def test_extract_data(self):
"""todo : check if this test makes sense"""
h = Histogram(str(get_shared_data("meri1.his")))
e = h.estimate_nonparametric()
assert e
def test_extract_data(self):
"""run and test the extract_data methods"""
h = Histogram(str(get_shared_data("meri2.his")))
m = h.estimate_DiscreteMixture("B", "NB")
d = m.extract_data()
assert d
def test_extract_data(self):
"""run and test the extract_data methods"""
h = Histogram(str(get_shared_data( "meri2.his")))
m = h.estimate_DiscreteMixture("B", "NB")
d = m.extract_data()
assert d
def test_mixture_1(self):
distributions = ["B", "NB", "NB", "NB"]
h = Histogram(get_shared_data( "peup2.his"))
m1 = h.estimate_mixture(distributions, NbComponent="Estimated")
assert m1
types = []
for d in distributions:
temp = distribution_identifier_type[d]
types.append(temp)
c = h.mixture_estimation2(types, 0, True, True,
likelihood_penalty_type['AIC'])
assert str(c)==str(m1)
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_mixture(self):
h = Histogram(get_shared_data("meri2.his"))
mixt = h.estimate_mixture(["B", "NB"])
assert ExtractHistogram(mixt, "Weight") == \
mixt.extract_weight()
assert ExtractHistogram(mixt, "Mixture") == \
mixt.extract_mixture()
assert ExtractHistogram(mixt, "Component", 1) == \
mixt.extract_component(1)
assert ExtractHistogram(mixt, "Component", 2) == \
mixt.extract_component(2)
try:
ExtractHistogram(mixt, "Component", 3)
assert False
except: # Bas distrubition index
assert True
def test_constructors(self):
h = Histogram([1, 2, 3, 4, 5, 6, 1, 2, 3])
assert h
# from histogram
dist = Distribution(h)
assert dist
#from parametric model
pm = _DiscreteParametricModel(h)
dist = Distribution(pm)
assert dist
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_convolution(self):
elementary = Histogram("data/nothofagus_antarctica_bud_2.his")
total = Histogram("data/nothofagus_antarctica_shoot_2.his")
convol1 = Estimate(Shift(total, 1), "CONVOLUTION",
Estimate(elementary, "NP"),
NbIteration=100,
Estimator="PenalizedLikelihood",
Weight=0.5)
convol2 = total.shift(1).estimate_convolution(
elementary.estimate_nonparametric(),
NbIteration=100,
Estimator="PenalizedLikelihood",
Weight=0.5)
assert convol1 and convol2
assert convol1 == convol2
def get_mixture_2(self):
"""create another mixture data"""
h = Histogram(get_shared_data("meri2.his"))
m = h.estimate_mixture("B", "NB")
return m
def test_mixture_2(self):
h = Histogram(get_shared_data( "peup2.his"))
m2 = h.estimate_mixture([Binomial(0, 10, 0.5), "NB"])
assert m2
def __init__(self):
self.meri1 = Histogram(get_shared_data("meri1.his"))
self.meri2 = Histogram(get_shared_data("meri1.his"))
self.meri3 = Histogram(get_shared_data("meri1.his"))
def test_binomial(self):
"""BINOMIAL Distribution"""
h = Histogram(get_shared_data("meri5.his"))
assert h.estimate_parametric('B')
def test_constructor_from_integers(self):
v = Histogram(0, 1, 2, 3)
assert v
def test_container(self):
""" container / iterator"""
h = Histogram(str(get_shared_data("meri1.his")))
assert h[0] == 0
assert h[10] == 1
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)
def hist_data(self):
h = Histogram(get_shared_data( "meri2.his"))
return h
def test_histo_extract_data(self):
h = Histogram(get_shared_data("meri2.his"))
mixt = h.estimate_mixture(["B", "NB"])
assert ExtractData(mixt)
assert mixt.extract_data() == ExtractData(mixt)
def get_mixture_2(self):
"""create another mixture data"""
h = Histogram(get_shared_data("meri2.his"))
m = h.estimate_mixture("B", "NB")
return m
def test_value_select_float(self):
meri1 = Histogram(get_shared_data("meri1.his"))
# note keep=False is equivalent to Mode=keep,is this correct ?
assert str(ValueSelect(meri1, 0, 10, Mode="Keep"))==\
str(meri1.value_select( min=0, max=10, keep=True))
def test_fit_histogram(self):
meri5 = Histogram(get_shared_data( "meri5.his"))
dist1 = Fit(meri5, Distribution("B", 0, 10, 0.437879))
dist2 = meri5.fit(Distribution("B", 0, 10, 0.437879))
assert str(dist1)==str(dist2)
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 build_data(self):
v = Histogram([0, 1, 2, 3])
assert v
return v
def create_data(self):
return Histogram(str(get_shared_data('fagus1.his')))
def test_constructor_from_list(self):
v = Histogram([0, 1, 2, 3])
assert v
def test_nonparametric(self):
h = Histogram(get_shared_data("meri1.his"))
e = h.estimate_nonparametric()
assert e
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_nb(self):
"""NegativeBinomial"""
h = Histogram(get_shared_data("peup2.his"))
assert h.estimate_parametric('NB')
def build_data(self):
data = Histogram("data/fagus1.his")
return data
def test_getters(self):
dist = Distribution(Histogram([1, 1, 1, 2, 2, 2, 3, 3, 3]))
assert dist.get_mean == 2
assert 0.3333 < dist.get_max < 0.3334
assert dist.get_mean == 2
assert 0.6666 < dist.get_variance < 0.6667
