n = x.shape[0]
x = x.reshape(n, -1)
trainx, testx, trainy, testy = train_test_split(x, y, test_size=0.32, random_state=32)
train_ind, test_ind = train_test_split(range(n), range(n), test_size=0.32, random_state=32)[:2]
# assert ((trainx==x[train_ind]).all())
# assert ((testx==x[test_ind]).all())
duration_model = LMNN_duration.load("./BIT/models/[K=300][rs=32]duration.model")
duration_trainx, duration_trainy = expand(trainx, trainy, K, G)
duration_testx, duration_testy = expand(testx, testy, K, G)
duration_trainx = duration_model.transform(duration_trainx)
duration_testx = duration_model.transform(duration_testx)
duration_knn = KNN(n_neighbor).fit(duration_trainx, duration_trainy)
print duration_trainx.shape
print duration_trainy.shape
prediction = duration_knn.predict(duration_testx)
print prediction.tolist()
mlmnn = MLMNN.load("./BIT/models/[K=300][rs=32]mlmnn.model")
mlmnn_knns = []
trunc = lambda x: x.reshape(x.shape[0], G, -1)[:, :g, :].reshape(x.shape[0], -1)
for g in range(1, G + 1):
mlmnn_knn = KNN(mlmnn.K).fit(mlmnn.transform(trunc(trainx), g), trainy)
mlmnn_knns.append(mlmnn_knn)
for g in range(1, G + 1):
# print '='*10
# acc = 0.
# for count, ind in enumerate(test_ind):
# ttest = mlmnn.transform(trunc(x[ind:ind+1]), g)
# if mlmnn_knns[g-1].predict(ttest)[0]==y[ind]:
# x = normalize(x)
print sum(pca.explained_variance_ratio_)
trainx, testx, trainy, testy = train_test_split(x, y, test_size=0.10)
print trainx.shape
print trainy.shape
print len(set(trainy))
from sklearn.svm import SVC
# from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import classification_report
# svm = OneVsRestClassifier(SVC(C=0.11)) # 0.90
svm = SVC(C=100.11) # 0.90
svm.fit(trainx, trainy)
print classification_report(testy, svm.predict(testx))
import sys; sys.path.append('/home/shaofan/Projects')
from FastML import NCA, KNN, MCML
knn = KNN(n_neighbors=8)
knn.fit(trainx, trainy)
print classification_report(testy, knn.predict(testx))
# nca = NCA()
# for i in range(100):
# trainx = nca.fit(trainx, trainy, max_iter=5, lr=5e-3).transform(trainx)
# testx = nca.transform(testx)
#
# print classification_report(testy, knn.predict(testx))
# sys.stdout.flush()
