object Relation extends Error with Serializable
The Relation companion object provides additional functions for the Relation
class.
FIX - apply methods - make compatible with RelationSQL
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- def apply(fileName: String, key: Int, domain: String): Relation
Read the relation with the given 'name' into memory loading its columns with data from the '.arff' file named 'fileName'.
Read the relation with the given 'name' into memory loading its columns with data from the '.arff' file named 'fileName'.
- fileName
the file name of the data file
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def apply(fileName: String, name: String, colName: ArrayBuffer[String], key: Int, domain: String): Relation
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'.
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'. This version assumes defaults for 'eSep' and 'skip' of ("," and 0).
- fileName
the file name of the data file
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def apply(fileName: String, name: String, domain: String, key: Int, eSep: String = ","): Relation
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'.
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'. In this version, the column names are read from the first line of the file. It uses 'col2' which is a temporary ReArray, and maintains indices.
- fileName
the file name of the data file
- name
the name of the relation
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- key
the column number for the primary key (< 0 => no primary key)
- eSep
the element separation string/regex (e.g., "," ";" " +")
- def apply(fileName: String, name: String, key: Int, domain: String, eSep: String, cPos: ArrayBuffer[Int]): Relation
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'.
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'. In this version, the column names are read from the first line of the file.
- fileName
the file name of the data file
- name
the name of the relation
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- eSep
the element separation string/regex (e.g., "," ";" " +")
- cPos
the sequence of column positions in the input file to be used (null => select all)
- def apply(fileName: String, name: String, colName: ArrayBuffer[String], key: Int, domain: String, skip: Int, eSep: String): Relation
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'.
Read the relation with the given 'name' into memory loading its columns with data from the CSV file named 'fileName'. Note: "ln.split (eSep, -1)" will keep all values even if empty "onethree" -> "one","",three"
- fileName
the file name of the data file
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- skip
the number of lines in the CSV file to skip (e.g., header line(s))
- eSep
the element separation string/regex (e.g., "," ";" " +")
- def apply(name: String): Relation
Read the relation with the given 'name' into memory using serialization.
Read the relation with the given 'name' into memory using serialization.
- name
the name of the relation to load
- def apply(name: String, colName: ArrayBuffer[String], row: ArrayBuffer[Row], key: Int): Relation
Create a relation from a sequence of row/tuples.
Create a relation from a sequence of row/tuples. These rows must be converted to columns.
- name
the name of the relation
- colName
the names of columns
- row
the sequence of rows to be converted to columns for the columnar relation
- key
the column number for the primary key (< 0 => no primary key)
- def apply(name: String, colName: ArrayBuffer[String], row: ArrayBuffer[Row], key: Int, domain: String): Relation
Create a relation from a sequence of row/tuples.
Create a relation from a sequence of row/tuples. These rows must be converted to columns.
- name
the name of the relation
- colName
the names of columns
- row
the sequence of rows to be converted to columns for the columnar relation
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def apply(name: String, key: Int, domain: String, colName: ArrayBuffer[String]): Relation
Create an unpopulated relation.
Create an unpopulated relation.
- name
the name of the relation
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- colName
the names of columns
- def apply(name: String, key: Int, domain: String, colName: String*): Relation
Create an unpopulated relation.
Create an unpopulated relation.
- name
the name of the relation
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- colName
the names of columns
- final def asInstanceOf[T0]: T0
- Definition Classes
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- def avg(r: Relation, cName: String): Vec
Get a Vec of average of the 'cName' column for the 'r' relation.
Get a Vec of average of the 'cName' column for the 'r' relation.
- r
the relation you want to operate on
- cName
average on column "cName"
- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def corr(r: Relation, i: Int = 0, j: Int = 1): Double
Compute the correlation of column 'i' and 'j' within columnar relation 'r'.
Compute the correlation of column 'i' and 'j' within columnar relation 'r'.
- r
the given relation
- i
the first column vector
- j
the second column vector
- def count(r: Relation, cName: String): Vec
Get a Vec of count of the 'cName' column for the 'r' relation.
Get a Vec of count of the 'cName' column for the 'r' relation.
- r
the relation you want to operate on
- cName
the column name for the column to be counted
- def count(r: Relation): IndexedSeq[Int]
Return the count (number of elements) of each of the columns of columnar relation 'r'.
Return the count (number of elements) of each of the columns of columnar relation 'r'.
- r
the given relation
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
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- def equals(arg0: AnyRef): Boolean
- Definition Classes
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- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def from(relations: Relation*): Relation
From function return cartesian product of all the relations.
From function return cartesian product of all the relations.
- relations
the relations making up the from clause
- def fromMatriD(xy: MatriD, name: String, colName: ArrayBuffer[String], key: Int = -1, domain: String = null): Relation
Create a relation from the 'xy' matrix of doubles.
Create a relation from the 'xy' matrix of doubles.
- xy
the matrix containing the data
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def fromMatriD_(x: MatriD, y: Vec, name: String, colName: ArrayBuffer[String], key: Int = -1, domain: String = null): Relation
Create a relation from the 'x' matrix of doubles and 'y' vector of doubles or integers.
Create a relation from the 'x' matrix of doubles and 'y' vector of doubles or integers.
- x
the matrix containing the data
- y
the vector containing the data
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def fromMatriI(xy: MatriI, name: String, colName: ArrayBuffer[String], key: Int = -1, domain: String = null): Relation
Create a relation from the 'xy' matrix of integers.
Create a relation from the 'xy' matrix of integers.
- xy
the matrix containing the data
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- def fromMatriII(x: MatriI, y: VectorI, name: String, colName: ArrayBuffer[String], key: Int = -1, domain: String = null): Relation
Create a relation from the 'xy' matrix of integers and 'y' vector of integers.
Create a relation from the 'xy' matrix of integers and 'y' vector of integers.
- x
the matrix containing the data
- y
the vector containing the data
- name
the name of the relation
- colName
the names of columns
- key
the column number for the primary key (< 0 => no primary key)
- domain
an optional string indicating domains for columns (e.g., 'SD' = 'StrNum', 'Double')
- final def getClass(): Class[_ <: AnyRef]
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- @native() @HotSpotIntrinsicCandidate()
- def hashCode(): Int
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- def max(r: Relation, cName: String): Vec
Get a Vec of max of the 'cName' column for the 'r' relation.
Get a Vec of max of the 'cName' column for the 'r' relation.
- r
the relation you want to operate on
- cName
max on column "cName"
- def max(r: Relation): IndexedSeq[Any]
Return the maximum of each of the columns of columnar relation 'r'.
Return the maximum of each of the columns of columnar relation 'r'.
- r
the given relation
- def mean(r: Relation): IndexedSeq[Any]
Compute the mean of each of the columns of columnar relation 'r'.
Compute the mean of each of the columns of columnar relation 'r'.
- r
the given relation
- def min(r: Relation, cName: String): Vec
Get a Vec of min of the 'cName' column for the 'r' relation
Get a Vec of min of the 'cName' column for the 'r' relation
- r
the relation you want to operate on
- cName
min on column "cName"
- def min(r: Relation): IndexedSeq[Any]
Return the minimum of each of the columns of columnar relation 'r'.
Return the minimum of each of the columns of columnar relation 'r'.
- r
the given relation
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- def sum(r: Relation, cName: String): Vec
Get a Vec of sum of the 'cName' column for the 'r' relation base on each group, the result will be the same size.
Get a Vec of sum of the 'cName' column for the 'r' relation base on each group, the result will be the same size.
- r
the relation to operate on
- cName
sum on column "cName"
- def sum(r: Relation): IndexedSeq[Any]
Compute the mean of each of the columns of columnar relation 'r'.
Compute the mean of each of the columns of columnar relation 'r'.
- r
the given relation
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- def variance(r: Relation): IndexedSeq[Any]
Compute the variance of each of the columns of columnar relation 'r'.
Compute the variance of each of the columns of columnar relation 'r'.
- r
the given relation
- 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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- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- def Ɛ(r: Relation): IndexedSeq[Any]
Compute the mean of each of the columns of columnar relation 'r'.
Compute the mean of each of the columns of columnar relation 'r'.
- r
the given relation
- def Ʋ(r: Relation): IndexedSeq[Any]
Compute the variance of each of the columns of columnar relation 'r'.
Compute the variance of each of the columns of columnar relation 'r'.
- r
the given relation
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