![]() ![]() I boot the Live image, enter command prompt and become superuser. I've compiled the drivers on Ubuntu 13.10 and am attempting to use the clonezilla-live-20140331-saucy-amd64 iso.įollowing the documentation page I get stuck pretty quickly. Get an empty Projection with the same parameters as the current object.Hi, I'm trying to add drivers for a HighPoint RocketRAID 2642 card to CloneZilla Live. Tune to improve accuracy.ĭtype ( numpy.dtype, optional) – Enforces a type for elements of the decomposed matrix. Tune to improve accuracy.Įxtra_dims ( int, optional) – Extra samples to be used besides the rank k. Power_iters ( int, optional) – Number of power iteration steps to be used. Otherwise - our own version will be used. Use_svdlibc ( bool, optional) – If True - will use sparsesvd library, ParametersĬorpus ( ) – Corpus in BoW format or as sparse matrix. load ( tmp_fname ) # load modelĮither corpus or id2word must be supplied in order to train the model. save ( tmp_fname ) # save model > loaded_model = LsiModel. add_documents ( common_corpus ) # update model with new documents > tmp_fname = get_tmpfile ( "lsi.model" ) > model. > from import common_corpus, common_dictionary, get_tmpfile > from gensim.models import LsiModel > model = LsiModel ( common_corpus, id2word = common_dictionary ) # train model > vector = model ] # apply model to BoW document > model. ![]() Model - right singular vectors (can be reconstructed if needed). The decomposition algorithm is described in “Fast and Faster: A Comparison of Two Streamed LsiModel ( corpus=None, num_topics=200, id2word=None, chunksize=20000, decay=1.0, distributed=False, onepass=True, power_iters=2, extra_samples=100, dtype= ) ¶īases:, Traffic due to data distribution across cluster nodes would likely make it Reading/decompressing the input from disk in its 4 passes. The stochastic algo could be distributed too, but most time is already spent > from import common_dictionary, common_corpus > from gensim.models import LsiModel > model = LsiModel ( common_corpus, id2word = common_dictionary ) > vectorized_corpus = model # vectorize input copus in BoW format 1 With dual core Xeon 2.0GHz, 4GB RAM, ATLAS Serial = Core 2 Duo MacBook Pro 2.53Ghz, 4GB RAM, libVecĭistributed = cluster of four logical nodes on three physical machines, each Multi-pass stochastic algo (with 2 power iterations) (2G corpus positions, 3.2M documents, 100K features, 0.5G non-zero entries in the final TF-IDF matrix), Wall-clock performance on the English Wikipedia Seen once and must be processed immediately (one-pass algorithm)ĭistributed computing for very large corpora, making use of a cluster of This module actually contains several algorithms for decomposition of large corpora, aĬombination of which effectively and transparently allows building LSI models for:Ĭorpora much larger than RAM: only constant memory is needed, independent ofĬorpora that are streamed: documents are only accessed sequentially, noĬorpora that cannot be even temporarily stored: each document can only be The SVD decomposition can be updated with new observationsĪt any time, for an online, incremental, memory-efficient training. Implements fast truncated SVD (Singular Value Decomposition). Module for Latent Semantic Analysis (aka Latent Semantic Indexing). Models.lsimodel – Latent Semantic Indexing ¶
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