Python GaussianArray.from_shapeの例

コード例 #1

ファイル: priors.py プロジェクト: fontaine618/NAIVI

 def __init__(self, N, mean=0., variance=1.):
     self.N = N
     self.mean = mean
     self.variance = variance
     self.prior = Prior(GaussianArray.from_shape((N, 1), mean, variance))
     self.marginal = GaussianArray.uniform((N, 1))
     self.from_adjacency = GaussianArray.uniform((N, 1))

コード例 #2

ファイル: factors.py プロジェクト: fontaine618/NAIVI

import numpy as np
from models.distributions.gaussianarray import GaussianArray
from models.distributions.bernoulliarray import BernoulliArray

# -----------------------------------------------------------------------------
# PRIOR
from models.vmp.vmp_factors2 import Prior
child = GaussianArray.uniform((3, 3))
self = Prior(child, 0., 1.)
self.forward()
self.to_elbo()

# -----------------------------------------------------------------------------
# ADD VARIANCE
from models.vmp.vmp_factors2 import AddVariance
parent = GaussianArray.from_shape((3, 3), 0., 1.)
child = GaussianArray.from_shape((3, 3), 0., 1.)
self = AddVariance(child, parent, 1.)

self.to_child()
print(child)
self.to_parent()
print(parent)
self.to_elbo()

# -----------------------------------------------------------------------------
# PROBIT
from models.vmp.vmp_factors2 import Probit
parent = GaussianArray.from_shape((5, 5), 0., 1.)
A = tf.where(tf.random.normal((5, 5)) > 0., 1., 0.)

コード例 #3

ファイル: model.py プロジェクト: fontaine618/NAIVI

a_u = GaussianArray(
    tf.tile(tf.expand_dims(alpha.precision(), 0), [N, 1, 1]),
    tf.tile(tf.expand_dims(alpha.mean_times_precision(), 0), [N, 1, 1]))
a_v = GaussianArray(
    tf.tile(tf.expand_dims(alpha.precision(), 1), [1, N, 1]),
    tf.tile(tf.expand_dims(alpha.mean_times_precision(), 1), [1, N, 1]))
s_uv = GaussianArray.from_array(tf.random.normal((N, N, K), 0., 1.),
                                0.1 * tf.ones((N, N, K)))
x = {"a_u": a_u, "a_v": a_v, "s_uv": s_uv}
v = GaussianArray.from_array(tf.random.normal((N, N, K + 2), 0., 1.),
                             0.1 * tf.ones((N, N, K + 2)))

self.to_v(x)
self.to_x(v)

# ----------

GaussianArray.from_shape((N, N, N + 2), 0.0, 1.0)

self.factors["concatenate"].to_x(
    GaussianArray.from_shape((N, N, N + 2), 0.0, 1.0))
for k, m in self.factors["concatenate"].message_to_x.items():
    print(m.shape())

self._update_latent_variable()

product = self.nodes["product"]
product.mean()
b = self.nodes["latent_variable"]
b.mean()

コード例 #4

import tensorflow as tf
import numpy as np
from models.distributions.gaussianarray import GaussianArray

from models.vmp.vmp_factors2 import Product
N = 3
K = 1
parent = GaussianArray.from_shape((N, K), 1.414, 1.)

upper = tf.linalg.band_part(tf.ones((N, N)), -1, 0) == 0
mean = tf.where(tf.expand_dims(upper, 2), 2., 0.)
variance = tf.where(tf.expand_dims(upper, 2), 5., np.inf)

child = GaussianArray.from_array(mean, variance)

self = Product(child, parent)


print(parent)
self.forward()
self.backward()
print(parent)

print(child)
self.to_child()
print(child.mean())

self.to_elbo()