def test_compound_two_distribution(self):
"""test to be checked"""
cdist1 = Compound("data/compound1.cd")
chisto1 = Simulate(cdist1, 200)
cdist2 = Estimate(chisto1, "COMPOUND",
ExtractDistribution(cdist1, "Sum"),
"Sum",
InitialDistribution=\
ExtractDistribution(cdist1, "Elementary"))
# If we call the method directly, we need to provide
# the default values and perform a conversion.
# Default is LIKELIHOOD -1 -1.0 SECOND_DIFFERENCE ZERO, which
# corresponds to 0, -1,-1,1,0
# In addition because the type is 's', the 2 distributions
# must be reversed.
cdist3 = chisto1.compound_estimation1(
ExtractDistribution(cdist1, "Elementary"),
ExtractDistribution(cdist1, "Sum"),
's', _stat_tool.LIKELIHOOD, -1, -1.,
_stat_tool.SECOND_DIFFERENCE,
_stat_tool.ZERO)
assert str(cdist2) == str(cdist3)
def test_plot_mixture_data(self):
mixt1 = Mixture(0.6, Distribution("B", 2, 18, 0.5), 0.4,
Distribution("NB", 10, 10, 0.5))
mixt_histo1 = Simulate(mixt1, 200)
if DISABLE_PLOT == False:
mixt1.plot()
mixt_histo1.plot()
def test_plot_mixture_data(self):
mixt1 = Mixture(0.6, Distribution("B", 2, 18, 0.5), 0.4,
Distribution("NB", 10, 10, 0.5))
mixt_histo1 = Simulate(mixt1, 200)
if DISABLE_PLOT == False:
mixt1.plot()
mixt_histo1.plot()
def test_plot_survival(self):
d1 = Distribution("B", 2, 18, 0.5)
if DISABLE_PLOT == False:
d1.plot(ViewPoint="Survival")
histo1 = Simulate(d1, 200)
if DISABLE_PLOT == False:
histo1.plot(ViewPoint="Survival")
def test_plot_survival(self):
d1 = Distribution("B", 2, 18, 0.5)
if DISABLE_PLOT == False:
d1.plot(ViewPoint="Survival")
histo1 = Simulate(d1, 200)
if DISABLE_PLOT == False:
histo1.plot(ViewPoint="Survival")
def test_merge(self):
mixt1 = Mixture(0.6, Distribution("B", 2, 18, 0.5),
0.4, Distribution("NB", 10, 10, 0.5))
mixt_histo1 = Simulate(mixt1, 200)
histo10 = mixt_histo1.extract_component(1)
histo11 = mixt_histo1.extract_component(2)
histo12 = Merge(histo10, histo11)
assert histo12
Plot(histo12)
def test_simulation_sequences():
#TODO The Simulate call does not work. Requires a proper input.
try:
from openalea.sequence_analysis import Sequences
seq = Sequences([1, 2, 3, 4])
Simulate(seq, 100)
except:
assert True
def test_plot_parametric_model(self):
dist1 = Distribution("NB", 0, 3.5, 0.3)
histo1 = Simulate(dist1, 200)
if DISABLE_PLOT == False:
Plot(histo1)
dist2 = Estimate(histo1, "NB", MinInfBound=0, InfBoundStatus="Fixed")
if DISABLE_PLOT == False:
Plot(dist2)
def test_simulate_compound():
c = Compound("data/compound1.cd")
s1 = Simulate(c, 1000)
assert s1
def test_simulate_convolution():
c = Convolution("data/convolution1.conv")
s1 = Simulate(c, 1000)
assert s1
def test_simulate_mixture():
m = Mixture("data/mixture1.mixt")
s1 = Simulate(m, 1000)
assert s1
def test_plot_convolution_data(self):
convol1 = Convolution("data/convolution1.conv")
convol_histo1 = Simulate(convol1, 200)
if DISABLE_PLOT == False:
convol_histo1.plot()
def test_compound(self):
comp = Compound("data/compound1.cd")
comp_histo = Simulate(comp, 200)
_histo = ExtractHistogram(comp_histo, "Sum")
_histo = ExtractHistogram(comp_histo, "Elementary")
def test_convolution(self):
convol = Convolution("data/convolution1.conv")
convol_histo = Simulate(convol, 200)
_histo = ExtractHistogram(convol_histo, "Elementary", 1)
_histo = ExtractHistogram(convol_histo, "Convolution")
def test():
"""Mixture tests from exploratory.aml
#
# Frequency distributions
#
# Objective: Analyzing the number of nodes of growth units in selected architectural
# position considering the respective roles of preformation and neoformation,
#
# Methods: comparison tests, one-way variance analysis,
# estimation of finite mixture of distributions.
#
# Wild cherry tree: number of nodes per growth unit (GU)
#
# Data: Dominique Fournier
#
# meri1.his: order 1,
# meri1.his: order 2,
# meri1.his: order 3, GU 1,
# meri1.his: order 3, GU 2,
# meri5.his: short shoots.
#
#
# Poplar: number of nodes per growth unit
#
# Data: Yves Caraglio and Herve Rey
#
# peup1.his: order 2,
# peup2.his: order 3,
# peup3.his: order 4,
# peup4.his: order 5,
# peup5.his: order 3, GU 4,
# peup6.his: order 3, acrotony.
#
#########################################################################
"""
plot.DISABLE_PLOT = DISABLE_PLOT
meri1 = Histogram(get_shared_data("meri1.his"))
meri2 = Histogram(get_shared_data("meri2.his"))
meri3 = Histogram(get_shared_data("meri3.his"))
meri4 = Histogram(get_shared_data("meri4.his"))
meri5 = Histogram(get_shared_data("meri5.his"))
Plot(meri1, meri2, meri3, meri4, meri5)
Compare(meri1, meri2, meri3, meri4, meri5, "N")
ComparisonTest("F", meri1, meri2)
ComparisonTest("T", meri1, meri2)
ComparisonTest("W", meri1, meri2)
ComparisonTest("F", meri1, meri3)
ComparisonTest("T", meri1, meri3)
ComparisonTest("W", meri1, meri3)
# Estimation of a mixture of two distributions assuming a first
# sub-population of GUs made only of a preformed part and a second
# sub-population made of both a preformed part and a neoformed part
_mixt1 = Estimate(meri2, "MIXTURE", "B", "B")
meri = Merge(meri1, meri2, meri3, meri4, meri5)
# model selection approach: estimation of both the mixture parameters and
# the number of components"""
mixt2 = Estimate(meri,
"MIXTURE",
"B",
"B",
"B",
"B",
NbComponent="Estimated")
mixt2 = Estimate(meri, "MIXTURE", "NB", "NB")
Plot(mixt2)
Plot(ExtractDistribution(mixt2, "Mixture"))
Plot(ExtractDistribution(mixt2, "Component", 1),
ExtractDistribution(mixt2, "Component", 2))
Display(mixt2)
_mixt_data = ExtractData(mixt2)
dist5 = Estimate(meri5, "BINOMIAL")
Display(dist5, Detail=2)
Plot(dist5)
histo5 = Simulate(dist5, 100)
Display(histo5, Detail=2)
Plot(histo5)
peup1 = Histogram(get_shared_data("peup1.his"))
peup2 = Histogram(get_shared_data("peup2.his"))
peup3 = Histogram(get_shared_data("peup3.his"))
peup4 = Histogram(get_shared_data("peup4.his"))
peup5 = Histogram(get_shared_data("peup5.his"))
peup6 = Histogram(get_shared_data("peup6.his"))
_mixt10 = Estimate(peup2,
"MIXTURE",
"B",
"NB",
"NB",
"NB",
NbComponent="Estimated")
peup = Merge(peup1, peup2, peup3, peup4, peup5, peup6)
_histo1 = Shift(peup, -1)
_histo2 = Cluster(peup, "Information", 0.8)
_histo3 = Cluster(peup, "Step", 10)
histo4 = Cluster(peup, "Limit", [13, 24])
Display(histo4, Detail=2)
Plot(histo4)
_mixt11 = Estimate(peup,
"MIXTURE",
"B",
"NB",
"NB",
"NB",
NbComponent="Estimated")
_mixt11 = Estimate(peup, "MIXTURE", "B", "NB")
def test_simulate_distribution():
c = Distribution("data/distribution1.dist")
s1 = Simulate(c, 1000)
assert s1
def test_plot_convolution_data(self):
convol1 = Convolution("data/convolution1.conv")
convol_histo1 = Simulate(convol1, 200)
if DISABLE_PLOT == False:
convol_histo1.plot()
def test():
meri1 = Histogram(get_shared_data("meri1.his"))
meri2 = Histogram(get_shared_data("meri2.his"))
meri3 = Histogram(get_shared_data("meri3.his"))
meri4 = Histogram(get_shared_data("meri4.his"))
meri5 = Histogram(get_shared_data("meri5.his"))
Plot(meri1, meri2, meri3, meri4, meri5)
Compare(meri1, meri2, meri3, meri4, meri5, "N")
ComparisonTest("F", meri1, meri2)
ComparisonTest("T", meri1, meri2)
ComparisonTest("W", meri1, meri2)
ComparisonTest("F", meri1, meri3)
ComparisonTest("T", meri1, meri3)
ComparisonTest("W", meri1, meri3)
# Estimation of a mixture of two distributions assuming a first
# sub-population of GUs made only of a preformed part and a second
# sub-population made of both a preformed part and a neoformed part
_mixt1 = Estimate(meri2, "MIXTURE", "B", "B")
meri = Merge(meri1, meri2, meri3, meri4, meri5)
#model selection approach: estimation of both the mixture parameters and
# the number of components
mixt2 = Estimate(meri,
"MIXTURE",
"B",
"B",
"B",
"B",
NbComponent="Estimated")
mixt2 = Estimate(meri, "MIXTURE", "NB", "NB")
Plot(mixt2)
Plot(ExtractDistribution(mixt2, "Mixture"))
print type(ExtractDistribution(mixt2, "Component", 1))
Plot(ExtractDistribution(mixt2, "Component", 1),
ExtractDistribution(mixt2, "Component", 2))
Display(mixt2)
_mixt_data = ExtractData(mixt2)
dist5 = Estimate(meri5, "BINOMIAL")
Display(dist5, Detail=2)
Plot(dist5)
histo5 = Simulate(dist5, 100)
Display(histo5, Detail=2)
Plot(histo5)
peup1 = Histogram(get_shared_data("peup1.his"))
peup2 = Histogram(get_shared_data("peup2.his"))
peup3 = Histogram(get_shared_data("peup3.his"))
peup4 = Histogram(get_shared_data("peup4.his"))
peup5 = Histogram(get_shared_data("peup5.his"))
peup6 = Histogram(get_shared_data("peup6.his"))
_mixt10 = Estimate(peup2,
"MIXTURE",
"B",
"NB",
"NB",
"NB",
NbComponent="Estimated")
peup = Merge(peup1, peup2, peup3, peup4, peup5, peup6)
_histo1 = Shift(peup, -1)
_histo2 = Cluster(peup, "Information", 0.8)
_histo3 = Cluster(peup, "Step", 10)
histo4 = Cluster(peup, "Limit", [13, 24])
Display(histo4, Detail=2)
Plot(histo4)
_mixt11 = Estimate(peup,
"MIXTURE",
"B",
"NB",
"NB",
"NB",
NbComponent="Estimated")
_mixt11 = Estimate(peup, "MIXTURE", "B", "NB")
def test2():
"""finite mixture of discrete distributions"""
mixt1 = Mixture("data//mixture1.mixt")
mixt1 = Mixture(0.6, Distribution("B", 2, 18, 0.5), 0.4,
Distribution("NB", 10, 10, 0.5))
mixt_histo1 = Simulate(mixt1, 200)
Plot(mixt_histo1)
# extraction of histograms/frequency distributions corresponding
# to a given mixture component
# (i.e. elementary distributions which are combined by mixture)
histo10 = ExtractHistogram(mixt_histo1, "Component", 1)
histo11 = ExtractHistogram(mixt_histo1, "Component", 2)
_histo12 = Merge(histo10, histo11)
_histo13 = ExtractHistogram(mixt_histo1, "Weight")
# estimation
mixt2 = Estimate(mixt_histo1,
"MIXTURE",
"B",
"NB",
MinInfBound=0,
InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed")
_mixt_histo2 = ExtractData(mixt2)
_histo14 = ExtractHistogram(ExtractData(mixt2), "Component", 1)
_histo15 = ToHistogram(ExtractDistribution(mixt2, "Component", 1))
# estimation and selection of the number of components
meri1 = Histogram(get_shared_data("meri1.his"))
meri2 = Histogram(get_shared_data("meri2.his"))
meri3 = Histogram(get_shared_data("meri3.his"))
meri4 = Histogram(get_shared_data("meri4.his"))
meri5 = Histogram(get_shared_data("meri5.his"))
#mixt3 = Estimate(meri1, "MIXTURE", Distribution("B", 6, 7, 0.5), "B")
mixt3 = Estimate(meri1, "MIXTURE", "B", "B")
Plot(mixt3)
# NbComponent="Fixed" (default) / "Estimated"
# Penalty="AIC"/ "AICc" / "BIC" / "BICc" (default), option
# valide if NbComponent="Estimated"
meri = Merge(meri1, meri2, meri3, meri4, meri5)
mixt2 = Estimate(meri,
"MIXTURE",
"B",
"B",
"B",
"B",
NbComponent="Estimated",
Penalty="BIC")
Display(mixt2, Detail=2)
dist_mixt = ExtractDistribution(mixt2, "Mixture")
Plot(dist_mixt)
