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 testAddTo(self):
""" tests the addTo(self, a, b) method and prints the result.
Final results should be [2,2,2,2,2].
"""
self.true_result = [2, 2, 2, 2, 2]
self.test_result = VectorOps.addTo(self.target, self.operand)
self.assertEqual(self.true_result, self.test_result)
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 testAddTo(self):
""" tests the addTo(self, a, b) method and prints the result.
Final results should be [2,2,2,2,2].
"""
self.true_result = [2, 2, 2, 2, 2]
self.test_result = VectorOps.addTo(self.target, self.operand)
self.assertEqual(self.true_result, self.test_result)
def calcDriftVector(self):
""" calculates drift vector.
Drift vector is the following vector:
u(t) = e(t) + Δv(t), where
Δv(t) = v(t) - v'(t)
"""
# calculate Δv(t)
self.dv = VectorOps.subFrom(self.cm.localStatistics, \
self.cm.lastStatistics)
# calculate drift vector
self.cm.drift = VectorOps.addTo(self.cm.estimate,\
self.dv)
def calcDriftVector(self):
""" calculates drift vector.
Drift vector is the following vector:
u(t) = e(t) + Δv(t), where
Δv(t) = v(t) - v'(t)
"""
# calculate Δv(t)
self.dv = VectorOps.subFrom(self.cm.localStatistics, \
self.cm.lastStatistics)
# calculate drift vector
self.cm.drift = VectorOps.addTo(self.cm.estimate,\
self.dv)
def calc_drift(self):
""" calculates drift vector.
Drift vector is computed as follows:
u(t) = e(t) + delta-v(t), where
delta-v(t) = v(t) = v'(t)
"""
# calculate delta-v
self.dv = VectorOps.subFrom(self.cm.localStatistics,\
self.cm.lastStatistics)
# calculate drift vector
self.cm.drift = VectorOps.addTo(self.cm.estimate, self.dv)
# now check if ball is monochromatic
if not self.check_ball(self.cm.localStatistics):
# if it is not, then send a REP-message to coordinator
# (the algorithm here is exactly the same as in REQ_receipt())
self.REQ_receipt()
def calc_drift(self):
""" calculates drift vector.
Drift vector is computed as follows:
u(t) = e(t) + delta-v(t), where
delta-v(t) = v(t) = v'(t)
"""
# calculate delta-v
self.dv = VectorOps.subFrom(self.cm.localStatistics,\
self.cm.lastStatistics)
# calculate drift vector
self.cm.drift = VectorOps.addTo(self.cm.estimate, self.dv)
# now check if ball is monochromatic
if not self.check_ball(self.cm.localStatistics):
# if it is not, then send a REP-message to coordinator
# (the algorithm here is exactly the same as in REQ_receipt())
self.REQ_receipt()
def calc_drift(self):
""" calculates drift vector.
Drift vector is computed as follows:
u(t) = e(t) + delta-v(t), where
delta-v(t) = v(t) = v'(t)
"""
# calculate delta-v
self.dv = VectorOps.subFrom(self.cm.localStatistics,\
self.cm.lastStatistics)
# calculate drift vector
self.cm.drift = VectorOps.addTo(self.cm.estimate, self.dv)
# now check if ball is monochromatic
if not self.check_ball(self.cm.localStatistics):
# if it is not, then initiate a balancing process,
self.coord_elem = self.createBalancingElement(self.node_name,\
self.cm.localStatistics, self.cm.drift, self.weight)
# initiating the balancing group with this very element
self.bp.addToBalancing(self.coord_elem)
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)
def ADJ_SLK_receipt(self, message):
""" Upon receipt of an ADJ_SLK message, add the value specified
in the message to the value of the slack vector (δ <- δ + Δδ).
"""
self.cm.slack = VectorOps.addTo(self.cm.slack, message.content)
def ADJ_SLK_receipt(self, message):
""" Upon receipt of an ADJ_SLK message, add the value specified
in the message to the value of the slack vector (δ <- δ + Δδ).
"""
self.cm.slack = VectorOps.addTo(self.cm.slack, message.content)
self.set_state('SAFE')
