在该计划中，我每隔2.5秒扫描一次40 x 64 x 64图像的时间序列拍摄的大量样本。 ＆＃39;体素的数量＆＃39;因此，每个图像中的（3D像素）是~168,000 ish（40 * 64 * 64），每个都是一个特征＆＃39;对于图像样本。
我想到了使用主成分分析（PCA），因为降低了n的高度维度。然后使用递归特征消除（RFE）进行跟进。
有9个课程要预测。因此是一个多类别的分类问题。下面，我将这个9级分类转换为二进制分类问题，并将模型存储在列表 models 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
现在循环浏览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)) #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的数字，表示9种可能的结果。我正在打印标签值，这就是我得到的：
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
我不明白标签值是浮点数的原因。它们应该是0-8的数字。
我正确加载了数据。执行predict()时出了点问题但是我仍然无法找到答案。