.. index:: single: regressor_common .. _regressor_common/0:

.. rst-class:: right

category

`regressor_common`

Shared predicates for regressor learning defaults, diagnostics, validation, dataset validation, export, and pretty-print helpers.

| Availability: | logtalk_load(regression_protocols(loader))

| Author: Paulo Moura | Version: 1:0:0 | Date: 2026-05-04

| Compilation flags: | static

| Remarks: | (none)

.. contents:: :local: :backlinks: top

Public predicates

(no local declarations; see entity ancestors if any)

Protected predicates

.. index:: regressor_diagnostics_data/2 .. _regressor_common/0::regressor_diagnostics_data/2:

regressor_diagnostics_data/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Default hook predicate for exposing diagnostics metadata from a regressor term. Importing implementations may override it when using a non-standard regressor representation.

| Compilation flags: | static

| Template: | regressor_diagnostics_data(Regressor,Diagnostics) | Mode and number of proofs: | regressor_diagnostics_data(+compound,-list(compound)) - one

.. index:: regressor_export_template/4 .. _regressor_common/0::regressor_export_template/4:

regressor_export_template/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Hook predicate that importing regressor implementations must define in order to expose the exported regressor template for a given functor.

| Compilation flags: | static

| Template: | regressor_export_template(Dataset,Regressor,Functor,Template) | Mode and number of proofs: | regressor_export_template(+object_identifier,+compound,+atom,-callable) - one

.. index:: regressor_term_template/2 .. _regressor_common/0::regressor_term_template/2:

regressor_term_template/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Hook predicate that importing regressor implementations must define in order to expose the learned regressor term template used by pretty-printing helpers.

| Compilation flags: | static

| Template: | regressor_term_template(Regressor,Template) | Mode and number of proofs: | regressor_term_template(+compound,-callable) - one

.. index:: dataset_attributes/2 .. _regressor_common/0::dataset_attributes/2:

dataset_attributes/2 ^^^^^^^^^^^^^^^^^^^^^^^^

Collects the dataset attribute declarations as Attribute-Values pairs.

| Compilation flags: | static

| Template: | dataset_attributes(Dataset,Attributes) | Mode and number of proofs: | dataset_attributes(+object_identifier,-list(pair)) - one

.. index:: dataset_examples/2 .. _regressor_common/0::dataset_examples/2:

dataset_examples/2 ^^^^^^^^^^^^^^^^^^^^^^

Collects the dataset examples as example(Id, TargetValue, AttributeValues) terms.

| Compilation flags: | static

| Template: | dataset_examples(Dataset,Examples) | Mode and number of proofs: | dataset_examples(+object_identifier,-list(compound)) - one

.. index:: check_examples/2 .. _regressor_common/0::check_examples/2:

check_examples/2 ^^^^^^^^^^^^^^^^^^^^

Validates that the collected examples list is non-empty, only contains numeric targets, and only uses declared attributes without duplicate bindings.

| Compilation flags: | static

| Template: | check_examples(Dataset,Examples) | Mode and number of proofs: | check_examples(+object_identifier,+list) - one_or_error

.. index:: print_regressor_template/1 .. _regressor_common/0::print_regressor_template/1:

print_regressor_template/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Pretty-printing helper predicate used by importing regressor implementations to show the learned regressor term template.

| Compilation flags: | static

| Template: | print_regressor_template(Regressor) | Mode and number of proofs: | print_regressor_template(+compound) - one

.. index:: base_regressor_diagnostics/6 .. _regressor_common/0::base_regressor_diagnostics/6:

base_regressor_diagnostics/6 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Builds common diagnostics metadata terms for a learned regressor and appends regressor-specific diagnostics terms.

| Compilation flags: | static

| Template: | base_regressor_diagnostics(Model,Target,TrainingExampleCount,Options,ExtraDiagnostics,Diagnostics) | Mode and number of proofs: | base_regressor_diagnostics(+atom,+atom,+integer,+list(compound),+list(compound),-list(compound)) - one

.. index:: valid_attribute_names/1 .. _regressor_common/0::valid_attribute_names/1:

valid_attribute_names/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of attribute names is a proper list of distinct atoms.

| Compilation flags: | static

| Template: | valid_attribute_names(AttributeNames) | Mode and number of proofs: | valid_attribute_names(+list(atom)) - zero_or_one

.. index:: valid_attribute_declarations/1 .. _regressor_common/0::valid_attribute_declarations/1:

valid_attribute_declarations/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of attribute declarations is a proper list of distinct Attribute-Values pairs where values are either continuous or a valid discrete value list.

| Compilation flags: | static

| Template: | valid_attribute_declarations(Attributes) | Mode and number of proofs: | valid_attribute_declarations(+list(pair)) - zero_or_one

.. index:: valid_discrete_values/1 .. _regressor_common/0::valid_discrete_values/1:

valid_discrete_values/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of categorical values is non-empty, contains only nonvar terms, and has no duplicates.

| Compilation flags: | static

| Template: | valid_discrete_values(Values) | Mode and number of proofs: | valid_discrete_values(+list) - zero_or_one

.. index:: valid_regression_encoders/1 .. _regressor_common/0::valid_regression_encoders/1:

valid_regression_encoders/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of encoders only contains valid continuous/3 or categorical/2 encoder terms with distinct attributes.

| Compilation flags: | static

| Template: | valid_regression_encoders(Encoders) | Mode and number of proofs: | valid_regression_encoders(+list(compound)) - zero_or_one

.. index:: valid_regressor_options/1 .. _regressor_common/0::valid_regressor_options/1:

valid_regressor_options/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of options is structurally valid for the receiving regressor implementation.

| Compilation flags: | static

| Template: | valid_regressor_options(Options) | Mode and number of proofs: | valid_regressor_options(+list(compound)) - zero_or_one

.. index:: valid_regressor_metadata/2 .. _regressor_common/0::valid_regressor_metadata/2:

valid_regressor_metadata/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when diagnostics metadata contains the expected model term and records a structurally valid effective options list.

| Compilation flags: | static

| Template: | valid_regressor_metadata(Model,Diagnostics) | Mode and number of proofs: | valid_regressor_metadata(+atom,+list(compound)) - zero_or_one

.. index:: valid_diagnostic_count/3 .. _regressor_common/0::valid_diagnostic_count/3:

valid_diagnostic_count/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when diagnostics contains a count term with the given functor and integer value.

| Compilation flags: | static

| Template: | valid_diagnostic_count(Functor,Diagnostics,Count) | Mode and number of proofs: | valid_diagnostic_count(+atom,+list(compound),+integer) - zero_or_one

.. index:: valid_linear_model_diagnostics/1 .. _regressor_common/0::valid_linear_model_diagnostics/1:

valid_linear_model_diagnostics/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when diagnostics contains structurally valid linear-model optimization metadata terms for convergence, completed iterations, and final parameter delta.

| Compilation flags: | static

| Template: | valid_linear_model_diagnostics(Diagnostics) | Mode and number of proofs: | valid_linear_model_diagnostics(+list(compound)) - zero_or_one

.. index:: valid_encoded_rows/2 .. _regressor_common/0::valid_encoded_rows/2:

valid_encoded_rows/2 ^^^^^^^^^^^^^^^^^^^^^^^^

True when encoded training rows match the feature count induced by the encoders and carry numeric targets.

| Compilation flags: | static

| Template: | valid_encoded_rows(Encoders,Rows) | Mode and number of proofs: | valid_encoded_rows(+list(compound),+list) - zero_or_one

.. index:: encoded_feature_count/2 .. _regressor_common/0::encoded_feature_count/2:

encoded_feature_count/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Counts the number of numeric features induced by a list of continuous and categorical encoders, including missing-value indicator features.

| Compilation flags: | static

| Template: | encoded_feature_count(Encoders,FeatureCount) | Mode and number of proofs: | encoded_feature_count(+list(compound),-integer) - one

.. index:: continuous_stats/5 .. _regressor_common/0::continuous_stats/5:

continuous_stats/5 ^^^^^^^^^^^^^^^^^^^^^^

Computes the mean and scaling factor used to encode a continuous attribute from the training examples according to the effective feature scaling option.

| Compilation flags: | static

| Template: | continuous_stats(Attribute,Examples,Options,Mean,Scale) | Mode and number of proofs: | continuous_stats(+atom,+list(compound),+list(compound),-float,-positive_float) - one

.. index:: examples_to_rows/3 .. _regressor_common/0::examples_to_rows/3:

examples_to_rows/3 ^^^^^^^^^^^^^^^^^^^^^^

Encodes dataset examples as numeric feature-vector and target pairs using a list of encoders.

| Compilation flags: | static

| Template: | examples_to_rows(Examples,Encoders,Rows) | Mode and number of proofs: | examples_to_rows(+list(compound),+list(compound),-list(pair)) - one

.. index:: encode_instance/3 .. _regressor_common/0::encode_instance/3:

encode_instance/3 ^^^^^^^^^^^^^^^^^^^^^

Validates and encodes an attribute-value list as a numeric feature vector using a list of continuous and categorical encoders.

| Compilation flags: | static

| Template: | encode_instance(Encoders,AttributeValues,Features) | Mode and number of proofs: | encode_instance(+list(compound),+list(pair),-list(float)) - one

.. index:: fit_linear_model/7 .. _regressor_common/0::fit_linear_model/7:

fit_linear_model/7 ^^^^^^^^^^^^^^^^^^^^^^

Builds linear-model encoders from the training dataset, encodes the examples, fits an ordinary least-squares bias plus weight vector using the linear_algebra least-squares solver, and returns solver diagnostics terms.

| Compilation flags: | static

| Template: | fit_linear_model(Dataset,Options,Encoders,TrainingExampleCount,Bias,Weights,TrainingDiagnostics) | Mode and number of proofs: | fit_linear_model(+object_identifier,+list(compound),-list(compound),-integer,-float,-list(float),-list(compound)) - one

.. index:: fit_ridge_model/7 .. _regressor_common/0::fit_ridge_model/7:

fit_ridge_model/7 ^^^^^^^^^^^^^^^^^^^^^

Builds linear-model encoders from the training dataset, encodes the examples, fits a bias plus weight vector by solving the ridge normal equations with partial pivoting, and returns ridge-specific diagnostics terms.

| Compilation flags: | static

| Template: | fit_ridge_model(Dataset,Options,Encoders,TrainingExampleCount,Bias,Weights,TrainingDiagnostics) | Mode and number of proofs: | fit_ridge_model(+object_identifier,+list(compound),-list(compound),-integer,-float,-list(float),-list(compound)) - one

.. index:: valid_feature_labels/1 .. _regressor_common/0::valid_feature_labels/1:

valid_feature_labels/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a list of regression-tree feature labels only contains valid feature/2 terms.

| Compilation flags: | static

| Template: | valid_feature_labels(FeatureLabels) | Mode and number of proofs: | valid_feature_labels(+list(compound)) - zero_or_one

.. index:: valid_regression_tree/2 .. _regressor_common/0::valid_regression_tree/2:

valid_regression_tree/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a regression tree only contains valid leaf/1 and node/5 terms using feature indexes within bounds.

| Compilation flags: | static

| Template: | valid_regression_tree(Tree,FeatureCount) | Mode and number of proofs: | valid_regression_tree(+compound,+positive_integer) - zero_or_one

Private predicates

.. index:: check_attribute_declarations/1 .. _regressor_common/0::check_attribute_declarations/1:

check_attribute_declarations/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Checks that dataset attribute declarations use distinct attribute names and valid value domains.

| Compilation flags: | static

| Template: | check_attribute_declarations(Attributes) | Mode and number of proofs: | check_attribute_declarations(+list(pair)) - one_or_error

| Exceptions: | An attribute is declared more than once or uses an invalid value domain: | domain_error(attribute_declarations,Attribute)

.. index:: known_attribute_values/3 .. _regressor_common/0::known_attribute_values/3:

known_attribute_values/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Collects the known numeric values for a continuous attribute from the training examples, skipping omitted and variable values.

| Compilation flags: | static

| Template: | known_attribute_values(Examples,Attribute,Values) | Mode and number of proofs: | known_attribute_values(+list(compound),+atom,-list(number)) - one

.. index:: check_attribute_bindings/2 .. _regressor_common/0::check_attribute_bindings/2:

check_attribute_bindings/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Checks that an attribute-value list contains only declared attributes and does not repeat any declared attribute; omitted declared attributes are allowed and treated as missing values.

| Compilation flags: | static

| Template: | check_attribute_bindings(AttributeNames,AttributeValues) | Mode and number of proofs: | check_attribute_bindings(+list(atom),+list(pair)) - one

.. index:: check_declared_attribute_bindings/2 .. _regressor_common/0::check_declared_attribute_bindings/2:

check_declared_attribute_bindings/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Checks that no declared attribute appears more than once in an attribute-value list.

| Compilation flags: | static

| Template: | check_declared_attribute_bindings(AttributeNames,AttributeValues) | Mode and number of proofs: | check_declared_attribute_bindings(+list(atom),+list(pair)) - one

.. index:: check_undeclared_attribute_bindings/2 .. _regressor_common/0::check_undeclared_attribute_bindings/2:

check_undeclared_attribute_bindings/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Checks that every attribute occurring in an attribute-value list is declared.

| Compilation flags: | static

| Template: | check_undeclared_attribute_bindings(AttributeValues,AttributeNames) | Mode and number of proofs: | check_undeclared_attribute_bindings(+list(pair),+list(atom)) - one

.. index:: attribute_occurrences/4 .. _regressor_common/0::attribute_occurrences/4:

attribute_occurrences/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Counts the number of times an attribute occurs in an attribute-value list.

| Compilation flags: | static

| Template: | attribute_occurrences(AttributeValues,Attribute,Count0,Count) | Mode and number of proofs: | attribute_occurrences(+list(pair),+atom,+integer,-integer) - one

.. index:: declared_attribute_names/2 .. _regressor_common/0::declared_attribute_names/2:

declared_attribute_names/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Collects the declared attribute names from dataset attribute declarations.

| Compilation flags: | static

| Template: | declared_attribute_names(Attributes,AttributeNames) | Mode and number of proofs: | declared_attribute_names(+list(pair),-list(atom)) - one

.. index:: encoder_attribute_names/2 .. _regressor_common/0::encoder_attribute_names/2:

encoder_attribute_names/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Collects the declared attribute names from a list of regression encoders.

| Compilation flags: | static

| Template: | encoder_attribute_names(Encoders,AttributeNames) | Mode and number of proofs: | encoder_attribute_names(+list(compound),-list(atom)) - one

.. index:: encode_instance_checked/3 .. _regressor_common/0::encode_instance_checked/3:

encode_instance_checked/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Encodes an already validated attribute-value list as a numeric feature vector using a list of continuous and categorical encoders.

| Compilation flags: | static

| Template: | encode_instance_checked(Encoders,AttributeValues,Features) | Mode and number of proofs: | encode_instance_checked(+list(compound),+list(pair),-list(float)) - one

.. index:: valid_linear_model_convergence/1 .. _regressor_common/0::valid_linear_model_convergence/1:

valid_linear_model_convergence/1 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

True when a linear-model optimization stop reason is recognized.

| Compilation flags: | static

| Template: | valid_linear_model_convergence(Convergence) | Mode and number of proofs: | valid_linear_model_convergence(+atom) - zero_or_one

.. index:: normalize_continuous/4 .. _regressor_common/0::normalize_continuous/4:

normalize_continuous/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^

Normalizes a continuous value using a stored mean and scale.

| Compilation flags: | static

| Template: | normalize_continuous(Value,Mean,Scale,Feature) | Mode and number of proofs: | normalize_continuous(+number,+float,+positive_float,-float) - one

.. index:: check_categorical_value/3 .. _regressor_common/0::check_categorical_value/3:

check_categorical_value/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Validates that a categorical value is declared for an attribute.

| Compilation flags: | static

| Template: | check_categorical_value(Attribute,Values,Value) | Mode and number of proofs: | check_categorical_value(+atom,+list,+nonvar) - one

.. index:: one_hot_encode/4 .. _regressor_common/0::one_hot_encode/4:

one_hot_encode/4 ^^^^^^^^^^^^^^^^^^^^

Encodes a declared categorical value using reference-level dummy coding plus a trailing missing-value indicator feature set to zero.

| Compilation flags: | static

| Template: | one_hot_encode(Values,Value,Encoded,Tail) | Mode and number of proofs: | one_hot_encode(+list,+nonvar,-list(float),+list(float)) - one

.. index:: missing_one_hot_encode/3 .. _regressor_common/0::missing_one_hot_encode/3:

missing_one_hot_encode/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Encodes a missing categorical value as reference-level dummy zeroes plus a trailing missing-value indicator feature set to one.

| Compilation flags: | static

| Template: | missing_one_hot_encode(Values,Encoded,Tail) | Mode and number of proofs: | missing_one_hot_encode(+list,-list(float),+list(float)) - one

.. index:: zero_vector_from_values/3 .. _regressor_common/0::zero_vector_from_values/3:

zero_vector_from_values/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Creates a zero vector with one element per supplied categorical value slot.

| Compilation flags: | static

| Template: | zero_vector_from_values(Values,Zeroes,Tail) | Mode and number of proofs: | zero_vector_from_values(+list,-list(float),+list(float)) - one

.. index:: build_linear_encoders/4 .. _regressor_common/0::build_linear_encoders/4:

build_linear_encoders/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Builds the encoder list used by linear models from dataset attribute declarations, training examples, and the effective feature scaling option.

| Compilation flags: | static

| Template: | build_linear_encoders(Attributes,Examples,Options,Encoders) | Mode and number of proofs: | build_linear_encoders(+list(pair),+list(compound),+list(compound),-list(compound)) - one

.. index:: train_linear_model/6 .. _regressor_common/0::train_linear_model/6:

train_linear_model/6 ^^^^^^^^^^^^^^^^^^^^^^^^

Fits an ordinary least-squares linear model bias and weight vector from encoded rows by delegating the solve and rank estimation to the linear_algebra library and returns solver diagnostics terms.

| Compilation flags: | static

| Template: | train_linear_model(Rows,FeatureCount,Options,Bias,Weights,TrainingDiagnostics) | Mode and number of proofs: | train_linear_model(+list(pair),+integer,+list(compound),-float,-list(float),-list(compound)) - one

.. index:: rows_to_design_matrix/3 .. _regressor_common/0::rows_to_design_matrix/3:

rows_to_design_matrix/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Transforms encoded training rows into a row-oriented design matrix with a leading intercept column and a target vector.

| Compilation flags: | static

| Template: | rows_to_design_matrix(Rows,DesignMatrix,Targets) | Mode and number of proofs: | rows_to_design_matrix(+list(pair),-list(list(float)),-list(float)) - one

.. index:: residual_sum_of_squares/4 .. _regressor_common/0::residual_sum_of_squares/4:

residual_sum_of_squares/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Computes the residual sum of squares for encoded training rows and a learned intercept plus weight vector.

| Compilation flags: | static

| Template: | residual_sum_of_squares(Rows,Bias,Weights,ResidualSumOfSquares) | Mode and number of proofs: | residual_sum_of_squares(+list(pair),+float,+list(float),-float) - one

.. index:: train_ridge_model/6 .. _regressor_common/0::train_ridge_model/6:

train_ridge_model/6 ^^^^^^^^^^^^^^^^^^^^^^^

Fits a ridge model bias and weight vector from encoded rows by solving a direct weighted linear system and returns diagnostics terms describing the solve.

| Compilation flags: | static

| Template: | train_ridge_model(Rows,FeatureCount,Options,Bias,Weights,TrainingDiagnostics) | Mode and number of proofs: | train_ridge_model(+list(pair),+integer,+list(compound),-float,-list(float),-list(compound)) - one

.. index:: ridge_feature_statistics/3 .. _regressor_common/0::ridge_feature_statistics/3:

ridge_feature_statistics/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Computes per-feature ridge penalty weights and active-feature flags from encoded training rows, dropping zero-variance columns from the direct solve.

| Compilation flags: | static

| Template: | ridge_feature_statistics(Rows,PenaltyWeights,ActiveFlags) | Mode and number of proofs: | ridge_feature_statistics(+list(pair),-list(float),-list(atom)) - one

.. index:: accumulate_feature_statistics/5 .. _regressor_common/0::accumulate_feature_statistics/5:

accumulate_feature_statistics/5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Accumulates per-feature sums and squared sums over encoded training rows.

| Compilation flags: | static

| Template: | accumulate_feature_statistics(Rows,Sums0,SumSquares0,Sums,SumSquares) | Mode and number of proofs: | accumulate_feature_statistics(+list(pair),+list(float),+list(float),-list(float),-list(float)) - one

.. index:: add_squared_vector/3 .. _regressor_common/0::add_squared_vector/3:

add_squared_vector/3 ^^^^^^^^^^^^^^^^^^^^^^^^

Adds the element-wise squares of a feature vector to an accumulated vector.

| Compilation flags: | static

| Template: | add_squared_vector(Features,AccumulatedSquares0,AccumulatedSquares) | Mode and number of proofs: | add_squared_vector(+list(float),+list(float),-list(float)) - one

.. index:: feature_penalty_profiles/5 .. _regressor_common/0::feature_penalty_profiles/5:

feature_penalty_profiles/5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Builds ridge penalty weights and active-feature flags from per-feature sums and squared sums.

| Compilation flags: | static

| Template: | feature_penalty_profiles(Sums,SumSquares,Count,PenaltyWeights,ActiveFlags) | Mode and number of proofs: | feature_penalty_profiles(+list(float),+list(float),+integer,-list(float),-list(atom)) - one

.. index:: active_feature_count/2 .. _regressor_common/0::active_feature_count/2:

active_feature_count/2 ^^^^^^^^^^^^^^^^^^^^^^^^^^

Counts the number of active encoded features retained for the direct ridge solve.

| Compilation flags: | static

| Template: | active_feature_count(ActiveFlags,Count) | Mode and number of proofs: | active_feature_count(+list(atom),-integer) - one

.. index:: compress_rows/3 .. _regressor_common/0::compress_rows/3:

compress_rows/3 ^^^^^^^^^^^^^^^^^^^

Filters encoded training rows down to the active feature subset selected for the direct ridge solve.

| Compilation flags: | static

| Template: | compress_rows(Rows,ActiveFlags,CompressedRows) | Mode and number of proofs: | compress_rows(+list(pair),+list(atom),-list(pair)) - one

.. index:: compress_features/3 .. _regressor_common/0::compress_features/3:

compress_features/3 ^^^^^^^^^^^^^^^^^^^^^^^

Filters a feature vector down to the active feature subset selected for the direct ridge solve.

| Compilation flags: | static

| Template: | compress_features(Features,ActiveFlags,CompressedFeatures) | Mode and number of proofs: | compress_features(+list(float),+list(atom),-list(float)) - one

.. index:: compress_vector/3 .. _regressor_common/0::compress_vector/3:

compress_vector/3 ^^^^^^^^^^^^^^^^^^^^^

Filters a numeric vector down to the active positions selected for the direct ridge solve.

| Compilation flags: | static

| Template: | compress_vector(Vector,ActiveFlags,CompressedVector) | Mode and number of proofs: | compress_vector(+list(float),+list(atom),-list(float)) - one

.. index:: build_ridge_system/5 .. _regressor_common/0::build_ridge_system/5:

build_ridge_system/5 ^^^^^^^^^^^^^^^^^^^^^^^^

Builds the regularized linear system for the intercept plus active ridge coefficients.

| Compilation flags: | static

| Template: | build_ridge_system(Rows,PenaltyWeights,Regularization,Matrix,Vector) | Mode and number of proofs: | build_ridge_system(+list(pair),+list(float),+float,-list(list(float)),-list(float)) - one

.. index:: accumulate_ridge_system/5 .. _regressor_common/0::accumulate_ridge_system/5:

accumulate_ridge_system/5 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Accumulates the unregularized normal-equation matrix and target vector for a set of active encoded rows.

| Compilation flags: | static

| Template: | accumulate_ridge_system(Rows,Matrix0,Vector0,Matrix,Vector) | Mode and number of proofs: | accumulate_ridge_system(+list(pair),+list(list(float)),+list(float),-list(list(float)),-list(float)) - one

.. index:: add_outer_product/4 .. _regressor_common/0::add_outer_product/4:

add_outer_product/4 ^^^^^^^^^^^^^^^^^^^^^^^

Adds the outer product of two vectors to an accumulated matrix.

| Compilation flags: | static

| Template: | add_outer_product(Vector1,Vector2,Matrix0,Matrix) | Mode and number of proofs: | add_outer_product(+list(float),+list(float),+list(list(float)),-list(list(float))) - one

.. index:: regularize_ridge_matrix/4 .. _regressor_common/0::regularize_ridge_matrix/4:

regularize_ridge_matrix/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Adds the ridge penalty weights to the feature-feature diagonal block of a linear system matrix.

| Compilation flags: | static

| Template: | regularize_ridge_matrix(Matrix0,PenaltyWeights,Regularization,Matrix) | Mode and number of proofs: | regularize_ridge_matrix(+list(list(float)),+list(float),+float,-list(list(float))) - one

.. index:: solve_linear_system/4 .. _regressor_common/0::solve_linear_system/4:

solve_linear_system/4 ^^^^^^^^^^^^^^^^^^^^^^^^^

Solves a square linear system using partial pivoting Gaussian elimination and returns the solver name.

| Compilation flags: | static

| Template: | solve_linear_system(Matrix,Vector,Solution,Solver) | Mode and number of proofs: | solve_linear_system(+list(list(float)),+list(float),-list(float),-atom) - one_or_error

.. index:: augment_rows/3 .. _regressor_common/0::augment_rows/3:

augment_rows/3 ^^^^^^^^^^^^^^^^^^

Pairs each matrix row with its corresponding right-hand-side value for elimination.

| Compilation flags: | static

| Template: | augment_rows(Matrix,Vector,Rows) | Mode and number of proofs: | augment_rows(+list(list(float)),+list(float),-list(compound)) - one

.. index:: triangularize/2 .. _regressor_common/0::triangularize/2:

triangularize/2 ^^^^^^^^^^^^^^^^^^^

Transforms an augmented matrix into upper-triangular form using partial pivoting.

| Compilation flags: | static

| Template: | triangularize(Rows0,UpperRows) | Mode and number of proofs: | triangularize(+list(compound),-list(compound)) - one_or_error

.. index:: select_pivot_row/3 .. _regressor_common/0::select_pivot_row/3:

select_pivot_row/3 ^^^^^^^^^^^^^^^^^^^^^^

Selects the pivot row with the largest leading magnitude and returns the remaining rows.

| Compilation flags: | static

| Template: | select_pivot_row(Rows0,PivotRow,RemainingRows) | Mode and number of proofs: | select_pivot_row(+list(compound),-compound,-list(compound)) - one

.. index:: leading_magnitude/2 .. _regressor_common/0::leading_magnitude/2:

leading_magnitude/2 ^^^^^^^^^^^^^^^^^^^^^^^

Returns the absolute leading coefficient magnitude of an augmented row.

| Compilation flags: | static

| Template: | leading_magnitude(Row,Magnitude) | Mode and number of proofs: | leading_magnitude(+compound,-float) - one

.. index:: ensure_non_zero/1 .. _regressor_common/0::ensure_non_zero/1:

ensure_non_zero/1 ^^^^^^^^^^^^^^^^^^^^^

Checks that a pivot coefficient is numerically non-zero.

| Compilation flags: | static

| Template: | ensure_non_zero(Value) | Mode and number of proofs: | ensure_non_zero(+float) - one_or_error

| Exceptions: | A pivot coefficient is numerically zero: | evaluation_error(zero_divisor)

.. index:: eliminate_rows/5 .. _regressor_common/0::eliminate_rows/5:

eliminate_rows/5 ^^^^^^^^^^^^^^^^^^^^

Eliminates the leading coefficient from remaining augmented rows using a pivot row.

| Compilation flags: | static

| Template: | eliminate_rows(Pivot,PivotTail,PivotValue,Rows0,Rows) | Mode and number of proofs: | eliminate_rows(+float,+list(float),+float,+list(compound),-list(compound)) - one

.. index:: scaled_row_difference/4 .. _regressor_common/0::scaled_row_difference/4:

scaled_row_difference/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Subtracts a scaled pivot tail from another row tail.

| Compilation flags: | static

| Template: | scaled_row_difference(PivotTail,RowTail,Factor,Difference) | Mode and number of proofs: | scaled_row_difference(+list(float),+list(float),+float,-list(float)) - one

.. index:: back_substitution/2 .. _regressor_common/0::back_substitution/2:

back_substitution/2 ^^^^^^^^^^^^^^^^^^^^^^^

Performs back-substitution on an upper-triangular augmented matrix.

| Compilation flags: | static

| Template: | back_substitution(UpperRows,Solution) | Mode and number of proofs: | back_substitution(+list(compound),-list(float)) - one_or_error

.. index:: maximum_linear_system_residual/4 .. _regressor_common/0::maximum_linear_system_residual/4:

maximum_linear_system_residual/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Computes the maximum absolute residual of a solved linear system.

| Compilation flags: | static

| Template: | maximum_linear_system_residual(Matrix,Vector,Solution,MaximumResidual) | Mode and number of proofs: | maximum_linear_system_residual(+list(list(float)),+list(float),+list(float),-float) - one

.. index:: expand_weights/3 .. _regressor_common/0::expand_weights/3:

expand_weights/3 ^^^^^^^^^^^^^^^^^^^^

Expands active ridge coefficients back to the full encoded feature vector, inserting zeroes for dropped zero-variance features.

| Compilation flags: | static

| Template: | expand_weights(ActiveFlags,ActiveWeights,Weights) | Mode and number of proofs: | expand_weights(+list(atom),+list(float),-list(float)) - one

Operators

(none)