Date: 2026-06-01T23:13:54+00:00

Actual source code: linear.c

      1: #include <../src/ml/regressor/impls/linear/linearimpl.h>

      3: const char *const PetscRegressorLinearTypes[] = {"ols", "lasso", "ridge", "RegressorLinearType", "REGRESSOR_LINEAR_", NULL};

      5: static PetscErrorCode PetscRegressorLinearSetFitIntercept_Linear(PetscRegressor regressor, PetscBool flg)
      6: {
      7:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

      9:   PetscFunctionBegin;
     10:   linear->fit_intercept = flg;
     11:   PetscFunctionReturn(PETSC_SUCCESS);
     12: }

     14: static PetscErrorCode PetscRegressorLinearSetType_Linear(PetscRegressor regressor, PetscRegressorLinearType type)
     15: {
     16:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     18:   PetscFunctionBegin;
     19:   linear->type = type;
     20:   PetscFunctionReturn(PETSC_SUCCESS);
     21: }

     23: static PetscErrorCode PetscRegressorLinearGetType_Linear(PetscRegressor regressor, PetscRegressorLinearType *type)
     24: {
     25:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     27:   PetscFunctionBegin;
     28:   *type = linear->type;
     29:   PetscFunctionReturn(PETSC_SUCCESS);
     30: }

     32: static PetscErrorCode PetscRegressorLinearGetIntercept_Linear(PetscRegressor regressor, PetscScalar *intercept)
     33: {
     34:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     36:   PetscFunctionBegin;
     37:   *intercept = linear->intercept;
     38:   PetscFunctionReturn(PETSC_SUCCESS);
     39: }

     41: static PetscErrorCode PetscRegressorLinearGetCoefficients_Linear(PetscRegressor regressor, Vec *coefficients)
     42: {
     43:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     45:   PetscFunctionBegin;
     46:   *coefficients = linear->coefficients;
     47:   PetscFunctionReturn(PETSC_SUCCESS);
     48: }

     50: static PetscErrorCode PetscRegressorLinearGetKSP_Linear(PetscRegressor regressor, KSP *ksp)
     51: {
     52:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     54:   PetscFunctionBegin;
     55:   if (!linear->ksp) {
     56:     PetscCall(KSPCreate(PetscObjectComm((PetscObject)regressor), &linear->ksp));
     57:     PetscCall(PetscObjectIncrementTabLevel((PetscObject)linear->ksp, (PetscObject)regressor, 1));
     58:     PetscCall(PetscObjectSetOptions((PetscObject)linear->ksp, ((PetscObject)regressor)->options));
     59:   }
     60:   *ksp = linear->ksp;
     61:   PetscFunctionReturn(PETSC_SUCCESS);
     62: }

     64: static PetscErrorCode PetscRegressorLinearSetUseKSP_Linear(PetscRegressor regressor, PetscBool flg)
     65: {
     66:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

     68:   PetscFunctionBegin;
     69:   linear->use_ksp = flg;
     70:   PetscFunctionReturn(PETSC_SUCCESS);
     71: }

     73: static PetscErrorCode EvaluateResidual(Tao tao, Vec x, Vec f, void *ptr)
     74: {
     75:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)ptr;

     77:   PetscFunctionBegin;
     78:   /* Evaluate f = A * x - b */
     79:   PetscCall(MatMult(linear->X, x, f));
     80:   PetscCall(VecAXPY(f, -1.0, linear->rhs));
     81:   PetscFunctionReturn(PETSC_SUCCESS);
     82: }

     84: static PetscErrorCode EvaluateJacobian(Tao tao, Vec x, Mat J, Mat Jpre, void *ptr)
     85: {
     86:   /* The TAOBRGN API expects us to pass an EvaluateJacobian() routine to it, but in this case it is a dummy function.
     87:      Denoting our data matrix as X, for linear least squares J[m][n] = df[m]/dx[n] = X[m][n]. */
     88:   PetscFunctionBegin;
     89:   PetscFunctionReturn(PETSC_SUCCESS);
     90: }

     92: static PetscErrorCode PetscRegressorSetUp_Linear(PetscRegressor regressor)
     93: {
     94:   PetscInt               M, N;
     95:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;
     96:   KSP                    ksp;
     97:   Tao                    tao;

     99:   PetscFunctionBegin;
    100:   PetscCall(MatGetSize(regressor->training, &M, &N));

    102:   if (linear->fit_intercept) {
    103:     /* If we are fitting the intercept, we need to make A a composite matrix using MATCENTERING to preserve sparsity.
    104:      * Though there might be some cases we don't want to do this for, depending on what kind of matrix is passed in. (Probably bad idea for dense?)
    105:      * We will also need to ensure that the right-hand side passed to the KSP is also mean-centered, since we
    106:      * intend to compute the intercept separately from regression coefficients (that is, we will not be adding a
    107:      * column of all 1s to our design matrix). */
    108:     PetscCall(MatCreateCentering(PetscObjectComm((PetscObject)regressor), PETSC_DECIDE, M, &linear->C));
    109:     PetscCall(MatCreate(PetscObjectComm((PetscObject)regressor), &linear->X));
    110:     PetscCall(MatSetSizes(linear->X, PETSC_DECIDE, PETSC_DECIDE, M, N));
    111:     PetscCall(MatSetType(linear->X, MATCOMPOSITE));
    112:     PetscCall(MatCompositeSetType(linear->X, MAT_COMPOSITE_MULTIPLICATIVE));
    113:     PetscCall(MatCompositeAddMat(linear->X, regressor->training));
    114:     PetscCall(MatCompositeAddMat(linear->X, linear->C));
    115:     PetscCall(VecDuplicate(regressor->target, &linear->rhs));
    116:     PetscCall(MatMult(linear->C, regressor->target, linear->rhs));
    117:   } else {
    118:     // When not fitting intercept, we assume that the input data are already centered.
    119:     linear->X   = regressor->training;
    120:     linear->rhs = regressor->target;

    122:     PetscCall(PetscObjectReference((PetscObject)linear->X));
    123:     PetscCall(PetscObjectReference((PetscObject)linear->rhs));
    124:   }

    126:   PetscCall(VecDestroy(&linear->coefficients));

    128:   if (linear->use_ksp) {
    129:     PetscCheck(linear->type == REGRESSOR_LINEAR_OLS, PetscObjectComm((PetscObject)regressor), PETSC_ERR_ARG_WRONGSTATE, "KSP can be used to fit a linear regressor only when its type is OLS");

    131:     if (!linear->ksp) PetscCall(PetscRegressorLinearGetKSP(regressor, &linear->ksp));
    132:     ksp = linear->ksp;

    134:     PetscCall(MatCreateVecs(linear->X, &linear->coefficients, NULL));
    135:     /* Set up the KSP to solve the least squares problem (without solving for intercept, as this is done separately) using KSPLSQR. */
    136:     PetscCall(MatCreateNormal(linear->X, &linear->XtX));
    137:     PetscCall(KSPSetType(ksp, KSPLSQR));
    138:     PetscCall(KSPSetOperators(ksp, linear->X, linear->XtX));
    139:     PetscCall(KSPSetOptionsPrefix(ksp, ((PetscObject)regressor)->prefix));
    140:     PetscCall(KSPAppendOptionsPrefix(ksp, "regressor_linear_"));
    141:     PetscCall(KSPSetFromOptions(ksp));
    142:   } else {
    143:     /* Note: Currently implementation creates TAO inside of implementations.
    144:       * Thus, all the prefix jobs are done inside implementations, not in interface */
    145:     const char *prefix;

    147:     if (!regressor->tao) PetscCall(PetscRegressorGetTao(regressor, &tao));

    149:     PetscCall(MatCreateVecs(linear->X, &linear->coefficients, &linear->residual));
    150:     /* Set up the TAO object to solve the (regularized) least squares problem (without solving for intercept, which is done separately) using TAOBRGN. */
    151:     PetscCall(TaoSetType(tao, TAOBRGN));
    152:     PetscCall(TaoSetSolution(tao, linear->coefficients));
    153:     PetscCall(TaoSetResidualRoutine(tao, linear->residual, EvaluateResidual, linear));
    154:     PetscCall(TaoSetJacobianResidualRoutine(tao, linear->X, linear->X, EvaluateJacobian, linear));
    155:     // Set the regularization type and weight for the BRGN as linear->type dictates:
    156:     // TODO BRGN needs to be BRGNSetRegularizationType
    157:     // PetscOptionsSetValue no longer works due to functioning prefix system
    158:     PetscCall(PetscRegressorGetOptionsPrefix(regressor, &prefix));
    159:     PetscCall(TaoSetOptionsPrefix(regressor->tao, prefix));
    160:     PetscCall(TaoAppendOptionsPrefix(tao, "regressor_linear_"));
    161:     switch (linear->type) {
    162:     case REGRESSOR_LINEAR_OLS:
    163:       regressor->regularizer_weight = 0.0; // OLS, by definition, uses a regularizer weight of 0
    164:       break;
    165:     case REGRESSOR_LINEAR_LASSO:
    166:       PetscCall(TaoBRGNSetRegularizationType(regressor->tao, TAOBRGN_REGULARIZATION_L1DICT));
    167:       break;
    168:     case REGRESSOR_LINEAR_RIDGE:
    169:       PetscCall(TaoBRGNSetRegularizationType(regressor->tao, TAOBRGN_REGULARIZATION_L2PURE));
    170:       break;
    171:     default:
    172:       break;
    173:     }
    174:     if (!linear->use_ksp) PetscCall(TaoBRGNSetRegularizerWeight(tao, regressor->regularizer_weight));
    175:     PetscCall(TaoSetFromOptions(tao));
    176:   }
    177:   PetscFunctionReturn(PETSC_SUCCESS);
    178: }

    180: static PetscErrorCode PetscRegressorReset_Linear(PetscRegressor regressor)
    181: {
    182:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

    184:   PetscFunctionBegin;
    185:   /* Destroy the PETSc objects associated with the linear regressor implementation. */
    186:   linear->ksp_its     = 0;
    187:   linear->ksp_tot_its = 0;

    189:   PetscCall(MatDestroy(&linear->X));
    190:   PetscCall(MatDestroy(&linear->XtX));
    191:   PetscCall(MatDestroy(&linear->C));
    192:   PetscCall(KSPDestroy(&linear->ksp));
    193:   PetscCall(VecDestroy(&linear->coefficients));
    194:   PetscCall(VecDestroy(&linear->rhs));
    195:   PetscCall(VecDestroy(&linear->residual));
    196:   PetscFunctionReturn(PETSC_SUCCESS);
    197: }

    199: static PetscErrorCode PetscRegressorDestroy_Linear(PetscRegressor regressor)
    200: {
    201:   PetscFunctionBegin;
    202:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetFitIntercept_C", NULL));
    203:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetUseKSP_C", NULL));
    204:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetKSP_C", NULL));
    205:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetCoefficients_C", NULL));
    206:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetIntercept_C", NULL));
    207:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetType_C", NULL));
    208:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetType_C", NULL));
    209:   PetscCall(PetscRegressorReset_Linear(regressor));
    210:   PetscCall(PetscFree(regressor->data));
    211:   PetscFunctionReturn(PETSC_SUCCESS);
    212: }

    214: /*@
    215:   PetscRegressorLinearSetFitIntercept - Set a flag to indicate that the intercept (also known as the "bias" or "offset") should
    216:   be calculated; data are assumed to be mean-centered if false.

    218:   Logically Collective

    220:   Input Parameters:
    221: + regressor - the `PetscRegressor` context
    222: - flg       - `PETSC_TRUE` to calculate the intercept, `PETSC_FALSE` to assume mean-centered data (default is `PETSC_TRUE`)

    224:   Level: intermediate

    226:   Options Database Key:
    227: . regressor_linear_fit_intercept (true|false) - fit the intercept

    229:   Note:
    230:   If the user indicates that the intercept should not be calculated, the intercept will be set to zero.

    232: .seealso: `PetscRegressor`, `PetscRegressorFit()`
    233: @*/
    234: PetscErrorCode PetscRegressorLinearSetFitIntercept(PetscRegressor regressor, PetscBool flg)
    235: {
    236:   PetscFunctionBegin;
    237:   /* TODO: Add companion PetscRegressorLinearGetFitIntercept(), and put it in the .seealso: */
    240:   PetscTryMethod(regressor, "PetscRegressorLinearSetFitIntercept_C", (PetscRegressor, PetscBool), (regressor, flg));
    241:   PetscFunctionReturn(PETSC_SUCCESS);
    242: }

    244: /*@
    245:   PetscRegressorLinearSetUseKSP - Set a flag to indicate that a `KSP` object, instead of a `Tao` one, should be used
    246:   to fit the linear regressor

    248:   Logically Collective

    250:   Input Parameters:
    251: + regressor - the `PetscRegressor` context
    252: - flg       - `PETSC_TRUE` to use a `KSP`, `PETSC_FALSE` to use a `Tao` object (default is false)

    254:   Options Database Key:
    255: . regressor_linear_use_ksp (true|false) - use `KSP`

    257:   Level: intermediate

    259:   Notes:
    260:   `KSPLSQR` with no preconditioner is used to solve the normal equations by default.

    262:   For sequential `MATSEQAIJ` sparse matrices QR factorization a `PCType` of `PCQR` can be used to solve the least-squares system with a `MatSolverType` of
    263:   `MATSOLVERSPQR`, using, for example,
    264: .vb
    265:   -ksp_type none -pc_type qr -pc_factor_mat_solver_type sp
    266: .ve
    267:   if centering, `PetscRegressorLinearSetFitIntercept()`, is not used.

    269:   Developer Notes:
    270:   It should be possible to use Cholesky (and any other preconditioners) to solve the normal equations.

    272:   It should be possible to use QR if centering is used. See ml/regressor/ex1.c and ex2.c

    274:   It should be possible to use dense SVD `PCSVD` and dense qr directly on the rectangular matrix to solve the least squares problem.

    276:   Adding the above support seems to require a refactorization of how least squares problems are solved with PETSc in `KSPLSQR`

    278: .seealso: `PetscRegressor`, `PetscRegressorLinearGetKSP()`, `KSPLSQR`, `PCQR`, `MATSOLVERSPQR`, `MatSolverType`, `MATSEQDENSE`, `PCSVD`
    279: @*/
    280: PetscErrorCode PetscRegressorLinearSetUseKSP(PetscRegressor regressor, PetscBool flg)
    281: {
    282:   PetscFunctionBegin;
    283:   /* TODO: Add companion PetscRegressorLinearGetUseKSP(), and put it in the .seealso: */
    286:   PetscTryMethod(regressor, "PetscRegressorLinearSetUseKSP_C", (PetscRegressor, PetscBool), (regressor, flg));
    287:   PetscFunctionReturn(PETSC_SUCCESS);
    288: }

    290: static PetscErrorCode PetscRegressorSetFromOptions_Linear(PetscRegressor regressor, PetscOptionItems PetscOptionsObject)
    291: {
    292:   PetscBool              set, flg = PETSC_FALSE;
    293:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

    295:   PetscFunctionBegin;
    296:   PetscOptionsHeadBegin(PetscOptionsObject, "PetscRegressor options for linear regressors");
    297:   PetscCall(PetscOptionsBool("-regressor_linear_fit_intercept", "Calculate intercept for linear model", "PetscRegressorLinearSetFitIntercept", flg, &flg, &set));
    298:   if (set) PetscCall(PetscRegressorLinearSetFitIntercept(regressor, flg));
    299:   PetscCall(PetscOptionsBool("-regressor_linear_use_ksp", "Use KSP instead of TAO for linear model fitting problem", "PetscRegressorLinearSetFitIntercept", flg, &flg, &set));
    300:   if (set) PetscCall(PetscRegressorLinearSetUseKSP(regressor, flg));
    301:   PetscCall(PetscOptionsEnum("-regressor_linear_type", "Linear regression method", "PetscRegressorLinearTypes", PetscRegressorLinearTypes, (PetscEnum)linear->type, (PetscEnum *)&linear->type, NULL));
    302:   PetscOptionsHeadEnd();
    303:   PetscFunctionReturn(PETSC_SUCCESS);
    304: }

    306: static PetscErrorCode PetscRegressorView_Linear(PetscRegressor regressor, PetscViewer viewer)
    307: {
    308:   PetscBool              isascii;
    309:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

    311:   PetscFunctionBegin;
    312:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
    313:   if (isascii) {
    314:     PetscCall(PetscViewerASCIIPushTab(viewer));
    315:     PetscCall(PetscViewerASCIIPrintf(viewer, "PetscRegressor Linear Type: %s\n", PetscRegressorLinearTypes[linear->type]));
    316:     if (linear->ksp) {
    317:       PetscCall(KSPView(linear->ksp, viewer));
    318:       PetscCall(PetscViewerASCIIPrintf(viewer, "total KSP iterations: %" PetscInt_FMT "\n", linear->ksp_tot_its));
    319:     }
    320:     if (linear->fit_intercept) PetscCall(PetscViewerASCIIPrintf(viewer, "Intercept=%g\n", (double)linear->intercept));
    321:     PetscCall(PetscViewerASCIIPopTab(viewer));
    322:   }
    323:   PetscFunctionReturn(PETSC_SUCCESS);
    324: }

    326: /*@
    327:   PetscRegressorLinearGetKSP - Returns the `KSP` context for a `PETSCREGRESSORLINEAR` object.

    329:   Not Collective, but if the `PetscRegressor` is parallel, then the `KSP` object is parallel

    331:   Input Parameter:
    332: . regressor - the `PetscRegressor` context

    334:   Output Parameter:
    335: . ksp - the `KSP` context

    337:   Level: beginner

    339:   Note:
    340:   This routine will always return a `KSP`, but, depending on the type of the linear regressor and the options that are set, the regressor may actually use a `Tao` object instead of this `KSP`.

    342: .seealso: `PetscRegressorGetTao()`
    343: @*/
    344: PetscErrorCode PetscRegressorLinearGetKSP(PetscRegressor regressor, KSP *ksp)
    345: {
    346:   PetscFunctionBegin;
    348:   PetscAssertPointer(ksp, 2);
    349:   PetscUseMethod(regressor, "PetscRegressorLinearGetKSP_C", (PetscRegressor, KSP *), (regressor, ksp));
    350:   PetscFunctionReturn(PETSC_SUCCESS);
    351: }

    353: /*@
    354:   PetscRegressorLinearGetCoefficients - Get a vector of the fitted coefficients from a linear regression model

    356:   Not Collective but the vector is parallel

    358:   Input Parameter:
    359: . regressor - the `PetscRegressor` context

    361:   Output Parameter:
    362: . coefficients - the vector of the coefficients

    364:   Level: beginner

    366: .seealso: `PetscRegressor`, `PetscRegressorLinearGetIntercept()`, `PETSCREGRESSORLINEAR`, `Vec`
    367: @*/
    368: PETSC_EXTERN PetscErrorCode PetscRegressorLinearGetCoefficients(PetscRegressor regressor, Vec *coefficients)
    369: {
    370:   PetscFunctionBegin;
    372:   PetscAssertPointer(coefficients, 2);
    373:   PetscUseMethod(regressor, "PetscRegressorLinearGetCoefficients_C", (PetscRegressor, Vec *), (regressor, coefficients));
    374:   PetscFunctionReturn(PETSC_SUCCESS);
    375: }

    377: /*@
    378:   PetscRegressorLinearGetIntercept - Get the intercept from a linear regression model

    380:   Not Collective

    382:   Input Parameter:
    383: . regressor - the `PetscRegressor` context

    385:   Output Parameter:
    386: . intercept - the intercept

    388:   Level: beginner

    390: .seealso: `PetscRegressor`, `PetscRegressorLinearSetFitIntercept()`, `PetscRegressorLinearGetCoefficients()`, `PETSCREGRESSORLINEAR`
    391: @*/
    392: PETSC_EXTERN PetscErrorCode PetscRegressorLinearGetIntercept(PetscRegressor regressor, PetscScalar *intercept)
    393: {
    394:   PetscFunctionBegin;
    396:   PetscAssertPointer(intercept, 2);
    397:   PetscUseMethod(regressor, "PetscRegressorLinearGetIntercept_C", (PetscRegressor, PetscScalar *), (regressor, intercept));
    398:   PetscFunctionReturn(PETSC_SUCCESS);
    399: }

    401: /*@C
    402:   PetscRegressorLinearSetType - Sets the type of linear regression to be performed

    404:   Logically Collective

    406:   Input Parameters:
    407: + regressor - the `PetscRegressor` context (should be of type `PETSCREGRESSORLINEAR`)
    408: - type      - a known linear regression method

    410:   Options Database Key:
    411: . -regressor_linear_type - Sets the linear regression method; use -help for a list of available methods
    412:    (for instance "-regressor_linear_type ols" or "-regressor_linear_type lasso")

    414:   Level: intermediate

    416: .seealso: `PetscRegressorLinearGetType()`, `PetscRegressorLinearType`, `PetscRegressorSetType()`, `REGRESSOR_LINEAR_OLS`,
    417:           `REGRESSOR_LINEAR_LASSO`, `REGRESSOR_LINEAR_RIDGE`
    418: @*/
    419: PetscErrorCode PetscRegressorLinearSetType(PetscRegressor regressor, PetscRegressorLinearType type)
    420: {
    421:   PetscFunctionBegin;
    424:   PetscTryMethod(regressor, "PetscRegressorLinearSetType_C", (PetscRegressor, PetscRegressorLinearType), (regressor, type));
    425:   PetscFunctionReturn(PETSC_SUCCESS);
    426: }

    428: /*@
    429:   PetscRegressorLinearGetType - Return the type for the `PETSCREGRESSORLINEAR` solver

    431:   Input Parameter:
    432: . regressor - the `PetscRegressor` solver context

    434:   Output Parameter:
    435: . type - `PETSCREGRESSORLINEAR` type

    437:   Level: advanced

    439: .seealso: `PetscRegressor`, `PETSCREGRESSORLINEAR`, `PetscRegressorLinearSetType()`, `PetscRegressorLinearType`
    440: @*/
    441: PetscErrorCode PetscRegressorLinearGetType(PetscRegressor regressor, PetscRegressorLinearType *type)
    442: {
    443:   PetscFunctionBegin;
    445:   PetscAssertPointer(type, 2);
    446:   PetscUseMethod(regressor, "PetscRegressorLinearGetType_C", (PetscRegressor, PetscRegressorLinearType *), (regressor, type));
    447:   PetscFunctionReturn(PETSC_SUCCESS);
    448: }

    450: static PetscErrorCode PetscRegressorFit_Linear(PetscRegressor regressor)
    451: {
    452:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;
    453:   KSP                    ksp;
    454:   PetscScalar            target_mean, *column_means_global, *column_means_local, column_means_dot_coefficients;
    455:   Vec                    column_means;
    456:   PetscInt               m, N, istart, i, kspits;

    458:   PetscFunctionBegin;
    459:   if (linear->use_ksp) PetscCall(PetscRegressorLinearGetKSP(regressor, &linear->ksp));
    460:   ksp = linear->ksp;

    462:   /* Solve the least-squares problem (previously set up in PetscRegressorSetUp_Linear()) without finding the intercept. */
    463:   if (linear->use_ksp) {
    464:     PetscCall(KSPSolve(ksp, linear->rhs, linear->coefficients));
    465:     PetscCall(KSPGetIterationNumber(ksp, &kspits));
    466:     linear->ksp_its += kspits;
    467:     linear->ksp_tot_its += kspits;
    468:   } else {
    469:     PetscCall(TaoSolve(regressor->tao));
    470:   }

    472:   /* Calculate the intercept. */
    473:   if (linear->fit_intercept) {
    474:     PetscCall(MatGetSize(regressor->training, NULL, &N));
    475:     PetscCall(PetscMalloc1(N, &column_means_global));
    476:     PetscCall(VecMean(regressor->target, &target_mean));
    477:     /* We need the means of all columns of regressor->training, placed into a Vec compatible with linear->coefficients.
    478:      * Note the potential scalability issue: MatGetColumnMeans() computes means of ALL columns. */
    479:     PetscCall(MatGetColumnMeans(regressor->training, column_means_global));
    480:     /* TODO: Calculation of the Vec and matrix column means should probably go into the SetUp phase, and also be placed
    481:      *       into a routine that is callable from outside of PetscRegressorFit_Linear(), because we'll want to do the same
    482:      *       thing for other models, such as ridge and LASSO regression, and should avoid code duplication.
    483:      *       What we are calling 'target_mean' and 'column_means' should be stashed in the base linear regressor struct,
    484:      *       and perhaps renamed to make it clear they are offsets that should be applied (though the current naming
    485:      *       makes sense since it makes it clear where these come from.) */
    486:     PetscCall(VecDuplicate(linear->coefficients, &column_means));
    487:     PetscCall(VecGetLocalSize(column_means, &m));
    488:     PetscCall(VecGetOwnershipRange(column_means, &istart, NULL));
    489:     PetscCall(VecGetArrayWrite(column_means, &column_means_local));
    490:     for (i = 0; i < m; i++) column_means_local[i] = column_means_global[istart + i];
    491:     PetscCall(VecRestoreArrayWrite(column_means, &column_means_local));
    492:     PetscCall(VecDot(column_means, linear->coefficients, &column_means_dot_coefficients));
    493:     PetscCall(VecDestroy(&column_means));
    494:     PetscCall(PetscFree(column_means_global));
    495:     linear->intercept = target_mean - column_means_dot_coefficients;
    496:   } else {
    497:     linear->intercept = 0.0;
    498:   }
    499:   PetscFunctionReturn(PETSC_SUCCESS);
    500: }

    502: static PetscErrorCode PetscRegressorPredict_Linear(PetscRegressor regressor, Mat X, Vec y)
    503: {
    504:   PetscRegressor_Linear *linear = (PetscRegressor_Linear *)regressor->data;

    506:   PetscFunctionBegin;
    507:   PetscCall(MatMult(X, linear->coefficients, y));
    508:   PetscCall(VecShift(y, linear->intercept));
    509:   PetscFunctionReturn(PETSC_SUCCESS);
    510: }

    512: /*MC
    513:      PETSCREGRESSORLINEAR - Linear regression model (ordinary least squares or regularized variants)

    515:    Options Database:
    516: +  -regressor_linear_fit_intercept - Calculate the intercept for the linear model
    517: -  -regressor_linear_use_ksp       - Use `KSP` instead of `Tao` for linear model fitting (non-regularized variants only)

    519:    Level: beginner

    521:    Notes:
    522:    By "linear" we mean that the model is linear in its coefficients, but not necessarily in its input features.
    523:    One can use the linear regressor to fit polynomial functions by training the model with a design matrix that
    524:    is a nonlinear function of the input data.

    526:    This is the default regressor in `PetscRegressor`.

    528: .seealso: `PetscRegressorCreate()`, `PetscRegressor`, `PetscRegressorSetType()`
    529: M*/
    530: PETSC_EXTERN PetscErrorCode PetscRegressorCreate_Linear(PetscRegressor regressor)
    531: {
    532:   PetscRegressor_Linear *linear;

    534:   PetscFunctionBegin;
    535:   PetscCall(PetscNew(&linear));
    536:   regressor->data = (void *)linear;

    538:   regressor->ops->setup          = PetscRegressorSetUp_Linear;
    539:   regressor->ops->reset          = PetscRegressorReset_Linear;
    540:   regressor->ops->destroy        = PetscRegressorDestroy_Linear;
    541:   regressor->ops->setfromoptions = PetscRegressorSetFromOptions_Linear;
    542:   regressor->ops->view           = PetscRegressorView_Linear;
    543:   regressor->ops->fit            = PetscRegressorFit_Linear;
    544:   regressor->ops->predict        = PetscRegressorPredict_Linear;

    546:   linear->intercept     = 0.0;
    547:   linear->fit_intercept = PETSC_TRUE;  /* Default to calculating the intercept. */
    548:   linear->use_ksp       = PETSC_FALSE; /* Do not default to using KSP for solving the model-fitting problem (use TAO instead). */
    549:   linear->type          = REGRESSOR_LINEAR_OLS;
    550:   /* Above, manually set the default linear regressor type.
    551:        We don't use PetscRegressorLinearSetType() here, because that expects the SetUp event to already have happened. */

    553:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetFitIntercept_C", PetscRegressorLinearSetFitIntercept_Linear));
    554:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetUseKSP_C", PetscRegressorLinearSetUseKSP_Linear));
    555:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetKSP_C", PetscRegressorLinearGetKSP_Linear));
    556:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetCoefficients_C", PetscRegressorLinearGetCoefficients_Linear));
    557:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetIntercept_C", PetscRegressorLinearGetIntercept_Linear));
    558:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearSetType_C", PetscRegressorLinearSetType_Linear));
    559:   PetscCall(PetscObjectComposeFunction((PetscObject)regressor, "PetscRegressorLinearGetType_C", PetscRegressorLinearGetType_Linear));
    560:   PetscFunctionReturn(PETSC_SUCCESS);
    561: }