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#!/usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
# generate reference files
if 0:
pMin = 0.1
pMax = 0.9
nMin = 2
nMax = 12
n1 = 5
nR = 5
f1 = open("t_DistFunc_binomial1.csv", "w")
f2 = open("t_DistFunc_binomial2.csv", "w")
for i1 in range(n1):
p = pMin + i1 * (pMax - pMin) / (n1 - 1.0)
for n in range(nMin, nMax + 1):
for k in range(n + 1):
f1.write(
f"{n}, {p}, {k}, {ot.DistFunc.dBinomial(n, p, k)}, "
f"{ot.DistFunc.logdBinomial(n, p, k)}\n"
)
for iR in range(nR):
f2.write(f"{n}, {p}, {iR}, {ot.DistFunc.rBinomial(n, p)}\n")
f1.close()
f2.close()
ot.RandomGenerator.SetSeed(0)
# binomial + logdBinomial
sample1 = ot.Sample.ImportFromCSVFile("t_DistFunc_binomial1.csv", ",")
for x in sample1:
n, p, k, ref, log_ref = x
n, k = int(n), int(k)
val = ot.DistFunc.dBinomial(n, p, k)
print(f"dbinomial({n}, {p}, {k}) ref={ref} val={val}")
ott.assert_almost_equal(val, ref)
log_val = ot.DistFunc.logdBinomial(n, p, k)
print(f"logdbinomial({n}, {p}, {k}) ref={log_ref} val={log_val}")
ott.assert_almost_equal(log_val, log_ref)
# rBinomial
sample2 = ot.Sample.ImportFromCSVFile("t_DistFunc_binomial2.csv", ",")
for x in sample2:
n, p, iR, ref = x
n, iR = int(n), int(iR)
val = ot.DistFunc.rBinomial(n, p)
print(f"rBinomial({n}, {p}) iR={iR} ref={ref} val={val}")
ott.assert_almost_equal(val, ref)
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