DenseLayeredTensorLike2

abstract def dim1: Int

abstract def dim2: Int

abstract def newTensor1: (Int) ⇒ Tensor1

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

def *(other: Tensor1): Tensor1

def *(v: Double): Tensor

def *=(ds: DoubleSeq): Unit

def *=(d: Double): Unit

def *=(i: Int, incr: Double): Unit

def +(that: Tensor): Tensor

def ++=(tensors: Iterable[Tensor]): DenseLayeredTensorLike2.this.type

def +=(ds: DoubleSeq, f: Double): Unit

def +=(i: Int, incr: Double): Unit

def +=(i: Int, j: Int, v: Double): Unit

def +=(ds: DoubleSeq, factor: DoubleSeq): Unit

Increment by the element-wise product of ds and factor.

def +=(a: Array[Double], factor: Double): Unit

def +=(a: Array[Double]): Unit

final def +=(ds: DoubleSeq): Unit

def +=(d: Double): Unit

def -(that: Tensor): Tensor

def -=(ds: DoubleSeq): Unit

final def -=(d: Double): Unit

def -=(i: Int, incr: Double): Unit

def /(v: Double): Tensor

def /=(ds: DoubleSeq): Unit

final def /=(d: Double): Unit

final def /=(i: Int, incr: Double): Unit

def :=(a: Array[Double], offset: Int): Unit

def :=(a: Array[Double]): Unit

def :=(ds: DoubleSeq): Unit

def :=(d: Double): Unit

def =+(a: Array[Double], offset: Int, f: Double): Unit

Increment given array (starting at offset index) with contents of this DoubleSeq, multiplied by factor f.

final def =+(a: Array[Double], f: Double): Unit

final def =+(a: Array[Double], offset: Int): Unit

final def =+(a: Array[Double]): Unit

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def abs(): Unit

def activeDomain: ArrayIntSeq

def activeDomain1: TruncatedArrayIntSeq

def activeDomain2: RangeIntSeq

def activeDomainSize: Int

def activeDomains: Array[IntSeq]

def activeElements: Iterator[(Int, Double)]

def addString(b: StringBuilder, start: String, sep: String, end: String): StringBuilder

Append a string representation of this DoubleSeq to the StringBuilder.

def apply(i: Int): Double

def apply(i: Int, j: Int): Double

def asArray: Array[Double]

Return the values as an Array[Double].

final def asInstanceOf[T0]: T0

def asSeq: Seq[Double]

With uncopied contents

def blankCopy: Tensor2

def clone(): AnyRef

def contains(d: Double): Boolean

def containsNaN: Boolean

def copy: Tensor2

def cosineSimilarity(t: DoubleSeq): Double

def defaultValue: Double

The default value at indices not covered by activeDomain.

def diag: Tensor1

def different(t: DoubleSeq, threshold: Double): Boolean

def dimensions: Array[Int]

def dimensionsMatch(t: Tensor): Boolean

def dot(t: DoubleSeq): Double

def ensureDimensionsMatch(t: Tensor): Unit

def entropy: Double

Assumes that the values are already normalized to sum to 1.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def exists(f: (Double) ⇒ Boolean): Boolean

def expNormalize(logZ: Double): Unit

Exponential the elements of the array such that they are normalized to sum to one, but do so efficiently by providing logZ.

def expNormalize(): Double

Exponentiate the elements of the array, and then normalize them to sum to one.

def expNormalized: Tensor

def exponentiate(): Unit

def finalize(): Unit

def foldActiveElements(seed: Double, f: (Int, Double, Double) ⇒ Double): Double

def foldLeft[B](z: B)(f: (B, Double) ⇒ B): B

def forall(f: (Double) ⇒ Boolean): Boolean

def forallActiveElements(f: (Int, Double) ⇒ Boolean): Boolean

def forallElements(f: (Int, Double) ⇒ Boolean): Boolean

def foreach(f: (Double) ⇒ Unit): Unit

def foreachActiveElement(f: (Int, Double) ⇒ Unit): Unit

def foreachElement(f: (Int, Double) ⇒ Unit): Unit

final def getClass(): Class[_]

def getInner(i: Int): Tensor1

def hashCode(): Int

final def index1(i: Int): Int

final def index2(i: Int): Int

def indexOf(d: Double): Int

def infinityNorm: Double

def inner(i: Int): Tensor1

def isDense: Boolean

final def isInstanceOf[T0]: Boolean

def isUniform: Boolean

def jsDivergence(p: DoubleSeq): Double

Assumes that the values are already normalized to sum to 1.

def klDivergence(p: DoubleSeq): Double

Assumes that the values in both DoubleSeq are already normalized to sum to 1.

def l2Similarity(t: DoubleSeq): Double

def leftMultiply(other: Tensor1): Tensor1

final def length: Int

def map(f: (Double) ⇒ Double): DoubleSeq

def max: Double

def maxIndex: Int

def maxIndex2: (Int, Int)

def maxNormalize(): Unit

def min: Double

def mkString: String

def mkString(sep: String): String

def mkString(start: String, sep: String, end: String): String

final def multiIndex(i: Int): (Int, Int)

final def ne(arg0: AnyRef): Boolean

def normalize(): Double

def normalizeLogProb(): Double

expNormalize, then put back into log-space.

def normalized: Tensor

final def notify(): Unit

final def notifyAll(): Unit

def numDimensions: Int

def oneNorm: Double

def oneNormalize(): Double

def outer(t: Tensor): Tensor

def printLength: Int

def sampleIndex(normalizer: Double)(implicit r: Random): Int

def sampleIndex(implicit r: Random): Int

Careful, for many subclasses this is inefficient because it calls the method "sum" to get the normalizer.

final def singleIndex(i: Int, j: Int): Int

final def size: Int

def stringPrefix: String

def substitute(oldValue: Double, newValue: Double): Unit

def sum: Double

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

def toArray: Array[Double]

Return the values as an Array[Double].

def toSeq: Seq[Double]

With copied contents

def toString(): String

def top(n: Int): TopN[String]

Return records for the n elements with the largest values.

def trace: Double

final def twoNorm: Double

def twoNormSquared: Double

def twoNormalize(): Double

def twoSquaredNormalize(): Double

def update(i: Int, t: Tensor1): Unit

def update(i: Int, j: Int, v: Double): Unit

def update(i: Int, d: Double): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def zero(): Unit