本文整理汇总了Python中sklearn.neural_network.BernoulliRBM.gibbs方法的典型用法代码示例。如果您正苦于以下问题：Python BernoulliRBM.gibbs方法的具体用法？Python BernoulliRBM.gibbs怎么用？Python BernoulliRBM.gibbs使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neural_network.BernoulliRBM的用法示例。
在下文中一共展示了BernoulliRBM.gibbs方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_gibbs_smoke
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import gibbs [as 别名]
def test_gibbs_smoke():
"""Check if we don't get NaNs sampling the full digits dataset.
Also check that sampling again will yield different results."""
X = Xdigits
rbm1 = BernoulliRBM(n_components=42, batch_size=40, n_iter=20, random_state=42)
rbm1.fit(X)
X_sampled = rbm1.gibbs(X)
assert_all_finite(X_sampled)
X_sampled2 = rbm1.gibbs(X)
assert_true(np.all((X_sampled != X_sampled2).max(axis=1)))示例2: test_gibbs_smoke
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import gibbs [as 别名]
def test_gibbs_smoke():
""" just seek if we don't get NaNs sampling the full digits dataset """
rng = np.random.RandomState(42)
X = Xdigits
rbm1 = BernoulliRBM(n_components=42, batch_size=10,
n_iter=20, random_state=rng)
rbm1.fit(X)
X_sampled = rbm1.gibbs(X)
assert_all_finite(X_sampled)示例3: test_gibbs
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import gibbs [as 别名]
def test_gibbs():
rng = np.random.RandomState(42)
X = Xdigits[:100]
rbm1 = BernoulliRBM(n_components=2, batch_size=5,
n_iter=5, random_state=rng)
rbm1.fit(X)
Xt1 = np.mean([rbm1.gibbs(X[0]) for i in range(100)], 0)
Xt2 = np.mean([rbm1._sample_visibles(rbm1._sample_hiddens(X[0], rng), rng)
for i in range(1000)], 0)
assert_almost_equal(Xt1, Xt2, decimal=1)示例4: test_fit_gibbs
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import gibbs [as 别名]
def test_fit_gibbs():
# Gibbs on the RBM hidden layer should be able to recreate [[0], [1]]
# from the same input
rng = np.random.RandomState(42)
X = np.array([[0.], [1.]])
rbm1 = BernoulliRBM(n_components=2, batch_size=2,
n_iter=42, random_state=rng)
# you need that much iters
rbm1.fit(X)
assert_almost_equal(rbm1.components_,
np.array([[0.02649814], [0.02009084]]), decimal=4)
assert_almost_equal(rbm1.gibbs(X), X)
return rbm1示例5: test_fit_gibbs_sparse
# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import gibbs [as 别名]
def test_fit_gibbs_sparse():
# Gibbs on the RBM hidden layer should be able to recreate [[0], [1]] from
# the same input even when the input is sparse, and test against non-sparse
rbm1 = test_fit_gibbs()
rng = np.random.RandomState(42)
from scipy.sparse import csc_matrix
X = csc_matrix([[0.], [1.]])
rbm2 = BernoulliRBM(n_components=2, batch_size=2,
n_iter=42, random_state=rng)
rbm2.fit(X)
assert_almost_equal(rbm2.components_,
np.array([[0.02649814], [0.02009084]]), decimal=4)
assert_almost_equal(rbm2.gibbs(X), X.toarray())
assert_almost_equal(rbm1.components_, rbm2.components_)