class
HashMatrix[T] extends Matrix[T]
Instance Constructors
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new
HashMatrix(initialSize: Int = 32, poolSize: Int = 4)(implicit arrayable: Arrayable[T])
Value Members
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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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def
+(other: String): String
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def
->[B](y: B): (HashMatrix[T], B)
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final
def
==(arg0: AnyRef): Boolean
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final
def
==(arg0: Any): Boolean
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def
apply(xr: Int, yr: Int): T
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def
applyAndUpdate(xr: Int, yr: Int, v: T): T
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def
area(gxf: Int, gyf: Int, gxu: Int, gyu: Int): Area[T]
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implicit
val
arrayable: Arrayable[T]
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final
def
asInstanceOf[T0]: T0
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def
clear(): Unit
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def
clearSpecialized(self: HashMatrix[T]): Unit
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def
clone(): AnyRef
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def
copy(a: Array[T], gxf: Int, gyf: Int, gxu: Int, gyu: Int): Unit
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def
ensuring(cond: (HashMatrix[T]) ⇒ Boolean, msg: ⇒ Any): HashMatrix[T]
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def
ensuring(cond: Boolean, msg: ⇒ Any): HashMatrix[T]
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def
ensuring(cond: Boolean): HashMatrix[T]
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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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def
findBlock(xb: Int, yb: Int): Block[T]
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def
foreach(f: (XY) ⇒ Unit): Unit
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def
formatted(fmtstr: String): String
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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def
increaseSize(): Unit
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final
def
isInstanceOf[T0]: Boolean
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final
def
ne(arg0: AnyRef): Boolean
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val
nil: T
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def
nonNilArea(gxf: Int, gyf: Int, gxu: Int, gyu: Int): Area[T]
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
orElse(xr: Int, yr: Int, v: T): T
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def
remove(x: Int, y: Int): T
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
update(x: Int, y: Int, v: T): Unit
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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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def
→[B](y: B): (HashMatrix[T], B)
Shadowed Implicit Value Members
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val
self: Any
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val
self: Any
Inherited from AnyRef
Inherited from Any
Inherited by implicit conversion any2stringadd from
HashMatrix[T] to StringAdd
Inherited by implicit conversion any2stringfmt from
HashMatrix[T] to StringFormat
Hash-based sparse matrix with an efficient O(1) indexing and update operations.
Ideal for sparse or dense data, particularly with many clusters that are tightly packed together. For spatial queries on sparse data, it is usually better to use
QuadMatrix
.