Word2Vec (deeplearning4j-nlp 0.0.3.2 API)

java.lang.Object
- org.deeplearning4j.models.word2vec.Word2Vec

All Implemented Interfaces:

Serializable, Persistable
```
public class Word2Vec
extends Object
implements Persistable
```
Leveraging a 3 layer neural net with a softmax approach as output, converts a word based on its context and the training examples in to a numeric vector

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class Word2Vec.Builder

Nested Classes
Modifier and Type	Class and Description
`static class`	`Word2Vec.Builder`

Field Summary

Fields
Modifier and Type Field and Description

static String UNK

Fields
Modifier and Type	Field and Description
`static String`	`UNK`

Constructor Summary

Constructors
Constructor and Description

Word2Vec()

Constructors
Constructor and Description
`Word2Vec()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`TreeSet<VocabWord>`	`analogy(String word0, String word1, String word2)`
`List<String>`	`analogyWords(String w1, String w2, String w3)` Brings back a list of words that are analagous to the 3 words presented in vector space
`boolean`	`buildVocab()` Builds the vocabulary for training
`Set<VocabWord>`	`distance(String word)`
`void`	`fit()` Train the model
`VocabCache`	`getCache()`
`int`	`getLayerSize()`
`SentenceIterator`	`getSentenceIter()`
`List<String>`	`getStopWords()`
`TokenizerFactory`	`getTokenizerFactory()`
`int`	`getWindow()`
`double[]`	`getWordVector(String word)` Get the word vector for a given matrix
`org.nd4j.linalg.api.ndarray.INDArray`	`getWordVectorMatrix(String word)` Get the word vector for a given matrix
`org.nd4j.linalg.api.ndarray.INDArray`	`getWordVectorMatrixNormalized(String word)` Returns the word vector divided by the norm2 of the array
`boolean`	`hasWord(String word)` Returns true if the model has this word in the vocab
`int`	`indexOf(String word)`
`void`	`iterate(VocabWord w1, VocabWord w2)` Train the word vector on the given words
`void`	`load(InputStream is)`
`void`	`plotTsne()` Create a tsne plot
`void`	`setCache(VocabCache cache)`
`void`	`setLayerSize(int layerSize)`
`void`	`setSentenceIter(SentenceIterator sentenceIter)` Note that calling a setter on this means assumes that this is a training continuation and therefore weights should not be reset.
`void`	`setTokenizerFactory(TokenizerFactory tokenizerFactory)`
`void`	`setup()`
`double`	`similarity(String word, String word2)` Returns the similarity of 2 words
`List<String>`	`similarWordsInVocabTo(String word, double accuracy)` Find all words with a similar characters in the vocab
`void`	`skipGram(int i, List<VocabWord> sentence, int b)` Train via skip gram
`Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`toVocabFloat()`
`List<VocabWord>`	`trainSentence(InputStream is)` Train on the given sentence returning a list of vocab words
`void`	`trainSentence(List<VocabWord> sentence, int doc)` Train on a list of vocab words
`List<VocabWord>`	`trainSentence(String sentence)` Train on the given sentence returning a list of vocab words
`Collection<String>`	`wordsNearest(String word, int n)` Get the top n words most similar to the given word
`void`	`write(OutputStream os)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - UNK
```
public static final String UNK
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - Word2Vec
```
public Word2Vec()
```
- Method Detail
  - similarWordsInVocabTo
```
public List<String> similarWordsInVocabTo(String word,
                                          double accuracy)
```
    Find all words with a similar characters in the vocab
    
    Parameters:
    
    word - the word to compare
    
    accuracy - the accuracy: 0 to 1
    
    Returns:
    
    the list of words that are similar in the vocab
  - indexOf
```
public int indexOf(String word)
```
  - getWordVector
```
public double[] getWordVector(String word)
```
    Get the word vector for a given matrix
    
    Parameters:
    
    word - the word to get the matrix for
    
    Returns:
    
    the ndarray for this word
  - getWordVectorMatrix
```
public org.nd4j.linalg.api.ndarray.INDArray getWordVectorMatrix(String word)
```
    Get the word vector for a given matrix
    
    Parameters:
    
    word - the word to get the matrix for
    
    Returns:
    
    the ndarray for this word
  - getWordVectorMatrixNormalized
```
public org.nd4j.linalg.api.ndarray.INDArray getWordVectorMatrixNormalized(String word)
```
    Returns the word vector divided by the norm2 of the array
    
    Parameters:
    
    word - the word to get the matrix for
    
    Returns:
    
    the looked up matrix
  - wordsNearest
```
public Collection<String> wordsNearest(String word,
                                       int n)
```
    Get the top n words most similar to the given word
    
    Parameters:
    
    word - the word to compare
    
    n - the n to get
    
    Returns:
    
    the top n words
  - analogyWords
```
public List<String> analogyWords(String w1,
                                 String w2,
                                 String w3)
```
    Brings back a list of words that are analagous to the 3 words presented in vector space
    
    Parameters:
    
    w1 -
    
    w2 -
    
    w3 -
    
    Returns:
    
    a list of words that are an analogy for the given 3 words
  - hasWord
```
public boolean hasWord(String word)
```
    Returns true if the model has this word in the vocab
    
    Parameters:
    
    word - the word to test for
    
    Returns:
    
    true if the model has the word in the vocab
  - fit
```
public void fit()
```
    Train the model
  - trainSentence
```
public List<VocabWord> trainSentence(InputStream is)
```
    Train on the given sentence returning a list of vocab words
    
    Parameters:
    
    is - the sentence to fit on
    
    Returns:
  - trainSentence
```
public List<VocabWord> trainSentence(String sentence)
```
    Train on the given sentence returning a list of vocab words
    
    Parameters:
    
    sentence - the sentence to fit on
    
    Returns:
  - distance
```
public Set<VocabWord> distance(String word)
```
    Parameters:
    
    word -
    
    Returns:
  - analogy
```
public TreeSet<VocabWord> analogy(String word0,
                                  String word1,
                                  String word2)
```
    Returns:
  - setup
```
public void setup()
```
  - buildVocab
```
public boolean buildVocab()
```
    Builds the vocabulary for training
  - plotTsne
```
public void plotTsne()
```
    Create a tsne plot
  - trainSentence
```
public void trainSentence(List<VocabWord> sentence,
                          int doc)
```
    Train on a list of vocab words
    
    Parameters:
    
    sentence - the list of vocab words to train on
  - skipGram
```
public void skipGram(int i,
                     List<VocabWord> sentence,
                     int b)
```
    Train via skip gram
    
    Parameters:
    
    i -
    
    sentence -
  - toVocabFloat
```
public Map<String,org.nd4j.linalg.api.ndarray.INDArray> toVocabFloat()
```
  - iterate
```
public void iterate(VocabWord w1,
                    VocabWord w2)
```
    Train the word vector on the given words
    
    Parameters:
    
    w1 - the first word to fit
  - similarity
```
public double similarity(String word,
                         String word2)
```
    Returns the similarity of 2 words
    
    Parameters:
    
    word - the first word
    
    word2 - the second word
    
    Returns:
    
    a normalized similarity (cosine similarity)
  - write
```
public void write(OutputStream os)
```
    Specified by:
    
    write in interface Persistable
  - load
```
public void load(InputStream is)
```
    Specified by:
    
    load in interface Persistable
  - getLayerSize
```
public int getLayerSize()
```
  - setLayerSize
```
public void setLayerSize(int layerSize)
```
  - getWindow
```
public int getWindow()
```
  - getStopWords
```
public List<String> getStopWords()
```
  - getSentenceIter
```
public SentenceIterator getSentenceIter()
```
  - getTokenizerFactory
```
public TokenizerFactory getTokenizerFactory()
```
  - setTokenizerFactory
```
public void setTokenizerFactory(TokenizerFactory tokenizerFactory)
```
  - getCache
```
public VocabCache getCache()
```
  - setCache
```
public void setCache(VocabCache cache)
```
  - setSentenceIter
```
public void setSentenceIter(SentenceIterator sentenceIter)
```
    Note that calling a setter on this means assumes that this is a training continuation and therefore weights should not be reset.
    
    Parameters:
    
    sentenceIter -

Class Word2Vec

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

UNK

Constructor Detail

Word2Vec

Method Detail

similarWordsInVocabTo

indexOf

getWordVector

getWordVectorMatrix

getWordVectorMatrixNormalized

wordsNearest

analogyWords

hasWord

fit

trainSentence

trainSentence

distance

analogy

setup

buildVocab

plotTsne

trainSentence

skipGram

toVocabFloat

iterate

similarity

write

load

getLayerSize

setLayerSize

getWindow

getStopWords

getSentenceIter

getTokenizerFactory

setTokenizerFactory

getCache

setCache

setSentenceIter