コード例 #1
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    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))
コード例 #2
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    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)
コード例 #3
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    def test_extract_data(self):
        """todo : check if this test makes sense"""

        s = self.simulate()
        #e = Estimate(s, "Compound",  Binomial(2, 5, 0.5), "Sum")
        d = s.extract_sum()
        assert d
        _eprime = Estimate(s, "COMPOUND", Binomial(0, 10, 0.5), "Sum")
コード例 #4
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    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))
コード例 #5
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    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)
コード例 #6
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    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
コード例 #7
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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")
コード例 #8
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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")
コード例 #9
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 def test_estimate(self):
     sim = self.simulate()
     # 3 Binomial distribution to match th original data
     est = Estimate(sim, "Mixture", "B", "B", "B")
     est.plot()
コード例 #10
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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")
コード例 #11
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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)
コード例 #12
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 def test_estimate(self):
     sim = self.simulate()
     # 3 Binomial distribution to match th original data
     est = Estimate(sim, "Mixture", "B", "B", "B")
     est.plot()