def writeVectors():
vecFileName = config.results_path + "vectors.out"
vecFile = open(vecFileName, 'w')
mats = sio.loadmat(config.corpus_path + 'vars.normalized.100.mat')
We_orig = mats.get('We')
params = sio.loadmat(config.corpus_path + 'params_rae.mat')
W1 = params.get('W1')
W2 = params.get('W2')
b1 = params.get('b1')
We = params.get('We')
b = params.get('b')
W = params.get('W')
hiddenSize = 100
nExamples = 5
print "loading data.."
rnnData_train = RNNDataCorpus()
rnnData_train.load_data_srl(load_file=config.train_data_srl,
nExamples=nExamples)
print 'writing vectors to: ', vecFileName
for ii in range(len(rnnData_train.allSNum)):
sNum = rnnData_train.allSNum[ii]
sStr = rnnData_train.allSStr[ii]
sTree = rnnData_train.allSTree[ii]
sKids = rnnData_train.allSKids[ii]
words_indexed = np.where(sNum >= 0)[0]
#L is only the part of the embedding matrix that is relevant for this sentence
#L is deltaWe
if We.shape[1] != 0:
L = We[:, words_indexed]
words_embedded = We_orig[:, words_indexed] + L
else:
words_embedded = We_orig[:, words_indexed]
# sl = words_embedded.shape[1]
tree = Tree()
tree.pp = all #np.zeros(((2*sl-1),1))
tree.nodeScores = np.zeros(len(sNum))
# tree.nodeNames = np.arange(1,(2*sl-1))
tree.kids = np.zeros((len(sNum), 2))
tree.nodeFeatures = np.zeros((hiddenSize, len(sNum)))
tree.nodeFeatures[:, :len(words_indexed)] = words_embedded
toMerge = np.zeros(shape=(words_indexed.shape), dtype='int32')
toMerge[:] = words_indexed[:]
while len(toMerge) > 1:
# find unpaired bottom leaf pairs (initially words) that share parent
i = -1
foundGoodPair = False
while (not foundGoodPair):
i += 1
if sTree[toMerge[i]] == sTree[toMerge[i + 1]]:
foundGoodPair = True
newParent = sTree[toMerge[i]]
kid1 = toMerge[i]
kid2 = toMerge[i + 1]
tree.kids[newParent, :] = [kid1, kid2]
# set new parent to be possible merge candidate
toMerge[i] = newParent
# delete other kid
toMerge = np.delete(toMerge, i + 1)
c1 = tree.nodeFeatures[:, kid1]
c2 = tree.nodeFeatures[:, kid2]
p = np.tanh(np.dot(W1, c1) + np.dot(W2, c2) + b1.flatten())
tree.nodeFeatures[:, newParent] = p
vec = tree.nodeFeatures[-1]
vecFile.write(" ".join([str(x) for x in vec]) + '\n')
vecFile.close()
print "finished! "
def writeVectors():
vecFileName = config.results_path+"vectors.out"
vecFile = open(vecFileName, 'w')
mats = sio.loadmat(config.corpus_path+'vars.normalized.100.mat')
We_orig = mats.get('We')
params = sio.loadmat(config.corpus_path+'params_rae.mat')
W1 = params.get('W1')
W2 = params.get('W2')
b1 = params.get('b1')
We = params.get('We')
b = params.get('b')
W = params.get('W')
hiddenSize = 100
nExamples = 5
print "loading data.."
rnnData_train = RNNDataCorpus()
rnnData_train.load_data_srl(load_file=config.train_data_srl, nExamples=nExamples)
print 'writing vectors to: ', vecFileName
for ii in range(len(rnnData_train.allSNum)):
sNum = rnnData_train.allSNum[ii]
sStr = rnnData_train.allSStr[ii]
sTree = rnnData_train.allSTree[ii]
sKids = rnnData_train.allSKids[ii]
words_indexed = np.where(sNum >= 0)[0]
#L is only the part of the embedding matrix that is relevant for this sentence
#L is deltaWe
if We.shape[1] != 0:
L = We[:, words_indexed]
words_embedded = We_orig[:, words_indexed] + L;
else :
words_embedded = We_orig[:, words_indexed]
# sl = words_embedded.shape[1]
tree = Tree()
tree.pp = all#np.zeros(((2*sl-1),1))
tree.nodeScores = np.zeros(len(sNum))
# tree.nodeNames = np.arange(1,(2*sl-1))
tree.kids = np.zeros((len(sNum),2))
tree.nodeFeatures = np.zeros((hiddenSize, len(sNum)))
tree.nodeFeatures[:,:len(words_indexed)] = words_embedded;
toMerge = np.zeros(shape=(words_indexed.shape), dtype='int32')
toMerge[:] = words_indexed[:]
while len(toMerge)>1 :
# find unpaired bottom leaf pairs (initially words) that share parent
i=-1;
foundGoodPair = False
while (not foundGoodPair ) :
i += 1
if sTree[toMerge[i]]==sTree[toMerge[i+1]]:
foundGoodPair = True
newParent = sTree[toMerge[i]]
kid1 = toMerge[i]
kid2 = toMerge[i+1]
tree.kids[newParent,:] = [kid1, kid2];
# set new parent to be possible merge candidate
toMerge[i] = newParent;
# delete other kid
toMerge = np.delete(toMerge,i+1)
c1 = tree.nodeFeatures[:,kid1]
c2 = tree.nodeFeatures[:,kid2]
p = np.tanh(np.dot(W1,c1) + np.dot(W2,c2) + b1.flatten())
tree.nodeFeatures[:,newParent] = p;
vec = tree.nodeFeatures[-1]
vecFile.write(" ".join([str(x) for x in vec])+'\n')
vecFile.close()
print "finished! "
