def calcNodeDriftVector(self):
""" calculates the drift vector of a node.
"""
self.weightEstimate = 1.0
self.ratioW = self.weightEstimate/self.weight
# compute statistics delta vector.
# delta = (weight*localStatistics - weightEstimate*localEstimate -
# (weight - weightEstimate)*globalEstimate) / weight
self.cm.delta = VectorOps.multAndAdd(self.cm.localStatistics, \
self.weight, self.localEstimate)
VectorOps.multAndAddTo(self.cm.delta, self.ratioW-self.weight,\
self.cm.globalV)
# update drift vector
VectorOps.cpy(self.cm.drift, self.cm.globalV)
VectorOps.addTo(self.cm.drift, self.cm.delta)
VectorOps.multAndAddTo(self.cm.drift, 1.0/self.weight, self.cm.slack)
def calcNodeDriftVector(self):
""" calculates the drift vector of a node.
"""
self.weightEstimate = 1.0
self.ratioW = self.weightEstimate / self.weight
# compute statistics delta vector.
# delta = (weight*localStatistics - weightEstimate*localEstimate -
# (weight - weightEstimate)*globalEstimate) / weight
self.cm.delta = VectorOps.multAndAdd(self.cm.localStatistics, \
self.weight, self.localEstimate)
VectorOps.multAndAddTo(self.cm.delta, self.ratioW-self.weight,\
self.cm.globalV)
# update drift vector
VectorOps.cpy(self.cm.drift, self.cm.globalV)
VectorOps.addTo(self.cm.drift, self.cm.delta)
VectorOps.multAndAddTo(self.cm.drift, 1.0 / self.weight, self.cm.slack)
def compute_balanced_vec(self):
""" computes the balanced vector from the elements that are
contained in balancing group only.
@return the balanced vector
"""
# initialize a balanced vector b
self.b = [0] * self.mf.getDimension()
self.total_weight = 0.0
# compute the balanced vector b
for elem in self.balancing_group:
self.w = elem.weight
self.total_weight += self.w
self.b = VectorOps.multAndAddTo(self.b, self.w, elem.drift)
self.b = VectorOps.multBy(self.b, 1.0/self.total_weight)
return self.b
def compute_balanced_vec(self):
""" computes the balanced vector from the elements that are
contained in balancing group only.
@return the balanced vector
"""
# initialize a balanced vector b
self.b = [0] * self.mf.getDimension()
self.total_weight = 0.0
# compute the balanced vector b
for elem in self.balancing_group:
self.w = elem.weight
self.total_weight += self.w
self.b = VectorOps.multAndAddTo(self.b, self.w, elem.drift)
self.b = VectorOps.multBy(self.b, 1.0 / self.total_weight)
return self.b
def computeBalancedVector(self):
""" computes the balanced_vector.
b = sum(w(i) * u(i))/sum(w(i)), where node i ε balancing_group.
"""
# initialize a balanced vector b
self.b = [0] * self.mf.getDimension()
self.total_weight = 0.0
# compute the balanced vector b
for elem in self.balancing_group:
self.w = elem.weight
self.total_weight += self.w
self.b = VectorOps.multAndAddTo(self.b, self.w, elem.drift)
self.b = VectorOps.multBy(self.b, 1.0 / self.total_weight)
# check if ball B(e(t), b) is monochromatic (step 1 of coord)
if self.mf.isMonochromatic(self.coord.cm.estimate, self.b):
self.successfulBalancing(self.b)
else:
self.unsuccessfulBalancing()
def computeBalancedVector(self):
""" computes the balanced_vector.
b = sum(w(i) * u(i))/sum(w(i)), where node i ε balancing_group.
"""
# initialize a balanced vector b
self.b = [0] * self.mf.getDimension()
self.total_weight = 0.0
# compute the balanced vector b
for elem in self.balancing_group:
self.w = elem.weight
self.total_weight += self.w
self.b = VectorOps.multAndAddTo(self.b, self.w, elem.drift)
self.b = VectorOps.multBy(self.b, 1.0/self.total_weight)
# check if ball B(e(t), b) is monochromatic (step 1 of coord)
if self.mf.isMonochromatic(self.coord.cm.estimate, self.b):
self.successfulBalancing(self.b)
else:
self.unsuccessfulBalancing()
