gensim.models¶
This package contains algorithms for extracting document representations from their raw bag-of-word counts.
Classes¶
CoherenceModel([model, topics, texts, ...]) |
Objects of this class allow for building and maintaining a model for topic coherence. |
Doc2Vec([documents, size, alpha, window, ...]) |
Class for training, using and evaluating neural networks described in http://arxiv.org/pdf/1405.4053v2.pdf |
HdpModel(corpus, id2word[, max_chunks, ...]) |
The constructor estimates Hierachical Dirichlet Process model parameters based |
LdaModel([corpus, num_topics, id2word, ...]) |
The constructor estimates Latent Dirichlet Allocation model parameters based |
LdaMulticore([corpus, num_topics, id2word, ...]) |
The constructor estimates Latent Dirichlet Allocation model parameters based |
LogEntropyModel(corpus[, id2word, normalize]) |
Objects of this class realize the transformation between word-document co-occurence matrix (integers) into a locally/globally weighted matrix (positive floats). |
LsiModel([corpus, num_topics, id2word, ...]) |
Objects of this class allow building and maintaining a model for Latent Semantic Indexing (also known as Latent Semantic Analysis). |
NormModel([corpus, norm]) |
Objects of this class realize the explicit normalization of vectors. |
Phrases([sentences, min_count, threshold, ...]) |
Detect phrases, based on collected collocation counts. |
RpModel(corpus[, id2word, num_topics]) |
Objects of this class allow building and maintaining a model for Random Projections (also known as Random Indexing). |
TfidfModel([corpus, id2word, dictionary, ...]) |
Objects of this class realize the transformation between word-document co-occurrence matrix (integers) into a locally/globally weighted TF_IDF matrix (positive floats). |
VocabTransform(old2new[, id2token]) |
Remap feature ids to new values. |
Word2Vec([sentences, size, alpha, window, ...]) |
Class for training, using and evaluating neural networks described in https://code.google.com/p/word2vec/ |