(trainx, trainy), (testx, testy) = load_mnist(percentage=0.01, skip_valid=True)
pca = PCA(whiten=True)
pca.fit(trainx)
components, variance = 0, 0.0
for components, ele in enumerate(pca.explained_variance_ratio_):
variance += ele
if variance > 0.90: break
components += 1
print 'n_components=%d'%components
pca.set_params(n_components=components)
pca.fit(trainx)
trainx = pca.transform(trainx)
testx = pca.transform(testx)
K = 3
dim = components
knn(trainx, testx, trainy, testy, np.eye(dim), K)
knn(trainx, testx, trainy, testy, np.random.rand(dim, dim), K)
lmnn = LMNN(K=K, mu=0.5)
lmnn.fit(trainx, trainy, lr=2e-6, max_iter=1500, gradient_cycle=100, verbose=True)
knn(trainx, testx, trainy, testy, lmnn.M, K)
lmnn = _LMNN(k=K, learn_rate=2e-6, max_iter=1000)
L = lmnn.fit(trainx, trainy, verbose=True).L
M = L.dot(L)
knn(trainx, testx, trainy, testy, M, K)
(trainx, trainy), (testx, testy) = load_mnist(percentage=0.01, skip_valid=True)
pca = PCA(whiten=True)
pca.fit(trainx)
components, variance = 0, 0.0
for components, ele in enumerate(pca.explained_variance_ratio_):
variance += ele
if variance > 0.90:
break
components += 1
print "n_components=%d" % components
pca.set_params(n_components=components)
pca.fit(trainx)
trainx = pca.transform(trainx)
testx = pca.transform(testx)
K = 3
dim = components
lmnn = LMNN(K=K, mu=0.5, dim=dim, maxS=100)
# trainx, testx, trainy, testy = train_test_split(X, Y, test_size=0.66)
knn(trainx, testx, trainy, testy, np.eye(dim), K)
knn(trainx, testx, trainy, testy, np.random.rand(dim, dim), K)
for _x in xrange(50):
lmnn.fit(trainx, trainy, lr=2e-5, max_iter=10, reset=20, verbose=True)
lmnn._init_neighbors()
train_acc = knn(trainx, trainx, trainy, trainy, lmnn.M.get_value(), K)
test_acc = knn(trainx, testx, trainy, testy, lmnn.M.get_value(), K)
print "train_acc={} test_acc={}".format(train_acc, test_acc)
