Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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466 lines
16 KiB
466 lines
16 KiB
import numpy as np
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from scipy.linalg import lu_factor, lu_solve
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from scipy.sparse import issparse, csc_matrix, eye
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from scipy.sparse.linalg import splu
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from scipy.optimize._numdiff import group_columns
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from .common import (validate_max_step, validate_tol, select_initial_step,
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norm, EPS, num_jac, validate_first_step,
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warn_extraneous)
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from .base import OdeSolver, DenseOutput
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MAX_ORDER = 5
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NEWTON_MAXITER = 4
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MIN_FACTOR = 0.2
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MAX_FACTOR = 10
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def compute_R(order, factor):
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"""Compute the matrix for changing the differences array."""
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I = np.arange(1, order + 1)[:, None]
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J = np.arange(1, order + 1)
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M = np.zeros((order + 1, order + 1))
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M[1:, 1:] = (I - 1 - factor * J) / I
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M[0] = 1
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return np.cumprod(M, axis=0)
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def change_D(D, order, factor):
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"""Change differences array in-place when step size is changed."""
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R = compute_R(order, factor)
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U = compute_R(order, 1)
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RU = R.dot(U)
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D[:order + 1] = np.dot(RU.T, D[:order + 1])
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def solve_bdf_system(fun, t_new, y_predict, c, psi, LU, solve_lu, scale, tol):
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"""Solve the algebraic system resulting from BDF method."""
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d = 0
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y = y_predict.copy()
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dy_norm_old = None
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converged = False
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for k in range(NEWTON_MAXITER):
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f = fun(t_new, y)
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if not np.all(np.isfinite(f)):
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break
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dy = solve_lu(LU, c * f - psi - d)
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dy_norm = norm(dy / scale)
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if dy_norm_old is None:
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rate = None
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else:
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rate = dy_norm / dy_norm_old
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if (rate is not None and (rate >= 1 or
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rate ** (NEWTON_MAXITER - k) / (1 - rate) * dy_norm > tol)):
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break
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y += dy
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d += dy
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if (dy_norm == 0 or
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rate is not None and rate / (1 - rate) * dy_norm < tol):
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converged = True
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break
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dy_norm_old = dy_norm
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return converged, k + 1, y, d
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class BDF(OdeSolver):
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"""Implicit method based on backward-differentiation formulas.
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This is a variable order method with the order varying automatically from
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1 to 5. The general framework of the BDF algorithm is described in [1]_.
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This class implements a quasi-constant step size as explained in [2]_.
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The error estimation strategy for the constant-step BDF is derived in [3]_.
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An accuracy enhancement using modified formulas (NDF) [2]_ is also implemented.
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Can be applied in the complex domain.
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Parameters
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----------
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fun : callable
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Right-hand side of the system. The calling signature is ``fun(t, y)``.
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Here ``t`` is a scalar, and there are two options for the ndarray ``y``:
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It can either have shape (n,); then ``fun`` must return array_like with
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shape (n,). Alternatively it can have shape (n, k); then ``fun``
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must return an array_like with shape (n, k), i.e. each column
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corresponds to a single column in ``y``. The choice between the two
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options is determined by `vectorized` argument (see below). The
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vectorized implementation allows a faster approximation of the Jacobian
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by finite differences (required for this solver).
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t0 : float
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Initial time.
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y0 : array_like, shape (n,)
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Initial state.
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t_bound : float
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Boundary time - the integration won't continue beyond it. It also
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determines the direction of the integration.
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first_step : float or None, optional
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Initial step size. Default is ``None`` which means that the algorithm
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should choose.
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max_step : float, optional
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Maximum allowed step size. Default is np.inf, i.e., the step size is not
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bounded and determined solely by the solver.
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rtol, atol : float and array_like, optional
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Relative and absolute tolerances. The solver keeps the local error
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estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
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relative accuracy (number of correct digits). But if a component of `y`
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is approximately below `atol`, the error only needs to fall within
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the same `atol` threshold, and the number of correct digits is not
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guaranteed. If components of y have different scales, it might be
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beneficial to set different `atol` values for different components by
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passing array_like with shape (n,) for `atol`. Default values are
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1e-3 for `rtol` and 1e-6 for `atol`.
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jac : {None, array_like, sparse_matrix, callable}, optional
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Jacobian matrix of the right-hand side of the system with respect to y,
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required by this method. The Jacobian matrix has shape (n, n) and its
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element (i, j) is equal to ``d f_i / d y_j``.
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There are three ways to define the Jacobian:
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* If array_like or sparse_matrix, the Jacobian is assumed to
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be constant.
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* If callable, the Jacobian is assumed to depend on both
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t and y; it will be called as ``jac(t, y)`` as necessary.
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For the 'Radau' and 'BDF' methods, the return value might be a
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sparse matrix.
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* If None (default), the Jacobian will be approximated by
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finite differences.
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It is generally recommended to provide the Jacobian rather than
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relying on a finite-difference approximation.
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jac_sparsity : {None, array_like, sparse matrix}, optional
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Defines a sparsity structure of the Jacobian matrix for a
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finite-difference approximation. Its shape must be (n, n). This argument
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is ignored if `jac` is not `None`. If the Jacobian has only few non-zero
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elements in *each* row, providing the sparsity structure will greatly
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speed up the computations [4]_. A zero entry means that a corresponding
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element in the Jacobian is always zero. If None (default), the Jacobian
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is assumed to be dense.
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vectorized : bool, optional
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Whether `fun` is implemented in a vectorized fashion. Default is False.
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Attributes
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----------
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n : int
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Number of equations.
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status : string
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Current status of the solver: 'running', 'finished' or 'failed'.
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t_bound : float
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Boundary time.
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direction : float
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Integration direction: +1 or -1.
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t : float
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Current time.
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y : ndarray
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Current state.
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t_old : float
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Previous time. None if no steps were made yet.
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step_size : float
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Size of the last successful step. None if no steps were made yet.
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nfev : int
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Number of evaluations of the right-hand side.
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njev : int
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Number of evaluations of the Jacobian.
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nlu : int
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Number of LU decompositions.
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References
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----------
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.. [1] G. D. Byrne, A. C. Hindmarsh, "A Polyalgorithm for the Numerical
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Solution of Ordinary Differential Equations", ACM Transactions on
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Mathematical Software, Vol. 1, No. 1, pp. 71-96, March 1975.
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.. [2] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
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COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
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.. [3] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations I:
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Nonstiff Problems", Sec. III.2.
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.. [4] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
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sparse Jacobian matrices", Journal of the Institute of Mathematics
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and its Applications, 13, pp. 117-120, 1974.
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"""
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def __init__(self, fun, t0, y0, t_bound, max_step=np.inf,
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rtol=1e-3, atol=1e-6, jac=None, jac_sparsity=None,
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vectorized=False, first_step=None, **extraneous):
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warn_extraneous(extraneous)
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super(BDF, self).__init__(fun, t0, y0, t_bound, vectorized,
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support_complex=True)
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self.max_step = validate_max_step(max_step)
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self.rtol, self.atol = validate_tol(rtol, atol, self.n)
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f = self.fun(self.t, self.y)
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if first_step is None:
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self.h_abs = select_initial_step(self.fun, self.t, self.y, f,
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self.direction, 1,
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self.rtol, self.atol)
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else:
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self.h_abs = validate_first_step(first_step, t0, t_bound)
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self.h_abs_old = None
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self.error_norm_old = None
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self.newton_tol = max(10 * EPS / rtol, min(0.03, rtol ** 0.5))
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self.jac_factor = None
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self.jac, self.J = self._validate_jac(jac, jac_sparsity)
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if issparse(self.J):
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def lu(A):
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self.nlu += 1
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return splu(A)
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def solve_lu(LU, b):
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return LU.solve(b)
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I = eye(self.n, format='csc', dtype=self.y.dtype)
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else:
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def lu(A):
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self.nlu += 1
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return lu_factor(A, overwrite_a=True)
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def solve_lu(LU, b):
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return lu_solve(LU, b, overwrite_b=True)
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I = np.identity(self.n, dtype=self.y.dtype)
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self.lu = lu
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self.solve_lu = solve_lu
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self.I = I
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kappa = np.array([0, -0.1850, -1/9, -0.0823, -0.0415, 0])
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self.gamma = np.hstack((0, np.cumsum(1 / np.arange(1, MAX_ORDER + 1))))
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self.alpha = (1 - kappa) * self.gamma
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self.error_const = kappa * self.gamma + 1 / np.arange(1, MAX_ORDER + 2)
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D = np.empty((MAX_ORDER + 3, self.n), dtype=self.y.dtype)
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D[0] = self.y
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D[1] = f * self.h_abs * self.direction
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self.D = D
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self.order = 1
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self.n_equal_steps = 0
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self.LU = None
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def _validate_jac(self, jac, sparsity):
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t0 = self.t
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y0 = self.y
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if jac is None:
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if sparsity is not None:
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if issparse(sparsity):
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sparsity = csc_matrix(sparsity)
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groups = group_columns(sparsity)
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sparsity = (sparsity, groups)
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def jac_wrapped(t, y):
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self.njev += 1
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f = self.fun_single(t, y)
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J, self.jac_factor = num_jac(self.fun_vectorized, t, y, f,
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self.atol, self.jac_factor,
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sparsity)
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return J
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J = jac_wrapped(t0, y0)
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elif callable(jac):
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J = jac(t0, y0)
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self.njev += 1
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if issparse(J):
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J = csc_matrix(J, dtype=y0.dtype)
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def jac_wrapped(t, y):
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self.njev += 1
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return csc_matrix(jac(t, y), dtype=y0.dtype)
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else:
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J = np.asarray(J, dtype=y0.dtype)
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def jac_wrapped(t, y):
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self.njev += 1
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return np.asarray(jac(t, y), dtype=y0.dtype)
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if J.shape != (self.n, self.n):
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raise ValueError("`jac` is expected to have shape {}, but "
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"actually has {}."
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.format((self.n, self.n), J.shape))
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else:
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if issparse(jac):
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J = csc_matrix(jac, dtype=y0.dtype)
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else:
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J = np.asarray(jac, dtype=y0.dtype)
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if J.shape != (self.n, self.n):
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raise ValueError("`jac` is expected to have shape {}, but "
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"actually has {}."
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.format((self.n, self.n), J.shape))
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jac_wrapped = None
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return jac_wrapped, J
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def _step_impl(self):
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t = self.t
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D = self.D
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max_step = self.max_step
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min_step = 10 * np.abs(np.nextafter(t, self.direction * np.inf) - t)
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if self.h_abs > max_step:
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h_abs = max_step
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change_D(D, self.order, max_step / self.h_abs)
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self.n_equal_steps = 0
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elif self.h_abs < min_step:
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h_abs = min_step
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change_D(D, self.order, min_step / self.h_abs)
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self.n_equal_steps = 0
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else:
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h_abs = self.h_abs
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atol = self.atol
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rtol = self.rtol
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order = self.order
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alpha = self.alpha
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gamma = self.gamma
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error_const = self.error_const
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J = self.J
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LU = self.LU
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current_jac = self.jac is None
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step_accepted = False
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while not step_accepted:
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if h_abs < min_step:
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return False, self.TOO_SMALL_STEP
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h = h_abs * self.direction
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t_new = t + h
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if self.direction * (t_new - self.t_bound) > 0:
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t_new = self.t_bound
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change_D(D, order, np.abs(t_new - t) / h_abs)
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self.n_equal_steps = 0
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LU = None
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h = t_new - t
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h_abs = np.abs(h)
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y_predict = np.sum(D[:order + 1], axis=0)
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scale = atol + rtol * np.abs(y_predict)
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psi = np.dot(D[1: order + 1].T, gamma[1: order + 1]) / alpha[order]
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converged = False
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c = h / alpha[order]
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while not converged:
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if LU is None:
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LU = self.lu(self.I - c * J)
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converged, n_iter, y_new, d = solve_bdf_system(
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self.fun, t_new, y_predict, c, psi, LU, self.solve_lu,
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scale, self.newton_tol)
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if not converged:
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if current_jac:
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break
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J = self.jac(t_new, y_predict)
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LU = None
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current_jac = True
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if not converged:
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factor = 0.5
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h_abs *= factor
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change_D(D, order, factor)
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self.n_equal_steps = 0
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LU = None
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continue
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safety = 0.9 * (2 * NEWTON_MAXITER + 1) / (2 * NEWTON_MAXITER
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+ n_iter)
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scale = atol + rtol * np.abs(y_new)
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error = error_const[order] * d
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error_norm = norm(error / scale)
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if error_norm > 1:
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factor = max(MIN_FACTOR,
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safety * error_norm ** (-1 / (order + 1)))
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h_abs *= factor
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change_D(D, order, factor)
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self.n_equal_steps = 0
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# As we didn't have problems with convergence, we don't
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# reset LU here.
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else:
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step_accepted = True
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self.n_equal_steps += 1
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self.t = t_new
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self.y = y_new
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self.h_abs = h_abs
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self.J = J
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self.LU = LU
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# Update differences. The principal relation here is
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# D^{j + 1} y_n = D^{j} y_n - D^{j} y_{n - 1}. Keep in mind that D
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# contained difference for previous interpolating polynomial and
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# d = D^{k + 1} y_n. Thus this elegant code follows.
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D[order + 2] = d - D[order + 1]
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D[order + 1] = d
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for i in reversed(range(order + 1)):
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D[i] += D[i + 1]
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if self.n_equal_steps < order + 1:
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return True, None
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if order > 1:
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error_m = error_const[order - 1] * D[order]
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error_m_norm = norm(error_m / scale)
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else:
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error_m_norm = np.inf
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if order < MAX_ORDER:
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error_p = error_const[order + 1] * D[order + 2]
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error_p_norm = norm(error_p / scale)
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else:
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error_p_norm = np.inf
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error_norms = np.array([error_m_norm, error_norm, error_p_norm])
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with np.errstate(divide='ignore'):
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factors = error_norms ** (-1 / np.arange(order, order + 3))
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delta_order = np.argmax(factors) - 1
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order += delta_order
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self.order = order
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factor = min(MAX_FACTOR, safety * np.max(factors))
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self.h_abs *= factor
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change_D(D, order, factor)
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self.n_equal_steps = 0
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self.LU = None
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return True, None
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def _dense_output_impl(self):
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return BdfDenseOutput(self.t_old, self.t, self.h_abs * self.direction,
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self.order, self.D[:self.order + 1].copy())
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class BdfDenseOutput(DenseOutput):
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def __init__(self, t_old, t, h, order, D):
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super(BdfDenseOutput, self).__init__(t_old, t)
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self.order = order
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self.t_shift = self.t - h * np.arange(self.order)
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self.denom = h * (1 + np.arange(self.order))
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self.D = D
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def _call_impl(self, t):
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if t.ndim == 0:
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x = (t - self.t_shift) / self.denom
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p = np.cumprod(x)
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else:
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x = (t - self.t_shift[:, None]) / self.denom[:, None]
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p = np.cumprod(x, axis=0)
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y = np.dot(self.D[1:].T, p)
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if y.ndim == 1:
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y += self.D[0]
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else:
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y += self.D[0, :, None]
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return y
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