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Python實(shí)現(xiàn)矩陣相乘的三種方法小結(jié)

更新時(shí)間：2018年07月26日 08:38:45 作者：Mr_Leeeee

今天小編就為大家分享一篇Python實(shí)現(xiàn)矩陣相乘的三種方法小結(jié)，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

問(wèn)題描述

分別實(shí)現(xiàn)矩陣相乘的3種算法，比較三種算法在矩陣大小分別為22∗2222∗22, 23∗2323∗23, 24∗2424∗24, 25∗2525∗25, 26∗2626∗26, 27∗2727∗27, 28∗2828∗28, 29∗2929∗29時(shí)的運(yùn)行時(shí)間與MATLAB自帶的矩陣相乘的運(yùn)行時(shí)間，繪制時(shí)間對(duì)比圖。

解題方法

本文采用了以下方法進(jìn)行求值：矩陣計(jì)算法、定義法、分治法和Strassen方法。這里我們使用Matlab以及Python對(duì)這個(gè)問(wèn)題進(jìn)行處理，比較兩種語(yǔ)言在一樣的條件下，運(yùn)算速度的差別。

編程語(yǔ)言

Python

具體代碼

#-*- coding: utf-8 -*-
from matplotlib.font_manager import FontProperties
import numpy as np
import time
import random
import math
import copy
import matplotlib.pyplot as plt

#n = [2**2, 2**3, 2**4, 2**5, 2**6, 2**7, 2**8, 2**9, 2**10, 2**11, 2**12]
n = [2**2, 2**3, 2**4, 2**5, 2**6, 2**7, 2**8, 2**9, 2**10, 2**11]
Sum_time1 = []
Sum_time2 = []
Sum_time3 = []
Sum_time4 = []
for m in n:
 A = np.random.randint(0, 2, [m, m])
 B = np.random.randint(0, 2, [m, m])
 A1 = np.mat(A)
 B1 = np.mat(B)
 time_start = time.time()
 C1 = A1*B1
 time_end = time.time()
 Sum_time1.append(time_end - time_start)

 C2 = np.zeros([m, m], dtype = np.int)
 time_start = time.time()
 for i in range(m):
  for k in range(m):
   for j in range(m):
    C2[i, j] = C2[i, j] + A[i, k] * B[k, j]
 time_end = time.time()
 Sum_time2.append(time_end - time_start)
 A11 = np.mat(A[0:m//2, 0:m//2])
 A12 = np.mat(A[0:m//2, m//2:m])
 A21 = np.mat(A[m//2:m, 0:m//2])
 A22 = np.mat(A[m//2:m, m//2:m])
 B11 = np.mat(B[0:m//2, 0:m//2])
 B12 = np.mat(B[0:m//2, m//2:m])
 B21 = np.mat(B[m//2:m, 0:m//2])
 B22 = np.mat(B[m//2:m, m//2:m])
 time_start = time.time()
 C11 = A11 * B11 + A12 * B21
 C12 = A11 * B12 + A12 * B22
 C21 = A21 * B11 + A22 * B21
 C22 = A21 * B12 + A22 * B22
 C3 = np.vstack((np.hstack((C11, C12)), np.hstack((C21, C22))))
 time_end = time.time()
 Sum_time3.append(time_end - time_start)
 time_start = time.time()
 M1 = A11 * (B12 - B22)
 M2 = (A11 + A12) * B22
 M3 = (A21 + A22) * B11
 M4 = A22 * (B21 - B11)
 M5 = (A11 + A22) * (B11 + B22)
 M6 = (A12 - A22) * (B21 + B22)
 M7 = (A11 - A21) * (B11 + B12)
 C11 = M5 + M4 - M2 + M6
 C12 = M1 + M2
 C21 = M3 + M4
 C22 = M5 + M1 - M3 - M7
 C4 = np.vstack((np.hstack((C11, C12)), np.hstack((C21, C22))))
 time_end = time.time()
 Sum_time4.append(time_end - time_start)

f1 = open('python_time1.txt', 'w')
for ele in Sum_time1:
 f1.writelines(str(ele) + '\n')
f1.close()

f2 = open('python_time2.txt', 'w')
for ele in Sum_time2:
 f2.writelines(str(ele) + '\n')
f2.close()

f3 = open('python_time3.txt', 'w')
for ele in Sum_time3:
 f3.writelines(str(ele) + '\n')
f3.close()

f4 = open('python_time4.txt', 'w')
for ele in Sum_time4:
 f4.writelines(str(ele) + '\n')
f4.close()

font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=8)
plt.figure(1)
plt.subplot(221)
plt.semilogx(n, Sum_time1, 'r-*')
plt.ylabel(u"時(shí)間(s)", fontproperties=font)
plt.xlabel(u"矩陣的維度n", fontproperties=font)
plt.title(u'python自帶的方法', fontproperties=font)
plt.subplot(222)
plt.semilogx(n, Sum_time2, 'b-*')
plt.ylabel(u"時(shí)間(s)", fontproperties=font)
plt.xlabel(u"矩陣的維度n", fontproperties=font)
plt.title(u'定義法', fontproperties=font)
plt.subplot(223)
plt.semilogx(n, Sum_time3, 'y-*')
plt.ylabel(u"時(shí)間(s)", fontproperties=font)
plt.xlabel(u"矩陣的維度n", fontproperties=font)
plt.title( u'分治法', fontproperties=font)
plt.subplot(224)
plt.semilogx(n, Sum_time4, 'g-*')
plt.ylabel(u"時(shí)間(s)", fontproperties=font)
plt.xlabel(u"矩陣的維度n", fontproperties=font)
plt.title( u'Strasses法', fontproperties=font)
plt.figure(2)
plt.semilogx(n, Sum_time1, 'r-*', n, Sum_time2, 'b-+', n, Sum_time3, 'y-o', n, Sum_time4, 'g-^')
#plt.legend(u'python自帶的方法', u'定義法', u'分治法', u'Strasses法', fontproperties=font)
plt.show()

以上這篇Python實(shí)現(xiàn)矩陣相乘的三種方法小結(jié)就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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