def run(centroid , group_num , train_X , train_y ,test_X , test_y, method='knn' , n_nb = 2):
# will load data as the patch size defined , 3 means 3*3 = 9 for each patch, and will return the dictionary included:
# 'data' (one patch) , 'target' (the sample of this patch belongs to ) , 'filename' (the file comes from)
bofs = []
vlad = my_vlad(centroid[group_num])
for file in train_X:
mydata,y = myData.load_sig_data(file , 3)
bofs.append(vlad.get_vlad(mydata['data']).flatten() )
#knn_init = KNeighborsClassifier()
#parameters = {'n_neighbors':[ 5, 10 , 15]}
#knn = grid_search.GridSearchCV(knn_init, parameters)
bofs_test = []
for file in test_X:
mydata,y = myData.load_sig_data(file , 3)
bofs_test.append(vlad.get_vlad(mydata['data']).flatten() )
print(len(bofs))
if(method == "knn"):
knn = KNeighborsClassifier(n_neighbors = n_nb)
knn.fit(bofs, train_y)
predicted = knn.predict(bofs_test)
score = knn.score(bofs_test,test_y)
return score
def run(centroid,
group_num,
train_X,
train_y,
test_X,
test_y,
method='knn',
n_nb=2,
seed=371986):
# will load data as the patch size defined , 3 means 3*3 = 9 for each patch, and will return the dictionary included:
# 'data' (one patch) , 'target' (the sample of this patch belongs to ) , 'filename' (the file comes from)
bofs = []
vlad = my_vlad(centroid[group_num])
for file in train_X:
mydata, y = myData.load_sig_data(file, 3)
bofs.append(vlad.get_vlad(mydata['data']).flatten())
#knn_init = KNeighborsClassifier()
#parameters = {'n_neighbors':[ 5, 10 , 15]}
#knn = grid_search.GridSearchCV(knn_init, parameters)
bofs_test = []
for file in test_X:
mydata, y = myData.load_sig_data(file, 3)
bofs_test.append(vlad.get_vlad(mydata['data']).flatten())
if (method == "knn"):
knn = KNeighborsClassifier(n_neighbors=n_nb)
knn.fit(bofs, train_y)
predicted = knn.predict(bofs_test)
score = knn.score(bofs_test, test_y)
if (method == "LinearSVM"):
svm = my_svm(iter=100)
w, obj, sp = svm.my_sgd(bofs,
train_y,
seed=seed,
stop=stop.perfor,
step=0.11,
t0=2,
C=1)
score = 100 - svm.predict(bofs_test, test_y)
return score
def run(class_num, subsample_size , centroid , cluster_num , group_num , window_size , train_X , train_y ,test_X , test_y):
# will load data as the patch size defined , 3 means 3*3 = 9 for each patch, and will return the dictionary included:
# 'data' (one patch) , 'target' (the sample of this patch belongs to ) , 'filename' (the file comes from)
bofs = []
vlad = my_vlad(centroid[group_num])
filename = "TRAIN_VLAD_%d_%d_%d_%d.txt" %(class_num, subsample_size , window_size, cluster_num[group_num])
bofs=get_vlad(train_X, filename , window_size, vlad)
bofs_test = []
filename = "TEST_VLAD_%d_%d_%d_%d.txt" %(class_num, subsample_size , window_size, cluster_num[group_num])
bofs_test=get_vlad(test_X, filename , window_size, vlad)
print(len(bofs))
return bofs , bofs_test
