Esempi in Python per logit

Esempio n. 1

File: pp_persistent_randomness.py Progetto: blei-lab/edward

def eye_color(person):
    random_variables = {x.name: x for x in
                        tf.get_collection('_random_variable_collection_')}
    if person + '/' in random_variables:
        return random_variables[person + '/']
    else:
        return Categorical(
            logits=ed.logit(tf.constant([1.0 / 3] * 3)), name=person)

Esempio n. 2

def eye_color(person):
    random_variables = {
        x.name: x
        for x in tf.get_collection('_random_variable_collection_')
    }
    if person + '/' in random_variables:
        return random_variables[person + '/']
    else:
        return Categorical(logits=ed.logit(tf.constant([1.0 / 3] * 3)),
                           name=person)

Esempio n. 3

File: mixture_gaussian_mh.py Progetto: neverspill/edward

    return x


N = 500  # num data points
K = 2  # num components
D = 2  # dimensionality of data
ed.set_seed(42)

# DATA
x_data = build_toy_dataset(N)

# MODEL
pi = Dirichlet(alpha=tf.constant([1.0] * K))
mu = Normal(mu=tf.zeros([K, D]), sigma=tf.ones([K, D]))
sigma = InverseGamma(alpha=tf.ones([K, D]), beta=tf.ones([K, D]))
c = Categorical(logits=ed.tile(ed.logit(pi), [N, 1]))
x = Normal(mu=tf.gather(mu, c), sigma=tf.gather(sigma, c))

# INFERENCE
T = 5000
qpi = Empirical(params=tf.Variable(tf.ones([T, K]) / K))
qmu = Empirical(params=tf.Variable(tf.zeros([T, K, D])))
qsigma = Empirical(params=tf.Variable(tf.ones([T, K, D])))
qc = Empirical(params=tf.Variable(tf.zeros([T, N], dtype=tf.int32)))

gpi = Dirichlet(alpha=tf.constant([1.4, 1.6]))
gmu = Normal(mu=tf.constant([[1.0, 1.0], [-1.0, -1.0]]),
             sigma=tf.constant([[0.5, 0.5], [0.5, 0.5]]))
gsigma = InverseGamma(alpha=tf.constant([[1.1, 1.1], [1.1, 1.1]]),
                      beta=tf.constant([[1.0, 1.0], [1.0, 1.0]]))
gc = Categorical(logits=tf.zeros([N, K]))

Esempio n. 4

File: mixture_gaussian_mh.py Progetto: zhaoyongboy/edward

    return x


N = 500  # number of data points
K = 2  # number of components
D = 2  # dimensionality of data
ed.set_seed(42)

# DATA
x_data = build_toy_dataset(N)

# MODEL
pi = Dirichlet(alpha=tf.constant([1.0] * K))
mu = Normal(mu=tf.zeros([K, D]), sigma=tf.ones([K, D]))
sigma = InverseGamma(alpha=tf.ones([K, D]), beta=tf.ones([K, D]))
c = Categorical(logits=tf.tile(tf.reshape(ed.logit(pi), [1, K]), [N, 1]))
x = Normal(mu=tf.gather(mu, c), sigma=tf.gather(sigma, c))

# INFERENCE
T = 5000
qpi = Empirical(params=tf.Variable(tf.ones([T, K]) / K))
qmu = Empirical(params=tf.Variable(tf.zeros([T, K, D])))
qsigma = Empirical(params=tf.Variable(tf.ones([T, K, D])))
qc = Empirical(params=tf.Variable(tf.zeros([T, N], dtype=tf.int32)))

gpi = Dirichlet(alpha=tf.constant([1.4, 1.6]))
gmu = Normal(mu=tf.constant([[1.0, 1.0], [-1.0, -1.0]]),
             sigma=tf.constant([[0.5, 0.5], [0.5, 0.5]]))
gsigma = InverseGamma(alpha=tf.constant([[1.1, 1.1], [1.1, 1.1]]),
                      beta=tf.constant([[1.0, 1.0], [1.0, 1.0]]))
gc = Categorical(logits=tf.zeros([N, K]))