From: Michael Orlitzky Date: Tue, 19 May 2015 03:41:41 +0000 (-0400) Subject: Simplify the random_cone() code by defaulting to lower bounds of zero. X-Git-Url: https://gitweb.michael.orlitzky.com/?a=commitdiff_plain;h=7d2f3fba7f494158dbce5f7a3eca1d15ee7f577e;p=sage.d.git Simplify the random_cone() code by defaulting to lower bounds of zero. --- diff --git a/mjo/cone/cone.py b/mjo/cone/cone.py index 507b6ce..6ade5e6 100644 --- a/mjo/cone/cone.py +++ b/mjo/cone/cone.py @@ -8,73 +8,114 @@ addsitedir(abspath('../../')) from sage.all import * -def random_cone(min_dim=None, max_dim=None, min_rays=None, max_rays=None): +def random_cone(min_dim=0, max_dim=None, min_rays=0, max_rays=None): r""" Generate a random rational convex polyhedral cone. Lower and upper bounds may be provided for both the dimension of the - ambient space and the number of generating rays of the cone. Any - parameters left unspecified will be chosen randomly. + ambient space and the number of generating rays of the cone. If a + lower bound is left unspecified, it defaults to zero. Unspecified + upper bounds will be chosen randomly. INPUT: - - ``min_dim`` (default: random) -- The minimum dimension of the ambient - lattice. + - ``min_dim`` (default: zero) -- A nonnegative integer representing the + minimum dimension of the ambient lattice. - - ``max_dim`` (default: random) -- The maximum dimension of the ambient + - ``max_dim`` (default: random) -- A nonnegative integer representing + the maximum dimension of the ambient lattice. - - ``min_rays`` (default: random) -- The minimum number of generating rays - of the cone. + - ``min_rays`` (default: zero) -- A nonnegative integer representing the + minimum number of generating rays of the + cone. - - ``max_rays`` (default: random) -- The maximum number of generating rays - of the cone. + - ``max_rays`` (default: random) -- A nonnegative integer representing the + maximum number of generating rays of the + cone. OUTPUT: A new, randomly generated cone. + EXAMPLES: + + If we set the lower/upper bounds to zero, then our result is + predictable:: + + sage: random_cone(0,0,0,0) + 0-d cone in 0-d lattice N + + In fact, as long as we ask for zero rays, we should be able to predict + the output when ``min_dim == max_dim``:: + + sage: random_cone(min_dim=4, max_dim=4, min_rays=0, max_rays=0) + 0-d cone in 4-d lattice N + TESTS: It's hard to test the output of a random process, but we can at least make sure that we get a cone back:: - sage: from sage.geometry.cone import is_Cone - sage: K = random_cone() - sage: is_Cone(K) # long time + sage: from sage.geometry.cone import is_Cone # long time + sage: K = random_cone() # long time + sage: is_Cone(K) # long time True + Ensure that an exception is raised when either lower bound is greater + than its respective upper bound:: + + sage: random_cone(min_dim=5, max_dim=2) + Traceback (most recent call last): + ... + ValueError: max_dim must be greater than or equal to min_dim. + + sage: random_cone(min_rays=5, max_rays=2) + Traceback (most recent call last): + ... + ValueError: max_rays must be greater than or equal to min_rays. + """ + # Catch obvious mistakes so that we can generate clear error + # messages. + + if min_dim < 0: + raise ValueError('min_dim must be nonnegative.') + + if min_rays < 0: + raise ValueError('min_rays must be nonnegative.') + + if max_dim is not None: + if max_dim < 0: + raise ValueError('max_dim must be nonnegative.') + if (min_dim > max_dim): + raise ValueError('max_dim must be greater than or equal to min_dim.') + + if max_rays is not None: + if max_rays < 0: + raise ValueError('max_rays must be nonnegative.') + if (min_rays > max_rays): + raise ValueError('max_rays must be greater than or equal to min_rays.') + + def random_min_max(l,u): r""" - We need to handle four cases to prevent us from doing - something stupid like having an upper bound that's lower than - our lower bound. And we would need to repeat all of that logic - for the dimension/rays, so we consolidate it here. + We need to handle two cases for the upper bounds, and we need to do + the same thing for max_dim/max_rays. So we consolidate the logic here. """ - if l is None and u is None: - # They're both random, just return a random nonnegative - # integer. - return ZZ.random_element().abs() - - if l is not None and u is not None: - # Both were specified. Again, just make up a number and - # return it. If the user wants to give us u < l then he - # can have an exception. - return ZZ.random_element(l,u) - - if l is not None and u is None: - # In this case, we're generating the upper bound randomly - # GIVEN A LOWER BOUND. So we add a random nonnegative - # integer to the given lower bound. - u = l + ZZ.random_element().abs() - return ZZ.random_element(l,u) - - # Here we must be in the only remaining case, where we are - # given an upper bound but no lower bound. We might as well - # use zero. - return ZZ.random_element(0,u) + if u is None: + # The upper bound is unspecified; return a random integer + # in [l,infinity). + return l + ZZ.random_element().abs() + else: + # We have an upper bound, and it's greater than or equal + # to our lower bound. So we generate a random integer in + # [0,u-l], and then add it to l to get something in + # [l,u]. To understand the "+1", check the + # ZZ.random_element() docs. + return l + ZZ.random_element(u - l + 1) + d = random_min_max(min_dim, max_dim) r = random_min_max(min_rays, max_rays) @@ -82,7 +123,8 @@ def random_cone(min_dim=None, max_dim=None, min_rays=None, max_rays=None): L = ToricLattice(d) rays = [L.random_element() for i in range(0,r)] - # We pass the lattice in case there are no rays. + # The lattice parameter is required when no rays are given, so we + # pass it just in case. return Cone(rays, lattice=L) @@ -140,7 +182,7 @@ def discrete_complementarity_set(K): The complementarity set of the dual can be obtained by switching the components of the complementarity set of the original cone:: - sage: K1 = random_cone(0,10,0,10) + sage: K1 = random_cone(max_dim=10, max_rays=10) sage: K2 = K1.dual() sage: expected = [(x,s) for (s,x) in discrete_complementarity_set(K2)] sage: actual = discrete_complementarity_set(K1) @@ -262,8 +304,8 @@ def lyapunov_rank(K): The Lyapunov rank should be additive on a product of cones:: - sage: K1 = random_cone(0,10,0,10) - sage: K2 = random_cone(0,10,0,10) + sage: K1 = random_cone(max_dim=10, max_rays=10) + sage: K2 = random_cone(max_dim=10, max_rays=10) sage: K = K1.cartesian_product(K2) sage: lyapunov_rank(K) == lyapunov_rank(K1) + lyapunov_rank(K2) True @@ -271,7 +313,7 @@ def lyapunov_rank(K): The dual cone `K^{*}` of ``K`` should have the same Lyapunov rank as ``K`` itself:: - sage: K = random_cone(0,10,0,10) + sage: K = random_cone(max_dim=10, max_rays=10) sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) True