Lab_6
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34
tasks/nikiforova-ts/Lab_6/Readme.md
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tasks/nikiforova-ts/Lab_6/Readme.md
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# Отчёт по лабораторной работе №6
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||||
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Выполнила: студентка гр. ИСЭбд-41 Никифорова Татьяна Сергеевна.
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## Создание приложения
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Выбрала язык Python.
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Запустим алгоритм с матрицей 3x3 c различными алгоритмами.
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![](picture/1.png)
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![](picture/2.png)
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## Бенчмарки
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Протекстируем обычный и паралелльный с различными размерами матриц.
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Матрица 5х5
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![](picture/31.png)
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![](picture/41.png)
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Матрица 8х8
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![](picture/3.png)
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![](picture/4.png)
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Вывод: Скоростные возможности параллельного алгоритма проявляются только при значительном количестве операций. В случае, если операций меньше, стандартный алгоритм может оказаться более быстрым.
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BIN
tasks/nikiforova-ts/Lab_6/picture/1.png
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tasks/nikiforova-ts/Lab_6/picture/2.png
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tasks/nikiforova-ts/Lab_6/picture/3.png
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tasks/nikiforova-ts/Lab_6/picture/3.png
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tasks/nikiforova-ts/Lab_6/picture/31.png
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tasks/nikiforova-ts/Lab_6/picture/4.png
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tasks/nikiforova-ts/Lab_6/picture/41.png
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145
tasks/nikiforova-ts/Lab_6/work/Lab6.py
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tasks/nikiforova-ts/Lab_6/work/Lab6.py
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import tkinter as tk
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from tkinter import ttk
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import numpy as np
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from multiprocessing import cpu_count, Pool
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import time
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class DeterminantCalculator:
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def __init__(self, root):
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self.root = root
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self.root.title("Determinant Calculator")
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self.matrix_size_label = ttk.Label(root, text="Matrix Size:")
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self.matrix_size_label.grid(row=0, column=0, padx=10, pady=10)
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self.matrix_size_entry = ttk.Entry(root)
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self.matrix_size_entry.grid(row=0, column=1, padx=10, pady=10)
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self.algorithm_label = ttk.Label(root, text="Algorithm:")
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self.algorithm_label.grid(row=1, column=0, padx=10, pady=10)
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self.algorithm_var = tk.StringVar()
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self.algorithm_var.set("Normal")
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self.algorithm_menu = ttk.Combobox(root, textvariable=self.algorithm_var, values=["Normal", "Parallel"])
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self.algorithm_menu.grid(row=1, column=1, padx=10, pady=10)
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|
||||
self.num_threads_label = ttk.Label(root, text="Number of Threads:")
|
||||
self.num_threads_label.grid(row=2, column=0, padx=10, pady=10)
|
||||
|
||||
self.num_threads_entry = ttk.Entry(root)
|
||||
self.num_threads_entry.insert(0, str(cpu_count()))
|
||||
self.num_threads_entry.grid(row=2, column=1, padx=10, pady=10)
|
||||
|
||||
self.generate_and_calculate_button = ttk.Button(root, text="Generate Matrix and Calculate Determinant", command=self.generate_and_calculate_determinant)
|
||||
self.generate_and_calculate_button.grid(row=3, column=0, columnspan=2, pady=10)
|
||||
|
||||
self.result_label = ttk.Label(root, text="Result:")
|
||||
self.result_label.grid(row=4, column=0, padx=10, pady=10)
|
||||
|
||||
self.time_label = ttk.Label(root, text="Time (s):")
|
||||
self.time_label.grid(row=5, column=0, padx=10, pady=10)
|
||||
|
||||
self.matrix_label = ttk.Label(root, text="")
|
||||
self.matrix_label.grid(row=6, column=0, columnspan=2, pady=10)
|
||||
|
||||
self.det_result = None # Глобальная переменная для хранения результата
|
||||
|
||||
def generate_matrix(self, size):
|
||||
matrix = np.random.randint(1, 10, size=(size, size))
|
||||
return matrix
|
||||
|
||||
def show_random_matrix(self, matrix):
|
||||
self.matrix_label.config(text=f"Random Matrix:\n{matrix}")
|
||||
|
||||
def calculate_determinant_parallel(self, matrix, num_threads):
|
||||
size = len(matrix)
|
||||
|
||||
if size == 1:
|
||||
return matrix[0, 0], 0
|
||||
|
||||
result = RawArray('d', [0.0]) # Разделяемый массив для хранения результата
|
||||
lock_object = np.ones(1) # Использование numpy для создания объекта блокировки
|
||||
|
||||
def calculate_submatrix(i):
|
||||
nonlocal result
|
||||
sub_matrix = self.get_submatrix(matrix, i)
|
||||
sub_determinant = matrix[0, i] * self.calculate_determinant(sub_matrix)
|
||||
|
||||
with lock_object.get_lock():
|
||||
result[0] += (-1) ** i * sub_determinant
|
||||
|
||||
with Pool(processes=num_threads) as pool:
|
||||
start_time = time.time()
|
||||
pool.map(calculate_submatrix, range(size))
|
||||
end_time = time.time()
|
||||
|
||||
return result[0], end_time - start_time
|
||||
|
||||
# ваш существующий код
|
||||
|
||||
def get_submatrix(self, matrix, column_index):
|
||||
size = len(matrix)
|
||||
sub_matrix = np.zeros((size - 1, size - 1))
|
||||
|
||||
for i in range(1, size):
|
||||
for j in range(size):
|
||||
if j < column_index:
|
||||
sub_matrix[i - 1, j] = matrix[i, j]
|
||||
elif j > column_index:
|
||||
sub_matrix[i - 1, j - 1] = matrix[i, j]
|
||||
|
||||
return sub_matrix
|
||||
|
||||
def calculate_determinant_group(self, matrix, row, col):
|
||||
sign = (-1) ** (row + col)
|
||||
minor = np.delete(np.delete(np.array(matrix), row, axis=0), col, axis=1)
|
||||
det_minor = self.calculate_determinant(minor)
|
||||
return sign * matrix[row][col] * det_minor
|
||||
|
||||
def calculate_determinant(self, matrix=None):
|
||||
if matrix is None:
|
||||
matrix_size = int(self.matrix_size_entry.get())
|
||||
matrix = self.generate_matrix(matrix_size)
|
||||
|
||||
size = len(matrix)
|
||||
if size == 1:
|
||||
return matrix[0][0]
|
||||
elif size == 2:
|
||||
return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0]
|
||||
else:
|
||||
det = 0
|
||||
for col in range(size):
|
||||
sign = (-1) ** col
|
||||
minor = np.delete(np.array(matrix), 0, axis=0)
|
||||
minor = np.delete(minor, col, axis=1)
|
||||
det += sign * matrix[0][col] * self.calculate_determinant(minor)
|
||||
return det
|
||||
|
||||
def generate_and_calculate_determinant(self):
|
||||
matrix_size = int(self.matrix_size_entry.get())
|
||||
algorithm = self.algorithm_var.get()
|
||||
num_threads = int(self.num_threads_entry.get())
|
||||
|
||||
random_matrix = self.generate_matrix(matrix_size)
|
||||
self.show_random_matrix(random_matrix)
|
||||
|
||||
if algorithm == "Parallel":
|
||||
det, time_taken = self.calculate_determinant_parallel(random_matrix, num_threads)
|
||||
else:
|
||||
start_time = time.time()
|
||||
det = self.calculate_determinant(random_matrix)
|
||||
time_taken = time.time() - start_time
|
||||
|
||||
result_str = f"Determinant: {det}"
|
||||
self.result_label.config(text=result_str)
|
||||
|
||||
time_str = f"Time (s): {time_taken:.6f}"
|
||||
self.time_label.config(text=time_str)
|
||||
|
||||
# Используем глобальную переменную для отображения результата в методе generate_and_calculate_determinant
|
||||
self.det_result = det
|
||||
|
||||
if __name__ == "__main__":
|
||||
root = tk.Tk()
|
||||
app = DeterminantCalculator(root)
|
||||
root.mainloop()
|
Loading…
Reference in New Issue
Block a user