Docs & Rules

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29 docs, listed Oct 3

haifengl/smileOpen repository

haifengl/smile — documented rules

354 of 354 rules, printed October 3, 2026.

	The sentence in your docs
Read Oct 3	`Tuple` is an interface representing one row of a `DataFrame`.	Tuple is a contract	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getBoolean`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getChar`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getByte`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getShort`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getInt`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getLong`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getFloat`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getDouble`	Holds
Read Oct 3	Available typed accessors: `getBoolean`, `getChar`, `getByte`, `getShort`, `getInt`, `getLong`, `getFloat`, `getDouble`, `getString`.	Tuple has `getString`	Holds
Read Oct 3	`StructType schema` — column descriptors	DataFrame has `schema`	Holds
Read Oct 3	`List<ValueVector> columns` — the actual data, one vector per column	DataFrame has `columns`	Holds
Read Oct 3	`RowIndex index` — optional row labels (may be `null`)	DataFrame has `index`	Holds
Read Oct 3	`describe()` returns a new `DataFrame` with one row per column and columns: `column`, `type`, `measure`, `count` (non-null), `mode`, `mean`, `std`, `min`, `25%`, `50%`, `75%`, `max`.	DataFrame has a `describe` method	Holds
Read Oct 3	`sort()` returns a new `DataFrame` with all rows reordered.	DataFrame has a `sort` method	Holds
Read Oct 3	If either frame has no `RowIndex`, falls back to `merge()`.	DataFrame has a `merge` method	Holds
Read Oct 3	`factorize()` converts `String` columns into `IntVector` columns annotated with a `NominalScale`.	DataFrame has a `factorize` method	Holds
Read Oct 3	Both `toArray()` and `toMatrix()` convert the DataFrame to a dense numeric representation with optional bias (intercept) column and categorical encoding.	DataFrame has a `toArray` method	Holds
Read Oct 3	Both `toArray()` and `toMatrix()` convert the DataFrame to a dense numeric representation with optional bias (intercept) column and categorical encoding.	DataFrame has a `toMatrix` method	Holds
Read Oct 3	`toString(from, to, truncate)`:	DataFrame has a `toString` method	Holds
Read Oct 3	JDBC types are mapped to SMILE types via `StructType.of(ResultSetMetaData)`.	StructType has an `of` method	Holds
Read Oct 3	Enum value LEVEL, Meaning Integer level code (default), Output columns per category 1 — raw code value	CategoricalEncoder has `LEVEL`	Holds
Read Oct 3	Enum value DUMMY, Meaning Dummy / treatment encoding, Output columns per category k−1 binary columns (reference = first level)	CategoricalEncoder has `DUMMY`	Holds
Read Oct 3	Enum value ONE_HOT, Meaning Full one-hot encoding, Output columns per category k binary columns	CategoricalEncoder has `ONE_HOT`	Holds
Read Oct 3	Method new DataFrame(ValueVector...), Description Construct from column vectors Method new DataFrame(RowIndex, ValueVector...), Description With row index	DataFrame has a `DataFrame` method	Holds
Read Oct 3	Method DataFrame.of(double[][], String...), Description From 2-D double array	DataFrame has an `of` method	Holds
Read Oct 3	Method new StructType(StructField...), Description Construct from fields	StructType has a `StructType` method	Holds
Read Oct 3	Method field(int) / field(String), Description Get field by ordinal or name	StructType has a `field` method	Holds
Read Oct 3	Method indexOf(String), Description Ordinal of named field	StructType has an `indexOf` method	Holds
Read Oct 3	Method length(), Description Number of fields	StructType has a `length` method	Holds
Read Oct 3	Method names() / dtypes() / measures(), Description Field property arrays	StructType has a `names` method	Holds
Read Oct 3	Method names() / dtypes() / measures(), Description Field property arrays	StructType has a `dtypes` method	Holds
Read Oct 3	Method names() / dtypes() / measures(), Description Field property arrays	StructType has a `measures` method	Holds
Read Oct 3	Method add(StructField), Description Append a field (mutable)	StructType has an `add` method	Holds
Read Oct 3	Method rename(String, String), Description Rename a field (mutable)	StructType has a `rename` method	Holds
Read Oct 3	Constructor / method new StructField(name, dtype), Description Without measure Constructor / method new StructField(name, dtype, measure), Description With measure	StructField has a `StructField` method	Holds
Read Oct 3	Constructor / method withName(String), Description Return renamed copy	StructField has a `withName` method	Holds
Read Oct 3	Constructor / method isNumeric(), Description True for non-nominal numeric fields	StructField has an `isNumeric` method	Holds
Read Oct 3	Constructor / method toString(Object), Description Format a value using measure or dtype	StructField has a `toString` method	Holds
Read Oct 3	Method size(), Description Element count	ValueVector has a `size` method	Holds
Read Oct 3	Method isNullable(), Description True if vector can contain nulls	ValueVector has an `isNullable` method	Holds
Read Oct 3	Method isNullAt(int), Description Null check at position	ValueVector has an `isNullAt` method	Holds
Read Oct 3	Method getNullCount(), Description Count of null positions	ValueVector has a `getNullCount` method	Holds
Read Oct 3	Method anyNull(), Description True if any null exists	ValueVector has an `anyNull` method	Holds
Read Oct 3	Method get(int), Description Boxed value (may be null)	ValueVector has a `get` method	Holds
Read Oct 3	Method getString(int), Description String form (uses measure)	ValueVector has a `getString` method	Holds
Read Oct 3	Method set(int, Object), Description Mutation	ValueVector has a `set` method	Holds
Read Oct 3	Method withName(String), Description Return renamed copy	ValueVector has a `withName` method	Holds
Read Oct 3	Method toIntArray() / toDoubleArray() / toStringArray(), Description Bulk export	ValueVector has a `toIntArray` method	Holds
Read Oct 3	Method toIntArray() / toDoubleArray() / toStringArray(), Description Bulk export	ValueVector has a `toDoubleArray` method	Holds
Read Oct 3	Method toIntArray() / toDoubleArray() / toStringArray(), Description Bulk export	ValueVector has a `toStringArray` method	Holds
Read Oct 3	Method intStream() / longStream() / doubleStream() / stream(), Description Streaming	ValueVector has an `intStream` method	Holds
Read Oct 3	Method intStream() / longStream() / doubleStream() / stream(), Description Streaming	ValueVector has a `longStream` method	Holds
Read Oct 3	Method intStream() / longStream() / doubleStream() / stream(), Description Streaming	ValueVector has a `doubleStream` method	Holds
Read Oct 3	Method intStream() / longStream() / doubleStream() / stream(), Description Streaming	ValueVector has a `stream` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has an `eq` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has a `ne` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has a `lt` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has a `le` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has a `gt` method	Holds
Read Oct 3	Method eq(Object) / ne / lt / le / gt / ge, Description Element-wise comparison masks	ValueVector has a `ge` method	Holds
Read Oct 3	Method isin(String...) / isin(int...), Description Membership mask	ValueVector has an `isin` method	Holds
Read Oct 3	Method isNull(), Description Per-element null mask	ValueVector has an `isNull` method	Holds
Read Oct 3	`Read` is a static-method interface;	Read is a contract	Holds
Read Oct 3	`Read.data(path)` examines the last path segment's file extension and delegates to the appropriate reader automatically.	Read has a `data` method	Holds
Read Oct 3	Extension(s) csv , txt , dat, Reader Read.csv	Read has `csv`	Holds
Read Oct 3	Extension(s) arff, Reader Read.arff	Read has `arff`	Holds
Read Oct 3	Extension(s) json, Reader Read.json	Read has `json`	Holds
Read Oct 3	Extension(s) sas7bdat, Reader Read.sas	Read has `sas`	Holds
Read Oct 3	Extension(s) avro, Reader Read.avro (format = schema file path)	Read has an `avro` method	Holds
Read Oct 3	Extension(s) parquet, Reader Read.parquet	Read has `parquet`	Holds
Read Oct 3	Extension(s) feather, Reader Read.arrow	Read has `arrow`	Holds
Read Oct 3	`Read.libsvm` returns a `SparseDataset<Integer>` (not a `DataFrame`):	Read has `libsvm`	Holds
Read Oct 3	`Write` is a static-method interface;	Write is a contract	Holds
Read Oct 3	Method Write.csv(DataFrame, Path), Description CSV with default format Method Write.csv(DataFrame, Path, CSVFormat), Description CSV with explicit format	Write has a `csv` method	Holds
Read Oct 3	Method Write.arrow(DataFrame, Path), Description Arrow/Feather	Write has an `arrow` method	Holds
Read Oct 3	Method Write.arff(DataFrame, Path, String), Description ARFF with relation name	Write has an `arff` method	Holds
Read Oct 3	Method Write.object(Serializable), Description Serialize to a temp file (auto-deleted) Method Write.object(Serializable, Path), Description Serialize to a specific file	Write has an `object` method	Holds
Read Oct 3	Method csv.schema(StructType), Description Override schema (fluent)	CSV has a `schema` method	Holds
Read Oct 3	Method csv.charset(Charset), Description Set charset (fluent)	CSV has a `charset` method	Holds
Read Oct 3	Method csv.read(String), Description Read all rows from string path	CSV has a `read` method	Holds
Read Oct 3	Method csv.inferSchema(Reader, int), Description Infer schema from first N rows	CSV has an `inferSchema` method	Holds
Read Oct 3	Method csv.write(DataFrame, Path), Description Write to Path	CSV has a `write` method	Holds
Read Oct 3	Method json.schema(StructType), Description Override schema (fluent)	JSON has a `schema` method	Holds
Read Oct 3	Method json.charset(Charset), Description Set charset (fluent)	JSON has a `charset` method	Holds
Read Oct 3	Method json.mode(Mode), Description SINGLE_LINE or MULTI_LINE (fluent)	JSON has a `mode` method	Holds
Read Oct 3	Method json.read(Path), Description Read all objects	JSON has a `read` method	Holds
Read Oct 3	Method arff.name(), Description @relation name	Arff has a `name` method	Holds
Read Oct 3	Method arff.schema(), Description Parsed StructType	Arff has a `schema` method	Holds
Read Oct 3	Method arff.read(), Description Read all data rows	Arff has a `read` method	Holds
Read Oct 3	Method arff.close(), Description Close underlying reader	Arff has a `close` method	Holds
Read Oct 3	Method Arff.write(df, Path, String), Description Static write method	Arff has a `write` method	Holds
Read Oct 3	Method Input.stream(String), Description InputStream for path or URI	Input has a `stream` method	Holds
Read Oct 3	Method Input.reader(String), Description BufferedReader (UTF-8) Method Input.reader(String, Charset), Description BufferedReader with charset	Input has a `reader` method	Holds
Read Oct 3	Method CacheFiles.dir(), Description Return cache directory path	CacheFiles has a `dir` method	Holds
Read Oct 3	Method CacheFiles.download(String), Description Download URL to cache (skip if exists) Method CacheFiles.download(String, boolean), Description Download URL force=true re-downloads	CacheFiles has a `download` method	Holds
Read Oct 3	Method CacheFiles.clean(), Description Delete all cached files	CacheFiles has a `clean` method	Holds
Read Oct 3	Method p(double x), Description PDF value at x	Distribution has a `p` method	Holds
Read Oct 3	Method logp(double x), Description Log-PDF (computed directly for stability)	Distribution has a `logp` method	Holds
Read Oct 3	Method cdf(double x), Description Cumulative distribution function P(X ≤ x)	Distribution has a `cdf` method	Holds
Read Oct 3	Method quantile(double p), Description Inverse CDF (quantile function)	Distribution has a `quantile` method	Holds
Read Oct 3	Method mean(), Description Distribution mean	Distribution has a `mean` method	Holds
Read Oct 3	Method variance(), Description Distribution variance	Distribution has a `variance` method	Holds
Read Oct 3	Method sd(), Description Standard deviation (default √variance() )	Distribution has a `sd` method	Holds
Read Oct 3	Method entropy(), Description Shannon entropy	Distribution has an `entropy` method	Holds
Read Oct 3	Method rand(), Description Draw one random sample	Distribution has a `rand` method	Holds
Read Oct 3	Method likelihood(double[] x), Description Likelihood of a data set	Distribution has a `likelihood` method	Holds
Read Oct 3	Method logLikelihood(double[] x), Description Log-likelihood of a data set	Distribution has a `logLikelihood` method	Holds
Read Oct 3	Method p(int k), Description PMF value at integer k	DiscreteDistribution has a `p` method	Holds
Read Oct 3	Method logp(int k), Description Log-PMF	DiscreteDistribution has a `logp` method	Holds
Read Oct 3	Method cdf(double k), Description CDF at k	DiscreteDistribution has a `cdf` method	Holds
Read Oct 3	Method randi(), Description Draw one integer sample	DiscreteDistribution has a `randi` method	Holds
Read Oct 3	Distributions in the exponential family implement an `M()` method for the M-step of the EM algorithm, enabling them to be used inside mixture models.	ExponentialFamily has a `M` method	Holds
Read Oct 3	The shared standard-normal singleton is obtained via `GaussianDistribution.getInstance()`.	GaussianDistribution has a `getInstance` method	Holds
Read Oct 3	Fit a mixture of any exponential-family distributions (Gaussian, Exponential, Gamma, etc.) using `ExponentialFamilyMixture.fit()`:	ExponentialFamilyMixture has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	GaussianDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	LogNormalDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	GammaDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	ExponentialDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	BetaDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	WeibullDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	PoissonDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	GeometricDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	ShiftedGeometricDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	NegativeBinomialDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	EmpiricalDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	MultivariateGaussianDistribution has a `fit` method	Holds
Read Oct 3	All parametric distributions provide a static `fit()` method:	MultivariateGaussianMixture has a `fit` method	Holds
Read Oct 3	`Mixture.variance()` and `MultivariateMixture.cov()` implement the law of total variance / Bienaymé formula:	Mixture has a `variance` method	Holds
Read Oct 3	`Mixture.variance()` and `MultivariateMixture.cov()` implement the law of total variance / Bienaymé formula:	MultivariateMixture has a `cov` method	Holds
Read Oct 3	Interface Interpolation, Purpose 1-D interpolant — double interpolate(double x)	Interpolation has an `interpolate` method	Holds
Read Oct 3	Interface Interpolation2D, Purpose 2-D interpolant — double interpolate(double x1, double x2)	Interpolation2D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	ShepardInterpolation has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	ShepardInterpolation1D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	ShepardInterpolation2D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	RBFInterpolation has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	RBFInterpolation1D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	RBFInterpolation2D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	KrigingInterpolation has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	KrigingInterpolation1D has an `interpolate` method	Holds
Read Oct 3	They all implement the `interpolate(double... x)` pattern (variadic `double[]` input) rather than a fixed-arity 2-D call.	KrigingInterpolation2D has an `interpolate` method	Holds
Read Oct 3	Available radial basis functions (all in `smile.math.rbf`): Class GaussianRadialBasis, Formula φ(r) exp(−r²/2r₀²), Properties SPD Gram → Cholesky sensitive to scale r₀	GaussianRadialBasis is in smile.math.rbf	Holds
Read Oct 3	Variograms are used by `KrigingInterpolation` to model spatial dependence. All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	KrigingInterpolation depends on Variogram	Holds
Read Oct 3	All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	Variogram has a `f` method	Holds
Read Oct 3	All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	ExponentialVariogram implements Variogram	Holds
Read Oct 3	All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	GaussianVariogram implements Variogram	Holds
Read Oct 3	All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	PowerVariogram implements Variogram	Holds
Read Oct 3	All variograms implement `Variogram` (which extends `smile.util.function.Function`) via `double f(double r)`.	SphericalVariogram implements Variogram	Holds
Read Oct 3	Strongly-typed column vectors (`IntVector`, `DoubleVector`, `StringVector`, …) live in `smile.data.vector`.	IntVector is in smile.data.vector	Holds
Read Oct 3	Strongly-typed column vectors (`IntVector`, `DoubleVector`, `StringVector`, …) live in `smile.data.vector`.	DoubleVector is in smile.data.vector	Holds
Read Oct 3	Strongly-typed column vectors (`IntVector`, `DoubleVector`, `StringVector`, …) live in `smile.data.vector`.	StringVector is in smile.data.vector	Holds
Read Oct 3	A rich collection of distance and similarity metrics implementing the `Distance<T>` interface: Metric Euclidean, Class EuclideanDistance	EuclideanDistance implements Distance	Holds
Read Oct 3	`Vector` is a specialized `DenseMatrix` with either one row or one column.	Vector extends DenseMatrix	Holds
Read Oct 3	Its subclasses `DenseMatrix64` (Float64) and `DenseMatrix32` (Float32) store data in column-major off-heap `MemorySegment` with a cache-optimised leading dimension.	DenseMatrix64 extends DenseMatrix	Holds
Read Oct 3	Its subclasses `DenseMatrix64` (Float64) and `DenseMatrix32` (Float32) store data in column-major off-heap `MemorySegment` with a cache-optimised leading dimension.	DenseMatrix32 extends DenseMatrix	Holds
Read Oct 3	`DenseMatrix` implements `java.io.Serializable`.	DenseMatrix uses java.io.Serializable	Holds
Read Oct 3	Method int numClasses(), Description Number of distinct classes	Classifier has a `numClasses` method	Holds
Read Oct 3	Method int numClasses(), Description Number of distinct classes	Classifier has a `classes` method	Holds
Read Oct 3	Method int predict(T x), Description Hard prediction — returns the predicted class label	Classifier has a `predict` method	Holds
Read Oct 3	Method double score(T x), Description Raw decision score (default throws UnsupportedOperationException )	Classifier has a `score` method	Holds
Read Oct 3	Method boolean isSoft(), Description true if predict(T, double[]) is available	Classifier has an `isSoft` method	Holds
Read Oct 3	Method boolean isOnline(), Description true if update(T, int) is available	Classifier has an `isOnline` method	Holds
Read Oct 3	Method void update(T x, int y), Description Online learning — update the model with a single new labelled sample	Classifier has an `update` method	Holds
Read Oct 3	Method int applyAsInt(T x), Description Alias for predict satisfies ToIntFunction<T>	Classifier has an `applyAsInt` method	Holds
Read Oct 3	Method double applyAsDouble(T x), Description Alias for score satisfies ToDoubleFunction<T>	Classifier has an `applyAsDouble` method	Holds
Read Oct 3	It stores the `IntSet classes` label encoder and provides default implementations of `numClasses()` and `classes()`.	AbstractClassifier has `classes`	Holds
Read Oct 3	It stores the `IntSet classes` label encoder and provides default implementations of `numClasses()` and `classes()`.	AbstractClassifier has a `numClasses` method	Holds
Read Oct 3	It stores the `IntSet classes` label encoder and provides default implementations of `numClasses()` and `classes()`.	AbstractClassifier has a `classes` method	Holds
Read Oct 3	An extension of `Classifier<Tuple>` for models that are trained on `DataFrame` objects via a `Formula`.	DataFrameClassifier extends Classifier	Holds
Read Oct 3	Method Formula formula(), Description The formula used at training time	DataFrameClassifier has a `formula` method	Holds
Read Oct 3	Method StructType schema(), Description The schema of the feature columns	DataFrameClassifier has a `schema` method	Holds
Read Oct 3	Method int predict(Tuple x), Description Predicts using a named-column Tuple	DataFrameClassifier has a `predict` method	Holds
Read Oct 3	All tree-based models (`DecisionTree`, `RandomForest`, `AdaBoost`, `GradientTreeBoost`) implement `DataFrameClassifier`.	DecisionTree implements DataFrameClassifier	Holds
Read Oct 3	All tree-based models (`DecisionTree`, `RandomForest`, `AdaBoost`, `GradientTreeBoost`) implement `DataFrameClassifier`.	AdaBoost implements DataFrameClassifier	Holds
Read Oct 3	The static `DataFrameClassifier.of(formula, classifier)` helper adapts any `Classifier<double[]>` into a `DataFrameClassifier`, applying the formula's feature extraction automatically.	DataFrameClassifier has an `of` method	Holds
Read Oct 3	A utility record that maps arbitrary integer class labels to the contiguous range `[0, k)` required by internal array indexing.	ClassLabels is a concrete implementation	Holds
Read Oct 3	Field k, Type int, Description Number of classes	ClassLabels has a `k` field	Holds
Read Oct 3	Field classes, Type IntSet, Description Encoder classes.indexOf(label) → index, classes.valueOf(index) → label	ClassLabels has a `classes` field	Holds
Read Oct 3	Field y, Type int[], Description The original label array remapped to [0, k)	ClassLabels has a `y` field	Holds
Read Oct 3	Field ni, Type int[], Description Per-class sample counts	ClassLabels has a `ni` field	Holds
Read Oct 3	Field priori, Type double[], Description Estimated prior probabilities ni[i] / n	ClassLabels has a `priori` field	Holds
Read Oct 3	`PlattScaling` is used internally by `OneVersusRest.fit` and `OneVersusOne.fit` when the base classifier supports `score()`.	OneVersusRest depends on PlattScaling	Holds
Read Oct 3	`PlattScaling` is used internally by `OneVersusRest.fit` and `OneVersusOne.fit` when the base classifier supports `score()`.	OneVersusOne depends on PlattScaling	Holds
Read Oct 3	`LogisticRegression`, `RandomForest`, `GradientTreeBoost`, `AdaBoost`, `DecisionTree`, `MLP`, and `Maxent` all expose a `fit(…, Properties)` overload and a nested `Options` record with `toProperties()` / `of(Properties)` for round-tripping:	LogisticRegression has a `fit` method	Holds
Read Oct 3	`LogisticRegression`, `RandomForest`, `GradientTreeBoost`, `AdaBoost`, `DecisionTree`, `MLP`, and `Maxent` all expose a `fit(…, Properties)` overload and a nested `Options` record with `toProperties()` / `of(Properties)` for round-tripping:	AdaBoost has a `fit` method	Holds
Read Oct 3	`LogisticRegression`, `RandomForest`, `GradientTreeBoost`, `AdaBoost`, `DecisionTree`, `MLP`, and `Maxent` all expose a `fit(…, Properties)` overload and a nested `Options` record with `toProperties()` / `of(Properties)` for round-tripping:	DecisionTree has a `fit` method	Holds
Read Oct 3	`LogisticRegression`, `RandomForest`, `GradientTreeBoost`, `AdaBoost`, `DecisionTree`, `MLP`, and `Maxent` all expose a `fit(…, Properties)` overload and a nested `Options` record with `toProperties()` / `of(Properties)` for round-tripping:	Maxent has a `fit` method	Holds
Read Oct 3	`Clustering` is a utility interface that defines the global `OUTLIER` constant and the shared `Options` record used by most partitioning algorithms.	Clustering is a contract	Holds
Read Oct 3	`Clustering` is a utility interface that defines the global `OUTLIER` constant and the shared `Options` record used by most partitioning algorithms.	Clustering has `OUTLIER`	Holds
Read Oct 3	`Partitioning` is the base class for density-based algorithms (DBSCAN, HDBSCAN, MEC, DENCLUE) that do not produce centroids.	HDBSCAN extends Partitioning	Holds
Read Oct 3	`Partitioning` is the base class for density-based algorithms (DBSCAN, HDBSCAN, MEC, DENCLUE) that do not produce centroids.	MEC extends Partitioning	Holds
Read Oct 3	`Partitioning` is the base class for density-based algorithms (DBSCAN, HDBSCAN, MEC, DENCLUE) that do not produce centroids.	DENCLUE extends Partitioning	Holds
Read Oct 3	`k` — number of clusters found (not counting the outlier pseudo-cluster).	Partitioning has `k`	Holds
Read Oct 3	`group[]` — per-point cluster label;	Partitioning has `group`	Holds
Read Oct 3	`CentroidClustering<T, U>` is a record returned by all centroid-based algorithms.	CentroidClustering is a concrete implementation	Holds
Read Oct 3	Method k(), Returns int, Description Number of clusters	CentroidClustering has a `k` method	Holds
Read Oct 3	Method centers(), Returns T[], Description Cluster centroids / medoids	CentroidClustering has a `centers` method	Holds
Read Oct 3	Method center(i), Returns T, Description Centroid of cluster i	CentroidClustering has a `center` method	Holds
Read Oct 3	Method group(), Returns int[], Description Per-point cluster labels Method group(i), Returns int, Description Label of data point i	CentroidClustering has a `group` method	Holds
Read Oct 3	Method proximity(i), Returns double, Description Squared distance of point i to its centroid	CentroidClustering has a `proximity` method	Holds
Read Oct 3	Method size(i), Returns int, Description Number of points in cluster i	CentroidClustering has a `size` method	Holds
Read Oct 3	Method distortions(), Returns double[], Description Average squared distance per cluster + overall	CentroidClustering has a `distortions` method	Holds
Read Oct 3	Method distortion(), Returns double, Description Overall average squared distance (WCSS / n)	CentroidClustering has a `distortion` method	Holds
Read Oct 3	Method radius(i), Returns double, Description RMS radius of cluster i sqrt(distortions[i])	CentroidClustering has a `radius` method	Holds
Read Oct 3	Method predict(x), Returns int, Description Assign a new point to the nearest centroid	CentroidClustering has a `predict` method	Holds
Read Oct 3	Method name(), Returns String, Description Algorithm name	CentroidClustering has a `name` method	Holds
Read Oct 3	Method toString(), Returns String, Description Formatted cluster summary table	CentroidClustering has a `toString` method	Holds
Read Oct 3	Sparse text path: `SpectralClustering.fit(SparseIntArray[], p, d)` builds a TF-IDF-weighted Laplacian for document term-count arrays.	SpectralClustering has a `fit` method	Holds
Read Oct 3	DBSCAN, HDBSCAN, DENCLUE, and MEC all label noise points with `Clustering.OUTLIER` (`Integer.MAX_VALUE`).	HDBSCAN depends on Clustering	Holds
Read Oct 3	DBSCAN, HDBSCAN, DENCLUE, and MEC all label noise points with `Clustering.OUTLIER` (`Integer.MAX_VALUE`).	DENCLUE depends on Clustering	Holds
Read Oct 3	DBSCAN, HDBSCAN, DENCLUE, and MEC all label noise points with `Clustering.OUTLIER` (`Integer.MAX_VALUE`).	MEC depends on Clustering	Holds
Read Oct 3	`SHAP<T>` is a generic interface implemented by any model that supports Shapley-value attribution.	SHAP is a contract	Holds
Read Oct 3	The interface also provides `shap(Stream<T>)` for batch processing.	SHAP has a `shap` method	Holds
Read Oct 3	`TreeSHAP` is an interface implemented by all SMILE tree-ensemble classifiers and regressors.	TreeSHAP is a contract	Holds
Read Oct 3	TreeSHAP is implemented by `RandomForest`, `GradientTreeBoost`, `AdaBoost`, and `DecisionTree`.	AdaBoost implements TreeSHAP	Holds
Read Oct 3	Method static MDS fit(double[][] proximity), Description 2-D MDS with default options	MDS has a `fit` method	Holds
Read Oct 3	Method double[] scores(), Description Eigenvalues of the top- d components	MDS has a `scores` method	Holds
Read Oct 3	Method double[] proportion(), Description Fraction of total variance in each component	MDS has a `proportion` method	Holds
Read Oct 3	Method double[][] coordinates(), Description Embedded coordinates [n][d]	MDS has a `coordinates` method	Holds
Read Oct 3	Method static IsotonicMDS fit(double[][] proximity), Description 2-D non-metric MDS with defaults	IsotonicMDS has a `fit` method	Holds
Read Oct 3	Method double stress(), Description Final normalized Kruskal stress	IsotonicMDS has a `stress` method	Holds
Read Oct 3	Method double[][] coordinates(), Description Embedded coordinates [n][d]	IsotonicMDS has a `coordinates` method	Holds
Read Oct 3	Method static SammonMapping fit(double[][] proximity), Description 2-D Sammon mapping with defaults	SammonMapping has a `fit` method	Holds
Read Oct 3	Method double stress(), Description Final Sammon stress (normalized)	SammonMapping has a `stress` method	Holds
Read Oct 3	Method double[][] coordinates(), Description Embedded coordinates [n][d]	SammonMapping has a `coordinates` method	Holds
Read Oct 3	Method static double[][] fit(double[][] data, Options options), Description Euclidean Isomap	IsoMap has a `fit` method	Holds
Read Oct 3	Method static double[][] fit(double[][] data, Options options), Description Standard LLE	LLE has a `fit` method	Holds
Read Oct 3	Method static double[][] fit(double[][] data, Options options), Description Euclidean Laplacian Eigenmaps	LaplacianEigenmap has a `fit` method	Holds
Read Oct 3	Method static <T> KPCA<T> fit(T[] data, MercerKernel<T> kernel, Options options), Description Fit KPCA	KPCA has a `fit` method	Holds
Read Oct 3	Method double[] apply(T x), Description Project a new point (out-of-sample extension)	KPCA has an `apply` method	Holds
Read Oct 3	Method double[][] coordinates(), Description Embedded training coordinates [n][d]	KPCA has a `coordinates` method	Holds
Read Oct 3	Method double[] latent(), Description Eigenvalues of kernel principal components	KPCA has a `latent` method	Holds
Read Oct 3	Method static TSNE fit(double[][] X), Description 2-D t-SNE with default options	TSNE has a `fit` method	Holds
Read Oct 3	Method double cost(), Description Final KL divergence	TSNE has a `cost` method	Holds
Read Oct 3	Method double[][] coordinates(), Description Embedded coordinates [n][d]	TSNE has a `coordinates` method	Holds
Read Oct 3	Method static double[][] fit(double[][] data, Options options), Description Euclidean UMAP	UMAP has a `fit` method	Holds
Read Oct 3	Ensemble models (`RandomForest`, `GradientTreeBoost`) implement `TreeSHAP` for feature importance.	RandomForest implements TreeSHAP	Holds
Read Oct 3	Uniform `Regression<T>` interface — a single `predict(T x)` for scalar output, plus batch, list, and dataset overloads;	Regression is a contract	Holds
Read Oct 3	Uniform `Regression<T>` interface — a single `predict(T x)` for scalar output, plus batch, list, and dataset overloads;	Regression has a `predict` method	Holds
Read Oct 3	optional online `update`.	Regression has `update`	Holds
Read Oct 3	Method boolean online(), Description Returns true if the model supports incremental updates	Regression has an `online` method	Holds
Read Oct 3	Method double applyAsDouble(T x), Description Alias for predict satisfies ToDoubleFunction<T>	Regression has an `applyAsDouble` method	Holds
Read Oct 3	Ensemble average. The static factory method `Regression.ensemble(models...)` returns a model whose prediction is the arithmetic mean of all constituent models.	Regression has an `ensemble` method	Holds
Read Oct 3	Method Formula formula(), Description The formula used at training time	DataFrameRegression has a `formula` method	Holds
Read Oct 3	Method StructType schema(), Description The feature schema of the design matrix	DataFrameRegression has a `schema` method	Holds
Read Oct 3	Method double predict(Tuple x), Description Predicts from a named-column Tuple	DataFrameRegression has a `predict` method	Holds
Read Oct 3	All tree-based models (`RegressionTree`, `RandomForest`, `GradientTreeBoost`) and `GLM`, `GAM`, `LinearModel` implement `DataFrameRegression`.	DataFrameRegression is a contract	Holds
Read Oct 3	All tree-based models (`RegressionTree`, `RandomForest`, `GradientTreeBoost`) and `GLM`, `GAM`, `LinearModel` implement `DataFrameRegression`.	RegressionTree implements DataFrameRegression	Holds
Read Oct 3	All tree-based models (`RegressionTree`, `RandomForest`, `GradientTreeBoost`) and `GLM`, `GAM`, `LinearModel` implement `DataFrameRegression`.	GLM implements DataFrameRegression	Holds
Read Oct 3	All tree-based models (`RegressionTree`, `RandomForest`, `GradientTreeBoost`) and `GLM`, `GAM`, `LinearModel` implement `DataFrameRegression`.	GAM implements DataFrameRegression	Holds
Read Oct 3	All tree-based models (`RegressionTree`, `RandomForest`, `GradientTreeBoost`) and `GLM`, `GAM`, `LinearModel` implement `DataFrameRegression`.	LinearModel implements DataFrameRegression	Holds
Read Oct 3	The static helper `DataFrameRegression.of(formula, model)` adapts any `Regression<double[]>` into a `DataFrameRegression`, applying formula extraction automatically:	DataFrameRegression has an `of` method	Holds
Read Oct 3	Method double[] coefficients(), Description Fitted weights (last element is the intercept when included in the design)	LinearModel has a `coefficients` method	Holds
Read Oct 3	Method double intercept(), Description The intercept term	LinearModel has an `intercept` method	Holds
Read Oct 3	Method double RSquared(), Description Coefficient of determination R²	LinearModel has a `RSquared` method	Holds
Read Oct 3	Method double adjustedRSquared(), Description R² adjusted for the number of predictors	LinearModel has an `adjustedRSquared` method	Holds
Read Oct 3	Method double RSS(), Description Residual sum of squares	LinearModel has a `RSS` method	Holds
Read Oct 3	Method double error(), Description Root mean square error of the residuals (σ̂)	LinearModel has an `error` method	Holds
Read Oct 3	Method double[][] ttest(), Description t-tests of coefficients estimate, std error, t-statistic, p-value	LinearModel has a `ttest` method	Holds
Read Oct 3	Method double F(), Description F-statistic for overall significance	LinearModel has a `F` method	Holds
Read Oct 3	Method double pvalue(), Description p-value of the F-test	LinearModel has a `pvalue` method	Holds
Read Oct 3	Method double[] fittedValues(), Description Training fitted values	LinearModel has a `fittedValues` method	Holds
Read Oct 3	Method double[] residuals(), Description Training residuals	LinearModel has a `residuals` method	Holds
Read Oct 3	Method double predict(Tuple x), Description Predict from a Tuple	LinearModel has a `predict` method	Holds
Read Oct 3	Method void update(double[] x, double y), Description Recursive Least Squares (RLS) update when enabled	LinearModel has an `update` method	Holds
Read Oct 3	Method String toString(), Description Human-readable summary resembling R's lm output	LinearModel has a `toString` method	Holds
Read Oct 3	Method double[] coefficients(), Description Fitted linear predictor coefficients (including intercept)	GLM has a `coefficients` method	Holds
Read Oct 3	Method double[][] ztest(), Description Coefficient z-tests estimate, std error, z-score, p-value	GLM has a `ztest` method	Holds
Read Oct 3	Method double[] fittedValues(), Description Fitted mean values on the response scale (after invlink )	GLM has a `fittedValues` method	Holds
Read Oct 3	Method double[] devianceResiduals(), Description Deviance residuals for each observation	GLM has a `devianceResiduals` method	Holds
Read Oct 3	Method double deviance(), Description Residual deviance of the fitted model	GLM has a `deviance` method	Holds
Read Oct 3	Method double logLikelihood(), Description Log-likelihood of the fitted model	GLM has a `logLikelihood` method	Holds
Read Oct 3	Method double AIC(), Description Akaike information criterion 2k − 2ℓ	GLM has an `AIC` method	Holds
Read Oct 3	Method double BIC(), Description Bayesian information criterion	GLM has a `BIC` method	Holds
Read Oct 3	Method double predict(Tuple x), Description Predicts on the response scale via invlink(Xβ)	GLM has a `predict` method	Holds
Read Oct 3	Method String toString(), Description R-style summary with deviance table, coefficients, z-tests	GLM has a `toString` method	Holds
Read Oct 3	Each family is a Java interface (`Bernoulli`, `Binomial`, `Poisson`, `Gaussian`) exposing a static factory that returns a `Model`.	Bernoulli is a contract	Holds
Read Oct 3	Each family is a Java interface (`Bernoulli`, `Binomial`, `Poisson`, `Gaussian`) exposing a static factory that returns a `Model`.	Poisson is a contract	Holds
Read Oct 3	Family Bernoulli, Factory Bernoulli.logit(), Link logit, Response type Binary (0/1) outcomes	Bernoulli has a `logit` method	Holds
Read Oct 3	Family Poisson, Factory Poisson.log(), Link log, Response type Non-negative count data	Poisson has a `log` method	Holds
Read Oct 3	Both model types expose a `predict(Tuple x)` method that accepts a single data row:	ClassificationModel has a `predict` method	Holds
Read Oct 3	Both model types expose a `predict(Tuple x)` method that accepts a single data row:	RegressionModel has a `predict` method	Holds
Read Oct 3	The `"svm"` and `"gaussian-process"` algorithms both accept a `smile.svm.kernel` / `smile.gaussian_process.kernel` property whose value is a short string parsed by `MercerKernel.of(String)`.	MercerKernel has an `of` method	Holds
Read Oct 3	Both `ClassificationModel` and `RegressionModel` implement `java.io.Serializable`.	ClassificationModel uses java.io.Serializable	Holds
Read Oct 3	Both `ClassificationModel` and `RegressionModel` implement `java.io.Serializable`.	RegressionModel uses java.io.Serializable	Holds
Read Oct 3	Interface ClassificationMetric, Method signature double score(int[] truth, int[] prediction), Who implements it Accuracy , Error , Precision , Recall , FScore , FDR , Fallout , Specificity , Sensitivity , MatthewsCorrelation	Error implements ClassificationMetric	Holds
Read Oct 3	Interface ClassificationMetric, Method signature double score(int[] truth, int[] prediction), Who implements it Accuracy , Error , Precision , Recall , FScore , FDR , Fallout , Specificity , Sensitivity , MatthewsCorrelation	FScore implements ClassificationMetric	Holds
Read Oct 3	Interface ClassificationMetric, Method signature double score(int[] truth, int[] prediction), Who implements it Accuracy , Error , Precision , Recall , FScore , FDR , Fallout , Specificity , Sensitivity , MatthewsCorrelation	MatthewsCorrelation implements ClassificationMetric	Holds
Read Oct 3	Interface ProbabilisticClassificationMetric, Method signature double score(int[] truth, double[] probability), Who implements it AUC , LogLoss	LogLoss implements ProbabilisticClassificationMetric	Holds
Read Oct 3	Interface ClusteringMetric, Method signature double score(int[] truth, int[] cluster), Who implements it RandIndex , AdjustedRandIndex , MutualInformation , NormalizedMutualInformation , AdjustedMutualInformation	RandIndex implements ClusteringMetric	Holds
Read Oct 3	Interface ClusteringMetric, Method signature double score(int[] truth, int[] cluster), Who implements it RandIndex , AdjustedRandIndex , MutualInformation , NormalizedMutualInformation , AdjustedMutualInformation	AdjustedRandIndex implements ClusteringMetric	Holds
Read Oct 3	Interface ClusteringMetric, Method signature double score(int[] truth, int[] cluster), Who implements it RandIndex , AdjustedRandIndex , MutualInformation , NormalizedMutualInformation , AdjustedMutualInformation	MutualInformation implements ClusteringMetric	Holds
Read Oct 3	Interface ClusteringMetric, Method signature double score(int[] truth, int[] cluster), Who implements it RandIndex , AdjustedRandIndex , MutualInformation , NormalizedMutualInformation , AdjustedMutualInformation	NormalizedMutualInformation implements ClusteringMetric	Holds
Read Oct 3	Interface ClusteringMetric, Method signature double score(int[] truth, int[] cluster), Who implements it RandIndex , AdjustedRandIndex , MutualInformation , NormalizedMutualInformation , AdjustedMutualInformation	AdjustedMutualInformation implements ClusteringMetric	Holds
Read Oct 3	Singleton instances (`Accuracy.instance`, `AUC.instance`, etc.) are provided for convenience when you need a reusable object reference.	AUC has `instance`	Holds
Read Oct 3	`CrossEntropy` is an interface (not a class);	CrossEntropy is a contract	Holds
Read Oct 3	Type Role, Kind enum, Purpose system , user , assistant , tool	Role is an enumeration	Holds
Read Oct 3	Type Message, Kind record, Purpose A single dialog turn — (Role role, String content)	Message has `role`	Holds
Read Oct 3	Type Message, Kind record, Purpose A single dialog turn — (Role role, String content)	Message has `content`	Holds
Read Oct 3	Type FinishReason, Kind enum, Purpose stop , length , tool_calls	FinishReason is an enumeration	Holds
Read Oct 3	`Tiktoken` is the BPE implementation compatible with OpenAI's tiktoken library (used by LLaMA-3):	Tiktoken is a concrete implementation	Holds
Read Oct 3	`Tokenizer` is the encoding/decoding interface.	Tokenizer is a contract	Holds
Read Oct 3	UTF-8-safe decoding (with a strict `tryDecode` variant that throws `CharacterCodingException` on invalid byte sequences)	Tiktoken has `tryDecode`	Holds
Read Oct 3	Class PositionalEncoding, Algorithm Sinusoidal (sin/cos, fixed), Used by Original Transformer	PositionalEncoding is a concrete implementation	Holds
Read Oct 3	Class LlamaModel, Role Top-level module — embedding + N × LlamaBlock + output projection	LlamaModel is a concrete implementation	Holds
Read Oct 3	Class LlamaModel, Role Top-level module — embedding + N × LlamaBlock + output projection	LlamaModel depends on LlamaBlock	Holds
Read Oct 3	Class LlamaBlock, Role Single decoder block GroupedQueryAttention + FeedForward + RMS norms	LlamaBlock depends on GroupedQueryAttention	Holds
Read Oct 3	Class LlamaBlock, Role Single decoder block GroupedQueryAttention + FeedForward + RMS norms	LlamaBlock depends on FeedForward	Holds
Read Oct 3	Class Llama, Role High-level entry point — build() , single-prompt generate() / chat()	Llama has a `build` method	Holds
Read Oct 3	Class Llama, Role High-level entry point — build() , single-prompt generate() / chat()	Llama has a `generate` method	Holds
Read Oct 3	Class Llama, Role High-level entry point — build() , single-prompt generate() / chat()	Llama has a `chat` method	Holds
Read Oct 3	Class InferenceEngine ( smile.llm.engine ), Role Request queue + Fluid Injection / Instant Eviction for serve	InferenceEngine is a concrete implementation	Holds
Read Oct 3	`Transform` is a functional interface that converts one or more `BufferedImage` objects into a 4-D `[N, C, H, W]` float tensor suitable for a vision model.	Transform is a contract	Holds
Read Oct 3	`ImageDataset` implements `Dataset<SampleBatch>` and reads images from a folder-per-class directory structure:	ImageDataset implements Dataset	Holds
Read Oct 3	`EfficientNet` extends `LayerBlock` and implements the EfficientNet-V2 architecture.	EfficientNet extends LayerBlock	Holds
Read Oct 3	Factory EfficientNet.V2S(), Variant EfficientNet-V2-S, Input size 384 × 384, Parameters ~21 M	EfficientNet has a `V2S` method	Holds
Read Oct 3	Factory EfficientNet.V2M(), Variant EfficientNet-V2-M, Input size 480 × 480, Parameters ~54 M	EfficientNet has a `V2M` method	Holds
Read Oct 3	Factory EfficientNet.V2L(), Variant EfficientNet-V2-L, Input size 480 × 480, Parameters ~119 M	EfficientNet has a `V2L` method	Holds
Read Oct 3	`VisionModel.forward(BufferedImage...)` automatically applies the model's associated `Transform`, so you never need to preprocess images manually.	VisionModel has a `forward` method	Holds
Read Oct 3	`VisionModel.forward(BufferedImage...)` automatically applies the model's associated `Transform`, so you never need to preprocess images manually.	VisionModel depends on Transform	Holds
Read Oct 3	The `EfficientNet` constructor accepts an `MBConvConfig[]` array to define a custom architecture, giving fine-grained control over each inverted-residual stage:	EfficientNet depends on MBConvConfig	Holds
Read Oct 3	`ImageNet` is an interface with two 1000-element string arrays and a set of utility methods for mapping between class indices and human-readable labels:	ImageNet is a contract	Holds
Read Oct 3	`GenAiChatModel` implements `smile.llm.LanguageModel` for a future serve backend.	GenAiChatModel implements LanguageModel	Holds
Read Oct 3	`Figure.toBufferedImage(width, height)` renders to a `BufferedImage` with no display required.	Figure has a `toBufferedImage` method	Holds
Read Oct 3	`Palette.jet(256, 0.8f)`.	Palette has a `jet` method	Holds
Read Oct 3	`Palette.get(int index)` returns a distinct color for the given group index (cycles through a built-in list of 10 visually distinct colous). It is used automatically by `ScatterPlot.of(x, labels, mark)` and similar factory methods.	ScatterPlot depends on Palette	Holds
Read Oct 3	`Palette.get(int index)` returns a distinct color for the given group index (cycles through a built-in list of 10 visually distinct colous).	Palette has a `get` method	Holds
Read Oct 3	It is used automatically by `ScatterPlot.of(x, labels, mark)` and similar factory methods.	ScatterPlot has an `of` method	Holds
Read Oct 3	All axis-level configuration is accessed via `figure.getAxis(i)`, where `i = 0` is the x-axis, `i = 1` is the y-axis, and `i = 2` is the z-axis (for 3-D figures).	Figure has a `getAxis` method	Holds
Read Oct 3	Legends are displayed automatically when a `Plot` has legends and `figure.isLegendVisible()` is `true` (the default).	Figure has an `isLegendVisible` method	Holds
Read Oct 3	Class Shape, Responsibility Abstract base — carries color knows how to paint(Renderer)	Shape has a `paint` method	Holds
Read Oct 3	Class Plot, Responsibility Shape subclass that also knows its data bounds ( getLowerBound / getUpperBound ) and optional legends/tooltips	Plot has `getLowerBound`	Holds
Read Oct 3	Class Plot, Responsibility Shape subclass that also knows its data bounds ( getLowerBound / getUpperBound ) and optional legends/tooltips	Plot has `getUpperBound`	Holds
Read Oct 3	Class Plot, Responsibility Shape subclass that also knows its data bounds ( getLowerBound / getUpperBound ) and optional legends/tooltips	Plot extends Shape	Holds
Read Oct 3	Class FigurePane, Responsibility Swing JPanel wrapping a Canvas with a toolbar	FigurePane depends on Canvas	Holds
Read Oct 3	It extends `VegaLite`, which provides the top-level properties shared by all specification types.	View extends VegaLite	Holds
Read Oct 3	`view.data()` returns a `Data` object that supports four source types.	View has a `data` method	Holds
Read Oct 3	`view.mark(type)` selects the geometric primitive and returns a `Mark` object for further configuration.	View has a `mark` method	Holds
Read Oct 3	`view.encode(channel, field)` binds a data field to a visual channel and returns a `Field` object for further refinement.	View has an `encode` method	Holds
Read Oct 3	`view.transform()` returns a `Transform` object that appends entries to the view-level `transform` array.	View has a `transform` method	Holds
Read Oct 3	`(String op, String field, double param, String as)`.	WindowTransformField has an `op` field	Holds
Read Oct 3	`(String op, String field, double param, String as)`.	WindowTransformField has a `field` field	Holds
Read Oct 3	`(String op, String field, double param, String as)`.	WindowTransformField has a `param` field	Holds
Read Oct 3	`(String op, String field, double param, String as)`.	WindowTransformField has an `as` field	Holds
Read Oct 3	Obtained from `field.axis()` or `config.axis()`.	Field has an `axis` method	Holds
Read Oct 3	Obtained from `field.legend()`.	Field has a `legend` method	Holds
Read Oct 3	`view.config()` returns a `Config` object;	View has a `config` method	Holds
Read Oct 3	`view.viewConfig()` returns a `ViewConfig` that controls the default single-view dimensions and borders.	View has a `viewConfig` method	Holds
Read Oct 3	`VegaLite.mapper` is the shared, stateless `ObjectMapper` instance used throughout the package.	VegaLite has `mapper`	Holds
Read Oct 3	At startup, `OnnxService` scans the folder specified by the property `smile.onnx.model`. Every `.onnx` file found is loaded into an `InferenceSession`.	OnnxService depends on InferenceSession	Holds
Read Oct 3	Each `Message` has a `role` (`system`, `user`, `assistant`, or `tool`) and `content`.	Message has `role`	Holds
Read Oct 3	Each `Message` has a `role` (`system`, `user`, `assistant`, or `tool`) and `content`.	Message has `content`	Holds
Read Oct 3	`metadata` (≤16 string pairs) and `items` (≤20 message items with `role` + text `content`).	Conversation has `metadata`	Holds
Read Oct 3	`smile.Main.main(String[] args)` is the single entry point for all CLI and GUI functionality.	Main has a `main` method	Holds

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