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 test1():
#ORIGINAL AML stops HERE
d11 = Binomial(0, 12, 0.1)
d12 = Binomial(2, 13, 0.6)
d13 = Binomial(3, 15, 0.9)
d21 = Poisson(0, 25.0)
d22 = Poisson(0, 5.0)
d23 = Poisson(0, 0.2)
m = _MultivariateMixture([0.1, 0.2, 0.7],
[[d11, d21], [d12, d22], [d13, d23]])
print m
#m2 = _MultivariateMixture("mixture_mv1.mixt")
#print m2
#print "Egalite des melanges construits par liste ",\
# "de distributions et par fichiers : ", str(str(m)==str(m2))
#m = _MultivariateMixture("mixture_mv_nonparam.mixt")
# print m
print "Simulation de melanges multivaries : "
v = m.simulate(5000)
print v
Plot(m, variable=1, Title="Simulated mixture")
print "Estimation de melanges multivaries ", \
# "d'apres un modele initial : "
m_estim_model = v.mixture_estimation(m, 100, [True, True])
extracted_mixture = m_estim_model.extract_mixture(1)
extracted_mixture.old_plot(variable=1, Title="Marginal distribution")
Plot(m_estim_model, variable=1, Title="Estimated mixture")
print "Estimation de melanges multivaries ", \
"d'apres un nombre de composantes : "
m_estim_nbcomp = v.mixture_estimation(2, 100, [True, True])
m_estim_nbcomp.plot(variable=1, Title="Estimated mixture")
clust_entropy = m_estim_nbcomp.cluster_data(v, True)
clust_plain = m_estim_nbcomp.cluster_data(v, False)
def test_plot_convolution(self):
convol1 = Convolution("data/convolution1.conv")
if DISABLE_PLOT == False:
Plot(convol1.extract_elementary(1), convol1.extract_elementary(2))
histo_b2 = Histogram("data/nothofagus_antarctica_bud_2.his")
histo_s2 = Histogram("data/nothofagus_antarctica_shoot_2.his")
convol31 = Estimate(Shift(histo_s2, 1),
"CONVOLUTION",
Estimate(histo_b2, "NP"),
NbIteration=100,
Estimator="PenalizedLikelihood",
Weight=0.5)
if DISABLE_PLOT == False:
Plot(convol31.extract_elementary(1))
def _test_plot_distribution_set(self):
d1 = Distribution("B", 2, 18, 0.5)
d2 = Distribution("NB", 10, 10, 0.5)
d3 = Distribution("U", 10, 20)
if DISABLE_PLOT == False:
Plot(d1, d2, d3)
d1.old_plot()
def test_merge_histo(self):
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"))
meri = Merge(meri1, meri2, meri3, meri4, meri5)
assert meri
meri_bis = meri1.merge([meri2, meri3, meri4, meri5])
assert meri_bis
assert str(meri)==str(meri_bis)
Plot(meri)
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_merge_vectors(self):
v1 = self.int_vector_data()
b = [[2, 78, 45],
[6, 2, 122],
[3, 4, 31],]
v2 = Vectors(b)
v = Merge(v1, v2)
assert v
a = v1.merge([v2])
b = v2.merge([v1])
assert str(a)==str(b)
assert str(a)==str(v)
Plot(v)
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_output_plot_viewpoint_survival():
d1 = Distribution("B", 2, 19, 0.5)
Plot(d1)
Plot(d1, ViewPoint='Survival')
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)