def ISE(numbeta, Beta_fonc_est, b_learned, T, nb=40, plot_beta=False, resolution=100):
ise = 0.0
Tmax = np.max(T)
Tmin = np.min(T)
Tgrid = np.linspace(Tmin, Tmax, nb, retstep=True)
step = Tgrid[1]
Tgrid = Tgrid[0]
grille = np.vstack((Tgrid, np.arange(nb)))
count = 0
for T_, i in grille.T:
for t_ in np.arange(0.01, T_, step, float):
count += 1
true_b = cg.beta_fonc(t_, T_, numbeta)
est_b = Beta_fonc_est(*([t_, T_] + list(b_learned[:, 0])))
ise = ise + (true_b - est_b) ** 2
ise = ise / count
print("ISE : " + str(ise))
if plot_beta:
x = np.linspace(0.1, np.max(T), num=resolution)
y = np.linspace(0.1, np.max(T), num=resolution)
arr = [x, y]
Grid = cg.expandnp(arr)
x = Grid[:, 0]
y = Grid[:, 1]
arrV = np.zeros(resolution ** 2, float)
n = 0
for i in range(resolution):
for j in range(resolution):
if j <= i:
arrV[n] = Beta_fonc_est[0](x[n], y[n])
n += 1
Xfig = x.reshape((resolution, resolution))
Yfig = y.reshape((resolution, resolution))
Zfig = arrV.reshape((resolution, resolution))
fig = plt.figure().add_subplot(111)
plt.imshow(
Zfig,
vmin=Zfig.min(),
vmax=Zfig.max(),
origin="lower",
extent=[Yfig.min(), Yfig.max(), Xfig.min(), Xfig.max()],
)
plt.colorbar()
return ise
