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_extract(self):
"""run and test the extract methods"""
m = self.data
assert m.extract_compound() == ExtractDistribution(m, "Compound")
assert m.extract_sum() == Binomial(2, 5, 0.5)
assert m.extract_sum() == ExtractDistribution(m, "Sum")
assert m.extract_elementary() == NegativeBinomial(0, 2, 0.5)
assert m.extract_elementary() == ExtractDistribution(m, "Elementary")
def test_compound(self):
comp = self.comp()
assert ExtractDistribution(comp, "Compound") == \
comp.extract_compound()
assert ExtractDistribution(comp, "Elementary") == \
comp.extract_elementary()
assert ExtractDistribution(comp, "Sum") == \
comp.extract_sum()
def test_convolution(self):
convol = self.conv()
assert ExtractDistribution(convol, "Convolution") == \
convol.extract_convolution()
assert ExtractDistribution(convol, "Elementary", 1) == \
convol.extract_elementary(1)
assert ExtractDistribution(convol, "Elementary", 2) == \
convol.extract_elementary(2)
def test_extract(self):
"""run and test the extract methods"""
m = self.data
assert m.extract_convolution() == ExtractDistribution(m, "Convolution")
assert m.extract(1) == Binomial(0, 10, 0.5)
assert m.extract(2) == NegativeBinomial(0, 1, 0.1)
assert ExtractDistribution(m, "Elementary", 1) == Binomial(0, 10, 0.5)
assert ExtractDistribution(m, "Elementary", 2) == \
NegativeBinomial(0, 1, 0.1)
def test_extract(self):
"""run and test the extract methods"""
m = self.data
assert m.extract_weight() == ExtractDistribution(m, "Weight")
assert m.extract_mixture() == ExtractDistribution(m, "Mixture")
assert ExtractDistribution(m, "Component", 1) == Binomial(0, 12, 0.1)
assert ExtractDistribution(m, "Component", 2) == Binomial(0, 12, 0.5)
assert ExtractDistribution(m, "Component", 3) == Binomial(0, 12, 0.8)
assert m.extract_component(1) == Binomial(0, 12, 0.1)
assert m.extract_component(2) == Binomial(0, 12, 0.5)
assert m.extract_component(3) == Binomial(0, 12, 0.8)
def test_hidden_semi_markov():
seq = Sequences(get_shared_data("pin_laricio_7x.seq"))
seq_cluster = Cluster(seq, "Step", 1, 10)
hsmc60 = HiddenSemiMarkov(get_shared_data('pin_laricio_6.hsc'))
hsmc6 = Estimate(seq_cluster, "HIDDEN_SEMI-MARKOV", hsmc60)
for process in range(1, hsmc6.nb_output_process):
for state in range(0, 6):
ExtractDistribution(hsmc6, "Observation", process, state)
def test_mixture(self):
mixt = self.mixt()
assert ExtractDistribution(mixt, "Weight")
assert ExtractDistribution(mixt, "Weight") == mixt.extract_weight()
assert ExtractDistribution(mixt, "Mixture") == mixt.extract_mixture()
assert ExtractDistribution(mixt, "Component", 1) == \
mixt.extract_component(1)
assert ExtractDistribution(mixt, "Component", 2) == \
mixt.extract_component(2)
assert ExtractDistribution(mixt, "Component", 3) == \
mixt.extract_component(3)
try:
ExtractDistribution(mixt, "Component", 0)
assert False
except: # Bas distrubition index
assert True
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 test():
vec10 = Vectors("data/chene_sessile.vec")
Plot(vec10)
# plot of the pointwise averages
Plot(Regression(vec10, "MovingAverage", 1, 2, [1]))
vec95 = ValueSelect(vec10, 1, 1995)
vec96 = ValueSelect(vec10, 1, 1996)
vec97 = ValueSelect(vec10, 1, 1997)
VarianceAnalysis(vec10, 1, 2, "N")
Compare(ExtractHistogram(vec95, 2), ExtractHistogram(vec96, 2), \
ExtractHistogram(vec95, 2), "N")
Plot(ExtractHistogram(vec95, 2), ExtractHistogram(vec96, 2), \
ExtractHistogram(vec97, 2))
ContingencyTable(vec10, 1, 4)
# one-way variance analysis based on ranks
VarianceAnalysis(vec10, 1, 4, "O")
Compare(ExtractHistogram(vec95, 4), ExtractHistogram(vec96, 4), \
ExtractHistogram(vec95, 4), "O")
# looks like it is not plotted
Plot(ExtractHistogram(vec95, 4), ExtractHistogram(vec96, 4),
ExtractHistogram(vec97, 4))
Plot(ExtractHistogram(vec95, 5), ExtractHistogram(vec96, 5),
ExtractHistogram(vec97, 5))
Plot(ExtractHistogram(vec95, 6), ExtractHistogram(vec96, 6),
ExtractHistogram(vec97, 6))
vec11 = ValueSelect(vec10, 4, 1)
vec12 = ValueSelect(vec10, 4, 2)
vec13 = ValueSelect(vec10, 4, 3, 4)
Plot(ExtractHistogram(vec11, 2), ExtractHistogram(vec12, 2),
ExtractHistogram(vec13, 2))
Plot(ExtractHistogram(vec11, 5), ExtractHistogram(vec12, 5),
ExtractHistogram(vec13, 5))
mixt20 = Estimate(ExtractHistogram(vec10, 2), \
"MIXTURE", "NB", "NB", "NB", "NB", \
NbComponent="Estimated")
Display(mixt20)
Plot(mixt20)
Plot(ExtractDistribution(mixt20, "Mixture"))
_mixt21 = Estimate(ExtractHistogram(vec10, 5), \
"MIXTURE", "NB", "NB", "NB", "NB", \
NbComponent="Estimated")
vec9596 = ValueSelect(vec10, 1, 1995, 1996)
Plot(ExtractHistogram(ValueSelect(vec9596, 4, 1), 6), \
ExtractHistogram(ValueSelect(vec9596, 4, 2), 6), \
ExtractHistogram(ValueSelect(vec9596, 4, 3, 4), 6))
# linear regression
regress10 = Regression(vec10, "Linear", 5, 2)
Display(regress10)
Plot(regress10)
# nonparametric regression (loess smoother)
_regress11 = Regression(vec10, "NearestNeighbors", 5, 2, 0.3)
_regress12 = Regression(vec9596, "Linear", 5, 6)
_regress13 = Regression(vec9596, "NearestNeighbors", 5, 6, 0.5)
_vec15 = SelectVariable(vec10, [1, 3, 6], Mode="Reject")
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 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)