RowMatrix

Instance Constructors

new RowMatrix(rows: RDD[Vector])

Alternative constructor leaving matrix dimensions to be determined automatically.
new RowMatrix(rows: RDD[Vector], nRows: Long, nCols: Int)

rows
rows stored as an RDD[Vector]
nRows
number of rows. A non-positive value means unknown, and then the number of rows will be determined by the number of records in the RDD rows.
nCols
number of columns. A non-positive value means unknown, and then the number of columns will be determined by the size of the first row.

Value Members

final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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def computeColumnSummaryStatistics(): MultivariateStatisticalSummary

Computes column-wise summary statistics.
def computeCovariance(): Matrix

Computes the covariance matrix, treating each row as an observation.
Computes the covariance matrix, treating each row as an observation.
returns
a local dense matrix of size n x n
def computeGramianMatrix(): Matrix

Computes the Gramian matrix A^T A.
def computePrincipalComponents(k: Int): Matrix

Computes the top k principal components.
Computes the top k principal components. Rows correspond to observations and columns correspond to variables. The principal components are stored a local matrix of size n-by-k. Each column corresponds for one principal component, and the columns are in descending order of component variance. The row data do not need to be "centered" first; it is not necessary for the mean of each column to be 0.
k
number of top principal components.
returns
a matrix of size n-by-k, whose columns are principal components
def computeSVD(k: Int, computeU: Boolean = false, rCond: Double = 1e-9): SingularValueDecomposition[RowMatrix, Matrix]

Computes singular value decomposition of this matrix.
Computes singular value decomposition of this matrix. Denote this matrix by A (m x n). This will compute matrices U, S, V such that A ~= U * S * V', where S contains the leading k singular values, U and V contain the corresponding singular vectors.
At most k largest non-zero singular values and associated vectors are returned. If there are k such values, then the dimensions of the return will be:
- U is a RowMatrix of size m x k that satisfies U' * U = eye(k),
- s is a Vector of size k, holding the singular values in descending order,
- V is a Matrix of size n x k that satisfies V' * V = eye(k).
We assume n is smaller than m. The singular values and the right singular vectors are derived from the eigenvalues and the eigenvectors of the Gramian matrix A' * A. U, the matrix storing the right singular vectors, is computed via matrix multiplication as U = A * (V * S^-1), if requested by user. The actual method to use is determined automatically based on the cost:
- If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian matrix first and then compute its top eigenvalues and eigenvectors locally on the driver. This requires a single pass with O(n²) storage on each executor and on the driver, and O(n² k) time on the driver.
- Otherwise, we compute (A' * A) * v in a distributive way and send it to ARPACK's DSAUPD to compute (A' * A)'s top eigenvalues and eigenvectors on the driver node. This requires O(k) passes, O(n) storage on each executor, and O(n k) storage on the driver.
Several internal parameters are set to default values. The reciprocal condition number rCond is set to 1e-9. All singular values smaller than rCond * sigma(0) are treated as zeros, where sigma(0) is the largest singular value. The maximum number of Arnoldi update iterations for ARPACK is set to 300 or k * 3, whichever is larger. The numerical tolerance for ARPACK's eigen-decomposition is set to 1e-10.
k
number of leading singular values to keep (0 < k <= n). It might return less than k if there are numerically zero singular values or there are not enough Ritz values converged before the maximum number of Arnoldi update iterations is reached (in case that matrix A is ill-conditioned).
computeU
whether to compute U
rCond
the reciprocal condition number. All singular values smaller than rCond * sigma(0) are treated as zero, where sigma(0) is the largest singular value.
returns
SingularValueDecomposition(U, s, V). U = null if computeU = false.

Note
The conditions that decide which method to use internally and the default parameters are subject to change.
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def finalize(): Unit

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final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

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def log: Logger

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def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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def logDebug(msg: ⇒ String): Unit

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def logError(msg: ⇒ String, throwable: Throwable): Unit

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def logError(msg: ⇒ String): Unit

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def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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def logInfo(msg: ⇒ String): Unit

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def logName: String

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def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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def logTrace(msg: ⇒ String): Unit

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def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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def logWarning(msg: ⇒ String): Unit

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def multiply(B: Matrix): RowMatrix

Multiply this matrix by a local matrix on the right.
Multiply this matrix by a local matrix on the right.
B
a local matrix whose number of rows must match the number of columns of this matrix
returns
a org.apache.spark.mllib.linalg.distributed.RowMatrix representing the product, which preserves partitioning
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 numCols(): Long

Gets or computes the number of columns.
Gets or computes the number of columns.

Definition Classes
RowMatrix → DistributedMatrix
def numRows(): Long

Gets or computes the number of rows.
Gets or computes the number of rows.

Definition Classes
RowMatrix → DistributedMatrix
val rows: RDD[Vector]

rows stored as an RDD[Vector]
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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final def wait(arg0: Long): Unit

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class RowMatrix extends DistributedMatrix with Logging

Instance Constructors

new RowMatrix(rows: RDD[Vector])

new RowMatrix(rows: RDD[Vector], nRows: Long, nCols: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def computeColumnSummaryStatistics(): MultivariateStatisticalSummary

def computeCovariance(): Matrix

def computeGramianMatrix(): Matrix

def computePrincipalComponents(k: Int): Matrix

def computeSVD(k: Int, computeU: Boolean = false, rCond: Double = 1e-9): SingularValueDecomposition[RowMatrix, Matrix]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def multiply(B: Matrix): RowMatrix

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numCols(): Long

def numRows(): Long

val rows: RDD[Vector]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Logging

Inherited from DistributedMatrix

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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