在这个程序中，我正在扫描一些大脑样本，这些样本是以每2.5秒40 x 64 x 64张图像的时间序列拍摄的。因此，每个图像中的"体素"(3D像素)的数量约为168,000 ish(40 * 64 * 64)，每个体素都是图像样本的一个"特征"。
我想到使用主成分分析(PCA)，因为惊人的高n来执行降维。然后使用递归特征消除(RFE)。
有9个类别需要预测。这是一个多类分类问题。下面，我将这个9类分类转换为二进制分类问题，并将模型存储在列表模型中。
models = []
model_count = 0
for 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做出预测。
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)) #Something goes wrong here
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)
标签应为0-8之间的数字，表示9种可能的结果。我打印的是标签值我得到的是:
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
我不明白为什么标签值是浮点数。应该是0-8之间的数字。
我正确加载了数据。执行predict()时出了问题，但我仍然找不到是什么。
您正在获得浮点值，因为您正在使用SVR:支持向量回归。你想要SVC，支持向量分类