object read
Data loading utilities.
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- final def ##(): Int
- Definition Classes
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- final def ==(arg0: Any): Boolean
- Definition Classes
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- def apply(file: Path): AnyRef
Reads a
Serializableobject/model. - def apply(file: String): AnyRef
Reads a
Serializableobject/model. - def arff(file: Path): DataFrame
Reads an ARFF file.
- def arff(file: String): DataFrame
Reads an ARFF file.
- def arrow(file: Path): DataFrame
Reads an Apache Arrow file.
- def arrow(file: String): DataFrame
Reads an Apache Arrow file.
- final def asInstanceOf[T0]: T0
- Definition Classes
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- def avro(file: Path, schema: Schema): DataFrame
Reads an Apache Avro file.
- def avro(file: String, schema: Schema): DataFrame
Reads an Apache Avro file.
- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def csv(file: Path, format: CSVFormat, schema: StructType): DataFrame
Reads a CSV file.
- def csv(file: String, format: CSVFormat, schema: StructType): DataFrame
Reads a CSV file.
- def csv(file: Path, delimiter: Char, header: Boolean, quote: Char, escape: Char, schema: StructType): DataFrame
Reads a CSV file.
- def csv(file: String, delimiter: Char = ',', header: Boolean = true, quote: Char = '"', escape: Char = '\\', schema: StructType = null): DataFrame
Reads a CSV file.
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def finalize(): Unit
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- def hashCode(): Int
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- final def isInstanceOf[T0]: Boolean
- Definition Classes
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- def jdbc(rs: ResultSet): DataFrame
Reads a JDBC query result to a data frame.
- def json(file: Path, mode: Mode, schema: StructType): DataFrame
Reads a JSON file.
- def json(file: String, mode: Mode, schema: StructType): DataFrame
Reads a JSON file.
- def json(file: Path): DataFrame
Reads a JSON file.
- def json(file: String): DataFrame
Reads a JSON file.
- def libsvm(file: Path): Dataset[Instance[SparseArray]]
Reads a LivSVM file.
- def libsvm(file: String): Dataset[Instance[SparseArray]]
Reads a LivSVM file.
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- def parquet(file: Path): DataFrame
Reads an Apache Parquet file.
- def parquet(file: String): DataFrame
Reads an Apache Parquet file.
- def sas(file: Path): DataFrame
Reads a SAS7BDAT file.
- def sas(file: String): DataFrame
Reads a SAS7BDAT file.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
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- def toString(): String
- Definition Classes
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- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- def wavefront(file: Path): (Array[Array[Double]], Array[Array[Int]])
Reads a Wavefront OBJ file.
Reads a Wavefront OBJ file. The OBJ file format is a simple format of 3D geometry including the position of each vertex, the UV position of each texture coordinate vertex, vertex normals, and the faces that make each polygon defined as a list of vertices, and texture vertices. Vertices are stored in a counter-clockwise order by default, making explicit declaration of face normals unnecessary. OBJ coordinates have no units, but OBJ files can contain scale information in a human readable comment line.
Note that we parse only vertex and face elements. All other information ignored.
- file
the file path
- returns
a tuple of vertex array and edge array.
- def wavefront(file: String): (Array[Array[Double]], Array[Array[Int]])
Reads a Wavefront OBJ file.
- def xstream(file: Path): AnyRef
Reads an object/model that was serialized by XStream.
- def xstream(file: String): AnyRef
Reads an object/model that was serialized by XStream.
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.