本文整理汇总了Python中gensim.models.ldamodel.LdaModel.expElogbeta[:]方法的典型用法代码示例。如果您正苦于以下问题：Python LdaModel.expElogbeta[:]方法的具体用法？Python LdaModel.expElogbeta[:]怎么用？Python LdaModel.expElogbeta[:]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gensim.models.ldamodel.LdaModel的用法示例。
在下文中一共展示了LdaModel.expElogbeta[:]方法的4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: vwmodel2ldamodel
# 需要导入模块: from gensim.models.ldamodel import LdaModel [as 别名]
# 或者: from gensim.models.ldamodel.LdaModel import expElogbeta[:] [as 别名]
def vwmodel2ldamodel(vw_model, iterations=50):
"""Convert :class:`~gensim.models.wrappers.ldavowpalwabbit.LdaVowpalWabbit` to
:class:`~gensim.models.ldamodel.LdaModel`.
This works by simply copying the training model weights (alpha, beta...) from a trained vwmodel
into the gensim model.
Parameters
----------
vw_model : :class:`~gensim.models.wrappers.ldavowpalwabbit.LdaVowpalWabbit`
Trained Vowpal Wabbit model.
iterations : int
Number of iterations to be used for inference of the new :class:`~gensim.models.ldamodel.LdaModel`.
Returns
-------
:class:`~gensim.models.ldamodel.LdaModel`.
Gensim native LDA.
"""
model_gensim = LdaModel(
num_topics=vw_model.num_topics, id2word=vw_model.id2word, chunksize=vw_model.chunksize,
passes=vw_model.passes, alpha=vw_model.alpha, eta=vw_model.eta, decay=vw_model.decay,
offset=vw_model.offset, iterations=iterations, gamma_threshold=vw_model.gamma_threshold,
dtype=numpy.float32
)
model_gensim.expElogbeta[:] = vw_model._get_topics()
return model_gensim示例2: malletmodel2ldamodel
# 需要导入模块: from gensim.models.ldamodel import LdaModel [as 别名]
# 或者: from gensim.models.ldamodel.LdaModel import expElogbeta[:] [as 别名]
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""Convert :class:`~gensim.models.wrappers.ldamallet.LdaMallet` to :class:`~gensim.models.ldamodel.LdaModel`.
This works by copying the training model weights (alpha, beta...) from a trained mallet model into the gensim model.
Parameters
----------
mallet_model : :class:`~gensim.models.wrappers.ldamallet.LdaMallet`
Trained Mallet model
gamma_threshold : float, optional
To be used for inference in the new LdaModel.
iterations : int, optional
Number of iterations to be used for inference in the new LdaModel.
Returns
-------
:class:`~gensim.models.ldamodel.LdaModel`
Gensim native LDA.
"""
model_gensim = LdaModel(
id2word=mallet_model.id2word, num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha, iterations=iterations,
gamma_threshold=gamma_threshold,
dtype=numpy.float64 # don't loose precision when converting from MALLET
)
model_gensim.expElogbeta[:] = mallet_model.wordtopics
return model_gensim示例3: vwmodel2ldamodel
# 需要导入模块: from gensim.models.ldamodel import LdaModel [as 别名]
# 或者: from gensim.models.ldamodel.LdaModel import expElogbeta[:] [as 别名]
def vwmodel2ldamodel(vw_model, iterations=50):
"""
Function to convert vowpal wabbit model to gensim LdaModel. This works by
simply copying the training model weights (alpha, beta...) from a trained
vwmodel into the gensim model.
Args:
vw_model : Trained vowpal wabbit model.
iterations : Number of iterations to be used for inference of the new LdaModel.
Returns:
model_gensim : LdaModel instance; copied gensim LdaModel.
"""
model_gensim = LdaModel(
num_topics=vw_model.num_topics, id2word=vw_model.id2word, chunksize=vw_model.chunksize,
passes=vw_model.passes, alpha=vw_model.alpha, eta=vw_model.eta, decay=vw_model.decay,
offset=vw_model.offset, iterations=iterations, gamma_threshold=vw_model.gamma_threshold
)
model_gensim.expElogbeta[:] = vw_model._get_topics()
return model_gensim示例4: malletmodel2ldamodel
# 需要导入模块: from gensim.models.ldamodel import LdaModel [as 别名]
# 或者: from gensim.models.ldamodel.LdaModel import expElogbeta[:] [as 别名]
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""
Function to convert mallet model to gensim LdaModel. This works by copying the
training model weights (alpha, beta...) from a trained mallet model into the
gensim model.
Args:
mallet_model : Trained mallet model
gamma_threshold : To be used for inference in the new LdaModel.
iterations : number of iterations to be used for inference in the new LdaModel.
Returns:
model_gensim : LdaModel instance; copied gensim LdaModel
"""
model_gensim = LdaModel(
id2word=mallet_model.id2word, num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha, iterations=iterations,
gamma_threshold=gamma_threshold)
model_gensim.expElogbeta[:] = mallet_model.wordtopics
return model_gensim