!date
import matplotlib.pyplot as plt, numpy as np, seaborn as sns, pandas as pd
%matplotlib inline
sns.set_context("poster")
sns.set_style('whitegrid')

# set random seed for reproducibility
np.random.seed(12345)

import pymc
import numpy as np

x = np.array([85, 95, 70, 65, 70, 90, 75, 85, 80, 85])
y = np.array([1., 1., 0., 0., 0., 1., 1., 0., 0., 1.])

w0 = pymc.Normal('w0', 0, 0.000001)    # uninformative prior (any real number)
w1 = pymc.Normal('w1', 0, 0.000001)    # uninformative prior (any real number)

@pymc.deterministic
def logistic(w0=w0, w1=w1, x=x):
    return 1.0 / (1. + np.exp(w0 + w1 * x))

observed = pymc.Bernoulli('observed', logistic, value=y, observed=True)

# when you initialize the Normal Stochastics
# their values are drawn from the prior
w0 = pymc.Normal('w0', 0, 0.000001)    # uninformative prior (any real number)
w0.value

w0 = pymc.Normal('w0', 0, 0.000001, value=0)    # uninformative prior (any real number)
w0.value

w0 = pymc.Normal('w0', 0, 0.000001, value=0)    # uninformative prior (any real number)
w1 = pymc.Normal('w1', 0, 0.000001, value=0)    # uninformative prior (any real number)

@pymc.deterministic
def logistic(w0=w0, w1=w1, x=x):
    return 1.0 / (1. + np.exp(w0 + w1 * x))

observed = pymc.Bernoulli('observed', logistic, value=y, observed=True)