

In [9]:

    
%matplotlib inline
import pandas as pd
import requests as req
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import scipy as sp
import pymc as pymc

Na Raça



In [10]:

    
xvals = np.linspace(-10,10)



In [11]:

    
y = sp.stats.norm.pdf(xvals, 0, 2.5)



In [12]:

    
plt.plot(xvals, y, 'ro')









    Out[12]:





[<matplotlib.lines.Line2D at 0x1862e7f0>]



In [13]:

    
disasters_array =   \
     np.array([ 4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
                   3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
                   2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
                   1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
                   0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
                   3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
                   0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])



In [15]:

    
plt.figure(figsize=(12,4))
plt.bar(np.arange(1851, 1962), disasters_array, color="#34BABD")
plt.xlabel("Ano")
plt.ylabel("# Desastres")
plt.title(u"Desastres de mineradores entre 1851 até 1962 em UK")
plt.xlim(1851, 1962)









    Out[15]:





(1851, 1962)



In [16]:

    
fig, axes = plt.subplots(1, 4, figsize=(18,4), sharex=True, sharey=True)
for i,l in enumerate([1, 5, 12, 25]):
    axes[i].hist(np.random.poisson(l, 1000), histtype="stepfilled")
    axes[i].annotate(r'$\lambda$=%i' % l, xy=(1, 1), xytext=(30, 350), fontsize=20)



In [17]:

    
fig, axes = plt.subplots(1, 4, figsize=(18,4))
for i,p in enumerate([(1, 10), (1, 100), (10, 10), (0.1, 100)]):
    axes[i].hist(np.random.gamma(*p, size=1000), histtype="stepfilled")
    axes[i].set_xlabel(r'$\alpha$=%i, $\beta$=%i' % (p[0], p[1]), fontsize=18)

PyMC



In [18]:

    
# Data
age = np.array([13, 14, 14,12, 9, 15, 10, 14, 9, 14, 13, 12, 9, 10, 15, 11, 15, 
                11, 7, 13, 13, 10, 9, 6, 11, 15, 13, 10, 9, 9, 15, 14, 14, 10, 14, 11, 13, 14, 10])
price = np.array([2950, 2300, 3900, 2800, 5000, 2999, 3950, 2995, 4500, 2800, 1990, 
                  3500, 5100, 3900, 2900, 4950, 2000, 3400, 8999, 4000, 2950, 3250, 
                  3950, 4600, 4500, 1600, 3900, 4200, 6500, 3500, 2999, 2600, 3250, 
                  2500, 2400, 3990, 4600, 450,4700])/1000.



In [19]:

    
from pymc import Normal, Gamma, deterministic, MCMC, Matplot

# Constant priors for parameters
a = Normal('a', 0, 0.0001)
b = Normal('b', 0, 0.0001)

# Precision of normal distribution of prices
tau = Gamma('tau', alpha=0.1, beta=0.1)

@deterministic
def mu(x=age, a=a, b=b):
    # Linear age-price model
    return a + b*x

# Sampling distribution of prices
p = Normal('p', mu, tau, value=price, observed=True)



In [20]:

    
M = MCMC(locals(), db='pickle')



In [21]:

    
M.sample(iter=20000, burn=10000)









    



 [-----------------100%-----------------] 20000 of 20000 complete in 3.0 sec



In [23]:

    
Matplot.plot(b)









    



Plotting b



In [ ]: