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from concurrent.futures import ThreadPoolExecutordef dgim(stream, window): # Buckets stored oldest -> newest as [size, timestamp_of_most_recent_1] buckets = [] for time, bit in enumerate(stream, 1): if bit == 1: buckets.append([1, time]) # If 3 buckets have the same size, merge the two oldest of them i = 0 while i < len(buckets) - 2: if buckets[i][0] == buckets[i + 1][0] == buckets[i + 2][0]: buckets[i + 1][0] = 2 # keep the newer timestamp buckets.pop(i) i = max(i - 2, 0) # re-check for cascaded merges else: i += 1 # Remove buckets that are completely outside the window buckets = [b for b in buckets if time - b[1] < window] count = sum(b[0] for b in buckets) # Oldest bucket may be partly outside the window: count only half if buckets: count -= buckets[0][0] // 2 return countdef exact_count(stream, window): return sum(stream[-window:])def process_stream(data): stream, window = data return dgim(stream, window)def main(): try: n = int(input(“Enter number of streams: “)) streams = [] for k in range(1, n + 1): line = input(f”Enter Stream {k} (0s and 1s): “) streams.append(list(map(int, line.split()))) window = int(input(“Enter window size: “)) except ValueError: print(“Invalid input! Please enter only numbers.”) return if (n <= 0 or window <= 0 or any(not s or any(b not in (0, 1) for b in s) for s in streams)): print(“Invalid input!”) return # Each stream is processed concurrently (one worker per stream) with ThreadPoolExecutor(max_workers=n) as executor: results = list(executor.map(process_stream, [(s, window) for s in streams])) print() for k, (s, r) in enumerate(zip(streams, results), 1): print(f”Stream {k}: estimated = {r}, exact = {exact_count(s, window)}”) print(“Total estimated 1s:”, sum(results)) print(“Total exact 1s:”, sum(exact_count(s, window) for s in streams))if name == “main”: main()
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from concurrent.futures import ThreadPoolExecutordef dgim(stream, window):    # Buckets stored oldest -> newest as [size, timestamp_of_most_recent_1]    buckets = []    for time, bit in enumerate(stream, 1):        if bit == 1:            buckets.append([1, time])            # If 3 buckets have the same size, merge the two oldest of them            i = 0            while i < len(buckets) - 2:                if buckets[i][0] == buckets[i + 1][0] == buckets[i + 2][0]:                    buckets[i + 1][0] *= 2      # keep the newer timestamp                    buckets.pop(i)                    i = max(i - 2, 0)           # re-check for cascaded merges                else:                    i += 1        # Remove buckets that are completely outside the window        buckets = [b for b in buckets if time - b[1] < window]    count = sum(b[0] for b in buckets)    # Oldest bucket may be partly outside the window: count only half    if buckets:        count -= buckets[0][0] // 2    return countdef exact_count(stream, window):    return sum(stream[-window:])def process_stream(data):    stream, window = data    return dgim(stream, window)def main():    try:        n = int(input("Enter number of streams: "))        streams = []        for k in range(1, n + 1):            line = input(f"Enter Stream {k} (0s and 1s): ")            streams.append(list(map(int, line.split())))        window = int(input("Enter window size: "))    except ValueError:        print("Invalid input! Please enter only numbers.")        return    if (n <= 0 or window <= 0            or any(not s or any(b not in (0, 1) for b in s) for s in streams)):        print("Invalid input!")        return    # Each stream is processed concurrently (one worker per stream)    with ThreadPoolExecutor(max_workers=n) as executor:        results = list(executor.map(process_stream,                                    [(s, window) for s in streams]))    print()    for k, (s, r) in enumerate(zip(streams, results), 1):        print(f"Stream {k}: estimated = {r}, exact = {exact_count(s, window)}")    print("Total estimated 1s:", sum(results))    print("Total exact 1s:", sum(exact_count(s, window) for s in streams))if __name__ == "__main__":    main()
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