.PHONY: lint
lint:
- PYTHONPATH="$(SRCDIR)" pylint \
- --reports=n \
- --good-names='b,c,e1,e2,h,A,C,G,K,_K,L,L_matrix,_L,indented_L,M' \
- $(SRCS)
+ PYTHONPATH="$(SRCDIR)" pylint --rcfile=./.pylintrc $(SRCS)
.PHONY: clean
clean:
... 'dual objective': 1.0,
... 'primal objective': None,
... 'gap': None,
- ... 'residual as primal infeasibility certificate': 3.986246886102996e-09}
+ ... 'residual as primal infeasibility certificate':
+ ... 3.986246886102996e-09}
>>> print(GameUnsolvableException(d))
Solution failed with result "primal infeasible."
CVXOPT returned:
"""
strict_ut = [[uniform(-10, 10)*int(i < j) for i in range(dims)]
- for j in range(dims)]
+ for j in range(dims)]
- strict_ut = matrix(strict_ut, (dims,dims))
- return (strict_ut - strict_ut.trans())
+ strict_ut = matrix(strict_ut, (dims, dims))
+ return strict_ut - strict_ut.trans()
def _random_lyapunov_like_icecream(dims):
Generate a random Lyapunov-like matrix over the ice-cream cone in
``dims`` dimensions.
"""
- a = matrix([uniform(-10,10)], (1,1))
- b = matrix([uniform(-10,10) for idx in range(dims-1)], (dims-1, 1))
+ a = matrix([uniform(-10, 10)], (1, 1))
+ b = matrix([uniform(-10, 10) for idx in range(dims-1)], (dims-1, 1))
D = _random_skew_symmetric_matrix(dims-1) + a*identity(dims-1)
row1 = append_col(a, b.trans())
row2 = append_col(b, D)
- return append_row(row1,row2)
+ return append_row(row1, row2)
def _random_orthant_params():
# We only check for positive/negative stability if the game
# value is not basically zero. If the value is that close to
# zero, we just won't check any assertions.
+ eigs = eigenvalues_re(L)
if soln.game_value() > options.ABS_TOL:
# L should be positive stable
- ps = all([eig > -options.ABS_TOL for eig in eigenvalues_re(L)])
- self.assertTrue(ps)
+ positive_stable = all([eig > -options.ABS_TOL for eig in eigs])
+ self.assertTrue(positive_stable)
elif soln.game_value() < -options.ABS_TOL:
# L should be negative stable
- ns = all([eig < options.ABS_TOL for eig in eigenvalues_re(L)])
- self.assertTrue(ns)
+ negative_stable = all([eig < options.ABS_TOL for eig in eigs])
+ self.assertTrue(negative_stable)
# The dual game's value should always equal the primal's.
dualsoln = game.dual().solution()