def __init__(self, n, source, target, bias, opts=None): MapSeeker.__init__(self, opts) shape = bias.shape if not isinstance(bias, Sparse): bias = Sparse(bias) bias.squeeze() self.bias = bias self._params = (n, source, target) self._space = Sparse(None, shape) self.prep(n, source, target)
class SparseNavMSC(MapSeeker):
def __init__(self, n, source, target, bias, opts=None):
MapSeeker.__init__(self, opts)
shape = bias.shape
if not isinstance(bias, Sparse):
bias = Sparse(bias)
bias.squeeze()
self.bias = bias
self._params = (n, source, target)
self._space = Sparse(None, shape)
self.prep(n, source, target)
def space(self):
return self._space.clone()
def prep(self, n, source, target):
ss = self.space()
if not hasattr(source[0], '__getitem__'):
source = [source]
for pt in source:
ss[pt[:2]]=pt[2]
self.source=ss
ts = self.space()
if not hasattr(target[0], '__getitem__'):
target = [target]
for pt in target:
ts[pt[:2]]=pt[2]
self.layers=[]
self.iter=0
for i in range(n):
self.layers.append({'source':None, 'target':None, 'gain':ones(self.nMaps(i)), 'dead':zeros(self.nMaps(i))})
self.layers[-1]['target']=ts
def applyBias(self, layer, source):
return source*self.bias
def getTrajectory(self):
path = self.bestPath()
s = self.source.clone()
pts = [maxPt(s)]
for i, p in enumerate(path):
s = self.doMap(i, p, True, s)
pts.append(maxPt(s))
return array(pts)
def test_append():
s = Sparse(test_list1)
assert len(s.array) == len(test_list1)
s.append(test_list2)
assert len(s.array) == len(test_list1) + len(test_list2)
assert s.array == test_list1 + test_list2
