def rss(d, p, pf, f):
#y = fnc.fv(cubic_spline_fit.fn, d[:,0], p, pf)
#rss_v = 0.0
#for i in range(len(d[:,0])):
# if(d[i,0] < 1.0):
# rss_v = rss_v + (y[i] - d[i,1])**2
# elif(d[i,0] < 1.5):
# rss_v = rss_v + (abs(y[i] - d[i,1]))**3
# else:
# rss_v = rss_v + (abs(y[i] - d[i,1]))**4
#return rss_v
y = fnc.fv(cubic_spline_fit.fn, d[:, 0], p, pf)
return sum((y[:] - d[:, 1])**2)
from f_fnc import fnc
import numpy
import matplotlib.pyplot as plt
p = numpy.asarray([0.01, 0.0, -0.01, 0.0])
pf = numpy.asarray([2.0, 3.0, 4.0, 5.0, 26.0, 26.0, 0.8, 1.8, 1])
x = numpy.linspace(0.0, 7.0, 1001)
y = fnc.fv("cubic_spline_zbl", x, p, pf)
plt.plot(x, y, 'k')
plt.ylim(-5.0, 10.0)
plt.show()
def fv(x, p, pf): return fnc.fv(test.fn, x, p, pf)
def fv(x, p, pf):
return fnc.fv(cubic_spline_fit.fn, x, p, pf)
def rss_opt(p):
y = fnc.fv(cubic_spline_fit.fn, cubic_spline_fit.d[:, 0], p,
cubic_spline_fit.pf)
return sum((y[:] - cubic_spline_fit.d[:, 1])**2)