Module: Colt::Matrix2DFloatingAlgebra

Included in:
DoubleMDMatrix2D, FloatMDMatrix2D
Defined in:
lib/colt/matrix/algebra.rb

Instance Method Summary collapse

Instance Method Details

#backward_solve(matrix1D) ⇒ Object

Solves the upper triangular system U*x=b;



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# File 'lib/colt/matrix/algebra.rb', line 184

def backward_solve(matrix1D)
  result = @colt_algebra.backwardSolve(@colt_matrix, matrix1D.colt_matrix)
  MDMatrix.from_colt_matrix(result)
end

#chol ⇒ Object

Constructs and returns the cholesky-decomposition of the given matrix. For a symmetric, positive definite matrix A, the Cholesky decomposition is a lower triangular matrix L so that A = L*L’; If the matrix is not symmetric positive definite, the IllegalArgumentException is thrown.



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# File 'lib/colt/matrix/algebra.rb', line 196

def chol
  result = @colt_algebra.chol(@colt_matrix).getL()
  MDMatrix.from_colt_matrix(result)
end

#cond ⇒ Object

Returns the condition of matrix A, which is the ratio of largest to smallest singular value.



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# File 'lib/colt/matrix/algebra.rb', line 206

def cond
  @colt_algebra.cond(@colt_matrix)
end

#det ⇒ Object

Returns the determinant of matrix A.



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# File 'lib/colt/matrix/algebra.rb', line 214

def det
  @colt_algebra.det(@colt_matrix)
end

#eig ⇒ Object 



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# File 'lib/colt/matrix/algebra.rb', line 222

def eig
  eig = @colt_algebra.eig(@colt_matrix)
  [MDMatrix.from_colt_matrix(eig.getD), 
   MDMatrix.from_colt_matrix(eig.getImagEigenvalues),
   MDMatrix.from_colt_matrix(eig.getRealEigenvalues),
   MDMatrix.from_colt_matrix(eig.getV)]
end

#forward_solve(matrix1D) ⇒ Object

Solves the lower triangular system L*x=b;



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# File 'lib/colt/matrix/algebra.rb', line 234

def forward_solve(matrix1D)
  result = @colt_algebra.forwardSolve(@colt_matrix, matrix1D.colt_matrix)
  MDMatrix.from_colt_matrix(result)
end

#inverse ⇒ Object

Returns the inverse or pseudo-inverse of matrix A.



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# File 'lib/colt/matrix/algebra.rb', line 243

def inverse
  result = @colt_algebra.inverse(@colt_matrix)
  MDMatrix.from_colt_matrix(result)
end

#lu ⇒ Object

Constructs and returns the LU-decomposition of the given matrix.



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# File 'lib/colt/matrix/algebra.rb', line 252

def lu
  result = @colt_algebra.lu(@colt_matrix)
  [result.isNonsingular(), result.det(), result.getPivot.to_a(),
   MDMatrix.from_colt_matrix(result.getL()),
   MDMatrix.from_colt_matrix(result.getU())]
end

#numerical_rank ⇒ Object

Returns the effective numerical rank of matrix A, obtained from Singular Value Decomposition.



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# File 'lib/colt/matrix/algebra.rb', line 264

def numerical_rank
  @colt_algebra.rank(@colt_matrix)
end

#power(val) ⇒ Object Also known as: **

Linear algebraic matrix power; B = A^k <==> B = A*A*…



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# File 'lib/colt/matrix/algebra.rb', line 272

def power(val)
  result = @colt_algebra.pow(@colt_matrix, val)
  MDMatrix.from_colt_matrix(result)
end

#solve(matrix) ⇒ Object

Solves A*X = B



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# File 'lib/colt/matrix/algebra.rb', line 283

def solve(matrix)
  result = @colt_algebra.solve(@colt_matrix, matrix.colt_matrix)
  MDMatris.from_colt_matrix(resul)
end

#solve_transpose(matrix) ⇒ Object

Solves X*A = B, which is also A’*X’ = B’.



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# File 'lib/colt/matrix/algebra.rb', line 292

def solve_transpose(matrix)
  result = @colt_algebra.solveTranspose(@colt_matrix, matrix.colt_matrix)
  MDMatris.from_colt_matrix(resul)
end

#svd ⇒ Object

Constructs and returns the SingularValue-decomposition of the given matrix.



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# File 'lib/colt/matrix/algebra.rb', line 301

def svd
  result = @colt_algebra.svd(@colt_matrix)
  [result.getInfo().val, result.cond(), result.norm2(), result.rank(), 
   result.getSingularValues().to_a(),
   MDMatrix.from_colt_matrix(result.getS()),
   MDMatrix.from_colt_matrix(result.getU()),
   MDMatrix.from_colt_matrix(result.getV())]
end

#trace ⇒ Object

Returns the sum of the diagonal elements of matrix A; Sum(A).



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# File 'lib/colt/matrix/algebra.rb', line 314

def trace
  @colt_algebra.trace(@colt_matrix)
end

#trapezoidal_lower ⇒ Object

Modifies the matrix to be a lower trapezoidal matrix.



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# File 'lib/colt/matrix/algebra.rb', line 322

def trapezoidal_lower
  result = @colt_algebra.trapezoidalLower(@colt_matrix)
  MDMatrix.from_colt_matrix(result)
end

#vector_norm2 ⇒ Object

Returns the two-norm (aka euclidean norm) of vector X.vectorize();



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# File 'lib/colt/matrix/algebra.rb', line 331

def vector_norm2
  @colt_algebra.vectorNorm2(@colt_matrix)
end