python类BayesianGaussianMixture()的实例源码

def test(epoch, prior):
    ans = np.zeros((50, 10))
    for data, lab in test_loader:
        C = prior.predict(data.numpy().reshape(data.numpy().shape[0],-1))
        for i in xrange(len(lab)):
            ans[C[i],lab[i]]+=1
    print(ans)
    s = np.sum(ans)
    v = 0
    for i in xrange(ans.shape[0]):
        for j in xrange(ans.shape[1]):
            if ans[i,j]>0:
                v += ans[i,j]/s*np.log(ans[i,j]/s/(np.sum(ans[i,:])/s)/(np.sum(ans[:,j])/s))
    print("Mutual information: "+str(v))

#prior = BayesianGaussianMixture(n_components=100, covariance_type='diag')

def train(epoch, prior):
    model.train()
    train_loss = 0
    #prior = BayesianGaussianMixture(n_components=1, covariance_type='diag')
    tmp = []
    for (data,_) in train_loader:
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        recon_batch, mu, logvar, z = model(data)
        tmp.append(z.cpu().data.numpy())
    print('Update Prior')
    prior.fit(np.vstack(tmp))
    print('prior: '+str(prior.weights_))
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        optimizer.zero_grad()
        recon_batch, mu, logvar, z = model(data)
        loss = loss_function(recon_batch, data, mu, logvar, prior, z)
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        #if batch_idx % args.log_interval == 0:
        #    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        #        epoch, batch_idx * len(data), len(train_loader.dataset),
        #        100. * batch_idx / len(train_loader),
        #        loss.data[0] / len(data)))

    print('====> Epoch: {} Average loss: {:.4f}'.format(
          epoch, train_loss / len(train_loader.dataset)))
    return prior

def train(epoch, prior):
    prior = BayesianGaussianMixture(n_components=50, covariance_type='diag', n_init=5, max_iter=1000)
    tmp = []
    for (data,_) in train_loader:
        #print(data.numpy().shape)
        tmp.append(data.numpy().reshape(data.numpy().shape[0],-1))
    prior.fit(np.vstack(tmp))
    return prior