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var RetreatAttendeeRegistrationStart = React.createClass({
  render: function() {
    return (
      <div>
        <h1>Retreat Attendee Registration</h1>
        <div className="form-group">
          <label>First Name</label>
          <input type="text"
                 id="first_name"
                 ref="first_name"

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class Tipper
  TAX = 0.05
  
  def initializer(amount:, discount_percentage: 0, tip_percentage:)
    @amount = amount
    @discount_percentage = discount_percentage
    @tip_percentage = tip_percentage
  end
  
  def total

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from math import *

def f(mu, sigma2, x):
    return 1 / sqrt(2. * pi * sigma2) * exp(-.5 * (x - mu) **2 / sigma2)

print f(10., 4., 10.)

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# In this exercise, you should implement the
# resampler shown in the previous video.

from math import *
import random

landmarks  = [[20.0, 20.0], [80.0, 80.0], [20.0, 80.0], [80.0, 20.0]]
world_size = 100.0

class robot:

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# ----------
# Background
# 
# A robotics company named Trax has created a line of small self-driving robots 
# designed to autonomously traverse desert environments in search of undiscovered
# water deposits.
#
# A Traxbot looks like a small tank. Each one is about half a meter long and drives
# on two continuous metal tracks. In order to maneuver itself, a Traxbot can do one
# of two things: it can drive in a straight line or it can turn. So to make a 

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# -----------
# User Instructions:
#
# Modify the the search function so that it becomes
# an A* search algorithm as defined in the previous
# lectures.
#
# Your function should return the expanded grid
# which shows, for each element, the count when
# it was expanded or -1 if the element was never expanded.

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```scss

// Media Queries, iPhone Portrait, iPhone Landscape, iPad Portrait...
@media (max-width: 480px) {
  // Mixins
  // CSS Elements
  // ID
  // Class
}

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print(__doc__)

# Author: Alexandre Gramfort <[email protected]>
#         Fabian Pedregosa <[email protected]>
#
# License: BSD 3 clause (C) INRIA


###############################################################################
# Generate sample data

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print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15

# import some data to play with

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decision trees: cross-validation

This script provides an example of using cross-validation to fine-tune parameters for learning a decision tree with scikit-learn.

A blog post about this code is available here, check it out!

Requirements