def moyenneMMD_MC(methodeMMD,
kernel,
law_p,
law_q,
m,
n,
repeat=100,
verbose=1):
mmd_list = np.zeros(repeat)
for i in range(repeat):
if verbose > 0:
print(i / repeat * 100, '% achevé', end='\r', flush=True)
X_ = law_p(m)
Y_ = law_q(n)
MMD = methodeMMD(kernel)
MMD.fit(Y_, X_)
mmd_list[i] = MMD.MMD
return mmd_list.mean()
def moyenneMMD_Bt(methodeMMD,
kernel,
X,
Y,
n_boot,
m_boot,
repeat=100,
verbose=1):
mmd_list = np.zeros(repeat)
for i in range(repeat):
if verbose > 0:
print(i / repeat * 100, '% achevé', end='\r', flush=True)
X_ = utils.bootstrap(X, m_boot)
Y_ = utils.bootstrap(Y, n_boot)
MMD = methodeMMD(kernel)
MMD.fit(Y_, X_)
mmd_list[i] = MMD.MMD
return mmd_list.mean()
def sampleMMD_MC(methodeMMD,
kernel,
law_p,
law_q,
m,
n,
finalSampleSize=1000,
verbose=1):
Liste_mmdl = []
for _ in range(finalSampleSize):
if verbose > 0:
print(_ / finalSampleSize * 100, '% achevé', end='\r', flush=True)
X = law_p(m)
Y = law_q(n)
MMD = methodeMMD(kernel)
MMD.fit(Y, X)
Liste_mmdl.append(MMD.MMD)
return np.r_[Liste_mmdl]