{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ML Logo](http://spark-mooc.github.io/web-assets/images/CS190.1x_Banner_300.png)\n",
    "# **Linear Regression Lab**\n",
    "#### This lab covers a common supervised learning pipeline, using a subset of the [Million Song Dataset](http://labrosa.ee.columbia.edu/millionsong/) from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD). Our goal is to train a linear regression model to predict the release year of a song given a set of audio features.\n",
    "#### ** This lab will cover: **\n",
    "+  ####*Part 1:* Read and parse the initial dataset\n",
    " + #### *Visualization 1:* Features\n",
    " + #### *Visualization 2:* Shifting labels\n",
    "+  ####*Part 2:* Create and evaluate a baseline model\n",
    " + #### *Visualization 3:* Predicted vs. actual\n",
    "+  ####*Part 3:* Train (via gradient descent) and evaluate a linear regression model\n",
    " + #### *Visualization 4:* Training error\n",
    "+  ####*Part 4:* Train using MLlib and tune hyperparameters via grid search\n",
    " + #### *Visualization 5:* Best model's predictions\n",
    " + #### *Visualization 6:* Hyperparameter heat map\n",
    "+  ####*Part 5:* Add interactions between features\n",
    " \n",
    "#### Note that, for reference, you can look up the details of the relevant Spark methods in [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) and the relevant NumPy methods in the [NumPy Reference](http://docs.scipy.org/doc/numpy/reference/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labVersion = 'cs190_week3_v_1_3'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Part 1: Read and parse the initial dataset **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (1a) Load and check the data **\n",
    "#### The raw data is currently stored in text file.  We will start by storing this raw data in as an RDD, with each element of the RDD representing a data point as a comma-delimited string. Each string starts with the label (a year) followed by numerical audio features. Use the [count method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.count) to check how many data points we have.  Then use the [take method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.take) to create and print out a list of the first 5 data points in their initial string format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load testing library\n",
    "from test_helper import Test\n",
    "import os.path\n",
    "baseDir = os.path.join('data')\n",
    "inputPath = os.path.join('cs190', 'millionsong.txt')\n",
    "fileName = os.path.join(baseDir, inputPath)\n",
    "\n",
    "numPartitions = 2\n",
    "rawData = sc.textFile(fileName, numPartitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6724\n",
      "[u'2001.0,0.884123733793,0.610454259079,0.600498416968,0.474669212493,0.247232680947,0.357306088914,0.344136412234,0.339641227335,0.600858840135,0.425704689024,0.60491501652,0.419193351817', u'2001.0,0.854411946129,0.604124786151,0.593634078776,0.495885413963,0.266307830936,0.261472105188,0.506387076327,0.464453565511,0.665798573683,0.542968988766,0.58044428577,0.445219373624', u'2001.0,0.908982970575,0.632063159227,0.557428975183,0.498263761394,0.276396052336,0.312809861625,0.448530069406,0.448674249968,0.649791323916,0.489868662682,0.591908113534,0.4500023818', u'2001.0,0.842525219898,0.561826888508,0.508715259692,0.443531142139,0.296733836002,0.250213568176,0.488540873206,0.360508747659,0.575435243185,0.361005878554,0.678378718617,0.409036786173', u'2001.0,0.909303285534,0.653607720915,0.585580794716,0.473250503005,0.251417011835,0.326976795524,0.40432273022,0.371154511756,0.629401917965,0.482243251755,0.566901413923,0.463373691946']\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "numPoints = rawData.count()\n",
    "print numPoints\n",
    "samplePoints = rawData.take(5)\n",
    "print samplePoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Load and check the data (1a)\n",
    "Test.assertEquals(numPoints, 6724, 'incorrect value for numPoints')\n",
    "Test.assertEquals(len(samplePoints), 5, 'incorrect length for samplePoints')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (1b) Using `LabeledPoint` **\n",
    "#### In MLlib, labeled training instances are stored using the [LabeledPoint](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) object.  Write the parsePoint function that takes as input a raw data point, parses it using Python's [unicode.split](https://docs.python.org/2/library/string.html#string.split) method, and returns a `LabeledPoint`.  Use this function to parse samplePoints (from the previous question).  Then print out the features and label for the first training point, using the `LabeledPoint.features` and `LabeledPoint.label` attributes. Finally, calculate the number features for this dataset.\n",
    "#### Note that `split()` can be called directly on a `unicode` or `str` object.  For example, `u'split,me'.split(',')` returns `[u'split', u'me']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.mllib.regression import LabeledPoint\n",
    "import numpy as np\n",
    "\n",
    "# Here is a sample raw data point:\n",
    "# '2001.0,0.884,0.610,0.600,0.474,0.247,0.357,0.344,0.33,0.600,0.425,0.60,0.419'\n",
    "# In this raw data point, 2001.0 is the label, and the remaining values are features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001.0 [0.884123733793,0.610454259079,0.600498416968,0.474669212493,0.247232680947,0.357306088914,0.344136412234,0.339641227335,0.600858840135,0.425704689024,0.60491501652,0.419193351817]\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "def parsePoint(line):\n",
    "    \"\"\"Converts a comma separated unicode string into a `LabeledPoint`.\n",
    "\n",
    "    Args:\n",
    "        line (unicode): Comma separated unicode string where the first element is the label and the\n",
    "            remaining elements are features.\n",
    "\n",
    "    Returns:\n",
    "        LabeledPoint: The line is converted into a `LabeledPoint`, which consists of a label and\n",
    "            features.\n",
    "    \"\"\"\n",
    "    lp = LabeledPoint(line.split(\",\")[0], line.split(\",\")[1:])\n",
    "    return lp\n",
    "    \n",
    "parsedSamplePoints = [parsePoint(x) for x in samplePoints]\n",
    "\n",
    "firstPointFeatures = parsedSamplePoints[0].features\n",
    "firstPointLabel = parsedSamplePoints[0].label\n",
    "print firstPointLabel,firstPointFeatures\n",
    "\n",
    "d = len(firstPointFeatures)\n",
    "print d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Using LabeledPoint (1b)\n",
    "Test.assertTrue(isinstance(firstPointLabel, float), 'label must be a float')\n",
    "expectedX0 = [0.8841,0.6105,0.6005,0.4747,0.2472,0.3573,0.3441,0.3396,0.6009,0.4257,0.6049,0.4192]\n",
    "Test.assertTrue(np.allclose(expectedX0, firstPointFeatures, 1e-4, 1e-4),\n",
    "                'incorrect features for firstPointFeatures')\n",
    "Test.assertTrue(np.allclose(2001.0, firstPointLabel), 'incorrect label for firstPointLabel')\n",
    "Test.assertTrue(d == 12, 'incorrect number of features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Visualization 1: Features**\n",
    "#### First we will load and setup the visualization library.  Then we will look at the raw features for 50 data points by generating a heatmap that visualizes each feature on a grey-scale and shows the variation of each feature across the 50 sample data points.  The features are all between 0 and 1, with values closer to 1 represented via darker shades of grey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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mXtVXdgMJFy61hqi+vl7bt29Xd3e3SkpKdNttt6Xlp4Sbm5v12muvSbrwannSpEmaN2+e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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb143242c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "sampleMorePoints = rawData.take(50)\n",
    "# You can uncomment the line below to see randomly selected features.  These will be randomly\n",
    "# selected each time you run the cell.  Note that you should run this cell with the line commented\n",
    "# out when answering the lab quiz questions.\n",
    "# sampleMorePoints = rawData.takeSample(False, 50)\n",
    "\n",
    "parsedSampleMorePoints = map(parsePoint, sampleMorePoints)\n",
    "dataValues = map(lambda lp: lp.features.toArray(), parsedSampleMorePoints)\n",
    "\n",
    "def preparePlot(xticks, yticks, figsize=(10.5, 6), hideLabels=False, gridColor='#999999',\n",
    "                gridWidth=1.0):\n",
    "    \"\"\"Template for generating the plot layout.\"\"\"\n",
    "    plt.close()\n",
    "    fig, ax = plt.subplots(figsize=figsize, facecolor='white', edgecolor='white')\n",
    "    ax.axes.tick_params(labelcolor='#999999', labelsize='10')\n",
    "    for axis, ticks in [(ax.get_xaxis(), xticks), (ax.get_yaxis(), yticks)]:\n",
    "        axis.set_ticks_position('none')\n",
    "        axis.set_ticks(ticks)\n",
    "        axis.label.set_color('#999999')\n",
    "        if hideLabels: axis.set_ticklabels([])\n",
    "    plt.grid(color=gridColor, linewidth=gridWidth, linestyle='-')\n",
    "    map(lambda position: ax.spines[position].set_visible(False), ['bottom', 'top', 'left', 'right'])\n",
    "    return fig, ax\n",
    "\n",
    "# generate layout and plot\n",
    "fig, ax = preparePlot(np.arange(.5, 11, 1), np.arange(.5, 49, 1), figsize=(8,7), hideLabels=True,\n",
    "                      gridColor='#eeeeee', gridWidth=1.1)\n",
    "image = plt.imshow(dataValues,interpolation='nearest', aspect='auto', cmap=cm.Greys)\n",
    "for x, y, s in zip(np.arange(-.125, 12, 1), np.repeat(-.75, 12), [str(x) for x in range(12)]):\n",
    "    plt.text(x, y, s, color='#999999', size='10')\n",
    "plt.text(4.7, -3, 'Feature', color='#999999', size='11'), ax.set_ylabel('Observation')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(1c) Find the range **\n",
    "#### Now let's examine the labels to find the range of song years.  To do this, first parse each element of the `rawData` RDD, and then find the smallest and largest labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011.0 1922.0\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "parsedDataInit = rawData.map(lambda x : parsePoint(x))\n",
    "onlyLabels = parsedDataInit.map(lambda x : x.label)\n",
    "minYear = onlyLabels.reduce(lambda x,y : min(x,y))\n",
    "maxYear = onlyLabels.reduce(lambda x,y : max(x,y))\n",
    "print maxYear, minYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Find the range (1c)\n",
    "Test.assertEquals(len(parsedDataInit.take(1)[0].features), 12,\n",
    "                  'unexpected number of features in sample point')\n",
    "sumFeatTwo = parsedDataInit.map(lambda lp: lp.features[2]).sum()\n",
    "Test.assertTrue(np.allclose(sumFeatTwo, 3158.96224351), 'parsedDataInit has unexpected values')\n",
    "yearRange = maxYear - minYear\n",
    "Test.assertTrue(yearRange == 89, 'incorrect range for minYear to maxYear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(1d) Shift labels **\n",
    "#### As we just saw, the labels are years in the 1900s and 2000s.  In learning problems, it is often natural to shift labels such that they start from zero.  Starting with `parsedDataInit`, create a new RDD consisting of `LabeledPoint` objects in which the labels are shifted such that smallest label equals zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pyspark.mllib.regression.LabeledPoint'>\n",
      "\n",
      "[LabeledPoint(79.0, [0.884123733793,0.610454259079,0.600498416968,0.474669212493,0.247232680947,0.357306088914,0.344136412234,0.339641227335,0.600858840135,0.425704689024,0.60491501652,0.419193351817])]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "parsedData = parsedDataInit.map(lambda x: LabeledPoint(x.label - 1922.0,x.features))\n",
    "\n",
    "# Should be a LabeledPoint\n",
    "print type(parsedData.take(1)[0])\n",
    "# View the first point\n",
    "print '\\n{0}'.format(parsedData.take(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Shift labels (1d)\n",
    "oldSampleFeatures = parsedDataInit.take(1)[0].features\n",
    "newSampleFeatures = parsedData.take(1)[0].features\n",
    "Test.assertTrue(np.allclose(oldSampleFeatures, newSampleFeatures),\n",
    "                'new features do not match old features')\n",
    "sumFeatTwo = parsedData.map(lambda lp: lp.features[2]).sum()\n",
    "Test.assertTrue(np.allclose(sumFeatTwo, 3158.96224351), 'parsedData has unexpected values')\n",
    "minYearNew = parsedData.map(lambda lp: lp.label).min()\n",
    "maxYearNew = parsedData.map(lambda lp: lp.label).max()\n",
    "Test.assertTrue(minYearNew == 0, 'incorrect min year in shifted data')\n",
    "Test.assertTrue(maxYearNew == 89, 'incorrect max year in shifted data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** Visualization 2: Shifting labels **\n",
    "#### We will look at the labels before and after shifting them.  Both scatter plots below visualize tuples storing i) a label value and ii) the number of training points with this label.  The first scatter plot uses the initial labels, while the second one uses the shifted labels.  Note that the two plots look the same except for the labels on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7MMc/42f4/TN+hj48Y/x555BPzxh/52zh555DRp/fL289XA70AOyd4fjfhhwy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H3JKJcnZ5iprq7P3Hihwo1ckcf7UvSsoKm2iIiIiMhm0kEpMq/WanMtV2S8VKXemjuG3meZxAI+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\np403XowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb084a36c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get data for plot\n",
    "oldData = (parsedDataInit\n",
    "           .map(lambda lp: (lp.label, 1))\n",
    "           .reduceByKey(lambda x, y: x + y)\n",
    "           .collect())\n",
    "x, y = zip(*oldData)\n",
    "\n",
    "# generate layout and plot data\n",
    "fig, ax = preparePlot(np.arange(1920, 2050, 20), np.arange(0, 150, 20))\n",
    "plt.scatter(x, y, s=14**2, c='#d6ebf2', edgecolors='#8cbfd0', alpha=0.75)\n",
    "ax.set_xlabel('Year'), ax.set_ylabel('Count')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2tdGDVrosal3KZBvVkuT660u2Qde02mDmbBLgmibbTZe0fMGySJrJF1jXLpc/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\nsV+vLfbPsCA2RAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0858c6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get data for plot\n",
    "newData = (parsedData\n",
    "           .map(lambda lp: (lp.label, 1))\n",
    "           .reduceByKey(lambda x, y: x + y)\n",
    "           .collect())\n",
    "x, y = zip(*newData)\n",
    "\n",
    "# generate layout and plot data\n",
    "fig, ax = preparePlot(np.arange(0, 120, 20), np.arange(0, 120, 20))\n",
    "plt.scatter(x, y, s=14**2, c='#d6ebf2', edgecolors='#8cbfd0', alpha=0.75)\n",
    "ax.set_xlabel('Year (shifted)'), ax.set_ylabel('Count')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (1e) Training, validation, and test sets **\n",
    "#### We're almost done parsing our dataset, and our final task involves split it into training, validation and test sets. Use the [randomSplit method](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.randomSplit) with the specified weights and seed to create RDDs storing each of these datasets. Next, cache each of these RDDs, as we will be accessing them multiple times in the remainder of this lab. Finally, compute the size of each dataset and verify that the sum of their sizes equals the value computed in Part (1a)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5371 682 671 6724\n",
      "6724\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "weights = [.8, .1, .1]\n",
    "seed = 42\n",
    "parsedTrainData, parsedValData, parsedTestData = parsedData.randomSplit(weights, seed)\n",
    "parsedTrainData.cache()\n",
    "parsedValData.cache()\n",
    "parsedTestData.cache()\n",
    "nTrain = parsedTrainData.count()\n",
    "nVal = parsedValData.count()\n",
    "nTest = parsedTestData.count()\n",
    "\n",
    "print nTrain, nVal, nTest, nTrain + nVal + nTest\n",
    "print parsedData.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Training, validation, and test sets (1e)\n",
    "Test.assertEquals(parsedTrainData.getNumPartitions(), numPartitions,\n",
    "                  'parsedTrainData has wrong number of partitions')\n",
    "Test.assertEquals(parsedValData.getNumPartitions(), numPartitions,\n",
    "                  'parsedValData has wrong number of partitions')\n",
    "Test.assertEquals(parsedTestData.getNumPartitions(), numPartitions,\n",
    "                  'parsedTestData has wrong number of partitions')\n",
    "Test.assertEquals(len(parsedTrainData.take(1)[0].features), 12,\n",
    "                  'parsedTrainData has wrong number of features')\n",
    "sumFeatTwo = (parsedTrainData\n",
    "              .map(lambda lp: lp.features[2])\n",
    "              .sum())\n",
    "sumFeatThree = (parsedValData\n",
    "                .map(lambda lp: lp.features[3])\n",
    "                .reduce(lambda x, y: x + y))\n",
    "sumFeatFour = (parsedTestData\n",
    "               .map(lambda lp: lp.features[4])\n",
    "               .reduce(lambda x, y: x + y))\n",
    "Test.assertTrue(np.allclose([sumFeatTwo, sumFeatThree, sumFeatFour],\n",
    "                            2526.87757656, 297.340394298, 184.235876654),\n",
    "                'parsed Train, Val, Test data has unexpected values')\n",
    "Test.assertTrue(nTrain + nVal + nTest == 6724, 'unexpected Train, Val, Test data set size')\n",
    "Test.assertEquals(nTrain, 5371, 'unexpected value for nTrain')\n",
    "Test.assertEquals(nVal, 682, 'unexpected value for nVal')\n",
    "Test.assertEquals(nTest, 671, 'unexpected value for nTest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Part 2: Create and evaluate a baseline model **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(2a) Average label **\n",
    "#### A very simple yet natural baseline model is one where we always make the same prediction independent of the given data point, using the average label in the training set as the constant prediction value.  Compute this value, which is the average (shifted) song year for the training set.  Use an appropriate method in the [RDD API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.9316700801\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "averageTrainYear = parsedTrainData.map(lambda lp : lp.label).reduce(lambda x,y : x+y)/float(nTrain)\n",
    "print averageTrainYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Average label (2a)\n",
    "Test.assertTrue(np.allclose(averageTrainYear, 53.9316700801),\n",
    "                'incorrect value for averageTrainYear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(2b) Root mean squared error **\n",
    "#### We naturally would like to see how well this naive baseline performs.  We will use root mean squared error ([RMSE](http://en.wikipedia.org/wiki/Root-mean-square_deviation)) for evaluation purposes.  Implement a function to compute RMSE given an RDD of (label, prediction) tuples, and test out this function on an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.29099444874\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "def squaredError(label, prediction):\n",
    "    \"\"\"Calculates the the squared error for a single prediction.\n",
    "\n",
    "    Args:\n",
    "        label (float): The correct value for this observation.\n",
    "        prediction (float): The predicted value for this observation.\n",
    "\n",
    "    Returns:\n",
    "        float: The difference between the `label` and `prediction` squared.\n",
    "    \"\"\"\n",
    "    return (label-prediction)**2\n",
    "\n",
    "def calcRMSE(labelsAndPreds):\n",
    "    \"\"\"Calculates the root mean squared error for an `RDD` of (label, prediction) tuples.\n",
    "\n",
    "    Args:\n",
    "        labelsAndPred (RDD of (float, float)): An `RDD` consisting of (label, prediction) tuples.\n",
    "\n",
    "    Returns:\n",
    "        float: The square root of the mean of the squared errors.\n",
    "    \"\"\"\n",
    "    sse=labelsAndPreds.map(lambda x:squaredError(x[0],x[1])).reduce(lambda x,y:x+y)\n",
    "    mse = float(sse)/labelsAndPreds.count()\n",
    "    rmse = np.sqrt(mse)\n",
    "    return rmse\n",
    "\n",
    "labelsAndPreds = sc.parallelize([(3., 1.), (1., 2.), (2., 2.)])\n",
    "# RMSE = sqrt[((3-1)^2 + (1-2)^2 + (2-2)^2) / 3] = 1.291\n",
    "exampleRMSE = calcRMSE(labelsAndPreds)\n",
    "print exampleRMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Root mean squared error (2b)\n",
    "Test.assertTrue(np.allclose(squaredError(3, 1), 4.), 'incorrect definition of squaredError')\n",
    "Test.assertTrue(np.allclose(exampleRMSE, 1.29099444874), 'incorrect value for exampleRMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(2c) Training, validation and test RMSE **\n",
    "#### Now let's calculate the training, validation and test RMSE of our baseline model. To do this, first create RDDs of (label, prediction) tuples for each dataset, and then call calcRMSE. Note that each RMSE can be interpreted as the average prediction error for the given dataset (in terms of number of years)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Train RMSE = 21.306\n",
      "Baseline Validation RMSE = 21.586\n",
      "Baseline Test RMSE = 22.137\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "labelsAndPredsTrain = parsedTrainData.map(lambda lp:(lp.label,averageTrainYear))\n",
    "rmseTrainBase = calcRMSE(labelsAndPredsTrain)\n",
    "\n",
    "labelsAndPredsVal = parsedValData.map(lambda lp:(lp.label,averageTrainYear))\n",
    "rmseValBase = calcRMSE(labelsAndPredsVal)\n",
    "\n",
    "labelsAndPredsTest = parsedTestData.map(lambda lp:(lp.label,averageTrainYear))\n",
    "rmseTestBase = calcRMSE(labelsAndPredsTest)\n",
    "\n",
    "print 'Baseline Train RMSE = {0:.3f}'.format(rmseTrainBase)\n",
    "print 'Baseline Validation RMSE = {0:.3f}'.format(rmseValBase)\n",
    "print 'Baseline Test RMSE = {0:.3f}'.format(rmseTestBase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Training, validation and test RMSE (2c)\n",
    "Test.assertTrue(np.allclose([rmseTrainBase, rmseValBase, rmseTestBase],\n",
    "                            [21.305869, 21.586452, 22.136957]), 'incorrect RMSE value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** Visualization 3: Predicted vs. actual **\n",
    "#### We will visualize predictions on the validation dataset. The scatter plots below visualize tuples storing i) the predicted value and ii) true label.  The first scatter plot represents the ideal situation where the predicted value exactly equals the true label, while the second plot uses the baseline predictor (i.e., `averageTrainYear`) for all predicted values.  Further note that the points in the scatter plots are color-coded, ranging from light yellow when the true and predicted values are equal to bright red when they drastically differ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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vv/5nFi7MZMmSaWMea7GYmTs3ndmzU/n662p2766hu9tPd7cPl8s26nkjzbad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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0a4240c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap, Normalize\n",
    "from matplotlib.cm import get_cmap\n",
    "cmap = get_cmap('YlOrRd')\n",
    "norm = Normalize()\n",
    "\n",
    "actual = np.asarray(parsedValData\n",
    "                    .map(lambda lp: lp.label)\n",
    "                    .collect())\n",
    "error = np.asarray(parsedValData\n",
    "                   .map(lambda lp: (lp.label, lp.label))\n",
    "                   .map(lambda (l, p): squaredError(l, p))\n",
    "                   .collect())\n",
    "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, 100, 20), np.arange(0, 100, 20))\n",
    "plt.scatter(actual, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=0.5)\n",
    "ax.set_xlabel('Predicted'), ax.set_ylabel('Actual')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.text.Text at 0xb0847bac>, <matplotlib.text.Text at 0xb08cfd0c>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lIto9kFZfT3bVd7S1xjQzW9HsXE2uy/NJAEBzI7QBAEKlUlPjHbaelKyWw9ov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\nRtq0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0aee0ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = np.asarray(parsedValData\n",
    "                         .map(lambda lp: averageTrainYear)\n",
    "                         .collect())\n",
    "error = np.asarray(parsedValData\n",
    "                   .map(lambda lp: (lp.label, averageTrainYear))\n",
    "                   .map(lambda (l, p): squaredError(l, p))\n",
    "                   .collect())\n",
    "norm = Normalize()\n",
    "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n",
    "\n",
    "fig, ax = preparePlot(np.arange(53.0, 55.0, 0.5), np.arange(0, 100, 20))\n",
    "ax.set_xlim(53, 55)\n",
    "plt.scatter(predictions, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=0.3)\n",
    "ax.set_xlabel('Predicted'), ax.set_ylabel('Actual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Part 3: Train (via gradient descent) and evaluate a linear regression model **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (3a) Gradient summand **\n",
    "#### Now let's see if we can do better via linear regression, training a model via gradient descent (we'll omit the intercept for now). Recall that the gradient descent update for linear regression is: $$ \\scriptsize \\mathbf{w}_{i+1} = \\mathbf{w}_i - \\alpha_i \\sum_j (\\mathbf{w}_i^\\top\\mathbf{x}_j  - y_j) \\mathbf{x}_j \\,.$$ where $ \\scriptsize i $ is the iteration number of the gradient descent algorithm, and $ \\scriptsize j $ identifies the observation.\n",
    "#### First, implement a function that computes the summand for this update, i.e., the summand equals $ \\scriptsize (\\mathbf{w}^\\top \\mathbf{x} - y) \\mathbf{x} \\, ,$ and test out this function on two examples.  Use the `DenseVector` [dot](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.DenseVector.dot) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.mllib.linalg import DenseVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18.0,6.0,24.0]\n",
      "[1.7304,-5.1912,-2.5956]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "def gradientSummand(weights, lp):\n",
    "    \"\"\"Calculates the gradient summand for a given weight and `LabeledPoint`.\n",
    "\n",
    "    Note:\n",
    "        `DenseVector` behaves similarly to a `numpy.ndarray` and they can be used interchangably\n",
    "        within this function.  For example, they both implement the `dot` method.\n",
    "\n",
    "    Args:\n",
    "        weights (DenseVector): An array of model weights (betas).\n",
    "        lp (LabeledPoint): The `LabeledPoint` for a single observation.\n",
    "\n",
    "    Returns:\n",
    "        DenseVector: An array of values the same length as `weights`.  The gradient summand.\n",
    "    \"\"\"\n",
    "    return (np.dot(lp.features,weights) - lp.label)*lp.features\n",
    "\n",
    "exampleW = DenseVector([1, 1, 1])\n",
    "exampleLP = LabeledPoint(2.0, [3, 1, 4])\n",
    "# gradientSummand = (dot([1 1 1], [3 1 4]) - 2) * [3 1 4] = (8 - 2) * [3 1 4] = [18 6 24]\n",
    "summandOne = gradientSummand(exampleW, exampleLP)\n",
    "print summandOne\n",
    "\n",
    "exampleW = DenseVector([.24, 1.2, -1.4])\n",
    "exampleLP = LabeledPoint(3.0, [-1.4, 4.2, 2.1])\n",
    "summandTwo = gradientSummand(exampleW, exampleLP)\n",
    "print summandTwo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Gradient summand (3a)\n",
    "Test.assertTrue(np.allclose(summandOne, [18., 6., 24.]), 'incorrect value for summandOne')\n",
    "Test.assertTrue(np.allclose(summandTwo, [1.7304,-5.1912,-2.5956]), 'incorrect value for summandTwo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (3b) Use weights to make predictions **\n",
    "#### Next, implement a `getLabeledPredictions` function that takes in weights and an observation's `LabeledPoint` and returns a (label, prediction) tuple.  Note that we can predict by computing the dot product between weights and an observation's features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2.0, 1.75), (1.5, 1.25)]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "def getLabeledPrediction(weights, observation):\n",
    "    \"\"\"Calculates predictions and returns a (label, prediction) tuple.\n",
    "\n",
    "    Note:\n",
    "        The labels should remain unchanged as we'll use this information to calculate prediction\n",
    "        error later.\n",
    "\n",
    "    Args:\n",
    "        weights (np.ndarray): An array with one weight for each features in `trainData`.\n",
    "        observation (LabeledPoint): A `LabeledPoint` that contain the correct label and the\n",
    "            features for the data point.\n",
    "\n",
    "    Returns:\n",
    "        tuple: A (label, prediction) tuple.\n",
    "    \"\"\"\n",
    "    return (observation.label, np.dot(weights,observation.features))\n",
    "\n",
    "weights = np.array([1.0, 1.5])\n",
    "predictionExample = sc.parallelize([LabeledPoint(2, np.array([1.0, .5])),\n",
    "                                    LabeledPoint(1.5, np.array([.5, .5]))])\n",
    "labelsAndPredsExample = predictionExample.map(lambda lp: getLabeledPrediction(weights, lp))\n",
    "print labelsAndPredsExample.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Use weights to make predictions (3b)\n",
    "Test.assertEquals(labelsAndPredsExample.collect(), [(2.0, 1.75), (1.5, 1.25)],\n",
    "                  'incorrect definition for getLabeledPredictions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (3c) Gradient descent **\n",
    "#### Next, implement a gradient descent function for linear regression and test out this function on an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LabeledPoint(79.0, [0.884123733793,0.610454259079,0.600498416968]), LabeledPoint(79.0, [0.854411946129,0.604124786151,0.593634078776])]\n",
      "[ 48.88110449  36.01144093  30.25350092]\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "def linregGradientDescent(trainData, numIters):\n",
    "    \"\"\"Calculates the weights and error for a linear regression model trained with gradient descent.\n",
    "\n",
    "    Note:\n",
    "        `DenseVector` behaves similarly to a `numpy.ndarray` and they can be used interchangably\n",
    "        within this function.  For example, they both implement the `dot` method.\n",
    "\n",
    "    Args:\n",
    "        trainData (RDD of LabeledPoint): The labeled data for use in training the model.\n",
    "        numIters (int): The number of iterations of gradient descent to perform.\n",
    "\n",
    "    Returns:\n",
    "        (np.ndarray, np.ndarray): A tuple of (weights, training errors).  Weights will be the\n",
    "            final weights (one weight per feature) for the model, and training errors will contain\n",
    "            an error (RMSE) for each iteration of the algorithm.\n",
    "    \"\"\"\n",
    "    # The length of the training data\n",
    "    n = trainData.count()\n",
    "    # The number of features in the training data\n",
    "    d = len(trainData.take(1)[0].features)\n",
    "    w = np.zeros(d)\n",
    "    alpha = 1.0\n",
    "    # We will compute and store the training error after each iteration\n",
    "    errorTrain = np.zeros(numIters)\n",
    "    for i in range(numIters):\n",
    "        # Use getLabeledPrediction from (3b) with trainData to obtain an RDD of (label, prediction)\n",
    "        # tuples.  Note that the weights all equal 0 for the first iteration, so the predictions will\n",
    "        # have large errors to start.\n",
    "        labelsAndPredsTrain = trainData.map(lambda lp: getLabeledPrediction(w,lp))\n",
    "        errorTrain[i] = calcRMSE(labelsAndPredsTrain)\n",
    "\n",
    "        # Calculate the `gradient`.  Make use of the `gradientSummand` function you wrote in (3a).\n",
    "        # Note that `gradient` sould be a `DenseVector` of length `d`.\n",
    "        gradient = trainData.map(lambda lp: gradientSummand(w, lp)).reduce(lambda x,y: x+y)\n",
    "\n",
    "        # Update the weights\n",
    "        alpha_i = alpha / (n * np.sqrt(i+1))\n",
    "        w -= alpha_i*gradient\n",
    "    return w, errorTrain\n",
    "\n",
    "# create a toy dataset with n = 10, d = 3, and then run 5 iterations of gradient descent\n",
    "# note: the resulting model will not be useful; the goal here is to verify that\n",
    "# linregGradientDescent is working properly\n",
    "exampleN = 10\n",
    "exampleD = 3\n",
    "exampleData = (sc\n",
    "               .parallelize(parsedTrainData.take(exampleN))\n",
    "               .map(lambda lp: LabeledPoint(lp.label, lp.features[0:exampleD])))\n",
    "print exampleData.take(2)\n",
    "exampleNumIters = 5\n",
    "exampleWeights, exampleErrorTrain = linregGradientDescent(exampleData, exampleNumIters)\n",
    "print exampleWeights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Gradient descent (3c)\n",
    "expectedOutput = [48.88110449,  36.01144093, 30.25350092]\n",
    "Test.assertTrue(np.allclose(exampleWeights, expectedOutput), 'value of exampleWeights is incorrect')\n",
    "expectedError = [79.72013547, 30.27835699,  9.27842641,  9.20967856,  9.19446483]\n",
    "Test.assertTrue(np.allclose(exampleErrorTrain, expectedError),\n",
    "                'value of exampleErrorTrain is incorrect')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (3d) Train the model **\n",
    "#### Now let's train a linear regression model on all of our training data and evaluate its accuracy on the validation set.  Note that the test set will not be used here.  If we evaluated the model on the test set, we would bias our final results.\n",
    "#### We've already done much of the required work: we computed the number of features in Part (1b); we created the training and validation datasets and computed their sizes in Part (1e); and, we wrote a function to compute RMSE in Part (2b)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation RMSE:\n",
      "\tBaseline = 21.586\n",
      "\tLR0 = 19.192\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "numIters = 50\n",
    "weightsLR0, errorTrainLR0 = linregGradientDescent(parsedTrainData,numIters)\n",
    "\n",
    "labelsAndPreds = parsedValData.map(lambda lp : getLabeledPrediction(weightsLR0, lp))\n",
    "rmseValLR0 = calcRMSE(labelsAndPreds)\n",
    "\n",
    "print 'Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}'.format(rmseValBase,\n",
    "                                                                       rmseValLR0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Train the model (3d)\n",
    "expectedOutput = [22.64535883, 20.064699, -0.05341901, 8.2931319, 5.79155768, -4.51008084,\n",
    "                  15.23075467, 3.8465554, 9.91992022, 5.97465933, 11.36849033, 3.86452361]\n",
    "Test.assertTrue(np.allclose(weightsLR0, expectedOutput), 'incorrect value for weightsLR0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** Visualization 4: Training error **\n",
    "#### We will look at the log of the training error as a function of iteration. The first scatter plot visualizes the logarithm of the training error for all 50 iterations.  The second plot shows the training error itself, focusing on the final 44 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Mbn7fMAAAAADA/9nybZAej0cff/yxurq6FAgE5HK55HA4NppexONxLS0tKRKJ0GQEAAAA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\n+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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1d0090c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = Normalize()\n",
    "clrs = cmap(np.asarray(norm(np.log(errorTrainLR0))))[:,0:3]\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, 60, 10), np.arange(2, 6, 1))\n",
    "ax.set_ylim(2, 6)\n",
    "plt.scatter(range(0, numIters), np.log(errorTrainLR0), s=14**2, c=clrs, edgecolors='#888888', alpha=0.75)\n",
    "ax.set_xlabel('Iteration'), ax.set_ylabel(r'$\\log_e(errorTrainLR0)$')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0aHx8nFk27Lve3l6N1NdrZKNexzbWHtt58m/NlZUrYxjqSMZklngLqqUjGtLC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\nYYQ2AAAAALAwQhsAAAAAWNj/A4xGx/ufo7FWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb06a89ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm = Normalize()\n",
    "clrs = cmap(np.asarray(norm(errorTrainLR0[6:])))[:,0:3]\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, 60, 10), np.arange(17, 22, 1))\n",
    "ax.set_ylim(17.8, 21.2)\n",
    "plt.scatter(range(0, numIters-6), errorTrainLR0[6:], s=14**2, c=clrs, edgecolors='#888888', alpha=0.75)\n",
    "ax.set_xticklabels(map(str, range(6, 66, 10)))\n",
    "ax.set_xlabel('Iteration'), ax.set_ylabel(r'Training Error')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Part 4: Train using MLlib and perform grid search **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(4a) `LinearRegressionWithSGD` **\n",
    "#### We're already doing better than the baseline model, but let's see if we can do better by adding an intercept, using regularization, and (based on the previous visualization) training for more iterations.  MLlib's [LinearRegressionWithSGD](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionWithSGD) essentially implements the same algorithm that we implemented in Part (3b), albeit more efficiently and with various additional functionality, such as stochastic gradient approximation, including an intercept in the model and also allowing L1 or L2 regularization.  First use LinearRegressionWithSGD to train a model with L2 regularization and with an intercept.  This method returns a [LinearRegressionModel](https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel).  Next, use the model's [weights](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.weights) and [intercept](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.intercept) attributes to print out the model's parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.mllib.regression import LinearRegressionWithSGD\n",
    "# Values to use when training the linear regression model\n",
    "numIters = 500  # iterations\n",
    "alpha = 1.0  # step\n",
    "miniBatchFrac = 1.0  # miniBatchFraction\n",
    "reg = 1e-1  # regParam\n",
    "regType = 'l2'  # regType\n",
    "useIntercept = True  # intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.682292427,14.7439059559,-0.0935105608897,6.22080088829,4.01454261926,-3.30214858535,11.0403027232,2.67190962854,7.18925791279,4.46093254586,8.14950409475,2.75135810882] 13.3335907631\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "firstModel = LinearRegressionWithSGD.train(parsedTrainData, iterations=numIters, \n",
    "                                           step=alpha, miniBatchFraction=miniBatchFrac,\n",
    "                                           regParam=reg, regType=regType, intercept=useIntercept)\n",
    "# weightsLR1 stores the model weights; interceptLR1 stores the model intercept\n",
    "weightsLR1 = firstModel.weights\n",
    "interceptLR1 = firstModel.intercept\n",
    "print weightsLR1, interceptLR1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST LinearRegressionWithSGD (4a)\n",
    "expectedIntercept = 13.3335907631\n",
    "expectedWeights = [16.682292427, 14.7439059559, -0.0935105608897, 6.22080088829, 4.01454261926, -3.30214858535,\n",
    "                   11.0403027232, 2.67190962854, 7.18925791279, 4.46093254586, 8.14950409475, 2.75135810882]\n",
    "Test.assertTrue(np.allclose(interceptLR1, expectedIntercept), 'incorrect value for interceptLR1')\n",
    "Test.assertTrue(np.allclose(weightsLR1, expectedWeights), 'incorrect value for weightsLR1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **(4b) Predict**\n",
    "#### Now use the [LinearRegressionModel.predict()](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.regression.LinearRegressionModel.predict) method to make a prediction on a sample point.  Pass the `features` from a `LabeledPoint` into the `predict()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.8013380112\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "samplePoint = parsedTrainData.take(1)[0]\n",
    "samplePrediction = firstModel.predict(samplePoint.features)\n",
    "print samplePrediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Predict (4b)\n",
    "Test.assertTrue(np.allclose(samplePrediction, 56.8013380112),\n",
    "                'incorrect value for samplePrediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (4c) Evaluate RMSE **\n",
    "#### Next evaluate the accuracy of this model on the validation set.  Use the `predict()` method to create a `labelsAndPreds` RDD, and then use the `calcRMSE()` function from Part (2b)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation RMSE:\n",
      "\tBaseline = 21.586\n",
      "\tLR0 = 19.192\n",
      "\tLR1 = 19.691\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "labelsAndPreds = parsedValData.map(lambda lp: (lp.label, firstModel.predict(lp.features)))\n",
    "rmseValLR1 = calcRMSE(labelsAndPreds)\n",
    "\n",
    "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}' +\n",
    "       '\\n\\tLR1 = {2:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Evaluate RMSE (4c)\n",
    "Test.assertTrue(np.allclose(rmseValLR1, 19.691247), 'incorrect value for rmseValLR1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (4d) Grid search **\n",
    "#### We're already outperforming the baseline on the validation set by almost 2 years on average, but let's see if we can do better. Perform grid search to find a good regularization parameter.  Try `regParam` values `1e-10`, `1e-5`, and `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.0171700716\n",
      "17.0175981807\n",
      "23.8007746698\n",
      "Validation RMSE:\n",
      "\tBaseline = 21.586\n",
      "\tLR0 = 19.192\n",
      "\tLR1 = 19.691\n",
      "\tLRGrid = 17.017\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "bestRMSE = rmseValLR1\n",
    "bestRegParam = reg\n",
    "bestModel = firstModel\n",
    "\n",
    "numIters = 500\n",
    "alpha = 1.0\n",
    "miniBatchFrac = 1.0\n",
    "for reg in [1e-10, 1e-5, 1]:\n",
    "    model = LinearRegressionWithSGD.train(parsedTrainData, numIters, alpha,\n",
    "                                          miniBatchFrac, regParam=reg,\n",
    "                                          regType='l2', intercept=True)\n",
    "    labelsAndPreds = parsedValData.map(lambda lp: (lp.label, model.predict(lp.features)))\n",
    "    rmseValGrid = calcRMSE(labelsAndPreds)\n",
    "    print rmseValGrid\n",
    "\n",
    "    if rmseValGrid < bestRMSE:\n",
    "        bestRMSE = rmseValGrid\n",
    "        bestRegParam = reg\n",
    "        bestModel = model\n",
    "rmseValLRGrid = bestRMSE\n",
    "\n",
    "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}\\n\\tLR1 = {2:.3f}\\n' +\n",
    "       '\\tLRGrid = {3:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1, rmseValLRGrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Grid search (4d)\n",
    "Test.assertTrue(np.allclose(17.017170, rmseValLRGrid), 'incorrect value for rmseValLRGrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** Visualization 5: Best model's predictions**\n",
    "#### Next, we create a visualization similar to 'Visualization 3: Predicted vs. actual' from Part 2 using the predictions from the best model from Part (4d) on the validation dataset.  Specifically, we create a color-coded scatter plot visualizing tuples storing i) the predicted value from this model and ii) true label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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hZjnc+XTV/m0zXO6VAuJWSKFSwlCKSnkexwrIOprBfIq+zoBEDIqVAHRINhFy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\nHKNjS5w/11S6PgKSKO5anFu2lxpoWRau66Io3ueW6fD0+SL9N27tS+hs2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FmUty1svTo4\nwZNPEVwsy0VVJOIxjcWVPF3tUQL+/cnD9jjIkkihaOIo8PC7dXq6q1E1hXTGIBp5TTC9qNn+mwle\nmqfLZrLIZrLIn/xR957rNL+QoqU5iiAIbCYLxKJaOUrn7mgHXhO6gyRuFFXGNC1UTeHkiQpm5wrc\nu/cFt259vidiNzszzerKGDf62w/9Lquvr+Dzz/08fLTAzZufH9rHbRuTkyN0du62G1hbS7OwkOTm\nzfZDpxhWVQW5dauDgYFhbt48RSDg9WNrK0c4HMWyLAqFLaJR77cEwbs330cwpL29ikePRnBduHLl\n8BH+9vY6Hj8eZ2pqnba21+esKBKDg4vYlsPVKy37jn1Rt8hlTQRRIBj0zk3TPGGddGqLaDR+IBHd\nDx0dVTx7NkwoHObxo6+5cX1/BdD9oGoyN/ubGBxcw+9XqK2NvfugA9DZUcHQ8BCVlTffu41j/Hg4\nJnXH+GjhZjKIH6BGVwDkTBbbtj9YCpUgCFz9wz/iIQKTv/k3Ekvz71QdcoDZugaKP/sDrv7RH/8g\nEYCPDQ+//CXnpMEyoQPI6hDTiu88NiyZpLKbyKp6YJ1djC0ymcxHR+qOsT8e3v8NFzp1wgeIdBwE\nQRC43KPyzfP7ROOVH40lx1Gxvr7O6tJLrl+oOPT9LwgC1y418eDJPPGwTn21SEFXGJxIezYFthch\nC/lFEk0aAZ+E7bgMjhcwbZVzvdXML+WZWNDov/kHyLKM67osLi6ysjyLYXjmyxsba3S0aNh2mFze\nYGYuSaFoYlkOsiwSDKicPtXAy8FFCoUsnW1BAFbWiyytFCnoFo7tsrqeJhpR6T1VUU5pdPEia4c5\nY7eUGmmYXkpmbU2QbwaWaGkME3hHtrIki+RyFk+ebdLVUUltbRDbFigWXVJpnXBI3WO67bouG5sF\n5hcy6IZX9yaILvPzGU6dqttTSweQyRg0NoZLf9eJxbyObb+uCgWTqakkubxnCC5JJYP15hjx+O6T\nkGUBy3Lw+aCiMsDI2Caneup48niAq9deL6TT6TTTMy+5eaNz19zJZotMz6yQz+tYpo2sSAT8Gq2t\nNYTD3m8FAho11Qq//OU/UFfX4Pn3qRp1dU3U13tRyPX1dRYWptH1IrbtKcpGoxWk01tEownAq2Mb\nHl5gcnKRGzcSZLN6yQhcLgunvG1ah8MaFy828eDBCJ9+ehpBEBgfX6e7+zqFQoFw+GiEcxumaaEX\niziO7amqCiKpVJJAwH9gNHM/CILApUudPHkyQT6/RE+PJ3C2vp4hnS7Qvw+ZdoFC3sA0QZJ8RCO7\nz0FRJIJByGRSRKOHJ1depK/IgwdfcfVK/aEJHXhKpZoqc+1qE3e+mSUSCeD3v5+aZjjko1g82sb1\nMX53OCZ1x/go4bouvOlF9z0gGwaGYXzQuhhRFLn6R3/EeGMD394fIDY1QdPiPD57t1KWLkrMNTSw\n1d5J65WrnD979veC0G1tbeHLTFAR3j0ejuseSvpfAEKiTj6bJhLdW9MCoLgGhqF/gN4e44dGMpkk\nICWJR97P/FsUBc60w9jwUy5d/eQD9+7HwejIU872RI58/0uSyNWLTdx/vMC3r9ZprA/S0xXH7xNR\nZLAdyGRNHr/KkEobCILIifY4PlHly0c5qmpaufnJBSzLYnDwBcuLk9TVKnQmQmglk+X1jSCbm1n+\n8V9eosgiZ3qr6GyLIZVIRzpj8GJwAccRWE3Ci5FFJEmgrTVCe3sFqiohirC1ZbCyVuDFYJK5SI7O\n9iixqO9whK70X9PyDMujUY1C0aK1JcbIZBZd36S9JUxjfbAc9XDxninrG0Umprzzr6sN0tQYxrK9\naF+xqBMIRMjksgg4+Et1WrPzKWbn08RjfloScTRVRhChWLQIhf2sruZJZ+Y42VVFxQ4yZpp2Of3O\nNO3ygnttLcfE5CaW5dDeXkmizYcsSziOQz5vMTG5QTZrkEjE8Jf83Tw5fa9dTZVwbJuKigDjE3O7\nasFHR15x5nRDueZwaWmTiUnPlLujvZJQKIaiSJimTTar8+rVFLmciaJKWJZNa0uc69cbCIdjyLKE\nrpvMzY3y7bf3ME2L5uYKOjqq8ftjiKKIaVpsbGyQTq9z924Bx3GxLBPXdbhypZl43OuX47joukU+\nb6CqMj6fjCiK+5I7QfAEZ6qqAqyupojHQxSLIpFIhM3NTZQjiAC5rmdVUCzmkWUBn0/xvANL9gSy\nbJJK5UmlqvH7A4e2JRAEgYsXO5icXOHLL8eprg6STOa8esEd37Mdl2LBxDBsNM1PJBpgbTW1b1qm\nqkoUCiaWbSMfQdgrm8uRaI0SCvoOfQyA6zgIgoAsS/R0VzExuUJfb/OR2tjV3rEC5k8Gx6TuGB8v\nPqCqoS1LP4jfiiAIdJ0+Q2ffaZaXlhi7exdjZRnMEiFVFJTaWrpu3ORyff3vBZnbxtjzh3Spe9Ms\ntxcxhxkKWQDHKJaI4D7eSYKMJr8fSTjGj4uxkad0N32/+R+LKOQmFkuS6j+t614oFHCtNMHA0X0z\ndcNm4PECtTUq5/taMWwZARfHtSkaDrZjIysy3ScqMEyHyZk8z0eLXLh0mU9625AkibXVVV48v0d3\nV4ieW3V7BC1kyaGtJUTPiSibSZ3BkS0EAVqaIqiKRMCvUFcTZGkly4PHK/R0V9HaHMUwLFxciron\nmqKoEnW1QbpPVpDJGrwc2qSh1k9H27ujq67rIgD5vAV497woeHYJp3trKRRNZma2+OLeMpJYEkzB\nI4AV8QBnztTj98l88fU0lu0giaIXtdBkLNsiFq3Atm3WN7b47vk8He0VXL/e4qUQugKiKCKKIo5t\nUF8Xo/dUPVvJHKPj64QCKr2nasr1fHbJv0SSRGzL5sm3i7iCQF9fPcGg+sbzTcbvU6msCGCaNtPT\nSYaGFl+fs7B9HVwURaJQyNHZUcHE+Ainz5zHNE1yuU1isU4sy+bBg1GiUZXLl/am5CmKRCCgki/o\nXhphooJQ2Ec0GiyRS51gMIokiTQ0aLS1tbC5mWdkZBXbrsDn8yKwqioTDPoIBBwKhSJTUxtUVvpZ\nX89SVxcBhHIdpaJIuK5H7lKpIuGwVhYw2TkOruttznR0VPP06SLRaIS2tpOlcZSw7cORB9v2POQ0\nTSIa9e19NwgCfp+C36cQDMrk8xl0XSEUCh/qvSMIAh0ddbS31zI3t8bc7CamYZNKFUr3jffH7w8Q\nCGplsifumBdvwu+XKRTyhEOHV9At5LN0tB093dxLY/bGsrY2xODQFE5P05HSP3e3916HHeN3gGNS\nd4yPEoIg4PreLxVjP1g+3w+6CBQEgfqGBur/w38Adpjc/p4+DQ3DIL86QTS89wWnSlB0ZfzC4Wwq\nfIJOMZ8jENwrzV0gQOSINSLH+PFhGAbFzDKRju//yknUWUxPjtO1j9z3x4zJ8RE6Wo4+V3XD5u7D\nOc71xqiIa7iui5G2iMarygIjUBaYBKCpEeYWM8wszJBItLOyvMTI0AA3r9ehvhFJcByHVCpJJKSg\nqgKiKFBd5edWhY/HT9cwTYeONi9tbCNZZHgsyZ/8vBUXgaJuEY34EQTPrkQUPTGUtY0iluVQGfdx\n83o9T5+vMzSapOfEwYR225I8l7e8lL5SdMN1BXyl2qRgQOVUTw09PTWlg/a3J+g+WcXDx0tcv9IA\nuGiqTCpdwO8PkC+YvBxa5eaNdkJBbY+he1E3cVyZYDBALpcnEvVz+WITY+PrfPd0ifPn6tF8EvmC\nSSzmR1Mlnj5fpru7hrY2L+X0zR7t/H9FkejqqkJWJObmZykWrXLKXr5g4verOI5FVVWcV6+mcJyz\nTE9NkkjEsCybu3eHOHmyukSs9sf4+CrJrRyf3O5EFAUMwyaVypWInYFpWmSzKSIRH5IkeumZNWHu\n35+ip6eFmhrveudyOWRZoLExSmNjjGfP5kskdLfqInjqlD6fgqJIpNNFgkEVWZb2bB4oioymKRSL\nBrYNp055ESRN0ygU3p2d4xG6JOGQtkf5cmefPB9EkESBSNhPoWiQSacIR6KHJimCIJBJFzl/vglF\nUQiFvSjmQYf7NIX8AeegKDK5XGHP+B2EdLpAKCQfKe2y3G9RxLFfr0EaGsIsLG7S3HR0sRPLshHE\nY6rwU8HvrwTbMT56VJw8QeoDELuCIhNIJH5UgrUtwfz7imQySQ3r+37WHHGZNg4frdBEB1Pfa2vh\nurAq1FJVVbXPUcf4mLC5uUlN/Ohek/uhsVphZWnqg7T1Y2J9fYHa6qNZWDiOy8CThTKhA+/ZosgC\nluWN53bc4M2/NzeEaa4zuX/vKwZfDdB/dS+hc12XdHqLYEBC1eSyuAd4EZXL56tZXcuxtJIllzN5\n9mKNG1cb0DQZnybh0yQyWa8+VpREtg+XJJFi0fYiL4LAuTNVZPMWs/OZg0/WdcnlLAzTYX2jSDpj\nsLqWZ3Ryi8bG3QRGgLJFwrYNwU6v5oa6MP6Awr99McPicpb1zTyWZaMXDR49mebalWZCJTGLnU/p\nQtFE1yEcieD3+xElH/mChQu0t1cgygIPHs3j9ymMjnrPt5XVLLV1Edp22h4IUCyarK/nWFrKsLKS\nIZUq7EpjC4e833/5crVMTqank7Q0x1EVCcuyqKjwkU6nWVldoK4uwldfv6Sy0u/VAW5kMc29ptiL\nS1usrWe4dNGTvC8WTVLpApl0kampVfJ53SPxJUIH3hjmcgadnZXcvz/C+PgSm5tpbFsnEFB2Rdiq\nqkKMjnoZGNs1dNskavvaR6M+cjmjpIpavryYpoMsSxSLhufH1tRWfk/6fD4MQySZzLG2lmJxcZPV\n1RTZ0vxyXTAMk2RyE03zIoPOAWmBC4tb1NTECIcDvBpcZHFpi2xGx7QMcrnsvscchPX1FDU1IVzX\nE/p521s9FPKRy5llS42dEARQFLF8374Lg0MLu8RajgJZlrGs19eksTHCygHm7+/CzEySpqYPK051\njB8Ox/T7GB8tTl65wpO794k+e/m92llItHLy5o0P1KtjHAaGYaCyf61bQ9BmZL2CbnftcKkw7J/T\nv1JUqGnv+722B/ipwNB1NHn/tKSjQpZFHOfDEMQfEwLOkTd6FpYy1FarZUK3DVFkFwE7CK1NEUYn\nxjjT27RvVMPQdRTZLRua2+xOjRYEgcsXqrkzsEwooHL+bDWq+rodnyZhGA6W5SDJIq5rY1kucong\nOU6phlYUuHCuhq/uzNPcGHoj0uNFxxaX8yws5wGRSETDFUE3BGRZ5eHjRerrQrQlYqiKJwTjqWWy\nox2XXN5kZjbNylqOiniA2roIK2s6UCSbNcgXlmlLxAnsEOpxXRfTtCkULURRJRKNlBfugUCATMZm\nczOHooh0tFdy5+4MgZCGZcOvfj2GLEtcudKCaXqG3KurWSYnN3GBeMyPosjYtkOhYLK1VaCpMUIi\n8ZoANjSGmZvfoi1Rwdpanr7eJoq6V7umahLpdJr1tVW++HKZSERFViCdyWMaNi9fLRDwa3R11RCL\nBXBdl+HhZW7famdlJcPExDqCANGoH0WVMAyL9fUsmYxOa2slDQ0x5uY2mZ9PEov5CQRUTpyoZG1t\nnfn5ZURRoLOzmqqqoJeW6ricPt3A119P0N5euSMS5+54RntprKGQF3kLBkVAQNctJEkknS4iijKV\nlbFyZM7zs5sjn8/x7NkWNTXhUn2gQyZTJJPRqasL0dgYxecTAQfdsDFzHkn0+9VyrZrjugy+WkKU\nJKIRjVzWRlUFTMMhnSmSz5ucONFCU1Pl4e5HAXTdwuc7nM9rS0s1s7ObtLfvJWSiKBzqvgVYXs7S\n0vx+dXACoGl+dN3A51PQVBnDOPoz03VdZucz3P6k9d1fPsZHgWNSd4yPFpFIBLezHePlIOoBeerv\ngiNAvquD6urqD9y7Y7wN27Ux+0EQoC4ssFQM0aAedtd074tw0m7kYs+Z9+7jMX48uPvlpH3f9n5i\neJ8eT80muXbp/SPRtuPQ1RZgYSlJbe3eWp5CMU8ktNt/zHGcXQqRsiwSi6gsrxa4fLFuTxt+v0S+\naBAJ+RBFkWxWJxDweQIapkXQ70UAHRdqqgNMzaZpqAviumDZLsmtIqMTaaqqQpw/14Df70WGZFkk\nnTE4cUJGVSQWFtM8eLRIJKxy9nTNrs0cx3F5/nKFbM6ivb2Cnh6v9k0UvRonXXfI5Q3yeYP19Ty/\n/XKcM6frCQRUDMNBUTRCofAum4OirlPIZ9E0icqqMLgujuNw8kQVuZzJ5cvNDA2vUlcbxjBsVlez\nTE8nqa4Jce5cE8F9LDscx2F+IcX9gZmyKmNDQ5ThoWVkSaSpMVYmGpbtMD29QiCQobevkupqP6GQ\nhvyGhUAqVWBsfJV83qC1tQpNk/n66wlqasNcuNCMf4cNhOO6XjqdIHD//hSvXi3S01PLp5927iL9\nW1t5gkEN07QZH19jcHCZs2cbUBQvDTaRqGBmJklHx7b9wPZ8cctRO1kWsSwHx3EAgWxWR9MCBIOe\ngX0ms4nruszNzTI29pympjA/+9kJ8vlUWU3Utm3AxbYdZmeTfPvtLB0d1SRaX5Ni07LJ54s4juer\ntrWVA1w++9SzW0il8qVUUG/cCgWTiYlNRkfn6ettpbbu3Vkjum4Tix0ua6i1pZo73wzS1nZI0rgP\nkskcmhY4smn5Tvj8ftKpwuv0zfd4AK1vZInH6w+0/zjGx4fjLe5jfNQ4/fnnjJw9zftQOhcY6e2h\n5w9+9nudCvm7gKqq6BwsodwRsxk2Gyk4795XcgGE3Y+qqUKIQPNp/P6jpbMd43cDVdMwzA/zurEd\nF1H6aYmkeBCPREZTaR2fTypH0XbCcTjUgq9YyFNfGyCVymFZu1P1LMtCFJxd4gmiuB1h293PpsbA\nHiuAbSiyiGM7JcLgohuUJPMlLKsUxZNEZFmkoz3G7EIeFwlBlMlkbSamc9zsT3D2TC2BgMp2fZZp\nOui6XY4MNjREuH0rQSzm5/6DhbIghW073B2Yp7IyyK2brTQ2hBElEQTBS88TBDSfgiRJVFaGOXe+\niStXWnn2fIVMBmLxyj2ELp/PY+hZYjEfgYCKJIpIkoSsKLQ0V7K6WsB1Nba2ijQ1xSgWLaamk1y7\nluB0X/2+hG57fFua43xyu7OskiiJAuGwjxcvV8rpdoZh8+DBOCdPVHLjRgdVVV498ZuEDrwo3KWL\nzZw718idO6MYusnNmx309dbvInTwWpTl22/nqKkJ85d/eZqmpjj5vLlrbvp8Crpu4fcrnDnTyK1b\n7bx6tczqagbHcWlpiTM7m9x1jCBQItKvSw80TUbXbfJ5A00LEA6Hyymfum6ztrbK4uIQn37awYkT\ndV5ary9ANquXajS9c9ZUmc7Oaj79tIv19SyDQ8s75p9EOOzD55PIZAoMDy9z7Vqi/DvBoI9MVi+n\n5/r8CidPVnH7dhvj4/PMTB/smQqwlcyjaf5D1+HJskR9fZyhoZU9nzmOi/AOsRLTtPju6RKtrZ0Y\n+vtnJIiCgKJo5PMGhmmhqkeL4eiGxYuX6yUPz2P8VHBM6o7xUaO6tpbOv/0bhvp6jkTsXGCsq4O6\nv/oLmts+PlPuf++Ix+OsuQcXZWuSy+UGl4FCGwX77S8bwxFQ1NeSznPFAMvRS5y/9tOUtf99REVF\nBatbH2a3d2ndoKqm6YO09WMiFq9hffPd/ozbmJlP0d4a3Pcz03KRDqHma5g6qirR3OBnaSm96zO9\nWMTn231NBECWZGzb3UXsFNkjlwcp+2ma5EXCilBRUU0ma2LbLpGwn2zewrRsBDxFQkWREAURXbcZ\nGd/iZn8LPp+M64JtuyVlSZd0xkDVPEVEl1L9FtCWiNPaGufxt0s4jsODR4t0dlTS0rLDA8x9XV/o\nOA4Cni2Dv0TQwiGN27faGBxaIJvZfU2KxSKWVSQc8e3ZDBTwhE4qKwKsruVpaa5gYyPP+Pg6n37S\niarKpf6+nby7uDQ3exGioaFVqquDVFSGUBQJ23Z4+GiWM2fqqagMllSbPS+4tyGfN4hG/Vzvb0NR\n91/aOY7L8+dLVFUF6e6uLVsMyLIXYQUvHVZVpVK93jY5U7h5s52FhS2WllJIkkgk4iOd3n8+bxM8\nn0+hWDTRdRufb/cG3MjIEpKU5cqV1l1RV03zUSiY6Lq5R5BFFAUuXmjG0C0mJl7XbDu2gwCMjKxQ\nKJi7xkqWRQJ+jXTas2UQ8NIgJVGkvz/BwsIqS4ube87BdV1evVpE88XIZo9mr3TyRCOFgsvY2Nqu\nfzct560q3IZhce/eNKf7rtLU1MzSSv5Iv/smAsEQliUyPbNFVdXhvVx13eTe/VnOnesvW2oc46eB\n4/TLY3z0aOs+ifQf/44X/99/pu3pSyL6233JsqrCdG83if/hr+js6/uRenmMndA0DbUyQSY/Q1jZ\nfyEYUR0uNYjcX+zgpLJAg5Lddze04GjkxSjfrqikbRlHixBVN/nyl39PZX07nd1nftIvHtd1WViY\nY2rsOY6VB9fGcSGdNRAEh1DQhyQAgkihNPUNwziwPdu2mZ2dYm5qENfW8WzvRVxBpqH5BIm2LlT1\n/Yxo3xeapqH4qsnm1wgFvt9rZ3JJ5uqtkx+oZz8eOrtO8fzbX1FdebjocqFoEtzHbdsTm1APFakT\n8BaxAb9M9g1VPtux8e1TjyoIIMsKlm2VF9LgSbJ7dWO7j9kO1ui6S0VlHFEQiETipNNb+H0CkbCP\nTKaIqjr4fDJBv0yhaPLdi3WuX21GVkRsx8F1vQhPoWChGw6iKOL3KRQKFqGQuit7t6kxQjqt8/Dx\nIpVVQerr95GJL8mBuq4XdRAlEWlHlERVJa5dbeLR40k+ud0LeASwUMwSi/oRSr+YTOYZn9ggn399\nz2WzOs5Cmra2GC9fLnPjRqIcybQsh2LRZnY2yfLyG8IwAtTWhqmuCpLNee0VizbPni0hioJn7j26\nQnt7JdFoAF3fvh4ycHDUxnEcnj9fpL+/zSOWlrNvDeXKSgZBYE+9VyCgkskUMQwLRfFIpEfMXn9H\nliWuX2/nyy/HqK+PEgioFIsm0ej+89l13bKIjZeO+/o9kErl0XWdy5dP7yHO+VyOaMSHZdlkMkWC\nARVJEsuWCbpu0ZqIc/fuFJNT68iyV7+XShU4c7oBWZb47ukc166+3szVNBlB8FJVg0GNQsFgfGKD\nTImUDgwMEouH8fk0Otrr8Ac0BgeXCYcbuXnzMi+e36Gm5t2WHNsQBIELF9p5/mKGx4/nOHWqFkWR\nkCVl3/vWdV1WV9O8erXGmbPXy+JfhaLIxMQKC0sbuKXxEwDHhbraOG3ttWhvicAJQCgcYXJymsYG\nldoa460m5K7rsrySZnBok/Pnb1BReXS1zGP8bnFM6o7xk0BLRwex/+1/ZXjgAVOvXlEzNErVZhK5\n9KCzBYH1WITVnpOEu09y8cYN4vGj+0Ed48Oh88xVxr94xXnl4PSWiOpws1lgYquJ0YxLrZQkoWzi\nFz3FucFiJbN2FZUanGjME4+G0bQiMO+pX2an+e63T3H9DXSfvU5V1U+ndtK2bYaHnrE8P0p9LM/l\nNm8BPTSVZ31Lp7vOoqHCwXZEEBUCoQjJjMjsIrx88m/MTjfSe/oK4bC3oNV1ncFX35Fcm6a5Sud6\nt4Isby8gHGzHZGH1Efe+fEYg0kDv6csEg/tHgn4IdJw8x/jYrzjX9f5tZHIWWqAW7SdoYxEMBrHd\nIMWihc/37levbbv7ptsVijaB0OF33QFPLfONKNvbpNW3iYRje+mT235khuXgw8uE2Fae9JQRJc/8\nudSeJElEYxUUCwXSmTyKLOO6Lqm0QdGwmZlLE4v5kBWxpNInoBsOpukCLvFYgGxOL4uY7Gds2dkR\n5x//eZXLl3eLSWyTTAEvVVcSBdIZA2kfWfZAQCUUUthMZqmIhygUCgT8CoIgMD+/xcTkBsGgyomu\nql3kpVA02dgoMDm1Ti5vsLKao6U5RjarMzi4TKFgkmir5MZNzyMQ18V2XAzDZn4+ydNni+Waut6+\neiYmVqmoCHD33iSraxl+8Yu+ksjIayKxnRq7X/bewkKKpqaYZwAvCDjC3uvrApOT61y61LLvNQ8E\nVLJZHUWRSoqWe7/j88k0N8eYnU0iSZ5VwmtLA+8Yt1R76LqeEqaiSGXD9G08ejTJuXOte3zTXBdM\nSycY8qNpCoZpkcnqmKaFKAoE/AqRUgT1ypUWZmc2SSTirK7mSKeLzM4lOdVTh2nY6Lq1K2KnqjIb\nGzbffTeJogqc6Kqm+mwdgihQLJieRUfR5tXgLKurRdrbTtHT04cgCNi2SrFo4PMdfjNMEATOnkmw\nurrF06fLpDN5Tvcl8PttZPm1r9/s7Cbz81mqqxu53v85fr8f0zQZGnxBPpsil5O4dql2l3Kt47gs\nLmV48GAITfNx6lQz4dD+BuUb61laW7uprW3g26dDCEKRzvYYlZWhXWR5emaTxaUCtbXN3Lh5EZ/v\naIbnx/g4cEzqjvGTQSQS4cof/hzrZ58xNTzC1NAgZlEH10X2adR0dfFpb++PHoU4xv6orKzkZaCV\njLFOWD04eVaTXE5VWvRUwGIuzst0BQUD1gyF9ooM/XUW4UgUWXpD1lyA2rBLbXiDgrHBk3sLNJ/+\nnETb92ANPxJ0Xef+nV/RVrnCZ6c9Bbl80eXesy26mwxOt2yvYb1wg+0YZNIbWHYMEDjfaaMqMzy6\nu0TfhZ8TCAR5cPdf6W3Jc/acjCDsvQckUaClTqOlDrYycwzcWeLspZ//aJYQVVVVvHoWJpPPEX6P\naJ3ruryYdOg+d/4H6N2Pg64TZ3kxcpdLZ+LvrPOVZRHTclB21NSZloODhCwdbfwMy0GRd88JURA8\nknDAMQLeotyrTzMwDAdREDGt1/5XougJZ+iGtUeFqcaTrQAAIABJREFUVhQEAoEA/kAA0zAoFgsI\ngkgu7zI2scUntxOksy6iICJKEn5fmHBYZmtrE9tx8PsVtrZyBAKe99ebdWrpjEF9fQTHhe3l7k4e\n4rqeMIjjeIvgg7QeujorGR5Z5vLlTgyjiD+g8ez5Io7t0H89Ua592wnH8WrZek/VEQ6rDA17BtW6\nYXPhQjPRqG/Hd10c24tw+v0KXV01dHZWlywRkriOg6pIVFWF6OqqYX09y8DAFCdPNhCNbht4e1FM\nx3EQpL2y+lNTG1wuKXCCl15oOy7yjjrIXE4v92E/bEdgbdst1cXtP14dHVV8880k9fURIhGfl27q\nvn6+e/NCYud08EzVvX/I53WSyQzNzb172i4Wi/g0uRwl9cigU7JfEEoput5vxWMBnj9fJBTSqKoK\ncepULZubee4PTNPYGGNico1TPfXltsdGV9nYzHCjv6VEqrzr6DoOsiyS3CoSDITov94NLoyNr/Fg\n4A6Xr9ygq6uX5y+ecvlSy5Hr82tqYqiqwrffrZNMhpiaXsW2TQRBRFFUGhsSfPpZS/n+yefzDNz/\ngp4TIfp6OkhtJfdEx0VRoKkx4kWsMzqPH43S19tK9RvRRMdxGBxe49LlPyAYDFJXX08ul2NyYpTR\n8TVsx/OEVBWNpqYuPu1uOlaT/onjmNQd4ycHWZbp6uulq2/vS+EYHw8EQeDKZ3/O/V9muC6O4Jff\nXmciCNAYsqkLwjfLPi7XbFFTHSIQ2Gs6/ib8KvQ3b/Lo1b8iINDa1vmhTuODw7Is7n39S840b1AR\n8R7BuuEw8HyLSydMIvtkkkoiRIMuWytpwHtxR4ISN3stvnr0S2xH4eYZCB6wYHsTsbDMjdMO95/8\nirOX/4SKiop3H/Q9IQgCl69/zoNv/pn+XhufdvgaO9d1eTZmUtN04Ufp6w+F2ro61tc7GBqf5lTX\n29O5QkGVVNog4PfmiG07ZHMO0djhz98tWRSk0hax+G5SJEkytmWUpeAPglebJpAvWgQC2p7oCoBt\nuYgH1AoJeMJJ25ttgpihNRGlpblx3++HIzHSqSQBv4QkecRWgD0RzomJTfpO1VAoWChh1SN0Ox4x\nlu1Fi/J5i3DI85uzLKesgriNSMRHsaiTy+ZRVZHnz5fw+xVOnqg5cExs20tvFEQBVVVoaY5xf2CL\na9c8IZedUSnHcUvS/zvGRBCoqvSi5C9eLNHXV8fcXBKfplBfFyUaDfDw4TzRaJRIJOBdK1tHkrya\nO2kHsUuni/h8Sql+rVBu33WdsrH6dpRuW63yIPj9Xg3ctgLpPgFSNE0hEFCYmUnS2hrfM5474dVl\nCti2i6aJGIbFb34zTHd3077zSC/miZQIsW07ZDJ5j9CJIk5J5GXnUS0tcZaWMmWPwIqKADdvJLh7\ndxrdsMukbnR0lVyuwNUrzQiCF7X2iOfrvvt9DqqqIZX+rftkLfMLSQYG7tDf/wnr6w0MDS1z6lQ9\nR0EuV+TJt8vcuPH5O6Nfuq5z/95vuXyhkkjY+244HCWd3iIa9e2buhkJa9zsb+TewCy9YoKqKi9z\nw3VdHj2Z57+z92ZPblxplufv+gbHDsS+M/bgLi7izhSZqaysrLS2sbEqs2kb6/f6o+q1zNr6bazG\nprq7KqtSSlESF1HcySAZG4Ox79jhu8+DA4gIxsIgRSkpJY6llBThcFy4XwD33O875/T3n9rWkRGN\nRjlx8ue7MVbH/qhT8jrqqONHQyQS4dzv/i9uGkfImgf7urm3qtGWLNDcFD0QoatCEnCuM8f04/9g\nY2PjfYf8o+O7W19wtGONhkSwmPZ9n9tPcpwe3J3QVSGAaKUDzPECJ0NFBuFkONq1Qjj0bjvIIVXi\n4lG4d+eP+2r0PiSi0ShnL/6eb55IZA9oPuB6Pt+N2ujpkwwO//yd2I4e+wRHdPPgaWabGYnjerya\nyfB4dIV7jxYplR3uPlqjVHawbY9cwSOeSAdEpVyiUMiTz+coFPOUy+Vdg5hDoTBlw2Zx2aS9bXul\nOxQOUzaDeeQ4geV/vmCSL5gUiham6dQ4Utnw8Fyx60I80Kx5tZbYQJeT5emzee4/fM2DRzM8e77A\n+kaRQsHE93UaG/bWFcqSFBC7fJCxpaly5TXcwKWxMirTckmlwkELKOwgdK4bhJnHYzqu6+K6LsWi\ngeO4uO5mKHag+/J4+Og1N29Ns7iYw3F91jMlHNcLzrUlRNuraMVkWUZVJEzT4d79WT7/zTCapmAY\nmwYfAQF7g4m8gaNH2xgbX6FQsHA9D9fzCYc1zp/v5vbtF1iWQygUwjAcZFlGiMCwpvp2czmDxqYo\nAlEhwYEpjagktPuVe5LJGDQ1RSvvY/cBKYpciYCoti7unFNCQFNTlGLR5PHjxR2uqlV4HhUjFR/T\ntFlby/Dv//6UpqZ22tr2aB8WQYXXxyeXLxGP6zWSVQ0534rGxij5/HaNfSikcOnSIYpFk3LZYWZm\nnZWVHMePteK6QTyC7/mINypSaiXwfSu6OtO0twmePH7A0WMncN00Dx7MHjhrbn29wO3b81y4cP2t\nhM73fW7f+jNnPknXCB0Em9jRWJJs1tjTqEhVZC5d6ODR4ykMw8ZxXL69NU1LywjdPbu329bxy0S9\nUldHHXX8qEgkElz+w//N/W//E2/9Ff3qHG26vWMHuGgLRouNLDsWJ9qlHW5pB4Ek4FTLGs8e3+bi\nZ7//QO/gwyGfzyOseVrSm9WRtaxLMmKTPgB/rV4ys1wCkiyu2bQ1+LQkfcrlItHoLoYR+0APSQx1\nlHk1Nf6TWVcnk0kuX/s/uH/3Br61ykCnR2uDuqOtqVh2GZ91WS+GGTp8ka7uX0YArhCCk5+c5dWr\nFF/efkw8bON7FkXDpqcrTkdHBFWRcFyPSEzizv01fASDvY0IKQd46CEJNSTVFrqOY5DLFpFkjUg4\nUnPY0/Uw4xNrtLYmd1xfSQhcF9YzJRRZQg8FrZSIgOjYtkspY6FpCtMzBRxvdx2ebbuoqo5tOUy9\nWmVuIUNLc4TW1jghrULITIfp1ytMz2RJN/Qiif2t2hVZJhyJABaKKlcMVGwM08EwHfSQgudvoRwV\nLZfr+ZimG7SKSgEJLZQM9JBKIq5RKtkIEej28nmDqVcZVldLNDREaGmJ47ousVgI03QYe7mKYdr0\n9TbQ0Z7AJSAvgZOjFoSqyxKTU2sMDTYTCiloIchkgmww3wNJSLXrtRnNzbb/jsZCNDfHmJxcQwhR\nqXwFrpS9vSlevHjFoUMtSJJSudYyrisqeXPBta3GPkTCQauqEq8EcXt+zYkyyB+sxmrsraWsaqxi\nsSpJ31mt833o7m6gt7eRGzemSKV0BgebicdDOI5HuWThVCqK2azB5OQ6rgsjwy08fTZDV1cHlhVB\nVdXauf0tzNw0bTRN3qYpFQRkb+voVVXGsnaSSl1XGRpq4umzWfI5g4sXepAVKZgnFWtUx7Fr1brA\nh0rgOjtJU39fE1/+eQrXPcGJk6d59WqSL78cpaVFZ2CgaYfxiO/7zM9nmJzcQNMSXLn62wNpgFdX\nV0nEXdKpnTt7mqoiJdLkCzkEQWuyqsrb7qKmygwNJPnizy8Ih5McOXKW1rZ3qyrW8fNHndTVUUcd\nPzrC4TCXP/8vGIbB+OgjRiefEPJLqDi4SFi+ipbuhGSUM/6dHVbr74K4DtbKDKZpfnSGGmMvHjHY\nbrH1q3d8psTxnndLYnTdIFdqcrbM2WHQVEGpWCIajfGuKd+dLRpfPXrG0PCRnyzPMRwOc/lXv6Nc\nLjMx9oxn9yfQFBdVCTRQpi0IRRoZHD7FyebmX2TO5KFD/RQLBVaWx+jtTpBKKAhp091OSDKdHU10\nd9j4vsvkdJbJ1yaX3jBNgCArLqwr2I5HsZRFljSisTgCGHtlcPrk9pZN13XJ5TLoIQlN01DfdEqU\ng0ViJOKTy5vMzOZpbUmyuFSkvW1z9yGoGjqUyvD85ThDgw1c/6xv14peU3OUru40a2s2L8YW6GhP\nkEzuXZqWhIQkBfpBSbjIckB0TcOhWLKxLBfbdrEsl/WMEcQYCIEeUpEkkBVBJKxt0WL5hMOBucfi\nUp7Xr7MMj7Rw/Hg7pVKlaiwgFg0hgPa2BKblMDW1zo2vp7hwvgc9rFIyDBTFQw9p4AcmJb/+9WD1\n6WiqjFXRGO7Wmvhmjcf3fQYGmnj5ciXQMG65dn19DXz99RSHD7dRKJjk8xbpdKxynBRo2URQmayS\nTNf1ak6TQkg1bVtA7KqkfG9iF+jfts6H7cf6vo9hOLS0xGlrS9LWlmRtrcDTp4usrORRVamitfMo\nlx0ScZ2zZ4aJVYw8DMNED8lYVoFySRBPJLe8Z1E5xiIR3/ndLRDbWKbn+buarfi+T1dnktt3ZmhM\n64QrLcy+ALwKcfbB81wc2w02QXy/puV7E4d64ky/mmRgcJje3n4OHepjaXGRe/dGcZwSWmVDxLZc\nLMunrb2H8xfOvtPvz8T4M44f3tvcTZFlUsk0jutilEsUi+VaJmDVaTQRjyAkk2vXf/+L/M6s4+2o\nk7o66qjjJ4Ou6xw/fZ7jp8/jOA62bQdtTKqK7/t88a//TFvX+0TNb0d/fI2JsaccPX7mA4z6w8Bx\nHLKrr2js2PzaNUwPx7GIvWNRUlNc1jeKSMJBr7RdarJXIbLv5lomS4KmeJnl5WVaW1vfbSA/EOFw\nmOMnz8LJszvmwy95UeL7Pg/u3yGkbnDtyqFgYQaBDsoPjBAEQXj26uoS8ajMqeNNrKyW+eb2Ilcu\ntO0gdhCQu2Rco1R2yOeyTM87HOr9hNGx16RSYSJhDcdxyOcyxBOBVbzrODWXyDfhOj73Hq7z298M\nMPpilbv3l7j+K414LKhOlEo2q2s2i8sbfHa1d099le/75HImyWSKVErQ2KRw78EkJ08corFh9+qy\nkKRapUmW5SD8WwG9slDW1FUkSUJVFRKJcKVtD3K5EuGwus35sNpyqOsaDx7O4Dg+n/2qPyA61Sqf\n65NIhLYt60OawuGRFjraE9y89YqjR9tIp6NIsiCXM8jnTFpaYgix+b71sEo+b9TIYW0Mu77L6rhU\notGgkphMbgZdK4pcy4NrbIyysVGgUCgRi0UqFTiJSEQnkynWPi+xWIhcziAWC70RSSBVAsW1SrVu\nZ9XVdX0cxyNVqRZVNYhVHhXcx6ANcGuFqqEhiixJDA8309fbhGnaGIaDoipEwvFt5CZwtXRpjsax\nLIdsNkMymaqYooDjupUq6y5zSWy6aAY6S7tWpaze52o1WZYlfM/jUG9q2+M1XZ4IzGGE5OM4Dq7r\n7WkS0tPTwFc3xukfGKqZ1rS1t9PW3o7v+1iWhed5aJq2LcT+oDAMA9vKEYu9PX9TkWVisTg+cXzf\nrwXKCxFUHHs6bebnZuns6n7ruer45aGuqaujjjr+IlAUhXA4jKZpCCHIZrM0aLk9XdfeBR1Jl5XZ\niR9+og+I9fV1WpLb9R/L6zYdDe9OYjVVMLdcoqt5c6kY0sA0y+81tq5mn4W5yfd67ofCm/PhlwbT\nNMnlcqyvr/PgwV1UeZ2jI0219yoIqlOyJFVazSCfy5BM6JiWj2G6NDeFOXG8iVt3l/fV9YR1mRcT\na8wtuBw/cYpz569z87tVcrky+VyGREJDqZhtyHIl+PuN85mmyze3Fzk60ko6FebS+S4SiRA3vpkl\nkzEoFm1W123mF3NB3twehM7zfbI5Ez0cR1NVFEVBkWQuX+zh4cNpCsXdc0dDWgjzjda6anaaENDW\nFmP69QahkBroroQgny8Tjmg7gro930eSJGZnN7AsjxPH27EdL3AT9QL9HQSxAQHd2fwHIJ7QOXu2\nmydPFpEkgabKJOI6c/OZWoi45wetgbIk8L2gne/N82xDNXqh0kI5NNjM+noJ23Zrjp2e59PZmWRu\nbgPf90mloliWS6Gw+Tlvbo6xuJirXRdVlQmHNbLZco0Aua5Pe3uSmZmNWjtmlQBVDVE8z6dUMonF\nNs1wymWb1dUimUyRfN5gY6OEJAlWVgo0NwcVW9/3efRolnhCY2iwBYQfuLZqCnoosqNa1dHRyOvX\ngeZZ0xSiUYVcLluJQFAol609g9YFAXnxK9f69fQG7e2Jyjg2CV21UqmqMtFIYKLjVQPpd2lDVhSJ\nUslEUfd2Bg2HBeYuGblCCEKhEOFw+L0IHcDy0hId7e+2GRd8XwQh6pLYNM7p6UqwsPD6vcZRx88f\n9UpdHXXU8VHAsix06WDmGW9D0GH0Yc71oWBZFiHFZevXrml7RNWDie63QgJs1ye0Rc4hSdQCat8V\nIU3Cek9CWMfe8H2fhfl5JiaeIQmDSFjG8zwWl1a4fL4tCOgOR1F3WUxapoks+4Q0BU0LV8KhbZJx\njbbWCK/nCvR2x3e83uKywdhUntbWRmzPpVgskkgkuHjpb/j3f/t/6OtWOTycrtmkCwGyouA6Ts1g\n5PVMntdzRU6f7KChIVI5TnDlYjf/698n+PcvZjjU00C+YPDZlUM7LNchIDmm6WAYLtFoYtP9EtBC\nOuDw6dkOnj6d4cL5nW61kiSQhLKrYyVAX28D//bHMfp6g2xKy7KRZYmQtn1h7fvgez7ZosH392b5\n1a8GsBwP4VCzyK8aUOymGXQr5Coe1zl2rI0XL5c5crgVIYJK1vZK0WZm256o6Lq2fupN0yEa08hk\ns+TzRiWLLRifqkrkciXW1/OEQirJZISNjRKuWyIS0VBVmVQqwtpakcaKo6bneeh6iI2NMpomo+sa\nPT1NfPnlc4aHW2pjDDifV8sbdJygWvXo0RwrK0USiRCyLOFVzFOKJYtYLESpZPPVV2O0tSaIJ0Jk\nNopcutRLPl/GrJjvaJqKpu1sP4zFdBwnqLIFRjgKtupimibhcIRcbh1d37ttMaiySZimTalkkUiE\nthE6ARhlh3BYDap1EJBssZPQ1e5xRf9nlMvEYruLm/WQgmmaP0p+m2kaRPYgsu+KUEjBNEsf5Fx1\n/PxQJ3V11FHHRwHP8yr1iQ+E3dJz/4LwPA9JbB/TbiYEB4X/RhBxoJR5v/csbdndruPD4PX0K8bH\nHtHWqnHuVBJdDyo6z54vcOp4I+mUHphKGDkKRYhFE9vIXdkoEY8FP9ECSMR1HNejXLZIp3Vu311E\nUyXUSih4Nhe4XLa2pjl/bhhdV1lZLTA+Psonn3yKruskE3FaWpPcfbCELHl0tocJhQIjEstyWVgq\nk81btLXGOP9pN5omY9luxYzFx7I92trSNLWcxHVcSuV7lA0H1/Nq2WaByYqH44KuR0imdlqxh/Uw\nufwGqaSOZVmYpl0L4952XDhK2cjX2j23olS2AUGhYJKI65TLNvFddFi5nMGDh4uYlsvQcHON+FSr\nPU7gAINpOmSyBiEtCFKvVrEkSdRIZWtrnGejSxw53IqqyqiqhO/5lc9htYV203Fym27Nf/PzGfzZ\ntl00LXjv1aBuSRI1oqxpSuUfGdO0sSwHWRYIoWBZgmKxTGdnkpcvV7hwIVKJcbCR5WAzQJJkikUT\nSYJ0OsziYo7W1vi2Sh0EOXaPHs2jKBIDA40cHmmpZM4JhBSQJcfxePFimULBJBZVEZLH3bvTpJJh\nisXArTQWCyNJErbtUihkAIlYPFlzsQQY6G9lcnK1Fg8Q1lVy+RKpVJqDfA0Jgipdb28TQWSCV6su\n+oBlO4Qj4VqeQ3X8e6FctonFdAoFA5/orto6IfGjfUd6vrdDG/i+kCSB5+3uSFrHLx91UldHHXV8\nFNA0Ddv7gF9J0sf19aZpGiV3exVBUyWs/Y0Ad4UPKLLY9lzPA0m8X/uPZXuV6kkdHwKjo48p5Ka5\nfrV922ItsPvPcGQ4yGhTFIl4TMPzfHL5LOFInJAWwnFdBB7yG3NYkSXiMZ1oVCedzrO4JqHrCqoi\nk2pIcfhIctvrNTVGefJsDtd1mXk9TU93jI6ONB0daYpFk6WlLKWcg++DpoYYGmohlYrg+T6WaeF6\nLr7vIYSEospEoiFOHEty9/4UWkjnzKk+YnEdyzJrNvuSkNDDKuoeuXUQtDnKska5bNPXl2ZyaoUj\nhzt2HKeqCsUSWJa7rSLmOB4PHi5y9swA9+/Pc/Fid0V7t31hvLZW4v7DBS5e7OXe97MMDQZVvYB0\nBfdDkoIWNiEEUUWiVLQoFEzi8VCgUxJsI3idnUkWF3N0daXQ9UAf5m4x7Ki101b0XzWCIKj9eWsb\nYLls09ISJZ83iEY1olEtIKoJvUK2g6pTPK7jeR65nIGqyhSLZRoamolGY5imieuuMT+fI5kMo6ph\nEonElg2jCJZl09KS5OHDOa5dG0TTlEBTJgT5vMGtW684e6abdDpSydaTKs8X1f/huh6dnSlGRlp4\nPbPB9PQGly/1kc+bPHu2xIWLfVvIqIymyUEcR3adeDxVc2Vtb2/g5ct5OjpKpFKRwIFS+DiOi6aF\ncBw3yPbbY/7k8gazcxmuXxtECAnbtmrXv1Aw0fVAk+tVyN5+dMmygvunqjKhkIxp7F6NC+bgzs2F\nDwFN07He54dgF1iWs2uFtI6/DtQ1dXXUUcdHgXg8zroVffuBB8B6EWKpvcOD/xJIpVKs5rcvCtIJ\nhZXsuxMxx/VpSGosb4njsx1Q1PdbdCxveKQb6/bXHwLjYy8wiq/59FTrjt33ldUCLU36Tl2PJEgm\nNMqlPJZtYxhlwvs4wEoCjo40IPA5eriDocFWOtpTO15PCEFHe4iFhQVevx6j99Cmu140GqK/v4XD\nIx0cOdzBwEBLzSBDEgJdDxGJRIhGY0QigTZKiMAuHr9EPrdKMhlBliTCephIJEo0EiUcDu9L6KqI\nxeJYFjQ2Rlhc2jtXMpFIUSwFWX0QELqbt2Y4eribzo40Q0MdfHVjchvpA8jlTe4/XODq1QEEAi0k\no2mVcVUMJnzfrxEbqGRBRkMBaSrZ2widqIjW+voaeTW9DkBDOsL6erGWfVY5dS1ioVqpC9oCt9+b\nqiay2ia4slIknY7UKnP5vInv+ywvF0inK/dFkkgmw9i2Syymkc2u4/s+uh7i0qUj3L8/z+qq8Qah\nq7iUlgq0tSU5ebKLb76ZwnE8hBAYhsU330xy+VIfTU0xfN9HVaWas2K12mWaDqWSTSIRXJ+B/iZG\nhlt4/mKZgYFGDh1Kc/fu9I4suaobZj6fwa25cgouXhzh+3uz5HLl2nUI5n0Y2/a3ZfFtRaFg8t13\nr7l4obdmbCJEUK0rlSyECCp/ArBtb1+yZNsuxaJFIh44Vem6irFLG7rv++TzDuHwu8fsHAQNDQ0s\nr+6uLX1XLK8UaWj4uH776vjp8HFtZe+BYrHI7du3mZmZwXVdkskk165do6mpqXbM3bt3ef78OaZp\n0tLSwtWrV0mn97aHraOOOvaHYRhMjD1jZW4c4bsEfSwK8YZ2ho6cIh5/t0y0t0HTNMLpQ2TLSyR/\n4G/nRDbN4atnP8zAPhB0XUfSmikZc0T0YDGSjMkUTQXLcdHe4dvYtCW62mNMzudrAceGLZF4VxtN\nggXLzGqIa6d6D/ycUqnE5PhzVlfnMMplisU8Ph4hTSMaTaCoOt09w/Qc6n1v84CfI3K5HAvzz7l6\nsWNX/U6pZBGL7W7GIIQgEdfI5rLIsoKs77/nGo9plMq5t44pHtMolgr4flD9+BAIhWR8fErlMpZl\nbGt1lmQ50Am+hdgJIJFMkstlKZftWnXmTUhCkEykyeUyGIbJs+erHDvSTWtr4GrY0Z5mdHSOm7dm\nOHumg0RSx3M9vrs7y6VLfeghhdV8gXhsMxy9Gj5efT2fatuaj5CokCqDtbUikhzQMUWRa66arhMQ\njp6eNH/+aoKurhTTc1lmZjaCds7K+TRNpre3kba2ONIWh0zLdikUgkW8qgbVrEymzOnTgfthOBzM\nkUymzOvXG/z2tyOb100IEgmdTKZMLBYin8+i61GePVugo2OI2dkCrrtEX19zrWpWLBZQVUG5bBKJ\nqPT1NfLFFy85caKDly+X+fRsD4mEjuO420hu9XqVSzam6ZBI6EiyVKtytrbGKRQMnr9YYnCgmY1M\nmceP5+jpSSPLgWGLokjIsiAWC1HIZ0kmg3WZrmtcuniYW7dfMDTYRHtHAs91UaMaxVIwQ9wt4/F8\nj/n5HM+fL/Hp2S4UJdApB2MMYhBM0yWZ1Cth9IEWcmY2R0PD9s3CaiyDaTokk5HaZogkiW1zuYrl\n5RzNzZ17umP+UCSTSUplGct20NQftix/9brA5asDH2hkdfzc8NGTOtM0+Zd/+Rc6Ozv5wx/+QDgc\nJpfLbSuDP3jwgCdPnnD9+nUSiQT379/nX//1X/mv//W/7ipAr6OOOvbG+vo6Lx7fxsnP0p9Y40ib\nvy0gdr04wdMbT7HUVgaPnaOj4+02zAfF0LFPGb/1grPh7Hufw3LAkFtIJBIfbFwfCgMjpxkfW+Bk\n/+bf9XWEmV6yGOp8Bz2cUFAUhe42ndfLRbqaQVL0bQvHg2I1Y9PQPHIg8rWyssLYiwd4ThZdNfGs\nMu2NMgNnIkTDCrbtUTYNHNdiZeMeX33xgHRjF8MjJ4hE9s4k+6VgbOwpxw6n9zRkcByP8D5kTZIE\noZCEYTgIsX8LlSyLmsHHflBVGTtvsZ+p/rugUDBYXFojHJYRwiSZ2O5W6jgu5XKOghuEn+t6eM/2\nN4EgkUghy2G+ublILAaD/Q0kt+zquK7HzGyGV9M5TAt8L8zyapl4PEIkEqwDwhGtEja9RLls0dgQ\npaEhSrTyuG0HRhiO4wWVJAGSvFmFqgwG1/GQ/MBOP6gumSSTQSueZbnkCyaCgJThB5Uew7D54otx\n+vsbuXChFyGo6eIMw2FycpXR0SU6OhJ0daUqZiSiNnYQzMxs0NOzab0PAbFbXS1gWQ7ffDNJf38j\nHR3Jms5N1xVWVgqMji6jKClGRk7Q0dGJ53lMTo7z1VcTpNMqXV0JPK9MOKwQCWuAz9pqEYHg9u1p\nFEUQCsnYthtcl4prZ6CZtHFsj1BIIRrVgsc4LcajAAAgAElEQVS2uKT6vk9/fyNffTXJyHALRw63\n8NVXExw71obr+pRKBp4H4XDVkdTDdd3ad00kEuJXV48xPr7Al1+OE4/pfHIqTCgUxnFsNE2hWDKY\nmlplcTFPW1ucq1d60bSgNdN2XIrFQGeoqjKJRBAcXyxaZHMGXV0p1tZKNSOUIDvPxra9iulMdBdN\n887PycRkhlOnT+0xiz8M+voO82p6jOHBprcfvAcymRLRWFN93ftXjI+e1D148IB4PM61a9dqf7fV\nncj3fR4/fszp06fp7e0F4Pr16/zzP/8z4+PjHDly5Kcech11/GwxNfGCuWd/4pPWDLHUzseFgMYY\nNMY2MJ0Nnj2cYWXhHCfPXPwgNvTpdJqi1E7eyBJ/T4nX89UIfUfP/eCx/BhoaWnh6cMEJSNTq9Z1\ntmp8eVfhUIuNdsDf4pAeEKRD7Tp//r5EIgLJ9O6ubfvB931Gp2XOXj7+1mNfvnjG+tJTjg1Fefg0\nT6pB4vSRxDYdk6ZJaJqE6/qoskNXm07ZXObWN/+LU2eu09DY+M5j/LnAcRzy2WUaTnTueYyiSNjO\n/kRM1xUKxRK+r+3rouO6/q6uk2/Ctl1UVeNdQ+l3w/Jylqej8xw72sziUoGwvrPdV1Fk4nEZz/cp\nl01yOZNEIrlnsLMgIH/Xrv+ejY0NXo6PUi7NUQu9FhId7Ye4cvVyLc9yeWmJh09eYFtlwGd1rURf\nn8+ZM8MIIfOnPz3ik1NtZLIGAIbpYFpOpSJTyTrzN1/C21K5E4HgDVFxiHS94DpXzUo8z6eQN3n4\naI611SKffNJJOKySTkcqIdDVql/g2jky0srQUAtzcxlu337F5ct9qKqCZVXbSV2mpta4erV/23Xx\nPI/x8VU+/3wESRJMTq4yPr5amxKu67G6WubcuWGy2QgdHcG8kySJwUpI9p+//E9mZl4SCQd5ftUL\n3t2V5re/PcLo6AINDYGxycZGabNKJ4IKaSSiocbkyvvabJ30PK8WZC6EoKUlxspygdbWOA0NQUtq\nY2OUeDyE7/mVsHiHcFihXC4Ri212eaiqzJEjXfT2NvP117Pcu7eGaZZZW1smldKQZejva+JQT0CI\njbKNYQTOxoosEYtpCBHMw0LBQg8pRMIqN29No2kqRtnm/v15BgcbEZIgrGtEo8o+H63tD2xsFEFE\nf/RNqc6ubr784hG9Pc5mm/A7wPd9nj5f4/jJa28/uI5fLD56Ujc9PU13dzd//OMfWVxcJBKJcOzY\nMQ4fPgxAPp+nXC7T1bVZLZBlmfb2dpaWluqkro46DoiJsVHWX/4Hl7sLB3JkDClwuqPI2OpN7t+x\nOXPhsw8yjk+v/p5b/5nncvsc+jtuOE6tazjpM3T3HPogY/nQEELw6cW/4fY3/y9XjlpoqkCWBKeG\n49x6nuXKUZf9CmZG0G2EqgQXRpZAC8W4P+nzm3Pv1lrn+z73Xjj0DF7a08a7iuejjzHyLzl7MsE3\nd+Y4NhSiuWFv/Z4sC+IxhVLZJBwSXDkb49t7f+LUmc9JNzS80zh/Lph+Ncmhnv0XfpGIxvLy/i2T\nQfaUCFrh9lnc5QtWpfKyP/IFi2gihhByheC9Z5bWSo7nYwtcvdJLoWjxajqz7/GSEEQjGqZZDZhO\n70rrAsIQPJJOpzl37vK+5xVC0NrWRmtbW+3vnj55iCQZ6LqOadrE4iE62jfnmaIoLC7kaoYfELRa\nIgJyJAC54nDpe37gYykE4bBacUYM9IQ+AttxSCR1Xr1a5+rVARoawpTLNvm8UWk3lCvVOrlGfjRN\npq+vkXQ6yrffvuLKlb7a2B4/nuf06c5KyLdfM1j57rsZBgebaq2YR460sXU547oe9+7NkUjoTE7O\n47qf1Cpgvu9z+9bXjBxOEIsmSCRDOxxIA71enmPHWoHAkCWVCuM4QVWr5ibpb1r+Q0A2fd+vjdf3\nYaC/kfsP5mhtjTM40MSTpws1fZ6QBPFYiFI5cDl1HPD92I5NwELBpK2tnRMnT+P7Pl9+8R/4/hrX\nfjWA53sIPCRZ2jGHgvG5KIpEMhEilze4890iJ461kU5HkKQQjx7PUSw6dHYm951bwZzYfIVi0eT+\ngxWuXP3tvs/7EJBlmVOnLnPzzjdcvdR1oA2bKnzf59GTJVpah0km93+Pdfyy8dEbpeTzeZ49e0Yq\nleIPf/gDR48e5ZtvvuHly5dAoO0AdghYw+Fw7bE66qhjfywvL7H44gvOdh6M0G3FUJOJmr3P2PMn\nH2Qs0WiUs5/9n3y70EneONhzfB+er4RZDZ3hzPkPQy5/LCQSCU6e+1u+fqZRMoLd78aUyvChBF8/\nVTB3idfzgYIBrtgsX/q+z72XPo1d5zhy+vd888jDsg9mue16PneeuSRaz9LXP7TvsXNzM+TWX/DJ\nkQTfP1xipE/bl9BVIQREIzK+W8b3bC6fSXD/+y93DfB9HxSLRSYnJng++oTRZ4+ZGH/Jxsbehhs/\nNtbXl2lp2t/op7kpxvKqscNM4k2Ewwql0v45ixNTOXorGW17wfd95hdM2tvb6ekZYvr1+12fYtHk\n6bM5Ll04FGjMJJlYLEw2+/YPaCikoIckCvndyezcfIa29p53Go9pmjx+/Ig///lP/Od//m/mF5a4\n890EL1/Oce/+JNGoQtmwasYc4bCKaTnbTDN838dzPfCDcOnq114tv80PHBEd28Uo2xRLFqWiybOn\ni+TzJr/97TCaGrTKKrKEabqUSkGba5UImaaLYTgUCmaFHGqcOtXJd9+9ro2lqytFc3OsRuZc1+Pm\nzVc0NUVrweZvIggLh9bWJBsbQZ5cuVwOtK6TE/zx3/8nrreBUTbJZIo1QrexUWJiYpnR0QUeP55D\nViQ8L3jNwO1SoCgyruvVogWqc1WSKtesai5DtWrnE45oNS1hLBbCstza49WIh0hYq1xbr3ZftmJi\nco3eviCvcOzlKC0tMNjfyo2vJ3EcZ9s92oqAbFevicfTp0vkc2UiYZVYTMN1DT452c2r6RyTU+v7\nzivDsNFD4cq1KnL7zgLnL1zbEaD+Y6GxqYmRw+e4cXMW0zyYG6bv+9x7sIAa6mRo+PCPPMI6PnZ8\n9JU63/dpbm7m3LmgnaqxsZGNjQ1GR0cZHh7e97n7tYOtrq5+0HF+zKgudP6SC546Pm48uPNnjiUL\nrJXebxe/NWpy99l3JBtaPpiYfPj059x6dBvFWqE7liMZ3tmN5niwkFNYNNI0dw7TNzDC2traB3n9\nHxeCwaO/4pund9FElu5mh0RUpbs9wZePy0RDDl3NHpEQWDZYroSmhbG9ILT56ZRHydHp7j1Ka1s3\nAO19F/jT/fvEQ2W6WiG6i3uiaXnMLnusF0IcGjhFQ2PrW78LHz/8jlNHQkzPFjFMC1WJsrp+cPtt\nH1hZzxJPpGhv8nhw/y4DgyNvfd6u5/J9VldXmZ2ZQMKgpVlDUyQkITDyLk9mnlA0BB0dfbS2tf+k\nJi3ZbI5cPky5vP+1icWijL7YoKV576qebXsUSjaWDfIu+VW247G2btF7yGd1tbjneTYyZULhFBsb\nG4QjUe6NZkgmdrpvvg3PXy7Q3ZMimzUpGzayHKapMcWjx4scOXwwp718waRUFjvez7Pnqxw/PnCg\n3+T19XVGRx9TKmVpbY2RTuvkckYlZFuibBTxXAvXVVhbK9QcMxVForExxuPHixw61IAgqMp4vr/N\nOt+v/qvSkun5Pvm8SSSi1TIhn79Yoa+vieXlIooiUyoF+WyRiEY+b7G8XKy8pkBVlVrFy/N8HCcI\nOjcMl6+/ngKC2IbV1RLFosnsbJZstkxfXyPJZITV1d03pj0vqG6Wyza5nMPGhsFXX/0JTfNpbg7T\n3OwRjUYxjBLLK3m+v/caSQgaG6M0NkZQVQnDrAbNZ/DcYLyG4dWyBr1K26nv+7ieh1ppHVbeIFeO\n6yNJYBhubbzl8uaffXzwgzZNH59M1sC0tkdeWJZDLhcEkJdKJcYnnnPuTDsISMQ0/vTFBKlkmO6u\nZK1yuRXFks3cbIZM1qCvN8WhnhT3Hy5w+pNOPN+nWFxneKiT8YlFXr4cp6M9RmtrbFs1zPeDTEPH\ntZidnQGhc+TIaQzDwDAOuLv4AaCoGj09x/nixhOiEZ/urgSxyM5NNMt2mJ3Ls7pu0909QEtrx1/V\nuvZjwk+xxt5qDLkfhP+2LcO/MP77f//vdHV18dlnm7vvz5494/79+/y3//bfyOVy/I//8T/4h3/4\nBxq36DX+7d/+jVAoxPXr13c97z/90z/92EOvo4466qijjjrqqKOOOup4b/zjP/7jgY776Ct1bW1t\nZDLbe/czmUxNAxKPx4lEIszOztZIneu6LCwscOHChT3P+/d///c/3qA/MmxsbPDFF1/w61//uh7z\nUMcOjD65R5vzgNQP1IFbLjzNHOLMpc8/zMDegOu6WJaFbdtIkoSqqj9ZW8xPAdcNdqodx0GSJDRN\nQ5IkLMuq/V25XObGjRsH+izbto1lWbiuiyRJhEKhd3ZFe/zoLn0dBnpI5sHjRT498e5mLBC0e5YM\nQTyeYGqmSDR1lJbWtrc/sYJiscjTJ3c4OpwgFjnYe7Btj8ej6/T0nqCpef82xQ+B0WeP6O5wiUXf\nPienXq3i+RYDvbvrXwzTARHCti10TaCqmxWFjazF1Ks8pz7p2aGT2orXszlMO8HQ8KYQy7IsHty/\nxYljjYQPKFidnV8H4dHZkaBQtAhpUbSKo89GpsDU1CKfnGxDfkuF3scnn7OIJ5I1B8kHj5Y4fvzT\nfU0oLMvi1q0b6LrL6dPdqKrE8nKO2dkNTpzoqGkEfT9w5jQMm0zGYHCwqeZOL0Rgjz89vU6xZHHi\neDuu6wVdBRVTkKCFL2hFlCUJIQWtg8WCSSyuUypZfP/9LE1NEY4caau9pu/75HIGQghiMW1bLENV\ncwY+rutXWhtdJElw+/ZrFhbKHDnSwOBgU0XLJt5qkuG6Pr4fmLhMTq4ghKCtNUk4HEOSBflcjng8\nxNpagdev1zl2rJVQKBiT5/kUi1bFiVIw+nyJT0524Lo+5bJNJKLhesH7r449MPcJ2imr2XVvzjrH\n9bl3b4bz53qCdsD7c5w9s+lz4FW0ggKB7biUyx6JRDD319cLvJoucfr0OYQQfH/3W04eb0BVZfKF\nPJGIwlZ5me14QWC44yErEpoqo6oSpuVg2w7RSKB/3Ngos7JWZniwGcf1MEyfWHS7yV7wHBejbDE2\nkeXMmYu7Bo//peE4zrbfAU3TfrQg9DreHR/TGvujJ3UnTpzgX/7lX7h//z79/f2srKzw/PnzWuVO\nCMHx48e5f/8+yWSyFmmgqiqDg4N7nvegpcxfEtLp9F/l+65jfzjFJQa73A9yrslslmQy+Vdjqez7\nPpZl4Xkemqb96O1+1faan+Kz7Ps+nl2gpTHG2nqZ3i6Vpob9fzKqi1zfByGJiq4meGwj65BKh4iE\nZR6Pr3L02NsdNyHQTz168C3XLrXWLOoPipbmCDduP6e1tfVHN2gZGDzMxupDenve/qPe2NDNg4cz\nLK+WODK8MwIhkzOJx1MIIchlNwiHJUKazMpqmdm5AtevjaDtY3gyPrGG7SW4eOnqjnPH43/Dndtf\ncOHTVmKxty9gR1/McfFCF2XDIRZr3KZfb2qKEdZVxsYXuXi+6605eGFdQQuF8Fy4eXuGC+c/o3Gf\neey6Ln/84/8kmYTr1w8jyxJrawVWV/N8/vnQDjOJxsYIGxtF5uYyNDaGa5ouIYKWwcamCI8fzzE9\nvcbJkx1omoJXYX6CIHJCrrTz+gQaq1hUxXE8Hj1aprs7zsBAc83pspqJoCgBoVNVudZ+vrUHquoc\nKctSxTwFDh1KsbBQply2UFWJcFgmkYiwHzcOCF1gwpLNFrEsm3OfHsI0PVLpBI7jooccHMdlcSnD\n558PVBwqNz+Hvh8lmy0TjYZ48RJSqRCyLJPJlEmlgntbNUtxXS/QFTqb5ii7bSOsrZdIpXUiUZVi\nwaStLUZT0yZRdz0fywp+Y4RQCYd9EokoKyt55hcsfvOb36EoCqZpEovJtLcn8QFFMUmlDk6ySmUL\nx3aIx3UaG3Xm5nO1cWxkDdKpnZtS+YLBne/m+c1vfv9Ww6g66tgPH8Ma+6Mndc3Nzfzud7/jzp07\n3Lt3j0QiweXLl7cRtlOnTuG6Ll9//XUtfPwPf/jDX83Cso46fggkPgyhA9DloJr2S/7s+b7P4sIC\nE2MPca0soYrzvG2D5Sq0dw3TPzDys6wiVjVrE2NPKJfWsc0NnowWWF4z6OtUKBRtwrqyLcbA94Pd\n87Jh43k+UmURGRgXBGYTYV2pGS3oIRnTLB94TE+f3OfE4eg7EzoIFtGXzjZz695trv367975+e+C\n9vZ2nj+7y9ER/62aNSEEpz7pZvT5It/eWeTwUIrGhmBBHbgxyrXKVyKZZnl5jVfTy5TKPr+6Mryn\ng+VGpsSLsQ3CkQ7Onz+z6zji8TgXL33O7Vtf0tURorc3vWfgse/7lEpWcN/DcXR955zu6mpEVWS+\nujHN0FADnR3Jmo7sTXiez+TkCnPzFp+eu/ZWp76JiTEcJ8dvfjNS03c9ejTHlSu9u7oDShKk01Fi\nsRDLy3laWhJbiJ1AliWOHWtnYmKNGzcmOXq0jZbWGAKB6/mBCYyo+mMGGqv1tRKLizkuX+7j8eMF\nNE0JNi2EwPehVLKJRjU0TcFxPITYqWip6tOCGAAJ33cr1TI4frydhw9nOXeuZ09C53lVYighyzLF\nYhDloKoyolKFFwh8L8jAe/RojksXD9Wumb/F1FEISCR0slmDnp40068zDPQ3EgopmKaDXvl8e1vM\nTCRJ1IxQdsP4+CrHjrbheT73Hsxx7GgrnucxN5tl6tUaCD/YhBACywqy5WQljB5q5OrV36BU9HWW\nZRHWgz/7XuCc+S6IhDUMIchkA6OUrbGd1UjC6hkty2Hq1Rpz8yaXLn3+V5GjWccvHx89qQPo6emh\np2d/d6yzZ89y9uzZn2hEddTxC4J/MMfEg0AWQbjsLxVTE2NMTTykNW3z6bCK/gZx832fueWn3Pn6\nGVqkjdNnL/9s2mTm52Z58fwe6YTH8cEo0Ugj2axPKq4w8SqPprpoKhSKJiCIRTUc16NUtlFkQTSi\noMjbF2G+XzH9KFpYtl+xHpfxDzjnHMchl1mk+djBWzXfRCikEA3bZDIZUqldwhc/EIQQtHf2M7ew\nRFfH223FhRAcPdJOvpBmfHyZR0/X6emKomkSkUiCjUyJUsnm1UwRnyhNTafw1pe49d0ivT1RolEV\nRQliCnI5k+nZEvF4M0eOffZWshSNRrn+679jduY1N2+/IBx26emKoesqkhBYtsvySonFJQPbUUkk\nG/Ztr2xtS5FuiDE1tcSXf56iuTlCW2uMUEgJ2txMl9m5HPOLeVpahrl2/WxtIb8XfN9nbGyUjo5E\nrSVxba1IOh3et0VRkgKi9OjRXMVWv5IbF5wUVZVpaY6RTOosrxR4NrpEZ2eCVDJMOKzieVA2bKam\n1lheznP2bDe/+mwACDLlZFlsMWzysW2XSKUleCuBrJJDz/O2/LdfeRyq9EJVZZLJMIWCRTS61cTG\nr22MBIHjgemK7wevOTOTYXCgGdNwSCTitWuWyxlEo2qNNFZ3WLYSO0kSKIpEe3uC27enGehvRNcV\ncjkDXQ9ex3U3yel+mxSm5WCaNsmEjmHYKIrgyZMFbt16xfBQA5+eaUMLKfiej1NxBDUtl0zGY2Gx\nyKNH3/PJJ58iy0EEhFT5DvHx3ytZUddVVFXBMCzW18uMPl+muSmKYTr4FDFNh9evcxiGRF/fCNev\nd38wc6866vhL42dB6uqoo44fEeLDfQ1YnvKzrFC9Db7v8/DBHYQ5ya/PhhBi95YgIQRdrTpdrbC6\nscTXX/5/XLjyt0Sj+1vd/6Ux9nKUtaVnfHauYXsFpLKu0zQZ23LQVAlNlXBcn42sgSwLkgl1T22X\nEJuB5KbpUchnCIXjlTDst2N6eopDXT9c4zLUn+Dly6ecO3/lB59rP/T3D/H1jQmam6KEDhggHI/p\nnD7Vg+N4PB9b4vFonp6eFmRFRdejnD57aEsV4SiGYTDz+hUbC0Uc10ZVdKKxdq5cPfROFXJJkug5\n1EvPoV6y2Sxzc6+xVgx8z0PToqTSvRw51sGNr/64r3avCk1TGBnpZHi4g+WlLMsreSyrhBCBTqy3\nt4NwpEhjU99bCR3A6uoKnldmaGgzlHt8fJljByD4sVgIWZZYWSnS2hqrEJRq6yKEdAVJFnzySSee\n5/Pq1RrzC7mgXVgSqKpMoWBy7dogiYpbqCQJdF3Ftj2qsivTdGv6NAjyGavxAFDV6FXaFitELmiF\nlGrtiJ7nMzjYxPPny7S2JitRAn7lWIGiiG2kyjRsDMNiY73E4EALnrfZ8ikkiampVY4d23QkrfLH\nraQOgpiHUsmipTnGxMQqAwNNKIpMuRyEhAfELth8Cf4/qExuZv0F7+/BgzmGh5orWrpZ8Hz6epMM\nDjQE2jfXo1yy8AnmXDweRipYNA2kGBmWmF/IcuOr/+DS5euoqloLZRdC1Fpj3xWyLIhGQzSkY8Tj\ncRaXioyNr9Pfn0bTEhw+cpxEIvFe566jjo8ZdVJXRx1/5RBqDNMJwsR/CHwfSq7+s6lMvQuePPoe\n3Zvk8PDBCUZTWuP8UYfb3/xvrlz7Lx8t2Z2cHCe3PsqF0407duR9gspAMq7yYqJIf6VhQghIxBSK\nJQfPA+ktUkLfB0kWJBMKY69W0UL7x9FUMTszxtVzP7y6lkzoFAuLuK77o+oeQ6EQp09f5eadG1y+\n0L6v7u1NlA2bpRWf3//d3+9r1qDr+gfPo0omkySTJ3Z9LBZPspEp0pA+mN4oCAdP0dq28749f7FK\nb1/8QOcZG3tGLKYQjwfXwrZdLMshHj/Y5+js2R5u3JhAUSSamqI1IxKAaDTE6moB3/eRJEFDQ5Te\n3gYkKdCR3b49zfHj7TQ0RLeYnQRti6urBeLxEL4PpmmTSGy/V1VdKVB7zTc/V0II8pUQTsdxaWyM\nYFkunsdbw+HX1vOMji5x7tNebNsnkdy8LwJBqWSRTG7P7a3lxW0hdkolo+7w4Rbu3HmNpil0daXI\n5QwkKSDprkuN2G1q8qj0Mvo8fDRPKhmmtTXOvfuz5HJljow0cagnqBRXWyldLyC31U0H1/NrLbod\n7Un0kMLNb7/k8pVfUyjYwX0RAt/bSUYPCtMMsu26Ohtoa02yti7xyalP3/1EddTxM0Kd1NVRx185\neg+f4dXLV4w0/7BQ6OWCoKlz5J0zsD52LMzPYeVfcuJo+O0Hv4FYROHUkMXd219y5bO//RFG98OQ\nzWaZm37I1XM7CR2ArkcwzBKJuErJ8LFsr7YYVFVBIqaQy1ukkqF9F16e7yOJwIBiadXBcBZrbWr7\nQfjurtqp90EkLGOa5o+unWlobOT4iSt8ffMbzp5qJJnYf974vs/iUoHRsSKXLv/2L+K+Z5omkxNj\nrK8v47gOgiAvq621m76+YcZe3uH8uR9mIlEqmfiEtxmtBH9fYnJyjExmFdd1EUKgqhpLS0t0d28e\nG4R3v9nuHLQ3biVeQSUs0M9dudLHt99O0dvbQEdHEkmScRwXVQ2Ctg3DqbWICiEoFEzu3Zuhr6+R\nzs5UYBbk+bXzd3YmuXnzFd3daWZmMkxPr1fISfD8aFSjszNVOWdQMQqHNVR1+1x3XZf19SDPrli0\ncBwPXVcpFk1SFRviXK7M5MQK+bxRcY8Ex/GZm8vy+789AsgkEkm2elHatks0qgXa1jf0aEIS+N4m\nsROCiuYOzp/v4e7dGQoFk6Gh5tqYFFWuvA+pdh0kKbhuDx/NkU6FOdST5tbtaTzXo6c7WSN0ENQb\nPbeac6cFrqeWi6pq265HQ0OUkWGHx4/u0dDYzupqgebmOFpIx7KczVbSd8DE1Dp9fUHFcnp6g56e\noXc+Rx11/NxQJ3V11PFXjq6uHr542MiwP/9eO6JVTGTTnD5z8sMN7CPB+MsHnD/8/tXHhqSG/Hqd\nfD5PPH6wKsVPhbEXjzkxEtuTXIX0MNlMkbAOfV1RXs0a9PeEg+oDwaJVVSUs2yWk7V1h8DyCoOay\ni6JodDUpLCws0NHRse/4fD6c3lNTJBzn4KHpPwRNzc2cv/hbnj19gFGep68nSmdHfJt2x7ZdXr3O\nMjNfprGpk6u/uvKTV7nX1tYYG3uGbeXp708zNNRWI9G27TIzO8+DB8/Z2MgxOJg+cLVuN0xMrjM4\ncKz238vLS4yNPQMMBgYaOXKko2bsYZo2jx5lmJ9f5/FjicHBlgoZq1rzb5I5SRJvVF/9StB3EOVx\n5Uo/z58v8cUXY3R3p+npSSHLAlkWGIZNKCSzslLg6dMFJElw/Hg7qVQYx3FrejxJCtonTdOhXLb4\n059e0t/fxLlzPeh6QOBc12NtrcjY2AqyLBgcbCKRiFIu2xSLHnpIIVQ5dnp6g8bGKPPzRmDdn7cx\nTZcXL5aJx0MsLWWJRFSGBpuIx1uwbZdy2aZUtpFlePBwju7uFsLh2DZ9oeO4aCF1d1IHtZiGoJpY\nldsFUQvnz/cwObXOn7+aoCEdoaMjAQjCYZVoNIhXWV7OMzW1jmk5NDZGWVkpsLiYY+RwM8+eLDIy\n1B7cAT+oxlXJsiSk2m9L2bCJxXZWcdvbkoyNT3P8xBVePL9Fc3OcsB4hl19/Z1IXmFkVOTLSi+/7\nvJ4t8Nm13nc6Rx11/BxRJ3V11PFXDkmSaO05yszGEj3p9zM5yZaASOcvzkEsn8+jiTwh7Ye1Tg52\nCcZfPuH02UsfaGQ/HLZtU8wvk0o27nmMJASKEsK0Lbo6Ivzp2xxd7RoRfXMRHdZlCkVnT1JXNXoQ\nwLPxIoN97SQSOncfP3krqRN8OAMD2/V+9MiJrYjFYpy/cBXLspiaGufGzVcgPDb1UiqHDo1w7Xr3\nTzquKp4/f8bGxjTHj7UTj7fseFzTFJbUhTQAACAASURBVAb6W+jva2ZhYY2bN19w5nQ/7e3vnsNk\nmjYrKwbHT7RWHCzvY9urnD7dRiSy/bMlhEDXNQYHmzl8uIly2ebmzQkGBgJi57pVMiftYXAhKqQt\nmHuu6zMy0kJHR5JCweL27Wl8H/J5E0kSNW3Z7353mHBYreng3mybXFoK2h7Pnu0mFFJJJgPiF1QF\nA+ORjo5k5XVMnj5dZHm5wJEjrYDANB0ymTLRqMbU1DpHj7bz+PFaYE4jBeY4r19naGv1ODzSgqIE\nraAbG2UkSUZVQ7S2pInHIiQSIebmstz4+ikXzo/UoikCs5FqZXH3Sng14iCoclLRGwbf+z3dKXq6\n08zP53g5tsHKciGosqmBvi6oBKrEYzqO7XH8eAeyLFhaypFK6Xg+eI5XybSTELKE63hIlfntOB74\n0p7zve9QgpWVJVxPJ5crk0iEkYSCbbtvbUvdiunXGdrag6iQ+YUs6XT7X+QzVkcdPzXqpK6OOurg\nyPEz3PjPGaLFlzRG302cXrbg7monV//mtz/S6P5yGH/5hIGutx/3NjSmNB5PzOA45w5kEvFT4NXU\nOL1db68MRWNxspk14hGfkf4Edx7l+OzTTdv6andkdSG8FZ7v43qgKArj0yUULUpLS1DtkcUGxWJx\nXxMZzxd7Lk7fFWXD/YvoGjVNY2TkKCMjR3/y194LT548xPfWuHih70DxC+0djYTDMvfvv8L1PLo6\n994IeBOO43Lz1mtOnw5Mar7//vb/z957NreRp2e/v86NHEiCOVM556yZsddr+3ns8vPO5Q/gD+aq\np8ovTtWpOvbx+vhZ7+4EaUYaaTTSSKJIiUHMGURG5z4vGgBFkZRIjWZnA35VKpXEBvDvRgPsq+/7\nvi4SCZtDh/rf+ThRFPE8h/b2GKlUmLt3pzBNG9/vQpKkfXUVBGHZErYdhEwnEjqffXaEUsng889f\n8dd/fYw7d6aIxbRaVMGbxiZCIzh8aanA5OQ6N28OIcsi5bJFoWAQi2nbKmJ12/xoVOPK5T5evFjh\n2dMlzpzpJhxSkGWR3/3uFYcPZxoiJVijz/LyOufO9TLQ/+6cq0oleE96e5Ok0yHu3R/jyuWjRKM6\nmiZTrdpIkozjOMjy3sdJEILPp6oq2/bBMB2SqRi9fd18+cUMx46dZWLiEdeuDeL7PnfuvODEiTZa\nW7Y+t0+fLnLmVBvKWzmFrutBre3a9XyKJYt4fO+bAt3dCT7/cpIrVz/j3je/5ca1bqLROPl8lnhc\n2Fcr9tp6mdm5IjevHyGXqzL+qsStW1ff+7gmTf4UaPq4NmnSBEmSuPbJ/+RpYYTlwv6/FgpV+Ga5\nm8uf/K+fZRbop6aQW6Ul+TFa4gRiIZOXL18yNTnJ7Ows6+vrDUOFn4P1tUU6MlvZaCtrZWbm80zN\nbDK/VKRQDGYsRUEgnkhRrPhEIxJDfTHufFfAsrdaI1VFwH4rxyqokgTn1thkmc2SzJmTnY2fd7Qp\njTD1vch09LO4XPzR+1qpWMhy/GcX1KZpsri4yOvpaaanplhYWKBa3X9m38fg9espLHOFU6e69y2W\nBQSSySRnz/bz/Pkcm5ulXbfzfZ/sZom5+Q2mpleYmFzm//x6jKNHL5BKpxkff0E4bHDo0M7K4Pbn\nAUXRME2XSsXC83wuXuyjUrHJ56sHbhMPMtYEwrWsw83NCuGwxosXK1y50kehYDI1tf6WoAscM/P5\nKi9frnL16kDD1dK2g8pWpWIFbYz1dTeOVy2y4njg1Dk5uY7rejx8OEtHe5SpqQ2qVRsIZsxM00IU\n/PcKOgiOy9JSjrm5TVZWivT2xPn8i6dYViDiZEnBMOrCzt3zO8Z1fai1l9apVm1MwyMei7O0lCPT\n3kN7Rye9vce5+/U0rusyPNzJ11/P8PDRHPPzOfL5KrbtbMuR9AHH9fAJhJjjeBQKJrFYAlEUsSwb\nwzCoVqsYholt27VWV5FQSESWZS5eus3dbxYol01i8SSFgoVtv7sde3GpwPPRda5ePsTGRplHT9a5\ndu3Tn/1z36TJ74vmmd6kSRMgcO679Yv/xXff/JapuWmGE1kysd2dx3IVmMjFqcpdXP/F3+wwP/jT\nwf1RVSLP8zCMKqZRBc/CyD0h1qJhG7C44vPD9yJdPYcYHDr0e68i2baFbcuMTaywkS3TkdEJ6SKy\nLGCaBuMrNpWqx2Bviu7OGIlkmo31FTozOroq8tXDPO0tCsN9geV7PXrO8/ygrcuDpVWbmYUKmUyC\ni2cz246lqopU3hNCPjx8mG+/maK788fZj0++LjI88vPlmGazWV69HMW08nS0h9FUCUGEStnj9XQF\nQQwzMnyMtkzmo1Ql98L3faYmX/DZZ8MHfqwgCKTSKc6f9/nN78a4cnmI7q5ULZja5fXMGnNz6yST\nIaIxFc/1MS2Xrq4Eoy++Z219jdXVWf7iL/Y2rPA8H9M0MIwqsiw08t/qAuvMmW6+/nqK//E/ThzA\nQCcIUE+nwwhC0EY4ObnOjRvDLC7mePBgjsOH23j8eIH29hihkNoQb4IAY2MrnD/fiywH836G4SDL\nItGoRrVqk8tVCOkKmi7v+t6dPNnJr/5zlKnpjaANtDPOi7FVNjcDoxTP9Xg9n2NwqKVWXdv9ssyy\nHKanV5mfXyOVVEimQiiyCPhkMjr/9V/fcfhQN319bUxObnDqVCcQVOzqkQzbKnKGRSikBi6elo1R\ndZBklXgihgBMTeW4fDn4zLS2tTM/P8e//dtD+vviHD+axnEcNtZzzM06bG5WqBp2wyDG8wJjJK/W\n5up5EI0mME0Ty8yhKEKjvdX3fAzbx3FA00LIsoBlWSQSCa5d/0sePrxDOOQyPJiiXAnEbygkNyqd\nvu+zulpiYmoTRVY4eriH775fQhCi3Lr1VweK+WjS5I+dpqhr0qRJA1mWuXLrl1QqFSbGfmB0bowW\ntYgqOYh4WJ7Chhkh0jLI4esX3hty/MfOm85yB6VSqWAaJUKaQDIuEd6QaM2EybRuzR16ns/iygT3\n7ozR3nWUI0dP/F7cQz3PY219jR9GfQ4NxTh9bKeYGAYs22NmrsTnX29w8mgGTdGQJIF0SuTWZYW1\nDZOHz8vYlks0IhMNy7iegGlDsQS9PSmuX0vtaMuC2mSZ8O4Lc03TULQExZJJLPphotd1PdY3PU6e\na/+gx/8YDMPg22+/IhrxOXa0hXg8vWObQyNQLptMTj3l+ajDpUs3iUZ/nNPkXiwtLtLREf7gc0xA\noK2thY72TTY3I7x69RrfDyouwyMtnL/Qg+8LCIJIKBRBUZSGGcd3303S2ipSLleIRCI7bhYZhkm1\nWkLXZRIJvSEWfd+tCSqZoSGNx4/n2dgoEw6rRCLqO/fF94Oqmu8HWYuO41EsWkQiEWwbWlpitLbG\nmJ3dwHVdSqWgOh24u0pYlothOITDKo4ThIhblks0GlSlQiEFRQkC4PO13EZJEhtixanN//X3txCL\nqvT2JBEEgUMjrXz51XTwWn5QDTx3tptKpYDvi7WK1tZ+vXq5yMLiOkNDKT77dLAR0l3fZHAgTTZb\noVx1mZhcYHOzwvHj7cFaRAXf8/G8YB5REAXwoVp10DSRSqWKqurE41tGPsWigaJEUVWV7x99S6Wy\nyshQkquXz2NbNtVqGUHwEcWgvbZUNoO/SyaKIiPLgZgURYVQKI5lmZRLOXRdIpnUdv1WDSIiTAr5\nLDMz05w8eZpwOMzt279kc3OTiVejVCoFEgkFzzNwbAfH9djYqBAK6SSTMYpFl5V1ndNnzv7BZ4M2\nafJT0BR1TZo02UE4HOb0+at4Zy+Ty+WwbRvP81BVlaPx+J/N3U8fEd8/eLWuVCqCZ5CMy40LGMtm\nR26ZKAr0dEbo7vB5/mqcx49KnD1/5ScVdq7rcvfObzg2EuLocHiHS96bqIrIoaE4g31R7j1apyWp\nEIuEkWUZH4nODoXO9igvXhVxfJ1EOoIiS+i6TCz67gtu03RRI+9v2T1x8gIP7/83N69mdhWH78L3\nfR48XuPI0Qu/96iNcrnMvXu/48K5joZN/V5EIhqnT3VRLpvcv/87zp+/STJ1cEOS9zE5Ncali+9u\nfdwPhw61srom0NnVR6Ewz81bQe5gEJYt73ReFASKxQq3bg1jWTbFYoFYLN4QdpVKBccxSCZD294n\nSRKxbTew16+1RZ461cV3381x+/YwhYLRCAffDc/zKBQMIpHghoDjuDx8OMv168eJRPRai7CLYWxw\n7FgnL16scPPmUE0A+kxNrTMy0lqbexNw3SBaQJbERqtlvWKo63LDvh9AkEU0MTgWR45kuH//NYOD\nLbVtlUY1cHR0mRMnMqiqjK4HoeT5/CaJRBJRFHny5DVg88ntrflHQRCCClwt1FwQQNMkYrEQvd0J\nHj1e4D9+9Zy/+58nAsMSUUAU5UaGXrFkoOsRNF1HluVtIsu2XR4+nOfc+Vt8ffdzujsFzp3ubfxc\n01Q0TcV1PVzXRVV9JHmVcEgjHNIoFk1AIR6P1GIiCoiCQyKxu5jbOkeCYxjSZaqlGZ498zh58iwA\nqVSKS5dvYFkWxWIRy7LwPA/TNOkf0JAkCVVVSSQSTUOUJn/WNGfqmjRpsieiKJJOp2lvb6ezs5OW\nlpY/G0EHkG7tYi1rHegxlXIZPINoWNqatfF9sgWIx3afzxMEgZOH42jiIi9Gf/iRq94b3/e5f+9L\nDvXDYH8a09pfZIAsi1y/2MrqhsH8ctAyKRBYlYuiSDbvcORQG92dcTJtEeIx7b0iannVpa2t7b2v\nHYvFOHn6Bl9/u4pl7d+d1fd9vnuyRkvbEbq6P4LbzQGwLIv7977gyqWu9wq6N4lENG5c7+O7R3ep\nVCrv3b7ucrgfbNsGTHT9x8+IZjIJZmcnqVaXuXx5sHGh/7bpRp1SySASCdoaw2ENWfYpl4O5PMMw\ncByDWGynOAtEYt2RMqh6DQ+34roe4+OryLJIqWTuOA71iIF83qhVDYM5s2++mWFkJEOkdjNBFAU8\nz+f16xVOnujgwvlevvn6NZbpoioS2WyF7q4kYm1du2Wm1ZfsOh6SLKIoEqoatAfWxaCmSsiyiGk6\njfV11lqKJUmguyvR2HdVlYhGFfKFHC/G5hFFhzOnO7cdm8BxM6g81ndb1xVM00YQBC6c66G7M85/\n/deLxnGpH6Ny2UKWdaLRKMpbgs6yHL7+eprjxy8xPv6Mvh6Rgf7dby5IkoiqKmiaSiSsU6nYSJJI\nIhHCdU1s26JcLiGKDpGwsq+eB9f1ME2Xi+c6ccxFJl6Nb/u5qqq0tLTQ2dlJd3c3Q0NDdHd309HR\nQTqdbgq6Jn/2NCt1TZo0abIHw4dO8Pj+JJl9mv25rotllUnEtn+1rmY92lpi76yKARwbjvLN9y8p\nFAaJx3/cHNluzM68JhUr09meDtq+ciV0bf9OgtcvZvjNVwv0dIaQaq1apbKNoqjoB8iSqlRsBDm2\n71nMtkyGE6dv8dX9OxwZDtPdGdtTNPq+z0a2wujLAn2DpxkYOPj82I/l6dPvOXWypWE1fxA0TeHS\nxU6+f3SfGzc/2/Yz3/dZXVlhYvIFjl1FEOsX7AKiqDE8dJTOrq5dj41lWYT0j3NDxvd9bKvMuXP7\naxc2DLsxHwdB22I+X2Fjw8DzgjDwfL6M7wc3EEIhDblWlRUEam2BwWxcYCzisrCQ4/XrLKGQjK6r\n9PUlSaXCDTFVLJqsr1dYXMgBwSxcOKLy6tUyxYLD8EgnkYjGD0+mSSVDyLJEKhXm/Pke7t6daohH\nx9268eE4Qd6c90bGWxAJAAhBlSuYXdt5DEK6gmE6qKqE7Xi4NWOh7u5ELWYgOI6e52PbDqVSheWl\nNW7c6Md2bPADMSdKEqIg1I5JULETpWBeznU9ymUTy7IZGkoyM5vlf//vbwmHZXRdQVNlJFlGFGUG\nBtrp6WlpZAMuLOYYH9/gzJlrmIZBWCvT29Oxr/NhZLidickVzp3tQBAgHtPY3CwgSQKJ+P7bpmfn\n8vT1pBEEgTMnM3z1zRjdPX1/wjPbTZp8XJqirkmTJk32IBwO40tJqkaJkP7+u8DVaoWwLu64Kz25\n4HL29Pvb6QRB4OiQzsTLZ5y/eP0DV703r6dfcP1CMAcpCgKyomE7Dqqyv6YNRRHp7Y4yu1BhsDeY\n+5p4XWZ4aH8Xf3UmXhcZGblyoMe0trZy8/bfMjX1kt/dnaa9Taa3MxxUToTARXB1rcrsgkGqpZtz\nFy//LGHvjuNQLKzR1jb0wc8Rj4UQhBUqlUpwDvo+U5MTzMy8pC0T4tzZ1h35boZhMTX1krGxx3R3\nD3L4yLFtgst13R2REx9KtVolHFb23dIavLZYE0GBI2M4HOTDJZPhbeuybY9KxcDz/FpVL7DpdxwX\nVZWIRFTicb0RL1Ct2hQKVRYW8kxMrJPJRNncrOK6Hv39ac6c6SIW09B1FVEUsGyH3KbJkycTOI6P\nUbXQ9a1LIVkW0XWZH54uYNsepmmja0owi8aWmAsy1wABJDEQW/jgel4wu1YzJ/F9H5/gpki5ZOC5\nHrIc7AfBwxFFqSbIDDzPQ9dlNtZLHD8eRB/UoxJ8z8dzHVxAEqVaKLpce02ntlaJYrHK5GSW7u4Y\nvd0xWlvC2E5QBRMEAVVVmF/I85vfzIGgIAphunsGuXnzApqm8dWXv+byhf3HVrSko/zwdPaNNllQ\n1YOfazOzBW5eG6kdZ4HDw3EmJ8Y4eercgZ+rSZM/R5qirkmTJk3eweGj53ky+t9cObX37A7UqxcG\nkfh28beadZHkEOHQ/qok6aTOk/HFdzrhfQi5XI5IyNkW4hsKRSgWsihxcd828cMDMe7cW2agJ0Kh\n5JAv+pxJ7/9OeqFoks1LnDp/8NkuTdM4duwUR4+eZHlpiYm5GSzTxPc9VE0nne7h9mcDP2sb1uvp\nKfr7f3yVdXgozeTEOCdPneW7h/cJhap8+unQHoHboOsqx493ceyYz8TEKve++YrLV240joWiKA0r\n/h+Dj49lGWgHqPopioxluTiOgyQFVab6fNfObUUUJZh3KxQMXLdKKKQQiYRwXa/W9ic12iCjUQ1d\nl0kmw5imzQ8/LCGJAhfO9+E4Pj7+NgEsCAKZTIxMJsbGRokHD2colYN4gWy2zOPH81y82EM8rvPF\nF5OoqkzVsPE9H8sOqoqqJDZm4oI5uy3BF1TjhYYLZH34rmrYeB4kEiFEUWBtLXC/tB0Pz/MpFitE\nIyqKItVCxyucPtUe9GoKgWWTINaFIjiuW4+xx/dcRElE1SReTaxjGjY3rgeOnbmcgSyrKCroWjAH\nly+atLVG6exIsrZmML/oMzx8CFVVKRaLKLKFph2sqjs81M6z0VXOnu6oGdOIlCv2vh8/M5sjlYo0\nKrQA7ZkYo+NzeCfO7HneN2nSZIvmp6RJkyZN3kFbJkO68wxPxo13zi+Zpommsq1Kt1nweD4NF850\n7vm43ejvlJidff1hC96DyYlRRga2uyrKkkQoHKNQstnnaBa6JhOJaEzNFHnwJM/li337rthUqjYP\nnhS4fPWzH2VcEgRid3H+wjWuXv+Uazf+ggsXrzM4NPyzz9XML0zR17vT5fKgZDIxVtcWePjgHukW\nhxMnuvZ1YSsIAocOtdPbp3D//t3GOatpGuWK86PXZVkWgnCwQPhwWCWbLSFJ4rYW5Poc2G7Unz8U\nUlDVQOjIsohh2Kiq1LDoF0UBWRZr/yfyye1hOjsTTE9nGzEGhhG8xtufX1GEv/yLw3iez8uXqzx+\nMs+NGwPE47WZO0nE93xiUY14XCca0eoaK6jECcFru56P721/bkkUkGviL1i3QyymNfLp8gUDgFLR\nolisEI9pjRsuCws5enoSbwSgv31sArMWz3NwXBtZFoNohNc5BHwunO9EVYI2zZAuY1o2AltzcC3p\nKKLoo+shhofbOXE8ytd3f4frukxNjTM8dHBX477eFkDh5asNPC8Q37K0M79yN5ZXSszOlTh1vPut\n/RTo6tRYXFw88HqaNPlzZF+3gf/93/99X1/gvh980f/d3/3dj15YkyZNmvyhcPjICcZeuHz77Bnn\nj2m1fKjtuI6NUmsj832fxXWXV3Mi1y/1Ne7q75dUQmF2PftR1l6nXMqT2GVOT9d08CFfLBKP7nQu\n3I14TOHRqE0qGd33xf3GZoXHzytcvvoLwuH9m4dYlsXM62ny+Q0s20QSJVRVp7u7/yfPdfsQRME7\nQIbaFrbjYBrBjFn9d6lhBBbuQ4MDB3quXK7MxkaeYjHPr/7j/ybT3kE0miASSbGxUaSl5cPbUl0n\nmGeLx8J8//0kpunUnHFlUqko3d0tOI61bT8sywpyCStmIwAcgqpc5a1qTpBzGMzPaZqEpik4jtuI\nCpicXGdoaHtIt+9DPm+QSAQVvqGhFp78sMjk1DqDA2lyuWpDMInClugPYjXg5o0h/uv/vOCvfnEE\nRZUbQmpgIMX09AZHj7bjA6omkctVCelbBiMCQcum47h7RnTk81UiYZVQSKFQMDAtm1ev1gCYmlrn\n3LmObedMoWjS0RGttXru8UYIIIkibm3mb2Z2E9uyOXth+0ylLIs7DJFEAeIxnXyhgCynaW2JcuSQ\ny/eP7mMYFdJH9996+SZnTvXy5OkcT54uc+pEK7IsYts2giC9kcweVGrFWsD79Osci0tlrl0e3PW7\nJxGTmZyeYGV5FssyEBBQVI329h66unuaFbwmTd5g3709+3XY2u92TZo0afLHxNFjp1mMp7n7w3fE\n9AojvRKJ2FaLkuf7uB5MzNnMrUI6FeXm1TbkD7jAVxQR2zI/5vLxfW9PAaTrOqIkUSgVEQSHkC7t\nmLMLcqRcDNPDR+bchcukkkke/PAIRcwxMhCmJb3dkt7zfOYXi0zPmYSjbdy4dRtd3595SD6f5+XL\n51TKGwz0xzl2JIKihPA8H8OwmZl7wrPnNr09wwwMDv9BuLLWQ7L3vT1gGkHYtigFlu6SKNcqND6R\nsEixkOfrb14wMtxJW1tiz/fQdT3m59d5/XqFSERhcLCFQ4fSlEomkUiMfL7E4uIaj743+eT2MRRV\nOXAOo2U5vHq1xNj4EseOddLfn67NowmYps3qaoE7d54Rj+scOpQhkQhRqViIosfQUAsTE+ucOdPd\nOFb1/fQ8v/F3YBoS7E8sFrRNBqIpaB1cWSly8mRnY5+rVRvDsNF1GVneMv05daqTL7+YoCUdxHaU\nyyahkIokCVQNC6Nq4XseohjEAZw728PycoFUKtQQUl2dCT7/YpLDR4KbB/Vql+142+JJgrm4oGL3\ntjDxgbHxVTraY5imQzyus5Etk2mLksvlOXykhbt3p0mnwxwaaSOZDGHb7vvnXP0gXkGSBGzbZXY2\nx81rvTsdREVhRxURgiplKCRTrVaJRCJ0diaYmJzFssUPuikBQWXtzOk+nj+f5ouvZojFNIYGkoT0\nrfZs3/epVgymZ/IsL5fp7Exx4+rwjnVXKhYTU6ssLWVpz4QZ7m9DVQNxaFoGiys/8Pn4IzId/QwP\nH22aqTRpwj5F3d///d//1Oto0qRJkz94urqDu8Obm5uMjz+h8nIdARdB8CmWHBTJ5fBwK7evxX+U\nKUXQavZxRcr7gr5VRUFNpnFcl2q1TLkStNnV8X0RTQ8RT4bQcjlCoRAtra3cuv1LisUikxMveDq+\nhCh4tQtrAdeT6O4Z5trNEVR1f1b6vu8zPjZKNjvN8aNtJJMDO7bRdYVkMozreszNr/DlFxNcunz7\nJ3EMPQgHqRp6nk++sImmisTj2g4x4Hk+sixy6dIgjuPy8uUKM7NrXDg/smNbw7D45t4Y3d1xrl8f\n3DY3CT6i6NPVlaKrK8Vvf/sDm5tZQuEQsWh832vO5Up8990kff0J/uqvjpJOB628gWmIQyQiMzzc\nyshIK9lshWfPFojHdXp6ksTjOqmUyIsXq5imjabJCAI1ERec7/U2ShAwDAtdVxqir+4OOTubo7U1\nUrPlD0xSIhEVx3EJh1XePCyqItHSEsF1faIxlULeQJIkLMtE0yTicZVi0UQQBEzTYWAgzRdfTnLs\nWKYxt4YgkMlEmZ/P0dubwvUCg5dy2UJJhLZJ4iAs3UNAJJjkC45NqWRSKpn0Xuxp7M+rl2v09iZ5\n+SrPYH8KTQ2qkKMvlkklw0iiiOO+++ZAow1UFFhbKxEOKTviFurb7aXdNU0mlzMIh4Mw+KGhOA++\nW2oc84PiA8VCgc72EEdG4jx/sc73j5fQNGnH8w30xRm41I7tCHi+j/TGzxcWcrycmOfE4ThHhtpw\nHKWRNVhfdzymcWTYZ3l1hW/uTnPi5DXaOw7W5t6kyZ8aTaOUJk2aNDkgqVSKy1c/Bba6EyZejRNi\njJ7OyI9+/mrVRdX236K4HyRZxbQcNPXdX/uyJBGLbomj3a4Jq4ZPKrN1kRWLxTh77vLWYz7wohCC\nOACRDa5def+sniSJDPSnybRFufft51y4+AmJxMHngT4m7j6i/zzPJ5/fJBpV3xJgb24ThG7XTUHO\nnetlZibLvftjXLt6tHFsDMPi66/HOH++e9dMPF1XKeSrjQrppUuHePDtSy5e6qVQyBNPJN5bsctm\nizx5MsWVq30YhoNeM0mpC7pgVm5r+1QqzJUr/Tx/vsT09DoXL/YDcPZsN/fvz3DjxiCSJNYqdDRm\n7eqVTsNwSCa3KmaCIJDNVpiZyfLJJyNUqzbFoklrSxi3Vt3brXVvZKSV0dEVLl/uJ5kUyOeDvDxd\nr1dD64IyyJdrz8SYm8vR15cKBBNw9EiGO3emiUY1YjEtmN9TJMpli2jNwXKrs1BoiCjfD/bj4YNZ\nLl3oaWTdvRhbJZXUqBfDZEkiFAqMZK5e6eHFizVWVkukW3RSqd2rT1vHBaqGw+uZTQ6P7N4y6dX2\nbzcEQFWEmtDV6OpM4LmvKVdMopGDxXH4QCGfQ1U8JFEO2lVViTMn22jPRBrfk29/pm3Ho5DPEk+k\nkUSRmdkNFhdXuH01gyQF85B7hnQnewAAIABJREFUrl8Q6GyP0dYS4d533+C4l+ju7t112yZN/hz4\nUc3I1WqVUqm040+TJk2a/LlQryL09PYzs/jjjSgAXi/a9PYNfpTnqtPXd4jXc8UDP+7tS2Xf91nP\nurS07D1386GCbnLiJZ67xskTHQc24rh+tYeHD77EMIwPeu2PRTKRYWNj7+Ps+5AvvFvQAZQrFqoq\nbzsO/f1pOjsiPH48BQSC5Jt7ews6CKIrELZCqmOxEGfODPLw4Ryua1EqFnEcB8uyME0Ty7JwvS1l\nWqmYPH48xaVLfdi2TzKZxrKC53tb0JXLJmtrJebnN8lmKwwNteJ58Pp1MB+aToc5fDjD3bvTmKZD\ntRrk14mNuIBA2NUdJOtyaXm5wOjoEqdOdSEIQaUmHFaoVG0sy91RoXJdj2y2QrlssbZeYmOjhOt6\npFIhTNOpOVbSqPZptazGvr4k8wt5LCtwlvQJtrl6rZ9Hj+bY2CgDQm0u0KdYMrc124piUHXCh3LZ\n4t69aU6d7CAe1/F9n2fPljENixMn2rGdLSdSXVeQFZFS0eL4sQzJpMb4+Hotm27XsygQdFUby3Sw\nLJdkUsPzfNzaTGLdeNMwHfR3OFmqmoxlWY3j39GeYPzl+p7b70WpWERVPEK6jKbJVKsOyyslWltC\njefe7TPtOkFsxMTkPC9eLjE9vcjl8y2NTgfD9FC1d2fdybLItYttTIw/ILuxceC1N2nyp8IHVeoe\nPXrEs2fPME1z292X+t3Zf/7nf/6oi2zSpEmTP3RCoRCCnKRStfYdX7Abpulie9GPnrHW3dPD5799\nxOGhD6+iASwul+joGvzoBiWu6zI7O86nt/s/6PG6rnDqZAvj4885c+bCR13bQRg5dJTRZ3f3NCMx\nTANNFd8p6Hx8JifWGRlu2/GzwcFWvrk3TalksLaep7cnsaegq/Pm72eAltY4Z84McefuKImExrGj\nnUQiamO7atUDRPRQmO8fTzFyqBXHFUgkkjXHSQnbtgmMRn3m5nJMT2+gaTLRaBAc7romxaJJpWLy\n5Eme7u4EiiLR2RlH0yS+/HKSeFzn7Nlu3hyH9LytmwLZbKVhKHLr1jClkonr+jVHSZV8voosS425\n1XLZZGJinY1shdbWMKoq0dEeZXExz0a2QiyqMTTUQrFkkojr6HpgXKLVcuo0TUYUBCpVC9EQCIUU\nZFlC12SuXunj4XfzzM5uMjLSRioVolqxyeWq6DUhA1CtWkxPbbC6WuTihSAaYW4+x9TkBh0dEU6e\n6Kq/ydsIh1QMIXi+E8cy/Md/jlMqW0Qj2wWN7wezjdVqEA8RCsm4blDx9HwPAQEPwPdwXR/bfnfp\nODAs2domGgsxP1/E22U+cC9c18VzTUK1yqUsiayvV0gmtF3n83zfZ3W1zMT0JvgeyYSCJMKrySI3\nL7dSKFTRVAVFlRBECUl8v6OtJIlcOdfCg6ffcev2L/e17iZN/tQ4sKgbGxvj8ePHnD17locPH3Lu\nXBAK+erVKyRJ4uzZsx99kU2aNGnyx8DIoVNMzNzh9NEPF3WTcxWGRi6/f8MDIooimUw/K2trdGSi\n73/AHkzNVrl87chHXFnA7OwMfT37d9PcjbbWKM9HZ3Ddsz9btEEsFsO0pdrs2M7zwDAqJOLvbm1z\nXY+1jSrnzu8uDEeGW5mYXCKXK3Hzxv4qun6tL9D3fV68mGNtLcfp012NWS5VkWlri6CqMj4+1arN\n5OQElYrJ+fODhEJbaw6FIpRKm5TLJi9frtLdneTq1QFUVW7MydUFom27vHy5yn//9zhHj7YzONhC\nKhWhpydJuWxy//5rdF2hpSWCqkpYlkc+X6FQMEgkQhw/3k48HqrtQ71yFVTxdF2hWg3s+r97NAvA\n8HALp05vVXrzuSrxeJAxmd2s8GpijVLJ4uLFPkK6jFsradUrmT6BM2TdhMXzLFRVwgcunO/Gtj1e\nvVqlUrHp7IijajK27VIuW6yvlzEMm5Z0mIH+JDOzm6yvl+npjnHtam9g9PEOdF1BVWVM0yGTifLk\n8SIXLvQ0XDA9z8eyXESpbnJiYxg+khTMI76pwfz6bKImUixVEQWBWCy0e+XvDYM7AYG2th4WF3P0\n9KT2dW5VqxVCoe37NjtfJJ3cWWHLZqs8frpMW4vKuZNxwqHgMnQzZ1EoWqRTamDKZLkUCiayEsbH\n35epj67LKOImpVKJaPTDv+OaNPlj5cCibnR0lLNnzzZE3eDgIK2trZw7d45/+7d/o1qt/hTrbNKk\nSZM/eNoyGcbHoqxlDdrSB5tJAcjmTNY2dY6d7X7/xh/AyOFj3P1qhmRCR9/FVOF9TL7OEU/2oL2n\nHepDmJl5yc1rHT/qOQRBoKc7wtzcLAMDH7d99SAcP3aWbx98y43rA9uqHbbjIEu7z3/V8X2fR48W\n6O9r2VPgtrZGefT9Aul0eFtY897PSS3w2+fBgwnicZlPPjnUeL14PISqRsnlyliWjSAIJBJRRkZU\nVNXHNMuIotB43wVBYGpqHcty+OSTkUY15s0ZuDqKInH8eAfd3UF2XKVi0d2dYGEhx1/UMuIqFYti\n0ayFo/tsbJT57LND2/bNr7U1iqJYu7wX0DSZ9fUS342tcPp0J21tWxfy9RW8WRBLp8JcudxHNlvh\n/r3XXLs2gKoEzpqKIgWtnGpQrRNlCSUm4dWEqe972I5HIqFz6WIvtu2ykS1jVG1kSSDTFiakSbx6\ntY7vuSiyQEd7mJMn2t5Y8/sRRQFdl7l0oZv/59/HGBoySKUCV1lZFhqZfeWyRTymIkvirkLNsV0c\n2yUR1/AB2w7Ecjwe2nb++b6P8MbMmmW5DI8c4vtHX5NOR7ZFUOxGINxNIm9s93o2TywaYjNnsJkz\nSCWD78Kl5RIvX61x43IL2lsCd2K6wKGhaCNmQlUkpJiIbTsUC3li8ffPfgKMDESYeDW6bca3SZM/\nFw48U5fP52lvb298abtu0BcuyzKnT59mbGzs466wSZMmTf5IEASBK9c+49krn43Ng0USbOZNHo85\nXL3xlz9Z9pqu61y4+AlfP1xvBDLvl9dzBVY3Q5z+CVobS6US4RD7EijvY6A/zdzsxEdY1YfT2tZG\nf/9xvn0w08gRAzCMasNkZDd83+fx4wXi8TgdHQm8XazoITjPMm1hYtH3i2vP9xsVrsePp2lp0Th6\ndEs8C0IgFGRZoLs7zeBgOwMDGbq6Uqyu5ujvT5NIhKhWS1hWcM5MTi7gOB4XLrxtoe/vEBh1QSmK\nImfOdGMYDr/5zUuuXQvMUiRJJBxW6eqKMzjYwvBwK7IsNYRHXShaloskiw3DER8fx3EZHV3m7Jmu\nhqCrT+NBUPGsVw3fJJUKc/lyL98+mEWSRCzbxXU9lpcLtLVuNzoSBQFVlQmFlKAlsRa5oKoSba1R\n+vrSDA+10NOdxAc0XeLypV76+1O0tETwa3ENb76Ttu3umV3p+z6O66HrGoODLTx7vka14qBpCqIY\nHN9KxSIeVxttp+GQQj5vvPH8HqWSRTymNWbZFEUkHJYpFKrbsu9My0VR1MZr5ws2yWSSS5dv8823\ni1Qq1q7rrGNZNqqyJbfmFwrML5Q4f6aHa5cHefRknXzeZCNb4eWrNW5eadsh6Hzfp1S2SSbUWrQF\nuJ6PLMuEQwqK7FIqFvDf7lndhZZ0iM3s0nu3a9LkT5ED36qt3+0LfhGolMvlxs90Xd/27yZNmjT5\nc0NVVW7c/hu+ufsbulqKDPZG3hk+7roer+fLzK1o3Lj9tz9JFexNkqkUFy79JXe//R3HRkJ0tr+7\n5dEwbMYn89h+K1euXv1JBGeQlfVxzJgVRcL33fdv+BPT1z+IKErcufuEUyfbSaejeJ6LJO0u6goF\ng6fPlmhvb2VkpBPTNKlWK4TDGp7n4nnbZ6N0XcbzvUZrmk89683ddtFuGDa+L7CwsIHv2wwP77R9\nlyRhx/MH+I0qXDweIp8v4Hlx5ubW+eyzQzUbfxff3736WG/BDMxPAlOSwcE0uVxlm4OjJEm1dQfR\nBt3dSRYWCmQyUSYn11ldLeK6QZthMNMn0N+fZmOjzPFj7SQS+q42rdWqTWgXES0IEImonD3TxfPn\nSxw50o7v+7yeyXLj+gAvX66ytFQI1s+Wb0BrawRdl9H1YI5NFGrGKATCZHmpWGuhlGrHVcRzPVwn\nCKQXaseoWnUIhYLPeV24+4Dv+biuhyTJiKJAPKbR15Ph6bNlOjrCDA4kKVeDCp0oCI1dHhxMMzGV\n5cLZLkzTxjAdEm/EZAi1nZblIJOvUjGJRIIKXt1kBWB1tUBbJgj0jsfjXLr8Gd98+wVHDsXp7to9\nIzE4p4NYiO9/WGFlpUgipvLlnXEqVYdyxeRXv84hSwKtaY2v7q2QSqqMDMaJhIPPvOv6QS5frcXU\n94NCQV0qhnSZYtnGMq33fj8KgoAkevi+j2maTE2Msboyi0BQBUYQESWdwaHjzfDyJn9yHPi3aDwe\nbzhctrW18eLFC/r7+xEEgbGxsY8+3N+kSZMmf2yoqsrN279kdmaaO49eEA9bDPdpxKJq4JDnBXem\np+YMNosyfYOnuPXp0O9tDiyRSHDz9t8yOTHG2NfTdLTJDPbGGlbvruuRzVWZmK7g+iGGRi7S2dn5\nk1UQnVpb4sdj/wHgPyU9vX0kkilevRzlydMpWlJw5EgbqlrvdPFYWi4wPb2JruscPz5EKhVUimRZ\nplCwUFWxkeH25uGX5aDC5Dh1x9W6tf/22aqS4xGN6jx5MsGFC7143k6Le0HYXdS9KQ5FMajojY/P\nc/hwG6Io1BwkJTzPx3GCx9ddC+uCrr6fwesKJJNhTpzoZHJyjbNne2rb1oVd8DyJhMbdO9Nk2qMM\nDaU5erQN3/MRa62GpukwNbXO1NQGw0NpIBADb2ZD+n5QsXqzffDt0zedDmPbQTVvamoDy3K4f3+G\nwYEU16711sxsgvk9x/GYmt7kq6+maGuLcPx4O4qm4Ls+nu+zvFSgoyPG2vqWA3g9rFwQgscLtWPm\nel5DLJumXTs+PoIgBmKmttDgPRe5ceMIo6PTfPHVNKoicvZMB9GoFjxvTewuzBcY6EsQjagk49rO\nEHKCT4WuS+RyJuGIhmW5KIre2HZiKs+581uV+Hg8zq3bf83k5Et+9+Vr2jMqg/0pQiGl8T2xkS3z\n5MkM1apJZ3uI21dbWVs3mF8s0dulMdwfRIw4joeuB99vm3mHJ8828Dw4MhwnVBN3juvh+wKKIu9o\ntQyHZIrl8r5uepmWwzd3f4Nn5xjqkTh2MbTteJiWy/TcAz4fe0B7xxBHj5/+2WZwmzT5mBxY1PX1\n9bG8vMzRo0c5e/Ysv/rVr/iXf/kXBEHAtm0++eSTn2KdTZo0afJHhSRJDA6NMDg0QjabZWJylEq5\niOe7CIJEOBxnYPgKZ1r2np36KdE0jeMnznDs+GkWF+Z5MjaJZRfxvaBSEE+kOXXu2u/FcEBRFGzn\nYwqx3//x3ItYLMb5C1dwHIdf//o/yBcW8H2vVo2QaGmJc/XqcdQ38gMdx6FYzKHVTDjC4Z3VpqCi\nIyJJQqNlTRSFbYLOMG0URcJxPDRNJhbTcV0X3/e3XcQG3Tc7KxZvn5a6LrO8vMHx44e3bSNJApIU\nvI7r1sMAtn4etH8GbpKiKJBOhxkdXW6Ejr/5PGurJV6MrXD9ej/pdBhVlXFcD0kUGtU4XZMJ6Spn\nTneiqhLFkkkkrCGKUmPNlYqFrr//EmdoKM34+AqvX2f57LPhWvulj+f6uI63lUEHDA2mGOxPspGt\ncvfuDFcu9xKOqFiWw9j4GtevDrC+vjPWqV5dFEWJYtHA84L4AoCq4eJ5ArIi7zhrbdtFVgKn0ZHh\nFjzXJRoRefVqjUo1iGZwXY94TOPQcIqFhQJnT+89lyoQvDWqGhj5VKsu8XhwI359vYQkxQiHtzup\nqqrKsWMnOXr0BMtLSzwdncA0NxsdW+tra3RmJC5+0osA3H+0SnubxqfXWxvvba5gEY8qtaB5n1RC\n5tLZFIbp8uhpjnRSx/WCzD7X3d0URRIFBFwc10GW9n5fF5cKVEvrXD0TIhbevcigqRJHh6McGfKZ\nX5rmqy8WuXbjFz95l0STJj81BxZ1Fy5s3cXp7u7mH/7hH5icnASgv7+frq6uj7e6Jk2aNPkTIJ1O\nk07f/LmXsSuCINDd00t3z88X2huJRCgU3j27s18Mw0ZW/vAuzmRZprdngEzGJJPZOyC9LujicR1R\nEinky5imsCOLrVAwiMZ0BAFEQUQUa9UySUIUwLJdDMMlkQjz7NkCw8MtNeEk4roerktD2DmOv6sz\noyCIWJbTEJyrq0U6O+M7IhK2thcar/Emvu9jWU5DnIqiQG9virm5Tfr7043tVpYLvHq1xq2bg4ii\nSD5fxQcUuV4x22J2dpMbNwYQxWAur1gyEQQNRZFqWXQe4bDeqBYG69gSmXXa2iJ8+dUUf/PLQ4Af\nVARFIRDL9dbL+nvj+kiyQGtrmMuXurn/7SxXLvfx6PsFjh7JoOsKiiIFbZ9vxZqIokC5YiJKGi2J\nGL4XiL94IkWpZJFI6I32zMZ7XLQYHNAwjRLRqMzmZoUzJ3trbaHBOWDbLul0GFGA75+sMDG1ycjQ\nHq6VtfdN1yWyWSMI/JZECoUqPzzLcfPWL3Z/XO297ezqovONa7zvH92nJVGhv0tAEgXufLvM4cEI\nHZktk6h6JmC9OCzLAq4buHPGIiq3r7Tx8IccmzkzqBbvcnOhTkiXMKoVotH4rj9fXikyObXA9XNx\nYrvcCNltn3q7IkQjFne/+i9uffK3KMqHOxc3afJz86OHGDKZDJlM5mOspUmTJk3ei+/7LC0ukM2u\nYlsmgiiiqiG6uvtIJpM/9/KafAChUAjPUzEM+51GIvthcirL4MDxd25TrVaZm52hapRxXQdFVolG\n4/T29SPLH2e2bzcGh0b44YevdhV19RmgYjFPIhGIEc91icXCFIsVPC+w8A9EiU8uV8VxvUZVQ0BA\nlkQc18VyPQzDJR4PIwgC+bzB8ePtwXZvCDvPE2pdNi6RyM7jPjDQzvT0OocPt+N5QaB3a2sE3/dr\nF+vStmpeMGPnIYpbVT7fpzYPJ1IomMRiGoIAqVSI2dlNenqSCIKAYTg8H13m1q0BJKk2uy8KFAsm\nsbiOpsoNB0xq5ieSJOH7PrIiEY1qVMpWIwA8EQ9ti3GoC9B6NEDQBu2Ryxl0dsSIRTUc1yWXr5KI\n6zuFae09EiQRSQgcKs+f6+I//78xTp3qJNMWVIUGB1uYnMpy8kT7G+8tVKo21apDW1vLtueVRZFo\nNEE+XyD2hvmJaTo4DoTDKpblY5kO0ahSa5X1yRcCYxRZkshmKyQSGmdOZ/j+yQrPRm1OHGvb2ufa\nIvzaPzwvULeSJLG6VuT5aIFr1z/DdV1ejo9SrdY+F4pKNJrc9XMxPfUKyV/h+OFWcpvrPJ7aYLA3\nvE3QQSCElbdmiiVJwHH9oA1VFLl4OsmvcyavpoocHkmzF4oiUqnubvBUrliMjc9z6VQYhIM5D6cS\nKqeGDR7c/4LrN/cWtu+iVCoxPzeNWS3jeg6qqhNPtNDd09ds7Wzye+On++3VpEmTJh8R0zSZnBhn\neXGSjjaBjhYVVRHxfTDMNSZeTFCuavQPHaO3t7/5i/SPjKGhY0xNv+D4sfb3b7wHnuezsmpw/ORO\nMxDf91lbXWVi8gW+V6V/IEFbm44kazi2Sy6/yJ07Y8RirRw6dIx4fPdqwI8hEongOkqQH6YHs16u\n61I1qtiWgSBCOCwj10wjwMfzHMJhFcN0yOWqaJrMxkaZrq4km7lKQwh7vo9p2lQqNqIokkxG3nCp\n9raJlLqwcxwX1wVV3T2/LJNJ8MUXMwwOpmvbO6iq1Kh2BW2kQqNC92Y+3dYUV2BYoqoytu2wuVlp\nBK/btosoCvg+jI+vcOJ44KxdKpvYlksopBKLapTKJpWKRUiXUTUZoWGKUnPb9H0kcasN1fd98vkq\n4bBaM87Zvl+27WJZDqbpEI2qhMIKlu2gawpCRKBYMhEFAT2koCjBcas7X9aPZ7Vi49guXV0x8jkD\nx/WQZZH2TIzR0WW8Y4Hhh2HYWJYbGKgoyq6NwYqiEIsnKZcKgEcopDA5lWVoMEPdRsW0PQRBIJev\nYlku4ZCMpkmBKHcCwSwIAieOtjK3UOR3X8zQ0RFlcCARVHkbJiTBwVteKXL/20VMS2Ro6ASPvrsL\nXon+Hp22uFJ7v8vkCivc+fIpsXg7hw6fIB6P4/s+01Mv+PRaKjj+gkqhaHH+5M7PTCCod+6zJAk4\njl9z9BS4eiHNr79Y4eihlp0b18/bd7RVv5pY5eRhHdvxicfDe263F20tOtMLOQqFwr4/+8ENxkUm\nXz1B9vP0d3iEkhKSJGDbHtmNcb4Y+5aWzAAjh08SiUTe/6RNmvwIDizq/vVf//W92/zTP/3TBy2m\nSZMmTXZjfn6WV2PfcmRQ59i1nS5sCTTa2yLYtsvr+ad8/tunXL76F03jpj8iOru6ePHiew4fcj84\n2mB+fpOursEd54fjONy79xWxmMeZ021EIjvbM9PpKEODGbLZEs+f3yUa6eDkqbMffd5xePgYL1+9\n4PSpHqpGFcMoEw6rRCJh8rkSoVAoEA+1kG1RDC4ew6KCp8nYtsd3j+aJRlU8z+f7x/McO9ZRa6tT\nSCbDFIvGtnXXq3tv/1/dHj+Z3P458X0oFguAS3t7jLXVIt3dycDN0asbsggNcxK/EZsQGLW4bt2y\nX8D3PapVi1BIIRrVkCUR1/NYWioyPb2BZQXbLi8XGOhPUiyaaKpEJBluzGQl4iE8z6datcjlqohC\nLYfN2TJ3EUWBSESlWrWJx8N4XhAeXi5bb8Qa+LU5NJ9EQsN1PUK63Gi5BJAlkUQihOsE6y6XAoMT\nz/URJQHf85FkkVBIIR7XiURU/t9fjbOZt4iEgxtNVcPh+egKvb1JQrpKOKzXDF72jjmRJYlEIoXr\nupRKZZ6PrtHZKbKwWGF9fY1yqUoqqaLraZJx7a3HioS0YPawXIsGSCVbWFuv8vW9efAD10tJFrEt\nF9vxKZUsfvnpEb59NINVGWWwK0p7eyuSuP2zl06FGeqH7GaZ509+SyzRT2tbB21pGsdsebXKYG94\nNwPSPRHq52Wt4zIckgnpMutZg9Y9Mj73ijRwHI/NXIkTQ2EqhvjOHMh3cahf5dX4Uy5cuvHebU3T\n5N6dX9May3PpkIKu7czza0nCSK/P+uYkj76ZoL33LIePnPigtTVpsh8OLOo6O3feATUMg5WVFVRV\n3fXnTZo0afKhTE9PsDL/hNuXUztaot5GUSQODcbpare5f+//cOHyL0gk9p5favKHgyAInDx5gfsP\nvuP61b4Di6lcrsrU6yo3bx3Z9v+2bXP3zm85dixJe/v723PT6SjXrkZ5+XKZhw/vcfHix41x6Ojs\nZGZmkonJRTIZnWQyjADYjoso7X5BGrQaCgiizw9PFxnob+HkqW4cx+HBgxnK5SDQu44oBi2V9YqY\nqsqYprOjtTXIINv+mr4PhXwOTRfRNZ3jx7q4c+cVsZiOpkoYhr1NJHm1Frp6pEJ9vUFlC7LZCpGI\nui2cWhJFFEVE0yRGRlopFAxaWkKkUkGFxfUCt0xBlBoiIRBtGqGQiu04uF4Qk/BmDp0oilQqQXue\nLEtEo2JjTY7jAgKlkkk6HcJxguMjCGAaDpoaBI3XK4CSLBCNafg+lEomiiyi63LtNbeOVziskkyG\nOHN6gNaWGJbl4Ps+3z6YoKdnaxay3vL4PkRR5Nlolps3/5q2TDvlcpmp//i/uHm1ndmFErq692Wb\nLIkk4kEbabli09UZ4chICvCx7cClM4ijUvj8qznuPXzNicNRMq0hHNejkN8kFk8h79LlkE6FuXYx\nzPjkIvfujfJXt7tq54vP0kqB21dTuK6/w8U2OBd3i8sIzE+CHMfgBsCZEym++GaFv/9lbxBx8Ba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Yrxzze32d6ziYQVrnzayIMnaSRZorcrBARh6J4vavo/3xcsrhRZXiuQbFS4fPbV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4ZsvzJU+e1/jk/ipdbRZ//lEbM/N1/t9/eIHnhhwfc5gYcTB0dokdKRVzywG3H9So1SLO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\nehF5wOv1znu6b1kWfr9/drvqssjq19rayp/+9Cc6OjpobGxkaGiIq1evc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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb06a89cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = np.asarray(parsedValData\n",
    "                         .map(lambda lp: bestModel.predict(lp.features))\n",
    "                         .collect())\n",
    "actual = np.asarray(parsedValData\n",
    "                    .map(lambda lp: lp.label)\n",
    "                    .collect())\n",
    "error = np.asarray(parsedValData\n",
    "                   .map(lambda lp: (lp.label, bestModel.predict(lp.features)))\n",
    "                   .map(lambda (l, p): squaredError(l, p))\n",
    "                   .collect())\n",
    "\n",
    "norm = Normalize()\n",
    "clrs = cmap(np.asarray(norm(error)))[:,0:3]\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, 120, 20), np.arange(0, 120, 20))\n",
    "ax.set_xlim(15, 82), ax.set_ylim(-5, 105)\n",
    "plt.scatter(predictions, actual, s=14**2, c=clrs, edgecolors='#888888', alpha=0.75, linewidths=.5)\n",
    "ax.set_xlabel('Predicted'), ax.set_ylabel(r'Actual')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (4e) Vary alpha and the number of iterations **\n",
    "#### In the previous grid search, we set `alpha = 1` for all experiments.  Now let's see what happens when we vary `alpha`.  Specifically, try `1e-5` and `10` as values for `alpha` and also try training models for 500 iterations (as before) but also for 5 iterations. Evaluate all models on the validation set.  Note that if we set `alpha` too small the gradient descent will require a huge number of steps to converge to the solution, and if we use too large of an `alpha` it can cause numerical problems, like you'll see below for `alpha = 10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = 1e-05, numIters = 5, RMSE = 56.970\n",
      "alpha = 1e-05, numIters = 500, RMSE = 56.893\n",
      "alpha = 1e+01, numIters = 5, RMSE = 355124752.221\n",
      "alpha = 1e+01, numIters = 500, RMSE = 33110728225678989137251839534941311488306376280786874812632607716020409015644103457295574688229365257796099669135380709376.000\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "reg = bestRegParam\n",
    "modelRMSEs = []\n",
    "\n",
    "for alpha in [1e-5,10]:\n",
    "    for numIters in [5,500]:\n",
    "        model = LinearRegressionWithSGD.train(parsedTrainData, numIters, alpha,\n",
    "                                              miniBatchFrac, regParam=reg,\n",
    "                                              regType='l2', intercept=True)\n",
    "        labelsAndPreds = parsedValData.map(lambda lp: (lp.label, model.predict(lp.features)))\n",
    "        rmseVal = calcRMSE(labelsAndPreds)\n",
    "        print 'alpha = {0:.0e}, numIters = {1}, RMSE = {2:.3f}'.format(alpha, numIters, rmseVal)\n",
    "        modelRMSEs.append(rmseVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Vary alpha and the number of iterations (4e)\n",
    "expectedResults = sorted([56.969705, 56.892949, 355124752.221221])\n",
    "Test.assertTrue(np.allclose(sorted(modelRMSEs)[:3], expectedResults), 'incorrect value for modelRMSEs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Visualization 6: Hyperparameter heat map **\n",
    "#### Next, we perform a visualization of hyperparameter search using a larger set of hyperparameters (with precomputed results).  Specifically, we create a heat map where the brighter colors correspond to lower RMSE values.  The first plot has a large area with brighter colors.  In order to differentiate within the bright region, we generate a second plot corresponding to the hyperparameters found within that region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8k3PwAgAAGM6KRCKReO7w1VdfqaKiQrt27Xri4g4msRJ7OmEASL3p1lRPgGeJ\n+FM9AZ4mOJHqCfAMkbFDz90n7mL1+/2qra01OnYBAABgjrirtaysTBMT/EsHAAAAL4a4g7epqUmX\nLl3SrVu3Yr6wBgAAAKSjuL9xVlhYqPLycv3v//6vLMua90trv/vd7xIyHAAAALBUcQfvTz/9pO7u\nbq1YsUJFRUWy2WzJmAsAAABIiLiDt6enR6+99pp++ctfJmMeAAAAIKHiPoY3HA6rqqoqGbMAAAAA\nCRd38FZVVenBgwfJmAUAAABIuLgPadi2bZuOHTumzMxMrVq1Sjk5OU/sY7fbEzIcAAAAsFRxX2nt\niy++ePYDWpY+//zzJQ2VDrjSGgDjcKW19MaV1tIXV1pLawu50lrcK7xbt2595u2WZcX7kAAAAEDS\nxB28jY2NyZgDAAAASIq4v7QGAAAAvEjiXuE9d+7cc/fZtm3booYBAAAAEo3gBQAAgNHiPkvDfPx+\nv27duqVLly7prbfeUl5eXiJmSynO0gDAOJylIb1xlob0xVka0tpCztKQkGN47Xa76uvrtW7dOp06\ndSoRDwkAAAAkREK/tOZ2uzUwMJDIhwQAAACWJKHBOzo6qszMuA8LBgAAAJIm7jrt6el5YlsoFNLo\n6KiuXbum9evXJ2QwAAAAIBHiDt7jx4/Pu91ms2n9+vV6/fXXlzoTAAAAkDBxB+9nn332xDabzSaH\nw8FlhQEAAJB24g7e/Pz8ZMwBAAAAJAWXFgYAAIDRFrTC+/XXXy/ocIVIJCLLsvTxxx8veTAAAAAg\nERYUvHa7/bn7BINBDQ8PL3kgAAAAIJEWFLzvvvvuU28Lh8O6evWqOjo6ZFmW1q3jmrwAAABIH0u6\nSkRfX5/a29s1OTmpqqoq7dixQyUlJYmaDQAAAFiyRQXvwMCAfvrpJw0PD6ukpEQHDhxQVVVVomcD\nAAAAliyu4B0dHdWZM2d0584d5efna+/evaqtrU3WbAAAAMCSLSh4p6endfbsWfX29sput2vnzp3a\nsGGDMjI4qxkAAADS24KC909/+pNCoZCqq6v12muvKTs7W2NjY0/dn+N4AQAAkC4WFLyhUEiSdOfO\nHd25c+eZ+1qWpc8//3zpkwEAAAAJsKDg3bNnT7LnAAAAAJJiQcH7yiuvJHsOAAAAICn41hkAAACM\nRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIwW16WFAQAvsLymVE+AZ/H8mOoJAGOxwgsAAACjEbwA\nAAAwGsGs8fcIAAAfgUlEQVQLAAAAoxG8AAAAMBrBCwAAAKMRvAAAADAawQsAAACjEbwAAAAwGsEL\nAAAAoxG8AAAAMBrBCwAAAKMRvAAAADAawQsAAACjEbwAAAAwGsELAAAAoxG8AAAAMBrBCwAAAKMR\nvAAAADAawQsAAACjEbwAAAAwGsELAAAAoxG8AAAAMBrBCwAAAKMRvAAAADAawQsAAACjEbwAAAAw\nGsELAAAAoxG8AAAAMBrBCwAAAKMRvAAAADAawQsAAACjEbwAAAAwGsELAAAAoxG8AAAAMBrBCwAA\nAKMRvAAAADAawQsAAACjEbwAAAAwGsELAAAAoxG8AAAAMBrBCwAAAKNlpnqA52lvb1dHR0fMNqfT\nqd/+9rcx+3R3dysQCMjtdqu5uVnFxcXR20OhkNra2tTX16dgMKjKyko1NzcrNzd32V4HAAAAUiPt\ng1eSXC6X3nnnnejPlmVFf3/+/HldunRJLS0tKigoUGdnp44cOaJPP/1UWVlZkqTTp0+rv79f+/bt\nU05Ojtra2nT06FF9+OGHMY8FAAAA87wQhzRYliWHwxH9ZbfbJUmRSERdXV1qaGhQTU2NXC6XWlpa\nFAwG1dvbK0mamZnRtWvX1NTUpMrKSpWUlGjv3r0aGxvTvXv3UvmyAAAAsAxeiBXeyclJ/fGPf5TN\nZpPb7db27dtVUFAgj8cjn8+nqqqq6L42m03l5eUaGhrSq6++quHhYYXD4Zh9nE6nXC6X7t+/H7Md\nAAAA5kn7Fd6ysjK9+eabOnDggHbv3i2v16tvv/1Wfr9fXq9XkuRwOGLu43A4orf5fD7ZbDZlZ2c/\nsY/P51ueFwEAAICUSfsV3urq6pify8rK9NVXX6mnp0dut/up9+PYXAAAAEgvwArvz2VmZsrlcmlq\nakpOp1OSnlip9fl80VVfh8OhUCikmZmZmH28Xm/0/gAAADDXCxe8oVBIExMTcjqdKigokNPp1N27\nd2NuHxwcVFlZmSSptLRUGRkZMft4vV6Nj49H9wEAAIC50v6Qhra2Nq1evVq5ubny+/3q6OjQ7Oys\n6urqJEmbNm1SZ2enCgsLo6cly8rKUm1trSQpOztb9fX1am1tVU5OTvS0ZC6XS5WVlal8aQAAAFgG\naR+8Dx8+1A8//CC/3y+HwyG3260PPvhAeXl5kqQtW7YoFArp5MmT0QtPHDhwIHoOXklqamqSZVk6\nduyYQqGQKisr9eabb3KcLwAAwEvAikQikVQPkY6sdameAADwUvH8mOoJ8DTBiVRPgGeIjB167j4v\n3DG8AAAAQDwIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUA\nAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0Qhe\nAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN\n4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA\n0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAA\nABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAF\nAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEI\nXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAY\njeAFAACA0QheAAAAGI3gBQAAgNEIXgAAABiN4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAA\ngNEIXgAAABiN4AUAAIDRCF4AAAAYLTPVAwwODurChQsaGRmR1+vV/v37VVNTE7NPe3u7uru7FQgE\n5Ha71dzcrOLi4ujtoVBIbW1t6uvrUzAYVGVlpZqbm5WbmxvdJxAI6NSpU+rv75ckrV69Wm+88Yay\ns7OX5XUCAAAgNVK+whsMBlVSUqLm5uZ5bz9//rwuXbqk5uZmHTp0SE6nU0eOHNHs7Gx0n9OnT+vW\nrVvat2+f3nvvPc3Ozuro0aOKRCLRff72t79pbGxMBw4c0Ntvv63R0VH97W9/S/rrAwAAQGqlPHir\nq6vV2Nj4xKquJEUiEXV1damhoUE1NTVyuVxqaWlRMBhUb2+vJGlmZkbXrl1TU1OTKisrVVJSor17\n92psbEz37t2TJI2Pj+vOnTvavXu33G63ysrKtHv3bvX392tiYmI5Xy4AAACWWcqD91k8Ho98Pp+q\nqqqi22w2m8rLyzU0NCRJGh4eVjgcjtnH6XTK5XJF9xkaGlJ2drbcbnd0H7fbrezsbD148GCZXg0A\nAABSIa2D1+v1SpIcDkfMdofDEb3N5/PJZrM9cSzuz/f5+WP8fB8AAACYKa2D91ksy3rm7Y8fvwsA\nAICXV1oHr9PplPRohfZxj6/YOhwOhUIhzczMPLHP3P0dDscTj/HzfQAAAGCmtA7e/Px8OZ1O3b17\nN7otFAppcHBQZWVlkqTS0lJlZGTE7OP1ejU+Ph7dp6ysTDMzMzHH6z548EAzMzPRfQAAAGCmlJ+H\nd3Z2VpOTk9GfPR6PRkZGZLfblZeXp02bNqmzs1OFhYUqKChQZ2ensrKyVFtbK0nKzs5WfX29Wltb\nlZOTo5ycHLW1tcnlcqmyslKSVFxcrOrqav3jH//Qrl27FIlE9I9//EOrV69WYWFhSl43AAAAlocV\nSfHBrgMDAzp8+PCjYSwreuxtXV2dWlpaJEnnzp3T1atXn3vhid7eXoVCoWdeeOL27duSpJqammde\neMJal4xXCwDAU3h+TPUEeJogpzBNZ5GxQ8/dJ+XBm64IXgDAsiJ40xfBm9YWErxpfQwvAAAAsFQE\nLwAAAIxG8AIAAMBoBC8AAACMRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIxG8AIAAMBoBC8AAACM\nRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIxG8AIAAMBoBC8AAACMRvACAADAaAQvAAAAjEbwAgAA\nwGgELwAAAIxG8AIAAMBoBC8AAACMRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIxG8AIAAMBoBC8A\nAACMRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIxG8AIAAMBoBC8AAACMRvACAADAaAQvAAAAjEbw\nAgAAwGgELwAAAIxG8AIAAMBoBC8AAACMRvACAADAaFYkEomkeggAAAAgWVjhBQAAgNEIXgAAABiN\n4AUAAIDRCF4AAAAYjeAFAACA0QheAAAAGI3gBQAAgNEyUz0Anm5wcFAXLlzQyMiIvF6v9u/fr5qa\nmiU/7sDAgNra2jQ+Pi6n06nXXntNGzZsiNnn4sWLunr1qqanp2W327V27Vrt2LFDNpttyc9vilR+\nPoFAQGfPntWtW7cUCASUn5+v119/XatWrVry87+oLl++rIsXL8rr9aq4uFg7d+7UypUrn7r/Qt7n\nGzduqL29XR6PRwUFBdq+ffsTn/HznvfmzZu6cuWKRkZGFAgE9NFHH2nFihUJfe2mSPRnODY2pvb2\ndo2Ojsrj8aipqUmbN29ejpdivHg+K6/Xq9bWVo2MjGhqakobN27Uzp07l3liJOvvrBcFK7xpLBgM\nqqSkRM3NzQl7zKmpKR09elTl5eX66KOP1NDQoNOnT+vmzZvRfXp6enT27Flt27ZNn376qfbs2aMb\nN27ozJkzCZvDBKn6fEKhkL7//ntNT0/r17/+tT799FPt3r1bubm5CZvjRdPX16fW1lY1NDToo48+\n0sqVK/WXv/xF09PT8+6/kPd5aGhIP/zwg+rq6vTRRx9p/fr1OnbsmB48eBDX8waDQZWXl+uXv/xl\n8t4AAyTjMwyFQiosLNSOHTvkdDplWdZyvRyjxftZhUIhORwObd26VS6Xi88hRZLxd9aLhBXeNFZd\nXa3q6uqn3h4KhXT27Fn19fUpEAjI5XJpx44dqqioeOp9rl69qvz8fDU1NUmSioqKNDw8rIsXL2rN\nmjWSpAcPHmjlypWqra2VJOXl5Wnt2rUaHh5O4Kt78aXq87l27ZoCgYDef/99ZWQ8+jdrXl5eAl/Z\ni+fixYuqr69XfX29JGnnzp26e/eurly5oh07djyx/0Le566uLlVVVWnLli2SpC1btmhwcFBdXV3a\nt2/fgp93/fr1kiSPx5PEd+DFl4zPsLS0VKWlpZLEP9gTKN7PKj8/P7qi293dvayz4v887+8s07HC\n+wI7ceKEHjx4oH379unjjz/W2rVr9Ze//EWTk5NPvc/Q0JAqKytjtlVVVWl4eFjhcFiStGrVKo2M\njERXsqampnTnzp2X+j+XL0ayPp/bt2/L7Xbr5MmT+sMf/qCvv/5anZ2delmvEh4KhTQyMqKqqqqY\n7VVVVRoaGpr3Pgt5nx88ePDEY1ZWVkYfczHPi/kl6zNE4vHnHi8qVnhfUFNTU+rr69O//Mu/yOl0\nSpJ+8Ytf6M6dO7p27dq8/8qWJJ/PF91/jsPhUDgclt/vl9Pp1KpVq9TY2Khvv/1WlmUpHA5rw4YN\n0ZUuPF8yP5+pqSlNT09r/fr1evvttzU5OamTJ08qHA5r27ZtSX9t6cbv9ysSicjhcMRsdzgc8nq9\n895nIe+z1+t94jGdTqd8Pt+inxfzS9ZniMTjzz1eVATvC2pkZESRSER/+tOfYraHQiHZ7XZJ0n/8\nx39Ej5Vav379go/buXnzps6ePatdu3bJ7XZrampKp0+fVkdHh7Zu3ZrYF2KoZH4+0qO/XHbt2iXL\nslRSUqKHDx/q4sWLL2XwAgDwPATvCyoSiciyLH344YdPfAEgKytLkvTxxx9Ht2VnZ0tSdOXqcT6f\nTxkZGdEQO3/+fMzxWS6XS7Ozs/rHP/5B8C5QMj8fp9OpjIyMmMctKiqS1+tVOByOHtf7srDb7bIs\nK7ryOme+FcA5C32ff/6Yj6/6LuZ5Mb9kfYZIPP7c40X1cv3NaJAVK1YoEonI5/OpoKAg5tfcX8iP\nb5v7C8DtduvevXsxj3X37l2VlpbGhNLPo2kurl7W40TjlczPp6ysTFNTUzGfxeTkpHJzc1+62JUk\nm82m0tJS3b17N2b73bt3VVZWNu99FvI+u93uJx7z3r170VMvLeZ5Mb9kfYZIPP7c40Vl+7d/+7d/\nS/UQmN/s7KzGx8fl9Xp19epVud1u2Ww2hcNhFRQUaHJyUpcuXVJeXp5sNpsmJyd1/fp1zczMqLCw\ncN7HLCwsVGdnp/x+v/Ly8nT79m11dnaqqalJxcXFkh4do9XV1aWCggJlZmZqeHhYbW1tqqio0Nq1\na5fzLUhrqfp8CgsLo+e/LCgo0IMHD/TTTz9p48aNKi8vX863IG1kZWWpvb1dubm5stls6uzs1ODg\noFpaWpSdna0zZ86op6cn+u39hbzPubm5Onv2rGw2m+x2u7q7u3Xt2jXt2rUregq45z2v9OicyRMT\nE/J4POrt7VVFRUX0S1Vzq/1IzmcYDoc1Ojoqr9er69evKy8vT3a7XbOzs6wCL0G8n5Wk6Llfb968\nqczMTOXn5ysQCDxxLDCS51l/Z839/5XJrAhLdmlrYGBAhw8flvRohXXuo6qrq1NLS4vC4bA6Ojp0\n/fp1PXz4UHa7XWVlZdq2bZtcLtdTH3dwcFCtra3Rk7Vv2bJFr776avT2cDis8+fP6/r165qenpbD\n4dDq1au1ffv2l+J/FAuVqs9HevQN9dbWVo2Ojio3N1f19fV67bXXXurzW165ckUXLlyQ1+uVy+VS\nU1NTdDX2+PHjmp6e1sGDB6P7L+R9nrvwxNTUlAoLC+e98MSznld6dBq5EydOSIr9c7Jt2zaOuf6Z\nRH+GHo9HX331laTY976ioiLmcRC/eD+rL774QlLs55Cfn6/PPvts+Yd/ST3v7yzTEbwAAAAwGgc6\nAQAAwGgELwAAAIxG8AIAAMBoBC8AAACMRvACAADAaAQvAAAAjEbwAgAAwGgELwAAAIxG8AIAAMBo\nmakeAIAZHr+ErvTo0pVOp1MVFRXavn278vLyUjjd/5m7vOa7776r8vLyhD3u3Ov/53/+56S81v7+\nfg0PD897OeIvv/xSFRUVy3550McvVSo9+sztdrvKy8vV2NiooqKiZZ1nOQWDQZ0/f14VFRWqqKhI\n9TgAnoPgBZBQLS0tKioqUigU0v3799XZ2anBwUF98sknysrKSvV4SbN69Wp98MEHcjgcSXn8/v5+\nXblyZd7g/c1vfpPS93bHjh2qqKhQOBzWgwcP1NHRoXv37unjjz9Wbm5uyuZKpmAwqI6ODlmWRfAC\nLwCCF0BCuVwulZSUSJLKy8sViUTU3t6u27dvq7a2NsXTJV4wGFRmZqbsdrvsdntSn8uyrHm3r1ix\nIqnP+zyFhYVyu92SpJUrVyonJ0cnTpxQT0+PGhoalvTYc+/vyyIcDkuSMjI44hBIpJfn/0UApMRc\n/E5PT8dsHx4e1rlz5zQ0NKRgMKiioiI1NDRo7dq1Mfvdv39fbW1tGh0dld1u1yuvvKK8vDz9/e9/\njzl84IsvvtC2bdueWAFdyH/uHx4e1oULFzQ8PCyv1yun0ym3261f/vKXMYcnzB22cODAAfX29qq/\nv19+v1//+q//qt7e3phDGn7+n/sfl5+fr88++0yS1NfXp+7ubo2PjysQCCg/P181NTXaunVrNPSO\nHz+unp6e6OucM/dc873G6elpnTlzRvfu3dPMzIzy8/NVX1+vzZs3R8PZ4/Hoq6++0uuvvy5Junz5\nsvx+v1wul5qamqIRG6+5+8195pcvX1ZfX58mJyc1OzurgoICrV+/Xps3b44Ju++++05+v1/Nzc06\nc+aMRkdHtXr1au3bt29B79Pce3Xz5k0dOnRIp06d0tDQkLKzs7Vx40Y1NDTo/v37+umnnzQ6Oqrc\n3Fxt3bpV69evj5nf6/Wqvb1dd+7ckc/nU25ururq6tTQ0KCMjIzo+yZJ586d07lz5yRJdXV10c9g\ncnJS7e3tGhgYiL7/Gzdu1MaNG6PPM/dn5M0339TIyIj6+vrk8/n0ySefGH04CJAKBC+ApPJ4PJIk\np9MZ3TYwMKDvv/9eZWVl2rVrl7Kzs9Xb26tjx46ppaVFdXV1kqTR0VEdOXJERUVFevPNN2Wz2XT1\n6lVdv379qaudP7eQ/Twej4qKirRu3To5HA55vV5dvnxZ33zzjT755JMnVm5PnDih1atXa+/evZqd\nnZ13Na6kpEQffPBBzLbJyUmdOHFCxcXFMduqq6u1efNmZWVlaWJiQufPn9eDBw908OBBSdLWrVsV\nDAZ148aNmMecO3zi56/R5/Pp22+/VTgcVmNjo/Lz83X79m21tbVpampKzc3NMftfvnxZRUVF2rlz\nZ3RF/i9/+Ys+++wzZWdnP/f9+7nJyUlJir5vU1NTWrdunQoKCmSz2TQ6OqrOzk5NTExoz549Mff1\ner368ccf9dprr2nHjh3R17aQ92lOOBzWX//6V7366qvasmWLent7dfbsWfn9fvX392vLli3Kzc3V\npUuX9OOPP6q4uDj6DzOv16tvvvlGGRkZ2rZtmwoKCqKH5ng8HrW0tMjpdOrAgQP6/vvvVV9fr/r6\n+pjXOz4+rm+//Vb5+fl6/fXX5XQ6defOHZ0+fVp+v/+Jf5SdOXNGZWVl2r17d8znCiBxCF4ACRUO\nhxUOh6PH8HZ0dMjpdGrNmjXRfU6ePKkVK1bo4MGD0aCpqqqS3+/XmTNnosHb2dmpjIwMvfPOO9GY\nWLVqlf7rv/4roTOvXbs2ZmU5Eomourpaf/zjH9Xb26tNmzbF7F9ZWflENP5cdnZ2zAqpz+fTDz/8\noKKiIu3duze6fevWrTHPW1ZWpqKiIn333XcaGxuTy+VSQUFB9PUvZNW1q6tLDx8+1KFDh1RaWirp\n0fsbiUR09epVbd68WYWFhTGzvvXWW9HPIjc3V998843u3LmjdevWPff5IpFI9HMfHh5WW1ubMjIy\novdtamqK2ffxwx6amppiojoQCOjXv/71E8fFLuR9mhMKhbR9+3bV1NRIenRoze3bt9XV1aWPPvoo\neghIaWmpfv/736u3tzcavOfOndPMzIw++eST6Op+RUWFMjMz1dbWptdeey0mkHNzc5/4TFpbW5Wd\nna333nsvemx1ZWWlQqGQzp8/r82bN8e85oKCAv3qV7967vsMYPEIXgAJ9ec//znm5xUrVujXv/51\n9C/+yclJTUxM6PXXX1ckElEkEonuu2rVKvX392tiYkJFRUUaHBxURUVFzAqrZVlau3Zt9D8jJ8Ls\n7Kw6Ojp08+ZNeTyemJkmJiae2P/xeF/o4x89elThcFhvv/12TOz8//buraeJrQ8D+DOdwgzQo1W0\nrWBiChdCgAoEo8EEIgkJUaM3xu/mZzARgyJBTNRIT6IYKApyKE2LmCKiWHuk+2Kn83aY1rZ7w6u7\neX53zExn1izm4umatf799u0b/H4/otEoEomE5tqFQa5SkUgEVqtVCbt57e3tCAaDiEajqsDb0tKi\nGiXOX/PwNJRSpqenVX+bTCaMjIwo54nFYggEAtje3kYymVSOEwQBX79+VQVGSZKKLgKrpp8EQUBL\nS4vyt06ng9lshiiKqvnOkiShoaFBdZ+bm5twOBxobGxU5tMCf/eRx+PB1taWaoT+sEwmg0gkgo6O\nDoiiqDnH4uIitre3Ve2r9nkiouox8BLRkRoaGoLVakUqlcLq6iqWlpawsbGBrq4uAH+PdAKAx+OB\nx+PRfF4QBCQSCQBAIpEo+nq3mle+hcGolKdPnyIajeLixYtobm5Wwvnk5CQymYzm+MLpGeUcHBxg\nenoae3t7uHHjhqpqQTqdxvj4OPR6Pfr7+2GxWKDX67G/v4+pqami165Efo5rqXbn+zfv8JQNURQB\noOLrDwwMwOl0KmXJCu9xf38fDx8+hMViwZUrV2AwGKDX6/H582e8fPkS2Wy2aBsLVdtPer1euYc8\nnU4HSZI059bpdKrPx+NxhEIh3Lt3T3Ns4bNZSjKZRC6Xw8LCAhYWFio6RzXPExH9Mwy8RHSkCl/3\nOhwO5HI5eL1e2O12nDp1SglXbre75MhWfvRRlmUlIBeKx+OabaIoasITANWIYjGpVArhcBi9vb3o\n6elRtmez2ZLhptL5wwDw/PlzRKNRjI6OakZrI5EI4vG4piZwuVBVjiRJRfsov+2oq0mYTCblf37Y\nxsYG0uk0RkZGVAsAY7FY0eOL9e1x9VMxDQ0NsNls6O/vL7q/XDiVJAmCIKC9vV21QK3Q4TrN1TxP\nRPTPMPAS0bEaGBjA+vo6PB4Prl+/DovFArPZjJ2dnZKhIs9utyMcDiORSCghLZfLYX19XRMSjEYj\nvnz5otoWiUSQTqfLtjGXy2kWnr1//76i0eFf8fv9WF5extDQEJxOp2Z//h4Oj0YuLS1pji0cdS1X\npsvpdOLt27eIxWKqILq8vPzb6sYW9m9+LnGlqumnf6u1tRXhcBhGo7HoiHBe/n4Of8nS6/VwOByI\nxWI4ceIEy4sR/SEYeInoWEmShJ6eHni9XoRCIZw7dw6Dg4N4/PgxHj16hPb2djQ1NSGZTGJ3dxc7\nOzvKAh63241QKISJiQm43W6IoohgMIhMJqMJo21tbQgEAggEArDb7djd3cXi4mLZKgP19fWw2+2Y\nn5+HLMswGAzY2trChw8ffhl4ylldXcWbN29w/vx5mM1mbG9vK/tEUcTJkydx+vRpSJKEFy9eoLe3\nF4Ig4OPHj5rgDvyv1u78/Lwy59Zms0Gn02n6oqurCysrK5icnERfXx8MBoPywxUdHR2q+bvH7ezZ\nsxBFETMzM+ju7kYmk0EwGEQqlSp6fLEvGdX0069U8gWmr68PkUgEDx48QGdnJ8xmM7LZLL5//45w\nOIzBwUE0NTWhvr4eRqMRGxsbcDgckCQJsizDaDTi8uXLGB8fx/j4OC5cuACDwYB0Oo29vT1sbm5q\nqkoQ0fFj4CWiI1Pq1WxnZycWFxfh8/nQ2toKh8OBW7duYW5uDrOzs0gmk5BlGVarVVUtwWazYWxs\nDB6PB8+ePYMkSWhra4PD4YDX61UF0u7ubqRSKSwvL+Pdu3dobm7GtWvXMDU1Vbadw8PDePXqFbxe\nLw4ODnDmzBmMjY1hcnKy4ns8vC+/2G1tbQ1ra2uq4/J1eGVZxujoKDweD2ZmZlBXV6fUnb1//77q\nMy6XC58+fUIwGMTc3BwA4O7duzAYDJo2ybKMmzdvwufzwefzIZVKwWQy4dKlS8pc6qNS7nW8xWLB\nyMgI/H4/pqamIMsyXC4Xurq6Ku7favqp2rYe3tbY2Kg8m/Pz8/jx4wfq6upgMpnQ0tKieuauXr0K\nr9eLJ0+eIJvNKnV4rVYrbt++jbm5Ofj9fvz8+ROSJMFsNqsWq5VqExEdPSH3b9/ZERH9n01MTGB/\nfx937tz53U0hIqL/AI7wEtEfbXZ2FjabDQaDAclkEisrK4hEIpofLCAiIiqFgZeI/mi5XA6vX79G\nPB6HIAiwWq0YHh6Gy+X63U0jIqL/CE5pICIiIqKaxnopRERERFTTGHiJiIiIqKYx8BIRERFRTWPg\nJSIiIqKaxsBLRERERDWNgZeIiIiIahoDLxERERHVNAZeIiIiIqppfwEZsOaVcXtr2QAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0af87ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "# Saved parameters and results, to save the time required to run 36 models\n",
    "numItersParams = [10, 50, 100, 250, 500, 1000]\n",
    "regParams = [1e-8, 1e-6, 1e-4, 1e-2, 1e-1, 1]\n",
    "rmseVal = np.array([[  20.36769649,   20.36770128,   20.36818057,   20.41795354,  21.09778437,  301.54258421],\n",
    "                    [  19.04948826,   19.0495    ,   19.05067418,   19.16517726,  19.97967727,   23.80077467],\n",
    "                    [  18.40149024,   18.40150998,   18.40348326,   18.59457491,  19.82155716,   23.80077467],\n",
    "                    [  17.5609346 ,   17.56096749,   17.56425511,   17.88442127,  19.71577117,   23.80077467],\n",
    "                    [  17.0171705 ,   17.01721288,   17.02145207,   17.44510574,  19.69124734,   23.80077467],\n",
    "                    [  16.58074813,   16.58079874,   16.58586512,   17.11466904,  19.6860931 ,   23.80077467]])\n",
    "\n",
    "numRows, numCols = len(numItersParams), len(regParams)\n",
    "rmseVal = np.array(rmseVal)\n",
    "rmseVal.shape = (numRows, numCols)\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, numCols, 1), np.arange(0, numRows, 1), figsize=(8, 7), hideLabels=True,\n",
    "                      gridWidth=0.)\n",
    "ax.set_xticklabels(regParams), ax.set_yticklabels(numItersParams)\n",
    "ax.set_xlabel('Regularization Parameter'), ax.set_ylabel('Number of Iterations')\n",
    "\n",
    "colors = LinearSegmentedColormap.from_list('blue', ['#0022ff', '#000055'], gamma=.2)\n",
    "image = plt.imshow(rmseVal,interpolation='nearest', aspect='auto',\n",
    "                    cmap = colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PH/7s9qzjBQAAwEoxr+ANBoOSpK6uLnV1dT1zW4vFog8++GDxkwEAAABxMK/g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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb066e3cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoom into the bottom left\n",
    "numItersParamsZoom, regParamsZoom = numItersParams[-3:], regParams[:4]\n",
    "rmseValZoom = rmseVal[-3:, :4]\n",
    "\n",
    "numRows, numCols = len(numItersParamsZoom), len(regParamsZoom)\n",
    "\n",
    "fig, ax = preparePlot(np.arange(0, numCols, 1), np.arange(0, numRows, 1), figsize=(8, 7), hideLabels=True,\n",
    "                      gridWidth=0.)\n",
    "ax.set_xticklabels(regParamsZoom), ax.set_yticklabels(numItersParamsZoom)\n",
    "ax.set_xlabel('Regularization Parameter'), ax.set_ylabel('Number of Iterations')\n",
    "\n",
    "colors = LinearSegmentedColormap.from_list('blue', ['#0022ff', '#000055'], gamma=.2)\n",
    "image = plt.imshow(rmseValZoom,interpolation='nearest', aspect='auto',\n",
    "                    cmap = colors)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** Part 5: Add interactions between features **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (5a) Add 2-way interactions **\n",
    "#### So far, we've used the features as they were provided.  Now, we will add features that capture the two-way interactions between our existing features.  Write a function `twoWayInteractions` that takes in a `LabeledPoint` and generates a new `LabeledPoint` that contains the old features and the two-way interactions between them.  Note that a dataset with three features would have nine ( $ \\scriptsize 3^2 $ ) two-way interactions.\n",
    "#### You might want to use [itertools.product](https://docs.python.org/2/library/itertools.html#itertools.product) to generate tuples for each of the possible 2-way interactions.  Remember that you can combine two `DenseVector` or `ndarray` objects using [np.hstack](http://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html#numpy.hstack)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.0,[2.0,3.0,4.0,6.0,6.0,9.0])\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "import itertools\n",
    "\n",
    "def twoWayInteractions(lp):\n",
    "    \"\"\"Creates a new `LabeledPoint` that includes two-way interactions.\n",
    "\n",
    "    Note:\n",
    "        For features [x, y] the two-way interactions would be [x^2, x*y, y*x, y^2] and these\n",
    "        would be appended to the original [x, y] feature list.\n",
    "\n",
    "    Args:\n",
    "        lp (LabeledPoint): The label and features for this observation.\n",
    "\n",
    "    Returns:\n",
    "        LabeledPoint: The new `LabeledPoint` should have the same label as `lp`.  Its features\n",
    "            should include the features from `lp` followed by the two-way interaction features.\n",
    "    \"\"\"\n",
    "    data_point = [x*y for x in lp.features for y in lp.features]\n",
    "    return LabeledPoint(lp.label,np.hstack((lp.features,data_point)))\n",
    "\n",
    "print twoWayInteractions(LabeledPoint(0.0, [2, 3]))\n",
    "\n",
    "# Transform the existing train, validation, and test sets to include two-way interactions.\n",
    "trainDataInteract = parsedTrainData.map(lambda lp : twoWayInteractions(lp))\n",
    "valDataInteract = parsedValData.map(lambda lp : twoWayInteractions(lp))\n",
    "testDataInteract = parsedTestData.map(lambda lp : twoWayInteractions(lp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n",
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Add two-way interactions (5a)\n",
    "twoWayExample = twoWayInteractions(LabeledPoint(0.0, [2, 3]))\n",
    "Test.assertTrue(np.allclose(sorted(twoWayExample.features),\n",
    "                            sorted([2.0, 3.0, 4.0, 6.0, 6.0, 9.0])),\n",
    "                'incorrect features generatedBy twoWayInteractions')\n",
    "twoWayPoint = twoWayInteractions(LabeledPoint(1.0, [1, 2, 3]))\n",
    "Test.assertTrue(np.allclose(sorted(twoWayPoint.features),\n",
    "                            sorted([1.0,2.0,3.0,1.0,2.0,3.0,2.0,4.0,6.0,3.0,6.0,9.0])),\n",
    "                'incorrect features generated by twoWayInteractions')\n",
    "Test.assertEquals(twoWayPoint.label, 1.0, 'incorrect label generated by twoWayInteractions')\n",
    "Test.assertTrue(np.allclose(sum(trainDataInteract.take(1)[0].features), 40.821870576035529),\n",
    "                'incorrect features in trainDataInteract')\n",
    "Test.assertTrue(np.allclose(sum(valDataInteract.take(1)[0].features), 45.457719932695696),\n",
    "                'incorrect features in valDataInteract')\n",
    "Test.assertTrue(np.allclose(sum(testDataInteract.take(1)[0].features), 35.109111632783168),\n",
    "                'incorrect features in testDataInteract')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (5b) Build interaction model **\n",
    "#### Now, let's build the new model.  We've done this several times now.  To implement this for the new features, we need to change a few variable names.  Remember that we should build our model from the training data and evaluate it on the validation data.\n",
    "####  Note that you should re-run your hyperparameter search after changing features, as using the best hyperparameters from your prior model will not necessary lead to the best model.  For this exercise, we have already preset the hyperparameters to reasonable values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation RMSE:\n",
      "\tBaseline = 21.586\n",
      "\tLR0 = 19.192\n",
      "\tLR1 = 19.691\n",
      "\tLRGrid = 17.017\n",
      "\tLRInteract = 15.690\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "numIters = 500\n",
    "alpha = 1.0\n",
    "miniBatchFrac = 1.0\n",
    "reg = 1e-10\n",
    "\n",
    "modelInteract = LinearRegressionWithSGD.train(trainDataInteract, numIters, alpha,\n",
    "                                              miniBatchFrac, regParam=reg,\n",
    "                                              regType='l2', intercept=True)\n",
    "labelsAndPredsInteract = valDataInteract.map(lambda lp: (lp.label, modelInteract.predict(lp.features)))\n",
    "rmseValInteract = calcRMSE(labelsAndPredsInteract)\n",
    "\n",
    "print ('Validation RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLR0 = {1:.3f}\\n\\tLR1 = {2:.3f}\\n\\tLRGrid = ' +\n",
    "       '{3:.3f}\\n\\tLRInteract = {4:.3f}').format(rmseValBase, rmseValLR0, rmseValLR1,\n",
    "                                                 rmseValLRGrid, rmseValInteract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Build interaction model (5b)\n",
    "Test.assertTrue(np.allclose(rmseValInteract, 15.6894664683), 'incorrect value for rmseValInteract')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** (5c) Evaluate interaction model on test data **\n",
    "#### Our final step is to evaluate the new model on the test dataset.  Note that we haven't used the test set to evaluate any of our models.  Because of this, our evaluation provides us with an unbiased estimate for how our model will perform on new data.  If we had changed our model based on viewing its performance on the test set, our estimate of RMSE would likely be overly optimistic.\n",
    "#### We'll also print the RMSE for both the baseline model and our new model.  With this information, we can see how much better our model performs than the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE:\n",
      "\tBaseline = 22.137\n",
      "\tLRInteract = 16.327\n"
     ]
    }
   ],
   "source": [
    "# TODO: Replace <FILL IN> with appropriate code\n",
    "labelsAndPredsTest = testDataInteract.map(lambda lp: (lp.label, modelInteract.predict(lp.features)))\n",
    "rmseTestInteract = calcRMSE(labelsAndPredsTest)\n",
    "\n",
    "print ('Test RMSE:\\n\\tBaseline = {0:.3f}\\n\\tLRInteract = {1:.3f}'\n",
    "       .format(rmseTestBase, rmseTestInteract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 test passed.\n"
     ]
    }
   ],
   "source": [
    "# TEST Evaluate interaction model on test data (5c)\n",
    "Test.assertTrue(np.allclose(rmseTestInteract, 16.3272040537),\n",
    "                'incorrect value for rmseTestInteract')"
   ]
  }
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