gCSJCCHEMnqCHJz97krqNdWzYvyGmfVbeLGqqalgxfwV5aXknfJ/GLicPb9jHtv0OLCYdmqYw4AuSZzeRYTESCKkEVQ23P8is4gwsRv2Y1NcY7QmxR16vaRqNXS68gRA5NhOKoqBqCt5AmNIsK73uwJgVIxvtyM2EoKnQ/UEkfOx7MrIIdajcRZHwMe1qMOcmvo9jRIKIEEIMsaVjC3Ub63hk6yM4/I6oNovBwvLTl1NbXcs5Zeec0OjHkQ79pr+/z4PFpMduNuALhvEGwhxw+THpdVhNBgiFsRgNmA36MauvMdrts0der2nQ5wlgtxgH/w6CYRW9TofZoKc4c2zCEox+5GZcczZC00ORD3dTbHvaVCi/MfKRcWri+xcHEkSEEAJw+p08vu1x6hrq+KTtk5j2uQVzqamq4YZ5N5BtzR6Tex75m/680kwCIY1upw+bSY/FqCMQ0uj1BCkx6nH5QhRkWEi3GHD5Q2NSX+NY22c1TWPAG6Sx28XskkyKMywx12enGQmFVYwWw+DXHNnHsKaNWTGy0Y7cjDuBPtj7ZGT0o+dvse0GO0xdHhn9KDgPRrDweTyRICKEmLQ0TWNj+0bqNtbx2LbHcAVcUe1pxjSumX0NNdU1LCpddNKjH0Md+Zu+TqejssCOyx/C6QugUxRCmorDE0CnQKbVyIx8G8CY1dc42vZZbzDMzg4n7Q4fBp2CxajnT+/tGVwQeuj6/X0eNCAQUlEUcPlCWE16ZuTbUBQF7xiFJZighc/UILStP1hq/QVQA9Htig6KvhYJH1OWgWGcj/QMYxx914QQYmw4fA4e3foodQ11bO7YHNO+sGghNVU1XDf3OjItmXHrx9Df9HNsJsrzbGzaF8ATCOMPqaiahsUYZm5pJka9jl1drjGtr3HkuTCbWvrY2ekkFNYozrJwamE6FqM+ZkHozedMZ/3WDrqdHXQO+Mi0GinIsDAj30aOzTzmxcgmTOEzTYPejQdLrT8G/p7YazLnRMLH9OshbfiaMxOFBBEhxKSgaRof7v+Q+oZ6nvjsCTxBT1S73WTnujnXUVtdS3VJdUL6NPQ3/V53gKYeNyaDjmk5abgDIfo9QRQFtrY68IdUqqZmj/kR8pUF6Uw/z8Z/vbYdXzBMZb6dDOvhtR9DF4RWFqTzjxfYmT81i8f+vg+3P0RFno00swGnLzjmxcjGfeEzz/5IrY+mdeD4PLbdUgDTrosEkOwF42LL7ViSICKEmND6vH089OlD1DfUs61rW0z7WaVnUVNVwzVzrsFusie0b0f+pm8z6Qd3oeTaItsvA2GV00syOKXAzu4eNzPy7dSeW4HBMPZrBNoHfPS4IjVMhk5/HG1BqE6ncO7MfIozLYO1Pbqc/qOesjsWRnqib8oIuiJVTpvWRaqeDjlrCJ0ZplwRWXRavDSyBXeSkiAihJhwNE3j/X3vU9dQx1OfP4Uv5ItqzzBncOO8G6mpqmF+0fwk9TL6N/1PWx10OX3YzQYCYXVwzUVlgZ0sm5lT9Dq6nX7aB3xx2RlyogtCR1qMbCwk8l4nRA1D1zuR8NHyNITcsdfkfyUy8jF1OZiyEt3DlCRBRAgxYfR4eli3ZR31DfVs79ke03522dnUVtWyfPZy0oypsfjv0G/6D2/YR2OnCwUNg14fteYCRr8zZLSl0E9mQejxipGNpUTea8Qcnx8ute7ZH9tur4iccFt+I6TPSHz/UpwEESHEuKZpGu80v0N9Qz1Pf/E0gXD07oNsSzYr5q+gpqqG2QWzT+peJ3rOyfG+rrIgnVu+Mp32AS9pRj2ZaZEgEAxHttGmWwyj2hlyIqXQJ8yC0ETxdR8utd67MbbdmBUpNFZ+I+SdPenWfYyGBBEhxLjU5e7iwc0PUt9QT2NvY0z7+dPOp6aqhitPvxKLwXLS9zvRc05G+nVTstOYV5rFh00H6HD46PMGCakqBp2ObKtx8KC74wWBEy2FPu4XhCZC2A+tL0XCR9sroA0ZnVL0kRLr5Sug9DLQn/y/u8lAgogQYtxQNZW/7PkL9Q31PL/9eYJqMKo915rLygUrubXqVmblzRqz+57ow300X6fTKcwqTufZza04fUFybSYyLAYGvCF2dDnJsBhZ8eVpwwaBky2FPu4WhCaCpkHPhwe33D4Owf7Ya3KqD5ZavyayA0aMigQRIUTKa3O2sWbTGlZvWk1zf3NM+4XlF1JTVcOyWcswG8xjeu8TfbifyMm229udFGdYyLeb6Bjw0THgIxTWMOgU3P4Q6zbsZVqOjVOKjh4IxqIUesovCE0UVxM0PRwJIK7YETesJZFaH+UrIGtO4vs3gUgQEUKkpLAa5rXdr1G3sY6Xdr5EeMiJo4W2wsHRj8qcyrj140Qf7id6su3MQjuBkEqvO4DNZCDdbCDNrMcTCLO7y8Uf3m7kexdWHnV0YqxKoafkgtBECDhg35+h+SHoei+2XZ8GZd+KhI/Cr4JOn/g+TkASRIQQKaXF0cIDmx7g/k330zLQEtWmoHDRjIuora7l8lMux6iPf+2FE324n+jJtlajhS/anYRVLSrE2MwKgVCYA27/MadXJmQp9HhTQ9D++sFS689D2DfkAgUKL4iEj7JvgXESTk/FmfxrFEIkXUgN8cquV6jbWMerja+iampUe0l6CbcsuIVVVauYnjU9oX072sNd0zScvhCBsEogFMak18U83E/0ZNsupy/mVFuInGxr0OspzrQec3pFdr6MQt9m2LMO9j4Kvs7Y9oxZh0ut26YmvHuTiQQRIUTSNPc3s7phNWs2r6HN2RbVplN0XFJ5CbXVtVw681IMuuT8uBr6cO/zBGnsctHnCRAKq3gCYWYU2PEGwsN+3fFCwaHrN+zpiTrV9tD1h062zU83s/eA+6jTK7Lz5Ti87dD8aGT0o//T2HZz7uFS6znVsuU2QSSICCESKhAO8OKOF6lvqOf13a+jDSl9XZZRxqqFq1hVtYopGVOS1MvDjny4b9rXT5czsoDUbNShaRoZ1siP0bUbmmN2wYwmFBy6fmeXkz3dbkyGEDazgeARVVZn5NvwBYefXpGdL0OEPLD/uYOl1t+AIaNt6IxQejmU3wTFF4PelJRuTmaKpmna8S9LjoGBATIzM3E4HGRkZCS7O0KIk7DrwC5WN6zmwS0P0uXuimrTK3ouP/VyaqtquWjGRehTcBHgzs4BfvXiF+zudpFm0qHX6Ugz6cm2GUk3GenzBllUnsN3FldGjTgcWUfEH4qEiMoC+zFDwdD7GPR6cmwmZuTbyE4zsavLxdzSTG47f8Zxt/JO2p0vmhpZbNq0DvY9BSFn7DW5X4KKFTD1ajDnJL6PE9xont8yIiKEiBt/yM+z25+lbmMdbze/HdNenlXOrVW3cvOCmylOL05CD0fOajSQZzdRnJmHJxhmd7eLlt7IaAeASa+jtd/LvLIszp2ZP/h1o90Oe0phBj+77HT+8HYjB9x+ijOt5Keb8QXD7OpyjXh6ZVLufBnYAU0PRT48+2LbbdMOl1rPOCXx/RNHJUFECDHmtvdsp35jPWu3rOWA90BUm0FnYNmsZdRU1bCkYgk6ZexPko0HdyCEP6xiNRnY0dJP54APRVGwmQxoaPiDKm39Xur/uofiTEvUaMdoQ8EpRel878LKwZGUvQfck3t6ZTj+Xtj3BOxZCwc+im03pEcOmCtfAQXnwjj59zaZSBARQowJb9DL0188TX1DPe/tja3BUJlTSW1VLTctuIkC2/irPmkzGTDrdWxvd3DA5UevU7AadQcXoSooQEhVaen18Nq2TioWH7166UhJYbFhhAPQ/urBLbcvwpAKuyg6KFoaCR9TvgGGSTYyNM5IEBFCnJRtXduo31jPQ58+RJ+vL6rNpDdx5WlXUltdy/nTzo8p7DWelGZZyU+38MHuHjQNzIMhJFIFPBDWyLAa0QFb9vfxyd5eMqzGmAAxmrUbk3J65Vg0DXo/OVhq/THwH4i9JmteZNHp9GvBmtpTfeIwCSJCiFFzB9w8+dmT1DfUs2H/hpj2WXmzqKmqYcX8FeSl5SWhh2NPp1M4Y3o2L2xpxR8OodNBWFXQgLCqYdQr5NrMOLwBdnS6+NO7uzEb9VGH3AEndHDepObeB82PRALIwPbYdkvh4VLr2fMT3z9x0iSICCFGbHPHZuo21vHI1kcY8A9EtVkMFpafvpza6lrOKTtnXI5+HG+04rTiDIozLTi8QQa8QUBBUcBi1JOfbiakqji8ISxGPbk2MwUZlsFD7r7oiPx9HaqYOtKD8yaloBNanomEj863YcgWb/QWmPLNyKLToq9BkmrMiLEh3z0hRmAyb4V0+p08vu1x6hrq+KTtk5j2uQVzqa2u5YZ5N5BlyUp8B8fIkdtsjzVa4Q2GCKsaOkXBYtRjNuhAUQirGr0uP8GwiqrBjDwbRQfLs6dbjNhMel77vBM0WDq7EJ0usmBypKfiTgpqGDrfioSPlmcg7Im9Jv9cqLgJyr4NpszE91HEhQQRIY5jJA+oiUbTND5p+4T6hnoe2/YYroArqj3NmMY1s6+htrqWs0rPGpejH0dq7HKy5oNmet2BY45WVOTZeeOzLjKtJoozw3Q5/QRVDYtBh04HDl8QVdUozU6jstAe9Xfi8ocJq9rg/8+w6qLKxNvNenZ1Ooc9FXfC6v8sEj6aHwZvW2y7vTIy7VJ+A9jLE98/EXcSRIQYxkgeUBMpjDh8Dh7d+ih1DXVs7tgc076waCG11bVcN/c6MswTo8igqmq8tq2TXneAmQWHA8TQ0YrL5ukGT8ednmfj0/39g6NkAGaDnrCqMX9KJjk2c9Q9AuFINU8FjUA4crLukWXidbrIrpsvOgYmRxDxdUHzY5EA0tcQ227MgmnXREY/chdJqfUJLiFB5A9/+AP33HMPHR0dzJ8/n9///vecddZZibi1ECdspA+o8T6crmkaH+7/kPqGep747Ak8weghcbvJznVzrqO2upbqkuok9TJ+Wvu97O52RZ10e4iiKBRnWmjscrGnxz14mq5ep3D+KfkMeIP0eYOggaJo/L2pF91RHpomfWQqRkPB4w/R2O3GGwhhtxgxWgy4/SH6PUFe3tJORZ5tQoXbQWEf7H8hEj7a14MWfTYPigFKvx4Z/Sj5OujNR38dMeHEPYg88cQT3Hnnnfzxj39k0aJF3HvvvSxdupQdO3ZQUDD+agmIyWOkD6jxOpze5+3joU8for6hnm1d22Lazyo9i5qqGq6Zcw12kz3h/UvUuhynL0ivJ4DJoEPTIN0SfUCd1aSncyByNPyRp+kqikJmmonMtMjZJAPeAFlpZno9Acpy0qJew27Wo9cpaKpGu8OLNxAix2ZCURQ0TSMQUpmWm4Y/pE6IcDtI06D7g4Ol1p+EoCP2mpwzI+Fj2tVgyY9tFxNe3IPIf//3f1NTU8PNN98MwB//+EdefvllHnjgAX784x/H+/ZCnDB3IDT4G/DRHHpAHe0U1FSlaRrv73ufuoY6nvr8KXwhX1R7pjmTG+bdQE1VDfOLkrcVMlHrchq7nDy3qY3dXS6ae1xYjAZy0kzMKLANTq94A5HzYcrzbMOeptsx4OfLFTl4g+pRD7k7pTAdTyDE9nYnWWlGNCAQCg8eaFdZYMeo143rcDvItedwqXXX7tj2tCmHS61nnpb4/omUEtcgEggE2LhxI3fdddfg53Q6HUuWLGHDhtjaA36/H7/fP/jngYGBmGuESBSbyRD1G/BQhx5QxzoFNZX0eHpYu3ktqzetZntPbC2Gs8vOpqaqhqtmX0WaMbkPwEStyzl0nwMuPwXpZvo9QcwGhS6nD6c/yIKyLLLTTLQ7fMwtzaQsO+24p+leu2gqwDFPvt3T7ea3r+0grGr0ewLodToKMizMyI8En5CqjrtwOyjQD/v+HBn96H4/tt1gi+x2KV8BBedDCh5sKJIjrj9Be3p6CIfDFBYWRn2+sLCQ7dtjfxjefffd/PKXv4xnl4QYsdIs67C/AR96QJVmHX3EJNlUTeWd5neo21jHs9ufJRAORLVnW7JZMX8FNVU1zC6YnaReRhvJupzXtnVgmq/DEwyf8JTNkfc5pTCd/HQLm1v6cftD2MwGXL4gn7cPkG+3kGs/fMhcZUE6N58z/ZhB41BAOlZpdrNBz+ySDIx6HUaDDpNeFzUVNJ7CLRAprd7+eiR87H8eVP+QCxQoujASPsq+FQkjQgyRUv/a77rrLu68887BPw8MDFBWVpbEHonJTKdTjvsb8EhOQU20TlcnD25+kPqGenb3xQ6Lnz/tfGqqarjy9CuxGCxJ6OGxHW9djtWo4+WtHXza6kCvU054ymbofXJsJhaUZQ3uZAlr0DXgp3paNledURb12iM5A+ZYpdlLs6xUFqSzrc3BzEz7uAu3QGTdR9/mg6XWH43sgBkq8/SDpdavi0zDCDGMuAaRvLw89Ho9nZ2dUZ/v7OykqKgo5nqz2YzZLCulReoY6W/AyaZqKn/Z8xfqNtbx/I7nCanRQ/t5aXmsnL+SW6tu5dS8U5PUy+M7tC7HarQw4A0SCKuDowZ9niA7Ol30uv2cWminNDvthKdsjrb+J8dm4szp2Th9IbzBEJ0DPpYtLD3qa57oGTDjNdwC4Gk7XGrdEbu4GXM+TLs2suU2e6FsuRUjFtcgYjKZqK6u5s0332TZsmUAqKrKm2++yXe/+9143lqIMZPKp6C2OdtYs2kNqzetprm/OaZ9ScUSaqpquOLUKzAbUj/k20wGAiGVD/ccwB0IE1JVDDod2VYj3oMLOzOtRrLSTOh1yglvpT7W+h9FUciwGlEUyE4zk26OXRt0ssZLuAUg5IaW5w6WWv8LaGp0u84Epd84uOX2YtCN/d+XmPjiPjVz5513ctNNN3HGGWdw1llnce+99+J2uwd30QgxHqTSKahhNcxru1+jbmMdL+18ifCQegyFtkJuWXgLqxauYkbOjCT18tiG25brDYbodvrpGPBRlGEm3WAiGFZpc3jp9waxmQ2U2q2kWw7/6DqRrdTJXv+TyuEWTYWudw9uuX0KQq7Ya/LPiYSPqcvBlJ34PooJJe5B5Oqrr6a7u5uf//zndHR0sGDBAtavXx+zgFUIMbwWRwv3b7qfBzY9QMtAS1SbgsLSyqXUVNVw+SmXY9Sn5m+mw23LPVRCPcNiJKxquP1hFEXBqNdhNurxDfgw63VU5Nli1o+Mdit1IqdIjhW8jhVuk3aukWM7NB/ccutpiW23lR8utZ5eGf/+iElD0TRNO/5lyTEwMEBmZiYOh4OMjIlRTlqI0QipIV7e+TJ1DXWsb1yPOmRovCS9hFULV3HLwluYnjU9OZ0codhtuQY8gdDgg/+SOUU809BKVpqRYFg7XAJdVVFV6PcEsJn1LDmtiHSLYfCclkjVUg2HN8QPvnbK4MN9JA/0I4ORPxTZsVJZYB+zKZLR1kNJ+LlGvh7Y90Rk9OPA32PbjRkw9epIAMk/R9Z9iBEbzfM7pXbNCCEimvqaBkc/2l3tUW06RcelMy+ltqqWS2ZegmEcHIE+km25b37RhTcYpuRgCfVDC0cDYRWjTuGLjgH2HvDQOeBly34/fe4AmgZmow4NOLcyb3AqZaQP9HhOkYy2HkrCzjUK+6HtlUj4aHs5sgX3SIoeipdGdr2UXg6GFN7BIyaE1P8JJsQkEQgHeGHHC9Q31PPG7jfQiB6sLMso49aqW7ll4S1MyRhfWyJHUi6/tc8LClEl1DOsh6eYSrOsNHW7eW/XATRNw6BTIr+ge8Fi1NPp9LOnJ7KeYTQP9His/xntOUVxP9dI0yIjHk3rYO/jEOiNvSZ7wcFS69eCNXZXoxDxIkFEiCTbdWAXqxtW8+CWB+lyR9dk0Ct6vnHqN6ipquGiGRehH6fVKEdSLl+vg4IMC+0O31EXkHYN+FE10NAw6hVAQ9Ui15j0Ch0OLw9v2IvRoOOAK8AphfbBs1w0DbLTjOzv8/Datg4qFsf3LJfRnlMUt3ON3Huh6eFIAHHujG23FsP06yPl1rPnnchbFeKkSRARIgn8IT/Pbn+W+oZ63mp6K6a9PKucmqoaVi5YSXF6cRJ6OLZGUi7fYjRw4WmFrN/WEbOAtK3fywF3AEUHM/Pt+IJh+rxBAkGV8MFzXrpdAfZ0uzEbdZRkWslPNwHK4bUmYRUN6HZ2MK8si3NnHv2AtbFYLDrac4rG9Fyj4ADsezoSPrreiW3XW2HKssjUS9ESKbUukk6CiBAJtL1nO/Ub61m7ZS0HvAei2ow6I8tmLaO2upavln8VnaJLUi9PztEe5CPdLnvOjDyKMy0xNTam5dro9wTxBsOEVI1eT5BgWEWvA38wsoA3GFZB09DrjPR7Any45wAQGRGxW4wYLZEaJZ0DPh77+z6KMy0xay7GarHoaM8pOulzjdQwdPzlYKn1ZyHsjb2mYHGk2FjZtyKLUIVIERJEhIgzb9DL0188Td3GOv66768x7ZU5ldRW1XLTgpsosBUkoYdjZ7gH+Ui3yx5aQNrS56Gpxw1Ewsr2jgFMeoUet59gWMViUHD7w6gaGPUKwTBoCoRUDbvZQMvBNScz8mzodJFQpyiQaTXi9odi1lyM5WLR0dYpOeG6Jv1bI+Gj+RHwRi9qBiD9lEj4mH492KaN+PsoRCJJEBEiTrZ1baN+Yz3rPl1Hv68/qs2kN3HlaVdSW13L+dPOj1kXMB6N5EE+0oqie3pcUYEmHNboGvChauDyRUZaVE0jqGroD/7VaYDdZAAUelwBVE0DTSEY1jDrIg90ly9EQYaFijxb1JqLsV4sOto6JaO63tsZOeOlaV3kzJehTDkw7ZrI1EvumbLlVqQ8CSJCjCF3wM2Tnz1JfUM9G/ZviGk/Le80aqtruXHejeSm5Sahh/Ex9EEODG69LUw30zHg4/XPOrnt/Bl85zjbZY8WaNz+IE0H3PR5Ilt2/aEwOp2CpkUCSDCsYTLoKMq04vQFUQ9+Tq+LhBUOloe3mvTMyLeRZjbQ5fQPrrmIx2LR0ZZyH+76pbMymOF9Bd5ZB+2vwZBquuiMUHLZwVLrl4LeNLpvoBBJJEFEiDGwuWMzdRvreGTrIwz4B6LaLAYLV82+itqqWs4uO3tCjH4MdeSDvM8TjFogatDrsJn0NOzrG3yQH+thfqyRiQyribOm5/DmF514gypGvY5AWCWsqoQAs0FPabYVg17BajIwNdvKJ74+AmEVtz+ExainIMPCjHwbOTYzTl8was3FmC4WPcJo65REXe8PkO3+mIKe36O8/1RkEepQuYsObrm9GswTJ9iKyUWCiBAnyOl38vi2x6lrqOOTtk9i2ucWzKW2upbr515PtnVin8dx6EHuC+rZ2urAGwgNLhANhjX6PUG6XX6+6BgYdkRhuJGJXLuZL1Xk8H5jb2Sth0WPQuSaKdmRSq297gAFGRZOKbTTdMBNSIUvlWdjMRpItxgGt/MOXXNx0otFhzHaOiU6927K9h8ste5uir0gbSqU3xj5yEjdk5SFGCkJIkKMgqZpfNL2CfUN9Ty27TFcgegDwdKMaVwz+xpqq2s5q/SsCTn6cTQ2kwGzXseOjgG8gRA5NtPgezcbFDSLnm5nmE+ae1ky69hnuBxvZKIkO43TigLo9DpCYRWbycD+Pi9OXwinL4TdYqAow8zuHg+nFkV2hvR7QxSbDIQ1Da8/dNQ1Gsk+BI9AH+x9MrLuo+dvse0GO0z9dmT0o+B8GKc7qoQ4GgkiQoyAw+fgka2PUN9Qz+aOzTHtc/IXcOWsFVx1+rXMKhibw9Li4Xg1Moa2F2dYaD84JTHctEJplpX8dAsfNfVSkG6OeZC7/WGKsyx0D/iHXWcxkpGJvHQL36wq5dMWB7u7XeTaTWiAgkauzQwog+swgBGt0UjkIXiD1CC0rY+Ej9YXQA1Etys6KPpaJHxMuQIMtrG7txApRIKIEMegaRof7v+QuoY6ntj2BN5QdG0Gu8nOZZVXMdV0OUHfdLraw6zu7mBGvit+h5SdhOPVyBjaHgip+IMqZqMOk0E3bE0NnU7hjOnZrN/WjtMXBAWMeh3BsDq4SPTUwnQc3uCw6yxGU2/knBl5g6HJatSjAJ5gOCYwjXSNxmgXl54QTYO+BtizLrLzxd8Te03m7MiOl+nXQ1rJyd9TiBQnQUSIIXq9vTy05SHqG+r5rPuzmPazSs+ipqqGMwu/zpN/76HXGaA40xi/Q8rGwPG21n51VgFvbe8abPcF9Wzc20u/N0h2mpGqqdlYjPph39tpxRmcUpROnzuAOxDG7Q+h1+kGF4ka9Tp8QXXYdRajHZkYydqL0azRiNsheJ79kVofTevA8Xlsu6UApl0XGf3IXiBbbsWkIkFECCK/bf9131+p21jHU58/hT/sj2rPNGdyw7wbqKmqYX7RfFRV4753dsfvkLIxdLwaGTs7nTz4t2ZsJgOnFEa23n7R3kdY1ZiabaXPE6T5gIczpmUzs8B+zPdWmmVlYVk2W1sdzM0wE1Q1THod6ZbIj5ldXa4RrbM4cmSisctJU08AnaKjssDOldWlcQ93Y3YIXtAVqXLatA463oQhhxiiM0emXMpvjJx2q4udihJiMpAgIia1bnc367aso76hnh0HdsS0n1N2DrXVtXz79G+TZjz8cIrbIWVxcLy+plsMfNY2wJcrclEUhQFvkD5PALvFiE6nw26J7EZx+kJkWI3HfG9HjmZ0Ov2DoxmuYywQHU5lQTrq6eDwBhjwBQlrkYJmb3zWhU5RUmakKYYahq63I1Mv+5+BkDv2mvxzIlMvU5eDKSvhXRQi1UgQEZOOqqm83fQ29Q31PPPFMwTVYFR7jjWHFfNWUFNdw+n5px/1NeJVdyIejtdXvU4hGFYHA0IgrBIKqxgPjmQY9Trc/khxMhj+vY3VOovGLidrN0Smkqbl2kgzGVJ22guITLc0rYucdOttjW23V0SmXabfAOkzEt8/IVKYBBExaXS4Onhw84OsbljN7r7dMe2Lpy+mpqqGb532LSwGy7CvFc+6E2PteH0NqxpGvQ5VjUwdmPQ6DHpdpDS6QTl4uJwOkz6yZfR47+1k11mMdbn1uPF1w97HoWkt9G6MbTdmRgqNla+AvLNl3YcQx5D8n5JCxJGqqbyx+w3qGup4YccLhNTo3+Lz0vJYOX8lt1bdyql5Iy8OlfS6E6NwvL46fSGm59kY8IUo0jTSLQay00x0O30Y04yD57OkWwwJeW8pPe0V9kPrS5HRj7ZXQBsyKqToofgSqFgBpZeDfvhAK4SQICImqDZnG2s2raG+oZ69jr0x7UsqllBbVcsVs67AdALnciSl7sQJOl5fc+1mlh/cNXOofXpeGr1uP/v6vGRZjUzPTRvxWo/jbRM+nkRMex2vnkoUTYOeDyPhY98TkeJjQ2VXHZx6uTayA0YIMWISRMSEEVbDrG9cT31DPS/tfInwkIPBiuxF3LzgZlYtXMWMnJOfp09I3YkxMrSvHQ4vYRVKsqxceFoBZ8/IY1pu2mC7PxSmLCeNgpCK2aDD4Q3iC6rHfW8jOYH3eH8v8Z72GnFQcjVF1nw0rQNXY+wLWUsjtT7KV0DW7BPqixBCgoiYAPY59vHApgd4YNMDtAy0RLUpKCytXEptVS2XnXIZRv3YbpGMW92JODjU1w929/DmF520O3x0DPh4pqGVLS0Ols4p5DuLZ5xQZVUYu7Ud8Zz2Ol5QWrUomwrva5Hw0fVe7Avo06DsysiW28Kvgk4/6j4IIaJJEBHjUjAc5OVdL1PfUM+ru15FG1KjoSS9hFsW3MKtVbcyLWtaXPsyZnUnEmBPj4v12zrodQcozbIedzfKoQWsIzFWazviNe11zJN9zQpzszZS5niKqe+8B/iHfKUSCR3lK6DsW2C0j+q+QojhSRAR40pTXxOrG1azZvMa2l3tUW06RcelMy+ltqqWS2ZegkEn/7yPNNoRi9Gu9RjLtR3xmPYaGpTyQts53fccs3wvYtN6YGiuyZh1eMutrWzU9xNCjIz8pBYpLxAO8MKOF6jbWMcbe96IaZ+aOZVVC1dxy8JbmJIxJQk9HB9GM2LhD4VHvdZjrNd2jPW0lzsQwhTq5LzwW8zufZ78cGwBO5eWSbDsarJn10BOtWy5FSIBJIiIlLXrwC5WN6zmwS0P0uXuimrTK3q+ceo3qK2u5WsVX0Mvc/XHNdIRC6c/yNtfdI96rUc81naMybRXyAP7n2fazjX8h/ImOo8a1RzGyB7TBTTovs4ngS/zf+bMJnucTLUJMRFIEBEpxR/y88wXz1DfUM/bzW/HtFdkV3DrwltZuWAlxenFSejh+DXSEQuXL3RCaz1Gs7bjWNtnR7WtdjiaCl1/Pbjl9s8QcjI0/rQZFvCF5RvsNF+KV8ka8Vk4QoixJUFEpIQvur+gvqGedVvWccB7IKrNqDPyzdO+SU1VDV8t/yo6RZekXo5vIx2xsJsNJ7zWYyRrO4619mRWcTrb250nXH8EgIGd0PQQND8E7tj6MUHLNP4auph3Axeht556RFBypVTtFyEmEwkiImm8QS9Pff4UdQ11vL/v/Zj2mTkzqa2uZcX8FRTYpEjUyRrpiIXZoD/myIl28PA5X1BlwBtEVbWYB/dwazuOtX32wz0HeHZzK8WZFmYW2IddkxIzamL1otv/JOxZCwc+in3jhvTIAXPlKzAWnMvUbjcF46D2ixCThQQRkXBbO7dS31DPQ58+RL+vP6rNrDdz5elXUltVy3nTzouZGhAnZyQjFqqqHXXkpNcdoLHLyd4DHtItBh77aB8fN/UddcTiaGs7jrVrx242EFJVnL4g+XbT4D2PtiZlT4+L17Z10tzVx4zwe5ytvEIJfwWiDy5E0UHRRZFdL1OuAMPhvoyn2i9CTAYSRERCuANunvzsSeoa6vhw/4cx7afnn05NVQ03zruR3LTcJPRw8jjeg/hoIyfeYJiGvX30e4NkpxmpmpqN2aDj780H+KzdwXVnTeXsGXnDPsyPtWvH6QvR5wmSazPR5wni9IXIsEZGYo5ck/JBYzdbt77J6b7nuFn3OmlKf+xNsuZC+U0w/TqwHnsN0Xiq/SLERCdBRMTVpvZN1DfU88jWRxjwD0S1WQwWrp59NTVVNZxddraMfhzDmC3gPMLxHsRHjpw0djn5vH0Apy9ERZ6NygI7oLC9w0Wf20+fN8jeHjeXzC3m4jlFg6MqQ/t8rF07gbBKKKySmWZkwBskEI7e1VKg72R+8GlmbXyVc7WmSL2PI+qsuZR8NqhL6c2/iuVLLpORDSHGGQkiYsw5/U4e2/YYdRvr2Ngeezz6vMJ51FbVcv2868myZCW+g+PIyR4gdzIOjZx8sreXP727m1ybmaJMC32eIJtb+vEGQtgtRgqNOtz+MB8399Lu8PHVWQVHXXQ6vyzzqGtPTHodBr0ObyCMXqfDpNdhVF3MDLzBab7nKAt+hKLTosJHCDON5iV8Yb6CvaZzGPBr9PcFOTsZJ/IKIU6KBBExJjRN45O2T6jbWMdj2x7DHXRHtduMNq6Zcw211bWcWXKmjH6MwFgcIHeydDqFDKsRs1FPQYblYL9ceAMhcmwmFEVB1RS8gTClWVb2HfDwP2/uojjTSklWdJ9b+z1kpRlpd/ii1p6kWwxkpxlp7hng8sLtLAu+xUzXGxjxxvSnxXAmX1iWscu8lIDu8Hu3mtSTPpFXCJEcEkTESen39fPIp49Q31DPls4tMe1VxVXUVNVw3dzryDBnJKGH49NYHSA3Fo6sP6Jp0OcJYLcYB/sUDKuDIxmeQIhup5+FZVmDox5H9rkkU092milq147du53rzU/zpbL15Ol7Yo566VGm4i6+hoc6zkO1To/LibxCiOSR/2rFqGmaxob9G6hvqOeJbU/gDUX/5ppuSuf6uddTU11DVXFVkno5vo3VAXJj4cj6I9lpRkJhFaMl8qND0zRcvtDgaInTH8JkUAgOOSzvUJ/7PEG+WVXKruY9ZPespVp9kanKdhiSLTxaOpt0S+nMWc68BRdTkZ9O9ju743IirxAiuSSIiBHr9fby0JaHqG+o57Puz2LaF5Uuora6lqtmX4XdJCeUnoyxPEDuZB25i2Z/nwcNCIRUFAVcvhBWk54Z+TaCqkYgpGI26DHpY4vO2Y0hSkKvsXDPO3yl9w0ULRx90JxiQCu5lAP5V3Mg62tMtdj48hELc+NxIq8QIvkkiIhhaZrGX/f9lbqNdTz1+VP4w9Hj5pnmTG6cdyM11TXMK5yXpF5OPGN9gNzJOrSLZv3WDrqdHXQO+Mi0GinIsDAj30aOzYzDEyAQUsm1m0g/OGKCplES2shpvhc4xfcKFsUJB4a8eM4ZkXof065BseSTB+QN04exPJFXCJF8EkTEUXW7u1m3ZR31DfXsOBB7Suk5ZedQW13Lt0//NmlG2aUw1uJxgNzJqixI5x8vsDN/ahaP/X0fbn9kO2+a2YDTF6RjwEd+upk0k4HMcAun+1/gNN9zZKktsS+WNgWm3wDlN0Lm6aPqgxQjE2JikSAiBqmayttNb1PfUM8zXzxDUI2uVpljzWHFvBXUVNdwev7IHx5i9EZzgFyi+3XuzHyKMy2DIxNdTj9mg54zSnScM/0jrK2PML1vU8zX+rHiL/4mGaetgsLFkeqnJ9gH2aIrxMShaJqmHf+y5BgYGCAzMxOHw0FGhuy4iJcOVwcPbn6Q1Q2r2d23O6Z98fTF1FTV8K3TvoXFYElCDyevI+uI+EOR6ZjKAntKTEWoqkZr7wB0vE5u1xNYu15CUaOn7lRNoVE5k32ZyylfeD0zSuTEZCEmg9E8v2VEZJJSNZU3dr9BXUMdL+x4gZAavegxLy2PmxfczK1Vt3JK7ilJ6qVIyakITYO+zeia1lG291HwdcVeknE6jqKr6clbjjVjGl9Ndp+FEClLgsgk0zrQyprNa1jdsJq9jthj0pdULKG2qpYrZl2BSW9KQg8nppMp054yUxGeNmh+BJrWgWNbTLOTbD7RltKg/wYW6xksnVqU9FEbIUTqkyAyCYTUEOsb11O3sY6Xd72MqkWf5VFoK+SWhbewauEqZuTMSFIvJ65klmk/aSE3tDwXCR+df4Eh/3Y0xcQ25XzeC11KX8ZiLOZIJdXG9gHaBnwJqf4qhBjfJIhMYPsc+7i/4X7u33Q/rc7WqDYFhYsrL6amqobLTrkMoz52i6g4ealQpn3UNBW63o2Ej31PQcgVe03e2ajTb+SBfWeysQNmFtixJbH6qxBi/JIgMsEEw0Fe3vUydRvrWN+4Ho3otcil6aWsWriKWxbewrSsaUnq5eSQSmXaj9fP1n4vwb7Pyet+gvSOJ1A8R9lyayuP1PsovwHSK2nt9fD5pp0UZxqPenaQ3axn494+PtnbyxnTciSMCCGOSoLIBNHU18TqhtU8sPkBOlwdUW06Rcdlp1xGTVUNF1dejEEn3/ZESKUy7ceyZ38z+zetYbrzaSqIrZaLMROmXhUJIPnnwBHv41jVX3vdARq7XBxw+xnwBvnTu7v5eGrf+JiKEkIknDyRxrFAOMDz25+nvqGeN/a8EdM+LXMat1bdys0LbqY0ozQJPZzcUqlMe5SwH9pewbX9AaZ2r6eC6PuHNT2N+rPJnL2KotOuAsPR+3+06q+97gCbW/rxBkKYDDoyrUZybebUnooSQiSVBJFxaOeBnaxuWM2Dmx+k29Md1WbQGfjGqd+gpqqGr1V8Db1On6ReTl6Hpjo6HD7CqobbHyLDmuQy7ZoGB/4eWfex93EI9DL0NKAuw2l8bl7GdvPX2dJjYW5PJrfpLByr7NjQ6q8AjV0uvIEQ2WlG+jxBCjIsFGVaKIKUmYoSQqQWCSLjhC/k49kvnqWuoY53mt+Jaa/IrqCmqoaVC1ZSZC9KfAcFEL1DxhsM0dLrpanbzVnl2eTaDxeDS1iZdvdeaHo4EkCcO2OanUo+OyyX84XlCnoMswY/X5wZPO600dDqr3azngNuPyaDjj5PcPAwvEPTUqkwFSWESD0SRFLcF91fUN9Qz9ota+n19ka1GXVGvnnaN6mtquWC8gvQnWDJbDE2hu6QKTFZsRr1fNzcx7s7ezhzejbFWdb4l2kPDsC+p6FpbWT3y1B6K468r7Ou43wCOV9Fp4/9MTDSaaMjD6Jr2NfHgDcYcxjeaF9TCDG5SBBJQd6glz9//mfqG+p5f9/7Me2n5J5CTVUNN82/iXxbfhJ6KIY61g6ZshwbaSYDf2/uZUeHE19QxWKMw4mxagg63oyMfOx/FsLe2GsKL4gsOi37FgNOA01v7CQrqJF+lNm70UwbHar++sneXv707m5ybWaKjrJAN9EnBgshxoe4/URobm7mV7/6FW+99RYdHR2UlJRwww038NOf/hSTSSp2Hs3Wzq3Ubazj4a0P0+/rj2oz6818+/RvU1NVw3nTzjvqdkmRPMPtkMm1mzlnRi5t/T6uOrOMGfn2MSvTrvZ+imv7A1jbnsAY6Ii9IOPUSPiYfj3YDm/XLs3SxvR0X51O4YxpOXw8tY9tbQ6GTg4m68RgIUTqi1sQ2b59O6qq8qc//YnKykq2bdtGTU0Nbreb3/72t/G67bjjDrh54rMnqNtYx0etH8W0n55/OrVVtdw4/0ZyrDlJ6KEYiePtkEkzGzDoFYoyLSe/PsLbAc2P4t+1FrPrU4YeJ+XSMtisu4SO7G+zYOFFVBbGHjgVj9N9U/XEYCFEakvo6bv33HMP9913H3v27BnR9RP59N2G9gbqN9bzyNZHcAacUW1Wg5WrZl9FbXUtX57yZRn9GAdaej387o2dZKUZB7eyHsnpC9LvCfKDr51yYkEk5IX9z0emXjpeBy0c1RzUDHzoW8Rr7ovYyjnMKMzBG1TJsZmG3TIbj9N9U/nEYCFEYqTs6bsOh4OcnMn7W73T7+SxbY9Rt7GOje0bY9rnFc6jtqqW6+ddT5YlK/EdFCds6FbWk53qiHyhCt0fHCy1/mRkEeoQ7YZ5vOr+Gi/1nYshLR9M4HYH6BjwUz01i8Zu97BbZuNxum9KnhgshEhZCQsijY2N/P73vx92Wsbv9+P3+wf/PDAQ+4N3vNE0jY/bPqZ+Yz2PbXsMd9Ad1W4z2rh2zrXUVNdwZsmZMvoxTo3ptISzEZoeiny4m2KaQ5Yy3vYvZWfaFfQo5WxoO4DFrB/8t2O3GOh1B3D5wyPaMhuP031T5sRgIUTKG3UQ+fGPf8x//ud/DnvNF198waxZh2sStLa2cvHFF7N8+XJqamqO+XV33303v/zlL0fbpZTU7+vnkU8fob6hni2dW2Laq4urqa2u5Zo515BhnljTTpPVkVtZd3e76BzwYTaMcIdMoA/2PhkZ/ej5W2y7wQ5Tl0P5ChrVBTz71m4qLHYCngChsIrRcvg/ZaNeh9sfIhBWyUozypZZIURKG/Uake7ubg4cODDsNRUVFYM7Y9ra2li8eDFf+tKXePDBB9Hpjl3r4mgjImVlZeNmjYimaWzYv4G6jXU8+dmTeEPRWyjTTelcP/d6aqprqCquSlIvRbwdqqx63GkJNQht6yPho/UFUAPR7YoOir4W2fUyZRkYIiMMR65H0TTYsOcAFqMesyHy35Y/FMYXVPlyRS6KwsmtTRFCiBMQ1zUi+fn55OePrHZFa2srF1xwAdXV1axZs2bYEAJgNpsxm83DXpOKer29PLTlIeoa6vi8+/OY9kWli6itruWq2VdhNw0trC0mmmGnJTQNejdC00Noex9F8ffEXpM5BypugmnXQVpJTPOR61Eq821kp5nodvow2SLh3+ULUZBhwW7W09jtli2zQoiUFrc1Iq2trSxevJhp06bx29/+lu7uw2eiFBWN/xLkmqbx3t73qG+o56nPn8If9ke1Z5ozuXHejdRU1zCvcF6SeilShrsFmh+JjH4MfAHAkWMkTnLYnXYFOXNXMbXi7KhTboc6cj1KY7eb4kwzA74gnQN+QMNuMVCUYaax2y1bZoUQKS9uQeSNN96gsbGRxsZGpkyZEtWWwB3DY67b3c3aLWtZ3bCaHQd2xLR/ZepXqKmq4dunf5s0owyFT2pBF7Q8EwkfnW8B0f/ug5qJHcavssP6Tb7QzqJ1IEzOpyZuTncdd5vr0PUouTYTmgYKGrk2M6CMffVWIYSIg4TWERmtVKkjomoqbze9TV1DHc9+8SxBNRjVnmPN4ab5N3Fr1a2cnn96knopUoIahq53IuGj5WkIuWMuaTOewVv+i3HkfYOAPnPw85qmsavLxdzSTG47f8aIRjGOXI9iNepRAE8wnNAtsyNeEyOEmDRSto7IeNPh6uDBzQ+yumE1u/t2x7Qvnr6Y2qpavnnaN7EYLEd5BTFpOL6IhI/mh8GzP7bdPgPKb6Qt+0p++0GIrEwj6frowmeKooz6hNpkb5M9sniZLxTGYtAzI9/O0jkyEiOEGBkJIkOomsobu9+grqGOF3a8QEiN3vaYn5bPygUrubXqVk7JPSVJvRQpwdcNex+PBJDeT2LbjVkw7WoovxHyIus+BjoG8IV2HbMU/Hg6oXboacNpJiueQIhtbQ7aHN5hK7oKIcQhEkQOah1oZc3mNaxuWM1ex96Y9q9VfI2aqhqumHUFJr0c2jdphf3Q+lIkfLS9AtqQwKAYoOSSyJbb0stAHz1SZjMZsBj0eAKho5aCHy8n1B7rtOF0ixG72cCuLtewFV2FEOKQ1P5pF2dhNcyrja9S31DPSztfQtXUqPYiexG3LLiFVVWrqMiuSFIvRdJpGvR8eLDU+hOR4mND5VRHwse0a8BScMyXiksp+CQY7rThE5liEkJMXpMyiOxz7OP+hvt5YPMD7B+Ins9XULi48mJqq2v5+syvY9TH/tYqRmbcL2J0NUHTw5EA4mqMaQ6Zi9FX3IhSvgKyZo/oJSfKCbXHO214PE0xCSGSa9IFEU3TuGDtBezpiz4BuDS9lFULV3HLwluYljUtSb2bOMbtIsaAA1qeioSPrvdimoOKlU+5gA/US9kb/BIVfZksLS2kchS3OKlS8CliokwxCSGSb9L9lFAUhZXzV/Lzd36OTtHx9Zlfp7a6losrL8agm3R/HXEx7hYxqiHoeCMSPvY/B2HfkAsUPNnn8YprCe/7zyMnM5c0k4HMk3hP4/2E2okyxSSESL5J+eS9ZeEtANy88GamZEw5ztViNMbVIsa+LQe33D4Cvs7Y9oxZUL4Cdep1rPk4yLYDjjF9T8neensyJsoUkxAi+SZlECnNKOVn5/8s2d2YkFJ+EaO3HZofjQSQ/k9j2825MO3ayMLTnDNAUWjt9bC7e2fqvqckmQhTTEKI5JuUQUTET0ouYgx5YP/zkfDR8ToM2R2Fzgill0fCR/ElMGR7dkq+pxQx3qeYhBDJJ0FEjKmUWcSoqZHFpk0Pwb4/Q8gZe0nul+grvIae3Cuw2gqO+QBNmfeUosbzFJMQIvkm509OETdJX8Q4sDMSPpofAndsYTps02D6jezN+CYvNWWwe4cLX+gAFkP/MXf1JP09CSHEBCZBRIyppCxi9PdGCo3tWQcHPoxtN6TD1OWRqZeCc2nsdh/c1eMY0a4eWZgphBDxI0FEjLmELGIMB6D91ci6j9YXYciJyCg6KLoocs7LlGVgiEwdnOiuHlmYKYQQ8SFBRMRFXBYxalrkcLmmdbD3MfAfiL0ma35k5GP6tWAtjmk+mV09sjDzsHFfNVcIkTIkiIi4GbNFjO59kVofTetgYHtsu6UIpl8XCSDZ84d/qZPcASMLM8dx1VwhREqSICJSU9AJLc9Ewkfn24AW3a63wJRvRsJH0RIYYVVc2QFzcsZd1VwhRMqTn7Yidahh6Hwzsuul5RkIe2KvKTg/Ej7KrgRT5qhvITtgTty4qporhBg3JIiI5OvfdrjUurcttj195sF1HzeAffpJ3Up2wJy4lK+aK4QYlySIiOTwdUHzY5EA0tcQ227KhmnXRAJI7iJQxi4YyA6YEyMVZoUQ8SBBRCRO2Af7X4iEj/b1oIWj2xUDlFwaCR+ll4HePKKXPZEdHLIDZvRkfY0QIh7kJ4aIL02D7g8i4WPfkxB0xF6Tc0YkfEy7Biz5o3r5k9nBITtgRkfW1wgh4kGCiIgP525ofjgSQFx7YtvTpkTWfJTfCJmnn9AtZAdHYsn6GiFEPEgQEWMn0B8Z9WhaFxkFGcpgi+x2KV8BBYtBpz/hW8kOjuSQ9TVCiLEmQUScHDUI7a9Fwsf+F0D1D7lAgcKvQsVNkbofRvuY3FZ2cCSPrK8RQowlCSJi9DQN+jYd3HL7KPi7Y6/JPB3Kb4pUPE2bMuZdkB0cySXra4QQY0WCiBg5T+vhUuuOz2LbzflHlFpfOKZbboeSHRxCCDExyE9pMbyQG1qejYSPjr8QU2pdZ4IpV0TCR/FS0MWGgniQHRxCCDExSBARsTQVOt+JhI+WpyJhZKj8cyLhY+rySPGxBJMdHEIIMTFIEBGHObYfXPfxMHhaYttt5ZHwUX4DpFcmvn9DyA4OIYQY/ySITHa+Htj3RCSAHPh7bLsxE6ZeFQkg+efEdd3HiZAdHEIIMb5JEJmMwn5oeyUSPtpejmzBPZKih+KLI8XGSr8BhpGtsziRUutjQXZwCCHE+CVBZLLQtMiIR9M62Ps4BHpjr8leeLDU+rVgLRzVy59MqXUhhBCTlwSRic69F5oOllp37oxttxbDtOsiBcey5p7QLaTUuhBCiBMlQWQiCg7Avqcj4aPrndh2vTVS5bTiJii8UEqtCyGESBoJIhOFGo7U+WhaB/ufhbA39prCCyJTL2VXgnFsRiik1LoQQoiTIUFkvOvfenDL7SPgbY9tTz8lMvIx/XqwTRvz20updSGEECdDgsh45O2EvY9GAkjf5th2U05kwWn5Csg9U0qtCyGESFnydBgvQl5ofSESPtpfAy0c3a4zQsllkfBRcinoTQnplpRaF0IIcTIkiKQyTYPu9yPhY9+TkUWoQ+UuOrjl9mow5ya8i1JqXQghxMmQIJKKnI3Q9FDkw90U2542NVJsrPxGyDg18f0bQkqtCyGEOFESRFJFoA/2PhkZ/ej5W2y7wR45YK58BRScB4ou8X0chpRaF0IIcSIkiCSTGoS29ZHw0foCqIHodkUHRV+LhI8py8CQ2ttfpdS6EEKI0ZIgkmiaBn0NsGcd7H0M/N2x12TOiWy5nXYdpJUkvo9CCCFEgkgQSRTP/kitj6Z14Pg8tt1SANOuh4oVkDU/5U65FUIIIeJBgkg8BV2RKqdN66DjTUCLbteZI1Mu5Sug+CLQybdDCCHE5CJPvrGmhiPnuzStg5anIeSOvSb/3Ej4mPptMGUluocTiqpqskBWCCHGMQkiY8XxxcFS6w9HpmGGss+IhI/yG8Bekfj+TUCNXc7BLcO+UBiLQc+MfDtL58iWYSGEGC8kiJwMXzfsfTwSQHo/iW03ZkUKjZWvgLwvy7qPMdTY5WTNB830ugMUZ1pIM1nxBEJsa3PQ5vBy8znTJYwIIcQ4IEFktMJ+aH0pEj7aXgFtyGFuigFKLomEj9LLQG9JTj8nMFXVeG1bJ73uADML7INl5dMtRuxmA7u6XLz+WScVeXaZphFCiBQnQWQkNA16PjxYav2JSPGxoXKqD5ZavyayA0bETWu/l93dkXLyypBRJkVRKM600NjlorXfK3VNhBAixUkQGY6rCZoejgQQV2Nsu7U0suZj+o2QNTvx/Zuk3IEQvlCYNNPRD9KzmvR0DvhwB0JHbRdCCJE6JIgMFXBAy1OR8NH1Xmy7Pg3KrozU+yi4AHT6xPdxkrOZDFgMejyBEOkWY0y7NxDGbNBjM8k/byGESHUJ+Unt9/tZtGgRW7ZsYdOmTSxYsCARtx05NQQdb0TCx/7nIOwbcoEChV+NTL2UfQuM9mT0UhxUmmVlRr6dbW0O7GZD1PSMpmm0O3zMLc2kNOvoIyZCCCFSR0KCyD//8z9TUlLCli1bEnG7kevbcnDL7SPg64xtz5gF5TfB9OvBVpb4/omj0ukUls4ppM3hZVdXZK2I1aTHGwjT7vCRYzNx0exCAFp6PVJjRAghUljcg8irr77K66+/ztNPP82rr74a79sdn6bBjnthz4PQ/2lsuzkPpl0bGf3IqZYttymqsiCdm8+ZPlhHpHPAh9mgZ25p5mAIue+d3VJjRAghUlxcg0hnZyc1NTU899xzpKUdf/eC3+/H7/cP/nlgYGDsO6Uoke23R4YQnQlKLz9Yav1i0JvG/r5izFUWpFOx2B5TWXVPj0tqjAghxDihi9cLa5rGypUrue222zjjjDNG9DV33303mZmZgx9lZXGaDilfEfnfvC/DmffBN9vh3KdgyjckhIwzOp1CWU4as4oyBrfqrt/Wwf4+D9lpRjQNdEqkxsjMAju97gCvf9aJqmrHeWUhhBCJMOog8uMf/xhFUYb92L59O7///e9xOp3cddddI37tu+66C4fDMfjR0tIy2u6NzNRvw2U74aK/wczbwJwTn/uIhPvb7h5e2drO/j4vf2/uZcOeA3zS3Eev2x9TY0QIIUTyjXpq5oc//CErV64c9pqKigreeustNmzYgNlsjmo744wzuP7661m7dm3M15nN5pjr48Jgg4yZ8b+PSKjGLieP/n0fB9wBCjPMmA16gmGVLqcPpz/IgrIsMqxGqTEihBApZNRBJD8/n/z8/ONe9z//8z/8+te/HvxzW1sbS5cu5YknnmDRokWjva0QwzpU9t3tD5FtNaJTdOgUBbNBj8mmo9cdYHe3m1ML7VJjRAghUkjcfhpPnTo16s92e6T2xowZM5gyZUq8bismqUNl3yvybARCGt1OHyabaXC60G4xcMDlZ49eYVF5rtQYEUKIFBG3xapCJNKhsu82s5HKAjtWk4FedwB/KIyqaagaOLxB7GYDF80ulHoiQgiRIhI2Pj19+nQ0TXYqiPg4sux7js3EgrIsGrtc9HkCuPwhNA1ybGauOWuqbN0VQogUIhPlYkIYWvY9x2bizOnZOH0h/KEwrf1ezpqewzkz8pLdVSGEEEeQqRkxIRwq+55jM7Gry4XTFySsaSgK9HmCTMlOY+mcIpmSEUKIFCNBREwYh8q+zynJpN8TpLnHTb8nyNzSTKmmKoQQKUqmZsSEcqyy7zISIoQQqUmCiJhwDpV9F0IIkfpkakYIIYQQSSMjIpOQqmq09ntx+oO4fCHsZgPpFqNMYQghhEg4CSKTTGOXk9e2dbKppY99vR68gTBWo56puWksLMtm6ZxCWdQphBAiYSSITCKNXU7WfNDMvgMeupw+wmGNdIsBfzDM/j4P/pBKm8MrO0yEEEIkjKwRmSQOHQp3wOUnpKqEVY1cu4l0i5Fcu5lQWCMUVjngCvD6Z52oqlTBFUIIEX8SRCaJQ4fCpVsM9HmC2C1GFCWyHuTQoXB9niDpFgONXS5a+71J7rEQQojJQILIJHHoUDi9TkcorGLURy9KNep1hFUVvU7BHwrjDoSS1FMhhBCTiQSRSeLQoXBhVcWg1xEMR0+9BMMqep2OsKphNuixmWT5kBBCiPiTIDJJHDoUzukLkZ1mxOULDp6GrGkaroOfd/pCVBbYKc2yJrnHQgghJgP5tXeSOHQoXJvDi9sfRq9TOOAKYDbq8AfDGPQ6DHoduXYTF80ulHoiQgghEkJGRCaRQ4fCfakil7KcNAx6BacvhEGvoywnjS9X5MrWXSGEEAklIyKTzJGHwkllVSGEEMkmQWQSkkPhhBBCpAqZmhFCCCFE0kgQEUIIIUTSyNSMiHLoZF53IITNZJB1I0IIIeJKgogYdOhk3t3dLnyhMBaDnhn5djmRVwghRNxIEBHA4ZN5e90BijMtpJmseAIhtrU55EReIYQQcSNrRMTgyby97gAzC+ykW4zodQrpFiMzC+z0uuVEXiGEEPEhQUQMnsxbnGkZPJH3EEVRKM60yIm8Qggh4kKCiBg8mTftGAfdWU16OZFXCCFEXEgQEYMn83qOETS8gbCcyCuEECIuJIiIwZN52x2+wRN5D9E0jXaHT07kFUIIERcSRMTgybw5NhO7ulw4fUFCqorTF2RXl4scm5zIK4QQIj4kiAjg8Mm8c0oy6fcEae5x0+8JMrc0U7buCiGEiBuZ9BeDjjyZVyqrCiGESAQJIiKKnMwrhBAikWRqRgghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkT1yDy8ssvs2jRIqxWK9nZ2SxbtiyetxNCCCHEOGOI1ws//fTT1NTU8B//8R989atfJRQKsW3btnjdTgghhBDjUFyCSCgU4vvf/z733HMPq1atGvz86aefHo/bCSGEEGKcisvUTENDA62treh0OhYuXEhxcTGXXHLJcUdE/H4/AwMDUR9CCCGEmLjiEkT27NkDwL/927/xr//6r7z00ktkZ2ezePFient7j/l1d999N5mZmYMfZWVl8eieEEIIIVLEqILIj3/8YxRFGfZj+/btqKoKwE9/+lOuvPJKqqurWbNmDYqi8Oc///mYr3/XXXfhcDgGP1paWk7u3QkhhBAipY1qjcgPf/hDVq5cOew1FRUVtLe3A9FrQsxmMxUVFezbt++YX2s2mzGbzaPp0glRVY3Wfi/uQAibyUBplhWdTon7fYUQQggRbVRBJD8/n/z8/ONeV11djdlsZseOHXzlK18BIBgM0tzczLRp006sp2OkscvJa9s62d3twhcKYzHomZFvZ+mcQioL0pPaNyGEEGKyicuumYyMDG677TZ+8YtfUFZWxrRp07jnnnsAWL58eTxuOSKNXU7WfNBMrztAcaaFNJMVTyDEtjYHbQ4vN58zXcKIEEIIkUBxqyNyzz33YDAYuPHGG/F6vSxatIi33nqL7OzseN1yWKqq8dq2TnrdAWYW2FGUyFRMusWI3WxgV5eL1z/rpCLPLtM0QgghRIIomqZpye7EsQwMDJCZmYnD4SAjI+OkXqul18Pv3thJVpqRdIsxpt3pC9LvCfKDr51CWU7aSd1LCCGEmMxG8/yeNGfNuAMhfKEwaaajDwJZTXr8oTDuQCjBPRNCCCEmr0kTRGwmAxaDHs8xgoY3EMZs0GM7RlARQgghxNibNEGkNMvKjHw77Q4fQ2ejNE2j3eGjssBOaZY1ST0UQgghJp9JE0R0OoWlcwrJsZnY1eXC6QsSUlWcviC7ulzk2ExcNLtQFqoKIYQQCTRpgghAZUE6N58znTklmfR7gjT3uOn3BJlbmilbd4UQQogkmHQLIioL0qlYbJfKqkIIIUQKmHRBBCLTNLJFVwghhEi+STU1I4QQQojUIkFECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSSNBRAghhBBJI0FECCGEEEkjQUQIIYQQSZPSlVUPnZI7MDCQ5J4IIYQQYqQOPbeHnnZ/NCkdRJxOJwBlZWVJ7okQQgghRsvpdJKZmTnsNYo2kriSJKqq0tbWRnp6Oooih9KNhYGBAcrKymhpaSEjIyPZ3RHI9yQVyfck9cj3JPUM9z3RNA2n00lJSQk63fCrQFJ6RESn0zFlypRkd2NCysjIkP+YU4x8T1KPfE9Sj3xPUs+xvifHGwk5RBarCiGEECJpJIgIIYQQImkkiEwyZrOZX/ziF5jN5mR3RRwk35PUI9+T1CPfk9QzVt+TlF6sKoQQQoiJTUZEhBBCCJE0EkSEEEIIkTQSRIQQQgiRNBJEhBBCCJE0EkQmsebmZlatWkV5eTlWq5UZM2bwi1/8gkAgkOyuTVr/9//+X84++2zS0tLIyspKdncmpT/84Q9Mnz4di8XCokWL+Pvf/57sLk1q7733HpdffjklJSUoisJzzz2X7C5NanfffTdnnnkm6enpFBQUsGzZMnbs2HFSrylBZBLbvn07qqrypz/9ic8++4zf/e53/PGPf+QnP/lJsrs2aQUCAZYvX853vvOdZHdlUnriiSe48847+cUvfkFDQwPz589n6dKldHV1Jbtrk5bb7Wb+/Pn84Q9/SHZXBPDuu+9y++238+GHH/LGG28QDAa56KKLcLvdJ/yasn1XRLnnnnu477772LNnT7K7Mqk9+OCD3HHHHfT39ye7K5PKokWLOPPMM/nf//1fIHLeVVlZGd/73vf48Y9/nOTeCUVRePbZZ1m2bFmyuyIO6u7upqCggHfffZfzzjvvhF5DRkREFIfDQU5OTrK7IUTCBQIBNm7cyJIlSwY/p9PpWLJkCRs2bEhiz4RIXQ6HA+CknhsSRMSgxsZGfv/73/MP//APye6KEAnX09NDOBymsLAw6vOFhYV0dHQkqVdCpC5VVbnjjjs455xzmDNnzgm/jgSRCejHP/4xiqIM+7F9+/aor2ltbeXiiy9m+fLl1NTUJKnnE9OJfD+EECLV3X777Wzbto3HH3/8pF7HMEb9ESnkhz/8IStXrhz2moqKisH/39bWxgUXXMDZZ59NXV1dnHs3+Yz2+yGSIy8vD71eT2dnZ9TnOzs7KSoqSlKvhEhN3/3ud3nppZd47733mDJlykm9lgSRCSg/P5/8/PwRXdva2soFF1xAdXU1a9asQaeTQbKxNprvh0gek8lEdXU1b7755uBiSFVVefPNN/nud7+b3M4JkSI0TeN73/sezz77LO+88w7l5eUn/ZoSRCax1tZWFi9ezLRp0/jtb39Ld3f3YJv8Bpgc+/bto7e3l3379hEOh9m8eTMAlZWV2O325HZuErjzzju56aabOOOMMzjrrLO49957cbvd3Hzzzcnu2qTlcrlobGwc/HNTUxObN28mJyeHqVOnJrFnk9Ptt9/Oo48+yvPPP096evrg+qnMzEysVuuJvagmJq01a9ZowFE/RHLcdNNNR/1+vP3228nu2qTx+9//Xps6dapmMpm0s846S/vwww+T3aVJ7e233z7qfxM33XRTsrs2KR3rmbFmzZoTfk2pIyKEEEKIpJEFAUIIIYRIGgkiQgghhEgaCSJCCCGESBoJIkIIIYRIGgkiQgghhEgaCSJCCCGESBoJIkIIIYRIGgkiQgghhEgaCSJCCCGESBoJIkIIIYRIGgkiQgghhEgaCSJCCCGESJr/H/AkSHxZbeyyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot Check Loss with epsilon results\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "# Parameters\n",
    "epsilon = 0.3\n",
    "\n",
    "# Fit Check Loss with epsilon (two quantiles)\n",
    "clf05 = plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.05, \"epsilon\": epsilon}, C=10.0 / n)\n",
    "clf05.fit(X=X, y=y)\n",
    "\n",
    "clf95 = plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.95, \"epsilon\": epsilon}, C=10.0 / n)\n",
    "clf95.fit(X=X, y=y)\n",
    "\n",
    "# Plot\n",
    "n_sample = 100\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = clf05.predict(X_sample)\n",
    "q95_sample = clf95.predict(X_sample)\n",
    "\n",
    "# sort by x0\n",
    "sort_idx = np.argsort(X_sample[:, 0])\n",
    "x0_sorted = X_sample[sort_idx, 0]\n",
    "y_sorted = y_sample[sort_idx]\n",
    "q05_sorted = q05_sample[sort_idx]\n",
    "q95_sorted = q95_sample[sort_idx]\n",
    "\n",
    "plt.scatter(x0_sorted, y_sorted, alpha=0.5, label=\"real_y\")\n",
    "plt.plot(x0_sorted, q05_sorted, \"orange\", linewidth=2, label=\"q05\")\n",
    "plt.plot(x0_sorted, q95_sorted, \"green\", linewidth=2, label=\"q95\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TkV1yRASA2x_"
   },
   "source": [
    "Epsilon creates a tolerance zone where small residuals incur zero loss, so the model only penalizes deviations exceeding this threshold.\n",
    "\n",
    "This produces tighter quantile intervals and more robust estimates, suitable for scenarios where small errors are acceptable but large deviations matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 97
    },
    "id": "ElZCOJ2lB0mP",
    "outputId": "b1c6058b-7a1d-41d0-8742-0a515a56bd50"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={&#x27;epsilon&#x27;: 0.2, &#x27;name&#x27;: &#x27;check_eps&#x27;, &#x27;qt&#x27;: 0.95})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={&#x27;epsilon&#x27;: 0.2, &#x27;name&#x27;: &#x27;check_eps&#x27;, &#x27;qt&#x27;: 0.95})</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={'epsilon': 0.2, 'name': 'check_eps', 'qt': 0.95})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Comparison: Original QR vs Check Loss with epsilon\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"is_categorical_dtype\")\n",
    "\n",
    "# Parameters\n",
    "epsilon = 0.2\n",
    "\n",
    "# Fit original QR\n",
    "clf_qr05 = plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.05}, C=10.0 / n)\n",
    "clf_qr05.fit(X=X, y=y)\n",
    "\n",
    "clf_qr95 = plq_Ridge_Regressor(loss={\"name\": \"QR\", \"qt\": 0.95}, C=10.0 / n)\n",
    "clf_qr95.fit(X=X, y=y)\n",
    "\n",
    "# Fit Check Loss with epsilon\n",
    "clf_eps05 = plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.05, \"epsilon\": epsilon}, C=10.0 / n)\n",
    "clf_eps05.fit(X=X, y=y)\n",
    "\n",
    "clf_eps95 = plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.95, \"epsilon\": epsilon}, C=10.0 / n)\n",
    "clf_eps95.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 365
    },
    "id": "Q3fIo_UwC7yN",
    "outputId": "89f16045-0070-4238-d335-109749350245"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "n_sample = 300\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "\n",
    "sort_idx = np.argsort(X_sample[:, 0])\n",
    "x0_sorted = X_sample[sort_idx, 0]\n",
    "y_sorted = y_sample[sort_idx]\n",
    "\n",
    "qr05_sorted = clf_qr05.predict(X_sample)[sort_idx]\n",
    "qr95_sorted = clf_qr95.predict(X_sample)[sort_idx]\n",
    "eps05_sorted = clf_eps05.predict(X_sample)[sort_idx]\n",
    "eps95_sorted = clf_eps95.predict(X_sample)[sort_idx]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x0_sorted, y_sorted, alpha=0.5, label=\"real_y\")\n",
    "plt.plot(x0_sorted, qr05_sorted, \"orange\", linewidth=2, label=\"QR_05\")\n",
    "plt.plot(x0_sorted, qr95_sorted, \"orange\", linewidth=2, linestyle=\"--\", label=\"QR_95\")\n",
    "plt.plot(x0_sorted, eps05_sorted, \"green\", linewidth=2, label=\"QR_eps_05\")\n",
    "plt.plot(x0_sorted, eps95_sorted, \"green\", linewidth=2, linestyle=\"--\", label=\"QR_eps_95\")\n",
    "plt.legend()\n",
    "plt.title(f\"Original QR vs Check Loss with epsilon (eps={epsilon})\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aC9eaV0NEFRE"
   },
   "source": [
    "## With Pipeline\n",
    "`plq_Ridge_Regressor` can be integrated into a scikit-learn Pipeline to streamline preprocessing including scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "NfN2cR3wEoOk"
   },
   "outputs": [],
   "source": [
    "# Simulate Data\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(42)\n",
    "n = 2000\n",
    "x = np.random.randn(n)\n",
    "noise = np.random.randn(n) * (0.3 + 0.5 * np.abs(x))\n",
    "y = 2 * x + noise\n",
    "X = x.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 155
    },
    "id": "X0SBCPmSFPH6",
    "outputId": "7656d2e6-1a90-4c97-f285-3762fe905c7c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;,\n",
       "                 plq_Ridge_Regressor(C=0.005,\n",
       "                                     loss={&#x27;epsilon&#x27;: 0.3, &#x27;name&#x27;: &#x27;check_eps&#x27;,\n",
       "                                           &#x27;qt&#x27;: 0.95}))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;reg&#x27;,\n",
       "                 plq_Ridge_Regressor(C=0.005,\n",
       "                                     loss={&#x27;epsilon&#x27;: 0.3, &#x27;name&#x27;: &#x27;check_eps&#x27;,\n",
       "                                           &#x27;qt&#x27;: 0.95}))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>plq_Ridge_Regressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>plq_Ridge_Regressor(C=0.005,\n",
       "                    loss={&#x27;epsilon&#x27;: 0.3, &#x27;name&#x27;: &#x27;check_eps&#x27;, &#x27;qt&#x27;: 0.95})</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('reg',\n",
       "                 plq_Ridge_Regressor(C=0.005,\n",
       "                                     loss={'epsilon': 0.3, 'name': 'check_eps',\n",
       "                                           'qt': 0.95}))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit Check Loss with epsilon (two quantiles)\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from rehline import plq_Ridge_Regressor\n",
    "\n",
    "epsilon = 0.3\n",
    "\n",
    "pipe5 = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.05, \"epsilon\": epsilon}, C=10.0 / n)),\n",
    "    ]\n",
    ")\n",
    "pipe5.fit(X=X, y=y)\n",
    "\n",
    "pipe95 = Pipeline(\n",
    "    [\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"reg\", plq_Ridge_Regressor(loss={\"name\": \"check_eps\", \"qt\": 0.95, \"epsilon\": epsilon}, C=10.0 / n)),\n",
    "    ]\n",
    ")\n",
    "pipe95.fit(X=X, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "id": "tnAZa4VCFb5-",
    "outputId": "f19104c4-f2ca-49c6-cfbe-1a17f8ba8387"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "n_sample = 100\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = pipe5.predict(X_sample)\n",
    "q95_sample = pipe95.predict(X_sample)\n",
    "\n",
    "sort_idx = np.argsort(X_sample[:, 0])\n",
    "x0_sorted = X_sample[sort_idx, 0]\n",
    "y_sorted = y_sample[sort_idx]\n",
    "q05_sorted = q05_sample[sort_idx]\n",
    "q95_sorted = q95_sample[sort_idx]\n",
    "\n",
    "plt.scatter(x0_sorted, y_sorted, alpha=0.5, label=\"real_y\")\n",
    "plt.plot(x0_sorted, q05_sorted, color=\"orange\", linewidth=2, label=\"q05\")\n",
    "plt.plot(x0_sorted, q95_sorted, color=\"green\", linewidth=2, label=\"q95\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee2clmrGFt9b"
   },
   "source": [
    "## Hyperparameter Tuning with GridSearchCV\n",
    "\n",
    "Due to its compatibility with the scikit-learn API, `GridSearchCV` can be applied to determine the optimal hyperparameters for the ReHLine model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tl_UV_G1F48c",
    "outputId": "f582a69a-8dae-419b-a57e-7a9dc9a06aa1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters (qt=0.05): {'reg__C': 0.05}\n",
      "Best CV Score (qt=0.05): 0.0911\n",
      "Best Parameters (qt=0.95): {'reg__C': 0.05}\n",
      "Best CV Score (qt=0.95): 0.0931\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.metrics import make_scorer, mean_pinball_loss\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Define the parameter grid to search\n",
    "param_grid = {\"reg__C\": [1.0 / n, 10.0 / n, 100.0 / n]}\n",
    "\n",
    "# Use pinball loss\n",
    "scorer05 = make_scorer(mean_pinball_loss, alpha=0.05, greater_is_better=False)\n",
    "scorer95 = make_scorer(mean_pinball_loss, alpha=0.95, greater_is_better=False)\n",
    "\n",
    "# Create the GridSearchCV objects\n",
    "grid_search5 = GridSearchCV(pipe5, param_grid, cv=5, scoring=scorer05)\n",
    "grid_search95 = GridSearchCV(pipe95, param_grid, cv=5, scoring=scorer95)\n",
    "\n",
    "grid_search5.fit(X, y)\n",
    "grid_search95.fit(X, y)\n",
    "\n",
    "# Print the best parameters and scores\n",
    "print(f\"Best Parameters (qt=0.05): {grid_search5.best_params_}\")\n",
    "print(f\"Best CV Score (qt=0.05): {-grid_search5.best_score_:.4f}\")\n",
    "\n",
    "print(f\"Best Parameters (qt=0.95): {grid_search95.best_params_}\")\n",
    "print(f\"Best CV Score (qt=0.95): {-grid_search95.best_score_:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "bwhb190FGHcW",
    "outputId": "8aa9eecb-9aa2-4539-b487-3c0776444044"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "n_sample = 100\n",
    "X_sample, y_sample = X[:n_sample], y[:n_sample]\n",
    "q05_sample = grid_search5.predict(X_sample)\n",
    "q95_sample = grid_search95.predict(X_sample)\n",
    "\n",
    "sort_idx = np.argsort(X_sample[:, 0])\n",
    "x0_sorted = X_sample[sort_idx, 0]\n",
    "y_sorted = y_sample[sort_idx]\n",
    "q05_sorted = q05_sample[sort_idx]\n",
    "q95_sorted = q95_sample[sort_idx]\n",
    "\n",
    "plt.scatter(x0_sorted, y_sorted, alpha=0.5, label=\"real_y\")\n",
    "plt.plot(x0_sorted, q05_sorted, color=\"orange\", linewidth=2, label=\"q05\")\n",
    "plt.plot(x0_sorted, q95_sorted, color=\"green\", linewidth=2, label=\"q95\")\n",
    "plt.title(f\"check Loss with eqsilon(eps={epsilon}, C05=100.0/n, C95=100.0/n)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}