def predict_new(self,
X,
only_topk=None,
csr_codes=None,
beam_size=2,
max_depth=None,
cond_prob=True,
normalized=False,
threads=-1):
if max_depth is None:
max_depth = self.depth
if cond_prob is None or cond_prob == False:
cond_prob = PostProcessor(Transform.identity, Combiner.noop)
if cond_prob == True:
cond_prob = PostProcessor(Transform.get_lpsvm(3), Combiner.mul)
assert isinstance(cond_prob, PostProcessor), tpye(cond_prob)
assert X.shape[1] == self.nr_features
if self.bias > 0:
X = smat_util.append_column(X, self.bias)
pX = PyMatrix.init_from(X, dtype=self.model_chain[0].pW.dtype)
max_depth = min(self.depth, max_depth)
pred_csr = csr_codes
for d in range(max_depth):
cur_model = self.model_chain[d]
local_only_topk = only_topk if d == (max_depth - 1) else beam_size
pred_csr = cur_model.predict_new(pX,
only_topk=local_only_topk,
csr_codes=pred_csr,
cond_prob=cond_prob,
threads=threads)
if normalized:
pred_csr = sk_normalize(pred_csr, axis=1, copy=False, norm='l1')
return pred_csr
def __init__(self, X, Y, C=None, bias=-1.0, dtype=None):
if dtype is None:
dtype = X.dtype
self.bias = bias
if self.bias > 0:
X = smat_util.append_column(X, self.bias)
self.pX = PyMatrix.init_from(X, dtype)
self.pY = PyMatrix.init_from(Y, dtype)
self.pC = PyMatrix.init_from(C, dtype)
Z = None if C is None else smat.csr_matrix(self.Y.dot(self.C))
self.pZ = PyMatrix.init_from(Z, dtype) # Z = Y * C
self.dtype = dtype
def __init__(self, X, Y, C=None, bias=-1.0, dtype=None, Z_pred=None):
if dtype is None:
dtype = X.dtype
self.bias = bias
if self.bias > 0:
X = smat_util.append_column(X, self.bias)
self.pX = PyMatrix.init_from(X, dtype)
self.pY = PyMatrix.init_from(Y, dtype)
self.pC = PyMatrix.init_from(C, dtype)
Z = None if C is None else smat.csr_matrix(self.Y.dot(self.C))
if Z_pred is not None and Z is not None:
print("Z", Z.shape)
print("Z_pred", Z_pred.shape)
Z = Z + Z_pred
Z = Z.tocsr()
self.pZ = PyMatrix.init_from(Z, dtype) # Z = Y * C
self.dtype = dtype
