object read
Data loading utilities.
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- final def ##: Int
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- def apply(file: Path): AnyRef
Reads a serialized object from a file.
- def apply(file: String): AnyRef
Reads a serialized object from a file.
- 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
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- def avro(file: Path, schema: Path): DataFrame
Reads an Apache Avro file.
- def avro(file: Path, schema: InputStream): DataFrame
Reads an Apache Avro file.
- def avro(file: String, schema: String): DataFrame
Reads an Apache Avro file.
- def avro(file: String, schema: InputStream): 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: String, header: Boolean, quote: Char, escape: Char, schema: StructType): DataFrame
Reads a CSV file.
- def csv(file: String, delimiter: String = ",", header: Boolean = true, quote: Char = '"', escape: Char = '\\', schema: StructType = null): DataFrame
Reads a CSV file.
- def data(path: String, format: String = null): DataFrame
Reads a data file.
Reads a data file. Infers the data format by the file name extension.
- path
the input file path.
- format
the optional file format specification. For csv files, it is such as
delimiter=\t,header=true,comment=#,escape=\,quote="
. For json files, it is the file mode (single-line or multi-line). For avro files, it is the path to the schema file.- returns
the data frame.
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- final def isInstanceOf[T0]: Boolean
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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
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- def toString(): String
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- final def wait(): Unit
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- final def wait(arg0: Long, arg1: Int): 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.
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.