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cars
plot(cars)

cars.lm = lm(dist~.,cars)
summary(cars.lm)

# dist = 3.03*speed - 17.58

plot(cars)
abline(cars.lm, col="red")

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# coding:utf-8
import numpy as np
from sklearn import linear_model
from matplotlib import pyplot as plt
from matplotlib import lines

data = np.loadtxt("../data/cars.csv", delimiter=",", skiprows=1, usecols=(1, 2))

dest = data[:, 0]
speed = data[:, 1]

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print "hello world"
list = []
list.append(1)
list.append(2)
list.append(3)
for n in list:
  print "%d" %n

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import pulp

problem = pulp.LpProblem('hamburg and omelet', pulp.LpMaximize)

# create variables
x = pulp.LpVariable('x', cat = 'Integer')
y = pulp.LpVariable('y', cat = 'Integer')

# maximize
problem += 400 * x + 300 * y

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# coding:utf-8
import FuncDesigner as fd
from openopt import LP

"""
max. 400x + 300y
sub. 60x+40y <= 3800
     20x+30y <= 2100
     20x+10y <= 1200
"""

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def insertion_sort(target):
    for i in range(len(target)):
        insert(target, i, target[i])


def insert(target, pos, value):
    i = pos - 1
    while (i >= 0 and target[i] > value):
        target[i + 1] = target[i]
        i = i - 1

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# coding:utf-8

from benchmarker import Benchmarker
import random
import sort


def initdata():
    data = range(100)
    random.shuffle(data)

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def sort(target):
    median_sort(target, 0, len(target) - 1)
    return target


def median_sort(target, left, right):
    if left < right:
        # find median
        mid_point = (left + right) / 2
        min_value = target[left]

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def quick_sort(target):
    if len(target) <= 1:
        return target

    pivot = target[0]
    left = []
    right = []
    for x in xrange(1, len(target)):
        if target[x] <= pivot:
            left.append(target[x])

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def select_sort(target):
    p = len(target)
    for i in xrange(p - 1):
        min = target[i]
        t = i
        for j in xrange(i + 1, p):
            if target[j] < min:
                min = target[j]
                t = j
        target[t] = target[i]