def calcSlackVector(self, element, b):
""" calculates the slack vector adjustments for a particular
node.
@param element is the element from the node we want to send slack.
@param b is the balanced vector.
@return is the slack vector for the particular node.
"""
self.slack = VectorOps.subFrom(VectorOps.mult(b, element.weight),\
VectorOps.mult(element.drift, element.weight))
return self.slack
def calcSlackVector(self, element, b):
""" calculates the slack vector adjustments for a particular
node.
@param element is the element from the node we want to send slack.
@param b is the balanced vector.
@return is the slack vector for the particular node.
"""
self.slack = VectorOps.subFrom(VectorOps.mult(b, element.weight),\
VectorOps.mult(element.drift, element.weight))
return self.slack
def recalcEstimateVector(self):
""" calculates the estimate vector. """
# init sum of weights in the minimum permitted value
# (so as to avoid division with zero)
self.sumw = 0.000000000001
# for every single node
for node in self.all_vectors:
# get the weight and the last statistic vector
self.w = float(self.all_vectors[node][0])
self.v = self.all_vectors[node][1]
# compute the denominator
self.sumw += self.w
# and compute the numerator
self.cm.estimate = VectorOps.addTo(self.cm.estimate,
VectorOps.mult(self.v, self.w))
# calculate the final estimate vector
self.cm.estimate = VectorOps.multBy\
(self.cm.estimate, 1.0/self.sumw)
def recalcEstimateVector(self):
""" calculates the estimate vector. """
# init sum of weights in the minimum permitted value
# (so as to avoid division with zero)
self.sumw = 0.000000000001
# for every single node
for node in self.all_vectors:
# get the weight and the last statistic vector
self.w = float(self.all_vectors[node][0])
self.v = self.all_vectors[node][1]
# compute the denominator
self.sumw += self.w
# and compute the numerator
self.cm.estimate = VectorOps.addTo(self.cm.estimate,
VectorOps.mult(self.v, self.w))
# calculate the final estimate vector
self.cm.estimate = VectorOps.multBy\
(self.cm.estimate, 1.0/self.sumw)
