Sinusoidal encoding turns a cyclic feature (like hour-of-day) into two smooth features using sine and cosine so models don’t treat the “end” and “start” as far apart. You’ll implement this encoding for an array of cyclic values and return the encoded features as NumPy arrays.

$$
\text{sin} = \sin\left(2\pi \frac{x}{p}\right), \quad \text{cos} = \cos\left(2\pi \frac{x}{p}\right)
$$

## Requirements

Implement the function
```python
def sin_cos_encode(values, period):
    \"""
    Applies sinusoidal (sin/cos) encoding to a cyclic feature.

    Args:
        values (np.ndarray): Raw cyclic values (e.g., hours in [0, 24)).
        period (float): The cycle length (e.g., 24 for hours, 7 for weekdays).

    Returns:
        tuple[np.ndarray, np.ndarray]: (sin_features, cos_features), each the same length as values.
    \"""
    # Your solution below
```

**Rules:**
- Use the formula above to compute one sine and one cosine value per input.
- Return a tuple of two NumPy arrays: all sine values, then all cosine values.
- Use NumPy for vectorized math (`np.sin`, `np.cos`, `np.pi`).
- Do not use any feature-engineering helper libraries (e.g., sklearn encoders).

## Example

```python
values = np.array([0.0, 6.0, 12.0, 18.0])
period = 24.0

sin_cos_encode(values, period)
```

Output:
```python
(
    array([0.0, 1.0, 0.0, -1.0]),
    array([1.0, 0.0, -1.0, 0.0])
)
```

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Sin and cosine feature encoding

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207. Sin and cosine feature encoding

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Similar Questions

Argument	Type
period	float
values	np.ndarray