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import xlrd | |
import xlwt | |
import numpy as np | |
def read_excel(filename, n=0): | |
"""Converts first sheet from an Excel file into an ndarray | |
Parameters | |
---------- | |
filename : string |
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import time | |
import redis | |
import urllib | |
import threading | |
import simplejson | |
class Generator(threading.Thread): | |
def __init__(self, generator, output_channel, _redis=None): | |
threading.Thread.__init__(self) | |
self.generator = generator |
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import csv | |
import pylab as p | |
data = [r for r in csv.reader(open('piratebay.csv', 'rb'))] | |
def categoryratio(c): | |
return sum([ int(r[3]) for r in data if r[1][0] == str(c) ]) | |
# Adapted from # http://scienceoss.com/bar-plot-with-custom-axis-labels/ | |
fig = p.figure() |
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heights = ["high", "medium", "short"] | |
dist = [0.2, 0.6, 0.2] | |
generator = position_generator(dist) | |
n = 10000 | |
count = 0 | |
high = 0 | |
med = 0 | |
low = 0 |
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def profit(supply, demand, sale_price=0.40, production_cost=0.25, welfare_price=0.10, lost=0.15): | |
operative_cost = production_cost * supply | |
if supply >= demand: | |
excedent = supply - demand | |
sale_income = demand * sale_price | |
welfare_gain = excedent * welfare_price | |
result = sale_income + welfare_gain - operative_cost | |
else: | |
not_baked_loss = lost * (demand - supply) | |
sale_income = supply * sale_price |
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demand_dist = (0.30, 0.45, 0.25) # [0]high, [1]medium, [2]low | |
positions = ( 36, 48, 60, 72, 84, 96) | |
high_demand = (0.05, 0.10, 0.25, 0.30, 0.20, 0.10) | |
med_demand = (0.10, 0.20, 0.30, 0.25, 0.10, 0.05) | |
low_demand = (0.15, 0.25, 0.35, 0.15, 0.05, 0.05) | |
demand_type = position_generator(demand_dist) | |
# be careful with order! | |
demand_amount = [ |
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import sys | |
import itertools | |
import numpy as np | |
from collections import Counter | |
from matplotlib import pyplot as plt | |
def dice_prob(n): | |
sides = [(1,2,3,4,5,6) for x in xrange(n)] | |
combined = itertools.product(*sides) | |
sums = [sum(i) for i in combined] |
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import sys | |
import math | |
import numpy as np | |
from matplotlib import pyplot as plt | |
# https://en.wikipedia.org/wiki/Binomial_distribution | |
# k successes, n trials, p probability of success | |
def binomial_pmf(k, n, p): | |
nk = math.factorial(n) / (math.factorial(k) * math.factorial(n-k)) | |
return nk * p**k * (1.0-p)**(n-k) |
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# from http://funcall.blogspot.sg/2009/03/not-lisp-again.html | |
def deriv(f, dx=.0001): | |
return lambda x: (f(x + dx) - f(x)) / dx | |
cube = lambda x: x**3 | |
d = deriv(cube) | |
for n in xrange(5): | |
print n, d(n) |
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# http://archive.ics.uci.edu/ml/datasets/online+retail | |
import csv | |
import sys | |
import time | |
import datetime | |
from sqlalchemy import * | |
from sqlalchemy import create_engine | |
from sqlalchemy import sql |
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