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linear/linear_app88rbf/linear_app88rbf.tex

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bölgeyi pürüzsüz fonksiyonlarla yaklaşık temsil etmenin en iyi yolu. Veri tabanı
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sadece bu değerleri taşıyacak.
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Bizim bu konuya girmemizin sebebi Google Elevation APİ ile aldığımız yükseklik
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Bizim bu konuya girmemizin sebebi Google Elevation API ile aldığımız yükseklik
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verilerini verimli şekilde kullanma ihtiyacı idi.
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Kaynaklar
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\begin{minted}[fontsize=\footnotesize]{python}
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np.random.seed(0)
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[1] Neto, {\em Radial Basis Functions}, \url{http://www.di.fc.ul.pt/~jpn/r/rbf/rbf.html}
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S = 200
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[2] Pouderoux, {\em Adaptive Hierarchical RBF Interpolation for Creating Smooth Digital Elevation Models}
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\url{https://hal.archives-ouvertes.fr/hal-00308008/document}
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x = np.linspace(36,37,D)
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y = np.linspace(32,33,D)
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\end{document}
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xx,yy = np.meshgrid(x,y)
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znew = func(xx,yy)
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xx = xx.reshape(D*D)
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yy = yy.reshape(D*D)
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znew = znew.reshape(D*D)
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from scipy.interpolate import Rbf
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rbfi = Rbf(xx,yy,znew)
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xx = xx.reshape(D,D)
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yy = yy.reshape(D,D)
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znew = znew.reshape(D,D)
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fig = plt.figure()
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ax = fig.gca(projection='3d')
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surf = ax.plot_surface(xx, yy, znew, cmap=cm.coolwarm,linewidth=0, antialiased=False)
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fig.colorbar(surf, shrink=0.5, aspect=5)
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plt.savefig('linear_app88rbf_03.png')
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\end{minted}
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\includegraphics[width=20em]{linear_app88rbf_03.png}
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Kaynaklar
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[1] Neto, {\em Radial Basis Functions}, \url{http://www.di.fc.ul.pt/~jpn/r/rbf/rbf.html}
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[2] Pouderoux, {\em Adaptive Hierarchical RBF Interpolation for Creating Smooth Digital Elevation Models}
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\url{https://hal.archives-ouvertes.fr/hal-00308008/document}
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\end{document}
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sk/2019/01/sql.md

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