def fit(self, x, y, testx, testy, verbose=False, autosave=True):
self.x = x = x.reshape(x.shape[0], self.M, -1)
self.y = y
self.testx = testx = testx.reshape(testx.shape[0], self.M, -1)
self.testy = y
self.Ms = []
self.PCAs = []
for i in xrange(self.M):
trainx, testx, _pca = pca(self.x[:, i, :], self.testx[:, i, :], self.Ppca, verbose=verbose, get_pca=True)
lmnn = LMNN_GPU(K=self.K, mu=self.mu, maxS=self.maxS, dim=trainx.shape[1])
print '[%d]pre-train-acc: %.3f%% pre-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
lmnn.fit(trainx, y, lr=self.lr, max_iter=self.max_iter, reset=self.reset, verbose=False)
print '[%d]post-train-acc: %.3f%% post-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
self.Ms.append(lmnn)
self.PCAs.append(_pca)
trainx = self.transform(self.x)
testx = self.transform(self.testx)
dim = trainx.shape[1]
print 'train-acc: %.3f%% test-acc: %.3f%% %s'%(knn(trainx, trainx, y, y, np.eye(dim), self.K), knn(trainx, testx, y, testy, np.eye(dim), self.K), ' '*30)
if autosave:
print 'Auto saving ...'
cPickle.dump(self, open('temp.MLMNN', 'w'))
return self
def fit(self, x, y, testx, testy, verbose=False, autosave=True):
self.x = x = x.reshape(x.shape[0], self.M, -1)
self.y = y
self.testx = testx = testx.reshape(testx.shape[0], self.M, -1)
self.testy = y
self.Ms = []
self.PCAs = []
for i in xrange(self.M):
trainx, testx, _pca = pca(self.x[:, i, :], self.testx[:, i, :], self.Ppca, verbose=verbose, get_pca=True)
lmnn = LMNN_GPU(K=self.K, mu=self.mu, maxS=self.maxS, dim=trainx.shape[1])
print '[%d]pre-train-acc: %.3f%% pre-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
lmnn.fit(trainx, y, lr=self.lr, max_iter=self.max_iter, reset=self.reset, verbose=False)
print '[%d]post-train-acc: %.3f%% post-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
self.Ms.append(lmnn)
self.PCAs.append(_pca)
trainx = self.transform(self.x)
testx = self.transform(self.testx)
trainx[np.isnan(trainx)] = 0
trainx[np.isinf(trainx)] = 0
testx[np.isnan(testx)] = 0
testx[np.isinf(testx)] = 0
print 'Final train shape:',trainx.shape
print 'Final test shape:',testx.shape
trainx, testx = pca(trainx, testx, self.Ppca, verbose=True)
print 'Final train shape:',trainx.shape
print 'Final test shape:',testx.shape
# integrated metric
lmnn = LMNN_GPU(K=self.K, mu=self.mu, maxS=10000, dim=trainx.shape[1])
for i in xrange(10):
print '[%d]pre-train-acc: %.3f%% pre-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
lmnn.fit(trainx, y, lr=2e-5, max_iter=50, reset=50, verbose=False)
print '[%d]post-train-acc: %.3f%% post-test-acc: %.3f%% %s'%(i, knn(trainx, trainx, y, y, lmnn._M, self.K), knn(trainx, testx, y, testy, lmnn._M, self.K), ' '*30)
if autosave:
print 'Auto saving ...'
cPickle.dump(self, open('temp.MLMNN', 'w'))
return self
variance += ele
if variance > 0.95: 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 = 9
dim = components
# knn(trainx, testx, trainy, testy, np.eye(dim), K)
# knn(trainx, testx, trainy, testy, np.random.rand(dim, dim), K)
# My LMNN
lmnn = LMNN_GPU(K=K, mu=0.5, maxS=10000, dim=dim)
print 'pre-train-acc: %.3f%% pre-test-acc: %.3f%% %s'%(knn(trainx, trainx, trainy, trainy, lmnn._M, K), knn(trainx, testx, trainy, testy, lmnn._M, K), ' '*30)
lmnn.fit(trainx, trainy, lr=5e-6, max_iter=1000, reset=50, verbose=True)
print 'pre-train-acc: %.3f%% pre-test-acc: %.3f%% %s'%(knn(trainx, trainx, trainy, trainy, lmnn._M, K), knn(trainx, testx, trainy, testy, lmnn._M, K), ' '*30)
#knn(trainx, testx, trainy, testy, lmnn.M, K)
# L = lmnn.L
# knn(trainx.dot(L), testx.dot(L), trainy, testy, np.eye(L.shape[1]), K)
# # metric learn
# lmnn = _LMNN(k=K, learn_rate=1e-5, max_iter=200)
# L = lmnn.fit(trainx, trainy, verbose=True).L
# knn(trainx, testx, trainy, testy, L.dot(L), K)
