def main():
x = np.arange(1, 11)
y = func(x, COEFFICIENTS)
yn = y + NOISE * np.random.randn(len(y))
solver = GNSolver(fit_function=func, max_iter=30)
init_guess = [1, 1, 1, 1] #1000000 * np.random.random(len(COEFFICIENTS))
a = solver.fit(x, yn, init_guess)
fit = solver.get_estimate()
residual = solver.get_residual()
print(a)
plt.figure()
plt.plot(x, y, label="Original, noiseless signal", linewidth=2)
plt.plot(x, yn, label="Noisy signal", linewidth=2)
plt.plot(x, fit, label="Fit", linewidth=2)
plt.plot(x, residual, label="Residual", linewidth=2)
plt.title("Gauss-Newton: curve fitting example")
plt.xlabel("X")
plt.ylabel("Y")
plt.grid()
plt.legend()
plt.show()
from model import func, der_func, der2_func
import numpy as np
from myGNE import GNSolver #
from datetime import datetime
from scipy.interpolate import griddata
import lhsmdu #latin hypercube
import tools
COEFFICIENTS = [2.4, 0.16]
x = np.linspace(2,6,15)
#x = np.mgrid[0.1:10:20j, 0.1:10:20j].reshape(2, -1).T
y = func(x,COEFFICIENTS)
maxIterations = 100
Ans = None
cnt = 0;
coeff = 20
coefff = 1
for NOISE in np.linspace(0,4*coeff,9):
SNR = 10*np.log10(np.abs(np.mean(y))/(np.abs(np.mean(y))+NOISE))
#NOISE = np.linalg.norm(y)/SNR - np.linalg.norm(y)
for DISTANCE in np.linspace(0,6/coefff,13):
ans = []
for r in range(3):
cnt+=1
tools.printProgressBar(cnt, 3*13*9, prefix="Progress", suffix="Complete", length = 50)
yn = y + NOISE * (np.array(lhsmdu.sample(y.shape[0],1)).flatten())
init_guess = ((np.array(lhsmdu.sample(len(COEFFICIENTS),1)).flatten()))*(10**DISTANCE)
g = GNSolver(func, der_func, der2_func, original_root=COEFFICIENTS,max_iter = maxIterations)
startTime = datetime.now()
a = g.fit(x,yn,init_guess, False)
if a is not None: