def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError("Cannot combine models that do not agree on categoricity")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
means = np.array(one.means) + np.array(two.means)
sizes = 1 / (1 / np.sqrt(np.array(one.sizes)) + 1 / np.sqrt(np.array(two.means)))**2
return MeanSizeModel(one.xx_is_categorical, xx, means, sizes)
def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError(
"Cannot combine models that do not agree on categoricity")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
means = np.array(one.means) + np.array(two.means)
sizes = 1 / (1 / np.sqrt(np.array(one.sizes)) +
1 / np.sqrt(np.array(two.means)))**2
return MeanSizeModel(one.xx_is_categorical, xx, means, sizes)
def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError("Cannot combine models that do not agree on categoricity")
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
conditionals = []
for ii in range(len(xx)):
conditionals.append(one.get_conditional(xx[ii]).convolve(two.get_conditional(xx[ii])).rescale())
return SplineModel(one.xx_is_categorical, xx, conditionals, True)
def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError("Cannot combine models that do not agree on categoricity")
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
if np.all(np.array(one.locations) == 0):
return two
if not isinstance(two, DeltaModel):
raise ValueError("Combining non-zero delta with non-delta is not yet implemented.")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
return DeltaModel(one.xx_is_categorical, xx, map(lambda ii: one.locations[ii] + two.locations[ii], range(len(xx))), 1)
def combine(one, two):
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
allxx = set(one.xx) | set(two.xx)
allxx = np.array(allxx)
midpts = (allxx[1:] + allxx[:-1]) / 2
onemodel = one.model.recategorize_x(map(model.get_bin_at, midpts), range(1, len(allxx)))
twomodel = two.model.recategorize_x(map(model.get_bin_at, midpts), range(1, len(allxx)))
model = Model.combine([onemodel, twomodel], [1, 1])
return BinModel(allxx, model, True)
def combine(one, two):
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
allxx = set(one.xx) | set(two.xx)
allxx = np.array(allxx)
midpts = (allxx[1:] + allxx[:-1]) / 2
onemodel = one.model.recategorize_x(map(model.get_bin_at, midpts),
range(1, len(allxx)))
twomodel = two.model.recategorize_x(map(model.get_bin_at, midpts),
range(1, len(allxx)))
model = Model.combine([onemodel, twomodel], [1, 1])
return BinModel(allxx, model, True)
def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError("Cannot combine models that do not agree on categoricity")
if one.yy_is_categorical or two.yy_is_categorical:
raise ValueError("Cannot combine categorical y models")
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
yy_one_min = min(one.yy)
yy_one_max = max(one.yy)
yy_two_min = min(two.yy)
yy_two_max = max(two.yy)
yy_step = min(median(diff(sort(one.yy))), median(diff(sort(two.yy))))
yy_one = arange(yy_one_min, yy_one_max, yy_step)
if yy_one[-1] + yy_step == yy_one_max:
yy_one = append(yy_one, [yy_one_max])
yy_two = arange(yy_two_min, yy_two_max, yy_step)
if yy_two[-1] + yy_step == yy_two_max:
yy_two = append(yy_two, [yy_two_max])
if not array_equal(yy_one, one.yy):
one = one.interpolate_y(yy_one)
if not array_equal(yy_two, two.yy):
two = two.interpolate_y(yy_two)
pp_one = one.lin_p()
pp_two = two.lin_p()
newpp = ones((len(xx), len(yy_one) + len(yy_two) - 1))
for ii in range(len(xx)):
newpp[ii,] = convolve(pp_one[ii,], pp_two[ii,])
newpp[ii,] = newpp[ii,] / sum(newpp[ii,]) # Scale
yy = append(arange(min(yy_one) + min(yy_two), max(yy_one) + max(yy_two), yy_step), [max(yy_one) + max(yy_two)])
return DDPModel('ddp1', 'combine', one.xx_is_categorical, xx, False, yy, newpp, scaled=True)
def combine(one, two):
if one.xx_is_categorical != two.xx_is_categorical:
raise ValueError("Cannot combine models that do not agree on categoricity")
if one.yy_is_categorical or two.yy_is_categorical:
raise ValueError("Cannot combine categorical y models")
if not one.scaled or not two.scaled:
raise ValueError("Cannot combine unscaled models")
(one, two, xx) = UnivariateModel.intersect_x(one, two)
yy_one_min = min(one.yy)
yy_one_max = max(one.yy)
yy_two_min = min(two.yy)
yy_two_max = max(two.yy)
yy_step = min(median(diff(sort(one.yy))), median(diff(sort(two.yy))))
yy_one = arange(yy_one_min, yy_one_max, yy_step)
if yy_one[-1] + yy_step == yy_one_max:
yy_one = append(yy_one, [yy_one_max])
yy_two = arange(yy_two_min, yy_two_max, yy_step)
if yy_two[-1] + yy_step == yy_two_max:
yy_two = append(yy_two, [yy_two_max])
if not array_equal(yy_one, one.yy):
one = one.interpolate_y(yy_one)
if not array_equal(yy_two, two.yy):
two = two.interpolate_y(yy_two)
pp_one = one.lin_p()
pp_two = two.lin_p()
newpp = ones((len(xx), len(yy_one) + len(yy_two) - 1))
for ii in range(len(xx)):
newpp[ii,] = convolve(pp_one[ii,], pp_two[ii,])
newpp[ii,] = newpp[ii,] / sum(newpp[ii,]) # Scale
yy = append(arange(min(yy_one) + min(yy_two), max(yy_one) + max(yy_two), yy_step), [max(yy_one) + max(yy_two)])
return DDPModel('ddp1', 'combine', one.xx_is_categorical, xx, False, yy, newpp, scaled=True)
