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Mar 17, 2025
* Potts variables (#14) * change ordering in alphabets * change default binarize from True to False in the load_dataset function * Sample state new chain (#17) * sample_state now does not modify in place the chains given as an input * new object for modifications in sample_state * Type hint (#18) * add return type to sample visibles and hiddens and change the docstring accordingly * changed some Chain in methods docstring to dict[str, Tensor] * UI (#19) * add print of hyperparameters at the beginning of the training * add overwrite option * check for file existence when training * change softlink to hardlink in save model rbm (#22) * add float16 as a possible dtype in scripts (#23) * Map model rework (#24) * fix concatenation wrong dim aats * add centered option fit_batch_pcd * rework map_model to ease using the code with new RBMs * rework map_model to ease using the code with new RBMs * update test for overwrite args * add map_model pt_sampling + match_args_dtype_function * update version number * fix: set gauge on all parameters (#28) * v0.2 (#29) * update installation instructions * break bash line on multiple lines * replace ref to epochs with ref to update * change TorchRBM to rbms in the doc * update rbms training tuto * update dataset tuto * remove debug print command * create EBM parent class * add sample_state method for EBM * add beta to sample_state method * change type hint map_model dict * change RBM to EBM for input/output to files * replace RBM with EBM + change pbar description to the name of the model * adapt PCD training for the EBM * fix name of args in call to sample_state * adapt PT sampling for the EBM * remove unused import * Update AIS for EBM * Move sample state gibbs implementation to RBM class. Kept it for compatibility. * add exact computation for generic EBM and RBM as special case * fix overwrite key in the tests * update version number * update tests workflow to work for develop * update doc * what's new page * link to whats_new.rst on the index page * update doc for EBM * use hidden magnetizations for the computation of the negative part of the gradient * move plot_trajectories to analysis repo and remove seaborn dependency * remove scikit learn dependency and use torch for one hot encoding * use hidden magnetization for the chains in PBRBM * fix potts initialization vbias * remove test of train without weights for proteins * ensure gauge on params init PBRBM
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See #27