122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
import itertools
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from collections.abc import Callable
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from concurrent.futures import ThreadPoolExecutor
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from queue import Queue
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from typing import Tuple, List
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import numpy as np
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_SUPLYER_TYPE = Callable[[], int | float] | int | float
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_QUEUE_TYPE = Queue[Tuple[List[float | int], List[float | int], int]]
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class Matrix:
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def __init__(self, size: int, suplyer: _SUPLYER_TYPE = 0):
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self.__size = size
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self.__matrix = self._generate_matrix(suplyer)
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def _generate_matrix(self, suplyer: _SUPLYER_TYPE):
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if suplyer:
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match suplyer:
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case int() | float():
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return [[suplyer for _ in range(self.__size)] for _ in range(self.__size)]
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case Callable():
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return [[suplyer() for _ in range(self.__size)] for _ in range(self.__size)]
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return [[0 for _ in range(self.__size)] for _ in range(self.__size)]
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def from_flat(self, numbers: List[int | float]):
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if len(numbers) != self.__size ** 2:
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raise Exception(f"Invalid matrix size {self.__size} ^ 2 != {len(numbers)}")
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x, y = 0, 0
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for number in numbers:
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self.__matrix[y][x] = number
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x += 1
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if x >= self.__size:
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x = 0
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y += 1
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@property
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def rows(self):
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return self.__matrix
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@property
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def columns(self):
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return [[self.__matrix[i][j] for i in range(self.__size)] for j in range(self.__size)]
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@property
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def size(self):
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return self.__size
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@staticmethod
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def random(*, size: int):
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import random
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return Matrix(size=size, suplyer=random.random)
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def to_numpy(self):
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return np.array(self.__matrix)
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def __eq__(self, other):
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return (isinstance(other, Matrix)
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and self.__size == other.__size)
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def __str__(self):
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return f"Matrix {self.__size}x{self.__size} \n" + "\n".join([str(
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" ".join([f"{element:.5f}" for element in row])
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) for row in self.__matrix])
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def __iter__(self):
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return iter(self.__matrix)
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def __getitem__(self, index):
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return self.__matrix[index]
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def __mul__(self, other):
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match other:
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case Matrix():
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return mul_matrixs(self, other)
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case tuple():
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other_matrix, count_threads = other
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return mul_matrixs(self, other_matrix, count_threads)
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return None
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def mul_row_and_column_in_thread(queue: _QUEUE_TYPE) -> list[tuple[int | float, int]]:
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result = []
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while queue.qsize():
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local_result = 0
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row, column, place = queue.get()
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for k in range(len(row)):
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local_result += row[k] * column[k]
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result.append((local_result, place))
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return result
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def mul_matrixs(m1: Matrix, m2: Matrix, threads: int = 0):
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if m1.size != m2.size:
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return None
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if threads == 0:
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threads = 1
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result = Matrix(size=m1.size, suplyer=0)
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thread_queues = [Queue() for _ in range(threads)]
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thread_iterator = 0
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for row_m1, column_m2 in itertools.product(m1.rows, m2.columns):
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thread_queues[thread_iterator].put((row_m1, column_m2, thread_iterator))
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thread_iterator += 1
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if thread_iterator >= threads:
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thread_iterator = 0
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with ThreadPoolExecutor(max_workers=threads) as executor:
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flat = []
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for item in executor.map(mul_row_and_column_in_thread, thread_queues):
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flat += item
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flat.sort(key=lambda x: x[1])
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result.from_flat([*map(lambda x: x[0], flat)])
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return result
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