Round #260

Implement a sliding window aggregation over a time-ordered sequence

**Sliding Window Aggregation Implementation in C#** Below is an example implementation of a sliding window aggregation over a time-ordered sequence in C#. ### SlidingWindowAggregator Class ```csharp using System; using System.Collections.Generic; using System.Linq; public class SlidingWindowAggregator { private readonly int _windowSize; private readonly Func<IEnumerable<double>, double> _aggregationFunction; public SlidingWindowAggregator(int windowSize, Func<IEnumerable<double>, double> aggregationFunction) { _windowSize = windowSize; _aggregationFunction = aggregationFunction; } public IEnumerable<double> Aggregate(IEnumerable<(DateTime, double)> sequence) { var queue = new Queue<(DateTime, double)>(); foreach (var (timestamp, value) in sequence) { queue.Enqueue((timestamp, value)); while (queue.Count > _windowSize) { queue.Dequeue(); } if (queue.Count == _windowSize) { var values = queue.Select(x => x.Item2); yield return _aggregationFunction(values); } } } } ``` ### Example Usage ```csharp using System; class Program { static void Main() { // Create a sample time-ordered sequence var sequence = new[] { (new DateTime(2022, 1, 1, 0, 0, 0), 10.0), (new DateTime(2022, 1, 1, 0, 1, 0), 20.0), (new DateTime(2022, 1, 1, 0, 2, 0), 30.0), (new DateTime(2022, 1, 1, 0, 3, 0), 40.0), (new DateTime(2022, 1, 1, 0, 4, 0), 50.0), }; // Create a sliding window aggregator with a window size of 3 and a mean aggregation function var aggregator = new SlidingWindowAggregator(3, values => values.Average()); // Apply the sliding window aggregation var aggregatedValues = aggregator.Aggregate(sequence); // Print the aggregated values foreach (var value in aggregatedValues) { Console.WriteLine(value); } } } ``` ### Explanation * The `SlidingWindowAggregator` class takes two parameters in its constructor: `windowSize` and `aggregationFunction`. * The `Aggregate` method applies the sliding window aggregation to the input sequence. * It uses a queue to maintain the current window of values. * For each value in the sequence, it adds the value to the queue and removes the oldest value if the queue size exceeds the window size. * When the queue size equals the window size, it applies the aggregation function to the values in the queue and yields the result. * In the example usage, we create a sample time-ordered sequence and a sliding window aggregator with a window size of 3 and a mean aggregation function. * We then apply the sliding window aggregation to the sequence and print the aggregated values. ### Advice * You can modify the `aggregationFunction` parameter to use different aggregation functions, such as sum, max, min, or standard deviation. * You can also adjust the `windowSize` parameter to change the size of the sliding window. * This implementation assumes that the input sequence is time-ordered. If the sequence is not time-ordered, you may need to sort it before applying the sliding window aggregation.

```csharp using System; using System.Collections.Generic; using System.Linq; public class SlidingWindowAggregator { private readonly int _windowSize; private readonly Func<IEnumerable<double>, double> _aggregationFunction; public SlidingWindowAggregator(int windowSize, Func<IEnumerable<double>, double> aggregationFunction) { _windowSize = windowSize; _aggregationFunction = aggregationFunction; } public IEnumerable<double> Aggregate(IEnumerable<double> sequence) { var queue = new Queue<double>(); foreach (var value in sequence) { queue.Enqueue(value); if (queue.Count > _windowSize) { queue.Dequeue(); } if (queue.Count == _windowSize) { yield return _aggregationFunction(queue); } } } } public class Program { public static void Main() { var sequence = Enumerable.Range(1, 10).Select(i => (double)i); var aggregator = new SlidingWindowAggregator(3, x => x.Average()); var aggregatedSequence = aggregator.Aggregate(sequence); foreach (var value in aggregatedSequence) { Console.WriteLine(value); } } } ``` This implementation defines a `SlidingWindowAggregator` class that takes a window size and an aggregation function in its constructor. The `Aggregate` method processes a sequence of values, maintaining a queue of the current window of values. When the queue reaches the desired size, it applies the aggregation function to the values in the queue and yields the result. In the `Main` method, an example sequence of numbers from 1 to 10 is created, and a `SlidingWindowAggregator` is instantiated with a window size of 3 and an aggregation function that calculates the average. The `Aggregate` method is then called on the sequence, and the resulting aggregated sequence is printed to the console. The output will be the average of each window of 3 consecutive values in the sequence: * (1 + 2 + 3) / 3 = 2 * (2 + 3 + 4) / 3 = 3 * (3 + 4 + 5) / 3 = 4 * (4 + 5 + 6) / 3 = 5 * (5 + 6 + 7) / 3 = 6 * (6 + 7 + 8) / 3 = 7 * (7 + 8 + 9) / 3 = 8 * (8 + 9 + 10) / 3 = 9 This demonstrates a sliding window aggregation over a time-ordered sequence, where each window of values is aggregated using a specified function.