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View Code? Open in Web Editor NEW[HELP REQUESTED] Generalized Additive Models in Python
Home Page: https://pygam.readthedocs.io
License: Apache License 2.0
[HELP REQUESTED] Generalized Additive Models in Python
Home Page: https://pygam.readthedocs.io
License: Apache License 2.0
eliminate corresponding columns and rows from U, D, Vt
see how jacques implemented his versioning in
https://github.com/jwkvam/bowtie/blob/master/bowtie/__init__.py
need to find optimal lambda vector somehow :)
Hello,
I was hoping to try out your implementation of GAM. However I am not sure how I can do that.
Thanks,
Vinod
pg 177
H is any positive semidefinite
matrix, which may be zero, but may also be used to allow lower bounds to
be imposed on the smoothing parameters, or to regularize an ill-conditioned problem.
For example, if it is required that λ1 ≥ 0.1 and λ2 ≥ 10, then we could set H =
0.1S1 + 10S2.
however, would like models to stay in main module
for the 2nd order difference penalty to make sense, the knots have to be evenly spaced.
easy.
needed for generating knots, basis functions, penalty matrices
kind of already do it...
to document, prioritized:
lol.
we dont need to compute the whole A (influence, hat) matrix for the edof estimation.
try doing just the diagonal.
I want to make predictions of time series. Can this project process time series data ?
ensure that AIC is computed right:
no constants missing from log likelihood, and deviance is defined correctly.
this is importnat to be able to compare models when data has different scales.
X_train = np.array([1,2,3,4])
y_train = np.array([2,3,4,5])
gam = LogisticGAM()
gam.fit(X_train, y_train)
I run the above codes and raised an error"AssertionError: y data is not in domain of link function"
I don't know how to use your API.
the edges are wrong
should not be allowed to fit n basis functions with less than n data-points.
one should be able to specify multiple penalties per feature, and each penalty should get its own lambda
its almost free.
this is a thing that people do.
estimated scale should go here otherwise no one will know where to find it.
class will read better if all properties that are specific to glms are self.glm_X_
not self.X_glm_
pg 164 in Wood introduces this concept. elaborated in appendix A.5
add these puppies.
any distribution might have a known scale, and the API should be abstract enough to allow this.
create a few standard penalty matrices to chose from:
woohoo!
maybe someone wants to use their own penalty
Using piecewise constant splines, no difference penalty
this will make it easy to make custom GAMs!
you should be able to penalize each feature differently, duh.
maybe even QR decomposition ?
right now its buried in the penalty matrix method.
it should be somewhere else.
want quick way of checking model.
it'll be more clear this way
would like to be able to see these per feature function
for example, logistic GAM shouldnt be allowed to fit on y in whole real line
there are some threads where people specifically request GAMs.
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