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# Bag of Words Vectorization
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Traditional text vectorization methods including TF-IDF and bag-of-words representations for document classification, information retrieval, and feature extraction tasks. Provides sparse vector representations that complement dense neural embeddings.
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## Capabilities
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### Text Vectorization Interface
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Base interface for text vectorization implementations providing consistent API across different vectorization strategies.
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```java { .api }
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/**
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 * Text vectorization interface for converting text to numerical representations
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 */
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public interface TextVectorizer {
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    // Core vectorization interface - implementations provide specific vectorization methods
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}
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```
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### Bag of Words Vectorizer
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Classic bag-of-words vectorization creating sparse representations based on word frequency counts.
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```java { .api }
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/**
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 * Bag of Words vectorization implementation
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 * Creates sparse vector representations based on word frequency counts
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 */
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public class BagOfWordsVectorizer implements TextVectorizer {
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    // Bag of words implementation with configurable vocabulary and normalization
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}
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```
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### TF-IDF Vectorizer
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Term Frequency-Inverse Document Frequency vectorization for weighted sparse representations emphasizing discriminative terms.
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```java { .api }
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/**
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 * TF-IDF vectorization implementation
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 * Creates weighted sparse vectors emphasizing discriminative terms
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 */
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public class TfidfVectorizer implements TextVectorizer {
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    // TF-IDF implementation with configurable term weighting schemes
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}
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```
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### Base Text Vectorizer
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Abstract base implementation providing common functionality for text vectorization implementations.
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```java { .api }
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/**
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 * Abstract base class for text vectorizers
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 * Provides common functionality and configuration patterns
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 */
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public class BaseTextVectorizer implements TextVectorizer {
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    // Common vectorization infrastructure and utilities
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}
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```
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### Vectorizer Builder
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Builder pattern for configuring text vectorizers with various parameters and data sources.
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```java { .api }
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/**
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 * Builder for text vectorizer configuration
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 * Supports various vectorization strategies and parameters
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 */
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public class Builder {
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    // Configurable builder for text vectorization components
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}
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```
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### Input Stream Creator
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Utility for creating input streams from various text sources for vectorization processing.
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```java { .api }
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/**
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 * Default input stream creator for text sources
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 * Handles various input formats and encodings
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 */
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public class DefaultInputStreamCreator {
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    // Input stream creation utilities for text processing
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}
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```
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**Usage Examples:**
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```java
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import org.deeplearning4j.bagofwords.vectorizer.*;
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// Basic bag of words vectorization
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Collection<String> documents = Arrays.asList(
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    "The quick brown fox jumps over the lazy dog",
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    "A fast brown animal leaps over the sleeping canine",
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    "Natural language processing with machine learning"
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);
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// Configure bag of words vectorizer
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BagOfWordsVectorizer bowVectorizer = new BagOfWordsVectorizer();
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// Additional configuration would be done here based on actual API
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// TF-IDF vectorization for document similarity
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TfidfVectorizer tfidfVectorizer = new TfidfVectorizer();
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// Configure TF-IDF parameters based on actual implementation
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// Example usage pattern (actual API may vary):
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// INDArray vectors = bowVectorizer.vectorize(documents);
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// INDArray tfidfVectors = tfidfVectorizer.vectorize(documents);
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// Builder pattern usage example
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Builder vectorizerBuilder = new Builder()
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    // Configure vectorization parameters
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    // .vocabulary(customVocabulary)
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    // .minWordFrequency(2)
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    // .maxFeatures(10000)
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    ;
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// TextVectorizer vectorizer = vectorizerBuilder.build();
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```
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## Integration with Neural Models
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Bag of words and TF-IDF vectorizers can be used as:
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- **Feature extraction**: Converting text to numerical features for traditional ML algorithms
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- **Preprocessing**: Initial text representation before neural processing  
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- **Baseline comparison**: Comparing neural embeddings against classical methods
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- **Hybrid approaches**: Combining sparse and dense representations for improved performance
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- **Cold start solutions**: Handling new documents without neural model inference overhead
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The sparse representations from these vectorizers complement the dense embeddings from Word2Vec, GloVe, and ParagraphVectors, providing multiple perspectives on text data for different use cases and performance requirements.