Ajay H - 4 months ago 28

Python Question

In the program, I am scanning a number of brain samples taken in a time series of 40 x 64 x 64 images every 2.5 seconds. The number of 'voxels' (3D pixels) in each image is thus ~ 168,000 ish (40 * 64 * 64), each of which is a 'feature' for an image sample.

I thought of using Principle Component Analysis (PCA) because of the rediculously high n to perform dimensionality reduction. Then follow this up with Recursive Feature Elimination (RFE).

There are 9 classes to predict. Thus a multi class classification problem. Below, I convert this 9-class classification to a binary classification problem and store the models in a list *models*.

`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

Now loop through these models and make predictions.

`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)

the labels should be numbers from 0-8 indicating the 9 possible outcomes. I'm printing the

`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

I don't get why the

I loaded the data correctly. Something goes wrong while executing

`predict()`

Answer

You are getting floating-point values because you are using SV**R**: support vector **regression**. You want SVC, support vector *classification*.