Seeded vs Unseeded Randomness Practice Problem

This data science coding problem helps you practice Random Sampling & Generators, seeded vs unseeded randomness, and implementation skills. Read the problem statement, write your solution, and strengthen your understanding of Random Sampling & Generators.

• Problem ID: 118
• Problem key: 118-seeded-vs-unseeded-randomness
• URL: https://datacrack.app/solve/118-seeded-vs-unseeded-randomness
• Difficulty: easy
• Topic: Random Sampling & Generators
• Module: NumPy Foundations

Problem Statement

# 🧩 Seeded vs Unseeded Randomness

---

### 🎯 Goal
Randomness in computing is pseudo-random. By setting the same **seed**, you reset the generator to its starting point and get the exact same sequence. If you don't reset the seed, the generator keeps moving forward, giving you a different sequence. This problem teaches the difference between resetting the seed before each call versus letting the generator continue.

---

### 🔍 How Seeds Work

```python
np.random.seed(42)
first = np.random.random(5)

# Resetting the same seed gives the same sequence
np.random.seed(42)
second = np.random.random(5)
print(np.array_equal(first, second))  # True

# Without resetting, the generator keeps moving forward
third = np.random.random(5)
print(np.array_equal(second, third))  # False
```

---

### 💻 Task
Implement `compare_seeded_randomness(seed, size)` that:
- generates two arrays after resetting the same seed before each one
- generates two arrays without resetting the seed between them
- returns `[seeded_same, unseeded_same]`

---

### 📥 Input
- `seed`: int — random seed to use
- `size`: int — number of random values to generate

### 📤 Output
- List `[seeded_same, unseeded_same]` containing boolean results

---

### 🧩 Starter Code
```python
import numpy as np

def compare_seeded_randomness(seed, size):
    """
    Compare resetting the seed vs not resetting the seed.

    Args:
        seed (int): Random seed
        size (int): Number of random values to generate

    Returns:
        list: [seeded_same, unseeded_same]
    """
    # 🧠 TODO: Show what happens when the same seed is reset
    # 🧠 TODO: Show what happens when the seed is not reset
    # 🧠 TODO: Return both comparison results
    pass
```

---

### 💡 Example
```python
compare_seeded_randomness(42, 5)
# Expected: [True, False]
```

---

### 🔑 Key Concepts
- **same seed reset → same sequence**: `np.random.seed(n)` resets the PRNG to the same initial state.
- **no reset → generator keeps moving forward → different sequence**: consecutive calls to random functions usually produce different values because the generator state advances.
- `np.array_equal(a, b)` checks whether two arrays are exactly identical
- `np.allclose(a, b)` checks whether two arrays are approximately equal, useful when tiny floating-point differences are expected

Starter Code

import numpy as np

def compare_seeded_randomness(seed, size):
    """
    Compare resetting the seed vs not resetting the seed.

    Args:
        seed (int): Random seed
        size (int): Number of random values to generate

    Returns:
        list: [seeded_same, unseeded_same]
    """
    # 🧠 TODO: Show what happens when the same seed is reset
    # 🧠 TODO: Show what happens when the seed is not reset
    # 🧠 TODO: Return both comparison results
    pass