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demo_LocalNN.py
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demo_LocalNN.py
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""" Generates data to show the effect of rescaling. Low density basisfunctions used. """
import pandas
from rbf import *
from rbf_2d import *
import basisfunctions, testfunctions
import matplotlib.pyplot as plt
import time
import mesh
import math
from scipy import spatial
start = time.time()
#x = np.linspace(0, 1, 10)
#y = np.linspace(2, 4, 10)
#print(x.ravel())
#tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
#pts = [0.46, 2.94]
#print(tree.query(pts,2))
for i in range(0,1):
nPoints = 16000
print("Number of points: ",nPoints)
#in_mesh = np.linspace((1,2),(10,20),nPoints)
in_mesh = np.random.random((nPoints,2))
tree = spatial.KDTree(list(zip(in_mesh[:,0],in_mesh[:,1])))
nearest_neighbors = []
shape_params = []
for j in range(0,nPoints):
queryPt = (in_mesh[j,0],in_mesh[j,1])
nnArray = tree.query(queryPt,2)
#print(nnArray[0][1])
nearest_neighbors.append(nnArray[0][1])
shape_params.append(0)
#print("nearest_nighbors: ",nearest_neighbors)
maxNN = max(nearest_neighbors)
func = lambda x,y: np.sin(2*x)+(0.0000001*y)
one_func = lambda x: np.ones_like(x)
in_vals = func(in_mesh[:,0],in_mesh[:,1])
#print(tree.query(pts))
#print("in_mesh: ", in_mesh)
for k in range(5,6):
for j in range(0,nPoints):
shape_params[j]=4.55228/(k*maxNN)
#shape_params[j]=4.55228/(i*nearest_neighbors[j])
#print("shape_params: ", shape_params)
#mesh_size = 1/math.sqrt(nPoints)
#print("mesh_size: ",mesh_size)
#shape_parameter = 4.55228/((5)*mesh_size)
#print("shape_parameter: ",shape_parameter)
bf = basisfunctions.Gaussian(list(shape_params))
#print("BF: ", bf)
#func = lambda x: (x-0.1)**2 + 1
#in_meshChange = [0, 0.02, 0.03, 0.1,0.23,0.25,0.52,0.83,0.9,0.95,1]
#for j in range(0,11):
# in_mesh[j] = in_meshChange[j]
#print(in_mesh)
#plot_mesh = np.random.random((nPoints,2))
#print(in_vals)
# evaluatine_vals = func(evaluate_mesh)
# basis_vals = func(basis_mesh)
#interp = NoneConsistent(bf, in_mesh, in_vals, rescale = False)
error_LOOCV = LOOCV(bf, in_mesh, in_vals, rescale = False)
errors = error_LOOCV()
#print("Error with i = ", i, ": ", errors)
print("LOOCV - Max Error with k = ", k, ": ", np.average(errors), " - and max error: ", max(errors))
#error_LOOCVSVD = LOOCVSVD(bf, in_mesh, in_vals, rescale = False)
#errorsSVD = error_LOOCVSVD()
#print("Error SVD: ", max(errorsSVD))
#print("Error Difference: ", errors - errorsSVD)
#end = time.time()
#print("Elapsed time: ", end - start)
#resc_interp = NoneConsistent(bf, in_mesh, in_vals, rescale = True)
#one_interp = NoneConsistent(bf, in_mesh, one_func(in_mesh), rescale = False)
'''
plt.scatter(in_mesh[:,0], in_mesh[:,1], label = "In Mesh")
plt.scatter(plot_mesh[:,0], plot_mesh[:,1], label = "Out Mesh")
#plt.legend()
#plt.show()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(in_mesh[:,0],in_mesh[:,1], in_vals, c='r', marker='o')
ax.set_xlabel('X coordinate')
ax.set_ylabel('Y ycoordinate')
ax.set_zlabel('Magnitude')
#plt.show()
'''
#fig = plt.figure()
#ax = Axes3D(fig)
#plt.scatter(in_mesh[:,0],in_mesh[:,1], errors, c = 'b', marker='o')
#plt.plot(evaluate_mesh, interp(evaluate_mesh), "--", label = "Interpolant $S_f$")
#plt.plot(evaluate_mesh, evaluate_vals, "--", label = "Interpolant $S_r$ of $g(x) = 1$")
#plt.plot(evaluate_mesh, evaluate_vals - interp(evaluate_mesh), "--", label = "Error on selected points")
#plt.tight_layout()
#plt.plot(in_mesh, in_vals, label = "Rescaled Interpolant")
#fig = plt.figure()
#ax = Axes3D(fig)
#surf = ax.plot_trisurf(in_mesh[:,0], in_mesh[:,1], in_vals)
#fig.colorbar(surf, shrink=0.5, aspect=5)
#plt.savefig('testSurrogate.pdf')
#plt.show()
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#ax.scatter(in_mesh[:,0],in_mesh[:,1], errors, c='r', marker='o')
#ax.set_xlabel('X coordinate')
#ax.set_ylabel('Y ycoordinate')
#ax.set_zlabel('Error Magnitude')
#plt.show()
#rint("RMSE no rescale =", interp.RMSE(func, plot_mesh))
#print("RMSE rescaled =", resc_interp.RMSE(func, plot_mesh))
end = time.time()
print("Elapsed time: ", end - start)