torch-choice v1.0.4a

What's Changed

• Enables users to control for the initialization strategies of different coefficients. by @TianyuDu in #33
  The model convergence can be sensitive to the initial weights of coefficients. We added a weight_initialization keyword argument to the ConditionalLogitModel class and {nest, item}_weight_initialization keyword arguments to the NestedLogitModel class. These arguments allow users to control the initialization of coefficients (e.g., initialize to zeros, uniform random, or Gaussian random). Please see the notebook here: ./tutorial/coefficient_initialization.ipynb or this documentation page for demonstrations.

Full Changelog: v1.0.3...v1.0.4a