def test1():
numpy.random.seed(42)
data = numpy.random.poisson(5, 1000).reshape(1000, 1)
for i in numpy.unique(data):
print(i, numpy.sum(data == i))
featureTypes = ["discrete"]
featureTypes = ["categorical"]
spn = SPN.LearnStructure(
data,
featureTypes=featureTypes,
row_split_method=Splitting.KmeansRows(),
col_split_method=Splitting.IndependenceTest(),
# spn = SPN.LearnStructure(data, featureNames=["X1"], domains =
# domains, families=families, row_split_method=Splitting.KmeansRows(),
# col_split_method=Splitting.RDCTest(),
min_instances_slice=100)
print(spn)
print(numpy.unique(data))
evdata = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
print(evdata)
ll = (spn.root.eval(numpy.asarray(evdata).reshape(len(evdata), 1)))
print("Probs", numpy.exp(ll))
print("Sum LL", numpy.sum(ll))
print(numpy.histogram(data, bins="auto", density=True))
import numpy
from tfspn.SPN import SPN, Splitting
import tensorflow as tf
if __name__ == '__main__':
gen = numpy.random.poisson(5, 1000)
data = numpy.transpose(numpy.vstack((gen, gen)))
spn = SPN.LearnStructure(data,
min_instances_slice=200,
families=["poisson", "poisson"],
row_split_method=Splitting.KmeansRows(),
col_split_method=Splitting.IndependenceTest())
# THIS PRODUCES THE FOLLOWING SPN:
#
# SumNode_0 SumNode(0.154*ProductNode_3, 0.188*ProductNode_6, 0.158*ProductNode_10, 0.076*ProductNode_13, 0.13999999999999999*ProductNode_18, 0.176*ProductNode_21, 0.108*ProductNode_24){
# ProductNode_3 ProductNode(PoissonNode_4, PoissonNode_5){
# PoissonNode_4 P(X_0_|λ=6.0)
# PoissonNode_5 P(X_1_|λ=6.0)
# }
# ProductNode_6 ProductNode(PoissonNode_7, PoissonNode_8){
# PoissonNode_7 P(X_0_|λ=5.0)
# PoissonNode_8 P(X_1_|λ=5.0)
# }
# ProductNode_10 ProductNode(PoissonNode_11, PoissonNode_12){
# PoissonNode_11 P(X_0_|λ=7.360759493670886)
# PoissonNode_12 P(X_1_|λ=7.360759493670886)
