wandou.math.algebra

SingularValueDecomposition

class SingularValueDecomposition extends Serializable

Constructs and returns a new singular value decomposition object; The decomposed matrices can be retrieved via instance methods of the returned decomposition object.

See also: http://en.wikipedia.org/wiki/Singular_value_decomposition also @see http://en.wikipedia.org/wiki/Eigenvalue,_eigenvector_and_eigenspace

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Instance Constructors

new SingularValueDecomposition(arg: Matrix)

arg
A rectangular matrix.

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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val a: Array[Array[Double]]
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[java.lang]
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@throws( ... )
def cond: Double

Returns the two norm condition number, which is max(S) / min(S).
Returns the two norm condition number, which is max(S) / min(S).
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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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def getCovariance(minSingularValue: Double): Matrix

returns
Returns the n × n covariance matrix. The covariance matrix is V × J × Vt where J is the diagonal matrix of the inverse of the squares of the singular values.
def getS: Matrix

returns
the diagonal matrix of singular values.
def getSingularValues: Array[Double]

Returns the diagonal of S, which is a one-dimensional array of singular values
Returns the diagonal of S, which is a one-dimensional array of singular values
returns
diagonal of S.
def getU: Matrix

Returns the left singular vectors U.
Returns the left singular vectors U.
returns
U
def getV: Matrix

Returns the right singular vectors V.
Returns the right singular vectors V.
returns
V
def hashCode(): Int

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def init(arg: Matrix): Unit
final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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def norm2: Double

Returns the two norm, which is max(S).
Returns the two norm, which is max(S).
final def notify(): Unit

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final def notifyAll(): Unit

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def rank: Int

Returns the effective numerical matrix rank, which is the number of nonnegligible singular values.
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

Returns a String with (propertyName, propertyValue) pairs.
Returns a String with (propertyName, propertyValue) pairs. Useful for debugging or to quickly get the rough picture. For example,
```
rank          : 3
trace         : 0
```
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SingularValueDecomposition → AnyRef → Any
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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