def forwardPropTree(W, WO, Wcat, Wv, Wo, sNum,sTree, sStr=None, sNN=None, indicies=None, params=None):
wsz = params.wordSize
r = params.rankWo
words = np.where(sNum>=0)[0]
numTotalNodes = len(sNum)
allV = Wv[:,sNum[words]]
allO = Wo[:,sNum[words]]
thisTree = Tree()
# set tree structure of tree
thisTree.pp = sTree #to check
# set which nodes are leaf nodes
thisTree.isLeafVec = np.zeros(numTotalNodes);
thisTree.isLeafVec[words] = 1;
thisTree.nodeNames = np.arange(len(sTree))
thisTree.nodeLabels = sNum;
# the inputs to the parent
thisTree.ParIn_z = np.zeros((wsz,numTotalNodes)) # empty for leaf nodes
thisTree.ParIn_a = np.zeros((wsz,numTotalNodes))
#node vectors
thisTree.nodeAct_a = np.zeros((wsz, numTotalNodes))
# the new operators
thisTree.nodeOp_A = np.zeros((wsz**2,numTotalNodes))
# the scores for each decision
thisTree.score = np.zeros(numTotalNodes);
# the children of each node (for speed)
thisTree.kids = np.zeros((numTotalNodes,2), dtype='int32');
# initialize the vectors and operators of the words (leaf nodes)
thisTree.nodeAct_a[:,words] = allV;
for thisWordNum in range(len(words)):
diag_a = np.diag(allO[:wsz,thisWordNum])
U = allO[wsz:wsz*(1+r),thisWordNum].reshape(wsz,r)
V = allO[wsz*(1+r):,thisWordNum].reshape(wsz, r)
A = diag_a + np.dot(U, np.transpose(V))
A = A.reshape(wsz**2)
thisTree.nodeOp_A[:, thisWordNum] = A
toMerge = np.zeros(shape=(words.shape), dtype='int32')
toMerge[:] = words[:]
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]
thisTree.kids[newParent,:] = [kid1, kid2];
# set new parent to be possible merge candidate
toMerge[i] = newParent;
# delete other kid
toMerge = np.delete(toMerge,i+1)
a = thisTree.nodeAct_a[:,kid1];
A = thisTree.nodeOp_A[:,kid1].reshape(wsz,wsz)
b = thisTree.nodeAct_a[:,kid2];
B = thisTree.nodeOp_A[:,kid2].reshape(wsz,wsz)
l_a = np.dot(B,a)
r_a = np.dot(A,b)
C = np.concatenate((l_a,r_a, np.ndarray([1])))
thisTree.nodeAct_a[:,newParent] = np.tanh(np.dot(W,C))
P_A = (np.dot(WO,np.vstack((A,B)))).reshape(wsz**2)
# save all this for backprop:
thisTree.ParIn_a[:,kid1] = l_a
thisTree.ParIn_a[:,kid2] = r_a
thisTree.nodeOp_A[:,newParent] = P_A
return thisTree
def forwardPropTree(W,
WO,
Wcat,
Wv,
Wo,
sNum,
sTree,
sStr=None,
sNN=None,
indicies=None,
params=None):
wsz = params.wordSize
r = params.rankWo
words = np.where(sNum >= 0)[0]
numTotalNodes = len(sNum)
allV = Wv[:, sNum[words]]
allO = Wo[:, sNum[words]]
thisTree = Tree()
# set tree structure of tree
thisTree.pp = sTree #to check
# set which nodes are leaf nodes
thisTree.isLeafVec = np.zeros(numTotalNodes)
thisTree.isLeafVec[words] = 1
thisTree.nodeNames = np.arange(len(sTree))
thisTree.nodeLabels = sNum
# the inputs to the parent
thisTree.ParIn_z = np.zeros((wsz, numTotalNodes)) # empty for leaf nodes
thisTree.ParIn_a = np.zeros((wsz, numTotalNodes))
#node vectors
thisTree.nodeAct_a = np.zeros((wsz, numTotalNodes))
# the new operators
thisTree.nodeOp_A = np.zeros((wsz**2, numTotalNodes))
# the scores for each decision
thisTree.score = np.zeros(numTotalNodes)
# the children of each node (for speed)
thisTree.kids = np.zeros((numTotalNodes, 2), dtype='int32')
# initialize the vectors and operators of the words (leaf nodes)
thisTree.nodeAct_a[:, words] = allV
for thisWordNum in range(len(words)):
diag_a = np.diag(allO[:wsz, thisWordNum])
U = allO[wsz:wsz * (1 + r), thisWordNum].reshape(wsz, r)
V = allO[wsz * (1 + r):, thisWordNum].reshape(wsz, r)
A = diag_a + np.dot(U, np.transpose(V))
A = A.reshape(wsz**2)
thisTree.nodeOp_A[:, thisWordNum] = A
toMerge = np.zeros(shape=(words.shape), dtype='int32')
toMerge[:] = words[:]
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]
thisTree.kids[newParent, :] = [kid1, kid2]
# set new parent to be possible merge candidate
toMerge[i] = newParent
# delete other kid
toMerge = np.delete(toMerge, i + 1)
a = thisTree.nodeAct_a[:, kid1]
A = thisTree.nodeOp_A[:, kid1].reshape(wsz, wsz)
b = thisTree.nodeAct_a[:, kid2]
B = thisTree.nodeOp_A[:, kid2].reshape(wsz, wsz)
l_a = np.dot(B, a)
r_a = np.dot(A, b)
C = np.concatenate((l_a, r_a, np.ndarray([1])))
thisTree.nodeAct_a[:, newParent] = np.tanh(np.dot(W, C))
P_A = (np.dot(WO, np.vstack((A, B)))).reshape(wsz**2)
# save all this for backprop:
thisTree.ParIn_a[:, kid1] = l_a
thisTree.ParIn_a[:, kid2] = r_a
thisTree.nodeOp_A[:, newParent] = P_A
return thisTree