trait
SeqLpNormedVectorSpace[A, SA <: SeqLike[A, SA]] extends SeqVectorSpace[A, SA] with NormedVectorSpace[SA, A]
Abstract Value Members
-
implicit abstract
def
cbf: CanBuildFrom[SA, A, SA]
-
abstract
def
nroot: NRoot[A]
-
abstract
def
p: Int
-
implicit abstract
def
scalar: Field[A]
-
abstract
def
signed: Signed[A]
Concrete 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
-
def
additive: AbGroup[SA]
-
final
def
asInstanceOf[T0]: T0
-
def
clone(): AnyRef
-
def
distance(v: SA, w: SA): A
-
def
divr(v: SA, f: A): SA
-
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
minus(x: SA, y: SA): SA
-
final
def
ne(arg0: AnyRef): Boolean
-
def
negate(sa: SA): SA
-
def
norm(v: SA): A
-
def
normalize(v: SA): SA
-
final
def
notify(): Unit
-
final
def
notifyAll(): Unit
-
def
plus(x: SA, y: SA): SA
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
-
def
timesl(r: A, sa: SA): SA
-
def
toString(): String
-
final
def
wait(): Unit
-
final
def
wait(arg0: Long, arg1: Int): Unit
-
final
def
wait(arg0: Long): Unit
-
def
zero: SA
Inherited from AnyRef
Inherited from Any
The L_p norm is equal to the
p
-th root of the sum of each element to the powerp
. For instance, ifp = 1
we have the Manhattan distance. If you'd like the Euclidean norm (p = 2
), then you'd probably be best to use anInnerProductSpace
instead.