sayancpe586fa25 5.0.0

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This is a package with two subpackages:

1. Differential -> function diff to calculate discrete time derivative differentialial -> discrete -> diff (x,t) It does discrete time derivative of two vectors (list) x and t and returs a list v

2. Matrix-> function rowswap(matrix: torch.Tensor, i: int, j: int) -> torch.Tensor Swap row i and row j in a matrix.

   Args: matrix: Input matrix. i : Index of the first row. j : Index of the second row.

   Returns: torch.Tensor: matrix with row i,j swapped.

        function rowscale(matrix: torch.Tensor, i: int, s_f: float) -> torch.Tensor
   

   Scale row i by a factor s_f.

   Args: matrix: input matrix. i : Index of the row to scale. s_f (float): Scaling factor.

   Returns: torch.Tensor: matrix with the scaled row i by factor s_f

        function rowreplacement(matrix: torch.Tensor, i: int, j: int, sf_a: float, sf_b: float) -> torch.Tensor
   

   Replace the ith row with: iRi + jRj.

   Args: matrix (torch.Tensor): input matrix. i (int): Index of the row to be replaced. j (int): Index of the source row. sf_a (float): Scaling factor for row i. sf_b (float): Scaling factor for row j.

   Returns: torch.Tensor: Result Matrix

        function rref(matrix: torch.Tensor) -> torch.Tensor
   

   Performs Reduced Row Echelion Form or Gausian Elemination on Marix

   Args: matrix (torch.Tensor): input matrix Returns: torch.Tensor: Result matrix RREF of input

        function uniform(a: float = 0.0, b: float = 1.0)
    Returns uniform values between 0 and 1
    E.G Use uniform(0,1)
   
        function exponentialdist(lmbd: float)
    Returns exponential distribution
   

   Args: Lambda value for exponential distribution E.G. Use exponentialdist(lmbd)

        function poissondist(lmbd: float):
    Returns poisson distribution
   

   Args: Lambda value for poisson distribution E.G. Use poissondist(lmbd)

   Added class LinearRegression, CauchyRegression, LogisticRegression Added class TorchNet