Searched refs:training (Results 1 – 7 of 7) sorted by relevance
| /petsc/src/ml/regressor/interface/ |
| H A D | regressor.c | 78 regressor->training = NULL; in PetscRegressorCreate()
261 PetscCall(MatDestroy(®ressor->training)); in PetscRegressorFit()
262 regressor->training = X; in PetscRegressorFit()
326 PetscCall(MatDestroy(®ressor->training)); in PetscRegressorReset()
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| /petsc/src/ml/regressor/impls/linear/ |
| H A D | linear.c | 100 PetscCall(MatGetSize(regressor->training, &M, &N)); in PetscRegressorSetUp_Linear()
113 PetscCall(MatCompositeAddMat(linear->X, regressor->training)); in PetscRegressorSetUp_Linear()
119 linear->X = regressor->training; in PetscRegressorSetUp_Linear()
474 PetscCall(MatGetSize(regressor->training, NULL, &N)); in PetscRegressorFit_Linear()
479 PetscCall(MatGetColumnMeans(regressor->training, column_means_global)); in PetscRegressorFit_Linear()
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| /petsc/include/petsc/private/ |
| H A D | regressorimpl.h | 29 Mat training; /* Matrix holding the training data set */ member
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| /petsc/doc/manual/ |
| H A D | regressor.md | 28 …or can be used to make predictions, the model must be fitted using an initial set of training data.
31 Fitting (or "training") a model is a relatively computationally intensive task that generally invol…
40 In the simplest usage of a regressor, the user provides a training (or "design") matrix
98 where `X` is training data, and `y` is target values.
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| /petsc/src/binding/petsc4py/src/petsc4py/PETSc/ |
| H A D | Regressor.pyx | 124 The matrix of training data
126 The vector of target values from the training dataset
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| /petsc/doc/community/meetings/2023/ |
| H A D | index.md | 231 relatively new training documentation like the "AMD lab notes" to enable
857 for solving ODEs and training neural ODEs.
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| /petsc/doc/ |
| H A D | petsc.bib | 24104 title = {Scalable training of L1-regularized log-linear models},
25689 @InProceedings{ blum.rivest:training,
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