import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.NegativeBinomial().__class__.__name__ == 'ComposedDistribution':
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif ot.NegativeBinomial(
).__class__.__name__ == 'CumulativeDistributionNetwork':
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
elif ot.NegativeBinomial().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
distribution = ot.NegativeBinomial()
dimension = distribution.getDimension()
if dimension == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
elif dimension == 2:
distribution.setDescription(['$x_1$', '$x_2$'])
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
pdf_graph = ot.Graph('PDF graph', 'x', 'PDF', True, 'topright')
cdf_graph = ot.Graph('CDF graph', 'x', 'CDF', True, 'bottomright')
palette = ot.Drawable.BuildDefaultPalette(10)
for i, v in enumerate([(2,0.83),(4,0.66),(20,0.33)]):
r, p = v
distribution = ot.NegativeBinomial(r, p)
pdf_curve = distribution.drawPDF().getDrawable(0)
cdf_curve = distribution.drawCDF().getDrawable(0)
pdf_curve.setColor(palette[i])
cdf_curve.setColor(palette[i])
pdf_curve.setLegend('r,p={},{}'.format(r, p))
cdf_curve.setLegend('r,p={},{}'.format(r, p))
pdf_graph.add(pdf_curve)
cdf_graph.add(cdf_curve)
fig = plt.figure(figsize=(10, 4))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=True)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=True)
fig.suptitle('NegativeBinomial(r,p)')
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.NegativeBinomial().__class__.__name__ == 'ComposedDistribution'):
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif (ot.NegativeBinomial().__class__.__name__ ==
'CumulativeDistributionNetwork'):
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
else:
distribution = ot.NegativeBinomial()
dimension = distribution.getDimension()
if dimension <= 2:
if distribution.getDimension() == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
else:
distribution.setDescription(['$x_1$', '$x_2$'])
pdf_graph = distribution.drawPDF()