def test_merge_correlation():
c1 = CorrelationData(1)
c2 = CorrelationData(2)
c3 = CorrelationData(3)
c = Merge(c1,c2,c3)
c_bis = c1.merge([c2,c3])
assert str(c)==str(c_bis)
c.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_merge_tops():
t1 = TopsData()
t2 = TopsData()
t = Merge(t1, t2)
t.plot()
def _test_merge_semi_markov_data():
sm1 = SemiMarkovData()
sm2 = SemiMarkovData()
sm = Merge(sm1, sm2)
def test_merge_sequences():
from test_sequences import Test as sequences_data
sequences = sequences_data()
data = sequences.build_data()
assert Merge(data, data)
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)