Ordinal encoding is a simple way to turn ordered categorical values (like sizes) into numbers so models can use them. You’ll implement an ordinal encoder that maps each category to an integer based on a provided ordering.

## Requirements

Implement the function
```python
import numpy as np

def ordinal_encode(values, ordered_categories, unknown_value=-1):
    \"""
    Ordinal-encodes a list of categorical values using a provided ordering.

    Args:
        values (np.ndarray): Raw categorical values to encode (e.g., ["S", "M", "L"]).
        ordered_categories (np.ndarray): Categories in increasing order
            (e.g., ["XS", "S", "M", "L", "XL"]).
        unknown_value (int): Value to use if an item in `values` is not found in
            `ordered_categories`.

    Returns:
        np.ndarray: Encoded integers where each category c is mapped to its index in
        `ordered_categories`.
    \"""
    # Your solution below
```

**Rules:**
- Use the mapping \( \text{code}(c) = \text{index}(c \text{ in ordered\_categories}) \).
- If a value is not present in `ordered_categories`, encode it as `unknown_value`.
- Return a **NumPy array** of integers.
- Do not use any prebuilt encoders (e.g., from scikit-learn).
- Keep it in a single function.

## Example

```python
values = np.array(["M", "S", "XL", "XS"])
ordered_categories = np.array(["XS", "S", "M", "L", "XL"])

ordinal_encode(values, ordered_categories, unknown_value=-1)
```

Output:
```python
np.array([2, 1, 4, 0])
```

Score Gap Analysis

Batch normalization forward pass

Ordinal encoding

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1344. Ordinal encoding

Examples

Constraints

Similar Questions

Argument	Type
values	np.ndarray
unknown_value	int
ordered_categories	np.ndarray