README.md

Reservoir sampling

Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far.

🔹 Variants of Reservoir Sampling

While Algorithm R is the simplest and most commonly used, there are other variants that improve performance in specific cases:

Algorithm	Description	Complexity
Algorithm R	Basic reservoir sampling, replaces elements with probability k/i	O(N)
Algorithm L	Optimized for large N, reduces replacements via skipping	O(N), fewer iterations
Weighted Reservoir Sampling	Assigns elements weights, prioritizing selection based on weight	O(N log k) (heap-based)
Random Sort Reservoir Sampling	Uses a min-heap priority queue, selecting k elements with highest random priority scores	O(N log k)

Algorithm Weighted R – Weighted Reservoir Sampling

Weighted Reservoir Sampling is an efficient algorithm for selecting k elements proportionally to their weights from a stream of unknown length N, using only O(k) memory.

This repository implements Weighted Algorithm R, an extension of Jeffrey Vitter's Algorithm R, which allows weighted sampling using a heap-based approach.

This algorithm uses a min-heap-based priority selection, ensuring O(N log k) time complexity, making it efficient for large streaming datasets.

📊 Mathematical Formula for Weighted Algorithm R

Problem Definition

We need to select k elements from a data stream of unknown length N, ensuring each element is selected with a probability proportional to its weight w_i.

Algorithm Steps

1. Initialize a Min-Heap of Size k

   • Store the first k elements with their priority scores: [ $p_i = \frac{w_i}{U_i}$ ] where ( $U_i$ ) is a uniform random number from (0,1].

2. Process Remaining Elements (i > k)

   • For each new element s_i:
     • Compute priority score: [ $p_i = \frac{w_i}{U_i}$ ]
     • If p_i is greater than the smallest priority in the heap, replace the smallest element.

3. After processing N elements, the reservoir will contain k elements selected proportionally to their weights.

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🔬 Probability Proof

For any element ( $s_i$ ) with weight ( $w_i$ ):

1. The priority score is: [ $p_i = \frac{w_i}{U_i}$ ] where ( $U_i \sim U(0,1]$ ).

2. The probability that s_i is among the top k elements: [ $P(s_i \text{ is selected}) \propto w_i$ ] meaning elements with higher weights are more likely to be selected.

✅ Conclusion: Weighted Algorithm R correctly samples elements proportionally to their weights, unlike uniform Algorithm R.

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🧪 Test Case Formula for Weighted Algorithm R

Test Case Design

To validate Weighted Algorithm R, we must check if:

• Higher-weight elements are chosen more frequently.
• Selection follows the weight distribution over multiple runs.

Mathematical Test

For T independent runs:

• Let count(s_i) be the number of times s_i appears in the reservoir.
• Expected probability: [ $P(s_i) = \frac{w_i}{\sum w_j}$ ]
• Expected occurrence over T runs: [ $\text{Expected count}(s_i) = T \times \frac{w_i}{\sum w_j}$ ]
• We verify that count(s_i) is statistically close to this value.

🎯 Algorithm L

Reservoir Sampling is a technique for randomly selecting k elements from a stream of unknown length N.
Algorithm L, introduced by Jeffrey Vitter (1985), improves upon traditional methods by using an optimized skipping approach, significantly reducing the number of random number calls.

Problem Definition

We need to select k elements from a data stream of unknown length N, ensuring each element has an equal probability k/N of being chosen.

Algorithm Steps

1. Fill the reservoir with the first k elements.

2. Initialize weight factor W using:

   $W = \exp\left(\frac{\log(\text{random}())}{k}\right)$

3. Skip elements efficiently using the geometric formula:

   $\text{skip} = \lfloor \frac{\log(\text{random}())}{\log(1 - W)} \rfloor$

4. If still in bounds, randomly replace an element in the reservoir.

5. Update W for the next iteration using:

   $W = W \times \exp\left(\frac{\log(\text{random}())}{k}\right)$

6. Repeat until the end of the stream.

Probability Proof

For each element ( $s_i$ ), we show that it has an equal probability of being selected:

1. The probability that ( $s_i$ ) reaches the selection process:

   $P(s_i \text{ is considered}) = \frac{k}{i}$

2. The probability that ( $s_i$ ) remains in the reservoir is:

   $P(s_i \text{ in final reservoir}) = \frac{k}{N}, \quad \forall i \in {1, ..., N}$

This confirms that Algorithm L ensures uniform selection.

🧪 Test Case Formula for Algorithm L

Test Case Design

To validate Algorithm L, we must check if:

• Each element is chosen with probability k/N.
• Selection is uniform over multiple runs.

Mathematical Test

For T independent runs:

• Let count(s_i) be the number of times s_i appears in the reservoir.

• Expected probability:

  $P(s_i) = \frac{k}{N}$

• Expected occurrence over T runs:

  $\text{Expected count}(s_i) = T \times \frac{k}{N}$

• We verify that count(s_i) is statistically close to this value.