示例#1
0
def uniform_doseless(start, end, height=None):
    scaled = False
    if height is None:
        height = 1 / (end - start)
        scaled = True

    conditional = SplineModelConditional()
    conditional.add_segment(SplineModel.neginf, start, [SplineModel.neginf])
    conditional.add_segment(start, end, [math.log(height)])
    conditional.add_segment(end, SplineModel.posinf, [SplineModel.neginf])

    return SplineModel(True, [''], [conditional], scaled)
示例#2
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    def apply_as_distribution(self, model):
        if model.kind() == 'ddp_model':
            if len(model.xx) != len(self.yy):
                raise ValueError("Wrong number of values.")

            for ii in range(len(model.xx)):
                if model.get_xx()[ii] != self.get_yy()[ii]:
                    raise ValueError("Wrong values in distribution.")

            vv = self.lin_p().dot(model.lin_p())

            return DDPModel('ddp1',
                            'distribution',
                            self.xx_is_categorical,
                            self.get_xx(),
                            model.yy_is_categorical,
                            model.get_yy(),
                            vv,
                            scaled=self.scaled)

        elif model.kind() == 'spline_model':
            if len(model.xx) != len(self.yy):
                raise ValueError("Wrong number of values.")

            for ii in range(len(model.xx)):
                if model.get_xx()[ii] not in self.get_yy():
                    raise ValueError("Wrong values in distribution.")

            pp = self.lin_p()
            conditionals = []

            for ii in range(len(self.xx)):
                conds = []

                for jj in range(len(model.xx)):
                    original = model.get_conditional(model.get_xx()[jj])
                    conditional = SplineModelConditional(
                        original.y0s, original.y1s, original.coeffs)
                    conditional.scale(
                        pp[ii, self.get_yy().index(model.get_xx()[jj])])
                    conds.append(conditional)

                conditionals.append(
                    SplineModelConditional.approximate_sum(conds))

            return SplineModel(self.xx_is_categorical,
                               self.get_xx(),
                               conditionals,
                               scaled=self.scaled)

        else:
            raise ValueError("Unknown model type in apply_as_distribution")
示例#3
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def test_exponential():
    fp = StringIO.StringIO()
    fp.write("dpc1,mean\n")
    fp.write("0,1\n")
    fp.seek(0)

    output = StringIO.StringIO()

    FeaturesInterpreter.init_from_feature_file(SplineModel(), fp, ',',
                                               (0, 100)).write(output, ',')
    print output.getvalue()

    fp.close()
    output.close()
def test_gtc(modelfile, distfile):
    with open(modelfile, "r") as modelfp:
        model = SplineModel()
        FeaturesInterpreter.init_from_feature_file(model, modelfp, ',', (SplineModel.neginf, SplineModel.posinf))
        
    with open(distfile, "r") as distfp:
        dist = DistributionModel()
        dist.init_from(distfp, ',')

    output = StringIO.StringIO()

    dist.apply_as_distribution(model).write(output, ',')
    print output.getvalue()

    output.close()
示例#5
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def test_non_gaussian():
    fp = StringIO.StringIO()
    fp.write("dpc1,mean,.025,.975\n")
    fp.write("0,0,-1,2\n")
    fp.seek(0)

    output = StringIO.StringIO()

    FeaturesInterpreter.init_from_feature_file(SplineModel(), fp, ',',
                                               ('-inf', 'inf')).write(
                                                   output, ',')
    print output.getvalue()

    fp.close()
    output.close()
示例#6
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def polynomial(lowbound, highbound, betas, covas, num=40):
    betas = np.array(betas)
    covas = np.mat(covas)

    if covas.shape[0] != covas.shape[1] and len(betas) != covas.shape[0]:
        return "Error: Please provide a complete covariance matrix."
    if np.any(linalg.eig(covas)[0] < 0):
        return "Error: Covariance matrix is not positive definite."

    xx = np.linspace(lowbound, highbound, num=num)
    xxs = {}
    for x in xx:
        xvec = np.mat([[1, x, x**2, x**3][0:len(betas)]])
        serr = np.sqrt(xvec * covas * np.transpose(xvec))
        xxs[x] = (betas.dot(np.squeeze(np.asarray(xvec))), serr[0,0])

    return SplineModel.create_gaussian(xxs, xx, False)
示例#7
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import sys, StringIO
sys.path.append("../lib/models")

from model import Model
from ddp_model import DDPModel
from spline_model import SplineModel

mod1 = DDPModel.create_lin([0, 1], dict(control=[.5, .5], treatment=[0, 1]))
mod2 = DDPModel.create_lin([0, 1], dict(control=[.5, .5], treatment=[1, 0]))

def printmod(mod):
    output = StringIO.StringIO()
    mod.write(output, ',')
    print output.getvalue()

printmod(Model.combine([mod1, mod2], [.5, .5]))
printmod(Model.combine([mod1, mod2], [.25, 2]))

mod1 = SplineModel.create_gaussian(dict(control=(0, 1), treatment=(1, 1e-3)))
mod2 = SplineModel.create_gaussian(dict(control=(0, 1), treatment=(0, 1e-3)))

def printmod2(mod):
    output = StringIO.StringIO()
    mod.write_gaussian_plus(output, ',')
    print output.getvalue()

printmod2(Model.combine([mod1, mod2], [.5, .5]))
printmod2(Model.combine([mod1, mod2], [.25, 2]))
示例#8
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from model import Model
from ddp_model import DDPModel
from spline_model import SplineModel

mod1 = DDPModel.create_lin([0, 1], dict(control=[.5, .5], treatment=[0, 1]))
mod2 = DDPModel.create_lin([0, 1], dict(control=[.5, .5], treatment=[1, 0]))


def printmod(mod):
    output = StringIO.StringIO()
    mod.write(output, ',')
    print output.getvalue()


printmod(Model.combine([mod1, mod2], [.5, .5]))
printmod(Model.combine([mod1, mod2], [.25, 2]))

mod1 = SplineModel.create_gaussian(dict(control=(0, 1), treatment=(1, 1e-3)))
mod2 = SplineModel.create_gaussian(dict(control=(0, 1), treatment=(0, 1e-3)))


def printmod2(mod):
    output = StringIO.StringIO()
    mod.write_gaussian_plus(output, ',')
    print output.getvalue()


printmod2(Model.combine([mod1, mod2], [.5, .5]))
printmod2(Model.combine([mod1, mod2], [.25, 2]))