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 knn(): return kNNLearner()