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demo_random_Refinement.py
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demo_random_Refinement.py
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""" Generates data to show the effect of rescaling. Low density basisfunctions used. """
import pandas
from rbf import *
import basisfunctions, testfunctions
import matplotlib.pyplot as plt
import time
import mesh
import math
from random import randint
import random
from scipy import spatial
start = time.time()
j = 0
nPoints = 8000
print("Number of points: ",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)
#random_point_removal = [randint(0, nPoints) for p in range(0, ntesting)]
ntesting = 320
random_point_removal_all = random.sample(range(0, nPoints), ntesting)
for i in range(0,3):
#print(random_point_removal)
ntesting = int(ntesting/2)
random_point_removal = []
for i in range(0,ntesting):
random_point_removal.append(random_point_removal_all[i])
basis_mesh = np.random.random((nPoints,2))
partial_mesh = np.random.random((nPoints-ntesting,2))
evaluate_mesh = np.random.random((ntesting,2))
evalAppend = 0
partialAppend = 0
for i in range(0,nPoints):
if i in random_point_removal:
evaluate_mesh[evalAppend,0] = in_mesh[i,0]
evaluate_mesh[evalAppend,1] = in_mesh[i,1]
basis_mesh[i,0] = 0
basis_mesh[i,1] = 0
evalAppend += 1
else:
partial_mesh[partialAppend,0] = in_mesh[i,0]
partial_mesh[partialAppend,1] = in_mesh[i,1]
basis_mesh[i,0] = in_mesh[i,0]
basis_mesh[i,1] = in_mesh[i,1]
partialAppend += 1
#print(evaluate_mesh)
mesh_size = maxNN
func = lambda x,y: np.sin(10*x)+(0.0000001*y)
one_func = lambda x: np.ones_like(x)
for k in range(3,9):
for j in range(0,nPoints):
shape_params[j]=4.55228/(k*maxNN)
shape_parameter = 4.55228/((k)*mesh_size)
#print("shape_parameter: ",shape_parameter)
bf = basisfunctions.Gaussian(shape_parameter)
#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.linspace(0, 1, 250)
in_vals = func(in_mesh[:,0],in_mesh[:,1])
evaluate_vals = func(evaluate_mesh[:,0],evaluate_mesh[:,1])
partial_vals = func(partial_mesh[:,0],partial_mesh[:,1])
interp = NoneConsistent(bf, partial_mesh, partial_vals, rescale = False)
#error_LOOCV = LOOCV(bf, in_mesh, in_vals, rescale = False)
#errors = error_LOOCV()
#print("Error: ", max(evaluate_vals - interp(evaluate_mesh)))
#resc_interp = NoneConsistent(bf, in_mesh, in_vals, rescale = True)
#one_interp = NoneConsistent(bf, in_mesh, one_func(in_mesh), rescale = False)
#plt.plot(plot_mesh, func(plot_mesh), label = "Target $f$")
#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")
errors = evaluate_vals - interp(evaluate_mesh)
#print("Testing error: ",errors)
print("k = ", k)
print("Random removal - Average Testing error: ", np.average(errors), " - Max Testing error: ", max(errors))
#plt.tight_layout()
#plt.plot(in_mesh, in_vals, label = "Rescaled Interpolant")
#rint("RMSE no rescale =", interp.RMSE(func, plot_mesh))
#print("RMSE rescaled =", resc_interp.RMSE(func, plot_mesh))
end = time.time()
print("Elapsed time for optimization: ", end - start)
#plt.legend()
#plt.show()
'''
plt.plot(plot_mesh, interp.error(func, plot_mesh))
plt.plot(plot_mesh, resc_interp.error(func, plot_mesh))
plt.grid()
plt.show()
df = pandas.DataFrame(data = { "Target" : func(plot_mesh),
"Interpolant" : interp(plot_mesh),
"RescaledInterpolant" : resc_interp(plot_mesh),
"OneInterpolant" : one_interp(plot_mesh),
"Error" : interp.error(func, plot_mesh),
"RescaledError" : resc_interp.error(func, plot_mesh)},
index = plot_mesh)
df.to_csv("rescaled_demo.csv", index_label = "x")
'''