02._Regressao.ipynb

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     "source": [
      "# Regress\u00e3o"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Exemplo - O problema dos pre\u00e7os de im\u00f3veis em Boston"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_boston\n",
      "boston = load_boston()\n",
      "print boston.DESCR"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
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       "stream": "stdout",
       "text": [
        "Boston House Prices dataset\n",
        "\n",
        "Notes\n",
        "------\n",
        "Data Set Characteristics:  \n",
        "\n",
        "    :Number of Instances: 506 \n",
        "\n",
        "    :Number of Attributes: 13 numeric/categorical predictive\n",
        "    \n",
        "    :Median Value (attribute 14) is usually the target\n",
        "\n",
        "    :Attribute Information (in order):\n",
        "        - CRIM     per capita crime rate by town\n",
        "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
        "        - INDUS    proportion of non-retail business acres per town\n",
        "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
        "        - NOX      nitric oxides concentration (parts per 10 million)\n",
        "        - RM       average number of rooms per dwelling\n",
        "        - AGE      proportion of owner-occupied units built prior to 1940\n",
        "        - DIS      weighted distances to five Boston employment centres\n",
        "        - RAD      index of accessibility to radial highways\n",
        "        - TAX      full-value property-tax rate per $10,000\n",
        "        - PTRATIO  pupil-teacher ratio by town\n",
        "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
        "        - LSTAT    % lower status of the population\n",
        "        - MEDV     Median value of owner-occupied homes in $1000's\n",
        "\n",
        "    :Missing Attribute Values: None\n",
        "\n",
        "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
        "\n",
        "This is a copy of UCI ML housing dataset.\n",
        "http://archive.ics.uci.edu/ml/datasets/Housing\n",
        "\n",
        "\n",
        "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
        "\n",
        "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
        "prices and the demand for clean air', J. Environ. Economics & Management,\n",
        "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
        "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
        "pages 244-261 of the latter.\n",
        "\n",
        "The Boston house-price data has been used in many machine learning papers that address regression\n",
        "problems.   \n",
        "     \n",
        "**References**\n",
        "\n",
        "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
        "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
        "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boston.data.shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "(506, 13)"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_samples = boston.data.shape[0]\n",
      "num_samples"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "506"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boston.data[0]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([  6.32000000e-03,   1.80000000e+01,   2.31000000e+00,\n",
        "         0.00000000e+00,   5.38000000e-01,   6.57500000e+00,\n",
        "         6.52000000e+01,   4.09000000e+00,   1.00000000e+00,\n",
        "         2.96000000e+02,   1.53000000e+01,   3.96900000e+02,\n",
        "         4.98000000e+00])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boston.target.shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "(506,)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Dist\u00e2ncia aos centros de emprego vs. pre\u00e7o"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(12,8)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(boston.data[:,5], boston.target)\n",
      "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n",
      "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
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       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x7f8a59970a90>"
       ]
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P2Lp1LXa7PdjxRC56msZORETkAhMfH8/Rox4yM4cDNcjMfIbExHDWr18f7GgikgUV0CIi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\nSCQz+k8P3msAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8a68b43ad0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "# Produzindo conjuntos de treinamento e teste"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import ShuffleSplit"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for train_idx, test_idx in ssplit:\n",
      "    pass"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_idx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([ 15, 362, 303, 100, 371, 389, 327,  44, 471, 274, 287,  52, 305,\n",
        "       410, 318, 230, 504, 169, 302, 189, 282, 211, 124,  39, 475, 481,\n",
        "       236,  30, 441, 454, 496, 155, 498, 505, 372,  49,  38, 117, 323,\n",
        "       386,  51, 262, 192, 113, 103, 123, 110, 417,  59, 349,   2, 220,\n",
        "       324, 258, 499,  25, 494,  23, 253, 404, 308,  19,  21, 359, 167,\n",
        "       298, 387, 411, 191,  63,  62, 447, 421, 442, 229, 135, 108, 235,\n",
        "        46,  56, 202, 128, 415, 289, 458, 140, 444, 428, 422, 381, 182,\n",
        "        76, 373, 172, 457, 166, 150, 132, 459, 337, 463, 204, 119,   3,\n",
        "       319, 143,  67, 352, 116, 495,  88, 228, 351, 254,  48, 249, 147,\n",
        "       503,  10, 353, 478, 345, 174, 398, 357, 339,  28, 433, 227, 111,\n",
        "        92, 440, 238,  85,  12, 199, 179,  41, 165, 134, 129, 177,  58,\n",
        "       270, 267, 322, 115,   0, 354, 244, 142, 106, 154, 466,  16, 137,\n",
        "       181, 401, 163,  45, 431, 291, 383, 489, 316,  33, 178, 286, 183,\n",
        "       131, 206, 271, 184, 394, 449, 355, 365, 311, 414,  36, 120, 473,\n",
        "       393, 399, 257,  40, 451,   9, 295, 151, 233, 278, 133, 336, 213,\n",
        "       460, 424, 239, 375, 112, 173, 201, 209,  66, 138,  14,  73, 197,\n",
        "       332, 397,   1, 141, 130, 148,  70, 377, 118, 122, 176, 250, 476,\n",
        "       380, 500, 180, 306, 296, 265,  11, 162, 310, 114,  75, 472, 315,\n",
        "       403,  20, 405, 136, 156, 486, 335, 292, 255,  80, 369,  96, 146,\n",
        "       231, 175, 437, 284, 341,  50, 224, 426, 453, 370, 275,   5, 264,\n",
        "       126, 186, 443,  81, 317,  84, 125, 309, 205,  90, 497, 203, 465,\n",
        "        89, 320,  22, 217, 107, 139, 358,  97, 241, 427, 487, 400, 492,\n",
        "       218, 348, 490, 269, 346,   7, 280, 430, 501, 225,   8, 196, 429,\n",
        "       456, 188, 366, 251, 402, 468,  61, 462, 419, 300, 304, 242,  74,\n",
        "       455, 388,  87, 450,  69,  94, 313,  77,  99, 364,  98,  79, 240,\n",
        "        95, 185, 145, 261, 331, 330, 368,  78, 461, 237, 432, 164, 266,\n",
        "       158, 207,  17,  27, 226, 293, 435, 436,  72, 288, 325, 273, 215,\n",
        "       413, 260,  57, 425, 491, 104, 438, 342, 452, 340, 200,  65, 159,\n",
        "       214, 420, 219, 276, 268, 326, 198, 395,  26, 152, 149, 232, 439,\n",
        "       170,  54])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_train = train_idx.shape[0]\n",
      "n_train"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "379"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_idx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([361, 263,  53, 407, 409, 171, 445, 391,  37, 378, 297, 418, 356,\n",
        "       367, 343, 484, 408, 406, 127, 334, 467, 256,  68, 392, 483,  31,\n",
        "         6, 344, 502, 379, 281,  24, 474, 222,  82, 246, 329, 301, 285,\n",
        "       194, 234, 109, 412, 385, 277, 221, 376, 363,  64, 396,  83, 469,\n",
        "       374, 212, 482, 312, 223, 279, 479, 382,   4, 338, 252, 290, 195,\n",
        "       477, 485, 390, 384, 283, 248, 144, 187, 157, 216, 208,  18, 210,\n",
        "       161, 347,  93, 328, 307, 423,  91, 247, 160, 153, 259, 121, 416,\n",
        "       480, 101, 493,  29, 464,  86, 321, 245, 299, 190, 102, 488,  13,\n",
        "        71,  34, 434,  35,  42, 294, 243, 470, 350,  47, 193, 314, 446,\n",
        "        32,  43, 105, 333, 448, 360,  60, 168,  55, 272])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_test = test_idx.shape[0]\n",
      "n_test"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "127"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_train + n_test"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "506"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Exemplo - regress\u00e3o utilizando apenas RM\n",
      "\n",
      "Vamos prever o pre\u00e7o dos im\u00f3veis utilizando apenas o **n\u00famero de c\u00f4modos** (RM)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = boston.data[train_idx,5].reshape(n_train,1)\n",
      "X.shape"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(379, 1)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = boston.target[train_idx]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "### Treinamento\n",
      "\n",
      "Aqui, vamos utilizar a **regress\u00e3o linear**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr = LinearRegression()\n",
      "\n",
      "regr.fit(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(X, y)\n",
      "plot(X, regr.predict(X))\n",
      "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n",
      "xlim((3,9))\n",
      "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n",
      "ylim((0,55))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(0, 55)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T5oLSTLOIiIhIKfLkk8/w3nursNtnABlYrd157bUhPPTQMG+HVmjffQc332xu//kntGvn\nnTg00ywiIiJSxixYsAy7/QkgDKiCzfYw8+cv93ZYhZKVBTVrmglzq1bgcHgvYS4oJc0iIiIipUjt\n2jXw9V11at/ffxX16tXwYkSFM38+BATA/v2weDGsWgWloSxb5RkiIiIipUh8fDxXXBFDZmZ7fHzs\nhIdvYc2a36lWrZq3QzsvhwOio2HzZmjYELZsgZKyInpByjOUNIuIiIiUMgcPHmT+/Pn4+fnRo0cP\nKlSo4O2QzmvZMoiJMbd/+AF69fJqOGdR0iwiIiIiXmMYcOWV8Pff5mIlSUlmaUZJowcBRURERMQr\n/vnHrFX++2/45htISSmZCXNBKWkWERGRcmPmzFnUqdOUSpXqcM89D5GRkeHtkMocw4CuXaFtW7BY\nwGaDAQO8HdWFU9IsIiIi5cJvv/3G4MEPk5j4MYcPr2D69J2MGDHK22GVKevXm7PLCxbA5MmQnW0u\nXFIWqKZZRERE3Co1NZXp06eTnp5Oz549iYyM9HZIAIwe/TSvvx4IPOcaiadKlS4kJ+/0ZlhlxsCB\nZhkGwNGjEB7u3XgKQzXNIiIiUqwOHjxIVNTljBr1O089lUDr1h34888/vR0WABERFQgISMg1spPw\n8JLddaI0iI8HHx8zYX7rLbM8ozQlzAWlmWYRERFxm9Gjx/Dmm0fIynrfNfIVrVt/Slzcr16NCyAl\nJYVLLmlHSko7srLqERQ0mRkzPqVnz57eDq3UeuAB+Ogjc/vQIahc2bvxFFVBZppLSEtpERERKQuS\nkw+TldU010hTUlJSATh69CibNm2iWrVqNGzYsNhjq1y5MuvX/81nn33G8eMn6NHjJ9q0aVPscZQF\nu3dD/frm9osvwtix3o2nOGimWURERNxm7ty53HbbY9hsc4DKBAcPYejQy7jttn506dIHqENm5i6G\nD7+PiRNf9na4UgSjRsHEieZ2UhLUKD0reJ+TFjcRERGRYvfuu5N47rlXyMiwM2DArXz44ZtcdFFz\nkpJeB/oBKYSEXMHPP39Kx44dvR2uFND+/VCzprn9xBMwYYJ343EnPQgoIiIixW7EiOGkpu4lLS2F\nKVPex8fHh/37dwI3uo6ojGHEsHnzZm+GWWotX76cdu26EBXVnpdffh2n0+nxe774Yk7CvGtX2UqY\nC0o1zSIiIuJR/v7+1KrVmMTEmcCtwEFgCVFRg70bWCm0du1aunW7CZvtbaA2r7wyivT0dF588VmP\n3C81Nefhvvvvhw8/9MhtSgXNNIuIiIjH/fDDdCIiHic8vAWBgU157LG76dChg7fDKnW++WYWNtsD\nwG1AR2y2yUyePM0j93r77ZyEeevW8p0wg2aaRUREpBi0atWKPXu2Eh8fT7Vq1ahVq5a3QyqVAgMD\nsFgO43CcHDlOQECAW+9x7BhUcLWvHjAgZ8GS8k4zzSIiIlIsQkJCaNmy5XkTZofDwSuvTOCKK7rQ\np89tbN26tRgjLPnuuWcIoaEz8PUdC3yI1Xo7zz33hNuu/+mnOQnzv/8qYc5N3TNERESkxHj44VF8\n+ukf2Gxj8PHZSHj4G2zcGEft2rW9HVqJsWPHDl5//W2OHDnB7bffSK9evS74mjYbhIWB0wldu8K8\neeYqf+WFWs6JiIhIqWK1VsRu/w8wZ6ODggbzv/+1Zfjw4d4NrAybOdMswwD4+29o29a78XiDVgQU\nERGRUsYHcOTs+WSfTGjEzTIzzTZyqanQrh38/jv4qnD3nPRHIyIiIiXGiBEPYbX2BWbi6zuO4OBY\n+vXr5+2wypxp0yAw0EyYY2Phzz+VMOenJP/XTeUZIiIi5YxhGLz//ofMmbOImjWr8NJLY6hfv763\nwypVHA4HY8Y8z9SpMwkOtjJ+/NPccsvNAGRkQFCQeVxICBw9ChaLF4MtIVTTLCIiIlLOjBnzPG+9\ntRCbbRKQjNV6Jz///DWLF8fw8svmMQ8/bPZhFpOSZhEREZFy5qKLLmHXrs+A1q6RiUBOW7q0NLBa\nvRFZyVWQpFnVKyIiIiJlSEhICLAv14iZMA8cCIahhLmoNNMsIiIiUobMmzePm24aQnr6gVNjGzYk\nEhWlXtfnoplmERERkXImObn7qYT5oot2sGfPXiXMbqCZZhEREZEyIndL66QkqFHDe7GUJpppFhER\nESkH5s7NSZibNTNrl5Uwu5dWBBQREREpxXLPLu/YAQ0aeC+WskwzzSIiIiKl0NKlOQlzhQrm7LIS\nZs/RTLOIiIhIKZN7dnn9eoiO9l4s5YVmmkVERERKibi40xNmw1DCXFyKK2m2AGuAH137lYBFwFZg\nIVCxmOIQERERKZV8fKBNG3P7zz/NhFmKT3ElzY8A/wEn397/w0yaLwaWuPZFRERE5AybN589u9yu\nnffiKa+KI2muA3QHJpPT/6438IVr+wvgxmKIQ0RERKRUqVnTbCEHsHChZpe9qTgeBHwTGAWE5xqr\nDpxc2/GAa19ERETELZKTk/n+++9xOp307t2b2rVL14p4u3dD/fo5+0qWvc/TM809gYOY9cznWmXF\nIKdsQ0REROSC7Nmzh+bNW/Poo7E89thfNG/ems2bN3s7rAJr3TonYZ41SwlzSeHpmeb2mKUY3YEg\nzNnmqZizyzWA/UBNzMT6LOPGjTu1HRMTQ0xMjEeDFRERkdLv2Wdf4fDhQTgcLwOQkfEGTzwxjp9+\n+sbLkZ3fwYNQPddn707n6bXM4j6xsbHExsYW6pzifCs6Ak8AvYDXgRTgNcyHACty9sOAhqH/WomI\niEghde16CwsW9AMGukYW0rr1a8TFLfFmWOfVrRvMn29uf/wx3Hefd+Mpb3zM/52cNy8u7sVNTmbB\nrwIzgXuABKB/McchIiIiZVTv3texYsUEbLYOQABW68v06tXV22Hl6dgxczW/kxwO8NUqGiVScb4t\nyzBLNQBSgeswW851AY4UYxwiIiJShg0bNpQRI3oSHNyCwMDGDBp0GWPHPuntsM4yeHBOwvzaa2bt\nshLmkqskV8qoPENERETKHLsdrNac/aws8Cvuz/7lNAUpz9D/Z0RERESKyahROQnzqFHm7LIS5tJB\nb5OIiIiIh2VlQUBAzr7dDkFB3otHCk8zzSIiIiIesnXrVrp0WXIqYR482JxdVsJc+qimWURERMQD\n4uJW06ZNq1P7YWGNWbXqF5o0aeLFqCQvqmkWERER8YJPPuG0hBkgLW0wL7wwwUsRyYVSTbOIiIiI\nm5zdNm4JcC0ATudFpKSs90ZY4gaaaRYRERFxg1mzchLmVq1gwoS3sFpHA/8Ba7FaX+LWW3t5M0S5\nAJppFhEREblAPrmqYXftgnr1wOl8mGPHjvPBB93x8fFl9OhHGDTodu8FKRdEDwKKiIiUEQ6HAwCL\nxeLlSMqPhQvhhhvM7Zo1Yd8+78YjRaMHAUVERMqBrKws7rjjPgIDrQQFhTBixBM4nU5vh1Xm+fjk\nJMybNilhLuuUNIuIiJRyzz33Mt9/vxuHI5ns7L1MmfIb7733gbfDKrP+/PP0cgzDgKZNvRePFA8l\nzSIiIqXc/PnLsdufAMKBKthsj/DLL8u8HVaZ5OMD7dub23FxZsJ8ptmzZ3PvvQ/x7LPPk5qaWrwB\niscoaRYRESnl6tSpjq/v6lP7fn6rqFevhhcjKns2bDh7drl167OPmzDhTe64YzSfftqYV1/dQ8uW\n7Tl27FjxBSoeowcBRURESrlt27Zx+eUdyMy8DB8fP8LD/2PNmt+pUUOJsztUrAhHj5rbS5dCTMy5\njw0NrUxa2l+AuepfSEgf3n33RoYMGeLxOKXoCvIgoFrOiYiIeElcXBw7duygRYsWNGvWrEjXMAyD\niRPfw25PB9ZRsWIQS5b8pITZDXbsgEaNcvbzm8szDIPMTDtQ9dSYw1EVu93umQClWKk8Q0RExAtG\nj36Wjh37ce+9M2jdOoZPPplSpOvMmDGDadOWk5m5i8zMRFJS7uDBB0e7Odryp2nTnIR5zpz8E2Yw\nZyv79u1PUNBgYC0wFYtlLl27dvVgpFJcVJ4hIiJSzDZu3EibNtdjt68HKgPxBAZeTnLyXsLCwgp1\nrSeffIoJE0KAsa6RBCIiriY1dY+boy4fkpKgVq2c/cKmIunp6Ywc+X/88ssSqlatwqRJr9G2bVv3\nBilupz7NIiIiJdDevXsJCGiOmTADNMHPryIHDx4s9LUuvrgRVutiIBMAH595NGjQ6PwnSZ46d85J\nmD//vPAJM0BQUBAffvgWu3atJy5uqRLmMkQzzSIiIsVs3759NGlyKTbbD8CVwLdUqjSSpKQdBAQE\nFOpa2dnZ9OzZn99++xeLpQb+/rtZsWJBkWuky6PDh6FSpZx9p/P0ThlS9hVkprkk/5VQ0iwiImXW\nzz//zIABd5Kd7SQsLIxffvmeyy+/vEjXMgyDVatWcfz4cVq1akWFChXcHG3ZdeutMGOGuf3mmzBy\npHfjEe9Q0iwiIlKCORwODh8+TKVKlfD1dX/FpNPp5J133mfx4j9o0KAW48Y9TeXKlfM/sRxIS4PQ\n0Jz97GywWLwXj3iXkmYREZFyJikpiSFDRrB+/QYMw8mRI1Wx24fh7/8ntWotZcOGlYTmzhbLoYcf\nhnffNbfHjoUXX/RuPOJ9SppFRKRADMNg06ZN2Gw2oqOjCQoK8nZIUgRZWVk0a3Y5u3b1IDv7FqAd\ncBAwyzXCwq7liy8eom/fvt4M02syMyEwMGc/Pf30fSm/1D1DRETylZ2dTY8et9CmTVc6d76byMjL\n2LNH7cpKo/j4eA4cSCM7+2WgKWYOkJMVOp0WFi1axGeffcahQ4e8FaZXvPBCToL8wANmZwwlzFIY\nmmkWESnn3nvvfUaPno3N9jMQiMXyPJ06rWXRotneDk0KKSEhgebNr8Bu3wlYgQHAUeBJfH1/wTA+\nIDi4Kz4+PgQH/01c3Arq16+f57UWLFjAjBlziYgIY+TIh6hbt24xvhL3cTjAL9f6x8ePn17LLAKa\naRYRkQL499/N2Gw9OTkj6XDcxKZNm70blBRJ/fr16dHjBqzWrsCbBAUlU6/eHlq0GEeNGj/i4/M4\nNtu3pKXN4vDhu/m//3shz+t8+eU0+vW7l88+i+Tttx1cemk79u3bV7wvxg3eey8nYe7Xz5xdVsIs\nReWX/yEiIlKWtWzZHKt1Bjbb/UAQfn7fEB0d5e2wpAh8fHz45pvP+PTTT4mLW0+LFv0YNuwB/Pz8\nuPrqnuzb1+rUsQ7HZezdG5fndcaOHY/NNh3ogMMBx4/bmDLlM8aOHVNMr+TCGAbkbkaSknJ6H2aR\noihM0twMuAhwArsATUOIiJQB998/lIULV7BwYUP8/CpQpYo/U6Ys8HZYUkQWi4WhQ4cydOjp4927\nx7B69URstg4AWK0T6d69T57XyMhIB3KyTIejMjZbuqdCdquvvoI77jC3r7oKfvvNu/FI2ZFfTXMD\n4FGgO5AI7HOdUxOoA/wEvAkkeCA21TSLiBQTwzDYvn07drudyMjIQq9KJyWfw+Fg+PDHmDLlYwDu\nvnsokya9gSWP5sSjRo1h0qRl2Gz/A/ZgtQ5nxYr5tGrV6qxjS5Lcq/glJuYsiS2SH3e0nJsJfALE\nAllnfM0f6ATcC/QvUoTnp6RZRETEzZxOJ8B5F1NxOBw899zLTJ8+h7CwUP73v+e49tpriyvEQvv5\nZ+jZ09xu1Ai2bfNuPFL6qE+ziIiIlElOp5MDBw5Qq1bNU2Px8dC4sReDklKrIElzQWqaKwJdgdqu\n/b3AAuDIhQQnIiIiUhSJiYm0a/cse/d+CoCfXxZZWf5ejkrKuvxazt0JrAJigGDXr87AauAuj0Ym\nIiIikoc6dWqfSpjhEIGBzZk/f75XY5KyL7+keSzQGngAeMn1637X2FjPhiYiIlJ2ff/9bHr2HMjA\ngfewfv16AA4ePMiwYSPp1q0/Eye+hcPhKNK1k5KSuO++EXTr1p+3337vVB1zabdmzekP+5mqYLff\nxKpVq7wRkpQjRe3TrGJjERGRIvr88y958MFnsdnG4eOTzI8/diY29hf69r2dAwe6kZXVl+XLJ7F5\n8zYmT36vUNc+cuQIrVp1IDm5Lw5HN5Yv/4jt23fxzjsTPPRqikfuZLl27cEkJnbH7EOQRXDw79Sr\nN/Rcp4rxp583AAAgAElEQVS4RX4PAt4FPAssxKxlBqgLdAFeBD7zXGh6EFBERMqmiy++nPj4CZhN\nqACeoXv3dSxfnsGJEyd7ZB/FYqmGzXa8UC0Ap02bxr33vklGxjYgA2iGxbKJzExbnh0zNm/eTGpq\nKtHR0YSHh1/gK3O/v/6CK6/M2TcMWLlyJddd1wsfn9Y4nQlcdVVTfv55Vp7t80QKwh0PAn4B/Ajc\nAJzsdhgLPA2kXlh4IiIi5ZNZdpE7EQ7E4XCeMeYP+BS6tGLnzp1kZOwAfgeigPE4HOM5cyLKMAwG\nDx7GrFk/4O9fBz+/fSxdOo9LLrmkKC/JI3LPLr/zDowYYW63bduW+Ph/WblyJRUrVuSqq646bws9\nEXcoSHlGKjAdqOzaT/FcOCIiImXfww/fw9NP34fN9jqQjNX6NqNHz6R//8HY7S/icLQjOPgtunfv\nT1BQUKGubbVaMee6ol0jo4FncDqdp83Ezpkzh++++wu7fSt2eyjwOf37383mzXkvrV2cNm2C5s1z\n9vP64Ll69er06tWr+IKSci+//5bVB74BkoG/Xb+SXWMXeTQyERGRMurhhx/kjTceo23bd+jUaQ4L\nFsymU6dOxMUtp1evLbRqNZ6HHmrF119PLvS1IyMjsVo3AZmukTjCw6vg7396S7YtW7aQkXE9EOoa\nuZGEhC0X8rLcwscnJ2F++um8E2YRb8ivpvkvzGWyvwOyXWN+wM3ASKCd50JTTbOIiEhhOZ1O+vQZ\nSGzsJiAah2MR06dPpk+fPqcd99NPP3HrrU+SlvY7EIGPz/tERU1j/fo/vRL3nj1Qr17OvtOZV6cM\nEc9wx4qA8UCTInzNHZQ0i4iIFIHT6WTx4sXs37+fK664gsjIyLOOMQyDRx4ZzccfTyYgoDpWawbL\nlv2S57Geljs5HjIEpkwp9hCknHNH0jwDs4b5C2CPa6weZleNypi9XjxFSbOIiIiHJSYmcvjwYZo0\naUJgYGCx3js1FSpXztnPzgY1wBBvcEfSHAjcA/QmZxntROAH4FPMXjaeoqRZRETkAuzYsYPHHnuG\nvXv3c8MN1zBu3NNn1TZ7S61akJRkbl9/PSxc6N14pHxzR9LsTUqaRUREiig5OZlmzVpx+PAwnM7L\nCQ6eSN++9fjqq8I/XOhOaWkQGpqzb7dDIRuEiLidu5LmrsCN5Mw07wXmAp5e5F1Js4iISBFNnTqV\nYcPmkJb2nWvkGBZLVdLT0/DzK+qCwBemXTv4+29zu2lTs7WcSEngjsVN3sZ82O9LzLIMgDrAw0B3\n1+8iIiJSRE6nk+nTpxMfv43LLmtJ7969T/4AvyAWiwUfn8xcI9n4+Pi45donGYbB7Nmz+fff9URG\nXsyAAQPyXGQkKwtyL2p45AhUqOC2MESKRVG7Z/i4vtbY7RHl0EyziIiUaYZhcOONt7FkSQI227VY\nrbMZOrQ3b7wx/oKvfeTIEZo1a82hQ/3Izm6N1foOd911BZMmvXnacU6nkzfffJdffllG3brVeeml\nsdSuXfscVz3d8OGP8eWXi7HZemO1LqRHjyi++WbKaYn5LbfAt9+a20FBZjmGSEnjjvKM9ZgPAq48\nY/wKYDLQoqjBFYCSZhERKdPi4uLo2LE/Ntt/QBCQQkBAQxITt1OlSpULvn5SUhLPPPMyu3fvp2vX\naxg58qFTM8Hbtm1j0KDhrF27lsxMf5zOZ/HzS6BSpW/YvHk1ERER5732vn37qFfvYhyO+kAWMIDg\n4M/4558FREVF4XSe3gkjKQlq1LjglyTiEe4ozxgMfACEYdYyg1meccz1NRERESmC+Ph4eva8GZut\nAmbCDFAJf/+KHD161C1Jc82aNZk8+b2zxk+cOMFVV13PoUMjcDonYXaWfZ/s7NWkpW3ip59+YtCg\nQee99ty5c3E4rMCHQAXgPhwOf44cOcLIkfD22znHag5MyoL8kuZVQFugJqe3nEvyZFAiIiJlmWEY\ndOnSlwMHhmM+PjQF6IGv72SqVAmlfv36Bb7W2rVrWbZsGVWrVuXmm28mIHfx8HnOSU+vhtP5mGvk\nBeArYDvgR0E+6V2+PA4YB1ztGplIVlZfOnS46tQx27ZBo0YFfikiJVpBH59NQomyiIiIWxw/fpy9\ne3cCozCbVN0HPEKDBo1YsmRegbtbzJr1LXfd9SBO5834+c3m7bcns2LF/HwT59DQUByOZCATCABO\nAIfx8ZlCYOBfdOv2Qb73rlQpHF/fvTidJ0fCMIxDp76u2WUpa85+xLXg1rgtChERkXIkNDTUtcjI\nOuASYCkhIbWZPPmtQs0y33//I9jtP5CR8T5pab+ycaODb08+dZeHlJQUbrnlLvr2vYugIAgO7gK8\nir//NVSuHMGNN+4jLm4FVatWzffeTzzxMGFhn2GxPOIaaQnA6tVKmKVsupBGjZe5LQoREZEiysrK\nYuvWrQQGBtKoUSO3tlTzFF9fXz7//BOGDOmCxdIJp3Mdffp0oGPHjgW+hmEYHDt2iJxn8n3Jzo7m\n0KFDeR7vdDrp3LkXmza1JitrCr6+PxIWNom7727JFVc8xh133FGoP7sGDRrw9NMbGD06Zx1sJctS\nlpXk7yzqniEiIud14MABrr66K0lJJ3A40ujcuQNz5nztlsU7Nm7cyD///EOtWrW4/vrrPZKMb9my\nhVWrVlGrVi06duxY6Htcc013/vqrMVlZrwLrCQ7uzR9/LKBly5ZnHbtz506ioztgs+3h5AfN4eHt\n+PHH17nmmmsKHXvuUJcuhZiYQl9CpMQoSPeMCynPWH8B54qIiFywoUMfJSHhOk6c2IrdvoOlSw/z\n7rvvX/B1v/pqOm3bduahh5bQr99j3HLLXQV6OK6wIiMjue2224iJiSlSUv7991/Srt02LJYIIiL6\n8cUX7+eZMAMEBQXhcNiBdNeIA6fzKIGBgYW655w5pyfMhqGEWcqH/P6F3pTHmOE67yPgwvvhnJtm\nmkVE5LwaNLiUhITPgFaukQ+4/fY1TJv2cZGv6XQ6CQmJID39N8zSh3RCQ1szZ847XHvttW6IumhS\nUlIYOfIp1q/fwmWXRfHmm69QsWJFwCzVKEjS3b//Xfz88y5stgEEBy+gZcs0VqyYjyV3Q+XzyH2L\n776Dfv2K9FJEShx39Gn+BvgacJ4x7kNOU0kRERGvaN68KXv3fk929mVANsHBP9Ky5XUXdM20tDSy\ns7OAaNdIEHAJSUnnbiJ14sQJDh48SJ06dQrU8q2wsrKy6NDhBrZvv5KsrOfYtGkGq1d3Z/XqFa7l\nsgs2Sz19+hQ++OBD/vprDVFRV/Loo48UKGFesQJyV3BoTkvKo/z+la0G7iLvUow9QF23R5RDM80i\nInJe+/bto33760lNteB0HqdduxbMm/ftBSeuTZq0ZPv2OzGMR4HVWK3dWL16BZGRkWcd+8knUxgx\n4lH8/CoQFGSwYMEcWrdufUH3P9OqVauIiRnEiRMbMX90OwkJaczKlT/RvHlzt97rTLnz8Y8+gqFD\nPXo7Ea9wx0zzSMzV//KiD2VERMSratWqxebNq9iwYQOBgYFERUWdWib6Qsyf/x3dut3Cjh1PERgY\nwpdfTs4zYf7vv/945JGnyMj4h4yMi0lLm0m3bv04cCDBrQ8O+vn5YRiZmB/8WgAHhpFV4LKKovj3\nX7j00px9zWNJeafuGSIiIudgt9sJCgo6ZwI8Y8YM7rtvFseP5/RGDgioSGLiNrcsg32Sw+Hgqqu6\nsG5dFdLT+xAcPIs2bTKIjf3ZI109cl/yxRdh7Nizj8nKymL8+IksWfInjRrV4ZVXnqVGjRpuj0Wk\nOHi6e4aIiIhXGYZBamoq2dnZHrl+cHDwWUnp119Pp169KKpVa8icOfPIzl4JpLq++hf+/hYiIiLc\nGofFYuHXX39k5MhIunWby+OPX8aCBd+fFltWVhYjRoyiatUG1K8fzYwZMwt9n507T0+YnU4zYTYM\ng5SUlNP+nO+8835ee+1Xli8fwtSpVi6//BqOHz9+Qa9TpCTTTLOIiJRKW7du5frrb2T//kR8fJx8\n/PEH3HnnHR695+LFi+nTZzA229dANazWB4iKMti4cRv+/s3JylrLzJmf06NHD4/GkZeRI0fzySdx\n2GyTgCSCg29j3ryviSlgP7jcyfLw4fC+q3Pf+vXrueGGvqSkHMJigalTp9CtW1cqVKhMdvYhIASA\nsLDr+OKLB+nbt69bX5dIcXBHTbOIiEiJ1LXrTezZMxzDeAj4jwce6ESrVi2Jjo7O99yi+vbbH7HZ\nRgJmKwmb7Q0OHhzM338vYO/evURHR1OnTh2P3f98Zs2ai802E4gEIrHbRzB79k/5Js0HD0L16jn7\nDgecLAt3Op1cf30fDhx4FhgMrObOO29g5cpY19G5cwzP1VeLlAQFKc+wkPOvwge4A3gAsHoqKBER\nkfNJS0tj9+54DONB10hzLJbrWbVqlUfvGxERhsWyJ9fIbkJDQ4mOjqZr165eS5gBQkPDMBtbmfz8\n9lCxYth5z6lQISdhvvFG82G/3M9RHjx4kKNHj2EmzACt8PNrz5YtW7jxxlsIDr4J+Ak/v6cJDd3u\n1T7WIp5WkKT5Z6Cpa3sMMAi4FLOHs4iISLGzWq0EBYUAca4RG4axyuNJ64gRw6lY8Xv8/R/Ax+cZ\nrNahvPHGOI/es6DefPN5rNa78fF5Fn//+4iI+Jnhwx/I89hjx8xyjGOu/lgZGTB79tnHVapUydW1\nY8PJM8nOXkedOnX46qvJPPpoO6688n369z9IXNxywsPDPfLaREqC/GqaOwJTgCGuYz8GngZSXNv3\nusaXeSA21TSLiMg5zZ49hzvuuA+LpSNO57/06XMN06Z94pFuErklJSUxZcpn2Gx2+vW70e09mS/E\nypUrmT37B8LCQrjnnrupnrvuwuWSS2C9a/WFVq0gv8n5adO+ZujQkfj5XYPTuZpBg/rwwQdveiB6\nEe8pSE1zft9ZYoDJwHAgAnjWte0DvAOMcB2npFlERIrd9u3biYuLo1atWnTo0AEfHx+WLVtGfHw8\n0dHRtGvXztshlhgZGRCUay3f48chNLRg527ZsoU1a9ZQv359rrzySs8EKOJF7kiaAV4AbgICgPGY\nM89VgBmAJ4uXlDSLiEihjBz5f0ye/C2GcTWwhKeeeoixY5/0dlgFcvDgQd5++z0OHTpCnz5d6d69\nu9uu3aMHzJtnbleuDIcOue3SImWCu5JmgOZAFhDv2q8KhAE7ihpcAShpFhGRAtu6dSstW16D3b4J\n88PRJAIDm7F791aqVavm7fDOKyUlhejoNhw6dAPZ2U2wWt/hjTfGcP/9913QdR0O8MvVJys5Gdy4\n5opImeHOxU3+w0yYq2Aun10PzybMIiIihXLgwAECAhpiJswANQkIqEFycnKBr7Flyxaio9sREBBC\n48YtWbt2rUdiPdNXX33FkSNXkp39AfAYNttsxox56YKuOXTo6QmzYShhFrkQ+SXNPwMnG17WxHx8\ndggwFXjUg3GJiIgUSlRUFE7ndswfXQbwNQEBaTRs2LBA52dkZBAT053//ruTrKwktm9/gs6de3D0\n6FFPhg2AzWYjOzv3bHhVMjLs5zx+69atzJw5k5UrV571NcMwO2N88om5v2uXOSYiFya/pPkicvrM\nDAEWAr2AK4C7C3D9IOBvYC3mbPV413glYBGw1XXNioUJWkRE5EyVKlXil1++p2rVB/Hx8ad27edZ\nvPhHgoODC3T+9u3bSUvzwzCGA+HAHTiddVl/stWEB/Xu3ZuAgGnALGAtwcH3MmBA/zyPnTr1K1q2\n7MC9935Dp079efjhnJrt5547vc+yYUC9ep6NXaS8yK+meS3Q0rX9K/AJMN21vw6zX3N+rIANc/XB\n34AngN7AIeB1YDTmZ2n/d8Z5qmkWEZEiyczMJCAgoFDn/PPPP7Rrdx1O5xXAdcD9BAc3Z9WqxTRr\n1swjcea2fPlyRowYw5EjR+jTpxsTJ7501mtIT08nIqI66el/AFHAEazWS1ixYg6tW7c6ddyGDRAV\n5fGQRcoMdzwI+BOwAEgEPgUaAocxE+F/MP/FFpQVszXdYOA7zB7QB4AaQCw5C6icpKRZRESKRUpK\nCk2bXkZKyl0YRhtgPH5+u7j99j58/vkHBbrG8ePHcTqdVKhQwWNxJiYm0qRJK+z2A6fGgoImkZ4+\n/NS+fnSKFJ47HgS8B7Om+S5gAGbCDGZ5xmcFjMMXc8b6ALAU2AhUd+3j+v3s7usiIiL5cDgc/PHH\nH/z666+cOHGiyNf5+eefsdvbYBgvYn4YOg+nM4XJk9/N99zs7GwGDrybypVrULVqbbp3v5n09PQC\n3Xfr1q0sWLCAPXv25H8wUKNGDcLCgoGvTo2dTJh//10Js4gn5Zc0HwDuB/pg1h6ftBSYWMB7ODFL\nPOoA1wCdzvi64folIiJSYOnp6Vx1VRduuOFe+vZ9hiZNLiUhIcFNVzfw9fUp0OqCEya8yQ8/7CIr\n6yBZWSksXerkqafG5XveK69MpGXLqxkwYAKRkZcxffqMfM+xWCwsXDiX8PA/T4/WgPbt8z1dRC6A\nXz5f//GMfQOzFvlXYFoh73UU85Hm1uSUZezH7MpxMK8Txo0bd2o7JiaGmJiYQt5SREQKatOmTYwf\n/xbHj9sYMuQWevfu7e2Qzuutt95h3bow0tMXAhbS0sZz332PsmjR7EJfq0ePHlitz5Ce/iwORyus\n1je47bb7sFgs+Z4bG7sSm+0+IASA9PRhLFv2ynnP2bJlCy+9NAG7fQ12ey3gX+6++xp69+5JSEjI\nec9t2fJS4D0A5s510Lt3/jGKyOliY2OJjY0t1Dn5Jc3/y2OsEnA7ZtnGmQ/vnakKkA0cAYKB64Hn\ngR8wSz5ec/0+J6+TcyfNIiLiOVu3bqVt246kpY3EMKqzcOHDTJp0lLvuGuTt0M7pv/+2k57eBTCT\nRoejK/Hx089/0jlUrlyZuLgVPPXUC+zZs4Zu3Xrz5JMF66zapEk9YmOXk5k5APDBYllOo0bnb1mR\nkJBAQEALV8IMcAkWS0WSkpJo3LhxnucsWQLXXZezb5ZiKGEWKYozJ2Off/75fM8p6IqAZ7IAq8m/\ne0YL4AvMMhBfzP7OEzAT75mYi6QkAP0xE+vc9CCgiEgxGTXqKf73Px8M4+QM6VIaN36C+PhVXo3r\nXLKzs7n66mv566/jwHLASkDACHr3PsGsWV8Uayypqam0adOR5OSKQAChobv4559l1K5d+5zn7N69\nm6ZNW2G3L8X8UbmAChXuYv/+BIKCgs46PneVyBdfwJ13uv1liJRrBXkQML+Z5nNxULA65PVAqzzG\nUzH7+YiISAmQlZWNYYTnGrGSnZ3ttXjyM378RNat88Vs4lQX8KVSpUp8+OFfxR5LpUqVWL/+b2Jj\nY3E4HHTs2JHw8PDznlOvXj0+/fR97r77aiyWCCwWOz/8MPOshDkuDtq0ydnXXJKI9+Q301zpHGOD\ngMaYZRqeoplmEZFisnr1aq6++gZstv8B1bFaR/Hcc4N58snHvBrXkiVLmD59NmFhVkaOfJD69esD\n0KFDD37//X7MThcHgLl06PADK1b85M1wC+3EiRPs37+fOnXqnJUw555dnjgRHn+8mIMTKUfcMdO8\nmtNnlA0gBbOv8rALiE1EREqQVq1a8csv3zFmzGukpdkYMuQBHnrIu9/mZ86cxZAhI7HZHsfXdz+f\nf34l69b9Rb169ahbtwYWSxwOR2+gOhbLburXr+nVeIsiNDT0rBrmrVshMjJnX/NHIiVDUWuai4Nm\nmkVEyrHGjVuxffvrnKzms1ge5cknQ3nllRfZs2cPrVtfjd1+KWBgta5n1aoV1KlTx6sxX6jcs8uP\nP27OMIuI53mypllERMRt0tLSWLx4MQ6Hg06dOhEREeFaIKTyqWMcjsrYbEcBqFu3Lps3r+aXX37B\nx8eHbt2+ICIi4pzX37lzJ3///TfVqlWjU6dOBeq/XJz27YPczw06nacn0ACrVq1i69atNGvWjJYt\nWxZvgCKimWYREfGulJQU2rTpyKFDVYEggoI28c8/y/jkky9488152GxvAfuxWu9nyZK5tGvXrlDX\nnz9/PjfdNAiLJQbD+I/OnS9h+PDBLF68lBo1qjJ06FDCwsI88toKwtc3pwRj4ED4+uuzjxk37hUm\nTPgAi+VKHI7feP750TzxxCPFG6hIGVaQmWYlzSIi4lUjRjzBRx/ZyMqaBIDF8iI9e27m+++n8sIL\n45k69TtCQ0N47bUxdO3atdDXr1KlLikp04COQAaBgZdjGPvJzHyEwMAN1K27mbVrf893URF3O3IE\nck+OZ2WBXx6f/yYkJNCsWRvS0zcA1YE9BAVdQkLCZqpXr15c4YqUaQVJmvNbRju3SzGX077J9atf\nkSMTERFx2bFjL1lZV53adziuIiFhL76+vowbN4bt21ezbt2KQiXMdrud2267B3//SqSkpABDgVlA\nIBkZl5GZ+QAwloyM6SQl1eLbb79198s6r0aNchLmq682Z5rzSpgBEhMTCQhoiJkwA9QlIKA2+/fv\nL45QRcSloDXNn2F2X98IOHONf+/2iEREpFy59toriY39CJutFxBAUNC7xMQUrgTjTA888CgzZvyG\n09kBc8npXZjraDkxF6U9uRCtDw5HXU6cOHFB9ysoux2s1pz9tLTT9/PSrFkznM6dwBLgWuAn4BCN\nGjXyXKAicpaClmf8h9lBvjjrJVSeISJSxmzdupW5c+cSGBjIwIEDqVq1Kg6Hg3vvfYipUz/Dx8eX\nrl17MWvWF3mujFdQlSrV5fBhA3O1wIau0bH4+k4gOvoytmxpQEbGi8B6rNahrFnzOxdffLEbXuG5\ndeoEsbHmdv36kJBQ8HOXLl1K374DsdtthIaG8+OPM2nfvr0nwhQpl9xZ0/wF8DrmTHNxUdIsIlKG\nrFy5ks6de5CZeSsWyzHCwmJZu/ZPatWqBZglFU6n0y21xfXrR7N7dwbwIebsLFgstzJ+fGuGDRvG\n0KEjWbRoCZUrV+GjjybSsWNHtm3bxgsvTCAl5SgDB/bmjjtuu+A4ALKzwd8/Zz819fRa5oJyOp0c\nOXKEiIiIEtf9Q6S0c2fSHIP5edZ+IMM1ZgCXFDG2glDSLCJShlx55Q389ddAYDAAfn6PM2yYD++8\n4/5mxHPnzqV//7vIzPQB7gO2ERS0nP/++4cGDRqcdfzu3btp0aItJ048iNN5EVbry7zwwjAef/zC\nOlQMGgTTpuXs68eaSMnkzgcBPwXuALoCvVy/el9IcCIiUr6kph4GckogsrMvJjn5sEfu1adPH374\nYQb+/g5gMxCJw9GTgQPvzfP4adO+wma7GafzGWAQNtt0Xn/9nSLf3zDMPssnE+a9e5Uwi5R2BU2a\nD2LONO8AEnL9EhERKZC+fbthtY4B9gAbsFon0q9fN4/dLy0tjaCgazB/fI0nK+tTVq9eyZEjR846\n1uFwYBiBuUaCcDodRbrvk0+avZdPMozTFy4RkdKpoN0z1gBfAz8Cma4xA3XPcKvFixczdeq3hIVZ\nefTRB/VktIiUKS+99AxHj45i2rTW+PsH8MwzT3LLLTd77H5WqxXDOIDZMcMXSMUwsvN8wHDAgP68\n+mp7bLZGQCMCA/+PBx64u9D3zF1qvHkzREYWNXoRKWkKWtP8uev3Mz9cGuK+UM5SrmqaZ836lsGD\nH8FmexJf34OEhn7KmjV/0LBhw/xPFhGRs2RlZXHFFZ3ZtKky6ekdsFq/ZPjwXkyY8PJZxyYlJdG0\n6aUcO1YV8MXffz/vvvsK999/X4Hu9cYb8PjjOfvl6MeXSJmgFQFLkaZN27Jly4vADQD4+j7JY49Z\nmDBhvHcDExEpxex2O++/P4mdO/dy9dVXMGDAgDw7T7z++uuMHbuNrKyPXSMrqVnzDvbt25rvPXJf\n7p9/4PLL3RS8iBSbgiTNBS3PiAQmATUw+zVfgvkg4EsXEJ/kkpGRAeT0IHI6K2G3H/ReQCJSbjkc\nDhYtWkRKSgrt27fPs9tESWAYBkuXLmXfvn20bds2zz7LwcHBPPHE43mcfTq7PR2HI3cfuAgyMzPO\neTzA1Klw55254ylo5CJSGhX0QcBPgKfJqWdeDwz0SETl1D333IbVOhz4Dfgeq/VNbr/9Fm+HJSLl\nTHZ2Nl269OWWW57igQd+IDq6LYsXL/Z2WGcxDINbbrmL3r0fYtiwn7nssg58913RH7Pp168vQUFT\ngOnAX1it9zBo0Ll/zPn45CTMixYpYRYpDwpanhEHXI75QOBlrrG1QEtPBOVSrsoznE4nr776P774\nYhZWazDjxz9F165dvR2WiJQz33zzDffe+y5pacswP4xcRI0aw0hK2ubt0E6zaNEi+vZ9lLS0OCAI\nWIXVej0nTqQUeeGPFStW8Oijz3H06DH69+/F88+Pwc/v9A9k582DHj1y9svRjymRMs2d5RnJQONc\n+zcDSUULS/Li6+vL00+P4umnR3k7FBEpx/bt20dW1uXk/HhoR0rKPm+GlKfExETMOZyTnTBakZGR\nRnp6OsHBwUW65tVXX01c3K/n/HruXHz6dLj11iLdhmPHjnHixAlq1KiBr29BP/AVEW8r6L/Wh4CP\nMGub9wGPAsM8FZSIiHhHu3bt8PP7DtgOGFgsr9GqVXtvh3WWNm3aYBgLMasFDXx83qJhw+Z5Jsxr\n1qzhhx9+YNeuXYW+j81m4623/j4tYTaMoiXMqamp3HTTQCpXrkmjRi2JimpLUpLmn0RKi8J+hhWK\nmWgf80AsZypX5RkiIiXFpEkf8eijj+F0Gvw/e/cdHkX1NXD8uyVbZpMQICEQeu+E3rsgSBGkSbMA\nooKCoi8iihRRithFFJEqHSmiFEGKdAEBAwIBpSm9BEKySbad949ZQiJtgUDk5/08zz7szt42E92c\nzJ57b8mS0SxfPp+oqKjMHtY1Zs6cTY8ez+HxuMmfvyg//rjgmvXtX3ppAF9/PROzuSwezzZmzPia\n1g4kTGIAACAASURBVK1bBdT+2bNnyZEjIvV19uwj2L//WcLDw297rPv376dKlVokJIQBvwDZMZvf\npFatGNau/eG221MUJWNlxJJzTwPTAc8N3rcAXYDJtzm2QKigWVGUTDN37jwmT55HcLDGoEH9iI6O\nzuwh3Vder5ekpCSCg4MzeygcP36ct956l7/+Os0jj9Tj5ZdfTE1r8Pl8JCYmEhISck29rVu30qBB\nB5zOXUAYsB1Na0x8/DlMJtNN+9y7F0qXTntEsFhe4oknfHz99djbPocaNR5myxYjUAMY4j/6F1my\nVOXiRXW3WVEyW0bkNAcD24D96JMBT/obzIk+MbAE+soaiqIo/zMmTpxM377DcTrfBs6wbFkjtm1b\nR8mSJTN7aPeNyWT6VwTMcXFxVKxYm/PnH8frrc+mTR9x+PAxPvvsfUCfD3K9gBngyJEjmEyV0ANm\ngMp4PMLFixfJnj37NeXdbjcpKSmEhKQ97wNAMcCAy9WQAwcm3tF5HDlyBHgSWIW+GFUQsJI8eQrc\nUXuKotx/t8ppHgtUBD5H/z+8NlALPdi+8t64ezlARVGU+23kyLE4nZOBrsArJCY+z4QJ9+ILNeVW\nfvjhBxITy+P1jgI64HR+z5dffo7P57tl3ejoaDye9cA+/5GZhIVlJVu2bNeUfffd99C00ukC5v79\n38Jufw1IAZKx2ydRu3alOzqPKlUqYTafRr8XVQ6oiaa9xowZX95Re4qi3H+BTAQU9MWDRwG9/Y/R\nwEau3VZbURTlgacHZGm/iAvC6711kKZkPP1nEZTmiBkQAknfK168OF988QFWazXs9lxERAzkxx8X\nXrMk3dKlSxk06DU8Hn33P4NhE02btuPtt9+kXj0TFkskFkskDRpYGTJk4B2dx6RJn1G69A6s1s2Y\nTEdo3jySI0f2/efSfhTlQaa20VYURfmHTz/9nIEDx+J0jgZOo2lvsHHjSsqXv5dL0yvXc+bMGUqW\nrMjFi73x+SqiaWPo0KEYkyd/EXAbSUlJnD9/npw5c16z7vK5cxAR8c8axwgLq0Fc3HFAnxBoMBju\naAJgWiLCqVOn0DSNLFmy3FVbiqJkrIyYCJiZVNCsKEqmEBEmTpzMpEnzcDjsvP12f2rUqJHZw/rP\nOnToEK++Opjjx/WJgG+99fo1we+dyJkTTp/WnxuN2/D5KqF/ATuHEiU+YN++rXfdh6IoDwYVNCuK\noijKPyQkQNq5g5cupdCoUVP27UvEYMiHyDp++ul7qlWrlnmDVBTlvsrIoDkMfY2cuv7Xa4G3gUt3\nOLZAqKBZURRFyVBVq8K2bfrzUqXg99/15263mxUrVhAfH0+dOnXIkydP5g1SUZT7LiOD5gXo2y5N\n9dd5An36b5u7GN+tqKBZURRFyRBuN1gsV19fugShoZk3HkVR/l0yMmj+DfjnFN/rHctIKmhWFEVR\n7lrbtrBggf5c0yAxMXPHoyjKv08gQXMgS84BJAF10ryuDTjvbFiKoijKgyo5OZmuXXvicGQnPDwf\nX389KbOHdEM+HxgMVwPmI0eSVcCsKModCzRofh59g5Oj/sdY/zFFURTlASMi/PLLLyxatIijR4/e\nsNz27dtZuHAhhw8fTj3Wp89rLFhwGqdzD+fPL+Kll4aycuXKuxrPlX4OHTp003Iul4uVK1fyww8/\ncPHixZuW7dMH0u6U7XDko0yZvLc1VpfLxU8//RRQf4qi/O+73dUzrmSAxWf0QK5DpWcoiqJkMBGh\ne/cXmDdvOSZTaTyeLcydO4XmzZunK/fii//H5MnzMJuj8Xg2M23aeNq2bUPOnEU5fXoxcGVL8VH0\n7XuOTz55/47G06dPfyZNmpvaz9SpX9KuXdtryiUkJFCjRiOOHvVhMIRitf7BL7+soWDBgv84PzCm\nuR1ks1UiOflzoDrwM8HB7fj77z9uuU5yYmIiNWs25vBhNwZDGBbLAbZsWU3hwoXv6DwVRfl3y4j0\njCf8/74KvAI8439cea0oiqI8QNauXcu8eatITIwhPv57nM7v6NTp6XQ77P3yyy9MmTIfpzOG+PjF\nOJ0reOKJ7ng8HrJmzQbEppa1WA4QEZH1jsaydetWJk2al6afQTz+eE+aN+/Ixo0b05UdPfoDDh4s\nzOXLvxAf/xPnzz/P88//HwC//vorrVt3oUiRGekC5o0bN2G1mtEDZoB6GI05b3lHG+D99z8iNjYf\nly9vJT5+JRcuvMhzz7160zqHDx+mc+ceNGjQio8/HhvQVt+Kojw4bhU0a/5/Q27wUBRFUTLBmjVr\nKFasEuHh+enUqQcJCQkB1dPTMaoCwf4jNXA640lKSkpXxmisCFy5G1sBn8/AxYsXGTt2BJrWE7P5\nZez2DuTIsZHevXtd04/L5eLSpWtXJU1JSeHZZ/sSEVGQli07I5LL388C4D18vpEsXdqAxo1bs3nz\n5tR6Bw4cJSWlPlduBPl89Tl8+BgxMTHUq9eU776bwZ9/dgFgyJDFiEC+fPlISfkDPasQ4A9crr8D\nWk7uRv3dyOnTp6lUqTZz5uRj7donefPNabz22qBb9qMoipIRRFEURdFdunRJ1q5dKzt27JC9e/eK\npoULLBL4Q6zWjvLoox0Daue3334Tuz1SIFb0ZIbxki9fyXRlYmNjxW6PENjtL/ONREYWFJ/PJyIi\nu3fvltGjR8vYsWMlLi7umj5GjBgjQUF2CQpySHR0TTl16lTqe927vyB2ezOBAwIrBEIEZgoUFbAK\nOAQaCrwrjz/eLbXe2LHjRNNqClwScIvV+pQ89dTz0qDBbP8YrzxWSbFiVVLrffzxWLHZIkTTKovV\nmkXGjRsf0HX64ovxomnVBS76++smXbr0vEn5L8Ru75JmHH+J3Z4loL4URcl8wC1zgm+1D+lnaYNY\nruZ6XGm47+3HwoqiKMrt2Lt3L3XrNsHjyYfHc4KCBXPg9T4GtAIgJWU8y5blDKitcuXK8cknI+jT\npxIGg41s2cJYvnxxujLFihXjq68+pmfPWoCF0NBgfvxx0ZWcP8qUKUOZMmWu2/6KFSt4550vcLtj\ngV/ZvfsTmjRpza5d+l3jhQsXkZS0HigIFMVgeBGD4Ul8vjzod4TDgT7A9/h8JfD5fCxatIikpETq\n1g1n1apcGAxBVKlSnalTl19nBMZ0qSYdOrThvfc+4eJFNyZTTiZNmslTT3VF07Tr1L3q2WefYfv2\nGKZNi8JgCKJSpaqMG/ftDcvrfaaZeYgp3TgURfnf97T/8RWwAf2TrC+wHvjyHvedyX9zKIqi/DtE\nR9cWg+FL/x3MJLFaq4nFUk7A5z+2W8zmENm5c+d161++fFkGDhwsrVt3lffe+0DcbrckJyfLyZMn\nxev13rDflJSUW5b5p2HDhgkMFHhGIFqgr0AuefPNYSIikidPCYGfU+/IWq1PSM2a9QRGprlLe0Ag\nRH7++Wdp06arOBwVxWJ5STStgAwaNEymTbuU7u6yftd9vMB80bQi8uWXX6WO57HHuorZ/Lq/rEds\ntvYyaNDQgM8nPj5ezp07l3qX/UZOnDghYWG5xGgcKfC9aFpN6dPn/wLuR1GUzEUAd5oD9QsQlOZ1\nkP/YvZTZ109RFOVfITQ0p8BfaQLFwZI1aw4JCmov8LZAlEAbcTjCZceOHenqulwuKV++llitHQUm\ni6Y9JG3adLlpfz6fTzwez22N0e12i4jIxIkTxWarIpBXIME/3tNitYbK2bNnZc6cuWK35xSDYahY\nLE9LZGQBKVOmgkBjAa+//EQpXLiCbNmyRRyOwgJJ/uPH0wXLCxfqfW/atEmaNGkntWs3l0mTpqQL\ncEuUqCawIU29ydK6ddfbOrdAHThwQB57rIvUqNFURo58/7b+2FAUJXMRQNAc6DrNYVxdbg70SYBh\ndxAIK4qiKLepTJlymExT0D/TL+JwLOKTT94nV67fgB3AVGA+iYmv8eGHX6Sru2XLFv74I56UlBnA\n0zidi/n++x/YtWvXdfsaMWIMNlsIVqud5s3b33KC4a5du8iXryQWi5VcuQpTrFgxihYFPc3C4S+V\ng6Cg7MTFxdGhQ3tWrJhH//4pDB1anKioPMTGVgAuABWBhoSEDOTbbydx/vx5zObCgM3fTlRqvyLQ\nurX+vEaNGkyY8CHnzp2ke/fuhIbmYMGChQBUrhyNxTIV8AHJ2O2zqFbt3mxmW7RoURYsmM6mTct4\n/fVXMRoD/RWrKMqDIND/o0dx9ZN5qv/5yHs1KEVRFOWqWbMmkDfvbByOQlitBXjqqUZ07dqV8PDc\nQE+gkb9kCMnJrnR13W43BoODqx/3VtxuqF69PsOHj8btdrNo0SImTZrEuHHjePfdr3G5fsfrvcSq\nVWZ69brxMmtJSUk0atSSv/4ahIibU6c+oHnzdsyfPwNNOwbMAxIxGMYSGmqiQIECANSuXZsWLR7B\narWwZ89vuN1fAZuBUdjtZxk37gPKly9PpUqV8PnSB/fZsr2Fx+NNd0xEiI6uzf79h4BSJCTY6NKl\nBzt37uShh2qQM+cGbLY82Gx5eeihMF599aU7+TFkuPPnzzN9+nRmzpxJXFxcZg9HUZQMlAt91kkr\nILAZJ3cns+/UK4qi3Hfx8fEyfPi70rPnizJr1qzUVAO32y0HDhxItxLFxImTRdOKCCzz5/PmkhUr\nVqRrLyEhQfLkKSZm85sC6wWeFGggcFzs9txSpkw1CQ6uLg7HE2I2hwo8myaVYY9ERRW/4VhjYmIk\nJKREupSJLFmqyfr162Xr1q2SP39pMZutUqpUVYmNjU2t9+qrb4jDUUg07SmBcIHBApMFektQUJQs\nW7ZMRER27ZJ0bZcrV1P+/PPPa8Yxd+5cgUL+lTVEYJxAlOTMWViCgxuIpnUUTcsmS5YsuWVu8v1y\n5MgRCQ/PKw5HawkOflQiIwvK33//ndnDUpT/LDIwp9kIPAm84X+dH32hz3sps6+foigPgJMnT0q7\ndk9KmTK1pFu33nLp0qXMHtIdS0pKkhIlKonV2kngI9G00vLGGzeftPb115MkOrquVKrUUL777rvr\nlvn777+lWbP2AmECz6cGl1ZrdbFa66bJJV4uEJFmguE3UqFC3Rv2ffLkSbFawwRO+ctfELs9hxw4\ncOCGdQ4ePCg2W4TAeX+dk/5l5qIFPhaD4RGpVKluumA5Z87x0qhRa4mJiblum++8847AK2nqnBcw\n+3O+r5zL1JueS1oej0eGDn1XypWrIw0aPHrDCZZ3o337p8RkGpo6ZrP5dXnyyecyvB9FUQLDXQbN\ndbi6fs6X/sdW/+vswPa7aTwAmX39FEX5l3M6nZI/fykxm18TWCtWazepUqX+v+Zu4u1asGCBBAfX\nSxPonRSz2Xbbk/Kux+fzSbZsuQV+8Ld9SoKCsojJlDbYPCcGg1WCgxuLpnWV4OAI2bp1603bHTz4\nHdG0/KJpPUTTCsmTT/aUpKSkG5bfsGGDZMlSLU2flwQsAnH+1950AbPdXl9gtRgMYyUkJIccPXr0\nmjbnz58vVmspgfjUO81mc5jA+2na+l1y5SoW0LV68cX/E02rI7BKYJwEB0fIoUOHAqobqOrVm6T5\nWYjAt2Kz5RKLxSHlytWUgwcPZmh/iqLcHHc5EdDH1WXlqgPPA4n+1+dJv5qGoijKfbd9+3bi4jQ8\nntFAPVJSJrBnz36OHbvxzm3/Zk6nE30C3ZUl8bMh4sPtdt912waDgcWL5xAa2oPQ0ErYbKXp0qUD\nVutc4ADgISjoHerUaciECd357LMG7NmzjSpVqly3vW3btjFv3jy6dGnPihUz6NjRgMdzlgULfiIy\nsgDr1q27br1SpUohcgT4Af131Gz0LQOubDKr/1pq0eIIdnsYSUmzgAaIvIDb3ZLFixdf0+Zjjz1G\nly4NsFqLYLOVJjT0bcaMGYKmTQD+BlKwWkfSoEHdgK7VlClTcDpnAA2BXrhcbVm0aFFAdQPVpEkd\nNO0jIB6IA0aQnNwIl+s4u3e3p379Zhnyc1cUJePcLGjeCHzuf55C+lXbI9CDakVRlExjNpvx+ZK5\n+nHkRsSN2XyrfZv+nRo2bIjRuAH4GtiN1dqD+vWbYrPZblU1ILVq1eLYsVhWrvyC2NgdTJ78FR98\nMBibrTJGo0auXCuoUKEEUVFRdO/enfz581+3nZdffp369dvRo8dMypevzS+/bGPWrIW4XJtISDhE\nfPxUHn20AykpKdfUzZo1K8uWLSAi4kUMBgvQn/RfbEJoaG6mTg3FaDQDztTjIqf48ccVDB/+DocP\nH049bjAYmDhxLHv2bGTduimcOPEHpUqVolKl3JhMRTCZQqlbN4Evv/wwoOtkMqXv12h0Zvh/U4MG\nDaBt28KYTBEYjZEEBZ0HJgNZEHmZixfdHDlyJEP7VBTl/ugKfA/8gb5qxgGgwz3uM7Pv1CuK8i/n\ndrulfPlaYrN1FpgmdntTad68/QObniGib3NdtepDkjt3SenSpafEx8ffVv0jR47I4493k9q1m8vI\nkWMCSu2Ij4+XQoVKi83WQWCwaFpumTJlWur7p0+flqeeel5q1Wom3bs/J5qWR+BCatpDUJBdQkMf\nSpdW4XDku+6kvbT27NkjV9dyFoElAmFSs+ZD8ttvv8mQIe+IppURmCIm01MCmphM/cRkeklCQnLI\n3r17r9vuRx99JppWQGCo2GwtpXTpqpKcnBzwNXz33dGiaaUEJovJ9Lpky5Y73QTMjOR2u2Xbtm2i\nafkEEv3X4YxYLPq61oqi3B9k4ERAgJLAi/5HyYxs+AYy+/opivIAuHz5sgwYMEhatOgk77wzSlwu\n1x21s3btWilSpIJkzZpbHnusi1y8ePGGZZcuXSoFCpSVrFnzSOfOz0hiYuKdDj9gSUlJ0q1bb8mW\nLa/ky1daFl7Z2SONM2fOSPbsefwTzBaJptWS559/+ZZtjx8/XjStVZrgdbtky5ZHRPTVN/LlKyFm\ncz+B78RiqSRmc710AbLVGi42Ww65uvnILrHZrh/0nTt3Tho0aCahoUXTtQEPCxQQ+FbgY3E4wmXf\nvn0yadIUadWqi+TNW1rg09TyBsNI6dDh6Wva9/l8YrOFCPzhL+uT4OB6Mnfu3ICvtc/nk6lTv5FW\nrbrIs8/2kWPHjgVc9074fD7p1Km7OBwVJCjoFXE4isrAgUPuaZ+KoqRHBgfNWYFooBL6CvQVM7Lx\n68js66coyn/EwYMH/VsxLxQ4IhZLN2nUqNV1y+7atUs0LcK/0sQhsdnaSfv2T93zMXbr1ltsthYC\nfwqsErs9UrZs2ZKuzMSJE8XhaJ8mED0rZrPtljvTjRkzRoKC+qarZ7dnERGRJUuWSEhInTTv7RfQ\nBLb7X8+U8PC8Mnz4aLHbc0hoaF0xGrOIyaSJ2WyXF154JfXO/4IFCwSCBX5Pbc9o3C7jx4+X0NDc\nAtvSHP8/efPNt1LHqE+cW5xmHLOkceO215yL2+0WozFIICW1rKY9IRMnTrzbH8E95fP5ZP78+TJ6\n9GhZvnx5Zg9HUf5zCCBoDjRJazjwNHCI9LnMDW47FFYURfmXmTdvHm53IfRJYzVwucaxZk0oXq8X\nk8mUruyPP/6Iy9UFaAJAcvJYliwpdc/HuGjRYpKTfwYKAYVITn6GJUuWUa1atVvWdbvdzJ07l1On\nTlGrVi1q1qyZ7v0mTZoweHBD3O5HgBLYbAN45JGWN2gtP0ajF4vlIUSMZMkSyvLl31GhQgU6dGjN\nc8/1Y9OmNrhcXwGXmTy5MZUqTaVLl860bfsccDlNW1EEBZXCZOpEcHAw8fFX3/H5fMTGHkh93bnz\no8TEvIXTWQDwomlv06nTgGtGZzabqVevCRs39sblegvYgchSDh3Kx+eff06HDh2IiIi4pt7+/ftZ\nunQpmqbRqVMnsmTJcsvrmpEMBgNt2rS5r30qinJvHAAs97nPzP6jQ1GU/4Ddu3eLzZZN4CnRN/7I\nLbBG7PYs182N/uKLL8RufyzNHc9fJDw8/12NIW06QM+eL153WbW8eUsJrE6TEtFV3n///XRlrpee\n0bNnH6lZs7E4HPUlKKifaFqUTJhw9a7r0aNHpWfPF6V69cYSHp5XwsJyS8eO3SUhIUFErqZnBAX1\nE1gkdntzadnycXG73XLmzJlr7mLraRQ701yfT6Vbt15Sp87lf6RjiEBHMZtzy5w5c6Rfv/4C+QXm\nCXwkkE1sttDUdBufzydvvz1SIiIKSmRkYRkz5qMb5q7HxcVJq1adJWvW3JIvX2mx27OI1fqc2Gxd\nJCIinxw/fjxd+Z9//lk0LVwslhfEbm8refIUk/Pnz9/+D1JRlAcWGZiesRCIzKjGApTZ109RlP+A\nJk3aisHwcZpAbqCYTNnl008/v275+Ph4KViwtFitHcVgGCyaFiXTpk0PqK+UlJTrpkqMHDlGbLbi\nou+K118sljB5+eVXpUCBclK8eBWZN+9bmT9/vtjtkWIwDBKL5QnJnbuoXLhw4Zq2Dh8+nDoRcNSo\n92X+/PkSHFxNwOM/v31is4XK6dOn5eGHW4vRGCzwmsBk0bSSMmLEe9e0efr0aXn66V5Sq1YzeeON\noZKSknLDc6xd+xExGD5JzSe2Wh//R6C8yf+vU6Cg5MyZT1q37izZsuUXgyFSoKFAZ39edIScOHEi\noGt7I/XqtRAYn9q/2fx/8sIL/dKVKVOmhj9Y18tYLN1k2LDhd9WvoigPFgIImg23KuBXBfgO2IO+\n/Bz+xh+9o3A4MP5zUBRFuXcqVWrIjh2vAw/7j8ygSpVJbN266oZ14uPjmThxIufOXaBp04epU6fO\nTfuIj4+nbdsnWb16KWZzEIMHD+HNN19LfT9Lliji41cDJfxHOmMwrEHkOyAOu70b33//DQ6Hgx9+\nWEpYWCg9evQga9astzy/SZMm0bfvWhITp/mPeDAaNYoXL0dsbAg+X17gynv7yZKlARcvnrxluzey\nf/9+atVqhMdTjqSk53C7W6W+Z7WGkJLiA2oDv5Mjh5VcuXKyb180LlcvYCXwPvA7sI6sWXtz9uyx\na1JkbuXPP//kwoULlCpViqpVG7N37yjgyhrNE2nTZh3z509NLZ87dwlOnPgWKOM/MoYXXjjJ2LGB\nLVGnKMqDz2AwwC3i4kBzmqcBo9CD5is5zSqiVRTlgde69cPs3/82TmcpIBlNG82zz7580zqhoaH0\n69cv4D569nyJ9euz4PNdxuU6w4gRD1GmTHFatdIDSpcrGbCnqRGMSFugKgBJSa8xY8Z8Jk0aR/Xq\n1W/r/OrUqYPIAOAnoDJm83BKlarCH38cwefriD5V5QoNr9dzW+3/U4kSJdi9exu5c+dKc3Qe0I6U\nlAXYbO146qmCVKnSgUqVKlGlSn08nvXo2waUBWZjtZbAbjexbNnC2wqYRYRnn+3LjBlzCQqKIijo\nPG3atOTIkaE4ndOBBDTtAx57bFC6es2aPcz06W+SnDwBOIWmfU6LFl9etw9FUf67bra5SVoJwKfA\namCt//HzvRmSoijK/fPGG/3p0aMGmhZNSEhN/u//OtCjR7cM7ePnn9eTkvIGYAXy4nT2YNWqqzvm\nNWxYD2iL/hH7JTALKJ36vtF4BofDzp0oWrQoCxZMJ1euXlgseYiK+pGCBfPgciUAjYFv0He+ew6r\n9XG6dXv6jvq5YuBAzz8C5mAgO7AbWIbPl43o6Ghq1KhBvXpN8HhS0HfFA/ARHJzCwIG9ad++DdOn\nz2Xv3r0B97148WJmzfqZpKSDxMfv5MKFwaxfv5WuXcuiaWUICanDoEHd6NKlU7p6n332Hm3aRGC3\nFyMsrCljxgykadOmd3UdFEX53xNoesaH6GkZi7mangGwI8NHdJVKz1AU5X9CdHRtYmJ6AV0AwWZ7\nnGHDqvDaa/0BSEhIIH/+0sTFGRDJRlDQSURS8Hhew2i8QHDwNHbs2EjhwoXveAxOp5Py5Wty7Fg0\nKSmVMZlG4PUagVzoiyMtJjLyEMeO7cViubN534Z0v1EE/VfMUqAPemD8ImDCZvuIMmVK8+uv7RD5\nE9gEdMFkWkmRIqc4duwYSUmvAMk4HOPYvHk1ZcuWvWX/o0aN4q23zuPxjPEfuYjVmpfk5Ms3raco\nihJIekagd5orAtWBEcAHaR6KojzALly4QGxsLMnJyZk9lP9pEyZ8SHBwPxyOzgQHN6BgwcO88ELv\n1PeDg4M5cmQPffu2ISTkNG73OTyeZEJCPqJbt0vs2rX5rgJmgCVLlnDyZDgpKVOAPni9y4E49C8N\nXwSWcfq0m59++um22x47Nn3AbDS+wdXfPRWBk8BbwBBgEMnJI9m37zAiFYGPgReApZQseZrw8Bwk\nJX0IDASG4XT2Z9SoTwMaR6lSpbBalwGXADAY5lC4cMYsB3jq1Clq1GhMUJCdyMiCLF++/Kblv/xy\nAlmy5MRqDaZNm644nc6bllcU5frOnz9PbGwsKSkpty58jwUaNNdHX5P5nw9FUR5QH3zwCVFRBalc\nuTl58hRl586dmT2k/1lVq1bl99+3M3bsw0ye/CI7dqzH4XCkK2Oz2Zg9exGXL78GXASmcPmyhx9+\nWEHu3Lnvegz6H0bZ0YNZL3oAa+FqLrUJyMb69etvq12DAfr00Z9v3gxr1/6MzTYdiAVcWCzDyJIl\nAghPUyucLFlCsdtHoWf/NUXTzvLSS71JTnb5x6kTCScxMSmgsbRs2ZInnmiMzVaE0NByhIeP5Ntv\np9zW+dzII4+0Z/v2yng8ZzlzZiJt2z7BH3/8cd2yK1eu5NVX3yE+fiUu118sW+bi+ecDz4FXFEU3\ncuT75M5dyP97qhi7d+/O7CH9a2Xq0iOK8r9s+/btomlRAkdTd3WLiiqS2cP6Tztw4IDY7fn+sTxb\nFTGbQ+S333676/aPHz8uoaGR/uXXXheo7N+d70X/usojxGDIKlOmTElXb9OmTTJgwBvy7rsj0m2L\nPXt2+jWX0xo3brzY7VnEaDRLnTqPyMSJk0TTCgqsElgjmlZEPvzwIylTpoIYDEFiMlmlf/83Ed6N\nBwAAIABJREFUxefzyRdffCWaVlJgncAK0bS8snjx4ts61yNHjsiOHTvE6XTe8fVKKykpSUwmi4A3\n9Xwdji4yefLk65Z/9dUBAu+kuT4HJCKiYIaMRVH+KzZt2iSallfgb///R1MkX76S96w/HvAFLu7Z\nhVGU/7rJkyeLw9E1zS91n5hM1tQNLZT77+zZsxIUFCJw2v8zSRDIKVBDSpWqIsnJyXfdR0xMjNSo\n8bA4HLkExgr08W/mUkCgmERFFZL4+PjU8gsWLBC7PVJgiFgs3SUysqCcOXMmXbDcrdsCqVGjqbRu\n3UViY2NT6/p8PnG73amvJ02aIsWKVZGiRSvL6NFjJDw8rwQFPSvwllitYRIdXVvq1Wsp8+fPl08+\nGStFilSSEiWqyowZM+/6vO+W1+sVmy1E9C3ERcAjwcGV5Pvvv79u+dGj3xOrtXOa67RQihevfJ9H\nrSgPtvHjx4umdU/z/5FXDAZT6oZHGY0MCpqNQM1blsp49+SiKIoisn79enE4Cglc8H8YrZSsWXPd\ncIc15f54442hYjLlEXhOoKzAswJeCQ6uLfPnzxcR/Y5xnTqPSEhIDilZsqrs2LFDXC6XnDhxIuBf\nJqNHjxG7vYWAS+ArgcpiMmVJDQKdTqecOnVK8ucvI/BTmo1B3k8XML/88gDRtGoCi8VgGC2hoTlk\nz549t+z/rbeGiNnc29/OWoHsAlMEZoum5ZF5876984t4j3z11UTRtCixWPqIw1FL6tVrJh6P57pl\nL168KIUKlRFNaylWay/RtHBZs2bN/R2wojzgVq1aJQ5HUYGL/s+KJRIRke+e9UcG3mnelVEN3YZ7\ndmEURRHp1+91sdtzSpYsdSQ4OEJWr16d2UNSRGTJkiViMBgFvhXwCYgEB3eSqVOnitfrleLFK4rJ\n9KbAcYFp4nCEi8ORXez2HBISEiGrVq26ZR/JyclSu3YTMRrz+oPzggJficMRLgMGvClBQZrYbNnF\naMwisOYfKSMi06bp7WhaVoFjad7rJEajRdq06XLTAL5Pn1cERvrrPCnweZo25ku1ag9n1OXMUJs3\nb5YPPvhAZs6cme4u+vVcvnxZvv76a/n4449l375992mEivK/w+fzSe/er4jdniv199S6devuWX9k\n4I6A7wNbgPmBNJpB/OegKMq9sn//fk6cOEHZsmWJiIjI7OEofvXrN2fz5ihcroHANoKD+/D779sx\nm80ULlye5OTTXP34rg60Ql9tYjUOR0eGDRuAx+PhkUceoVy5cgBs376dn376iWzZstG1a1cAgoND\nEVmJvomKA5utET7fflyuLUBuYDiwDn1jFN23387nyJEjmM1mBg4cSlLSLiC//92ngGjs9hUMGFCX\nIUPeuO75rV27lmbNOpOUNBN9C4CG6Ct4AHxL9epfs3nzzVenUBTlv2Hv3r2cPn2asmXLEh4efusK\ndyiQJecCDZoTAA19yvWVtakECL3TwQVABc2KovxnrFu3jk8++RqDwUCPHh0ZP346GzZsIDIyFxMn\nfkz16tWJj48nPDwKt/swEAG4gSLATKAWAAZDUSyWfHg8+YBFVKpUkWrVyjJx4mxcrq5YLLHky3eS\nbdvWkj17JC7XbqAQ4MNiKYTX2wqv95Nrxpc163u89VYQgwePxOVqh8GQjMGwCIgiOXkosBd9Y5Zf\ngY00bDiDVasW3vB858yZy4ABw7l06TwJCUl4PKMAG5r2BjNnjkvdLVFRFOV+yMigOTOooFlRlP+E\n1atX07JlJ5zOIYBgtw9l4cLp1K1bF7s9/U6Ar78+mLFj55GY2B67/WeSk3ch8htQADiGvpPgaqAl\n0Nt/fBDQDn2fKkHTWjJqVBMmTZrDrl2/AxHYbBHkynWBM2dCSUxcj74cnS44uC4TJ77I11/PZuXK\nh9DXVQajcRBVqmzk8OGTnD3rQGQeUJCgoD5062Zi/Phrg+/rWb9+PaNHf47H46VPn6dp3rz5nV5K\nRVGUO5LRQXMroC76Heafge/veGSBUUGzoijpJCUlsXv3bjRNo3Tp0lc+5O6bhIQE9uzZQ1hYGMWL\nF8+w/h9+uC0rV7YAugGfA68CPkCIjq7GihULyJEjR2r5xYsXs337rxQsWICzZy8ydOhogoKq4nRu\nxOOpjn7X+QQw1l9jB3rQfAgAs/kVChRYzV9/RZOSMgD4BYulH9Omjadjx8fTjOwzoAKaNpkPP6zK\n+PGz2bnzDfTttwG+oXnzpUyY8CFVq9bn0qWcgIfw8Hj/nezsKIqiPAgCCZrNAbY1CqgCzPA32Bd9\nRY2BdzE+RVGUgB07doxatRoTH2/H44mjbt3KfP/9HMzmQD/G7s7evXupV68pLlcO3O4TtGnTnG++\n+SpDAmePxwvYgDXA2+ipF2uBAsTE9KNdu6fp2bMTTqeTJk2a8Oijj/Loo4+m1m/RogmxsbFcvtyO\nXr0G4nTmBdJunmJD38b6LBCL2TydQ4cu4fNtRb+jfAmXK2e6gNlobIbP9x5wClhMvXr9OXv2IrGx\nQ3E6iwHJaNpo2rbtT65cudi371fWr1+PwWCgbt26aJp219dFURTlQbQbfbuoK0z+Y/fSPZshqSjK\ng6dBg5ZiMl3ZMCJFNO0hGTt27H3rv0yZ6mIwjE9dQ9nhqChz5szJkLYXLlwodntugU4CdQReS7Oa\nxBkBm1gsjcRq7Spmc6i0bdvxhisyTJ36jYSFRQloAl8KLBdNqyRFikSL3R4mkZGFZO7cuWI22/wr\ncMg/HsmiaQWlcuXaYrdnkaioorJ8+XIREfF4PPLyywMkJCRCHI5wqVChhrz44ity+PDhDLkOiqIo\nmYUAFroIdBttAcLSvA4LpHFFUZSMsn9/LF7vY/5XFpzO5sTExN63/g8dikXkSv8OkpIeJjY2Y/pv\n3bo106d/SsGCMRgMJ4Ft6POuAbYCobhcK0lJ+QaPZyzz52+nXLkaFChQnnr1WqTbWvbJJ7sSF3ec\n7dvX0bDhMipUGM2wYV2Jjd2B0xnHqVN/0r59e958802CglpcZzRWoD7PPvskTudFjh8/QJMmTQAw\nmUx89NEoxo//FBErO3d2ZNw4C+XL1+Do0aMZci0URVH+rQINmkeiJ8VN9T9+BUbcq0EpiqL8U5ky\npTGZZqL/ve5E0xZSsWLp+9Z/sWKlMRhm+l9dwm5fQunSGdd/mzZt2LJlNcHBTvSl8UuiT+brDLRN\nU7I8cBm3uypHj37OunVVqFGjAX/99Ve69iIiIoiLu0BMzAbGjPmEVatWpXv/00/74nbv8L/6GZjj\nf34aWH3Tc3vrrfdwOqcBffH5RpKQ0JEJEybe8bkriqI8CAINmmcBNYAF6Gs1VwdmB1AvL3qS3u/A\nHvRcaIBswErgALCC9HexFUVRrjFlyljy519McHBx7PZCNG2an2eeeea+9T937iRy5vyMkJCS2GyF\n6dq1IY899titK96GJ554noQEK3qe8QVgA3qO80/AYfTVP4f5//0KGABMJDHRR9OmbXG5XACICI0b\ntyYm5mG83njOnPma1q07c/ToUZxOMBggLu7KiqGFgONAP6AwRmNh+vd/lpo1b7wRbHJyMpA19bXX\nm42kpJSMvBTXJSKsWrWKGTNmcPDgwXven6IoSlq3msFSifRpGFfKXzm2g5vL6X/sAoLR71C3Rp8i\nfg54D/1TPyvw+j/q+lNMFEVRdG63mwMHDqBpGgUKFLjvq2ckJydz8OBBwsLCyJs3b4a3HxSUBY+n\nKfqca4D26B+VldCDZBfQANiIfvfZjh5Ef4XR+A3duzdiwoQviYuLIzIyH253PFc+tkNC2pEnzzj2\n7dNX4TAaj+LztUOf0/0J4AS2YbO1ZN68maxa9TNRUZH06vU8wcHBqWNcvnw5Q4aMYOfOBNzuscBJ\n7PZerF37A1WrVs3wa3KFz+ejbdsnWLlyFwZDGXy+1cyaNTHdhEhFUZQ7lRFLzq3l5rnLDW5vSCxC\nXwNpLFAP/XvAnP5+SvyjrAqaFUVBRBg//mumTp1PSIjG8OEDqFatWmYP67bt2LGDN94YwcWLl+nS\npRUvvtgLg8HA4cOHefXVwfz99yl+/XUXPt9E4EoguBhN601kZDjly5clOroEs2Yt5sKFs5w7l4jI\neOAl4BEgCpPpE+bN+5oWLVoQHByGyxUDFEYPtq+uu2wwlEOkM3paxg7gKPoKG2sJCemI1xuM0/kM\nVutOChT4g507N2C32xk79gsGDBiN09kTk2kBBsNflCpVgl69OvPdd6uIi4vn8cdb8PLLL2b4HzRL\nly7l8ccHkpCwFT3veguhoa24ePHUff/jSVGU/z2BBM33UwH0T+YQIC7NccM/Xl+RiXMoFUX5t3j/\n/Y9F00oJLBYYL5oWLjExMZk9rIBMmzZdIiMLi8MRISZTFoEhAnPF4YiW4cNHybZt28RkChUYKrBM\nDIYogScEfAI+MRielB49XrimXZ/PJ1Wq1BOoIfBsmpUvVkqhQtEiIvL551+KpuUWo3Frmve9AtY0\nq2b4BKLFaCwhVuuzYrNlk6AgTeDP1PeDgx+SWbNmiYhIcHC4wN7U9hyO5jJq1ChxOMIFPhP4QTSt\nggwePDxDrp/P55PTp0/LuXPnZMKECaJpT6c5F48YDCZxuVwZ0peiKP9tBLDAxe1E1GXRZ6bY0hyb\nFmDdYPRbGsPR7zbHkTYhTk/ey/aPOv5zUBTlvyx//rIcOzYRuPLV/2BefdXF228PZuPGjRgMBmrV\nqnXNznmZbe3atTRr1oWkpPlAHvSstF/R7/o+T2Tkd7hcl4mLqwz84K91DCiF1RqFwWCgYMFQNm1a\nSVjYtdM+Dh06RLlyVUlM7AMM8R/dS2RkK06dOogIGNPMWunU6U1mzdLQc6QTubJMf3Bwe7p2DScm\n5gC//LIZr9cFXEZP/QBNe4pPPqnDM888g8Wi4XafBLIAYLM9R6NGJ1i2rAhe70epYwgPb8bZs0fu\n6vo5nU5atHicTZs2IOKldu06bN78K0lJq4GSGI3vU7z4XPbu3XZX/SiKokDGbm4yFD2dojSwBP27\nwA0EFjQHoU8e/AY9YIaraRmngFzAmet2OnRo6vP69etTv379AIerKMr/Cv2DzJ3mtZvk5CRKl67C\nuXOheDznsdvjGTXqbZ555hmMxkDnN99bP/ywnKSkXujzpkHPSmuGfv+gJsnJHpKTIf187GCMRhdr\n1kzFaDRSsWJFgoKCAIiNjWXRokVYLBbq1atHs2btcLlyAB+hz9POg93ehw4dHqN/f3j//autisBz\nz10GNOAh4Hn06SS/AD9TvPgbTJu2Ha/3BNAdPcB/G9iJwbCUhx4aCkCzZq1ZuvQZ3O53gd0YjQso\nWvRJli9POwnQg8FgxOfzMXv2bP7880+io6Np2bLlbaVRDBgwmM2bNVJSzgAeNm9uRbNm9VmypDoe\nj5eCBYuxZMmCgNtTFEVJa+3ataxdu/aetL0HfUOT3/yvI9Gnc9+KAT2w/ugfx69MAAR9AuCo69TN\n7Dv1iqL8C3z55VeiaYUEvhGD4T1xOMLl0Uc7SFBQP/8mIKUFBojZHC1t2z4hPp8vs4csIiIjRowU\niyVtOsESgYr+5z2lSZNHJCgoVCBSoJ/AHIGyUqRIWWnUqI0MGjRMkpOTRURky5Yt4nCEi9n8klit\n3cRszibwpr+thQK5xGaLkD59+qfbqOTAgavj2bVrlz+NYoRAXYFQyZu3tGzbtk169nxR4CN/vXiB\nNmI0ZpEyZWrIpk2bUtsYNGiYmEw5BHKIwRApFSrUlEOHDkloaKQYDO8KzBRNKylDhw6XQoXKitGY\nR6CeaFoJ6dfv9du6fhUq1BdYmeZ8ZkrTpu3F4/HIpUuXMuRnpCiKcgUZuP/Ile+/fkX/Xs4ABLKq\nf23Ah756xk7/oyl6KsZP3HzJucy+foqi/EvMmjVbmjZtLx06PC0xMTFSrdrDAtMFsgic9wdVTtG0\nfLJr16677u/ChQvSunUXyZGjkJQvX0d27Nhx3XI+n08uXbp0TaCenJwsf//9t+TOXVRsto4CLwtk\nE1gmkCRGY0mZMWOGVKpUR6CQQBOBcgLBYrE8JTBH7PZHpXHjVuLz+aRGjYcFpqQJIHMJrEvzerJY\nLMPTBcwNGrSUiIiCUrt2Uzl06JCIiGzfvl3atn1SmjV7XBYuXJg63jFj3heb7VEBj4CIwfCx1KrV\nNN05OZ1OCQqyp8mHdklwcBlZvXq17N+/Xzp37iEPP9xOJkyYKIULlxXo7P9DoKVAcwkKCpEzZ84E\n/DPo0OFpMZtfT82ttliekT59/i/g+oqiKLeDDAyav0DPQX4eOOgPgidnVOM3kNnXT1GUf6lXXhko\nFksjgYLpAkWHo6qsXbv2rtuvUaORWCzPCsQKTJbQ0Eg5efJkujI7d+6UnDkLidlsl5CQcFm+fLl4\nPB7p3r23mEwWMZms0qxZO/nggw+kY8eOAjaBhwQKidFYTJo1aychITkEDvrHv1GgqH9ynr5VuM0W\nLseOHZPixasKbEhzroUFOgq4BRLSXYPNm12SO3cxMZmGCBwQo3G05MpVWBITE294vtOnzxCDIVSg\nkBgMVSUsLEpiY2PTlTlz5oxYrWFpxicSGtpCFixYkK7chg0bxG4vnaZcskC42O255eDBgwFdf5/P\nJytXrpSIiLwSElJDQkKqSrFiFSQuLi7An6CiKMrtIQO20R6Hfre4F/rkvS+Bh4Gn0JPeFEVR7rt3\n3x1M7do29OkQI/3/TiIx8Xdy5859V21fvnyZbds24nKNA4oBTyNSg3Xr1qWWcbvdNG7cilOnhuPx\nOLl8eQFt23Zl2LB3mD07Bq/3DF5vHGvWJHPixHnq16+PzfYo8DIwA59vBz/+uBiv14M+7QP0bbOD\n0ozEiMFgwuv10rZtM2y2fuh5xu8SFJSAwbAc6AM4UmuIwJw5b3D8eAJe7xCgKD7fayQmhqbbavuK\nvXv38tZbb9G9+/OILAG+RqQiISGhFCtWLF3Z8PBwChcuisk0yH+95yGylerVq6cr5/V6MZksaY6Y\nACFrVhsFChS45fX3er20atWJ1q2fISUlEpPpKJ9+2ovfftt03QmRiqIo98utguYDwBj0peLeAyqg\nb0v1280qKYqi3Es2m43PPx+D3Z4VWIq+sM94goOLXbOd9O2yWq3oNxzO+Y/4EDmJw3E1OP37779J\nShL0La4B6mA2R7N06Sqczt7oWWx2kpJeYvXqTQQHB2MyXQSao08MPIXFYufZZ3uiaR2BZcAWDIa/\nCAp6GViBzfY0FSqUI3/+/NSrVwOP5wDwB7AMu92ESBz6l4BQsOALtG37FIMGDWbcuMnoK3Q4/WNL\nweM5l26DEoAff/yRKlXqMXr0SVyuckB//9jGcfLkMeLj49OVNxgMrFy5iBo1dqBpJSlSZAQrVnxH\nrly50pWrWrUqkZFezGb9POBxHA4LGzasxGy+9dzzKVOmsGrVCRITY4mP30Z8fH+++GI6NpvtlnUV\nRVHupVsFzR+jT8uuh74s3CT0XOYh6LdgFEVRMkWOHDnw+eKB6cB5YCle7wmioqLuql2LxcLrrw/E\n4WgIjMZma0ORImYaN26cWiY8PByP5yJwyH/kIi7XPgoXzk9Q0ObUcibTFvLli+Kxxx4jKuoMVmtn\nYDSa9jDDhw/jgw9GMGhQW6pU+ZBmzbazfv0yOnZMJjr6HaKifufEidM88kh7nnnmJTyeGejzqtcT\nH388tQ+zOSvHj59j/vxyjBgxDpfL53+nHDAaqEONGuUpVapUuvPs2bMfTucM3O6vgfXoU02mATGY\nzeZrgmyAqKgo1q9fRmLieQ4e3HnNXWbQ/6DZvPknOnZMolKl0fTuXYDTpw9SsGDBgK7//v0HcTqb\noG9gAj5fK/7440BAdRVFUf5tKqDnNHvvcT+Znd6iKMq/3EcffSaalkuCgzuKpuWX/v0HZVjb3377\nrfTt+6p8+OGH4nQ6r3lf3zwkpwQHdxSHo6D07fuanDlzRvLmLS4hIY0kJKS5RETkk8OHD4uISHx8\nvIwaNVr69n1Vfvjhhxv2u2fPHrFYwgW6C2wTo3GogENgX7rcZZgtcEGgisCn/gmFEQI/CKwRyC9Q\nUWy2kGvGf/LkSdG0bAIn0rTXXyyWsmK3R8jMmbMz7Drerm+++UYcjsr+VTx8YjINk7p1m2XaeBRF\n+W8gAzc3MaMvMNoRfZHPNcAs4Ls7jYgD4D8HRVGUG9u1axe7d++maNGi173zeS/FxMTw22+/UahQ\nIWrVqgVAQkICP/74I16vl8aNG5M1a9ZbtHKVy+Uid+7CnDuXgr6M/ZUvA59AX+r+irzAM8An6HO0\nz/rLjkKfrw3wIwZDZ9asWUC9evVSa77zznu8885IPB4rXm8jYDzwJ1ZrE/r3f4YuXbpQokSJ278Y\nGURE6NatN7NnzyUoKCvZs1tZv345efPmzbQxKYryvy+QzU1uFTQ/jB4oNwe2ogfKi4GEDBjfraig\nWVGUB4LH42HChAns3h1LxYpl6N69+x1tsnLw4EGioxuQlHQRfbKd9o8SfYEJmEwF8HpPod+/KI++\nGmgd4BXgHX/ZOZQp8ym7d29Mrf3LL7/QsGF7nM4t/rabAVsJDc3ORx+Npnv3pwMea0pKCl988SUH\nDhymZs3KdOnS5bY2L7mVv//+m8uXL1OkSJHUDV4URVHulYzYEfB19ED5/9BzmhVFUZQ0RIRHH+3I\nzz9fwOlsjqZNYeXKDcyePfmmQeSOHTt47bXhXLwYz+OPt+DVV18ie/bspKScBwqg3zFOu+mqF3gB\ni2USLVtWYOHCnfh85f3vVULfXPVD9BU4gjEah/P++3PS9bl3714MhvrAlbzvjRiNVk6ePIym/TNA\nTy8uLo5+/d7gt9/2UbZscfbvP8CePRpJSQ2ZOvUjNm78lS+++Oc+VncuT548qc//+OMP+vUbxPHj\np2natB7Dhr2pAmlFUZQ0MjO1RVEU5YacTqccPnxYkpOTJSYmRjQtn0CKPzc4TiyWbLJ3795r6r33\n3ocSFhYlmpZdzOYsAmMFlommVZI33xwqIiIVKtRKtxYyfCZQTOBJgTCx2cKkd++XxG7PlibPeY/Y\n7VmlV68XpHDhclK5ch1Zs2bNNf1v2LBBHI4CAqcEjgrMkezZ89xyF0W32y1lylQTi+U5gZ/EbH5U\njMZCqZuh6OccfE/WUT516pRkzRolRuNogRVitzeSJ57omeH9KIry30YGbm6SGTL7+imKolxjwYKF\nomlZRdPySGhoDvn8888lNLSCP3jcIhAlkF0slhCZPn1mar2ZM2eJphUX+N0fsFYXeNtfb69kz55P\njh9PO9FP/IF4R9G3Cg8X+FbgkGhaVWnfvrPY7dkkS5ZqYrdnk2++mRHQ+J9/vq8YDCECWQWs8sQT\nPW5ZJyYmRoKDi6QJ5pf4JyBeGadXbLZwOX78+HXrLly48JrNUgI1efJkcTg6pOnropjNVvF4PHfU\nnqIoyvWQAZubKIqiKH4nT56ka9dncDpX4HT+RXz8dF5/fRiadgmj8V2gFfqeUOdwuTbRs2dfDh3S\nl6X79ttlOJ39gVJAPvTl4Jb7W04hLm4jV/ZlqVx5OVfSLOA8BoMb+AxoCxTE6RzCsWPn+PPPPSxZ\n8gGHDv1O166dCcTOnb9jNPZHX6bvGAsWrGfp0qU3rWMymfD53FxdNKkKsA+D4TNgP0FBr1CsWNFr\n1mweNmwk1as34amnJlK+fG0mTJh0wz4uXLjAhx9+yNtvD2fHjh3p+oaUNCVTAEOG5k8riqIEQgXN\niqIoAdq/fz9BQaWAyv4jjYEsfPPNeKpUWY2+qUgr/3tlCAqqwp49ewCIjMyGyZR2veH9wCVgLlAe\nn0/P4fV6Ydu2pmzbtpny5auTM+cR8uY1YzTGptY0GA6QPXsYuXLlolatWuTMmTPgc4iJ+RWvtxf6\nfJccJCW1SRekXk+JEiUoX74YNlsnYDY22wuUL1+WqlW/I2fOljRpcopVqxanC2QPHjzI6NEf43Tu\nJD7+e5KSNtK37ytcunTpmvbPnTtHmTJVeeONnQz7f/buO7zJqn3g+DcdGU+aDmihrAJlb2QP2cp0\noCxBQAFBQVHUH6KiIiLCi0wVZAgq4mApSxCUvREKKFD2hpZRCqVNZ3L//kgorayOtClwPtf1Xm/y\n5Dzn3E9Tw53T+zlneByNGrVmxYoVADzxxBP4+PyDl9f/AT+haU8wYMDALN1oqSiK8qBy90y9oijZ\ntGLFCild+hEJDCwhvXoNkPj4eHeHlC3Hjh0TozFQ4GxqWYXR6CfR0dESHx8vJpOfQJjztYtiMhWS\nvXv3iojImTNnJH/+omIwvCDe3q+KyZRf9PqrqWUHzz9/+zEPHz4s1ao1FJ3OIjpdafHy6i4WSwHZ\nt29flq6hTJkaAj86x00Qs7m+zJkzR2w2m7z//sdSsGApKVasosyc+W2686xWq7z77ofSsmVHeffd\nD2+7dnVaq1evFj+/xunKTXx8SsnBgwfFarVKXFxcattPPx0p3t6907RdJmXK1Eh9/fz589K372vS\nunUn+eKLyWKz2bJ07YprnDp1Sho1aiP584dIvXqPy9GjR90dkqJkG6qmWVEUd9m9e7doWpDAcoHD\nYjQ+JT169HN3WBly4cIFWbNmzW3rcEePHicmU0Hx82spJlOgzJr1feprc+fOF00LFD+/x8VkCpah\nQ4enOzcyMlImTpwon346MV0ymZBw+ziuX78uBQqUEJ1ugsAx8fD4SAICisqRI0eyfG07d+4UX9+C\n4ufXQszm0vLEE53FZrPJJ5+MEk2rI/CvwCbRtBBZunRplsdxbKCS31nn7UiEfX0LSpcuL4iXl1G8\nvIzSvn03SUxMlLfeekfg0zQ/kwNSsGCpLI+t5JzExEQJCSkvnp4jnL+TYyU4ODTdlyBFuR/hws1N\n3MF5DYqi3I9GjRrFhx9ewWb73HnkHD4+j3D9+kW3xnUnZ8+eZfHixRw+fJhvvvkRb++1wBeGAAAg\nAElEQVSKJCUd5vXXX2b06OHp2h46dIhjx45RoUKFW7aHPn36NPv37yckJIRKlSrdMk7t2rBzp+Nx\nlSrwzz93jmnLli20aTOImJgdziOCj08Z/v57WboNSM6cOcOSJUvw8vKiSJEiHD58mGLFitGhQ4fb\nljFcvnyZXbt24e/vT506ddDpdFSsWJ/w8DE41nsG+Jpu3cL48ccZ9/rR3dHSpct47rmeiHhjMHjS\nufMzzJlzEKt1MeCJydSJ11+vQevWzWnX7nms1vlAEUym/vTsWZapUydmeWwlZ+zbt4/69TsQG3uz\nXMjXtwarVn1N3bp13RiZomSPK9ZpVhRFyRIfHx+8vfdiu3HvGGcwmy3uDOmOwsPDqVevGYmJrUlM\nXAgsA5oAl/nyy5p06PAEtWvXTm1frlw5ypUrd9u+QkJCCAkJueV4UhIYDDefx8SA5TY/jvDwcMaO\n/Yq4uASaNq1NSspFHDe/GYBrxMdH8uabH1K9egWGDn2HU6dOUb9+c5KT22GzxZCc/Afe3p3R639h\n9uwFLFnyyy03zQUGBtKqVat0xywWH+BM6nMPjzP4+/vc9ed2L08++QRXr17g0qVLFChQgFatOmG1\n9sdxgyPEx7/KmjUTGD36E6ZP/5zBg1/Eao3l2WefYdKk/2VrbCVn+Pj4kJISjWOPMx8ggZSUS/j4\nZO93RVGU7HH3TL2iKNkQHR0tRYuWFb2+h8BwMZkKy+zZc9wd1m21adNJdLpxAlcELOlKJyyWTvLT\nTz/du5O7aN/+Zn++vrdvM2PGTClRorLodGbnUnRTxGQqLI88Uk80rbHASPH0LCKeng0EZonB8LxU\nqVJPWrR4WnS6L9LEPFhgoECi+PhUuO16zbezbt060bRA0emGiqfna+LnFyzHjx/P1nX/10svvSbe\n3oNSY/X0/EA6d37RpWNkhFquLnu6desjZnNdgZGiaY/K0093veda34qS16HKMxRFcafo6GimT5/B\nlStXadu2FU2aNHF3SLdVs2ZzwsKGAC2B4sBE4FngJJpWn23bVlGlSpW79nHq1CmuXLlCuXLlUnfX\ns9vB0/NmmwsXoECBW8/94YcfeeWVYVitNYGywAjnKyuArhiNHjRpUp+//lqPzXYBMAOCxVKToCA4\nfnws0Nx5zmwcS9n9hK9ve2bN6kGHDh0y9HPYs2cP8+YtxGjU06vXixQrVixD52XUpUuXqFmzEVev\nFgG80LQjLF++ABGhTJky+Pr6AmC1Wjl48CCBgYG3nbXPqlWrVtG1a2+ioyMoW/YRli37hdKlS7us\n/4eF3W5nzpw57N27n0qVyvHCCy84lwZUlPtXRsozVNKsKMpDb/jwzxgz5g+s1rnA38DzGAz5gWjG\njBnF668PuOO5IsLAgYOZOfM79PpC6PUxrFu3nClTKjFlStp2dx6/SZOn2LChJ7ABKAq843xlEzAI\nmILJ1I6UlGSSky9zo7LO17cx7dqFsnjxKazWX4B4oC3wOlAcs7kn4eFh90x+N2zYwJEjR6hcuXKO\n16XGxsby119/ISL8++9BPvtsDAZDCCKRLFs2H39/f5o3b0dycgBJSefp378v48ePyva4p0+fpkKF\nms666UbodF9SrNh0Tp7cr9Z8VhQlQ0lzXubGSXpFUR4mKSkp0r//IDEYLGIy+cvbb78nBw4ckKio\nqHueu2zZMjGbKwhEO8sOpqcr78hIhUObNp0EpgpsFiggMFfgL4HKAo6VNszmnlKqVEUxGF4Q2CSe\nnh9LoUKl5MqVK9K378DU2IODQ8XTUy+FCpXOUGnGG28MEbO5lJjNL4imFZVRo8Zm4CeWfXv37hVN\nKyRw2vmz+kP8/YMlNLSqwLfOY1FiNpeVlStXZnu8hQsXiq/vU+neG4Mhn1y4cMEFV6Moyv0OVZ6h\nKIqSsz7//HOGDo0gOXn8La9l9CNs27ZttGjxJFbrIOAwOt0y5/kvAyOBFHx86jJr1nusWLGOrVt3\nUbp0SaZMGXPbWeSVK1cyfPgEkpKSGTCgB717v3jbcQ8dOsQjjzQhPj4cCADOYTBU5MyZowQFBWUs\n+CyaN28eL700l+vXF6Ye0+v9SUmJw26/DhgBMBgGMnp0KQYNGpSt8TZv3kyrVr2Ii9sLmIBj6PXV\niImJwpD2Dk1FUR5KGZlpVlsqKYqiZEP58uXR61elOxYS0uWeCfPly5c5ceIENpuNevXqsWnTKvr3\nv8SAAfnYvXsNP/zwFSbTTDStLz4+9Xn00ZJ06NCBWbMmEx6+jaVLf75twrx+/XqeffYFtm7txa5d\nbzFw4GfMnPntbWOIjIxEry+NI2EGKIK3d0EuXbqUhZ9E5pQrV46UlK1APyAfEIhOB8WKlQNuJNLR\n2O0LGDr0U8zmfLzyyiBSUlKyNF6DBg1o164hPj510bS+aFpjJk4crxJmRVEyTM00K4qi3ENUVBSb\nNm3CaDTSrFkz9Hp96mvTpwsvv3zzozR//mKsXbv8jjcOighvvPEO06ZNw8vLl0KF8rNu3e8ULVr0\nlrZ79+5l27ZtBAcH8+STT2Zo6+jnn+/LTz9VBQY6j6ykatWR7N274Za2ly9fpmTJisTGfg+0Bn4m\nf/4hnD17BKPReM+xsqtp0zasX38F+BWIw2h8kqFDX2D8+MnYbMFYrUeAIFJSlgMWNK07b7zRmM8+\n+xgRYcuWLURGRlKzZk1KlChxz/FEhBUrVnD69Glq1apFrVq17nmOoigPB7VOs6IoSjYdOnSIBg1a\nkJJSBZEoQkO92bLlTzRNI+1n7M8/nyM09BwVK4bfdc3ahQsXMmvWSpKSTpKUFMDJkx/TrVs/NmxY\nfkvbatWqUa1atUzF6+3theOGwBvi8fK6/Ud9YGAgy5cv5JlnuhEdHUmhQiVZunRJriTMAJcvxwBj\ngCIAJCS8w+7dGzh16iDh4eEMGzaGP/5oDTjWxLZah7No0TuMHDmMHj36sWjROjw9K5GS8grz5n1H\nu3bt7jqeTqejbdu26Y5ZrVZmzpxJZORFmjVrwmOPPZYDV6ooyoNAJc2KoriMiPDll1NYuHAlBQoE\n8NlnH1CmTBl3h5Utffu+RXT0O4i8DgiHDnWhb98V/PTTzWXcHH8UK8KN5O9uwsL2EBfXAUdJAths\nL7F373SXxTto0MssWPA4cXEeOGZnh/PRR1/fsX2jRo24fPkMCQkJuZYs3xAYmA84hGMjGfDyOkRw\ncD4sFgt16tShdOnieHkd5GZFxkECA/OxevVqFi3a7KxP1oCtdO36FNeuXczUShgJCQnUqdOM48cL\nER9fnYkT+zBmzHu8+uorrr1QRVGUHOa+WygV5SGSkpIiFy9eFJvNlu2+3n33I9G0mgILxcPjM/Hz\nKyhnzpxxQZTuExJSWWB3ulUXbvxv0SJHmx9//EmKFCkv+fOHyGuvvS1JSUl37G/mzJmiaY0EEgVE\ndLrpUrVqQ5fGHBYWJt26vSTPPtvTJStPZEZ8fLxERUVlaLOLsLAw8fEJEr3+FTEau0u+fEXl1KlT\nqa+fO3dOgoJCxGh8XvT6V8THJ0h27dolM2fOFLO5Z5r3wi4eHt5itVozFetPP/0kPj7NBezOfg6K\nyeSvNupQlIcQGVg9Iy9z989PUR54K1asEB+f/GIwBEi+fEVk27Zt2erPYgkSOJ5mSa8+MnHiRBdF\n6x7PPddb9PreAinpEuYbVq9eLSZTYYENAofFZGoub7757h37S0lJkTZtOojZXFp8fRtLvnxF5N9/\n/810XHa7XXbv3i1//vmnXLp0KSuX5nKffDJKvL1Notf7SpUq9SQiIuKe5xw7dkz69+8vRqOP6PV+\n4udXUNavX5/6+sWLF+Wrr76S8ePHy7Fjx0REZM+ePWIyFRQ46PziMVlKlqyc6XinTp0qmtY7zfsa\nL56e3mrHQEV5CKGSZkVR7iQyMlLM5kCBTc6EYZH4+xeS+Pj4LPd5u6R5woQJLow69129ejVdstys\n2c/pZiJfffVNgdFp2uyRokUr3rVPu90u27dvlz///FOuXLmS6Zjsdrt0795XNC1E/PyaiK9vwWx/\n4cmu5cuXi6aVFjgvYBcvr3ekadMn7nnelStXxMcnyLkutQisFIulgFy7du2u582c+a0YDD5iMARI\nsWLl5ODBg5mO+fDhw6JpgQKLBU6JXt9LHn+8fab7URTl/kcGkma15JyiPKT279+Pl1dFoKHzyNOk\npGicOnUqy30OGPAKmtYJWISHx/8wGJbRsWNHV4TrFrt3g7+/X+rz2Ng41qx5Ll3dbL58vnh5nU5z\n1iksFstd+9XpdNSpU4fHHnuMgICAu7a9naVLl/Lbb9uxWg9w7do6YmKm0KnTi5nux5W2bdtOfPxz\nQCFAR0rKG+zateOe5x0+fBgPjxCghfNIS3S6YI4ePXrX83r3fpGYmChOnz7IqVPhlCtXLtMxlylT\nht9/n0+pUsPw969P27aJzJ//Xab7URTl4aBuBFSUh1TRokVJSjoIXAKCgGMkJ1+iYMGCWe5z1Kjh\nFCwYxK+/fkPBgvn47LMNt11KLTclJSXh7e2d6a2S0zYPCprMlCkFMZtv/QIwYMArTJ1al6tXE0lJ\nCcZonM6ECT+kvp6SkoJOp8PT0zPL1/Bfx44dIzm5CWB2HmnD+fPPuaRvm82GiNxxxY07CQkphsn0\nI1ZrCo5/WjZRqNC93/siRYqQlHQCOIfjRsqzJCWdoVChQvc8V6/XU6BAgUzF+V9Nmzbl6NHd2epD\nUZSHg5ppVpSHVNmyZXnrrdfQtBpYLM9iMjVkwoTP8ff3z3KfOp2ON98cyMaNy1iwYDZly5Z1YcSZ\nc+bMGapWbYDJZMZiCWTu3HkZOu/YsfQJM/zNpUsl6dSpD/7+Bdi4cWO69sHBwezb9zcjRpRh6FBP\nNm5cQatWrUhKSuK553phNJoxGn144413EBetPV+9enW8vJYBFwDQ6WZStmz1bPVpt9t55ZVBznjN\n9OzZj+Tk5Ayf37NnT2rVMuLjUxOL5SksloHMnj3lnucVLVqUYcPeR9NqY7F0QNPq8MknH2UoaVYU\nRclNanMTRXnIhYWFcfToUSpXrkzFihXdHY7LVKvWkP37W2GzfQDsRdNas337aipXrnzHc9Imy0bj\nTBISTgGvAIVxrCe8DYtlM8eP7ycwMPCu4//f/w1lypQ9xMfPBRLQtLaMGdPbZcuZffzxSEaN+h/e\n3vnw8/Nm7drfs/UlZezYiQwbNh+rdSnghaZ14K23GjNixIcZ7sNms7F27VquXbtGgwYNMpX47t27\nl0OHDlGhQoV0G8MkJiayZMkSYmJiaNasGaGhoZm5LEVRlAzJyOYmKmlWFCWdP/74gwULlpEvny9v\nvjnwvpzxS05OxmAwIZIIOMoiNK03EyfWp2/fvre0j4yEtJdZtGgFzp0LRiQQ2AisBr4FvPHz28Si\nRSNo2rTpXWOoWrUR//47ArjR7geeeGIFS5f+lN3LS3XlyhWio6MJCQnB29s7W3099tizrF7dFejk\nPPIHtWuPY8eOP7MdZ1bFx8dTt25zTpwwYLeHoNOtYMWKX2nUqJHbYlIU5cGUkaRZlWcoipJq1qzv\n6NChHzNnlmL8+DiqVq3LhQsX3B1Wpnl5eWE2BwB7nEeS8fDYS3Bw8C1tTaabCXOnTvD22+9x4UIT\nRNYC84EPcCSSPwJdSUo6ett+/qtIkWA8PHamPvf23knx4vc+LzPy5ctHqVKlsp0wiwhWazTwPvA4\n8AeenjspWtS18WbWN998w9GjBYiNXYvVOpu4uOn06TMoy/1t3LiRBg1aU7VqI8aMGY/dbndhtIqi\nPOjUjYCKoqQaOnQkVut8oC42G8TExDJ79mwGDx7s7tAyRafTMWvW17zwQht0urZ4ePzDo4+WTLfN\nckwM+N1cGIOkJPD2hi5dLpCcXD9NbzWBSIzGFnh6dqBXr+6UL1/+njFMmjSSunWbkpS0FUggIOAI\nH320yWXX6EpTpkxjz55IYDJwDeiK2ezNuHHbc2S8iIgIrFYrJUqUuOsNkhERF4iPr8HNyZ8aXL6c\ntS9xe/bsoXXrZ7FaJwCF+eSTd4iPT2DYsPez1N+DLD4+ntOnTxMcHIxf2v9IFOUhp2aaFUVJlZgY\nD9ys1U1JCcRqjXdfQNnQqVNHdu5cxxdfNOLnnz/h99/n4+Hh+MirUOFmwlyvnmN14BuTta1bN0HT\nvgIigDiMxtF07NiWL75oybJlM/jyy88zNH5wcDDFi5cgOXk1yckbqFixYpaWl8sNU6bMJj5+MtAS\nx6z6MNq0aUvJkiUz3dfRo0eZO3cumzZtuuXGR7vdTvfufSlZshJVqzalcuW6XLx48Y59NW3aGE37\nDjgGJKHXj6Bx4yaZjgng55/nYbX2B7oDzYmL+4bp02enayMirF+/nrlz53LixIksjXO/27RpE8HB\nJalVqx3BwcX59tvZ9z5JURS3c9f61ory0HrttbdF05oJ7BSYL5oWJHv37nV3WC4TH59+G+zY2Fvb\n2O12GTLkQ/H2Nomnp16efbZ7ljZ86dVrgBgMvZw7CSaIydRGRowY5YKryLjY2FiZMGGCDB78rixf\nvvyO7apVa+zc4EOcO+yNkH79BmZ6vAULForJFCgWi2PHwx49+qXbCOabb74RTasvcN25Acrb0q5d\n57v2OWHCl2I0WsTT01uaNXtCoqOjMx2XiMgHH3wknp5vpnn/N0rx4lVSX7fb7fLss93FbC4vFksH\n0bRA+f3337M01v0qKSlJ/PyCBZY7f0bhYjIFydGjR90dmqLkONSOgIqiZEZycrL83/8NlRIlqkq1\nao1kzZo17g7JZVq2vJksFyp07/Y2my1b2ylXrtxQYF2aJO0HadfuuSz3l1nx8fFSqVIdMRqfERgh\nmlZSxo69/Zbmv/32m2haIYHJotN9JmZzYKa39rbZbKJp/s4vXCIQK2ZzWVm7dm1qm759XxOYkOZn\nsk8KFy53z77tdrskJydnKp7/OnHihPj6FhSdbpjANNG04vLNN7NSX//999/Fx6eqQHxqUu3vH5yt\nMe83p0+fdv4e3Pxvxde3jSxZssTdoSlKjkPtCKgoyg2LFi2iWLGKBAQUoUePfsTH31p24eXlxeef\nf8qJE3vZs2cDzZo1c0OkrmWzOZaSW7XK8TwqCs6fv/d5Hh4e2dqQpEKF0nh7/47jc9iO0biCypVL\np75utVq5dOmSy9Zu/q8lS5Zw6pRGQsJC4AOs1r8YOvSD247Xvn17Fi36ns6dd9Kz5ym2bVtLsWLF\nePrpbvj5FSI0tBpr1qy563ixsbEkJSUCNZxHzOh0NTh79iwiQlRUFKGhRTGZVgIpAHh4/E6ZMqXv\n1GUqnU6X6c1W/qtEiRLs2rWJ3r0v07HjFn76aRJ9+vRKff3s2bPY7bUAo/NIfa5du5Sptarvd0FB\nQUACcOMG1giSk3erZf4U5T7g5u8civLg2L59u5hMBQXWCpwUo7G9PP983zu2P3XqlCxfvlwOHDiQ\ni1FmXnx8vKxZs0bWrl0rCQkJt7zeq1f6cozcFBkZKSVKVBKLpab4+FSSRx55VGKd9SDvv/+xeHmZ\nxGDwl8qV60pkZKTLx58xY4ZoWo80158gnp7eGZ6xbdHiKdHrewucEVgqmhYohw4dumN7u90uISEV\nRKeb4hzvX9G0ArJjxw5p2LCl6PW+4u2tSXBwadG00uLrW0+Cg0Pl+PHjrrrkbAkLCxNNCxY4JGAX\nD4+xUr58TXeHlet++22RaFp+8fNrLCZTkIwY8T93h6QouQJVnqEoiojIxx8PFw+P99IkUKfE1/f2\nf3r+5Zd5ommB4uf3uJhMBeXjjz/L5Wgz5tKlSxIaWkUsllpisdSUMmWqS1RUlIiI2O3pk+UzZ7I/\n3u+//y6fffaZzJs3L12d7t1YrVbZuHGjbN26NTVZXbRokZjN5QUuOOt6B0vz5k9lP8D/OH78uJjN\ngQLzBY6JXv+itGiRsXFsNpt4enqnKVUQMZl6y9dff33X8w4ePCjFipUTvd5XjEaLzJnzkzz/fF8x\nGF4USBa4KppWT957731Zv369XL9+Pd35W7dulVGjRsk333yTpTry7JoxY5bo9WbR632lZMnKcuzY\nsVyPIS84d+6c/PXXX3LkyBF3h6IouQaVNCuKIiIyYcIEMRieS5NIrpMiRW6tJY2LixOj0U9gj7Nd\npJhMwbJ//343RH13L77YX7y9BwrYBeyi178i/fq9Lu+/7/rZ5bfffl/M5vLi6TlYzOaa0qXLixlO\nnP/rvfeGCnycJsbT4ueX/dpZu90u06bNkLZtu0ifPq/KmTNnZNOmTVKuXC3Jl6+YPPNMd7l69WqG\n+3LUJ4c7Y7SL2dxMfvrppwydGxUVlfoloWTJ6gJ/p7ner6Vbt5duOe+772aLphUSL6+3RdNaSvXq\nDW/714OclpycLFFRUVl+fxVFuT+hkmZFUUREoqOjJSSkvBgMz4lO976YTAVl/vwFt7Q7ceKEaFrR\ndEmnn19LWbZsmRuivrt69VoJLEsT62/p4nZVZcmFCxfEYPATuOzs2yqaFiJ79uzJUn/Tpk0TTWvh\nnHkVgdlSsWLdbMf53nvDxGyuLvCDeHoOkaCgELl06VKW+5s8+WsxmYqJTvehmExPSeXKdbM0+9u8\n+VPi4fF5avJtMDwnH3884pZ2FktQmi9rdvHxaSY//vjjLe2uXr0qXbr0ksKFy0mtWs2y/D64wrRp\nM6RkyWpSokRVmTDhC5VoK8p9DJU0K4pyw9WrV2XChAkybNjHsnXr1tu2SUxMdC45dSMZ/Uc0LVBO\nnDiRu8FmwFtvvSdGYweBpDQJqOtml48dOyZbtmyRnTt3io9Pyf98kWiYblWIzEhKSpLGjduIj08V\n8fVtI76+BWXXrl3Zjtdk8hM4laacootMnTo1S31t2rRJAgOLiYeHl2iavwwePFisVmuW+jpy5IgE\nBhYTX9/HxWKpJVWq1Eut7b7Bbrc7y0GsqfEbjf3kq6++uqW/xo3biF7fS2CfwDfi61tQIiIishRb\ndvz88y+iaaECGwS2iKZVkGnTvsn1OBRFcQ1U0qwoSmRkpMycOVNmzZolly9fvmf7zZs3i59fsJjN\nIWI0+smPP/6cC1FmntVqlebNn0iXzG7YkOiSvt9++30xGgPF17eW+PoWlKCg4uLhMU4gSmC2+PsX\nyvJ6wSIiKSkpsmbNGlm0aJFcuHDBJTEbDD7OOukbSecLMnny5Aydu3nzZpk8ebKsWLFCrly5IhZL\nAecXJ7vAZ2Iy+cqCBQuyPJN65coVWbp0qaxatUoSE2//HjVr9oTo9a84Z/TXiMkUKPv27UvXJi4u\nTry8jOm+JFksz8gvv/ySpbiyo2XLjgJz0vz+LZL69VvnehyKorgGKmlWlIfbkSNHJCCgsGjac2I2\nd5ACBYrL2bNn73leQkKCHDt27JYZwbzkp5/S1y676k/j69atE7M5NE05xm8SGFhMqld/VAwGi5Qp\n84iEhYW5ZCxX6tt3oGhaU4E/RaebIL6+BeVMBu6AHD16nGhaiJhM/cRsrijt2nUUP7/azmv/SqCo\nQE8xmSpJ1669c6wEISoqSlq2fEZMJj8pVKj0bUuCkpKSxMvLIBCZpoyjgVvWEe7YsafA2DS/g9Pk\n8cefzfU4FEVxDTKQNOtyIfnNKuc1KIqSVe3bP8/SpVWw298FwNPzfXr0iObbb792c2TZo0vzybVi\nBbRunf0+ly5dyrBh47lw4QKXLuUjOXkjjo9IOzqdnsTEeLxv7LXtRjc+F3XOH8L69esZMmQkMTHX\nCQ7258qVeAoVCmLcuOFUrFjxrn1dvXqVggVDSEo6ABQFYjGZKmCzxZKUtBeoCOwHigNWzOaq/PXX\nHOrVq5eDV3h377//MZMmLcBq7YXBsI0yZc6wc+d6DAZDrsbxzz//0KBBC6zWfoh4oWlTWLNmGXXr\n1s3VOBRFcQ3nZ+pd82K1uYmiPMDOnbuI3V499bnNVp2zZy+6MaLs2bs3fcIsAvXqXWX//v3ExsZm\nud/Vq1fTpUs/du9+g/PnvyA5+QwwzvnqfAoXDnV7wpyUlMQLL7yCweCD2ZyP4cM/IywsjLZtO7J9\n+4uEh49m+/YonnqqBStWzL9nwgxw5coVvL0DcCTMAD7o9WV55pknMJkaAt44EmYADS+vcly86N7f\nn5Ejh/Httx/Rv/8ZPvmkDtu3r7lnwnzhwgXCw8NJTEzM8Djx8fEcOHCAy5cv3/b1qlWrsnPnRt5+\n28ZbbyWwdetqlTAriuI2bp6oV5T737Bhn4qmNXPW4l4QTasjEyd+6e6wssRkulmKMX++49js2XPE\naPQXi6Wc+PgEyl9//ZWlvrt27eMsRbgxxkrR6fKJxVJJAgIKu+RGvXuxWq2yePFimTt3rly8ePGW\n199++30xmVo6y0ZOiKZVknbtnhb4ME3ce6VQobIZHjMpKUmCg0NFp5vqrBP+XXx8gmT27NkybNgw\n8fcvLDrdROdrq8RsDsxQeU9e8s47H4rB4C8WSxkpWLCkhIeH3/OcHTt2SEBAYbFYyorB4Cfjxk0S\nEZHDhw/Ljz/+KKtXr1YrZSjKAwZV06woD7fk5GTp3XuAeHkZxdtbk9dfHyw2m83dYWXKkSO3X3f5\n1KlTYjIFCux3vrZWfHwCs7TKQ69e/UWn+yzNOAukWrVGsnv37lyp67527ZqUK1dDLJZGYrE8JQEB\nhW9J7sqVqyOwKU2MM6Rixeri6flGmmMbJSSkcqbGDg8Pl9Klq4tO5yEFCpSQ0NCKYrE0EIulvZjN\n+aVEicqi03lIYGCIrF692pWXneNWrlwpZnMZgUsCIjrdFKlQofZdz7Hb7RIUFCKwUG5sBKRphWXc\nuPGiaUFisXQWH58K0rFjT5U4K8oDBJU0K4oi4tjhLa/+A2+32+XLL6dI3botpXXrjulusgsNvZks\n//e+sFWrVomfX7N0CbWPT0k5fPhwpmP4559/xGwOFJ3uU4FJYjIVlN9//z1D5ytmGakAACAASURB\nVCYnJ8tHH42Q2rUfk2ee6Z6lXeQ+/PBjMRi6O1erENHpJkmTJu3StWncuJ3A16nX6uX1uvTq1U/8\n/ILFw2OowBTRtBCZNeu7TI8v4ljRY/To/4nR2DE1DpghtWs3l5SUlCz1mR0JCQnyf/83VGrVaiFd\nuvTK0gz32LFjRa9P+6Xiunh5Ge56ztWrV8Xb2/yf36vnxGAwy81NWuLFbK4kq1atyurlKYqSx5CB\npFnVNCvKQ8DDwyP1xrG85tNP/8e7705j+/aB/PFHMxo1asmGDcfQ6eD4cUcbEWjXLv15oaGhJCXt\nA047j4Rhs0VTuHDhTMdQpUoVtm1bS+/eETz//D6WL/+Ftm3bZujcvn0HMnbsWv7++y0WL65A7dqN\nuXTpUqbGP3bsLImJDbhxD4pIA06fPpeuzRdfjMTH5yOMxt5oWicCA5cxatQnhIVtpl+/WLp23cXc\nuZPp1euFTI19g6enJydPniMhoT4374VpwNmzZ/H09MxSnxlx7tw5Hn20NT4+gZQvX4udO3cC0LFj\nTyZP/pedO/+PhQsLUbt2E2JiYjLVd5kyZfD2XgPcqHdfSrFiZe96jq+vLyaTGVjjPHIZu30LycmJ\nQE3nMSNQg7Nnz2YqHkVRlJzi7i8diqLkgoIFSwnsTTOzdzz18W02hEtn/PgvxGjML35+DUXT8sv8\n+QtzJMZ9+/bJ0qVLb5lFTklJcS6BFp0as9ncUb77LnOzvTNnzhJNq+GsV04Ug6GbvPhi/1vanTx5\nUiZPniwzZsyQK1euZOuabueXX34Rs7myc73nZNHre0vnzi+6fJwbbDablC37iHh6fiAQIfCjWCwF\n5OjRo+LtrQnEp1mPuYUsXrw4U/3b7XZ54YVXxGQqLH5+9cXfv5Ds3LnznuetXr1afHyCxM+vgZhM\nBWTIkI+kTJnqotONd87C7xOTqYD8+++/Wb10RVHyGFR5hqIoeV1wcGmBsHR/Dnesu5yx80+cOCFr\n166V8+fP50h8w4ePEpMpWPz8WovJFCizZ89Jfc1mszk327iUGrde31bGjh2bqTHsdru8/vpg8fIy\niJeXUVq0eFK+/fZbGTFihCxevPiOpTXJycny/fffy4gRI2TlypXZus4bcQwZ8mFqHI0atZarV69m\nu987OXfunBiNgWnKQUR8fVvJvHnzxMvLJBCbJmluLEuXLs3SOPv27ZP169dnakOaixcvyrp161LL\nfY4ePSqhoVXEy0sTo9EiP/xwj290iqLcV1BJs6Ioed24cZPE03NlanJkMLwrx48fd3dYIiJy8OBB\nMZkKOGdBRWC/GI2+EhMTk9pmwIA3xWSqJzBPYLBAfjGb88s///yT6fESExPl+vXr0qFDDzGba4uH\nx7tiNleUQYOG3NLWZrPJY489JWZzI/HweFc0rZSMGDE6W9f73zjuZseOHdKhQ09p1+65DNd//1dM\nTIxzRvnGZiVJYjaXlw0bNkiXLi+Kpj0mME+8vQdK8eIV88RmO1evXnVLjbeiKDkLlTQripKXXb+e\nfna5U6fecuDAAXeHlWrlypXi59c8XYxmc4l0NxvabDapUaOBQDWBVwTOik43SVq37pilMffs2SOa\nFiJgdY4ZJQaDn0RGRqZrt3btWvHxqSyQ5Gx3Try9TVlaPSSzdu7cKZoWKDBJYKZoWhFZuDBrpTFD\nhw4Xs7ms6HTvi9ncSFq2bC82m02Sk5NlxIjR0qLFM9Kv30C5dOmSi69CURTlJjKQNHvlQvKrKIpy\ni/79YepUx+Phw+GjjwBm5srY586d4/jx45QqVequNw5WrFiR5OS9QBhQA1iOl1c8xYoVS23j4eFB\nYGBh4BmgGwAiZYiKWpql2KKjo/HyKgqYnEfy4e2dn2vXrlGwYMF07Tw8SuDYgASgEB4eRmJjYzGZ\nTOSkL76YgdX6DvA6AFZrfkaOnMSzzz6b6b4+/fQj6tevyc6duyhRog/du3fHw8MDDw8PPvhgCB98\n4OLgFUVRskglzYqi5KrERDAa0z/X63Nv/Fmzvue1195Cry9HUtIhpk6dRM+e3W/btmjRosyePZ0e\nPVqg02no9XaWLVuIMe0FAJ07t2XTps+wWh8B9GjaMDp2fC5L8VWvXh1Pz5PAt8ATeHh8R0CANyVL\nlkzXrl69eoi8AswHmuLpOYnQ0FIEBgZmadzMSEmxAWnfND02my3L/bVr1452/10eRVEURckwd8/U\nK4riYh9+eLPMYeDA3B8/IiJCjMYAgXBnHAfEZAq47Q58acXHx8upU6ckKSnptq/b7XYZMWK0BAQU\nET+/YHnnnQ+ytYnM3r17pUKF2qJpAVKzZpM71nhv27ZNSpWqJpoWIA0btpJz585leczM2Lhxo5hM\nQQKzBX4TTSsp3303O9P9HDt2TAYMGCTPP99XVqxYkQORKoqiZAwZKM/Imwu3OjivQVGU+53NBl5p\n/q4VFwealvtxbN++nZYtXyUmZmfqMV/fR1i9ega1atXK/YBymIjk2Prcq1evZsSISSQmJvHqqz3p\n3r1bps4/efIk1arVIza2D3Z7ITTtf0yf/j+efz5z/SiKoriC87Pyrh+YanMTRVFy1MSJNxPm555z\nzO+6I2EGx4YoyckncNQoA+wkJeX0LaUP97sNGzYQHByKp6cXFSrU5siRIy4fo0WLFqxbt4StW//I\ndMIMMH36TOLinsduHwm8htX6PR999LnL41QURXEVVdOsKEqOEAGPNF/Lr1yBgAD3xQMQFBTE7Nkz\n6NnzMby8grHZLjBnzkzy58/P33//zYEDByhXrhz16tVzb6DZEBERQbt2HYmN/R54jEOHvqZ58yc5\neXJ/ju7sl1kJCYnYbH5pjviTlJTktngURVHuRc00K4rict9/fzNhbtbMkUC7O2G+oWPHZ4mIOMHm\nzXM5f/44zzzTnk8/HUPTps/y2mt/0qJFFz744BN3h5llYWFheHg8ArQBvBF5naioa0RERLg7tHS6\ndeuMpn0FzAM2omkv06ePKs1QFCXvUjXNiqK4VNoS2vPnoVChnBnn119/ZezYGeh0Ot59tz9PPvlk\nlvqJiIigZMmKJCbuBwoDlzAaKxIe/jclSpRwZci54u+//6ZZsy7Exe0DNOAMen0FoqIi8fHxcXd4\n6axevZohQ0YSGxtHjx4deO+9/8PDQ83lKIqS+zJS06zKMxRFcYklS+Dppx2Py5eH8PCcG2vRokX0\n6PEGVusEwE6XLi+zYIEnbdu2zXRfFy5cwGAoSmLijfWagzAYShIREXFfJs21atXiqaeasXRpfVJS\nGuDh8TvDh3+a5xJmcNRF79zZwt1hKIqiZIiaaVYUJdvSzi4fPw45fV9d06ZPsX59N+DGWsjf06rV\nMv74Y36m+4qNjaVo0TJcu/Y10B5YgcXyIqdPH8Lf39+FUeceEWH58uWcOHGCGjVq0KBBA3eHpCiK\nkqepmWZFUXLUunWOmmUAPz+4ejV3xnXc0JaY5kgiXl5Zu8nNx8eHlSsX8eSTnYmO7oqvb34WL154\n3ybM4PjwV5uFKIqiuJaaaVYUJUvSzi7/+y9Urpx7Y//111889dTzxMd/AtjQtI9Zvnw+TZo0yXKf\nIkJcXBxmsznH1ja+3yQlJTFy5Bg2btxJmTLFGTVqGPny5XN3WIqiKC6XkZnmvPwvg0qaFSUP2rUL\n0u4D4q7/TNeuXcsXX8zCw0PHoEF9adSokXsCcaGYmBhMJhPe3t7uDgWA9u27sXLlBRISEoFD6PU6\nNm36ndq1a7s7NGw2G+++O4wffpiL0Whk5Mj31MYoiqJkmUqaFUVxqbQTsFu3wn28nHGeEhERQevW\nHThwYC86nTBq1CjefvsNt8Z05coVgoOLk5xcGagPvAqsxmz+gJMnDxAYGOjW+IYOHc7EiX9itU4D\notC0bixa9C2PP/64W+NSFOX+pHYEVBTFJQ4eTJ8wi6iE2ZU6d+7NgQNNSEmJJTk5nI8+msDatWvd\nGtPNSYvDwFigFNAPkSps27bNfYE5/fzzIqzW8UAloDFW69vMnbvY3WEpivIAU0mzouQiu93OV19N\noUOHFxgy5AOuXbvm7pDuqUgRqFDB8XjlSveVYzxIRIQff/yJzp17MXDg2+zcuZmUlP/DMclRnMTE\nzm5PTPPnz0/jxk2BOOCK86gND4+LeWL5OovFBzib+tzT8yz+/u6PS1GUB5dKmhUlF/XtO5AhQ37k\n11+bMGnSOerUaUZCQoK7w7qt06cds8vnzzuei0DLljk/7pEjR2jW7ElKlKhKt24v3RdfLDJr9Oix\n9Os3gvnzGzJ1Kjh2j/7D+WoKRuN2ihQp4sYIHZYtm0/NmnXw8GgAfIbR2JZq1QrnifrxsWM/QtNe\nAYbh5fUqvr6/MGjQa+4OK8N+++03KlduQJkytfj88wmockRFyftyuqZ5FtAOuAhUcR7LB8wFigMn\ngc7A7RaqUjXNygMlLi4Of/8gUlIiAV9AsFgaMnfuh7Rp08alY4kIhw8fJjk5mfLly+PllbnVJWvW\nhLAwx+N586BTJ5eGd0fR0dGUKVON6OhB2O1N0esnU7PmaTZvXvVArWjh61uA69c3AWWBJLy9n0Sn\n24Re3xo4Ts2ahfnrr8WZft9ygoiwYMECtm3bSWhoCH379kWv17s7LAB27tzJggW/YTab6N27V574\nopERq1ev5qmnemC1zgD80LRX+eST3m6vY1eUh1leuBGwERALzOZm0jwGuOz8/yFAAPDubc5VSbPy\nQLl27RpBQUVITo4GHKsjWCyt+OGHATx9Yys9F0hMTKRdu05s3bobDw8DISH52LBhBfnz57/nuZcu\nQYECN5/b7elrmXPa0qVL6d79S2JiVjmP2NDr83Pu3FG333jmSpoWQHz8AeezlsB1vLziqFatAp98\n8i6tWrVyrkWtPIh69nyZH36oBLzuPLKe8uWHEB7u/lpxRXlY5YUbATcC0f859hTwvfPx9zi24FKU\nB56fnx+NGjXHYOgJbMbDYzQGw0EaN27s0nHGjBnPli2C1Xqc2NgjHD1al4EDh9zzvHbtbibM06c7\nyjFye3LXaDRit18Bbnxhvo5IMgaDIXcDyWHdu/dA07oD3YEngBOkpJwhPNyTkydPqYT5AadpRnS6\nK2mORGEyGd0Wj6IoGeOOmuaCwAXn4wvO54ryUFiy5Gd69AiifPm3adlyF9u3ryUgIMClY4SFHSA+\nvgOO2WwdSUld2LNn/x3bx8Q4kuPlyx3PbTbo29elIWVYkyZNKFXKgNH4HDAZTWvFiy/2wWKxuCeg\nHDJ58jhef/1RvLz2At1wTG4YsVrbExZ25/dKeTC8+eYAzOYp6HQfAGMxmfozcuS9v9gqiuJe7r4R\nULg5paQoDzyz2cyMGV8QHr6NFSvmExoa6vIxqlUrh8m0GEgBBG/vX6lSpfxt2/bq5dj+GmD0aMfs\nsocbPxX0ej2bN69i6NBq9Oz5L5Mm9WPatEnuCyiHeHt7M2rUcJo2bYSn56/Oo4lo2jKqVbv9e6U8\nOMqVK8euXZt47bV4+vY9xapVC11+X4OiKK6XG398LQEs5WZN80GgKRAJFALWArf7V0KGDRuW+qRp\n06Y0bdo0B8NUlAdDQkICjz/ent27D6PTGSlUyMDmzasICgpK0wZMppvnJCdDHrjn7KFz9uxZGjZ8\nnOhob2y2qzRuXJulS+fmiRsA3W3//v0cPHiQsmXLUqVKlXufoCiKkgnr1q1j3bp1qc+HDx8OeWBH\nwBKkT5rHAFHA/3DcAOiPuhFQUVzKbrezf/9+kpOTqVy5crrVDt55Bz7/3PF48GAYM8ZNQSqA40vO\nvn370DSNChUqPFCrhNyO3W7nxx9/5ODBw1StWpnOnTvfcs3jx3/Jhx9+hpdXXVJSdjBs2GDeeedN\nN0WsKMrDIC+snvEz0AQIxFG//BGwGJgHhKCWnFOUXJOSAt7eN59brelnmxUlp4kIHTv2ZOXKo8TF\ntcJsXkLXrg2ZMePL1DYRERGULFmRxMS9OP6ZOIfRWJXDh/dQrFgxt8WuKMqDLS+sntEVKAzogWLA\ntzi2lnoMxwKlLbl9wqwoiguNGXMzYX7xRUftskqYldy2f/9+/vhjPXFxa4CPiYtbxw8//MSZM2dS\n25w/fx6DoTiOhBmgCAZDKOfOnXNHyIqiKKlU4ZyiPMDsdki7etnVqzdv/FOU3BYTE4OXV0Hgxjc2\nX7y98xMTE5PapnTp0ohEAH8CjwNrsdlOUa5cudwPWFEUJQ13r56hKEoO+eabmwlzmzaO2WWVMCvu\nVLVqVQyGS+h0XwFn8fAYTUCAB2XKlElt4+fnx5Ilc/H17Y7RGIjF0oVFi352+dKMiqIomZWX7zhR\nNc2KkgX/XTbu4kVIs3CGorjVoUOH6NatH8eOHaZChcr8/PMMSpQocUs7m83G5cuXCQwMVJu9KIqS\n4/LCjYDZoZJmRcmkBQugUyfH4xo1YNcu98aj3H9sNhuASlQVRXmo5IUbARVFySU63c2E+dQplTAr\nmWO32+nf/02MRjMGg0bPni+TkpLi7rAURVHyDJU0K8p97s8/HQkzQHCwozwjJOTu5ygQHh7OjBkz\nWLRoUersqiudPHmSWbNmMW/ePBISElzev6uNH/8Fs2fvICXlHDbbJRYuPMbw4aPcHZaiKEqeocoz\nFOU+lnZPiPBwKK92YM6QJUuW0LXrS0A7PDz2U7NmEH/9tdhlO/Ft2bKFli2fBlqj052leHErO3as\nRdO0bPW7cuVKFi5cRkCAL4MGvUahQoVcEi/AY489y+rVz+FYOh/gD2rXHseOHX+6bIwHyb59+5g2\n7VtEhD59evDII4+4OyRFUbJBlWcoygNq27b0CbPIg50wnz17lqef7kalSg146aWBXL9+PVv99eo1\nAKt1EVbrt8TGbmXXrmh+++03F0ULL730JnFxU4iL+4HY2DUcO1aE6dOnZ6qPyMhIOnbsSaVKDXjh\nhVeYMmUqzz7blxkzQhk3LpaqVety4cIFl8UcEhKMl9fNmh5Pz10ULRrssv4fJLt376ZevWZMnuzL\n5Mn5ePTRlmzdutXdYSmKksPUOs2Kcp9Jmyz//TfUqpX7MdhsNk6ePImPjw8FCxbM0bFiY2OpW7cZ\nFy50w2brz7FjMzh4sCMbN/6RpS2nRYRr1y4CNZxHPElJqU5kZKTLYr548UKa/nUkJNTk3LmM95+Q\nkED9+o9x9mw7UlL6cvToD/z881CSk5cB9bHZ4Nq1OL7//nveeecdl8T86acfsHx5Q+LiDiLihdG4\ng3HjNrik7wfNiBHjiYv7AHgDAKu1IMOGjWXVqoXuDUxRlBylZpoV5T6xb9+ts8vuSJgjIiKoWLE2\nVas2o3jx8vTu/So5WUq1ZcsWYmODsdmGA41ITJzFzp07uXjxYpb60+l01KzZCC+v4YAN+Bed7lca\nNmzospibNm2MwTASSAROommzaNascYbPDwsLIypKT0rKaKARSUlTSU5OBgJT29hsQVit8S6LuXDh\nwoSHhzF1ame+/ro9Bw/upmTJki7r/0ESFxdP2vcCAp3HFEV5kKmkWVHuA/7+UKWK4/GaNY6E2V16\n9hzA8eOtsVpPkZh4irlzdzBnzpwcG8/b2xsRK3DjopMQSc5W/fGiRXOoWnULHh5GNK0x06aNo0aN\nGvc+MYNmzvySRx+NwsPDgl5fmeHDB9K2bdsMn++45nhuXnMynp4eGI19gV3AQozGmbRv/7TLYgYI\nCAjg+eefp0ePHgQGBt77hIdUnz5d0LQPgbXABjTtXV56qYu7w1IUJYepGwEVJQ87cQJCQ28+zwv/\nSRQoEMqlSyuBG7u4/Y+BAy/yxRfjcmS8pKQkatRoxNGj5UhMbIGmzaZdu6LMm/d9tvtOTnYk31kp\n88jJ/m02G/XqtWDfvoIkJLTFZPqFxo1NVKlSgfnzl+Lr68vEiZ/QvHnzHIlbubdZs75j9OivsNuF\nN9/sy4ABL+fY75GiKDlPbW6iKPexihUdK2IALFoET7t2UjHL6td/nB07nsBufwNIRtPaMHZsB/r3\n759jY16/fp2RI8dw8OAJHn20JoMGDXTZShd5ldVqZdSoz/n33yPUqVOVwYPfxNvb291hKYqiPJBU\n0qwo96HISEi7klhe+8/gyJEjNGz4OImJRbDZLlK/fkWWL1+gEjpFURTlvqWSZkW5z7Ro4ahZBvju\nO3jhhdwbW0T49ddfOXLkCFWqVKFt27Z3/HNzTEwMYWFh+Pj4UKNGDTw81O0RiqIoyv1LJc2Kcp+4\nehUCAm4+t9vTr5SR00SEHj36sWjRLhITW2AwLKNfv/aMH692hFMURVEefGpzE0W5D3TrdjNhnjDB\nUY6R2/cT7d+/n99++4O4uI2kpHxOXNxmpkyZSkRERO4Gojxw9u7dS+3azSlSpDxdu/YhJibmtu3O\nnDnDY4+1p3Dhcjz++DOcPXs2lyNVFEW5uwf7ThpFySFxcXHs27cPf39/ypYtm6W75q1WMJtvPk9J\nAU9PFwaZCdHR0Xh7FwVuBJQPb+8goqOjXbpVc065evUqBw8eJDg4mBIlSrg7HMUpIiKCxo1bERMz\nEqjHb799TmRkN9auXZauXWJiIo8+2pJz557DZvuMixfn8eijrTh0KAyDweCe4BVFUf5DzTQrSiaF\nh4dTokRFWrYcwCOPNKNnz5czvbnH66/fTJiHDnXMLrsrYQaoWrUqHh6ngdlANDrdF1gsdkqVKuW+\noDJo8+bNhISUo3XrgVSoUJsPPvjE3SEpTmvXrsVufxToA1QiMXEGGzf+RXx8+o1A9u/fT3S0Jzbb\nMKAiNtswrlwRwm8sH6MoipIHqKRZUTKpS5c+REW9R0zMLuLjj/Dbb7uYP39+hs612aB8efjyS8fz\nhAT49NMcDDaD/Pz8WLt2OWXLTsJgKE6VKnNZv35Fnp/lExGefvo5rl//lmvX/iYh4QATJ85i27Zt\n7g5NATRNQ6e7yM1NWqLQ6bhlpRVN07DZrgIJziOJpKRcRdO0XIxWURTl7lTSrCiZdOzYIUSedT4z\nY7W24uDBg/c8b/168PKCQ4dg6VLH7HJeykmrVavGoUO7SEiIYe/ezZQpU+beJ7mZ1Wrl6tVLwI3d\n9oLQ6R7N0Puh5LzWrVtTrFgCRuNzwHg07THeeee9W9bYLleuHC1aPIqmtQEmoGltePzxJvfF76Ci\nKA8PVdOsKJlUtmxF9u79BZHXgRg0bTmVKn14x/YiUL8+bN8O+fPD+fOg1+devA8yTdPIl68gly4t\nAtoDEdjt66lY8XV3h6YARqORHTvW8uWXX3Hy5CmaN/+ITp063dJOp9Px669zmDFjBnv2hPPII13o\n27ev2mFPUZQ8JS9/Iqkl55Q86ciRIzRu3Jq4OCPJyRfp0aMr06ZNuu0/8H//DXXqOB7/8gt06ZLL\nwT4Etm/fTuvWz2C3B5KUdJb33x/Chx8OcXdYiqIoyn1ErdOsKDkkISGBQ4cO4e/vT/HixW95XQTa\ntIGVKx03+F2/DiaTGwJ9SMTGxnLkyBGCg4Pvi9U+/ktEmDNnDmFh/1ChQhl69+79wG8TriiKkpeo\npFlR3ODff6FqVcfjb76BPn3cG4+S9/XuPYC5c//Gau2Apq2kceN8LF++QJUnKIqi5BKVNCtKLuva\n1VGGAXDtGvj6ujceJe87f/48oaGVSUw8BViAJMzm8mzcuJBHHnnE3eEpiqI8FNSOgIqSS44ccezi\n98svMHGiozxDJcxKRsTGxuLl5Qf4OI/o8fQsQGxsrDvDUhRFUf5DJc2Kkk0vvwxlyzoeX74Mb7zh\n3niU+0toaCgFC1rw9BwGHMPDYxJG4wWqV6/u7tAURVGUNFTSrChZdPq0Y3Z5+nQYMcIxu5w/v7uj\nUu43Xl5ebNiwgsaN9xAY2ILatZexadMqLBaLu0NTFEVR0lA1zYqSBYMHw9ixjscRERAc7N54lIdX\nVFQUvXq9xo4df1OsWAjfffcllSpVcndYiqIo9xV1I6CiuFhkJNxY0WzwYBgzxr3xKA83EaFWrSb8\n+281kpNfQ6dbh7//Jxw+vJfAwEB3h6coinLfUDcCKooLjRhxM2E+dUolzIr7Xb58mf37/yU5eRJQ\nDpGXsdmqsmXLFneHpiiK8sBRq+cryj1cuXKzVvnll2HqVPfGoyg3mEwmbLZEIBrID9iw2yMxm81u\njkxRFOXBo2aaFeUuJk26mTAfPqwS5vuJiJAbJV7uLCPz8fHh1VcHYja3AMZgMrWnYsUAmjRp4raY\nFEVRHlQqaVaU24iJcayMMWgQdOniWBmjTBl3R6VkhN1uZ9CgIRiNFoxGC6+8Mgibzebycc6fP0+d\nOs35//buPkqq+jzg+HdfgGVRQWQVfDubkKPBxLcajFVRE6jHJGJE0zRtQxttWppIjDUxNnqsNjkq\nPS0So6T1FTRqQ2tB6KYe0YOLIiji8k6ggawoShQVqbpEBLZ/PHcz4zC77Ky73LnL93POnvndO3fu\n75l7l+WZZ373/qqr+3LIIUcyZ86cbu+jM6ZMmcRdd13NZZf9lptvHsP8+f/jFNyS1AO8EFAqcM89\n8M1vRnvFCjj++HTjUWluueWnXHfdQ7S0PAJUUlv7Fa655nyuvfYH3drPySePYuXKs9i16x+AF6it\nvZAlS+YzYsSIbu1HktTzvBBQKkFLC1RVRcJ83nmwe7cJcxY1NMyjpeX7wFDgUFparqahYV639rFj\nxw5WrHiWXbt+BPQDTqei4otegCdJvZhJswTMmAEDBkSivHgxPPpoDM9Q9hx+eB1VVSt+v1xZuYJh\nw+q6tY8+ffpQU3MAsCZZs5OKitXU1XVvP5Kk8lHOaYHDM9TjduyIiUm2boXTToOFC02Ws27jxo2c\ncsqZbN9+BlBFv36NPP/8UwwfPrxb+/n5zx9kwoTv0do6jurqZYwcOYTHH3+Eqqqqbu1HktTznNxE\n6kBDA4wdG+3GRvCGA73Hli1bmD17Nq2trYwdO5ahPTRlY1NTEwsXLmToYcDnKgAADE9JREFU0KGM\nGzfOhFmSMsqkWSpi50445hhoboYRI2DlyhjLLEmS9k9eCCgVmDcP+vSJhPnRR2HNGhNmSZK0d97M\nU/uF3bth5EhoaooxzC+9FMmzJElSZ1hpVq+3aFFUk5ua4OGHYfNmE2ZJklQaK83qtVpbYcyYGJJR\nUxN3yKipSTsqSZKURVaa1SstWwaVlZEwT58O27ebMEuSpK6z0qxe5+KLYebMaL/zDhxwQLrxSJKk\n7LPSrF5j7dqYmGTmTLj99hieYcIsSZK6g5Vm9QqXXgrTpkX7rbfg4IPTjUeSJPUuVpqVac3NUV2e\nNg0mTYrqsgmzJEnqblaalVlXXAG33hrt116DQw9NNx5JktR7WWlW5rz6alSXb70VrrkmqssmzJIk\nqSeZNCtTrr8ejjgi2i+/DDfemG48kiRp/+DwDGXCG29AXV20J06E225LNx5JkrR/sdKssjd5ci5h\nXr/ehFmSJO17VppVtrZtg0GDoj1+PNx/f7rxSJKk/ZeVZpWlO+/MJcyrV5swS5KkdFlpVll5773c\nLH5jx8Ls2XGnDEmSpDRZaVbZeOCBXML8wgswZ44JsyRJKg9WmpW699+HIUPg3XfhrLOgsdFkWZIk\nlRcrzUrVI49ATU0kzAsWwPz5JsySJKn8WGlWKj74AD7+cdi0CU44AZYuhUo/wkmSpDJlmqJ9bu5c\n6Ns3Eua5c2H5chNmSZJU3qw0a5/ZtQtOOglWrYKjj4YNG6Da30BJkpQB1ve0TzzzTCTIq1bBrFmw\ncaMJsyRJyg7TFvWo1ta4I8aCBXDQQfD669CvX9pRSZIklcZKs3pMU1OMVV6wIO7BvG2bCbMkScom\nK83qdq2tcMEF0NAQy+++CwMGpBuTJEnSR2GlWd1q9eqoLjc0wB13RAJtwixJkrLOSrO6zfjxMQwD\n4O23YeDAdOORJEnqLlaa9ZFt2BCz+D3wAEyeHNVlE2ZJktSbWGnWRzJxIkydGu0tW2DIkHTjkSRJ\n6glWmtUlmzZFdXnqVLj++qgumzBLkqTeKs2k+TxgLfBr4OoU41CJrr0Wjjoq2q+8AjfcsOc2jY2N\n+zIkdSPPXbZ5/rLN85ddnrveL62kuQq4nUicjwP+FBiRUizqpNdfj+ryTTfBFVdEdfnww4tv6x+P\n7PLcZZvnL9s8f9nluev90kqaTwXWAy8CHwC/AL6cUizqhEmT4LDDot3cDFOmpBuPJEnSvpTWhYBH\nAC/nLW8CPptSLOrA1q0weHC0L7kE7r033XgkSZLSUJFSvxcTQzP+Oln+OpE0fydvm/XA8H0clyRJ\nkvY/G4BPdLRBWpXmV4Cj8paPIqrN+ToMXJIkSertqomMvh7oCyzDCwElSZKkPXwBWEcMw/hhyrFI\nkiRJkiRJknqTGuA5YsjGGuDmdMNRF1QBS4H/TjsQlexFYAVx/hanG4q6YBDwMPAr4u/naemGo046\nlvg31/azDbg81YhUqh8Cq4GVwENAv3TDUQm+S5y3VUk7c2qTx2rgWeDMFGNR6a4EHgTmpB2IStYM\nDE47CHXZfcClSbsaGJhiLOqaSmAzH75YXuWtHvgNuUR5BvCXqUWjUnyaSJhriILf43Rw57Y0p9Hu\nSEvy2Jd4E2+lGItKcyTwReBu0ruloT4az1s2DQRGAW13U99JVCyVLWOIC+Vf3tuGKhv/R0zUVkt8\nWK0l7hKm8vdJYnTD74BdwHzgovY2LtekuZIYnvEa8CTxNaOyYQpwFbA77UDUJa3AE8AScvdRVzZ8\nDNgCTAOagLvIfWun7Pga8fW+suMtYDLwEvAq8Dbxd1TlbxVRbBhM/L38ElH8y6SBxPCMc1KOQ51z\nPjA1aZ+DY5qzaFjyWEd8cB2VYiwqzWeIatfIZPknwI/SC0dd0Jf44FOXdiAqyXCiuHcIUWmeBfx5\nqhGpFJcShaL5wM+I4l9R5VppbrMN+CXxn4HK3+nABcS42H8HPg/cn2pEKtXm5HEL8Yf/1BRjUWk2\nJT/PJ8sPA3+QXjjqgi8ALxD//pQdnwEWAm8Sw6JmEv8fKhvuJc7h2cS3BOvSDac0Q4grwAH6A08B\no9MLR110Nlaas6YWODBpDwCeAc5NLxx1wVPAMUn7BuCf0gtFXfALvIAsi04kvubvT1wTch9wWaoR\nqRSHJo9HE3ceOijFWEp2PDEebxlx66ur0g1HXXQ23j0jaz5G/LtbRvwH4KRD2XMiUWleTlS7vHtG\ndgwA3iD3wVXZ8gNyt5y7D+iTbjgqwVPEuVsGfC7lWCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nUo8bBfxh2kGU6G/I3ds+C44lYpakTCn3GQEl7X92AUuJ+7TPBA5I1tcDu4Ef5207hJg6+rZ29nU+\nMclHZxwCXEfcqzMrrgPeImaxKqaR3KyAv6TrN+3/Bu0f41IcCPw90AL8VTfsr5jpwMVdeN3lwPju\nDUWSJKnnvJPXng58L2nXAxuIaYbbfItIsH/azr6eBA7rZL+jiAleekJF8rOvPUn3TKX9Dbonad4X\npgEXdeF1BwKLuzkWSb2IlWZJ5WwRMDxvuYWY5vSUZPmrwH9QPCE9CugLvJYsTwduJaYH30CuGnkO\nMeX700AzcDu5qYxfBG4iEvMlRAI6F1gPTMjr6yoi4VpOrrJdD6wjZgdbmcTzz0l7RRJ7oXpgLZH4\nrQMeJKYyfwb4X2Bkst0A4N6kz6XAl5P1/YmpmNcQVfr+eft+ERictK9M4lgJfLdIHACXJDE8B5ye\nt74OeDjpe3HBc22qgH9J9r8cmJisH03uW4R7iPPTFtvejnMFxY9fBXHO1gKPE1Pitv0+jCZmmC3s\nbxIxA9jyZJ8QH9beBD7VzvGQJEkqK22V5irgv4BvJ8v1RMJ0PpHoHAk8QSS4xaqgXytYPw2YkbRH\nAL9O2ucQSXOb24C/SNrN5JK2W4jkawAxLOS3yfpzgTuSdmWyr1FJvLuAU5PnLiYSwQoisdsIDC2I\nuZ4YbvKpZLslRLIHcAEwK2nfBHw9aR9MJNS1RDJ8d7L++GRfbZXmZiJpPiV5H/2T97IKOKkgjmFJ\nfIcQ0wEvIFfNfwg4I2kfTSTohb5FfJhpK8wcDNQALwGfSNbdRy5h78xxbu/4XZS3fhiwNVnXXn+D\niQS7Tf5U4/+YxC5Je7DSLKnc9CcqjpuJ6uy/FTz/GPBHRFI8g/Ydnewj3yPJ46/o/LCNOcnjSqLy\n/R7wBvA+kXCdm/wsJYaOHEsuUdtI7iv/M4iEsxV4HZhPrnKcr5mogrYmj08k61cRSTVJf39LDL+Y\nCewkjtUo4IG8eFcU7LsCODN5zfbkvcxMXpfvs8m+3yQS7xnkqrdjiMruUmA2MayhtuD1o4kPEruT\n5a3EcWkmqscQSexZea/Z23Fu7/iNylu/GZiX7Ke9/rYBvyM+jIwjvr1o8yq5YyxJH1KddgCSVGA7\ncDKRPD9GDD2Ylff8B0RyeiVwHHBhB/sqHLaxo8hzO/lwASF/SANE0gaRAOa/fje5v6E3A3cWvK6e\nSPw6iqd1j4hz/RX2md8fwKVEhbnQ3sZOtxZsU1Ekjo62qSCS6h10bG/vtbDfzhzn9t5bsfXF+oNc\n9X808BVi6MjodmKSpN+z0iypXG0n7mhwI3smRZOBq2n/rhFQfPhDe9sdR4x3HQR8vp3t2kvMHiMS\n2AHJuiOIcb+Fngb+hPi7W0dUPbt64dljwHfyltvGeD8F/FnS/jRwQpF4nyY+aLQNz7gwWZdvMXA2\nMZShD/DHec/NJc5Lm8KhHRBjiycQQ2wgN4SkntwY9fFEtbhQe8e52PF7jnjPbeuHAZ9LXrOuSH+N\nxHseBDxKfPA6Ma+fYcT4aknag5VmSeUmv9K3jPh6/avAs3nPrSE3lraV4tXBZ/hwcle477b2y8T4\n21XE1/lNHcRV7PWPE2OkFyXL7xDjjQu3n0XcA3p5sv4qYphBsX72FvOPgZ8Qwy8qgd8QY57/lRi7\nvYYYgrKkyP6XEhdFtiXsdyUx5dtMXNC4iPhgsjTvucuBqclrqonE99sFr78bOCaJ7wOiCv8z4uLC\n/0xet5jc0JvC91jsPbd3/GYRH3TWEGOYFybbv99Of0OIYTo1RIL+d3l9nQp8H0mSpP3MPKJ6KO3N\nQcDzaQchqXxV7X0TScqsLcTwg8aU41D5m0D8nhRePClJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkqTe7f8Bo/O4l+Bi27cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8a597b2310>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "Erro quadr\u00e1tico m\u00e9dio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean((regr.predict(X) - y)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "46.796244399594485"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "#### Score\n",
      "\n",
      "O coeficiente $R^2$ \u00e9 definido como $(1 - \\frac{u}{v})$ em que\n",
      "\n",
      "$u = \\sum_{i} (y_i - \\hat{y_i})^2$\n",
      "\n",
      "$v = \\sum_{i} (y_i - \\mu_y)^2$\n",
      "\n",
      "O melhor *score* poss\u00edvel \u00e9 1.0 e o pior poss\u00edvel 0.0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr.score(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "0.48450411944260396"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "### Teste"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_t = boston.data[test_idx,5].reshape(n_test, 1)\n",
      "y_t = boston.target[test_idx]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Erro quadr\u00e1tico m\u00e9dio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean((regr.predict(X_t) - y_t)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "34.312008286451317"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "*Score*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr.score(X_t, y_t)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0.47519490527319708"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(X_t, y_t)\n",
      "plot(X_t, regr.predict(X_t))\n",
      "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n",
      "xlim((3,9))\n",
      "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n",
      "ylim((0,55))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "(0, 55)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M1KdZRERyzfbt2xk/fjLx8Yk88siDtG7d2qXjduzYwe23tyI29nWgHDbbS7z5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\niIiIiIiIiIiIiIiIiIiIiIgUb/8Pgoa+nAOB7TwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8a597238d0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(X, y, c='b', marker='o')\n",
      "scatter(X_t, y_t, c='r', marker='s')\n",
      "plot(X, regr.predict(X))\n",
      "plot(X_t, regr.predict(X_t), 'r-')\n",
      "xlabel(u'RM (n\u00famero m\u00e9dio de c\u00f4modos)')\n",
      "xlim((3,9))\n",
      "ylabel(u'Valor m\u00e9dio (em US$ 1.000)')\n",
      "ylim((0,55))"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "(0, 55)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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geA1oi6dqOns7l8vM999/z3vvvcexY8cCFW5ArOj1eHbC/GP728EwlDBLiWik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\n/cCtwD5SUv5h+3Yf9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ipVxM5+/C0hTz1wcYO+RpUXiQgtW3Zk8+amJCf/H4axkJCQN/j7\n742Eh4d7OzyllCoz9EJApYqSYaQnzGdffFsTZuV1J0+eZOvWzSQnfwjUQ+RRXK4mLF++3NuhKaVU\nuaOz5yuVh/OT5+K4O0Otsggh3gtHqXQBAQG4XInAGaAC4MLtPordbvdyZEopVf7oSLNSuTGM9IT5\n7H1P6uhyGSIilESJlzfLyAIDA3nyycHY7V2AdwkI6EnDhqF07NjRazEppVR5pUmzUjmIjVqTbWaM\nC9PKqdLN7XbzzDNDsNkc2GwOHnvsGVwuV5H3c+TIEVq3vgGr1Y8KFaoyY8aMIu8jP8aMeYfx44fw\n5JNHGTnyRhYtmqNLcCulVDHQCwGVyipDshzT4dbMS2KrUm/06I949dUfcDqnARZMszcvv3wrQ4e+\nWKT9NGvWns2bO+ByvQaswzR7snbtIho0aFCk/SillCp+eiGgUh6I37QrU8IsLrcmzGXQrFkLcDqf\nByKAK3A6hzBr1oIi7SMpKYlNm1bicr0B+ANtMYzuegGeUkqVY5o0KwVgGAQ0vQqA+FoNQQTDUpq/\niFGXUrlyRXx8NqXft1g2ERlZsUj78PX1xWYLBLalbUnBMLZSsWLR9qOUUqr0KM1ZgZZnqGKXdPAo\nfjUi0+9LUjKGr9aDlmUHDhygRYt2xMdfD/jg77+QNWsWU6dOnSLt59tvv+fRR59D5E6s1g20ahXO\nH39Mw8fHp0j7UUopVfx0cROlcpOhFMNt9cWSnOTFYFRROnHiBNOnT0dEuO2224iIiCiWftavX8/y\n5cuJiIjgzjvv1IRZKaXKKE2alcpBSowTa/DFeWxd52LxCdJ5bZVSSqnLlV4IqFQWZxu3y5QwI6IJ\ns1JKKaXypCPN6rLgTkzGYvNLv5984iy+4cFejEgppZRSpYWONCsFnLyxT3rC7Lb6gogmzEoppZTy\niI40q3JLXG4M68ULsxL2HsFWKzKXI5RSSil1OdKRZnXZOn7/s5kSZkQ0YVZKKaVUgelIsyp/Mkwl\nF7dhF/amV3oxGKWUUkqVdjrSrC4rx194L1PCjIgmzEoppZQqEjrSrMqHDMlyzML1BHVs5sVglFJK\nKVWW6EizKvdOvPd1ttFlTZiVUkopVdR0pFmVXRmS5TNTFhDaq7MXg1FKKaVUWaUjzapcOvX1zGyj\ny5owK6WUUqo4adKsyhbDoMLDtwNw6tPJoN9GKKWUUqoEaHmGKhPOzlpKyG3tL27Q14ZSSimlioiW\nZ6jywTDSE+YTIz7VhFkppZRSJU5HmlWpdX75ZhzXN7m4QV8PSimllCoGOtKsyi7DSE+Yjz85XBNm\npZRSSnmVjjSrUiXuyDnsVULS74vLjWEpzS9TpZRSSpV1OtKsypSlj3+fnjCf6P04iGjCrJRSSqlS\noTRnJDrSfJlIjEkkKTgcB7FsCO5A09MLNVlWSimlVInRkWZV6q16aRr+wTYcxLJp3FKuObtIE2al\nlFJKlTqlOTvRkeZyLNmZzImg2lR2HWKnrQl1z/+Fxaqf4ZRSSilV8nSkWZVK60b+jq/dj8quQ6wb\n+Tv14jdqwqyUUkqpUk1HmlWJcSW52Bt0DXUTt3DIpzoRsXuw2qzeDksppZRSlzkdaValxqb/LcPH\n30rdxC2s+s9UqqYc0IRZKaWUUmWGjjSrYiVuYVNoB5rGLOUcQdjOHcc/yN/bYSmllFJKpdORZuVV\n279fj+FjoWnMUpY9/h3Bck4TZqWUUkqVSTrSrIqcuIU1kbfT+vgsAOKOxWK/wu7lqJRSSimlcqYj\nzarE7Z6+FcPHQuvjs1h832cgogmzUkoppco8HWlWRWZp7X602/cdAOcOnCW4erCXI1JKKaWUypuO\nNKsScWDBHjAM2u37joW3vw8imjArpZRSqlzRkWZVKIua/B8dN48F4NSOE1SoF+7liJRSSimlPKMj\nzarYRK85BIZBx81jWdhxGIhowqyUUkqpcsubSXM3YAewCxjixTiUhxZeP5TI1tUAOLruMJ0Wvp59\nn4ULSzYoVWT0uSvb9Pkr2/T5K7v0uSv/vJU0+wCfkJo4NwTuBRp4KRaVTye2HgfDoNPyt1nU7BkQ\nIaJ55Rz31T8eZZc+d2WbPn9lmz5/ZZc+d+Wft5Lm1sBuYD+QDPwE3OGlWFQ+LOz2DhUbVQLg0JJ9\ndFw/xssRKaWUUkqVHG8lzVWAfzLcP5S2TZUyZ/edSR1dnvcSS+o+DCJUbVfT22EppZRSSpUob82e\ncReppRkD0+7fD7QBBmfYZzdQp4TjUkoppZRSl589wJW57WAtoUCyOgxUy3C/GqmjzRnlGrhSSiml\nlFLlnZXUjL4m4AdsQC8EVEoppZRSKptbgJ2klmG85OVYlFJKKaWUUkopEsT/fwAACLFJREFUpZRS\nSpUnNmAVqSUb24CR3g1HFYAP8Bcw09uBKI/tBzaR+vyt9m4oqgBCgF+A7aT+/bzWu+GofKpH6v+5\nC7dzwFNejUh56iVgK7AZ+AHw9244ygNPk/q8bUn7ucwx0/61AiuBdl6MRXnuWeB7YIa3A1Ee2weE\neTsIVWDfAP3TfrYCwV6MRRWMBYgm88XyqnSrCezlYqI8CXjQa9EoTzQiNWG2kTrg9we5zNzmzWW0\nc+NM+9eP1JM47cVYlGeqAt2BL/DelIaqcPR5K5uCgfbAhLT7KaSOWKqy5UZSL5T/J68dVakRQ+pC\nbSapH1ZNUmcJU6VffVKrGxIAF7AI6HWpnUtr0mwhtTzjGBBF6teMqmwYA7wAuL0diCoQAf4E1nJx\nHnVVNtQCTgBfAeuB8Vz81k6VHfeQ+vW+KjtOA+8DB4EjwFlS/46q0m8LqYMNYaT+vexB6uBfmRRM\nanlGJy/HofLnVmBs2s+d0Jrmsigy7d+KpH5wbe/FWJRnWpI62tUq7f4HwBveC0cVgB+pH3wqejsQ\n5ZE6pA7uVSB1pHkqcJ9XI1Ke6E/qQNEiYBypg385Kq0jzRecA2aT+magSr+2wO2k1sX+CNwATPRq\nRMpT0Wn/niD1D39rL8aiPHMo7bYm7f4vQHPvhaMK4BZgHan//1TZ0RJYDpwitSzqV1LfD1XZMIHU\n57Ajqd8S7PRuOJ4JJ/UKcIAAYDHQxXvhqALqiI40lzUm4Ej72Q4sA7p6LxxVAIuBq9J+fh34r/dC\nUQXwE3oBWVnUlNSv+QNIvSbkG+BJr0akPHFF2r/VSZ15KMiLsXisMan1eBtInfrqBe+GowqoIzp7\nRllTi9T/dxtIfQPQRYfKnqakjjRvJHW0S2fPKDvs8P/t3XuIFWUYx/Hv8RKum+GVWio5UCRpVySj\nbEvb8i8p07KIDCvIlDKyrP7xj5As6EJl9ywVStgubgURq6LmZuYmrauyaWWrBdlFk5A0b7v98TzT\nvM7O7FmX1T2rvw8sO+edmfd95z3L8sx7njMvO4lvXKVreZT4kXMLgZ6d2x05Cquw9249MLqT+yIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIsdcOXBFZ3fiKN1L/Gz7rmAI1mcRkS6l\n2FcEFJGTz2GgDntO+2LgVC/PA03A7ODYgdjS0XMz6hqLLfLRFgOAWdizOruKWcBf2CpWaVYSrwr4\nGe1/aP9kssf4aPQBHgf2Avd0QH1pFgAT2nHedGBSx3ZFRERE5NjZE2wvAB727TywFVtmODIVC7Bf\nyqhrBXB6G9stxxZ4ORZy/nO8raBjltKeTMcEzcfDfGB8O87rA9R2cF9E5ASimWYRKWZrgHOC13ux\nZU6H++uJwPukB6RnA6cAv/vrBcCL2PLgW4lnI0dhS77XAI3Ay8RLGW8D5mCB+TosAF0C/AhMCdqa\niQVc9cQz23lgC7Y62EbvzzO+vcH7npQHNmOB3xbgPWwp89XA98Blflwp8I63WQfc6OUl2FLMDdgs\nfUlQ9zagv2/P8H5sBB5M6QfAXd6HtcCVQfkg4ENvuzaxL9IdeNbrrwfu9/IK4k8R3sben6hvhcY5\nR/r45bD3bDOwFFsSN/p7qMBWmE229zS2Ali91wl2s7YLGJYxHiIiIiJFJZpp7g58BEzz13ksYBqL\nBTpnAcuwADdtFvS2RPl8oNK3zwd+8O1RWNAcmQvc6duNxEHb81jwVYqlhfzm5WOAN3y7m9dV7v09\nDIzwfROwQDCHBXbbgTMSfc5j6SbD/Lh1WLAHcANQ5dtzgDt8ux8WUPfGguF5Xn6h1xXNNDdiQfNw\nv44Sv5ZNwCWJfpR5/wZgywF/STybvwgY6duDsQA9aSp2MxNNzPQDegE/A+d62ULigL0t45w1fuOD\n8jJgt5dltdcfC7Aj4VLjT3jfRURa0EyziBSbEmzGcQc2O/t6Yn81cD0WFFeSbbDXEfrYf39H29M2\nPvXfG7GZ73+AncB+LOAa4z91WOrIEOJAbTvxR/4jsYCzGfgD+IJ45jjUiM2CNvvvZV6+CQuq8fbu\nw9IvFgOHsLEqB94N+rshUXcOuMrP2efXstjPC13ude/CAu9K4tnb67CZ3TrgEyytoXfi/ArsRqLJ\nX+/GxqURmz0GC2KvDs4pNM5Z41celO8Alns9We39DfyL3YzchH16EfmVeIxFRI7Qo7M7ICKSsA+4\nFAueq7HUg6pg/0EsOJ0BDAXGtVJXMm3jQMq+Qxw5gRCmNIAFbWABYHh+E/H/0KeANxPn5bHAr7X+\nNLfocdxess2wPYC7sRnmpEK5082JY3Ip/WjtmBwWVB+gdYWuNdluW8Y569rSytPag3j2vwK4GUsd\nqcjok4jI/zTTLCLFah/2RIMnaRkUPQc8RvZTIyA9/SHruKFYvmtf4NqM47ICs2osgC31sjOxvN+k\nGuBW7P/uIGzWs71fPKsGHgheRzneq4DbffsC4KKU/tZgNxpResY4LwvVAtdgqQw9gVuCfUuw9yWS\nTO0Ayy2egqXYQJxCkifOUZ+EzRYnZY1z2vitxa45Ki8DRvs5W1LaW4ldc1/gc+zG6+KgnTIsv1pE\npAXNNItIsQln+tZjH69PBL4O9jUQ59I2kz47uJojg7tk3dH2L1j+7Sbs4/xvW+lX2vlLsRzpNf56\nD5ZvnDy+CnsGdL2Xz8TSDNLaKdTn2cALWPpFN+AnLOf5NSx3uwFLQVmXUn8d9qXIKGB/y/sU2oF9\noXENdmNSF+ybDrzi5/TAAt9pifPnAed5/w5is/CvYl8u/MDPqyVOvUleY9o1Z41fFXaj04DlMH/l\nx+/PaG8glqbTCwvQHwraGgE8goiIiMhJZjk2eyhSyGnAN53dCREpXt0LHyIi0mX9iaUfrOzkfkjx\nm4L9nSS/PCkiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJ7b/AMp9atid\nIC3+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8a51710290>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Exemplo - regress\u00e3o utilizando todas as dimens\u00f5es"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "### Treinamento"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr = LinearRegression()\n",
      "X = boston.data[train_idx]\n",
      "y = boston.target[train_idx]\n",
      "regr.fit(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Erro quadr\u00e1tico m\u00e9dio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean((regr.predict(X) - y)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "22.91041859376821"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "*Score*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr.score(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "0.74762448229637368"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### Teste"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Erro quadr\u00e1tico m\u00e9dio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_t = boston.data[test_idx]\n",
      "y_t = boston.target[test_idx]\n",
      "mean((regr.predict(X_t) - y_t)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "19.870238886388343"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "*Score*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr.score(X_t, y_t)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "0.69608300062305262"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Exerc\u00edcio\n",
      "\n",
      "### A base de dados sobre diabetes\n",
      "\n",
      "Dez vari\u00e1veis idade, sexo, \u00edndice de massa corporal, press\u00e3o arterial m\u00e9dia e seis medi\u00e7\u00f5es sangu\u00edneas de $n = 442$ pacientes com **diabetes**. \n",
      "\n",
      "Medida quantitativa de interesse: **\u00edndice de progress\u00e3o da doen\u00e7a ap\u00f3s 1 ano**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_diabetes\n",
      "diabetes = load_diabetes()"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "### Exerc\u00edcio\n",
      "\n",
      "Realizar regress\u00e3o para prever o \u00edndice de progress\u00e3o da doen\u00e7a a partir das 10 vari\u00e1vies"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "source": [
      "### Solu\u00e7\u00e3o"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_samples = diabetes.data.shape[0]\n",
      "num_samples"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "442"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for train_idx, test_idx in ssplit:\n",
      "    pass"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_idx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([ 88, 325, 273, 279, 104, 146, 403,  48, 331, 401, 329, 381, 191,\n",
        "         4,  80, 101, 202,  47,  93, 422, 278, 321, 298,  38, 197, 174,\n",
        "       286, 225, 149, 404, 129, 272,  91,  39, 190, 166,  36, 239, 398,\n",
        "        17, 209,  82,  54, 409,  97, 318, 378, 419, 154, 208,  96, 275,\n",
        "       441, 355, 372, 284, 171,  22,  19, 315,  55,   6, 192, 258,  42,\n",
        "        52, 289,  30, 291, 268,   1, 428, 185, 218, 200, 241, 326, 130,\n",
        "       345, 319, 238, 250, 227, 211, 353, 263, 232, 430, 314, 281, 138,\n",
        "        77, 344,  67, 396, 357, 100, 170,  63,   2, 413, 342,  49,   0,\n",
        "       231, 254, 386, 150, 245, 244, 237, 135, 358, 307, 145, 434, 407,\n",
        "       248, 352, 420,  79, 399,  94, 242, 178, 277, 303, 123,  85, 113,\n",
        "       297,  87,  16, 346, 433, 229, 310, 293, 195, 205, 379, 371, 167,\n",
        "       259, 194,  44,  46, 189, 156, 251, 230, 287, 429, 269,  31,  78,\n",
        "        21,   9,  81,  98,  26, 305, 240, 290,  59, 157, 369, 196, 158,\n",
        "       410, 349, 347,  72, 252, 122, 366, 356, 341, 221, 306, 423, 116,\n",
        "       115, 343, 440,  23, 338, 246, 391, 380, 118, 300,   3,  15, 400,\n",
        "       132, 181, 322,  99, 389, 106, 323, 282, 226, 177, 110,  27, 262,\n",
        "       186, 164,  64, 188,  56, 224,  90, 324, 382, 361, 212, 376, 128,\n",
        "        50, 213, 183, 274, 161,  20,  83, 141, 421, 437, 333, 288, 395,\n",
        "       152, 412, 162,   5, 313, 351, 133, 105, 438,  74, 121, 108, 163,\n",
        "       142, 387,  45, 139, 173, 111, 117,  62, 109,  75, 266,  34, 214,\n",
        "       223, 260, 439, 264, 426, 408, 112, 144, 165, 172,  29, 406, 204,\n",
        "        71, 370, 126, 417, 255, 362, 270, 257, 402, 393, 125, 120,  10,\n",
        "       160, 431,  89, 360, 193, 187, 219, 153, 309,  61,  40,  43, 102,\n",
        "        35, 168, 228, 182, 367,  92, 243, 363, 179, 103, 432, 340, 312,\n",
        "       137, 124, 397, 236, 151, 184, 140, 424, 203, 261,  60,  12, 114,\n",
        "       247,  65,   8, 416, 234, 392])"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_idx"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "array([267, 155, 332, 143, 334,  37, 276, 285, 180, 339,  25, 253, 235,\n",
        "        41, 256, 350,  11, 427, 368, 336, 365, 216, 308, 377, 220, 296,\n",
        "       107, 316, 418, 384, 411, 292, 385, 280,  68, 394, 294, 374, 233,\n",
        "       302, 176, 359, 364,  13,  58, 169, 175, 425,  28,  53, 301, 199,\n",
        "       119,  57,  84,  14,  70, 383, 136, 330,   7,  24,  76,  95, 436,\n",
        "       348, 271, 354, 222, 311, 388, 415, 304, 249, 335,  18, 206, 295,\n",
        "       217, 435, 127, 373, 131, 147, 265, 283, 210, 320,  73, 327, 215,\n",
        "       207, 299, 414,  66, 134, 148,  32, 198,  69,  51, 337, 375, 328,\n",
        "        33, 390, 405, 317,  86, 159, 201])"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr = LinearRegression()\n",
      "X = diabetes.data[train_idx]\n",
      "y = diabetes.target[train_idx]\n",
      "regr.fit(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "source": [
      "Erro quadr\u00e1tico m\u00e9dio"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean((regr.predict(X) - y)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "2854.5161687412092"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_t = diabetes.data[test_idx]\n",
      "y_t = diabetes.target[test_idx]\n",
      "mean((regr.predict(X_t) - y_t)**2)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "2957.3328332580109"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr.score(X_t, y_t)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "0.49383612489744777"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "## Lasso \n",
      "\n",
      "- Lasso \u00e9 capaz de produzir **modelos esparsos**\n",
      "    - Modelos que usam um **subconjunto** das vari\u00e1veis\n",
      "- O ideia \u00e9 reduzir os coeficientes de algumas vari\u00e1veis **zero**\n",
      "- Modelos Lasso s\u00e3o assim mais f\u00e1ceis de interpretar\n",
      "- Pode ser entendido como uma aplica\u00e7\u00e3o da **faca de Occam**\n",
      "    - *Prefira modelos mais **simples** *"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ssplit = ShuffleSplit(num_samples, n_iter=1, test_size=0.25)\n",
      "\n",
      "for train_idx, test_idx in ssplit:\n",
      "    pass\n",
      "\n",
      "X = boston.data[train_idx]\n",
      "y = boston.target[train_idx]\n",
      "\n",
      "X_t = boston.data[test_idx]\n",
      "y_t = boston.target[test_idx]"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import Lasso"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lasso_regr = Lasso()\n",
      "lasso_regr.set_params(alpha=0.001)\n",
      "lasso_regr.fit(X, y)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "Lasso(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=1000,\n",
        "   normalize=False, positive=False, precompute='auto', tol=0.0001,\n",
        "   warm_start=False)"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lasso_regr.score(X_t, y_t)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "fragment"
      }
     },
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "0.7053705432249564"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boston_vars = ['CRIM (per capita crime rate by town)',\n",
      "            'ZN (proportion of residential land zoned for lots over 25,000 sq.ft.)',\n",
      "            'INDUS (proportion of non-retail business acres per town)',\n",
      "            'CHAS (Charles River dummy variable, = 1 if tract bounds river; 0 otherwise)',\n",
      "            'NOX     nitric oxides concentration (parts per 10 million)',\n",
      "            'RM  (average number of rooms per dwelling)',\n",
      "            'AGE (proportion of owner-occupied units built prior to 1940)',\n",
      "            'DIS (weighted distances to five Boston employment centres)',\n",
      "            'RAD (index of accessibility to radial highways)',\n",
      "            'TAX (full-value property-tax rate per $10,000)',\n",
      "            'PTRATIO (pupil-teacher ratio by town)',\n",
      "            'B (1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town)',\n",
      "            'LSTAT (lower status of the population)']"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "skip"
      }
     },
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for descr, coefficient in zip(boston_vars, lasso_regr.coef_):\n",
      "    print \"%.3f\\t%s\" % (coefficient, descr)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "subslide"
      }
     },
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.107\tCRIM (per capita crime rate by town)\n",
        "0.061\tZN (proportion of residential land zoned for lots over 25,000 sq.ft.)\n",
        "0.051\tINDUS (proportion of non-retail business acres per town)\n",
        "2.754\tCHAS (Charles River dummy variable, = 1 if tract bounds river; 0 otherwise)\n",
        "-17.696\tNOX     nitric oxides concentration (parts per 10 million)\n",
        "3.505\tRM  (average number of rooms per dwelling)\n",
        "0.032\tAGE (proportion of owner-occupied units built prior to 1940)\n",
        "-1.401\tDIS (weighted distances to five Boston employment centres)\n",
        "0.432\tRAD (index of accessibility to radial highways)\n",
        "-0.016\tTAX (full-value property-tax rate per $10,000)\n",
        "-0.883\tPTRATIO (pupil-teacher ratio by town)\n",
        "0.010\tB (1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town)\n",
        "-0.668\tLSTAT (lower status of the population)\n"
       ]
      }
     ],
     "prompt_number": 137
    }
   ],
   "metadata": {}
  }
 ]
}