def __call__(self, start, length, slicelen):
assert start >= 0
slicelen = min(length, slicelen)
iostream = open(self.path, "rb")
buf = np.empty(slicelen, dtype=self.dtype)
itemsize = buf.dtype.itemsize
iostream.seek(itemsize * start)
state = (buf, iostream, start, length, slicelen)
return Seq.from_next(state, next_data_chunk)
def __call__(self, start, length, slicelen):
assert start >= 0 and start < len(self.vector)
length = min(length, len(self.vector) - start)
slicelen = min(length, slicelen)
state = start, length, slicelen
def f(from_to):
return self.vector[from_to[0]:from_to[1]]
return Seq.map(f, Seq.from_next(state, next_slice_indices))
def growtree(factors, gcovariate, hcovariate, fm, eta, maxdepth, lambda_, gamma, minh, slicelen):
length = len(gcovariate)
maxnodecount = 2 ** maxdepth
nodeids = np.zeros(length, dtype=np.uint8) if maxnodecount <= np.iinfo(np.uint8).max else np.zeros(length, dtype=np.uint16)
loss0 = np.finfo(np.float32).max
nodes0 = [LeafNode((0.0, 0.0), True, {f : np.full(len(f.levels), True) for f in factors}, loss0)]
state0 = TreeGrowState(nodeids, nodes0, factors, gcovariate, hcovariate, gamma, lambda_, minh, slicelen)
layers = Seq.tolist(Seq.take(Seq.from_next(state0, nextlayer), maxdepth))
predict(state0.nodes, nodeids, fm, eta, lambda_)
return layers, fm
def slicer(start, length, slicelen):
length = min(self._length - start, length)
slicelen = min(length, slicelen)
buf = np.ones(slicelen, dtype = np.uint8)
return Seq.map((lambda x: buf[x[0]:x[1]]), Seq.from_next((start, length, slicelen), ch.next_slice_indices))
