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breeze

linalg

package linalg

This package contains everything relating to Vectors, Matrices, Tensors, etc.

If you're doing basic work, you probably want breeze.linalg.DenseVector and breeze.linalg.DenseMatrix, which support most operations. We also have breeze.linalg.SparseVectors and (basic!) support for a sparse matrix (breeze.linalg.CSCMatrix).

This package object contains Matlab-esque functions for interacting with tensors and matrices.

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Type Members

  1. sealed trait Axis extends AnyRef

    This trait is commonly used for breeze.linalg.sum and its kin for summing along a particular axis of a Matrix.

  2. class BitVector extends Vector[Boolean] with VectorLike[Boolean, BitVector]

    A BitVector is a Vector of Booleans backed by a java.util.Bitset.

    A BitVector is a Vector of Booleans backed by a java.util.Bitset. Much better memory usage and sometimes faster.

  3. trait Broadcasted[+T, B] extends NumericOps[Broadcasted[T, B]]

    A Broadcasted is a type that represents "broadcasting" (a la numpy).

    A Broadcasted is a type that represents "broadcasting" (a la numpy).

    Unlike Numpy, broadcasting in Breeze is explicit: matrix(*, ::) lifts UFuncs and operators so that they apply over each row matrix(::, *) is the same, but for columns

  4. case class BroadcastedColumns[T, ColType](underlying: T) extends BroadcastedLike[T, ColType, BroadcastedColumns[T, ColType]] with Product with Serializable

    Class for classes that are broadcasting their columns.

    Class for classes that are broadcasting their columns. That is denseMatrix(::, *) /= denseVector

    T

    the type of the tensor

    underlying

    the tensor (or equivalent) being broadcasted

  5. trait BroadcastedColumnsOps extends AnyRef
  6. trait BroadcastedLike[T, B, Self <: Broadcasted[T, B]] extends Broadcasted[T, B] with NumericOps[Self]
  7. trait BroadcastedOps extends BroadcastedColumnsOps with BroadcastedRowsOps
  8. case class BroadcastedRows[T, RowType](underlying: T) extends BroadcastedLike[T, RowType, BroadcastedRows[T, RowType]] with Product with Serializable

    Class for classes that are broadcasting their rows.

    Class for classes that are broadcasting their rows. That is denseMatrix(*, ::) /= denseVector

    T

    the type of the tensor

    underlying

    the tensor (or equivalent) being broadcasted

  9. trait BroadcastedRowsOps extends AnyRef
  10. class Broadcaster extends AnyRef
  11. class CSCMatrix[V] extends Matrix[V] with MatrixLike[V, CSCMatrix[V]] with Serializable

    A compressed sparse column matrix, as used in Matlab and CSparse, etc.

    A compressed sparse column matrix, as used in Matlab and CSparse, etc.

    In general, you should probably NOT use the class's constructors unless you know what you are doing. We don't validate the input data for performance reasons. So make sure you understand the https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_.28CSC_or_CCS.29 correctly. Otherwise, please use the factory methods under CSCMatrix and CSCMatrix#Builder to construct CSC matrices.

  12. trait CanPadLeft[Input, Dimensions, Output] extends AnyRef
  13. trait CanPadRight[Input, Dimensions, Output] extends AnyRef
  14. trait Counter[K, V] extends Tensor[K, V] with CounterLike[K, V, Map[K, V], Counter[K, V]]
  15. trait Counter2[K1, K2, V] extends Tensor[(K1, K2), V] with Counter2Like[K1, K2, V, Counter[K2, V], Counter2[K1, K2, V]]
  16. trait Counter2Like[K1, K2, V, T <: Counter[K2, V], +This <: Counter2[K1, K2, V]] extends TensorLike[(K1, K2), V, This]

    A map-like tensor that acts like a collection of key-value pairs where the set of values may grow arbitrarily.

  17. trait CounterLike[K, V, +M <: Map[K, V], +This <: Counter[K, V]] extends TensorLike[K, V, This] with Serializable

    A map-like tensor that acts like a collection of key-value pairs where the set of values may grow arbitrarily.

  18. final class DenseMatrix[V] extends Matrix[V] with MatrixLike[V, DenseMatrix[V]] with Serializable

    A DenseMatrix is a matrix with all elements found in an array.

    A DenseMatrix is a matrix with all elements found in an array. It is column major unless isTranspose is true, It is designed to be fast: Double- (and potentially Float-)valued DenseMatrices can be used with blas, and support operations to that effect.

    Annotations
    @SerialVersionUID()
  19. class DenseVector[V] extends StorageVector[V] with VectorLike[V, DenseVector[V]] with Serializable

    A DenseVector is the "obvious" implementation of a Vector, with one twist.

    A DenseVector is the "obvious" implementation of a Vector, with one twist. The underlying data may have more data than the Vector, represented using an offset into the array (for the 0th element), and a stride that is how far elements are apart from one another.

    The i'th element is at offset + i * stride

    Annotations
    @SerialVersionUID()
  20. class HashVector[E] extends Vector[E] with VectorLike[E, HashVector[E]] with Serializable

    A HashVector is a sparse vector backed by an OpenAddressHashArray

  21. trait ImmutableNumericOps[+This] extends HasOps
  22. implicit final class InjectNumericOps[T] extends AnyVal with ImmutableNumericOps[T]
  23. class LapackException extends RuntimeException with LinearAlgebraException
  24. trait LinearAlgebraException extends RuntimeException

    Marker trait for exceptions thrown from the breeze.linalg package.

  25. trait LowPriorityCounter2 extends AnyRef
  26. trait LowPriorityMatrix extends AnyRef
  27. trait LowPrioritySliceMatrix extends AnyRef
  28. trait Matrix[V] extends MatrixLike[V, Matrix[V]]
  29. trait MatrixConstructors[Mat[T] <: Matrix[T]] extends AnyRef
  30. class MatrixEmptyException extends IllegalArgumentException with LinearAlgebraException
  31. trait MatrixLike[V, +Self <: Matrix[V]] extends Tensor[(Int, Int), V] with TensorLike[(Int, Int), V, Self]

  32. class MatrixNotSquareException extends IllegalArgumentException with LinearAlgebraException
  33. class MatrixNotSymmetricException extends IllegalArgumentException with LinearAlgebraException
  34. class MatrixSingularException extends RuntimeException with LinearAlgebraException

    Thrown when trying to solve using a singular matrix.

  35. trait NormBasedDistance extends UFunc

    TODO

  36. class NotConvergedException extends RuntimeException with LinearAlgebraException

    Exception thrown if a routine has not converged.

  37. trait NumericOps[+This] extends ImmutableNumericOps[This]

    In some sense, this is the real root of the linalg hierarchy.

    In some sense, this is the real root of the linalg hierarchy. It provides methods for doing operations on a Tensor-like thing. All methods farm out to some implicit or another. We use this when we don't care about the index into the Tensor, or if we don't really have an index.

  38. class PCA extends AnyRef

    Perform Principal Components Analysis on input data.

    Perform Principal Components Analysis on input data. Handles scaling of the when computing the covariance matrix. Lazily produces the scores (the translation of the data to their new coordinates on the PC axes.

    Input is a matrix that has data points as rows. Variable naming and documentation inspired and used directy from the 'princomp' function in R.

  39. trait QuasiTensor[K, V] extends HasOps

    We occasionally need a Tensor that doesn't extend NumericOps directly.

    We occasionally need a Tensor that doesn't extend NumericOps directly. This is that tensor.

  40. trait RandomGeneratorUFunc[T] extends UFunc
  41. class SliceMatrix[K1, K2, V] extends Matrix[V] with MatrixLike[V, SliceMatrix[K1, K2, V]]
  42. trait SliceMatrixOps extends AnyRef
  43. class SliceVector[K, V] extends Vector[V] with VectorLike[V, SliceVector[K, V]]

    A SliceVector is a vector that is a view of another underlying tensor.

    A SliceVector is a vector that is a view of another underlying tensor. For instance:

    val m = DenseMatrix(...)
m( (1,2), (3,4), (4,5))

    will give a SliceVector such that apply/update at index 0 will map to m(1,2), index 1 to m(3,4), etc.

  44. trait SliceVectorOps extends VectorOps
  45. class SparseVector[V] extends StorageVector[V] with VectorLike[V, SparseVector[V]] with Serializable

    A vector backed by binary search (with breeze.collection.mutable.SparseArray).

    A vector backed by binary search (with breeze.collection.mutable.SparseArray). There is a parallel array of ints (in 0 until length) and values, sorted by index value. To quickly access all stored values use the following loop:

    var offset = 0
while( offset < v.activeSize) {
  val index: Int = v.indexAt(offset)
  val value: E = v.valueAt(offset)

  offset += 1
}
    Annotations
    @SerialVersionUID()
  46. trait StorageVector[V] extends Vector[V] with Storage[V]
  47. implicit class String2File extends AnyRef

    Add methods to the string class in order to make file reading easier

  48. trait Tensor[K, V] extends TensorLike[K, V, Tensor[K, V]]

    A Tensor defines a map from an index set to a set of values.

  49. trait TensorLike[K, V, +This <: Tensor[K, V]] extends QuasiTensor[K, V] with NumericOps[This]
  50. final case class Transpose[+T](inner: T) extends NumericOps[Transpose[T]] with Product with Serializable

    Represents the Transpose of an instance of a type.

    Represents the Transpose of an instance of a type. The most common use is for Transpose[DenseVector[T]] to represent row vectors

  51. trait Vector[V] extends VectorLike[V, Vector[V]]

    A Vector represents the mathematical concept of a vector in math.

  52. class VectorBuilder[E] extends NumericOps[VectorBuilder[E]] with Serializable

    A VectorBuilder is basically an unsorted Sparse Vector.

    A VectorBuilder is basically an unsorted Sparse Vector. Two parallel arrays are maintained, one of indices, and another of values. The indices are not sorted. Moreover, <B> indices are not unique in the index array. Furthermore, apply(i) and update(i, v) are linear in the number of active values in the array.

    + and - are linear operations: they just append to the end. Component wise multiply, divide, and dot product are also linear, but require creating a HashVector copy. (TODO: maybe a SparseVector?)

    In general, these should never be used, except for building, or for doing feature vector type things where you just need a sparse vector with a fast dot product with a "real" vector.

    Annotations
    @SerialVersionUID()
  53. trait VectorConstructors[Vec[T] <: Vector[T]] extends AnyRef

    Trait that can mixed to companion objects to enable utility methods for creating vectors.

  54. trait VectorLike[V, +Self <: Vector[V]] extends Tensor[Int, V] with TensorLike[Int, V, Self]

    Trait for operators and such used in vectors.

  55. trait VectorizedReduceUFunc extends UFunc
  56. sealed trait View extends AnyRef

    Trait used for methods that can return a view or a copy.

  57. trait ZippedValues[V1, V2] extends AnyRef

    Usually used as the return type from zipValues

  58. trait diagLowPrio extends UFunc
  59. trait diagLowPrio2 extends UFunc with diagLowPrio
  60. sealed trait diffLowPrio extends AnyRef
  61. sealed trait maxLowPrio extends AnyRef
  62. sealed trait minLowPrio extends AnyRef
  63. sealed trait minkowskiDistanceLowPrio extends AnyRef
  64. sealed trait normalizeLowPrio extends AnyRef
  65. trait pinvLowPrio extends AnyRef
  66. sealed trait squaredDistanceLowPrio extends UFunc
  67. sealed trait sumLowPrio extends AnyRef

Value Members

  1. implicit def RangeToRangeExtender(re: Range): RangeExtender
  2. def axpy[A, X, Y](a: A, x: X, y: Y)(implicit axpy: linalg.scaleAdd.InPlaceImpl3[Y, A, X]): Unit

    Computes y += x * a, possibly doing less work than actually doing that operation

  3. def copy[T](t: T)(implicit canCopy: CanCopy[T]): T

    Copy a T.

    Copy a T. Most tensor objects have a CanCopy implicit, which is what this farms out to.

  4. def cov(x: DenseMatrix[Double], center: Boolean = true): DenseMatrix[Double]

    Compute the covariance matrix from the given data, centering if necessary.

    Compute the covariance matrix from the given data, centering if necessary. Very simple, just does the basic thing.

  5. def cross[V1](a: DenseVector[V1], b: DenseVector[V1])(implicit ring: Ring[V1], man: ClassTag[V1]): DenseVector[V1]

    Vector cross product of 3D vectors a and b.

  6. def csvread(file: File, separator: Char = ',', quote: Char = '"', escape: Char = '\\', skipLines: Int = 0): DenseMatrix[Double]

    Reads in a DenseMatrix from a CSV File

  7. def csvwrite(file: File, mat: Matrix[Double], separator: Char = ',', quote: Char = '\u0000', escape: Char = '\\', skipLines: Int = 0): Unit
  8. def linspace(a: Double, b: Double, length: Int = 100): DenseVector[Double]

    Generates a vector of linearly spaced values between a and b (inclusive).

    Generates a vector of linearly spaced values between a and b (inclusive). The returned vector will have length elements, defaulting to 100.

  9. def lowerTriangular[T](X: Matrix[T])(implicit arg0: Semiring[T], arg1: ClassTag[T], arg2: Zero[T]): DenseMatrix[T]

    The lower triangular portion of the given real quadratic matrix X.

    The lower triangular portion of the given real quadratic matrix X. Note that no check will be performed regarding the symmetry of X.

  10. def mmwrite[T](file: File, mat: Matrix[T])(implicit arg0: Numeric[T]): Unit
  11. def padLeft[T](v: DenseMatrix[T], dimensions: Dimensions2, mode: OptPadMode)(implicit canPad: CanPadLeft[DenseMatrix[T], Dimensions2, DenseMatrix[T]]): DenseMatrix[T]
  12. def padLeft[T](v: DenseMatrix[T], dimensions: Dimensions1)(implicit canPad: CanPadLeft[DenseMatrix[T], Dimensions1, DenseMatrix[T]]): DenseMatrix[T]
  13. def padLeft[T](v: DenseVector[T], dimensions: Dimensions1, mode: OptPadMode)(implicit canPad: CanPadLeft[DenseVector[T], Dimensions1, DenseVector[T]]): DenseVector[T]
  14. def padLeft[T](v: DenseVector[T], dimensions: Dimensions1)(implicit canPad: CanPadLeft[DenseVector[T], Dimensions1, DenseVector[T]]): DenseVector[T]
  15. def padRight[T](v: DenseMatrix[T], dimensions: Dimensions2, mode: OptPadMode)(implicit canPad: CanPadRight[DenseMatrix[T], Dimensions2, DenseMatrix[T]]): DenseMatrix[T]
  16. def padRight[T](v: DenseMatrix[T], dimensions: Dimensions1)(implicit canPad: CanPadRight[DenseMatrix[T], Dimensions1, DenseMatrix[T]]): DenseMatrix[T]
  17. def padRight[T](v: DenseVector[T], dimensions: Dimensions1, mode: OptPadMode)(implicit canPad: CanPadRight[DenseVector[T], Dimensions1, DenseVector[T]]): DenseVector[T]
  18. def padRight[T](v: DenseVector[T], dimensions: Dimensions1)(implicit canPad: CanPadRight[DenseVector[T], Dimensions1, DenseVector[T]]): DenseVector[T]
  19. def princomp(x: DenseMatrix[Double], covmatOpt: Option[DenseMatrix[Double]] = None): PCA

    Performs a principal components analysis on the given numeric data matrix and returns the results as an object of class PCA.

    Performs a principal components analysis on the given numeric data matrix and returns the results as an object of class PCA.

    If the no covariance matrix is supplied, one obtained from the given data is used.

  20. val rand: randomDouble.type

    Alias for randomDouble

  21. def ranks[V](x: Vector[V])(implicit arg0: Ordering[V]): Array[Double]

    Returns the rank of each element in the given vector, adjusting for ties.

  22. def scale(x: DenseMatrix[Double], center: Boolean = true, scale: Boolean = false): DenseMatrix[Double]

    A generic function (based on the R function of the same name) whose default method centers and/or scales the columns of a numeric matrix.

    A generic function (based on the R function of the same name) whose default method centers and/or scales the columns of a numeric matrix.

    If ‘scale’ is ‘TRUE’ then scaling is done by dividing the (centered) columns of ‘x’ by their standard deviations if ‘center’ is ‘TRUE’, and the root mean square otherwise. If ‘scale’ is ‘FALSE’, no scaling is done.

  23. def strictlyLowerTriangular[T](X: Matrix[T])(implicit arg0: Semiring[T], arg1: ClassTag[T], arg2: Zero[T]): DenseMatrix[T]

    The lower triangular portion of the given real quadratic matrix X with the diagnal elements is zero!

  24. def strictlyUpperTriangular[T](X: Matrix[T])(implicit arg0: Semiring[T], arg1: ClassTag[T], arg2: Zero[T]): DenseMatrix[T]

    The upper triangular portion of the given real quadratic matrix X with the diagnal elements is zero!

  25. def upperTriangular[T](X: Matrix[T])(implicit arg0: Semiring[T], arg1: ClassTag[T], arg2: Zero[T]): DenseMatrix[T]

    The upper triangular portion of the given real quadratic matrix X.

    The upper triangular portion of the given real quadratic matrix X. Note that no check will be performed regarding the symmetry of X.

  26. lazy val usingNatives: Boolean

    val to determine if breeze is using natives or f2jblas

  27. object * extends Broadcaster
  28. object Axis
  29. object BitVector
  30. object BroadcastedColumns extends Serializable
  31. object BroadcastedRows extends Serializable
  32. object Broadcaster
  33. object CSCMatrix extends MatrixConstructors[CSCMatrix] with Serializable
  34. object CanPadLeft
  35. object CanPadRight
  36. object Counter extends CounterOps with Serializable
  37. object Counter2 extends LowPriorityCounter2 with Counter2Ops
  38. object DenseMatrix extends MatrixConstructors[DenseMatrix] with Serializable
  39. object DenseVector extends VectorConstructors[DenseVector] with Serializable
  40. object HashVector extends Serializable
  41. object LSMR extends SerializableLogging

    Nearly direct port of http://www.mathworks.com/matlabcentral/fileexchange/27183-lsmr--an-iterative-algorithm-for-least-squares-problems (BSD licensed code)

    Nearly direct port of http://www.mathworks.com/matlabcentral/fileexchange/27183-lsmr--an-iterative-algorithm-for-least-squares-problems (BSD licensed code)

    http://web.stanford.edu/group/SOL/software/lsmr/

    The only difference is that they square the regularization factor.

  42. object LU extends UFunc

    Computes the LU factorization of the given real M-by-N matrix X such that X = P * L * U where P is a permutation matrix (row exchanges).

  43. object Matrix extends MatrixConstructors[Matrix] with LowPriorityMatrix
  44. object NotConvergedException extends Serializable
  45. object NumericOps
  46. object Options

  47. object SliceMatrix extends LowPrioritySliceMatrix with SliceMatrixOps
  48. object SliceVector
  49. object SparseVector extends Serializable
  50. object Tensor
  51. object Vector extends VectorConstructors[Vector]
  52. object VectorBuilder extends VectorBuilderOps with Serializable
  53. object View
  54. object accumulate extends UFunc

    Returns a cumulative sum of the vector (ie cumsum).

  55. object all extends UFunc

    all(t) true if all elements of t are non-zero all(f, t) returns true if all elements of t satisfy f

  56. object any extends UFunc

    any(t) true if any element of t is non-zero any(f, t) returns true if any element of t satisfies f

  57. object argmax extends UFunc
  58. object argmin extends UFunc
  59. object argsort extends UFunc with LowPriorityArgSort
  60. object argtopk extends UFunc with LowPriorityArgTopK
  61. object chebyshevDistance extends NormBasedDistance

    A Chebyshev distance metric implementation between two points

  62. object cholesky extends UFunc

    Computes the cholesky decomposition A of the given real symmetric positive definite matrix X such that X = A A.t.

    Computes the cholesky decomposition A of the given real symmetric positive definite matrix X such that X = A A.t.

    TODO: For higher dimensionalities, the return value really should be a sparse matrix due to its inherent lower triangular nature.

  63. object clip extends ElementwiseUFunc
  64. object cond extends UFunc

    Computes the condition number of the given real matrix.

  65. object convert extends MappingUFunc
  66. object cosineDistance extends UFunc

    The cosine distance between two points: cosineDistance(a,b) = 1 - (a dot b)/(norm(a) * norm(b))

  67. object det extends UFunc

    Computes the determinant of the given real matrix.

  68. object diag extends UFunc with diagLowPrio2

    returns a vector along the diagonal of v.

    returns a vector along the diagonal of v. Requires a square matrix?

  69. object diff extends UFunc with diffLowPrio
  70. object dim extends UFunc

    breeze 7/15/14

  71. object eig extends UFunc

    Eigenvalue decomposition (right eigenvectors)

    Eigenvalue decomposition (right eigenvectors)

    This function returns the real and imaginary parts of the eigenvalues, and the corresponding eigenvectors. For most (?) interesting matrices, the imaginary part of all eigenvalues will be zero (and the corresponding eigenvectors will be real). Any complex eigenvalues will appear in complex-conjugate pairs, and the real and imaginary components of the eigenvector for each pair will be in the corresponding columns of the eigenvector matrix. Take the complex conjugate to find the second eigenvector.

    Based on EVD.java from MTJ 0.9.12

  72. object eigSym extends UFunc

    Computes all eigenvalues (and optionally right eigenvectors) of the given real symmetric matrix X.

  73. object euclideanDistance extends NormBasedDistance

    A Euclidean distance metric implementation between two points

  74. object evdr extends UFunc

    Approximate truncated randomized EVD

  75. object fliplr extends UFunc

    mirrors the columns (left<->right).

  76. object flipud extends UFunc

    mirrors the rows (up down)

  77. object hsplit extends UFunc
  78. object inv extends UFunc

    Computes the inverse of a given real matrix.

    Computes the inverse of a given real matrix. In general, you should avoid using this metho in combination with *. Instead, wherever you might want to write inv(A) * B, you should write A \ B.

  79. object isClose extends UFunc
  80. object kron extends UFunc

    Returns the Kronecker product of two matrices a and b, usually denoted a ⊗ b.

  81. object logAndNormalize extends UFunc
  82. object logDiff extends MappingUFunc
  83. object logNormalize extends UFunc
  84. object logdet extends UFunc

    Computes the log of the determinant of the given real matrix.

    Computes the log of the determinant of the given real matrix. The value returned is (sign of determinant, log of determinant). This method can be more accurate than just using breeze.linalg.det, if det is very small.

  85. object manhattanDistance extends NormBasedDistance

    A Manhattan distance measure implementation between two points

  86. object max extends UFunc with maxLowPrio with VectorizedReduceUFunc
  87. object min extends UFunc with minLowPrio with VectorizedReduceUFunc
  88. object minMax extends UFunc
  89. object minkowskiDistance extends UFunc with minkowskiDistanceLowPrio

    A Minkowski distance metric implementation between two points

  90. object mpow extends UFunc

    Raises m to the exp'th power.

    Raises m to the exp'th power. Relies on eigenvalue decomposition when m's eigenvalues are real, if not it relies on exponentiation by squaring.

  91. object norm extends UFunc
  92. object normalize extends UFunc with normalizeLowPrio

    Normalizes the argument such that its norm is 1.0 (with respect to the argument n).

    Normalizes the argument such that its norm is 1.0 (with respect to the argument n). Returns value if value's norm is 0.

  93. object pinv extends UFunc with pinvLowPrio
  94. object product extends UFunc
  95. object ptp extends UFunc
  96. object qr extends UFunc

    QR Factorization

    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)

    returns

    (Q, R) Q - A matrix with orthonormal columns R - The upper-triangular matrix

  97. object qrp extends UFunc

    QR Factorization with pivoting

    QR Factorization with pivoting

    input: A m x n matrix output: (Q,R,P,pvt) where AP = QR Q: m x m R: m x n P: n x n : permutation matrix (P(pvt(i),i) = 1) pvt : pivot indices

  98. object randn extends RandomGeneratorUFunc[Double]

    Gives Gaussian-distributed random Double(s) + randn()...

    Gives Gaussian-distributed random Double(s) + randn()... returns a Gaussian random variable with mean 0, variance 1 + randn( n: Int )... returns a DenseVector with n randn's + randn( (n1: Int, n2: Int) )... returns an n1 x n2 DenseMatrix with randn's

  99. object randomDouble extends RandomGeneratorUFunc[Double]

    Gives a random Double.

    Gives a random Double. + randomDouble()... returns a random double, in [0, 1] + randomDouble( n: Int )... returns a DenseVector with n random doubles, in [0, 1] + randomDouble( n: Int, (r1: Double, r2: Double) )... returns a DenseVector with n random doubles, in [r1, r2] + randomDouble( (n1: Int, n2: Int) )... returns an n1 x n2 DenseMatrix with n random doubles, in [0, 1] + randomDouble( (n1: Int, n2: Int), (r1: Double, r2: Double) )... returns an n1 x n2 DenseMatrix with n random doubles, in [r1, r2]

  100. object randomInt extends RandomGeneratorUFunc[Int]

    Gives a random Int.

    Gives a random Int. + randomInt()... returns a random Int, in [0, 1] + randomInt( n: Int )... returns a DenseVector with n random Ints, in [0, 1] + randomInt( n: Int, (r1: Int, r2: Int) )... returns a DenseVector with n random Ints, in [r1, r2) + randomInt( (n1: Int, n2: Int) )... returns an n1 x n2 DenseMatrix with n random Ints, in [0, 1] + randomInt( (n1: Int, n2: Int), (r1: Int, r2: Int) )... returns an n1 x n2 DenseMatrix with n random Ints, in [r1, r2)

  101. object rank extends UFunc

    Computes the rank of a matrix.

    Computes the rank of a matrix.

    The rank of the matrix is computed using the SVD method. The singular values of the SVD which are greater than a specified tolerance are counted.

    returns

    the rank of the matrix (number of singular values)

  102. object reshape extends UFunc

    breeze 7/4/14

  103. object reverse extends UFunc

    Returns a reversed copy of the DenseVector.

  104. object roll extends UFunc

    roll the array

  105. object rot90 extends UFunc

    Rotates a matrix by 90 * k degrees counter clockwise.

    Rotates a matrix by 90 * k degrees counter clockwise. if k is not specified, it defaults to 1.

  106. object scaleAdd extends UFunc
  107. object shuffle extends UFunc

    Return the given DenseVector, Array, or DenseMatrix as a shuffled copy by using Fisher-Yates shuffle.

    Return the given DenseVector, Array, or DenseMatrix as a shuffled copy by using Fisher-Yates shuffle. Additionally, can return the given Array as a shuffled copy with the corresponding shuffle index information, or return the given Array as a shuffled copy using the inverse of the given shuffle index information, reversing the shuffle.

  108. object softmax extends UFunc

    Computes the softmax (a.k.a.

    Computes the softmax (a.k.a. logSum) of an object. Softmax is defined as \log \sum_i \exp(x(i)), but implemented in a more numerically stable way. Softmax is so-called because it is a differentiable function that tends to look quite a lot like max. Consider log(exp(30) + exp(10)). That's basically 30. We use softmax a lot in machine learning.

  109. object split extends UFunc

    split the array

  110. object squaredDistance extends UFunc with squaredDistanceLowPrio

    Computes the squared distance between two vectors.

  111. object sum extends VectorizedReduceUFunc with sumLowPrio
  112. object svd extends UFunc

    Computes the SVD of a M-by-N matrix Returns an M-by-M matrix U, a vector of singular values, and a N-by-N matrix V'

  113. object svdr extends UFunc

    Approximate truncated randomized SVD

  114. object tanimotoDistance extends UFunc
  115. object tile extends UFunc

  116. object trace extends UFunc

    Computes the determinant of the given real matrix.

  117. object unique extends UFunc

    deduplicates the array

  118. object vsplit extends UFunc
  119. object where extends UFunc

    where(a) returns those indices that are non-zero

    where(a) returns those indices that are non-zero

    where(cond, a, b) returns the value from a if cond is non-zero, and the value from b otherwise

  120. object zipValues extends UFunc

Inherited from AnyRef

Inherited from Any

Ungrouped