Anthony Lee sicktastic

## knn_classification.py
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

## knn_regression.py
print(__doc__)

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause (C) INRIA


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

## scss_structure.scss
```scss

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

## heuristic_function.py
# -----------
# 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.

## studentMain.py
# ----------
# 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

## particle_filter_resampling_wheel_solution.py
# 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:

## 1_maximize_gaussian.py
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.)

## tipper.rb
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

## registration_step_1.jsx
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"

## localization.py
def localize(colors,measurements,motions,sensor_right,p_move):
    # initializes p to a uniform distribution over a grid of the same dimensions as colors

    def sense(p, colors, measurement):
        aux = [[ 0.0 for row in range(len(p[0]))] for col in range(len(p))]

        s = 0.0

        for i in range(len(p)):
            for j in range(len(p[i])):
	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
	print(__doc__)

	# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
	# Fabian Pedregosa <fabian.pedregosa@inria.fr>
	#
	# License: BSD 3 clause (C) INRIA


	###############################################################################
	# Generate sample data
	```scss

	// Media Queries, iPhone Portrait, iPhone Landscape, iPad Portrait...
	@media (max-width: 480px) {
	// Mixins
	// CSS Elements
	// ID
	// Class
	}
	# -----------
	# 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.
	# ----------
	# 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
	# 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:
	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.)
	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
	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"
	def localize(colors,measurements,motions,sensor_right,p_move):
	# initializes p to a uniform distribution over a grid of the same dimensions as colors

	def sense(p, colors, measurement):
	aux = [[ 0.0 for row in range(len(p[0]))] for col in range(len(p))]

	s = 0.0

	for i in range(len(p)):
	for j in range(len(p[i])):