def train_test(ds_path):
data = SparseDataSet(ds_path)
g, c, fold = 0.25, 128, 2
##################################################
#### This part of the code does Does statistical
#### feature selection ...
#labels = np.array([int(n) for n in data.labels.L])
#ranks = rank_feat(data.getMatrix().T, labels)
#ranks = [(abs(r),i) for i, r in enumerate(ranks)]
#ranks.sort()
#ranks.reverse()
#feats = [f[1] for f in ranks]
#data.keepFeatures(feats[:2662])
data.attachKernel('gaussian', gamma=g)
s = SVM(C=c)
r = s.cv(data, numFolds=fold)
o = open(ds_path + '.pkl', 'wb')
pickle.dump(r, o)
o.close()
print ds_path
def train_test(ds_path):
data = SparseDataSet(ds_path)
g, c, fold = 0.25, 128, 2
##################################################
#### This part of the code does Does statistical
#### feature selection ...
#labels = np.array([int(n) for n in data.labels.L])
#ranks = rank_feat(data.getMatrix().T, labels)
#ranks = [(abs(r),i) for i, r in enumerate(ranks)]
#ranks.sort()
#ranks.reverse()
#feats = [f[1] for f in ranks]
#data.keepFeatures(feats[:2662])
data.attachKernel('gaussian', gamma = g)
s=SVM(C=c)
r = s.cv(data, numFolds=fold)
o = open(ds_path+'.pkl', 'wb')
pickle.dump(r, o)
o.close();
print ds_path
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L = labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree = 5)
self.svminstance.train(data)
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L=labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree=5)
self.svminstance.train(data)
