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")
def test_tc(modelfile, distfile):
with open(modelfile, "r") as modelfp:
model = DDPModel()
model.init_from(modelfp, ',')
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()
