forked from floli/PyRBF
-
Notifications
You must be signed in to change notification settings - Fork 2
/
demo_2d.py
130 lines (104 loc) · 4.06 KB
/
demo_2d.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
""" 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
from matplotlib import cm
import time
import mesh
import math
from mpl_toolkits.mplot3d import Axes3D
start = time.time()
j = 0
#nPointsRange = [1000,2000,4000,8000,10000,12000,16000]
for i in range(0,1):
nPoints = 1000
print("Number of points: ",nPoints)
#in_mesh = np.linspace((1,2),(10,20),nPoints)
in_mesh = np.random.random((nPoints,2))
#print("in_mesh: ", in_mesh)
plt.scatter(in_mesh[:,0], in_mesh[:,1], label = "In Mesh")
plt.show()
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(shape_parameter)
#func = lambda x: (x-0.1)**2 + 1
func = lambda x,y: np.sin(10*x)+(0.0000001*y)
funcTan = lambda x,y: np.arctan(125*(pow(pow(x-1.5,2) + pow(y-0.25,2),0.5) - 0.92))
one_func = lambda x: np.ones_like(x)
#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))
in_vals = funcTan(in_mesh[:,0],in_mesh[:,1])
#print(in_vals)
# evaluatine_vals = func(evaluate_mesh)
# basis_vals = func(basis_mesh)
interp = NoneConsistent2D(bf, in_mesh, in_vals, rescale = False)
error_LOOCV = LOOCV(bf, in_mesh, in_vals, rescale = False)
errors = error_LOOCV()
#print("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)
'''
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")
'''