object
qr extends UFunc
Type Members
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type
Impl[V, VR] = UImpl[qr.this.type, V, VR]
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type
Impl2[V1, V2, VR] = UImpl2[qr.this.type, V1, V2, VR]
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type
Impl3[V1, V2, V3, VR] = UImpl3[qr.this.type, V1, V2, V3, VR]
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type
Impl4[V1, V2, V3, V4, VR] = UImpl4[qr.this.type, V1, V2, V3, V4, VR]
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type
SinkImpl2[S, V1, V2] = generic.UFunc.SinkImpl2[qr.this.type, S, V1, V2]
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type
SinkImpl3[S, V1, V2, V3] = generic.UFunc.SinkImpl3[qr.this.type, S, V1, V2, V3]
Value Members
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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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final
def
apply[V1, V2, V3, V4, VR](v1: V1, v2: V2, v3: V3, v4: V4)(implicit impl: Impl4[V1, V2, V3, V4, VR]): VR
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final
def
apply[V1, V2, V3, VR](v1: V1, v2: V2, v3: V3)(implicit impl: Impl3[V1, V2, V3, VR]): VR
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final
def
apply[V1, V2, VR](v1: V1, v2: V2)(implicit impl: Impl2[V1, V2, VR]): VR
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final
def
apply[V, VR](v: V)(implicit impl: Impl[V, VR]): VR
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final
def
asInstanceOf[T0]: T0
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implicit
def
canZipMapValuesImpl[T, V1, VR, U](implicit handhold: ScalarOf[T, V1], impl: Impl2[V1, V1, VR], canZipMapValues: CanZipMapValues[T, V1, VR, U]): Impl2[T, T, U]
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def
clone(): AnyRef
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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
inPlace[V, V2, V3](v: V, v2: V2, v3: V3)(implicit impl: generic.UFunc.InPlaceImpl3[qr.this.type, V, V2, V3]): V
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final
def
inPlace[V, V2](v: V, v2: V2)(implicit impl: generic.UFunc.InPlaceImpl2[qr.this.type, V, V2]): V
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final
def
inPlace[V](v: V)(implicit impl: generic.UFunc.InPlaceImpl[qr.this.type, V]): V
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final
def
isInstanceOf[T0]: Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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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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final
def
withSink[S](s: S): WithSinkHelp[qr.this.type, S]
QR Factorization
Previous versions of Breeze had qr(m, skipQ), where we could skip the computation in making Q if we didn't want it. That is now supplanted by qr.justR(m)
Supports complete and reduced mode of factorization of matrix A with dimensions (m, n). If mode is complete matrices Q and R have dimensions (m, m), (m, n). If mode is reduced matrices Q and R have dimensions (m, k), (k, n) with k = min(m, n).
Complete QR factorization can be called by qr(A).
Reduced QR factorization can be called by qr.reduced(A). If computation of Q is unnecessary, it can be skipped by qr.reduced.justR(A)
(Q, R) Q - A matrix with orthonormal columns R - The upper-triangular matrix