77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
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import numpy as np
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import time
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from Schenk_Brandenberger_S10_Aufg3 import *
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from Gauss_Algorithm import *
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import matplotlib.pyplot as plt
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#3b
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dim = 3000
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A = np.diag( np.diag( np.ones( ( dim , dim ) )*4000 ) )+ np.ones( ( dim , dim ) )
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dum1 = np.arange( 1 , np.int32( dim /2+1) , dtype = np.float64 ).reshape( ( np.int32( dim / 2 ) , 1 ) )
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dum2 = np.arange( np.int32( dim / 2 ) ,0 , -1 , dtype=np.float64 ).reshape( ( np.int32( dim / 2 ) , 1 ) )
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x = np.append( dum1 , dum2 , axis=0)
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b = A@x
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x0 = np.zeros( ( dim , 1 ) )
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tol = 1e-4
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startLinalgSolve = time.time()
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x_linalg_solve = np.linalg.solve(A,b)
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stoppLinalgSolve = time.time()
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startJacobi = time.time()
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[x_jacobi, n, n2] = Schenk_Brandenberger_S10_Aufg3(A,b,x0,tol,0)
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stoppJacobi = time.time()
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startGaussSeidel = time.time()
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[x_gauss_seidel, n, n2] = Schenk_Brandenberger_S10_Aufg3(A,b,x0,tol,0)
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stoppGaussSeidel = time.time()
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startGauss = time.time()
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x_gauss = Witschi_Floian_S6_Aufg2(A,b)[2]
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stoppGauss = time.time()
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print("***********Time Estimation***********")
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print("Time for np.linalg.solve:")
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print(stoppLinalgSolve-startLinalgSolve)
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print("Time for Jacobi:")
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print(stoppJacobi-startJacobi)
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print("Time for Gauss-Seidel:")
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print(stoppGaussSeidel-startGaussSeidel)
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print("Time for Gauss:")
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print(stoppGauss-startGauss)
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#In this algorithm for Jacobi and Gauss-Seidel is the B calculated every single time
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""" Time for np.linalg.solve:
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0.6146464347839355
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Time for Jacobi:
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319.44120621681213
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Time for Gauss-Seidel:
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307.4256772994995
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Time for Gauss:
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95.12803220748901 """
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#In this algorithm for Jacobi and Gauss-Seidel is the B calculated one time
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""" Time for np.linalg.solve:
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0.5016989707946777
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Time for Jacobi:
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18.02965211868286
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Time for Gauss-Seidel:
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17.64187240600586
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Time for Gauss:
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105.31756782531738 """
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print(x_gauss)
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#3c
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#x_axis = np.array(["x0","x1","x2"])
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#plt.plot(x_axis, x_linalg_solve)
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x_axis = np.arange(dim)
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plt.plot(x_axis, x_gauss_seidel-x)
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plt.plot(x_axis, x_jacobi-x)
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plt.plot(x_axis, x_gauss-x)
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plt.plot(x_axis, x_linalg_solve-x)
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plt.legend(["Gauss Seidel", "Jacobi", "Gauss", "Linalg"])
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plt.show()
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#Das Gauss-Verfahren ist genauer wie das Jacobi und Gauss-Seidel Verfahren
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