If True, all non zero counts are set to 1. In the mapping should not be repeated and should not have any gapīetween 0 and the largest index. Given, a vocabulary is determined from the input documents. Indices in the feature matrix, or an iterable over terms. vocabulary Mapping or iterable, default=NoneĮither a Mapping (e.g., a dict) where keys are terms and values are This parameter is ignored if vocabulary is not None. Max_features ordered by term frequency across the corpus. If not None, build a vocabulary that only consider the top min_df float in range or int, default=1įrequency strictly lower than the given threshold. If float, the parameter represents a proportion of documents, integer When building the vocabulary ignore terms that have a documentįrequency strictly higher than the given threshold (corpus-specific max_df float in range or int, default=1.0 Since v0.21, if input is filename or file, the data isįirst read from the file and then passed to the given callableĪnalyzer. If a callable is passed it is used to extract the sequence of features Word boundaries n-grams at the edges of words are padded with space. Option ‘char_wb’ creates character n-grams only from text inside Whether the feature should be made of word n-gram or character Parameters input or callable, default=’word’ That does some kind of feature selection then the number of features willīe equal to the vocabulary size found by analyzing the data. If you do not provide an a-priori dictionary and you do not use an analyzer This implementation produces a sparse representation of the counts using CountVectorizer ( *, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype= ) ¶Ĭonvert a collection of text documents to a matrix of token counts. The proposed approach may potentially be applied for developing control algorithms for the adaptability of a 2D array to incoming wave _ ¶ class sklearn.feature_extraction.text. These results demonstrate a computationally effective method for accounting for nonlinear effects in large WEC arrays. Several optimisation strategies are proposed to improve the overall performance of the WEC array. Subsequently, simulations of a 2D array with 18 WECs and a pillar in the centre (representing the tower of a wind turbine) are carried out to understand wave interference effects. A single heave absorber is firstly investigated to establish the accuracy of the new model in capturing the nonlinear behaviour of the pumping system. In this model, the nonlinear PTO forces are approximated by a truncated Fourier series, while the dynamics of the WEC array are described by a set of linear motion equations in the frequency domain, and the hydrodynamic coefficients are obtained with the boundary element method. This paper presents a nonlinear frequency domain model and uses this to assess the performance of a wave energy converter (WEC) array with a nonlinear power take-off (PTO).
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