Semiparametric validation jupyter-examples #35
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This commit unifies the sample generators for all mixture types (NMM, NMV, NV) by integrating canonical form support directly into the main generator function. Previously, each mixture type had separate classical and canonical generators. Now, the generator checks mixture_form inside the function and switches behavior accordingly. In canonical form, the beta parameter is not used. This simplifies the generator API and reduces code duplication. - Updated nm_generator.py, nmv_generator.py, nv_generator.py - Remembered `mixture_form` in AbstractMixture class - Updated tests and notebook to use the new unified API BREAKING CHANGE: canonical_generate() methods were removed; use generate() instead.
Desiment
changed the title
* added base StatisticalProcedure protocol * replaced NDArray[np.float64] with np.ndarray
* Rename NVSemiParametricGEstimationPostWidder → NVEstimationDensityPW * Rename SemiParametricGEstimationPostWidder → NMVEstimationDensityPW * Rename folders in src/procedures * Rename tests/algorithms → tests/procedures lgorithms/semiparametric_sigma_estimation/__init__.py -> tests/procedures/nm_procedures/semiparametric_sigma_estimation/__init__.py
In the previous commit "NVSemiParametricGEstimationPostWidder" were mistakenly replaced by "NMVEstimationDensityPW"
- Updated `moment`, `pdf`, `cdf`, and `logpdf` methods to support both scalar and list inputs for efficient batch computation. - Extracted shared evaluation logic (e.g., `moment`, `pdf`, `cdf`, `logpdf`) into the `AbstractMixtures` base class to eliminate code duplication across subclasses. - Centralized input validation, RQMC integration, and distribution handling within the base class for better maintainability. - Fixed type annotations to align with abstract method signatures and resolve `mypy` type-checking issues. These changes improve code clarity, enable vectorized evaluations, and make future extensions easier to implement.
feat: dynamic integrator selection
feat: unify mixture generators and support canonical forms
feat: add batch support and refactor mixture evaluation logic
…ures Api/renaming of statistical procedures
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Fix characteristic_function_normal for NMSemiParametricGEstimation