def setUp(self):
self.learner = knn.kNNLearner(distance_constructor=Euclidean())
svmCnt = []
knnCnt = []
traFile = "C:\\Users\\Parker\\Documents\\NPS Projects\\thesis\\status\\Databases\\train\\%strain.csv" % str(i+1)
testFile = "C:\\Users\\Parker\\Documents\\NPS Projects\\thesis\\status\\Databases\\train\\%stest.csv" % str(i+1)
train = data.Table(traFile)
test = data.Table(testFile)
svmLearner = svm.SVMLearner(svm_type=svm.SVMLearner.C_SVC, kernel_type=svm.SVMLearner.RBF, kernel_func=None, \
C=1, nu=0.5, p=0.1, gamma=0.0, degree=3, coef0=0, \
shrinking=True, probability=True, verbose=False, \
cache_size=200, eps=0.001, normalization=False)
svmLearner.tune_parameters(train, parameters=["gamma","C"], folds=8)
svmClassifier = svmLearner(train)
knnClassifier = knn.kNNLearner(train, k=8)
for t in test:
svmCnt.append(svmClassifier(t))
knnCnt.append(knnClassifier(t))
voteIdx = 0
imp = (length - gen)
svmVotes = np.zeros(length/numV) #create arrays for each voting block
knnVotes = np.zeros(length/numV)
j = 0
while j < length : #for the length of the vector
svm_neigh = 0
knn_neigh = 0
def setUp(self):
self.learner = knn.kNNLearner(distance_constructor=Euclidean())
def knn():
return kNNLearner()