import bayes_lib as bl
import autograd
import autograd.numpy as agnp
import autograd.scipy as agsp
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
#x_true = agnp.random.normal(3, 1, size = (100, 10))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
train_images = mnist.train.next_batch(100)[0]
with bl.Model() as m:
X = bl.Placeholder('X', dimensions=agnp.array([100, 784]))
encoder = bl.ml.neural_network.DenseNeuralNetwork(
'Encoder',
X,
layer_dims=[784, 50, 20],
nonlinearity=bl.math.utils.sigmoid)
decoder = bl.ml.neural_network.DenseNeuralNetwork(
'Decoder',
encoder,
layer_dims=[20, 50, 784],
nonlinearity=bl.math.utils.sigmoid,
last_layer_nonlinearity=bl.math.utils.sigmoid)
y = bl.rvs.Bernoulli('obs', decoder, observed=X)
fit_params = agnp.loadtxt("pos.txt", delimiter=',')
m.set_param(fit_params)
out = m.evaluate(decoder, feed_dict={X: train_images})
for i in range(out.shape[1]):
s1 = theta3**2
s2 = theta4**2
rho = np.tanh(theta5)
return np.sum(
multivariate_normal.logpdf(
X, np.array([theta1, theta2]),
np.array([[s1, rho * s1 * s2], [rho * s1 * s2, s2]])))
tp = np.array([0.7, -2.9, -1, -0.9, 0.6, 4])
x = sample(0.7, -2.9, -1, -0.9, 0.6, 4)
with bl.Model() as model_post:
X = tf.placeholder(tf.float32, shape=[None, 2])
theta1 = bl.rvs.Uniform(-3, 3)
theta2 = bl.rvs.Uniform(-3, 3)
theta3 = bl.rvs.Uniform(-3, 3)
theta4 = bl.rvs.Uniform(-3, 3)
theta5 = bl.rvs.Uniform(-3, 3)
rho = tf.tanh(theta5)
corr = rho * (theta3**2) * (theta4**2)
mu = tf.stack([theta1, theta2])
cov = [[theta3**4, corr], [corr, theta4**4]]
y = bl.rvs.Multivariate_Normal(mu, cov, observed=X)
import bayes_lib as bl
import tensorflow as tf
import seaborn as sns
import pandas as pd
import autograd.numpy as np
import autograd.scipy as agsp
import matplotlib.pyplot as plt
with bl.Model() as model:
x = bl.rvs.Normal(0., 2., name='b')
inf_alg = bl.inference.samplers.MultivariateUniSliceSampler()
chain, tchain = inf_alg.run(model,
np.array(np.random.normal(0, 1)).reshape(1, ))
plt.hist(tchain)
plt.show()
"""
inf_alg = bl.inference.variational.ReparameterizedVariationalInference(model, init = np.array([0,0,0,0]))
res, v_dist = inf_alg.run(feed_dict = {X: X_data, y: y_data}, iter_func = iterfunc)
samples = v_dist.sample(res.position, 1000)
sns.pairplot(pd.DataFrame(samples))
plt.show()
inf_alg = bl.inference.samplers.A_MVNMetropolisHastings_Adapt()
chain = inf_alg.run(model, feed_dict = {X: X_data, y: y_data}, n_iters = 10000)
sns.pairplot(pd.DataFrame(chain[5000:,:]))
plt.show()
plt.plot(chain[5000:,0])
def gen_data(n):
logpdfs = []
#theta = agnp.random.multivariate_normal(agnp.array([3,3]), agnp.eye(2), size = n)
theta = agnp.random.uniform(0, 5, size=(n, 2))
data = []
for i in range(n):
y = agnp.random.multivariate_normal(theta[i, :], agnp.eye(2))
logpdfs.append(
agsp.stats.multivariate_normal.logpdf(y, theta[i, :], agnp.eye(2)))
data.append(y)
return logpdfs, theta, agnp.array(data)
#data = agnp.hstack([agnp.random.normal(3, 2, size = (1000,1)),agnp.random.normal(10, .1, size = (1000,1)), agnp.random.normal(-5, 3, size = (1000,1))])
with bl.Model() as m_surrogate_density:
#X = bl.Placeholder('X', dimensions = agnp.array([1000,3]))
X = bl.Placeholder('X', dimensions=agnp.array([100, 2]))
theta = bl.Placeholder('theta', dimensions=agnp.array([100, 2]))
made = bl.ml.made.ConditionalGaussianMADE('made',
2,
2,
theta, [20, 21],
X,
nonlinearity=bl.math.utils.relu)
init = agnp.random.normal(-3, 3, size=m_surrogate_density.n_params)
def iter_func(t, p, o):
print("Objective value: %f" % o)