在程序中，我正在扫描一系列大脑样本，这些样本是以40 x 64 x 64的图像形式每2.5秒采集一次的。每张图像中的“体素”（3D像素）数量大约为168,000左右（40 * 64 * 64），每个体素都是图像样本的一个“特征”。

由于特征数量极高，我考虑使用主成分分析（PCA）来进行降维处理。接着使用递归特征消除（RFE）进一步处理。

需要预测的类别有9个，因此这是一个多类分类问题。下面，我将这个9类分类问题转换为二元分类问题，并将模型存储在一个名为models的列表中。

models = []model_count = 0for i in range(0,DS.nClasses):    for j in range(i+1,DS.nClasses):        binary_subset = sample_classes[i] + sample_classes[j]        print 'length of combined = %d' % len(binary_subset)        X,y = zip(*binary_subset)        print 'y = ',y        estimator = SVR(kernel="linear")        rfe = RFE(estimator , step=0.05)        rfe = rfe.fit(X, y)        #save the model        models.append(rfe)        model_count = model_count + 1        print '%d model fitting complete!' % model_count

现在遍历这些模型并进行预测。

predictions = []for X,y in test_samples:    Votes = np.zeros(DS.nClasses)    for mod in models:        #X = mod.transform(X)        label = mod.predict(X.reshape(1,-1)) #这里出了问题        print 'label is type',type(label),' and value ',label        Votes[int(label)] = Votes[int(label)] + 1    prediction = np.argmax(Votes)    predictions.append(prediction)    print 'Votes Array = ',Votes    print "We predicted %d , actual is %d" % (prediction,y)

标签应该是0到8之间的数字，表示9种可能的结果。我打印了label的值，得到的结果如下：

label is type <type 'numpy.ndarray'>  and value  [ 0.87011103]label is type <type 'numpy.ndarray'>  and value  [ 2.09093105]label is type <type 'numpy.ndarray'>  and value  [ 1.96046739]label is type <type 'numpy.ndarray'>  and value  [ 2.73343935]label is type <type 'numpy.ndarray'>  and value  [ 3.60415663]label is type <type 'numpy.ndarray'>  and value  [ 6.10577602]label is type <type 'numpy.ndarray'>  and value  [ 6.49922691]label is type <type 'numpy.ndarray'>  and value  [ 8.35338294]label is type <type 'numpy.ndarray'>  and value  [ 1.29765466]label is type <type 'numpy.ndarray'>  and value  [ 1.60883217]label is type <type 'numpy.ndarray'>  and value  [ 2.03839272]label is type <type 'numpy.ndarray'>  and value  [ 2.03794106]label is type <type 'numpy.ndarray'>  and value  [ 2.58830013]label is type <type 'numpy.ndarray'>  and value  [ 3.28811133]label is type <type 'numpy.ndarray'>  and value  [ 4.79660621]label is type <type 'numpy.ndarray'>  and value  [ 2.57755697]label is type <type 'numpy.ndarray'>  and value  [ 2.72263461]label is type <type 'numpy.ndarray'>  and value  [ 2.58129428]label is type <type 'numpy.ndarray'>  and value  [ 3.96296151]label is type <type 'numpy.ndarray'>  and value  [ 4.80280219]label is type <type 'numpy.ndarray'>  and value  [ 7.01768046]label is type <type 'numpy.ndarray'>  and value  [ 3.3720926]label is type <type 'numpy.ndarray'>  and value  [ 3.67517869]label is type <type 'numpy.ndarray'>  and value  [ 4.52089242]label is type <type 'numpy.ndarray'>  and value  [ 4.83746684]label is type <type 'numpy.ndarray'>  and value  [ 6.76557315]label is type <type 'numpy.ndarray'>  and value  [ 4.606097]label is type <type 'numpy.ndarray'>  and value  [ 6.00243346]label is type <type 'numpy.ndarray'>  and value  [ 6.59194317]label is type <type 'numpy.ndarray'>  and value  [ 7.63559593]label is type <type 'numpy.ndarray'>  and value  [ 5.8116106]label is type <type 'numpy.ndarray'>  and value  [ 6.37096926]label is type <type 'numpy.ndarray'>  and value  [ 7.57033285]label is type <type 'numpy.ndarray'>  and value  [ 6.29465433]label is type <type 'numpy.ndarray'>  and value  [ 7.91623641]label is type <type 'numpy.ndarray'>  and value  [ 7.79524801]Votes Array =  [ 1.  3.  8.  5.  5.  1.  7.  5.  1.]We predicted 2 , actual is 8

我不知道为什么label值是浮点数。它们应该是0到8之间的数字。

我正确地加载了数据。在执行predict()时出了问题，但我仍然找不到问题出在哪里。

回答：

你得到浮点值是因为你使用了SVR：支持向量回归。你需要的是SVC，支持向量分类。