HalfspaceIntersection(halfspaces, interior_point, incremental=False, qhull_options=None)
Halfspace intersections in N dimensions.
.. versionadded:: 0.19.0
Parameters ---------- halfspaces : ndarray of floats, shape (nineq, ndim+1) Stacked Inequalities of the form Ax + b <= 0 in format A; b interior_point : ndarray of floats, shape (ndim,) Point clearly inside the region defined by halfspaces. Also called a feasible point, it can be obtained by linear programming. incremental : bool, optional Allow adding new halfspaces incrementally. This takes up some additional resources. qhull_options : str, optional Additional options to pass to Qhull. See Qhull manual for details. (Default: 'Qx' for ndim > 4 and '' otherwise) Option 'H' is always enabled.
Attributes ---------- halfspaces : ndarray of double, shape (nineq, ndim+1) Input halfspaces. interior_point :ndarray of floats, shape (ndim,) Input interior point. intersections : ndarray of double, shape (ninter, ndim) Intersections of all halfspaces. dual_points : ndarray of double, shape (nineq, ndim) Dual points of the input halfspaces. dual_facets : list of lists of ints Indices of points forming the (non necessarily simplicial) facets of the dual convex hull. dual_vertices : ndarray of ints, shape (nvertices,) Indices of halfspaces forming the vertices of the dual convex hull. For 2-D convex hulls, the vertices are in counterclockwise order. For other dimensions, they are in input order. dual_equations : ndarray of double, shape (nfacet, ndim+1) normal, offset forming the hyperplane equation of the dual facet (see `Qhull documentation <http://www.qhull.org/>`__ for more). dual_area : float Area of the dual convex hull dual_volume : float Volume of the dual convex hull
Raises ------ QhullError Raised when Qhull encounters an error condition, such as geometrical degeneracy when options to resolve are not enabled. ValueError Raised if an incompatible array is given as input.
Notes ----- The intersections are computed using the `Qhull library <http://www.qhull.org/>`__. This reproduces the 'qhalf' functionality of Qhull.
Examples --------
Halfspace intersection of planes forming some polygon
>>> from scipy.spatial import HalfspaceIntersection >>> halfspaces = np.array([-1, 0., 0.],
... [0., -1., 0.],
... [2., 1., -4.],
... [-0.5, 1., -2.]) >>> feasible_point = np.array(0.5, 0.5) >>> hs = HalfspaceIntersection(halfspaces, feasible_point)
Plot halfspaces as filled regions and intersection points:
>>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot('111', aspect='equal') >>> xlim, ylim = (-1, 3), (-1, 3) >>> ax.set_xlim(xlim) >>> ax.set_ylim(ylim) >>> x = np.linspace(-1, 3, 100) >>> symbols = '-', '+', 'x', '*' >>> signs = 0, 0, -1, -1 >>> fmt = 'color': None, 'edgecolor': 'b', 'alpha': 0.5 >>> for h, sym, sign in zip(halfspaces, symbols, signs): ... hlist = h.tolist() ... fmt'hatch' = sym ... if h1== 0: ... ax.axvline(-h2/h0, label='{
}
x+{
}
y+{
}
=0'.format( *hlist)) ... xi = np.linspace(xlimsign, -h2/h0, 100) ... ax.fill_between(xi, ylim0, ylim1, **fmt) ... else: ... ax.plot(x, (-h2-h0*x)/h1, label='{
}
x+{
}
y+{
}
=0'.format( *hlist)) ... ax.fill_between(x, (-h2-h0*x)/h1, ylimsign, **fmt) >>> x, y = zip( *hs.intersections) >>> ax.plot(x, y, 'o', markersize=8)
By default, qhull does not provide with a way to compute an interior point. This can easily be computed using linear programming. Considering halfspaces of the form :math:`Ax + b \leq 0`, solving the linear program:
.. math::
max \: y
s.t. Ax + y ||A_i|| \leq -b
With :math:`A_i` being the rows of A, i.e. the normals to each plane.
Will yield a point x that is furthest inside the convex polyhedron. To be precise, it is the center of the largest hypersphere of radius y inscribed in the polyhedron. This point is called the Chebyshev center of the polyhedron (see 1_ 4.3.1, pp148-149). The equations outputted by Qhull are always normalized.
>>> from scipy.optimize import linprog >>> from matplotlib.patches import Circle >>> norm_vector = np.reshape(np.linalg.norm(halfspaces:, :-1, axis=1), ... (halfspaces.shape0, 1)) >>> c = np.zeros((halfspaces.shape1,)) >>> c-1 = -1 >>> A = np.hstack((halfspaces:, :-1, norm_vector)) >>> b = - halfspaces:, -1: >>> res = linprog(c, A_ub=A, b_ub=b) >>> x = res.x:-1 >>> y = res.x-1 >>> circle = Circle(x, radius=y, alpha=0.3) >>> ax.add_patch(circle) >>> plt.legend(bbox_to_anchor=(1.6, 1.0)) >>> plt.show()
References ---------- .. Qhull http://www.qhull.org/ .. 1 S. Boyd, L. Vandenberghe, Convex Optimization, available at http://stanford.edu/~boyd/cvxbook/