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.pre-commit-config.yaml

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- id: auto-walrus
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.14.7
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rev: v0.14.10
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hooks:
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- id: ruff-check
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- id: ruff-format
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- id: validate-pyproject
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.19.0
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rev: v1.19.1
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hooks:
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- id: mypy
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args:

DIRECTORY.md

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* [Minimum Squares To Represent A Number](dynamic_programming/minimum_squares_to_represent_a_number.py)
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* [Minimum Steps To One](dynamic_programming/minimum_steps_to_one.py)
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* [Minimum Tickets Cost](dynamic_programming/minimum_tickets_cost.py)
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* [Narcissistic Number](dynamic_programming/narcissistic_number.py)
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* [Optimal Binary Search Tree](dynamic_programming/optimal_binary_search_tree.py)
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* [Palindrome Partitioning](dynamic_programming/palindrome_partitioning.py)
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* [Range Sum Query](dynamic_programming/range_sum_query.py)
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"""
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Find all narcissistic numbers up to a given limit using dynamic programming.
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A narcissistic number (also known as an Armstrong number or plus perfect number)
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is a number that is the sum of its own digits each raised to the power of the
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number of digits.
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For example, 153 is a narcissistic number because 153 = 1^3 + 5^3 + 3^3.
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This implementation uses dynamic programming with memoization to efficiently
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compute digit powers and find all narcissistic numbers up to a specified limit.
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The DP optimization caches digit^power calculations. When searching through many
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numbers, the same digit power calculations occur repeatedly (e.g., 153, 351, 135
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all need 1^3, 5^3, 3^3). Memoization avoids these redundant calculations.
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Examples of narcissistic numbers:
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Single digit: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
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Three digit: 153, 370, 371, 407
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Four digit: 1634, 8208, 9474
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Five digit: 54748, 92727, 93084
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Reference: https://en.wikipedia.org/wiki/Narcissistic_number
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"""
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def find_narcissistic_numbers(limit: int) -> list[int]:
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"""
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Find all narcissistic numbers up to the given limit using dynamic programming.
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This function uses memoization to cache digit power calculations, avoiding
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redundant computations across different numbers with the same digit count.
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Args:
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limit: The upper bound for searching narcissistic numbers (exclusive)
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Returns:
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list[int]: A sorted list of all narcissistic numbers below the limit
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Examples:
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>>> find_narcissistic_numbers(10)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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>>> find_narcissistic_numbers(160)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153]
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>>> find_narcissistic_numbers(400)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371]
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>>> find_narcissistic_numbers(1000)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371, 407]
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>>> find_narcissistic_numbers(10000)
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 153, 370, 371, 407, 1634, 8208, 9474]
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>>> find_narcissistic_numbers(1)
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[0]
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>>> find_narcissistic_numbers(0)
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[]
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"""
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if limit <= 0:
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return []
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narcissistic_nums = []
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# Memoization: cache[(power, digit)] = digit^power
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# This avoids recalculating the same power for different numbers
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power_cache: dict[tuple[int, int], int] = {}
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def get_digit_power(digit: int, power: int) -> int:
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"""Get digit^power using memoization (DP optimization)."""
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if (power, digit) not in power_cache:
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power_cache[(power, digit)] = digit**power
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return power_cache[(power, digit)]
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# Check each number up to the limit
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for number in range(limit):
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# Count digits
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num_digits = len(str(number))
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# Calculate sum of powered digits using memoized powers
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remaining = number
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digit_sum = 0
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while remaining > 0:
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digit = remaining % 10
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digit_sum += get_digit_power(digit, num_digits)
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remaining //= 10
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# Check if narcissistic
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if digit_sum == number:
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narcissistic_nums.append(number)
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return narcissistic_nums
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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# Demonstrate the dynamic programming approach
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print("Finding all narcissistic numbers up to 10000:")
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print("(Using memoization to cache digit power calculations)")
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print()
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narcissistic_numbers = find_narcissistic_numbers(10000)
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print(f"Found {len(narcissistic_numbers)} narcissistic numbers:")
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print(narcissistic_numbers)

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