Compute **cosine similarity** between two vectors, a common linear algebra measure of how aligned they are. It’s especially useful when you care about direction more than magnitude.

## Requirements

Implement the function
```python
import numpy as np

def cosine_similarity(x, y):
    \"""
    Computes cosine similarity between two vectors.

    Cosine similarity is defined as:
        cos(x, y) = (x · y) / (||x|| * ||y||)

    Args:
        x (np.ndarray): First vector.
        y (np.ndarray): Second vector.

    Returns:
        float: Cosine similarity score.
    \"""
    # Your solution below
```

**Rules:**
- Use the formula: $$\cos(x, y) = \frac{\sum_{i=1}^{n} x_i y_i}{\sqrt{\sum_{i=1}^{n} x_i^2}\sqrt{\sum_{i=1}^{n} y_i^2}}$$
- Return a single `float`.
- Do not use any prebuilt similarity/distance utilities (e.g., from `scipy`).
- Use NumPy for vector operations (`np.dot`, `np.linalg.norm`).

## Example

```python
x = np.array([1.0, 2.0, 3.0])
y = np.array([4.0, 5.0, 6.0])

cosine_similarity(x, y)
```

Output:
```python
0.974631846
```

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Cosine similarity

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313. Cosine similarity

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Similar Questions

Argument	Type
x	np.ndarray
y	np.ndarray