import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
factory = ot.BetaFactory()
ref = factory.build()
dimension = ref.getDimension()
if dimension <= 2:
sample = ref.getSample(50)
distribution = factory.build(sample)
if dimension == 1:
distribution.setDescription(['$t$'])
pdf_graph = distribution.drawPDF(256)
cloud = ot.Cloud(sample, ot.NumericalSample(sample.getSize(), 1))
cloud.setColor('blue')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(111)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
else:
sample = ref.getSample(500)
distribution.setDescription(['$t_0$', '$t_1$'])
pdf_graph = distribution.drawPDF([256] * 2)
cloud = ot.Cloud(sample)
cloud.setColor('red')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
continuousSampleCollection[i] = continuousDistributionCollection[
i].getSample(size)
continuousSampleCollection[i].setName(
continuousDistributionCollection[i].getName())
sampleCollection[i] = continuousSampleCollection[i]
for i in range(discreteDistributionNumber):
discreteSampleCollection[i] = discreteDistributionCollection[i].getSample(
size)
discreteSampleCollection[i].setName(
discreteDistributionCollection[i].getName())
sampleCollection[continuousDistributionNumber +
i] = discreteSampleCollection[i]
factoryCollection = ot.DistributionFactoryCollection(3)
factoryCollection[0] = ot.UniformFactory()
factoryCollection[1] = ot.BetaFactory()
factoryCollection[2] = ot.NormalFactory()
aSample = ot.Uniform(-1.5, 2.5).getSample(size)
model, best_bic = ot.FittingTest.BestModelBIC(aSample, factoryCollection)
print("best model BIC=", repr(model))
model, best_result = ot.FittingTest.BestModelKolmogorov(
aSample, factoryCollection)
print("best model Kolmogorov=", repr(model))
# BIC adequation
resultBIC = ot.SquareMatrix(distributionNumber)
for i in range(distributionNumber):
for j in range(distributionNumber):
value = ot.FittingTest.BIC(sampleCollection[i],
distributionCollection[j], 0)
resultBIC[i, j] = value
print("Q90_SS = ",Q90_SS)
print("")
print("Q99_SS = ",Q99_SS)
print("")
mean_sample = sample_Y.computeMean()[0]
standardDeviation_sample = np.sqrt(sample_Y.computeCovariance()[0,0])
# for i in range(N-1):
# print(format(str('{0:.3f}'.format(asample_Y[i]))))
################
### Loi Beta ###
################
fittedRes = ot.BetaFactory().buildEstimator(sample_YF)
Beta = fittedRes.getDistribution()
Beta_PDF = Beta.drawPDF(-10.,55.,251)
Beta_PDF.setLegends('PC meta-modele : '+str(N)+' realisations')
Beta_draw = Beta_PDF.getDrawable(0)
Beta_draw.setLegend('PC meta-modele : '+str(N)+' realisations')
viewer.View(Beta_draw)
Mean_Beta = Beta.getMean()
Q90_Beta = Beta.computeQuantile(0.90)
Q99_Beta = Beta.computeQuantile(0.99)
print("")
print("Mean_Beta = ",Mean_Beta)
print("")
print("Q90_Beta = ",Q90_Beta)
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
# %%
# Create a sample from a continuous distribution
distribution = ot.Beta(2.0, 2.0, 0.0, 1.)
sample = distribution.getSample(1000)
graph = ot.UserDefined(sample).drawCDF()
view = viewer.View(graph)
# %%
# **1. Specify the model only**
# %%
# Create the list of distribution estimators
factories = [ot.BetaFactory(), ot.TriangularFactory()]
# %%
# Rank the continuous models by the Lilliefors p-values:
estimated_distribution, test_result = ot.FittingTest.BestModelLilliefors(
sample, factories)
test_result
# %%
# Rank the continuous models wrt the BIC criteria (no test result):
ot.FittingTest.BestModelBIC(sample, factories)
# %%
# Rank the continuous models wrt the AIC criteria (no test result)
ot.FittingTest.BestModelAIC(sample, factories)
print(sample_X)
asample_Y = np.array(sample_Y).flatten()
asample_YF = (asample_Y-F0)*100/F0
mean_sample = sample_Y.computeMean()[0]
standardDeviation_sample = np.sqrt(sample_Y.computeCovariance()[0,0])
# for i in range(N-1):
# print(format(str('{0:.3f}'.format(asample_Y[i]))))
################
### Loi Beta ###
################
fittedRes = ot.BetaFactory().buildEstimator(sample_YF)
Beta = fittedRes.getDistribution()
Beta_PDF = Beta.drawPDF(-10.,55.,251)
Beta_PDF.setLegends('PC meta-modele : '+str(N)+' realisations')
Beta_draw = Beta_PDF.getDrawable(0)
Beta_draw.setLegend('PC meta-modele : '+str(N)+' realisations')
viewer.View(Beta_draw)
Mean_Beta = Beta.getMean()
Q90_Beta = Beta.computeQuantile(0.90)
Q99_Beta = Beta.computeQuantile(0.99)
print("")
print("Mean_Beta = ",Mean_Beta)
print("")
print("Q90_Beta = ",Q90_Beta)