def test_distribution(self):
seq70 = self.data
MovingAverage(seq70, Distribution("B", 0, 16, 0.5), BeginEnd=True)
MovingAverage(seq70,
Distribution("B", 0, 16, 0.5),
BeginEnd=True,
Output="Residual")
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_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_to_histogram(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
h = d.simulate(1000)
d2 = ToDistribution(h)
assert h and d2
h2 = ToHistogram(d2)
assert h2
assert h == h2
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_to_histogram(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
h = d.simulate(1000)
d2 = ToDistribution(h)
assert h and d2
h2 = ToHistogram(d2)
assert h2
assert h == h2
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_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(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 create_sequence_data(self):
seq66 = Sequences(get_shared_data("laricio_date66.seq"))
seq69 = MovingAverage(VariableScaling(seq66, 3, 100),
Distribution("B", 0, 6, 0.5),
BeginEnd=True,
Output="Residual")
return seq69
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_output_display_detail():
d1 = Distribution("B", 2, 19, 0.5)
print Display(d1)
print Display(d1, Detail=1)
print Display(d1, Detail=2)
try:
print Display(d1, Detail=3)
assert False
except:
assert ValueError
def test_output_display_viewpoint_data():
from openalea.stat_tool import Vectors
v = Vectors('data/vectors.vec')
Display(v, ViewPoint='Data')
Display(v)
#ddata viewpoint is dedicated to Vectors or sequences
try:
d1 = Distribution("B", 2, 19, 0.5)
Display(d1, ViewPoint="Data")
assert False
except:
assert True
def test_multimodial(self):
"""multinomial not yet implemented"""
try:
Distribution("MULTINOMIAL", 10)
assert False
except:
assert True
try:
d = Multinomial()
assert False
except:
assert True
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_neg_binomial(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == 0.5
assert d.get_parameter == 1
assert d.get_ident() == 3
d = NegativeBinomial(0, 1, 0.5)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_uniform(self):
d = Distribution("UNIFORM", 0, 10)
assert list(d.simulate(1000))
assert d.get_sup_bound == 10
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == -1
assert d.get_ident() == 4
d = Uniform(0, 10)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_poisson(self):
d = Distribution("POISSON", 0, 2)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == 2
assert d.get_ident() == 2
d = Poisson(0, 2)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_neg_binomial(self):
d = Distribution("NEGATIVE_BINOMIAL", 0, 1, 0.5)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == 0.5
assert d.get_parameter == 1
assert d.get_ident() == 3
d = NegativeBinomial(0, 1, 0.5)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_poisson(self):
d = Distribution("POISSON", 0, 2)
assert list(d.simulate(1000))
assert d.get_sup_bound == -1
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == 2
assert d.get_ident() == 2
d = Poisson(0, 2)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
def test_uniform(self):
d = Distribution("UNIFORM", 0, 10)
assert list(d.simulate(1000))
assert d.get_sup_bound == 10
assert d.get_inf_bound == 0
assert d.get_probability == -1
assert d.get_parameter == -1
assert d.get_ident() == 4
d = Uniform(0, 10)
assert list(d.simulate(1000))
m = d.simulate(1000).extract_model()
assert isinstance(m, _DiscreteParametricModel)
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 test_output_plot_viewpoint_survival():
d1 = Distribution("B", 2, 19, 0.5)
Plot(d1)
Plot(d1, ViewPoint='Survival')
def test_output_display_viewpoint_survival():
d1 = Distribution("B", 2, 19, 0.5)
print Display(d1, ViewPoint="Survival")
convol1 = Convolution("data/convolution1.conv")
print Display(convol1, ViewPoint="Survival")
def test_simulate_distribution():
c = Distribution("data/distribution1.dist")
s1 = Simulate(c, 1000)
assert s1
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
def test_distribution(self):
data = self.correlation
ComputeWhiteNoiseCorrelation(data, Distribution("BINOMIAL", 0, 4, 0.5))
def build_data(self):
d1 = Binomial(0, 10, 0.5)
d2 = Distribution("BINOMIAL", 0, 10, 0.5)
assert d1 == d2
return d1
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_constructor_failure(self):
try:
_h = Distribution("Whatever", 1, 1)
assert False
except KeyError:
assert True
