:py:mod:`regression`
====================

.. py:module:: regression


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   regression.CCS_regressor




.. py:class:: CCS_regressor(N=100, xmin=0, xmax=1, dx=None, sw=False, eps=False)

   .. py:method:: merge_intervals(x)


   .. py:method:: rubber_band()


   .. py:method:: solver(pp, qq, gg, hh, aa, bb, maxiter=300, tol=(1e-10, 1e-10, 1e-10))
      :staticmethod:

      The solver for the objective.

      :param pp: P matrix as per standard Quadratic Programming(QP)
                 notation.
      :type pp: matrix
      :param qq: q matrix as per standard QP notation.
      :type qq: matrix
      :param gg: G matrix as per standard QP notation.
      :type gg: matrix
      :param hh: h matrix as per standard QP notation
      :type hh: matrix
      :param aa: A matrix as per standard QP notation.
      :type aa: matrix
      :param bb: b matrix as per standard QP notation
      :type bb: matrix
      :param maxiter: maximum iteration steps (default: 300).
      :type maxiter: int, optional
      :param tol: tolerance value of the solution
                  (default: (1e-10, 1e-10, 1e-10)).
      :type tol: tuple, optional

      :returns: dictionary containing solution details
      :rtype: sol (dict)


   .. py:method:: fit(x, y)


   .. py:method:: const(n_switch)


   .. py:method:: predict(x)


   .. py:method:: spline_construction(N)

      This function constructs the matrices A, B, C, D.

      :param N: Number of knots

      :returns: constructed matrices
      :rtype: cc, dd, bb, aa (matrices)


   .. py:method:: model(x)

      Constructs the v matrix.

      :param self:

      :returns: The v matrix for a pair.
      :rtype: ndarray