def singlepopulationfitness(singlepopulation):
geophysicsbase = GeoPhysicsBase(matrix_size, 0.25)
geophysicsbase.g[0] = singlepopulation['population_properties'][0] + geophysicsbase.g[0]
geophysicsbase.g[1] = singlepopulation['population_properties'][1] + geophysicsbase.g[1]
geophysicsbase.g[2] = singlepopulation['population_properties'][2] + geophysicsbase.g[2]
svd_regularization = SVDRegularization(geophysicsbase.A, geophysicsbase.g)
f = svd_regularization.reconstruct_with_tikhonov(1E-11)
return svd_regularization.getGCValue(geophysicsbase.A, geophysicsbase.g, f)
def singlepopulationfintessleastsquare(singlepopulation):
geophysicsbase = GeoPhysicsBase(matrix_size, 0.25)
geophysicsbase.g[0] = singlepopulation['population_properties'][0] + geophysicsbase.g[0]
geophysicsbase.g[1] = singlepopulation['population_properties'][1] + geophysicsbase.g[1]
geophysicsbase.g[2] = singlepopulation['population_properties'][2] + geophysicsbase.g[2]
leastsquare = TReg(geophysicsbase.A, geophysicsbase.g, 1E-11)
f = leastsquare.getf()
ro = np.dot(geophysicsbase.A, f)
ro = LA.norm(ro - geophysicsbase.g)**2
return ro
def plot_tsvd_plot(matrix_size):
geophysics = GeoPhysicsBase(matrix_size, 0.25)
geophysics.g[0] = geophysics.g[0]
interation = CGLS(geophysics.A, geophysics.g)
x = np.arange(matrix_size)
plt.plot(x, geophysics.fexact, 'r', linewidth=2, label='pre defined f')
plt.plot(x, interation.x, 'bs', linewidth=2, label='reconstructed f')
plt.tick_params(axis='both', which='major', labelsize=18)
plt.legend(loc='upper right', shadow=True, fontsize='18')
plt.show()
