Aggregators

Aggregators are special type of nodes that includes a generic CPT for any numbers of parents.

pyAgrum proposes a list of such aggregators. Some of then are used below.

import pyagrum as gum
import pyagrum.lib.notebook as gnb

min_x = 0
max_x = 15

bn = gum.BayesNet()
l = [bn.add(gum.RangeVariable(item, item, min_x, max_x)) for item in ["a", "b", "c", "d", "e", "f"]]

gum.config["notebook", "histogram_line_threshold"] = 15

nmax = bn.addMAX(gum.RangeVariable("MAX", "MAX", min_x, max_x))
bn.addArc(l[0], nmax)
bn.addArc(l[1], nmax)
bn.addArc(l[2], nmax)

nmin = bn.addMIN(gum.RangeVariable("MIN", "MIN", min_x, max_x))
bn.addArc(l[3], nmin)
bn.addArc(l[4], nmin)
bn.addArc(l[5], nmin)

nampl = bn.addAMPLITUDE(gum.RangeVariable("DELTA", "DELTA", 0, max_x - min_x))
bn.addArc(nmax, nampl)
bn.addArc(nmin, nampl)

nmedian = bn.addMEDIAN(gum.RangeVariable("MEDIAN", "MEDIAN", min_x, max_x))
for n in [l[0], l[1], l[2], l[3]]:
  bn.addArc(n, nmedian)
## potential for median has a size : 16^5=2^20 double !

nexists = bn.addEXISTS(gum.LabelizedVariable("EXISTS_0", "EXISTS"), 0)
bn.addArc(l[0], nexists)
bn.addArc(l[1], nexists)
bn.addArc(l[2], nexists)

nforall = bn.addFORALL(gum.LabelizedVariable("FORALL_1", "FORALL"), 1)
bn.addArc(l[3], nforall)
bn.addArc(l[4], nforall)
bn.addArc(l[5], nforall)

ncount = bn.addCOUNT(gum.RangeVariable("COUNT_1", "COUNT_1,", 0, 3), 1)
bn.addArc(l[0], ncount)
bn.addArc(l[1], ncount)
bn.addArc(l[2], ncount)

for nod in l:
  bn.cpt(nod).fillWith(1).normalize()

gnb.showInference(bn, size="13")

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## dot | neato | fdp | sfdp | twopi | circo | osage | patchwork
gum.config["notebook", "graph_rankdir"] = "LR"
gnb.showInference(bn, size="13", evs={"MEDIAN": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]})
gum.config.reset()

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## if the roots do not have uniform but random distribution
for nod in l:
  bn.generateCPT(nod)

gnb.showInference(bn, size="13")

svg

gnb.showInference(bn, size="13", evs={"MEDIAN": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]})

svg