Turn raw text into numbers using **Bag-of-Words (BoW) vectorization**, a simple NLP preprocessing step that counts how often each word appears. Given a fixed vocabulary, represent each sentence as a count vector where each entry corresponds to a vocabulary word.

## Requirements

Implement the function
```python
def bag_of_words_vectorize(texts, vocabulary):
    \"""
    Builds Bag-of-Words vectors for a list of texts using a fixed vocabulary.

    Args:
        texts (np.ndarray): Input sentences/documents.
        vocabulary (np.ndarray): Unique tokens defining the vector order.

    Returns:
        np.ndarray: A matrix of shape (len(texts), len(vocabulary)),
        where result[i][j] is the count of vocabulary[j] in texts[i].
    \"""
    # Your solution below
```

**Rules:**
- Tokenize each text by lowercasing and splitting on whitespace.
- For each text, count occurrences of each vocabulary token and output counts in the exact vocabulary order.
- Words not in `vocabulary` are ignored.
- Return the result as a **NumPy array**.
- Do not use any prebuilt vectorizers (e.g., scikit-learn).
- Use only NumPy and built-in Python libraries.

## Example

```python
texts = ["I like cats", "I like like dogs"]
vocabulary = ["i", "like", "cats", "dogs"]

bag_of_words_vectorize(texts, vocabulary)
```

Output:
```python
array([
    [1, 1, 1, 0],
    [1, 2, 0, 1]
])
```

Neural Network Classifier

Mean Reciprocal Rank

Bag of words vectorization

Solve this easy level ML coding question from General. The premier LeetCode for ML platform to master Python, SQL, and Machine Learning algorithms.

199. Bag of words vectorization

Examples

Constraints

Similar Questions

Argument	Type
texts	np.ndarray
vocabulary	np.ndarray