def test_reduce_poly_inequalities_real_relational(): with assuming(Q.real(x), Q.real(y)): assert reduce_rational_inequalities([[Eq(x**2, 0)]], x, relational=True) == Eq(x, 0) assert reduce_rational_inequalities([[Le(x**2, 0)]], x, relational=True) == Eq(x, 0) assert reduce_rational_inequalities( [[Lt(x**2, 0)]], x, relational=True) is False assert reduce_rational_inequalities( [[Ge(x**2, 0)]], x, relational=True) is True assert reduce_rational_inequalities([[Gt(x**2, 0)]], x, relational=True) == Or( Lt(x, 0), Gt(x, 0)) assert reduce_rational_inequalities([[Ne(x**2, 0)]], x, relational=True) == Or( Lt(x, 0), Gt(x, 0)) assert reduce_rational_inequalities([[Eq(x**2, 1)]], x, relational=True) == Or( Eq(x, -1), Eq(x, 1)) assert reduce_rational_inequalities([[Le(x**2, 1)]], x, relational=True) == And( Le(-1, x), Le(x, 1)) assert reduce_rational_inequalities([[Lt(x**2, 1)]], x, relational=True) == And( Lt(-1, x), Lt(x, 1)) assert reduce_rational_inequalities([[Ge(x**2, 1)]], x, relational=True) == Or( Le(x, -1), Ge(x, 1)) assert reduce_rational_inequalities([[Gt(x**2, 1)]], x, relational=True) == Or( Lt(x, -1), Gt(x, 1)) assert reduce_rational_inequalities([[Ne(x**2, 1)]], x, relational=True) == Or( Lt(x, -1), And(Lt(-1, x), Lt(x, 1)), Gt(x, 1)) assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x, relational=True) == And( Le(-1.0, x), Le(x, 1.0)) assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x, relational=True) == And( Lt(-1.0, x), Lt(x, 1.0)) assert reduce_rational_inequalities([[Ge(x**2, 1.0)]], x, relational=True) == Or( Le(x, -1.0), Ge(x, 1.0)) assert reduce_rational_inequalities([[Gt(x**2, 1.0)]], x, relational=True) == Or( Lt(x, -1.0), Gt(x, 1.0)) assert reduce_rational_inequalities([[Ne(x**2, 1.0)]], x, relational=True) == \ Or(Lt(x, -1.0), And(Lt(-1.0, x), Lt(x, 1.0)), Gt(x, 1.0))
def test_issue_19822(): expr = And(Gt(n - 2, 1), Gt(n, 1)) assert simplify(expr) == Gt(n, 3)
def gt(self, a, b): return Gt(a, b)
def test_reduce_poly_inequalities_complex_relational(): cond = Eq(im(x), 0) assert reduce_poly_inequalities([[Eq(x**2, 0)]], x, relational=True) == And( Eq(re(x), 0), cond) assert reduce_poly_inequalities([[Le(x**2, 0)]], x, relational=True) == And( Eq(re(x), 0), cond) assert reduce_poly_inequalities([[Lt(x**2, 0)]], x, relational=True) is False assert reduce_poly_inequalities([[Ge(x**2, 0)]], x, relational=True) == cond assert reduce_poly_inequalities([[Gt(x**2, 0)]], x, relational=True) == And( Or(Lt(re(x), 0), Lt(0, re(x))), cond) assert reduce_poly_inequalities([[Ne(x**2, 0)]], x, relational=True) == And( Or(Lt(re(x), 0), Lt(0, re(x))), cond) assert reduce_poly_inequalities([[Eq(x**2, 1)]], x, relational=True) == And( Or(Eq(re(x), -1), Eq(re(x), 1)), cond) assert reduce_poly_inequalities([[Le(x**2, 1)]], x, relational=True) == And( And(Le(-1, re(x)), Le(re(x), 1)), cond) assert reduce_poly_inequalities([[Lt(x**2, 1)]], x, relational=True) == And( And(Lt(-1, re(x)), Lt(re(x), 1)), cond) assert reduce_poly_inequalities([[Ge(x**2, 1)]], x, relational=True) == And( Or(Le(re(x), -1), Le(1, re(x))), cond) assert reduce_poly_inequalities([[Gt(x**2, 1)]], x, relational=True) == And( Or(Lt(re(x), -1), Lt(1, re(x))), cond) assert reduce_poly_inequalities([[Ne(x**2, 1)]], x, relational=True) == And( Or(Lt(re(x), -1), And(Lt(-1, re(x)), Lt(re(x), 1)), Lt(1, re(x))), cond) assert reduce_poly_inequalities([[Le(x**2, 1.0)]], x, relational=True) == And( And(Le(-1.0, re(x)), Le(re(x), 1.0)), cond) assert reduce_poly_inequalities([[Lt(x**2, 1.0)]], x, relational=True) == And( And(Lt(-1.0, re(x)), Lt(re(x), 1.0)), cond) assert reduce_poly_inequalities([[Ge(x**2, 1.0)]], x, relational=True) == And( Or(Le(re(x), -1.0), Le(1.0, re(x))), cond) assert reduce_poly_inequalities([[Gt(x**2, 1.0)]], x, relational=True) == And( Or(Lt(re(x), -1.0), Lt(1.0, re(x))), cond) assert reduce_poly_inequalities( [[Ne(x**2, 1.0)]], x, relational=True) == And( Or(Lt(re(x), -1.0), And(Lt(-1.0, re(x)), Lt(re(x), 1.0)), Lt(1.0, re(x))), cond)
def check(self, mu, sigma): _value_check(len(mu) == len(sigma.col(0)), "Size of the mean vector and covariance matrix are incorrect.") #check if covariance matrix is positive definite or not. _value_check(all([Gt(i, 0) != False for i in sigma.eigenvals().keys()]), "The covariance matrix must be positive definite. ")
def test_floor(): assert floor(nan) is nan assert floor(oo) is oo assert floor(-oo) is -oo assert floor(zoo) is zoo assert floor(0) == 0 assert floor(1) == 1 assert floor(-1) == -1 assert floor(E) == 2 assert floor(-E) == -3 assert floor(2 * E) == 5 assert floor(-2 * E) == -6 assert floor(pi) == 3 assert floor(-pi) == -4 assert floor(S.Half) == 0 assert floor(Rational(-1, 2)) == -1 assert floor(Rational(7, 3)) == 2 assert floor(Rational(-7, 3)) == -3 assert floor(-Rational(7, 3)) == -3 assert floor(Float(17.0)) == 17 assert floor(-Float(17.0)) == -17 assert floor(Float(7.69)) == 7 assert floor(-Float(7.69)) == -8 assert floor(I) == I assert floor(-I) == -I e = floor(i) assert e.func is floor and e.args[0] == i assert floor(oo * I) == oo * I assert floor(-oo * I) == -oo * I assert floor(exp(I * pi / 4) * oo) == exp(I * pi / 4) * oo assert floor(2 * I) == 2 * I assert floor(-2 * I) == -2 * I assert floor(I / 2) == 0 assert floor(-I / 2) == -I assert floor(E + 17) == 19 assert floor(pi + 2) == 5 assert floor(E + pi) == 5 assert floor(I + pi) == 3 + I assert floor(floor(pi)) == 3 assert floor(floor(y)) == floor(y) assert floor(floor(x)) == floor(x) assert unchanged(floor, x) assert unchanged(floor, 2 * x) assert unchanged(floor, k * x) assert floor(k) == k assert floor(2 * k) == 2 * k assert floor(k * n) == k * n assert unchanged(floor, k / 2) assert unchanged(floor, x + y) assert floor(x + 3) == floor(x) + 3 assert floor(x + k) == floor(x) + k assert floor(y + 3) == floor(y) + 3 assert floor(y + k) == floor(y) + k assert floor(3 + I * y + pi) == 6 + floor(y) * I assert floor(k + n) == k + n assert unchanged(floor, x * I) assert floor(k * I) == k * I assert floor(Rational(23, 10) - E * I) == 2 - 3 * I assert floor(sin(1)) == 0 assert floor(sin(-1)) == -1 assert floor(exp(2)) == 7 assert floor(log(8) / log(2)) != 2 assert int(floor(log(8) / log(2)).evalf(chop=True)) == 3 assert (floor(factorial(50) / exp(1)) == 11188719610782480504630258070757734324011354208865721592720336800) assert (floor(y) < y) == False assert (floor(y) <= y) == True assert (floor(y) > y) == False assert (floor(y) >= y) == False assert (floor(x) <= x).is_Relational # x could be non-real assert (floor(x) > x).is_Relational assert (floor(x) <= y).is_Relational # arg is not same as rhs assert (floor(x) > y).is_Relational assert (floor(y) <= oo) == True assert (floor(y) < oo) == True assert (floor(y) >= -oo) == True assert (floor(y) > -oo) == True assert floor(y).rewrite(frac) == y - frac(y) assert floor(y).rewrite(ceiling) == -ceiling(-y) assert floor(y).rewrite(frac).subs(y, -pi) == floor(-pi) assert floor(y).rewrite(frac).subs(y, E) == floor(E) assert floor(y).rewrite(ceiling).subs(y, E) == -ceiling(-E) assert floor(y).rewrite(ceiling).subs(y, -pi) == -ceiling(pi) assert Eq(floor(y), y - frac(y)) assert Eq(floor(y), -ceiling(-y)) neg = Symbol("neg", negative=True) nn = Symbol("nn", nonnegative=True) pos = Symbol("pos", positive=True) np = Symbol("np", nonpositive=True) assert (floor(neg) < 0) == True assert (floor(neg) <= 0) == True assert (floor(neg) > 0) == False assert (floor(neg) >= 0) == False assert (floor(neg) <= -1) == True assert (floor(neg) >= -3) == (neg >= -3) assert (floor(neg) < 5) == (neg < 5) assert (floor(nn) < 0) == False assert (floor(nn) >= 0) == True assert (floor(pos) < 0) == False assert (floor(pos) <= 0) == (pos < 1) assert (floor(pos) > 0) == (pos >= 1) assert (floor(pos) >= 0) == True assert (floor(pos) >= 3) == (pos >= 3) assert (floor(np) <= 0) == True assert (floor(np) > 0) == False assert floor(neg).is_negative == True assert floor(neg).is_nonnegative == False assert floor(nn).is_negative == False assert floor(nn).is_nonnegative == True assert floor(pos).is_negative == False assert floor(pos).is_nonnegative == True assert floor(np).is_negative is None assert floor(np).is_nonnegative is None assert (floor(7, evaluate=False) >= 7) == True assert (floor(7, evaluate=False) > 7) == False assert (floor(7, evaluate=False) <= 7) == True assert (floor(7, evaluate=False) < 7) == False assert (floor(7, evaluate=False) >= 6) == True assert (floor(7, evaluate=False) > 6) == True assert (floor(7, evaluate=False) <= 6) == False assert (floor(7, evaluate=False) < 6) == False assert (floor(7, evaluate=False) >= 8) == False assert (floor(7, evaluate=False) > 8) == False assert (floor(7, evaluate=False) <= 8) == True assert (floor(7, evaluate=False) < 8) == True assert (floor(x) <= 5.5) == Le(floor(x), 5.5, evaluate=False) assert (floor(x) >= -3.2) == Ge(floor(x), -3.2, evaluate=False) assert (floor(x) < 2.9) == Lt(floor(x), 2.9, evaluate=False) assert (floor(x) > -1.7) == Gt(floor(x), -1.7, evaluate=False) assert (floor(y) <= 5.5) == (y < 6) assert (floor(y) >= -3.2) == (y >= -3) assert (floor(y) < 2.9) == (y < 3) assert (floor(y) > -1.7) == (y >= -1) assert (floor(y) <= n) == (y < n + 1) assert (floor(y) >= n) == (y >= n) assert (floor(y) < n) == (y < n) assert (floor(y) > n) == (y >= n + 1)
def newtons_method(expr, wrt, atol=1e-12, delta=None, debug=False, itermax=None, counter=None): """ Generates an AST for Newton-Raphson method (a root-finding algorithm). Returns an abstract syntax tree (AST) based on ``sympy.codegen.ast`` for Netwon's method of root-finding. Parameters ========== expr : expression wrt : Symbol With respect to, i.e. what is the variable. atol : number or expr Absolute tolerance (stopping criterion) delta : Symbol Will be a ``Dummy`` if ``None``. debug : bool Whether to print convergence information during iterations itermax : number or expr Maximum number of iterations. counter : Symbol Will be a ``Dummy`` if ``None``. Examples ======== >>> from sympy import symbols, cos >>> from sympy.codegen.ast import Assignment >>> from sympy.codegen.algorithms import newtons_method >>> x, dx, atol = symbols('x dx atol') >>> expr = cos(x) - x**3 >>> algo = newtons_method(expr, x, atol, dx) >>> algo.has(Assignment(dx, -expr/expr.diff(x))) True References ========== .. [1] https://en.wikipedia.org/wiki/Newton%27s_method """ if delta is None: delta = Dummy() Wrapper = Scope name_d = 'delta' else: Wrapper = lambda x: x name_d = delta.name delta_expr = -expr / expr.diff(wrt) whl_bdy = [ Assignment(delta, delta_expr), AddAugmentedAssignment(wrt, delta) ] if debug: prnt = Print([wrt, delta], r"{0}=%12.5g {1}=%12.5g\n".format(wrt.name, name_d)) whl_bdy = [whl_bdy[0], prnt] + whl_bdy[1:] req = Gt(Abs(delta), atol) declars = [Declaration(Variable(delta, type=real, value=oo))] if itermax is not None: counter = counter or Dummy(integer=True) v_counter = Variable.deduced(counter, 0) declars.append(Declaration(v_counter)) whl_bdy.append(AddAugmentedAssignment(counter, 1)) req = And(req, Lt(counter, itermax)) whl = While(req, CodeBlock(*whl_bdy)) blck = declars + [whl] return Wrapper(CodeBlock(*blck))
def test_DiscreteMarkovChain(): # pass only the name X = DiscreteMarkovChain("X") assert isinstance(X.state_space, Range) assert X.index_set == S.Naturals0 assert isinstance(X.transition_probabilities, MatrixSymbol) t = symbols('t', positive=True, integer=True) assert isinstance(X[t], RandomIndexedSymbol) assert E(X[0]) == Expectation(X[0]) raises(TypeError, lambda: DiscreteMarkovChain(1)) raises(NotImplementedError, lambda: X(t)) nz = Symbol('n', integer=True) TZ = MatrixSymbol('M', nz, nz) SZ = Range(nz) YZ = DiscreteMarkovChain('Y', SZ, TZ) assert P(Eq(YZ[2], 1), Eq(YZ[1], 0)) == TZ[0, 1] raises(ValueError, lambda: sample_stochastic_process(t)) raises(ValueError, lambda: next(sample_stochastic_process(X))) # pass name and state_space # any hashable object should be a valid state # states should be valid as a tuple/set/list/Tuple/Range sym, rainy, cloudy, sunny = symbols('a Rainy Cloudy Sunny', real=True) state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain], Tuple(1, exp(sym), Str('World'), sympify=False), Range(-1, 5, 2), [rainy, cloudy, sunny]] chains = [DiscreteMarkovChain("Y", state_space) for state_space in state_spaces] for i, Y in enumerate(chains): assert isinstance(Y.transition_probabilities, MatrixSymbol) assert Y.state_space == state_spaces[i] or Y.state_space == FiniteSet(*state_spaces[i]) assert Y.number_of_states == 3 with ignore_warnings(UserWarning): # TODO: Restore tests once warnings are removed assert P(Eq(Y[2], 1), Eq(Y[0], 2), evaluate=False) == Probability(Eq(Y[2], 1), Eq(Y[0], 2)) assert E(Y[0]) == Expectation(Y[0]) raises(ValueError, lambda: next(sample_stochastic_process(Y))) raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1)))) Y = DiscreteMarkovChain("Y", Range(1, t, 2)) assert Y.number_of_states == ceiling((t-1)/2) # pass name and transition_probabilities chains = [DiscreteMarkovChain("Y", trans_probs=Matrix([[]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[0, 1], [1, 0]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[pi, 1-pi], [sym, 1-sym]]))] for Z in chains: assert Z.number_of_states == Z.transition_probabilities.shape[0] assert isinstance(Z.transition_probabilities, ImmutableDenseMatrix) # pass name, state_space and transition_probabilities T = Matrix([[0.5, 0.2, 0.3],[0.2, 0.5, 0.3],[0.2, 0.3, 0.5]]) TS = MatrixSymbol('T', 3, 3) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) YS = DiscreteMarkovChain("Y", ['One', 'Two', 3], TS) assert YS._transient2transient() == None assert YS._transient2absorbing() == None assert Y.joint_distribution(1, Y[2], 3) == JointDistribution(Y[1], Y[2], Y[3]) raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol)) assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2) assert (P(Eq(YS[3], 2), Eq(YS[1], 1)) - (TS[0, 2]*TS[1, 0] + TS[1, 1]*TS[1, 2] + TS[1, 2]*TS[2, 2])).simplify() == 0 assert P(Eq(YS[1], 1), Eq(YS[2], 2)) == Probability(Eq(YS[1], 1)) assert P(Eq(YS[3], 3), Eq(YS[1], 1)) == TS[0, 2]*TS[1, 0] + TS[1, 1]*TS[1, 2] + TS[1, 2]*TS[2, 2] TO = Matrix([[0.25, 0.75, 0],[0, 0.25, 0.75],[0.75, 0, 0.25]]) assert P(Eq(Y[3], 2), Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float(0.375, 3) with ignore_warnings(UserWarning): ### TODO: Restore tests once warnings are removed assert E(Y[3], evaluate=False) == Expectation(Y[3]) assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3) TSO = MatrixSymbol('T', 4, 4) raises(ValueError, lambda: str(P(Eq(YS[3], 2), Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO)))) raises(TypeError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M'))) raises(ValueError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4))) raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6))) raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1))) # extended tests for probability queries TO1 = Matrix([[Rational(1, 4), Rational(3, 4), 0],[Rational(1, 3), Rational(1, 3), Rational(1, 3)],[0, Rational(1, 4), Rational(3, 4)]]) assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Probability(Eq(Y[0], 0)), Rational(1, 4)) & TransitionMatrixOf(Y, TO1)) == Rational(1, 16) assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \ Probability(Eq(Y[0], 0))/4 assert P(Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P(Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P(Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P(Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Y[1], 1)) == 0.1*Probability(Eq(Y[0], 0)) # testing properties of Markov chain TO2 = Matrix([[S.One, 0, 0],[Rational(1, 3), Rational(1, 3), Rational(1, 3)],[0, Rational(1, 4), Rational(3, 4)]]) TO3 = Matrix([[Rational(1, 4), Rational(3, 4), 0],[Rational(1, 3), Rational(1, 3), Rational(1, 3)],[0, Rational(1, 4), Rational(3, 4)]]) Y2 = DiscreteMarkovChain('Y', trans_probs=TO2) Y3 = DiscreteMarkovChain('Y', trans_probs=TO3) assert Y3._transient2absorbing() == None raises (ValueError, lambda: Y3.fundamental_matrix()) assert Y2.is_absorbing_chain() == True assert Y3.is_absorbing_chain() == False TO4 = Matrix([[Rational(1, 5), Rational(2, 5), Rational(2, 5)], [Rational(1, 10), S.Half, Rational(2, 5)], [Rational(3, 5), Rational(3, 10), Rational(1, 10)]]) Y4 = DiscreteMarkovChain('Y', trans_probs=TO4) w = ImmutableMatrix([[Rational(11, 39), Rational(16, 39), Rational(4, 13)]]) assert Y4.limiting_distribution == w assert Y4.is_regular() == True TS1 = MatrixSymbol('T', 3, 3) Y5 = DiscreteMarkovChain('Y', trans_probs=TS1) assert Y5.limiting_distribution(w, TO4).doit() == True assert Y5.stationary_distribution(condition_set=True).subs(TS1, TO4).contains(w).doit() == S.true TO6 = Matrix([[S.One, 0, 0, 0, 0],[S.Half, 0, S.Half, 0, 0],[0, S.Half, 0, S.Half, 0], [0, 0, S.Half, 0, S.Half], [0, 0, 0, 0, 1]]) Y6 = DiscreteMarkovChain('Y', trans_probs=TO6) assert Y6._transient2absorbing() == ImmutableMatrix([[S.Half, 0], [0, 0], [0, S.Half]]) assert Y6._transient2transient() == ImmutableMatrix([[0, S.Half, 0], [S.Half, 0, S.Half], [0, S.Half, 0]]) assert Y6.fundamental_matrix() == ImmutableMatrix([[Rational(3, 2), S.One, S.Half], [S.One, S(2), S.One], [S.Half, S.One, Rational(3, 2)]]) assert Y6.absorbing_probabilities() == ImmutableMatrix([[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half], [Rational(1, 4), Rational(3, 4)]]) # test for zero-sized matrix functionality X = DiscreteMarkovChain('X', trans_probs=Matrix([[]])) assert X.number_of_states == 0 assert X.stationary_distribution() == Matrix([[]]) # test communication_class # see https://drive.google.com/drive/folders/1HbxLlwwn2b3U8Lj7eb_ASIUb5vYaNIjg?usp=sharing # tutorial 2.pdf TO7 = Matrix([[0, 5, 5, 0, 0], [0, 0, 0, 10, 0], [5, 0, 5, 0, 0], [0, 10, 0, 0, 0], [0, 3, 0, 3, 4]])/10 Y7 = DiscreteMarkovChain('Y', trans_probs=TO7) tuples = Y7.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([1, 3], [0, 2], [4]) assert recurrence == (True, False, False) assert periods == (2, 1, 1) TO8 = Matrix([[0, 0, 0, 10, 0, 0], [5, 0, 5, 0, 0, 0], [0, 4, 0, 0, 0, 6], [10, 0, 0, 0, 0, 0], [0, 10, 0, 0, 0, 0], [0, 0, 0, 5, 5, 0]])/10 Y8 = DiscreteMarkovChain('Y', trans_probs=TO8) tuples = Y8.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([0, 3], [1, 2, 5, 4]) assert recurrence == (True, False) assert periods == (2, 2) TO9 = Matrix([[2, 0, 0, 3, 0, 0, 3, 2, 0, 0], [0, 10, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0, 3, 3], [0, 0, 0, 3, 0, 0, 6, 1, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 10, 0, 0, 0, 0], [4, 0, 0, 5, 0, 0, 1, 0, 0, 0], [2, 0, 0, 4, 0, 0, 2, 2, 0, 0], [3, 0, 1, 0, 0, 0, 0, 0, 4, 2], [0, 0, 4, 0, 0, 0, 0, 0, 3, 3]])/10 Y9 = DiscreteMarkovChain('Y', trans_probs=TO9) tuples = Y9.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([0, 3, 6, 7], [1], [2, 8, 9], [5], [4]) assert recurrence == (True, True, False, True, False) assert periods == (1, 1, 1, 1, 1) # test custom state space Y10 = DiscreteMarkovChain('Y', [1, 2, 3], TO2) tuples = Y10.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([1], [2, 3]) assert recurrence == (True, False) assert periods == (1, 1) # testing miscellaneous queries T = Matrix([[S.Half, Rational(1, 4), Rational(1, 4)], [Rational(1, 3), 0, Rational(2, 3)], [S.Half, S.Half, 0]]) X = DiscreteMarkovChain('X', [0, 1, 2], T) assert P(Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) assert E(X[1]**2, Eq(X[0], 1)) == Rational(8, 3) assert variance(X[1], Eq(X[0], 1)) == Rational(8, 9) raises(ValueError, lambda: E(X[1], Eq(X[2], 1))) raises(ValueError, lambda: DiscreteMarkovChain('X', [0, 1], T)) # testing miscellaneous queries with different state space X = DiscreteMarkovChain('X', ['A', 'B', 'C'], T) assert P(Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) a = X.state_space.args[0] c = X.state_space.args[2] assert (E(X[1] ** 2, Eq(X[0], 1)) - (a**2/3 + 2*c**2/3)).simplify() == 0 assert (variance(X[1], Eq(X[0], 1)) - (2*(-a/3 + c/3)**2/3 + (2*a/3 - 2*c/3)**2/3)).simplify() == 0 raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
def test_Union_as_relational(): x = Symbol('x') assert (Interval(0,1) + FiniteSet(2)).as_relational(x) ==\ Or(And(Ge(x,0), Le(x,1)) , Eq(x,2)) assert (Interval(0,1, True, True) + FiniteSet(1)).as_relational(x) ==\ And(Gt(x,0), Le(x,1))
def test_DiscreteMarkovChain(): # pass only the name X = DiscreteMarkovChain("X") assert isinstance(X.state_space, Range) assert isinstance(X.index_of, Range) assert not X._is_numeric assert X.index_set == S.Naturals0 assert isinstance(X.transition_probabilities, MatrixSymbol) t = symbols('t', positive=True, integer=True) assert isinstance(X[t], RandomIndexedSymbol) assert E(X[0]) == Expectation(X[0]) raises(TypeError, lambda: DiscreteMarkovChain(1)) raises(NotImplementedError, lambda: X(t)) raises(ValueError, lambda: sample_stochastic_process(t)) raises(ValueError, lambda: next(sample_stochastic_process(X))) # pass name and state_space # any hashable object should be a valid state # states should be valid as a tuple/set/list/Tuple/Range sym = symbols('a', real=True) state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain], Tuple(1, exp(sym), Str('World'), sympify=False), Range(-1, 7, 2)] chains = [ DiscreteMarkovChain("Y", state_spaces[0]), DiscreteMarkovChain("Y", state_spaces[1]), DiscreteMarkovChain("Y", state_spaces[2]) ] for i, Y in enumerate(chains): assert isinstance(Y.transition_probabilities, MatrixSymbol) assert Y.state_space == Tuple(*state_spaces[i]) assert Y.number_of_states == 3 assert not Y._is_numeric # because no transition matrix is provided assert Y.index_of[state_spaces[i][0]] == 0 assert Y.index_of[state_spaces[i][1]] == 1 assert Y.index_of[state_spaces[i][2]] == 2 with ignore_warnings( UserWarning): # TODO: Restore tests once warnings are removed assert P(Eq(Y[2], 1), Eq(Y[0], 2), evaluate=False) == Probability(Eq(Y[2], 1), Eq(Y[0], 2)) assert E(Y[0]) == Expectation(Y[0]) raises(ValueError, lambda: next(sample_stochastic_process(Y))) raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1)))) Y = DiscreteMarkovChain("Y", Range(1, t, 2)) assert Y.number_of_states == ceiling((t - 1) / 2) raises(NotImplementedError, lambda: Y.index_of) # pass name and transition_probabilities chains = [ DiscreteMarkovChain("Y", trans_probs=Matrix([[]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[0, 1], [1, 0]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[pi, 1 - pi], [sym, 1 - sym]])) ] for Z in chains: assert Z.number_of_states == Z.transition_probabilities.shape[0] assert isinstance(Z.transition_probabilities, ImmutableDenseMatrix) assert isinstance(Z.state_space, Tuple) assert Z._is_numeric # pass name, state_space and transition_probabilities T = Matrix([[0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]]) TS = MatrixSymbol('T', 3, 3) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) YS = DiscreteMarkovChain("Y", ['One', 'Two', 3], TS) assert YS._transient2transient() == None assert YS._transient2absorbing() == None assert Y.joint_distribution(1, Y[2], 3) == JointDistribution(Y[1], Y[2], Y[3]) raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol)) assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2) assert (P(Eq(YS[3], 2), Eq(YS[1], 1)) - (TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] + TS[1, 2] * TS[2, 2])).simplify() == 0 assert P(Eq(YS[1], 1), Eq(YS[2], 2)) == Probability(Eq(YS[1], 1)) assert P(Eq(YS[3], 3), Eq(YS[1], 1)) is S.Zero TO = Matrix([[0.25, 0.75, 0], [0, 0.25, 0.75], [0.75, 0, 0.25]]) assert P(Eq(Y[3], 2), Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float( 0.375, 3) with ignore_warnings( UserWarning): ### TODO: Restore tests once warnings are removed assert E(Y[3], evaluate=False) == Expectation(Y[3]) assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3) TSO = MatrixSymbol('T', 4, 4) raises( ValueError, lambda: str(P(Eq(YS[3], 2), Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO)))) raises(TypeError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M'))) raises( ValueError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4))) raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6))) raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1))) # extended tests for probability queries TO1 = Matrix([[Rational(1, 4), Rational(3, 4), 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) assert P( And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Probability(Eq(Y[0], 0)), Rational(1, 4)) & TransitionMatrixOf(Y, TO1)) == Rational(1, 16) assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \ Probability(Eq(Y[0], 0))/4 assert P( Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P( Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P( Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P( Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Y[1], 1)) == 0.1 * Probability(Eq(Y[0], 0)) # testing properties of Markov chain TO2 = Matrix([[S.One, 0, 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) TO3 = Matrix([[Rational(1, 4), Rational(3, 4), 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) Y2 = DiscreteMarkovChain('Y', trans_probs=TO2) Y3 = DiscreteMarkovChain('Y', trans_probs=TO3) assert Y3._transient2absorbing() == None raises(ValueError, lambda: Y3.fundamental_matrix()) assert Y2.is_absorbing_chain() == True assert Y3.is_absorbing_chain() == False TO4 = Matrix([[Rational(1, 5), Rational(2, 5), Rational(2, 5)], [Rational(1, 10), S.Half, Rational(2, 5)], [Rational(3, 5), Rational(3, 10), Rational(1, 10)]]) Y4 = DiscreteMarkovChain('Y', trans_probs=TO4) w = ImmutableMatrix([[Rational(11, 39), Rational(16, 39), Rational(4, 13)]]) assert Y4.limiting_distribution == w assert Y4.is_regular() == True TS1 = MatrixSymbol('T', 3, 3) Y5 = DiscreteMarkovChain('Y', trans_probs=TS1) assert Y5.limiting_distribution(w, TO4).doit() == True TO6 = Matrix([[S.One, 0, 0, 0, 0], [S.Half, 0, S.Half, 0, 0], [0, S.Half, 0, S.Half, 0], [0, 0, S.Half, 0, S.Half], [0, 0, 0, 0, 1]]) Y6 = DiscreteMarkovChain('Y', trans_probs=TO6) assert Y6._transient2absorbing() == ImmutableMatrix([[S.Half, 0], [0, 0], [0, S.Half]]) assert Y6._transient2transient() == ImmutableMatrix([[0, S.Half, 0], [S.Half, 0, S.Half], [0, S.Half, 0]]) assert Y6.fundamental_matrix() == ImmutableMatrix( [[Rational(3, 2), S.One, S.Half], [S.One, S(2), S.One], [S.Half, S.One, Rational(3, 2)]]) assert Y6.absorbing_probabilities() == ImmutableMatrix( [[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half], [Rational(1, 4), Rational(3, 4)]]) # testing miscellaneous queries T = Matrix([[S.Half, Rational(1, 4), Rational(1, 4)], [Rational(1, 3), 0, Rational(2, 3)], [S.Half, S.Half, 0]]) X = DiscreteMarkovChain('X', [0, 1, 2], T) assert P( Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) assert E(X[1]**2, Eq(X[0], 1)) == Rational(8, 3) assert variance(X[1], Eq(X[0], 1)) == Rational(8, 9) raises(ValueError, lambda: E(X[1], Eq(X[2], 1))) raises(ValueError, lambda: DiscreteMarkovChain('X', [0, 1], T)) # testing miscellaneous queries with different state space X = DiscreteMarkovChain('X', ['A', 'B', 'C'], T) assert P( Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) a = X.state_space.args[0] c = X.state_space.args[2] assert (E(X[1]**2, Eq(X[0], 1)) - (a**2 / 3 + 2 * c**2 / 3)).simplify() == 0 assert (variance(X[1], Eq(X[0], 1)) - (2 * (-a / 3 + c / 3)**2 / 3 + (2 * a / 3 - 2 * c / 3)**2 / 3)).simplify() == 0 raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
def __gt__(self, other): return Gt(self.symbol, _param_to_symbol(other))
def test_ContinuousMarkovChain(): T1 = Matrix([[S(-2), S(2), S.Zero], [S.Zero, S.NegativeOne, S.One], [Rational(3, 2), Rational(3, 2), S(-3)]]) C1 = ContinuousMarkovChain('C', [0, 1, 2], T1) assert C1.limiting_distribution() == ImmutableMatrix( [[Rational(3, 19), Rational(12, 19), Rational(4, 19)]]) T2 = Matrix([[-S.One, S.One, S.Zero], [S.One, -S.One, S.Zero], [S.Zero, S.One, -S.One]]) C2 = ContinuousMarkovChain('C', [0, 1, 2], T2) A, t = C2.generator_matrix, symbols('t', positive=True) assert C2.transition_probabilities(A)(t) == Matrix( [[S.Half + exp(-2 * t) / 2, S.Half - exp(-2 * t) / 2, 0], [S.Half - exp(-2 * t) / 2, S.Half + exp(-2 * t) / 2, 0], [ S.Half - exp(-t) + exp(-2 * t) / 2, S.Half - exp(-2 * t) / 2, exp(-t) ]]) with ignore_warnings( UserWarning): ### TODO: Restore tests once warnings are removed assert P(Eq(C2(1), 1), Eq(C2(0), 1), evaluate=False) == Probability(Eq(C2(1), 1), Eq(C2(0), 1)) assert P(Eq(C2(1), 1), Eq(C2(0), 1)) == exp(-2) / 2 + S.Half assert P( Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 1), Eq(P(Eq(C2(1), 0)), S.Half)) == (Rational(1, 4) - exp(-2) / 4) * (exp(-2) / 2 + S.Half) assert P( Not(Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 2)) | (Eq(C2(1), 0) & Eq(C2(2), 1) & Eq(C2(3), 2)), Eq(P(Eq(C2(1), 0)), Rational(1, 4)) & Eq(P(Eq(C2(1), 1)), Rational(1, 4))) is S.One assert E(C2(Rational(3, 2)), Eq(C2(0), 2)) == -exp(-3) / 2 + 2 * exp(Rational(-3, 2)) + S.Half assert variance(C2(Rational(3, 2)), Eq( C2(0), 1)) == ((S.Half - exp(-3) / 2)**2 * (exp(-3) / 2 + S.Half) + (Rational(-1, 2) - exp(-3) / 2)**2 * (S.Half - exp(-3) / 2)) raises(KeyError, lambda: P(Eq(C2(1), 0), Eq(P(Eq(C2(1), 1)), S.Half))) assert P(Eq(C2(1), 0), Eq(P(Eq(C2(5), 1)), S.Half)) == Probability(Eq(C2(1), 0)) TS1 = MatrixSymbol('G', 3, 3) CS1 = ContinuousMarkovChain('C', [0, 1, 2], TS1) A = CS1.generator_matrix assert CS1.transition_probabilities(A)(t) == exp(t * A) C3 = ContinuousMarkovChain( 'C', [Symbol('0'), Symbol('1'), Symbol('2')], T2) assert P(Eq(C3(1), 1), Eq(C3(0), 1)) == exp(-2) / 2 + S.Half assert P(Eq(C3(1), Symbol('1')), Eq(C3(0), Symbol('1'))) == exp(-2) / 2 + S.Half #test probability queries G = Matrix([[-S(1), Rational(1, 10), Rational(9, 10)], [Rational(2, 5), -S(1), Rational(3, 5)], [Rational(1, 2), Rational(1, 2), -S(1)]]) C = ContinuousMarkovChain('C', state_space=[0, 1, 2], gen_mat=G) assert P(Eq(C(7.385), C(3.19)), Eq(C(0.862), 0)).round(5) == Float(0.35469, 5) assert P(Gt(C(98.715), C(19.807)), Eq(C(11.314), 2)).round(5) == Float(0.32452, 5) assert P(Le(C(5.9), C(10.112)), Eq(C(4), 1)).round(6) == Float(0.675214, 6) assert Float(P(Eq(C(7.32), C(2.91)), Eq(C(2.63), 1)), 14) == Float(1 - P(Ne(C(7.32), C(2.91)), Eq(C(2.63), 1)), 14) assert Float(P(Gt(C(3.36), C(1.101)), Eq(C(0.8), 2)), 14) == Float(1 - P(Le(C(3.36), C(1.101)), Eq(C(0.8), 2)), 14) assert Float(P(Lt(C(4.9), C(2.79)), Eq(C(1.61), 0)), 14) == Float(1 - P(Ge(C(4.9), C(2.79)), Eq(C(1.61), 0)), 14) assert P(Eq(C(5.243), C(10.912)), Eq(C(2.174), 1)) == P(Eq(C(10.912), C(5.243)), Eq(C(2.174), 1)) assert P(Gt(C(2.344), C(9.9)), Eq(C(1.102), 1)) == P(Lt(C(9.9), C(2.344)), Eq(C(1.102), 1)) assert P(Ge(C(7.87), C(1.008)), Eq(C(0.153), 1)) == P(Le(C(1.008), C(7.87)), Eq(C(0.153), 1)) #test symbolic queries a, b, c, d = symbols('a b c d') query = P(Eq(C(a), b), Eq(C(c), d)) assert query.subs({ a: 3.65, b: 2, c: 1.78, d: 1 }).evalf().round(10) == P(Eq(C(3.65), 2), Eq(C(1.78), 1)).round(10) query_gt = P(Gt(C(a), b), Eq(C(c), d)) query_le = P(Le(C(a), b), Eq(C(c), d)) assert query_gt.subs({ a: 13.2, b: 0, c: 3.29, d: 2 }).evalf() + query_le.subs({ a: 13.2, b: 0, c: 3.29, d: 2 }).evalf() == 1 query_ge = P(Ge(C(a), b), Eq(C(c), d)) query_lt = P(Lt(C(a), b), Eq(C(c), d)) assert query_ge.subs({ a: 7.43, b: 1, c: 1.45, d: 0 }).evalf() + query_lt.subs({ a: 7.43, b: 1, c: 1.45, d: 0 }).evalf() == 1 #test issue 20078 assert (2 * C(1) + 3 * C(1)).simplify() == 5 * C(1) assert (2 * C(1) - 3 * C(1)).simplify() == -C(1) assert (2 * (0.25 * C(1))).simplify() == 0.5 * C(1) assert (2 * C(1) * 0.25 * C(1)).simplify() == 0.5 * C(1)**2 assert (C(1)**2 + C(1)**3).simplify() == (C(1) + 1) * C(1)**2
def test_DiscreteMarkovChain(): # pass only the name X = DiscreteMarkovChain("X") assert isinstance(X.state_space, Range) assert X.index_set == S.Naturals0 assert isinstance(X.transition_probabilities, MatrixSymbol) t = symbols('t', positive=True, integer=True) assert isinstance(X[t], RandomIndexedSymbol) assert E(X[0]) == Expectation(X[0]) raises(TypeError, lambda: DiscreteMarkovChain(1)) raises(NotImplementedError, lambda: X(t)) raises(NotImplementedError, lambda: X.communication_classes()) raises(NotImplementedError, lambda: X.canonical_form()) raises(NotImplementedError, lambda: X.decompose()) nz = Symbol('n', integer=True) TZ = MatrixSymbol('M', nz, nz) SZ = Range(nz) YZ = DiscreteMarkovChain('Y', SZ, TZ) assert P(Eq(YZ[2], 1), Eq(YZ[1], 0)) == TZ[0, 1] raises(ValueError, lambda: sample_stochastic_process(t)) raises(ValueError, lambda: next(sample_stochastic_process(X))) # pass name and state_space # any hashable object should be a valid state # states should be valid as a tuple/set/list/Tuple/Range sym, rainy, cloudy, sunny = symbols('a Rainy Cloudy Sunny', real=True) state_spaces = [(1, 2, 3), [Str('Hello'), sym, DiscreteMarkovChain], Tuple(1, exp(sym), Str('World'), sympify=False), Range(-1, 5, 2), [rainy, cloudy, sunny]] chains = [ DiscreteMarkovChain("Y", state_space) for state_space in state_spaces ] for i, Y in enumerate(chains): assert isinstance(Y.transition_probabilities, MatrixSymbol) assert Y.state_space == state_spaces[i] or Y.state_space == FiniteSet( *state_spaces[i]) assert Y.number_of_states == 3 with ignore_warnings( UserWarning): # TODO: Restore tests once warnings are removed assert P(Eq(Y[2], 1), Eq(Y[0], 2), evaluate=False) == Probability(Eq(Y[2], 1), Eq(Y[0], 2)) assert E(Y[0]) == Expectation(Y[0]) raises(ValueError, lambda: next(sample_stochastic_process(Y))) raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1)))) Y = DiscreteMarkovChain("Y", Range(1, t, 2)) assert Y.number_of_states == ceiling((t - 1) / 2) # pass name and transition_probabilities chains = [ DiscreteMarkovChain("Y", trans_probs=Matrix([[]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[0, 1], [1, 0]])), DiscreteMarkovChain("Y", trans_probs=Matrix([[pi, 1 - pi], [sym, 1 - sym]])) ] for Z in chains: assert Z.number_of_states == Z.transition_probabilities.shape[0] assert isinstance(Z.transition_probabilities, ImmutableDenseMatrix) # pass name, state_space and transition_probabilities T = Matrix([[0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]]) TS = MatrixSymbol('T', 3, 3) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) YS = DiscreteMarkovChain("Y", ['One', 'Two', 3], TS) assert Y.joint_distribution(1, Y[2], 3) == JointDistribution(Y[1], Y[2], Y[3]) raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol)) assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2) assert (P(Eq(YS[3], 2), Eq(YS[1], 1)) - (TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] + TS[1, 2] * TS[2, 2])).simplify() == 0 assert P(Eq(YS[1], 1), Eq(YS[2], 2)) == Probability(Eq(YS[1], 1)) assert P(Eq(YS[3], 3), Eq( YS[1], 1)) == TS[0, 2] * TS[1, 0] + TS[1, 1] * TS[1, 2] + TS[1, 2] * TS[2, 2] TO = Matrix([[0.25, 0.75, 0], [0, 0.25, 0.75], [0.75, 0, 0.25]]) assert P(Eq(Y[3], 2), Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float( 0.375, 3) with ignore_warnings( UserWarning): ### TODO: Restore tests once warnings are removed assert E(Y[3], evaluate=False) == Expectation(Y[3]) assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3) TSO = MatrixSymbol('T', 4, 4) raises( ValueError, lambda: str(P(Eq(YS[3], 2), Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO)))) raises(TypeError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M'))) raises( ValueError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4))) raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6))) raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1))) # extended tests for probability queries TO1 = Matrix([[Rational(1, 4), Rational(3, 4), 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) assert P( And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Probability(Eq(Y[0], 0)), Rational(1, 4)) & TransitionMatrixOf(Y, TO1)) == Rational(1, 16) assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \ Probability(Eq(Y[0], 0))/4 assert P( Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P( Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) == Rational(1, 4) assert P( Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P( Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [None, 'None', 1]) & TransitionMatrixOf(X, TO1)) is S.Zero assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Y[1], 1)) == 0.1 * Probability(Eq(Y[0], 0)) # testing properties of Markov chain TO2 = Matrix([[S.One, 0, 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) TO3 = Matrix([[Rational(1, 4), Rational(3, 4), 0], [Rational(1, 3), Rational(1, 3), Rational(1, 3)], [0, Rational(1, 4), Rational(3, 4)]]) Y2 = DiscreteMarkovChain('Y', trans_probs=TO2) Y3 = DiscreteMarkovChain('Y', trans_probs=TO3) assert Y3.fundamental_matrix() == ImmutableMatrix( [[176, 81, -132], [36, 141, -52], [-44, -39, 208]]) / 125 assert Y2.is_absorbing_chain() == True assert Y3.is_absorbing_chain() == False assert Y2.canonical_form() == ([0, 1, 2], TO2) assert Y3.canonical_form() == ([0, 1, 2], TO3) assert Y2.decompose() == ([0, 1, 2], TO2[0:1, 0:1], TO2[1:3, 0:1], TO2[1:3, 1:3]) assert Y3.decompose() == ([0, 1, 2], TO3, Matrix(0, 3, []), Matrix(0, 0, [])) TO4 = Matrix([[Rational(1, 5), Rational(2, 5), Rational(2, 5)], [Rational(1, 10), S.Half, Rational(2, 5)], [Rational(3, 5), Rational(3, 10), Rational(1, 10)]]) Y4 = DiscreteMarkovChain('Y', trans_probs=TO4) w = ImmutableMatrix([[Rational(11, 39), Rational(16, 39), Rational(4, 13)]]) assert Y4.limiting_distribution == w assert Y4.is_regular() == True assert Y4.is_ergodic() == True TS1 = MatrixSymbol('T', 3, 3) Y5 = DiscreteMarkovChain('Y', trans_probs=TS1) assert Y5.limiting_distribution(w, TO4).doit() == True assert Y5.stationary_distribution(condition_set=True).subs( TS1, TO4).contains(w).doit() == S.true TO6 = Matrix([[S.One, 0, 0, 0, 0], [S.Half, 0, S.Half, 0, 0], [0, S.Half, 0, S.Half, 0], [0, 0, S.Half, 0, S.Half], [0, 0, 0, 0, 1]]) Y6 = DiscreteMarkovChain('Y', trans_probs=TO6) assert Y6.fundamental_matrix() == ImmutableMatrix( [[Rational(3, 2), S.One, S.Half], [S.One, S(2), S.One], [S.Half, S.One, Rational(3, 2)]]) assert Y6.absorbing_probabilities() == ImmutableMatrix( [[Rational(3, 4), Rational(1, 4)], [S.Half, S.Half], [Rational(1, 4), Rational(3, 4)]]) TO7 = Matrix([[Rational(1, 2), Rational(1, 4), Rational(1, 4)], [Rational(1, 2), 0, Rational(1, 2)], [Rational(1, 4), Rational(1, 4), Rational(1, 2)]]) Y7 = DiscreteMarkovChain('Y', trans_probs=TO7) assert Y7.is_absorbing_chain() == False assert Y7.fundamental_matrix() == ImmutableDenseMatrix( [[Rational(86, 75), Rational(1, 25), Rational(-14, 75)], [Rational(2, 25), Rational(21, 25), Rational(2, 25)], [Rational(-14, 75), Rational(1, 25), Rational(86, 75)]]) # test for zero-sized matrix functionality X = DiscreteMarkovChain('X', trans_probs=Matrix([[]])) assert X.number_of_states == 0 assert X.stationary_distribution() == Matrix([[]]) assert X.communication_classes() == [] assert X.canonical_form() == ([], Matrix([[]])) assert X.decompose() == ([], Matrix([[]]), Matrix([[]]), Matrix([[]])) assert X.is_regular() == False assert X.is_ergodic() == False # test communication_class # see https://drive.google.com/drive/folders/1HbxLlwwn2b3U8Lj7eb_ASIUb5vYaNIjg?usp=sharing # tutorial 2.pdf TO7 = Matrix([[0, 5, 5, 0, 0], [0, 0, 0, 10, 0], [5, 0, 5, 0, 0], [0, 10, 0, 0, 0], [0, 3, 0, 3, 4]]) / 10 Y7 = DiscreteMarkovChain('Y', trans_probs=TO7) tuples = Y7.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([1, 3], [0, 2], [4]) assert recurrence == (True, False, False) assert periods == (2, 1, 1) TO8 = Matrix([[0, 0, 0, 10, 0, 0], [5, 0, 5, 0, 0, 0], [0, 4, 0, 0, 0, 6], [10, 0, 0, 0, 0, 0], [0, 10, 0, 0, 0, 0], [0, 0, 0, 5, 5, 0] ]) / 10 Y8 = DiscreteMarkovChain('Y', trans_probs=TO8) tuples = Y8.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([0, 3], [1, 2, 5, 4]) assert recurrence == (True, False) assert periods == (2, 2) TO9 = Matrix( [[2, 0, 0, 3, 0, 0, 3, 2, 0, 0], [0, 10, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 0, 0, 0, 0, 3, 3], [0, 0, 0, 3, 0, 0, 6, 1, 0, 0], [0, 0, 0, 0, 5, 5, 0, 0, 0, 0], [0, 0, 0, 0, 0, 10, 0, 0, 0, 0], [4, 0, 0, 5, 0, 0, 1, 0, 0, 0], [2, 0, 0, 4, 0, 0, 2, 2, 0, 0], [3, 0, 1, 0, 0, 0, 0, 0, 4, 2], [0, 0, 4, 0, 0, 0, 0, 0, 3, 3]]) / 10 Y9 = DiscreteMarkovChain('Y', trans_probs=TO9) tuples = Y9.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([0, 3, 6, 7], [1], [2, 8, 9], [5], [4]) assert recurrence == (True, True, False, True, False) assert periods == (1, 1, 1, 1, 1) # test canonical form # see https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf # example 11.13 T = Matrix([[1, 0, 0, 0, 0], [S(1) / 2, 0, S(1) / 2, 0, 0], [0, S(1) / 2, 0, S(1) / 2, 0], [0, 0, S(1) / 2, 0, S(1) / 2], [0, 0, 0, 0, S(1)]]) DW = DiscreteMarkovChain('DW', [0, 1, 2, 3, 4], T) states, A, B, C = DW.decompose() assert states == [0, 4, 1, 2, 3] assert A == Matrix([[1, 0], [0, 1]]) assert B == Matrix([[S(1) / 2, 0], [0, 0], [0, S(1) / 2]]) assert C == Matrix([[0, S(1) / 2, 0], [S(1) / 2, 0, S(1) / 2], [0, S(1) / 2, 0]]) states, new_matrix = DW.canonical_form() assert states == [0, 4, 1, 2, 3] assert new_matrix == Matrix([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [S(1) / 2, 0, 0, S(1) / 2, 0], [0, 0, S(1) / 2, 0, S(1) / 2], [0, S(1) / 2, 0, S(1) / 2, 0]]) # test regular and ergodic # https://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf T = Matrix([[0, 4, 0, 0, 0], [1, 0, 3, 0, 0], [0, 2, 0, 2, 0], [0, 0, 3, 0, 1], [0, 0, 0, 4, 0]]) / 4 X = DiscreteMarkovChain('X', trans_probs=T) assert not X.is_regular() assert X.is_ergodic() T = Matrix([[0, 1], [1, 0]]) X = DiscreteMarkovChain('X', trans_probs=T) assert not X.is_regular() assert X.is_ergodic() # http://www.math.wisc.edu/~valko/courses/331/MC2.pdf T = Matrix([[2, 1, 1], [2, 0, 2], [1, 1, 2]]) / 4 X = DiscreteMarkovChain('X', trans_probs=T) assert X.is_regular() assert X.is_ergodic() # https://docs.ufpr.br/~lucambio/CE222/1S2014/Kemeny-Snell1976.pdf T = Matrix([[1, 1], [1, 1]]) / 2 X = DiscreteMarkovChain('X', trans_probs=T) assert X.is_regular() assert X.is_ergodic() # test is_absorbing_chain T = Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]]) X = DiscreteMarkovChain('X', trans_probs=T) assert not X.is_absorbing_chain() # https://en.wikipedia.org/wiki/Absorbing_Markov_chain T = Matrix([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 0, 1], [0, 0, 0, 2]]) / 2 X = DiscreteMarkovChain('X', trans_probs=T) assert X.is_absorbing_chain() T = Matrix([[2, 0, 0, 0, 0], [1, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 1], [0, 0, 0, 0, 2]]) / 2 X = DiscreteMarkovChain('X', trans_probs=T) assert X.is_absorbing_chain() # test custom state space Y10 = DiscreteMarkovChain('Y', [1, 2, 3], TO2) tuples = Y10.communication_classes() classes, recurrence, periods = list(zip(*tuples)) assert classes == ([1], [2, 3]) assert recurrence == (True, False) assert periods == (1, 1) assert Y10.canonical_form() == ([1, 2, 3], TO2) assert Y10.decompose() == ([1, 2, 3], TO2[0:1, 0:1], TO2[1:3, 0:1], TO2[1:3, 1:3]) # testing miscellaneous queries T = Matrix([[S.Half, Rational(1, 4), Rational(1, 4)], [Rational(1, 3), 0, Rational(2, 3)], [S.Half, S.Half, 0]]) X = DiscreteMarkovChain('X', [0, 1, 2], T) assert P( Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) assert E(X[1]**2, Eq(X[0], 1)) == Rational(8, 3) assert variance(X[1], Eq(X[0], 1)) == Rational(8, 9) raises(ValueError, lambda: E(X[1], Eq(X[2], 1))) raises(ValueError, lambda: DiscreteMarkovChain('X', [0, 1], T)) # testing miscellaneous queries with different state space X = DiscreteMarkovChain('X', ['A', 'B', 'C'], T) assert P( Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), Rational(1, 4)) & Eq(P(Eq(X[1], 1)), Rational(1, 4))) == Rational(1, 12) assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == Rational(2, 3) assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) is S.Zero assert P(Ne(X[2], 2), Eq(X[1], 1)) == Rational(1, 3) a = X.state_space.args[0] c = X.state_space.args[2] assert (E(X[1]**2, Eq(X[0], 1)) - (a**2 / 3 + 2 * c**2 / 3)).simplify() == 0 assert (variance(X[1], Eq(X[0], 1)) - (2 * (-a / 3 + c / 3)**2 / 3 + (2 * a / 3 - 2 * c / 3)**2 / 3)).simplify() == 0 raises(ValueError, lambda: E(X[1], Eq(X[2], 1))) #testing queries with multiple RandomIndexedSymbols T = Matrix([[Rational(5, 10), Rational(3, 10), Rational(2, 10)], [Rational(2, 10), Rational(7, 10), Rational(1, 10)], [Rational(3, 10), Rational(3, 10), Rational(4, 10)]]) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) assert P(Eq(Y[7], Y[5]), Eq(Y[2], 0)).round(5) == Float(0.44428, 5) assert P(Gt(Y[3], Y[1]), Eq(Y[0], 0)).round(2) == Float(0.36, 2) assert P(Le(Y[5], Y[10]), Eq(Y[4], 2)).round(6) == Float(0.583120, 6) assert Float(P(Eq(Y[10], Y[5]), Eq(Y[4], 1)), 14) == Float(1 - P(Ne(Y[10], Y[5]), Eq(Y[4], 1)), 14) assert Float(P(Gt(Y[8], Y[9]), Eq(Y[3], 2)), 14) == Float(1 - P(Le(Y[8], Y[9]), Eq(Y[3], 2)), 14) assert Float(P(Lt(Y[1], Y[4]), Eq(Y[0], 0)), 14) == Float(1 - P(Ge(Y[1], Y[4]), Eq(Y[0], 0)), 14) assert P(Eq(Y[5], Y[10]), Eq(Y[2], 1)) == P(Eq(Y[10], Y[5]), Eq(Y[2], 1)) assert P(Gt(Y[1], Y[2]), Eq(Y[0], 1)) == P(Lt(Y[2], Y[1]), Eq(Y[0], 1)) assert P(Ge(Y[7], Y[6]), Eq(Y[4], 1)) == P(Le(Y[6], Y[7]), Eq(Y[4], 1)) #test symbolic queries a, b, c, d = symbols('a b c d') T = Matrix([[Rational(1, 10), Rational(4, 10), Rational(5, 10)], [Rational(3, 10), Rational(4, 10), Rational(3, 10)], [Rational(7, 10), Rational(2, 10), Rational(1, 10)]]) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) query = P(Eq(Y[a], b), Eq(Y[c], d)) assert query.subs({ a: 10, b: 2, c: 5, d: 1 }).evalf().round(4) == P(Eq(Y[10], 2), Eq(Y[5], 1)).round(4) assert query.subs({ a: 15, b: 0, c: 10, d: 1 }).evalf().round(4) == P(Eq(Y[15], 0), Eq(Y[10], 1)).round(4) query_gt = P(Gt(Y[a], b), Eq(Y[c], d)) query_le = P(Le(Y[a], b), Eq(Y[c], d)) assert query_gt.subs({ a: 5, b: 2, c: 1, d: 0 }).evalf() + query_le.subs({ a: 5, b: 2, c: 1, d: 0 }).evalf() == 1 query_ge = P(Ge(Y[a], b), Eq(Y[c], d)) query_lt = P(Lt(Y[a], b), Eq(Y[c], d)) assert query_ge.subs({ a: 4, b: 1, c: 0, d: 2 }).evalf() + query_lt.subs({ a: 4, b: 1, c: 0, d: 2 }).evalf() == 1 #test issue 20078 assert (2 * Y[1] + 3 * Y[1]).simplify() == 5 * Y[1] assert (2 * Y[1] - 3 * Y[1]).simplify() == -Y[1] assert (2 * (0.25 * Y[1])).simplify() == 0.5 * Y[1] assert ((2 * Y[1]) * (0.25 * Y[1])).simplify() == 0.5 * Y[1]**2 assert (Y[1]**2 + Y[1]**3).simplify() == (Y[1] + 1) * Y[1]**2
def low(self): return Piecewise((0, Gt(0, self.n + self.m - self.N) != False), (self.n + self.m - self.N, True))
def test_piecewise(): eq = Piecewise((x - 2, Gt(x, 2)), (2 - x, True)) - 3 assert set(solveset_real(eq, x)) == set(FiniteSet(-1, 5)) absxm3 = Piecewise((x - 3, S(0) <= x - 3), (3 - x, S(0) > x - 3)) y = Symbol('y', positive=True) assert solveset_real(absxm3 - y, x) == FiniteSet(-y + 3, y + 3)
def test_BernoulliProcess(): B = BernoulliProcess("B", p=0.6, success=1, failure=0) assert B.state_space == FiniteSet(0, 1) assert B.index_set == S.Naturals0 assert B.success == 1 assert B.failure == 0 X = BernoulliProcess("X", p=Rational(1, 3), success='H', failure='T') assert X.state_space == FiniteSet('H', 'T') H, T = symbols("H,T") assert E(X[1] + X[2] * X[3] ) == H**2 / 9 + 4 * H * T / 9 + H / 3 + 4 * T**2 / 9 + 2 * T / 3 t, x = symbols('t, x', positive=True, integer=True) assert isinstance(B[t], RandomIndexedSymbol) raises(ValueError, lambda: BernoulliProcess("X", p=1.1, success=1, failure=0)) raises(NotImplementedError, lambda: B(t)) raises(IndexError, lambda: B[-3]) assert B.joint_distribution(B[3], B[9]) == JointDistributionHandmade( Lambda( (B[3], B[9]), Piecewise((0.6, Eq(B[3], 1)), (0.4, Eq(B[3], 0)), (0, True)) * Piecewise((0.6, Eq(B[9], 1)), (0.4, Eq(B[9], 0)), (0, True)))) assert B.joint_distribution(2, B[4]) == JointDistributionHandmade( Lambda( (B[2], B[4]), Piecewise((0.6, Eq(B[2], 1)), (0.4, Eq(B[2], 0)), (0, True)) * Piecewise((0.6, Eq(B[4], 1)), (0.4, Eq(B[4], 0)), (0, True)))) # Test for the sum distribution of Bernoulli Process RVs Y = B[1] + B[2] + B[3] assert P(Eq(Y, 0)).round(2) == Float(0.06, 1) assert P(Eq(Y, 2)).round(2) == Float(0.43, 2) assert P(Eq(Y, 4)).round(2) == 0 assert P(Gt(Y, 1)).round(2) == Float(0.65, 2) # Test for independency of each Random Indexed variable assert P(Eq(B[1], 0) & Eq(B[2], 1) & Eq(B[3], 0) & Eq(B[4], 1)).round(2) == Float(0.06, 1) assert E(2 * B[1] + B[2]).round(2) == Float(1.80, 3) assert E(2 * B[1] + B[2] + 5).round(2) == Float(6.80, 3) assert E(B[2] * B[4] + B[10]).round(2) == Float(0.96, 2) assert E(B[2] > 0, Eq(B[1], 1) & Eq(B[2], 1)).round(2) == Float(0.60, 2) assert E(B[1]) == 0.6 assert P(B[1] > 0).round(2) == Float(0.60, 2) assert P(B[1] < 1).round(2) == Float(0.40, 2) assert P(B[1] > 0, B[2] <= 1).round(2) == Float(0.60, 2) assert P(B[12] * B[5] > 0).round(2) == Float(0.36, 2) assert P(B[12] * B[5] > 0, B[4] < 1).round(2) == Float(0.36, 2) assert P(Eq(B[2], 1), B[2] > 0) == 1 assert P(Eq(B[5], 3)) == 0 assert P(Eq(B[1], 1), B[1] < 0) == 0 assert P(B[2] > 0, Eq(B[2], 1)) == 1 assert P(B[2] < 0, Eq(B[2], 1)) == 0 assert P(B[2] > 0, B[2] == 7) == 0 assert P(B[5] > 0, B[5]) == BernoulliDistribution(0.6, 0, 1) raises(ValueError, lambda: P(3)) raises(ValueError, lambda: P(B[3] > 0, 3)) # test issue 19456 expr = Sum(B[t], (t, 0, 4)) expr2 = Sum(B[t], (t, 1, 3)) expr3 = Sum(B[t]**2, (t, 1, 3)) assert expr.doit() == B[0] + B[1] + B[2] + B[3] + B[4] assert expr2.doit() == Y assert expr3.doit() == B[1]**2 + B[2]**2 + B[3]**2 assert B[2 * t].free_symbols == {B[2 * t], t} assert B[4].free_symbols == {B[4]} assert B[x * t].free_symbols == {B[x * t], x, t}
def test_ceiling(): assert ceiling(nan) is nan assert ceiling(oo) is oo assert ceiling(-oo) is -oo assert ceiling(zoo) is zoo assert ceiling(0) == 0 assert ceiling(1) == 1 assert ceiling(-1) == -1 assert ceiling(E) == 3 assert ceiling(-E) == -2 assert ceiling(2 * E) == 6 assert ceiling(-2 * E) == -5 assert ceiling(pi) == 4 assert ceiling(-pi) == -3 assert ceiling(S.Half) == 1 assert ceiling(Rational(-1, 2)) == 0 assert ceiling(Rational(7, 3)) == 3 assert ceiling(-Rational(7, 3)) == -2 assert ceiling(Float(17.0)) == 17 assert ceiling(-Float(17.0)) == -17 assert ceiling(Float(7.69)) == 8 assert ceiling(-Float(7.69)) == -7 assert ceiling(I) == I assert ceiling(-I) == -I e = ceiling(i) assert e.func is ceiling and e.args[0] == i assert ceiling(oo * I) == oo * I assert ceiling(-oo * I) == -oo * I assert ceiling(exp(I * pi / 4) * oo) == exp(I * pi / 4) * oo assert ceiling(2 * I) == 2 * I assert ceiling(-2 * I) == -2 * I assert ceiling(I / 2) == I assert ceiling(-I / 2) == 0 assert ceiling(E + 17) == 20 assert ceiling(pi + 2) == 6 assert ceiling(E + pi) == 6 assert ceiling(I + pi) == I + 4 assert ceiling(ceiling(pi)) == 4 assert ceiling(ceiling(y)) == ceiling(y) assert ceiling(ceiling(x)) == ceiling(x) assert unchanged(ceiling, x) assert unchanged(ceiling, 2 * x) assert unchanged(ceiling, k * x) assert ceiling(k) == k assert ceiling(2 * k) == 2 * k assert ceiling(k * n) == k * n assert unchanged(ceiling, k / 2) assert unchanged(ceiling, x + y) assert ceiling(x + 3) == ceiling(x) + 3 assert ceiling(x + k) == ceiling(x) + k assert ceiling(y + 3) == ceiling(y) + 3 assert ceiling(y + k) == ceiling(y) + k assert ceiling(3 + pi + y * I) == 7 + ceiling(y) * I assert ceiling(k + n) == k + n assert unchanged(ceiling, x * I) assert ceiling(k * I) == k * I assert ceiling(Rational(23, 10) - E * I) == 3 - 2 * I assert ceiling(sin(1)) == 1 assert ceiling(sin(-1)) == 0 assert ceiling(exp(2)) == 8 assert ceiling(-log(8) / log(2)) != -2 assert int(ceiling(-log(8) / log(2)).evalf(chop=True)) == -3 assert (ceiling(factorial(50) / exp(1)) == 11188719610782480504630258070757734324011354208865721592720336801) assert (ceiling(y) >= y) == True assert (ceiling(y) > y) == False assert (ceiling(y) < y) == False assert (ceiling(y) <= y) == False assert (ceiling(x) >= x).is_Relational # x could be non-real assert (ceiling(x) < x).is_Relational assert (ceiling(x) >= y).is_Relational # arg is not same as rhs assert (ceiling(x) < y).is_Relational assert (ceiling(y) >= -oo) == True assert (ceiling(y) > -oo) == True assert (ceiling(y) <= oo) == True assert (ceiling(y) < oo) == True assert ceiling(y).rewrite(floor) == -floor(-y) assert ceiling(y).rewrite(frac) == y + frac(-y) assert ceiling(y).rewrite(floor).subs(y, -pi) == -floor(pi) assert ceiling(y).rewrite(floor).subs(y, E) == -floor(-E) assert ceiling(y).rewrite(frac).subs(y, pi) == ceiling(pi) assert ceiling(y).rewrite(frac).subs(y, -E) == ceiling(-E) assert Eq(ceiling(y), y + frac(-y)) assert Eq(ceiling(y), -floor(-y)) neg = Symbol("neg", negative=True) nn = Symbol("nn", nonnegative=True) pos = Symbol("pos", positive=True) np = Symbol("np", nonpositive=True) assert (ceiling(neg) <= 0) == True assert (ceiling(neg) < 0) == (neg <= -1) assert (ceiling(neg) > 0) == False assert (ceiling(neg) >= 0) == (neg > -1) assert (ceiling(neg) > -3) == (neg > -3) assert (ceiling(neg) <= 10) == (neg <= 10) assert (ceiling(nn) < 0) == False assert (ceiling(nn) >= 0) == True assert (ceiling(pos) < 0) == False assert (ceiling(pos) <= 0) == False assert (ceiling(pos) > 0) == True assert (ceiling(pos) >= 0) == True assert (ceiling(pos) >= 1) == True assert (ceiling(pos) > 5) == (pos > 5) assert (ceiling(np) <= 0) == True assert (ceiling(np) > 0) == False assert ceiling(neg).is_positive == False assert ceiling(neg).is_nonpositive == True assert ceiling(nn).is_positive is None assert ceiling(nn).is_nonpositive is None assert ceiling(pos).is_positive == True assert ceiling(pos).is_nonpositive == False assert ceiling(np).is_positive == False assert ceiling(np).is_nonpositive == True assert (ceiling(7, evaluate=False) >= 7) == True assert (ceiling(7, evaluate=False) > 7) == False assert (ceiling(7, evaluate=False) <= 7) == True assert (ceiling(7, evaluate=False) < 7) == False assert (ceiling(7, evaluate=False) >= 6) == True assert (ceiling(7, evaluate=False) > 6) == True assert (ceiling(7, evaluate=False) <= 6) == False assert (ceiling(7, evaluate=False) < 6) == False assert (ceiling(7, evaluate=False) >= 8) == False assert (ceiling(7, evaluate=False) > 8) == False assert (ceiling(7, evaluate=False) <= 8) == True assert (ceiling(7, evaluate=False) < 8) == True assert (ceiling(x) <= 5.5) == Le(ceiling(x), 5.5, evaluate=False) assert (ceiling(x) >= -3.2) == Ge(ceiling(x), -3.2, evaluate=False) assert (ceiling(x) < 2.9) == Lt(ceiling(x), 2.9, evaluate=False) assert (ceiling(x) > -1.7) == Gt(ceiling(x), -1.7, evaluate=False) assert (ceiling(y) <= 5.5) == (y <= 5) assert (ceiling(y) >= -3.2) == (y > -4) assert (ceiling(y) < 2.9) == (y <= 2) assert (ceiling(y) > -1.7) == (y > -2) assert (ceiling(y) <= n) == (y <= n) assert (ceiling(y) >= n) == (y > n - 1) assert (ceiling(y) < n) == (y <= n - 1) assert (ceiling(y) > n) == (y > n)
def test_pretty_relational(): expr = Eq(x, y) ascii_str = \ """\ x = y\ """ ucode_str = \ u"""\ x = y\ """ assert pretty(expr) == ascii_str assert upretty(expr) == ucode_str expr = Lt(x, y) ascii_str = \ """\ x < y\ """ ucode_str = \ u"""\ x < y\ """ assert pretty(expr) == ascii_str assert upretty(expr) == ucode_str expr = Gt(x, y) ascii_str = \ """\ y < x\ """ ucode_str = \ u"""\ y < x\ """ assert pretty(expr) == ascii_str assert upretty(expr) == ucode_str expr = Le(x, y) ascii_str = \ """\ x <= y\ """ ucode_str = \ u"""\ x ≤ y\ """ assert pretty(expr) == ascii_str assert upretty(expr) == ucode_str expr = Ge(x, y) ascii_str = \ """\ y <= x\ """ ucode_str = \ u"""\ y ≤ x\ """ assert pretty(expr) == ascii_str assert upretty(expr) == ucode_str expr = Ne(x / (y + 1), y**2) ascii_str_1 = \ """\ x 2\n\ ----- != y \n\ 1 + y \ """ ascii_str_2 = \ """\ x 2\n\ ----- != y \n\ y + 1 \ """ ucode_str_1 = \ u"""\ x 2\n\ ───── ≠ y \n\ 1 + y \ """ ucode_str_2 = \ u"""\ x 2\n\ ───── ≠ y \n\ y + 1 \ """ assert pretty(expr) in [ascii_str_1, ascii_str_2] assert upretty(expr) in [ucode_str_1, ucode_str_2]
def test_DiscreteMarkovChain(): # pass only the name X = DiscreteMarkovChain("X") assert X.state_space == S.Reals assert X.index_set == S.Naturals0 assert X.transition_probabilities == None t = symbols('t', positive=True, integer=True) assert isinstance(X[t], RandomIndexedSymbol) assert E(X[0]) == Expectation(X[0]) raises(TypeError, lambda: DiscreteMarkovChain(1)) raises(NotImplementedError, lambda: X(t)) # pass name and state_space Y = DiscreteMarkovChain("Y", [1, 2, 3]) assert Y.transition_probabilities == None assert Y.state_space == FiniteSet(1, 2, 3) assert P(Eq(Y[2], 1), Eq(Y[0], 2)) == Probability(Eq(Y[2], 1), Eq(Y[0], 2)) assert E(X[0]) == Expectation(X[0]) raises(TypeError, lambda: DiscreteMarkovChain("Y", dict((1, 1)))) # pass name, state_space and transition_probabilities T = Matrix([[0.5, 0.2, 0.3], [0.2, 0.5, 0.3], [0.2, 0.3, 0.5]]) TS = MatrixSymbol('T', 3, 3) Y = DiscreteMarkovChain("Y", [0, 1, 2], T) YS = DiscreteMarkovChain("Y", [0, 1, 2], TS) assert YS._transient2transient() == None assert YS._transient2absorbing() == None assert Y.joint_distribution(1, Y[2], 3) == JointDistribution(Y[1], Y[2], Y[3]) raises(ValueError, lambda: Y.joint_distribution(Y[1].symbol, Y[2].symbol)) assert P(Eq(Y[3], 2), Eq(Y[1], 1)).round(2) == Float(0.36, 2) assert str(P(Eq(YS[3], 2), Eq(YS[1], 1))) == \ "T[0, 2]*T[1, 0] + T[1, 1]*T[1, 2] + T[1, 2]*T[2, 2]" TO = Matrix([[0.25, 0.75, 0], [0, 0.25, 0.75], [0.75, 0, 0.25]]) assert P(Eq(Y[3], 2), Eq(Y[1], 1) & TransitionMatrixOf(Y, TO)).round(3) == Float( 0.375, 3) assert E(Y[3], evaluate=False) == Expectation(Y[3]) assert E(Y[3], Eq(Y[2], 1)).round(2) == Float(1.1, 3) TSO = MatrixSymbol('T', 4, 4) raises( ValueError, lambda: str(P(Eq(YS[3], 2), Eq(YS[1], 1) & TransitionMatrixOf(YS, TSO)))) raises(TypeError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], symbols('M'))) raises( ValueError, lambda: DiscreteMarkovChain("Z", [0, 1, 2], MatrixSymbol('T', 3, 4))) raises(IndexError, lambda: str(P(Eq(YS[3], 3), Eq(YS[1], 1)))) raises(ValueError, lambda: str(P(Eq(YS[1], 1), Eq(YS[2], 2)))) raises(ValueError, lambda: E(Y[3], Eq(Y[2], 6))) raises(ValueError, lambda: E(Y[2], Eq(Y[3], 1))) # extended tests for probability queries TO1 = Matrix([[S(1) / 4, S(3) / 4, 0], [S(1) / 3, S(1) / 3, S(1) / 3], [0, S(1) / 4, S(3) / 4]]) assert P( And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Probability(Eq(Y[0], 0)), S(1) / 4) & TransitionMatrixOf(Y, TO1)) == S(1) / 16 assert P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), TransitionMatrixOf(Y, TO1)) == \ Probability(Eq(Y[0], 0))/4 assert P( Lt(X[1], 2) & Gt(X[1], 0), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) == S(1) / 4 assert P( Ne(X[1], 2) & Ne(X[1], 1), Eq(X[0], 2) & StochasticStateSpaceOf(X, [0, 1, 2]) & TransitionMatrixOf(X, TO1)) == S(0) raises( ValueError, lambda: str( P(And(Eq(Y[2], 1), Eq(Y[1], 1), Eq(Y[0], 0)), Eq(Y[1], 1)))) # testing properties of Markov chain TO2 = Matrix([[S(1), 0, 0], [S(1) / 3, S(1) / 3, S(1) / 3], [0, S(1) / 4, S(3) / 4]]) TO3 = Matrix([[S(1) / 4, S(3) / 4, 0], [S(1) / 3, S(1) / 3, S(1) / 3], [0, S(1) / 4, S(3) / 4]]) Y2 = DiscreteMarkovChain('Y', trans_probs=TO2) Y3 = DiscreteMarkovChain('Y', trans_probs=TO3) assert Y3._transient2absorbing() == None raises(ValueError, lambda: Y3.fundamental_matrix()) assert Y2.is_absorbing_chain() == True assert Y3.is_absorbing_chain() == False TO4 = Matrix([[S(1) / 5, S(2) / 5, S(2) / 5], [S(1) / 10, S(1) / 2, S(2) / 5], [S(3) / 5, S(3) / 10, S(1) / 10]]) Y4 = DiscreteMarkovChain('Y', trans_probs=TO4) w = ImmutableMatrix([[S(11) / 39, S(16) / 39, S(4) / 13]]) assert Y4.limiting_distribution == w assert Y4.is_regular() == True TS1 = MatrixSymbol('T', 3, 3) Y5 = DiscreteMarkovChain('Y', trans_probs=TS1) assert Y5.limiting_distribution(w, TO4).doit() == True TO6 = Matrix([[S(1), 0, 0, 0, 0], [S(1) / 2, 0, S(1) / 2, 0, 0], [0, S(1) / 2, 0, S(1) / 2, 0], [0, 0, S(1) / 2, 0, S(1) / 2], [0, 0, 0, 0, 1]]) Y6 = DiscreteMarkovChain('Y', trans_probs=TO6) assert Y6._transient2absorbing() == ImmutableMatrix([[S(1) / 2, 0], [0, 0], [0, S(1) / 2]]) assert Y6._transient2transient() == ImmutableMatrix( [[0, S(1) / 2, 0], [S(1) / 2, 0, S(1) / 2], [0, S(1) / 2, 0]]) assert Y6.fundamental_matrix() == ImmutableMatrix( [[S(3) / 2, S(1), S(1) / 2], [S(1), S(2), S(1)], [S(1) / 2, S(1), S(3) / 2]]) assert Y6.absorbing_probabilites() == ImmutableMatrix( [[S(3) / 4, S(1) / 4], [S(1) / 2, S(1) / 2], [S(1) / 4, S(3) / 4]]) # testing miscellaneous queries T = Matrix([[S(1) / 2, S(1) / 4, S(1) / 4], [S(1) / 3, 0, S(2) / 3], [S(1) / 2, S(1) / 2, 0]]) X = DiscreteMarkovChain('X', [0, 1, 2], T) assert P( Eq(X[1], 2) & Eq(X[2], 1) & Eq(X[3], 0), Eq(P(Eq(X[1], 0)), S(1) / 4) & Eq(P(Eq(X[1], 1)), S(1) / 4)) == S(1) / 12 assert P(Eq(X[2], 1) | Eq(X[2], 2), Eq(X[1], 1)) == S(2) / 3 assert P(Eq(X[2], 1) & Eq(X[2], 2), Eq(X[1], 1)) == S(0) assert P(Ne(X[2], 2), Eq(X[1], 1)) == S(1) / 3 assert E(X[1]**2, Eq(X[0], 1)) == S(8) / 3 assert variance(X[1], Eq(X[0], 1)) == S(8) / 9 raises(ValueError, lambda: E(X[1], Eq(X[2], 1)))
def test_reduce_poly_inequalities_real_relational(): x = Symbol('x', real=True) y = Symbol('y', real=True) assert reduce_rational_inequalities([[Eq(x**2, 0)]], x, relational=True) == Eq(x, 0) assert reduce_rational_inequalities([[Le(x**2, 0)]], x, relational=True) == Eq(x, 0) assert reduce_rational_inequalities([[Lt(x**2, 0)]], x, relational=True) == False assert reduce_rational_inequalities([[Ge(x**2, 0)]], x, relational=True) == And( Lt(-oo, x), Lt(x, oo)) assert reduce_rational_inequalities([[Gt(x**2, 0)]], x, relational=True) == Or( And(Lt(-oo, x), Lt(x, 0)), And(Lt(0, x), Lt(x, oo))) assert reduce_rational_inequalities([[Ne(x**2, 0)]], x, relational=True) == Or( And(Lt(-oo, x), Lt(x, 0)), And(Lt(0, x), Lt(x, oo))) assert reduce_rational_inequalities([[Eq(x**2, 1)]], x, relational=True) == Or( Eq(x, -1), Eq(x, 1)) assert reduce_rational_inequalities([[Le(x**2, 1)]], x, relational=True) == And( Le(-1, x), Le(x, 1)) assert reduce_rational_inequalities([[Lt(x**2, 1)]], x, relational=True) == And( Lt(-1, x), Lt(x, 1)) assert reduce_rational_inequalities([[Ge(x**2, 1)]], x, relational=True) == Or( And(Le(1, x), Lt(x, oo)), And(Le(x, -1), Lt(-oo, x))) assert reduce_rational_inequalities([[Gt(x**2, 1)]], x, relational=True) == Or( And(Lt(1, x), Lt(x, oo)), And(Lt(x, -1), Lt(-oo, x))) assert reduce_rational_inequalities([[Ne(x**2, 1)]], x, relational=True) == Or( And(Lt(-oo, x), Lt(x, -1)), And(Lt(-1, x), Lt(x, 1)), And(Lt(1, x), Lt(x, oo))) assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x, relational=True) == And( Le(-1.0, x), Le(x, 1.0)) assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x, relational=True) == And( Lt(-1.0, x), Lt(x, 1.0)) assert reduce_rational_inequalities([[Ge(x**2, 1.0)]], x, relational=True) == Or( And(Lt(Float('-inf'), x), Le(x, -1.0)), And(Le(1.0, x), Lt(x, Float('+inf')))) assert reduce_rational_inequalities([[Gt(x**2, 1.0)]], x, relational=True) == Or( And(Lt(Float('-inf'), x), Lt(x, -1.0)), And(Lt(1.0, x), Lt(x, Float('+inf')))) assert reduce_rational_inequalities([[Ne(x**2, 1.0)]], x, relational=True) == \ Or(And(Lt(-1.0, x), Lt(x, 1.0)), And(Lt(Float('-inf'), x), Lt(x, -1.0)), And(Lt(1.0, x), Lt(x, Float('+inf'))))
def test_reduce_poly_inequalities_real_interval(): assert reduce_rational_inequalities([[Eq(x**2, 0)]], x, relational=False) == FiniteSet(0) assert reduce_rational_inequalities([[Le(x**2, 0)]], x, relational=False) == FiniteSet(0) assert reduce_rational_inequalities([[Lt(x**2, 0)]], x, relational=False) == S.EmptySet assert reduce_rational_inequalities( [[Ge(x**2, 0)]], x, relational=False) == \ S.Reals if x.is_real else Interval(-oo, oo) assert reduce_rational_inequalities( [[Gt(x**2, 0)]], x, relational=False) == \ FiniteSet(0).complement(S.Reals) assert reduce_rational_inequalities( [[Ne(x**2, 0)]], x, relational=False) == \ FiniteSet(0).complement(S.Reals) assert reduce_rational_inequalities([[Eq(x**2, 1)]], x, relational=False) == FiniteSet(-1, 1) assert reduce_rational_inequalities([[Le(x**2, 1)]], x, relational=False) == Interval(-1, 1) assert reduce_rational_inequalities([[Lt(x**2, 1)]], x, relational=False) == Interval( -1, 1, True, True) assert reduce_rational_inequalities( [[Ge(x**2, 1)]], x, relational=False) == \ Union(Interval(-oo, -1), Interval(1, oo)) assert reduce_rational_inequalities( [[Gt(x**2, 1)]], x, relational=False) == \ Interval(-1, 1).complement(S.Reals) assert reduce_rational_inequalities( [[Ne(x**2, 1)]], x, relational=False) == \ FiniteSet(-1, 1).complement(S.Reals) assert reduce_rational_inequalities([[Eq(x**2, 1.0)]], x, relational=False) == FiniteSet( -1.0, 1.0).evalf() assert reduce_rational_inequalities([[Le(x**2, 1.0)]], x, relational=False) == Interval( -1.0, 1.0) assert reduce_rational_inequalities([[Lt(x**2, 1.0)]], x, relational=False) == Interval( -1.0, 1.0, True, True) assert reduce_rational_inequalities( [[Ge(x**2, 1.0)]], x, relational=False) == \ Union(Interval(-inf, -1.0), Interval(1.0, inf)) assert reduce_rational_inequalities( [[Gt(x**2, 1.0)]], x, relational=False) == \ Union(Interval(-inf, -1.0, right_open=True), Interval(1.0, inf, left_open=True)) assert reduce_rational_inequalities([[Ne( x**2, 1.0)]], x, relational=False) == \ FiniteSet(-1.0, 1.0).complement(S.Reals) s = sqrt(2) assert reduce_rational_inequalities( [[Lt(x**2 - 1, 0), Gt(x**2 - 1, 0)]], x, relational=False) == S.EmptySet assert reduce_rational_inequalities( [[Le(x**2 - 1, 0), Ge(x**2 - 1, 0)]], x, relational=False) == FiniteSet(-1, 1) assert reduce_rational_inequalities( [[Le(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, False, False), Interval(1, s, False, False)) assert reduce_rational_inequalities( [[Le(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, False, True), Interval(1, s, True, False)) assert reduce_rational_inequalities( [[Lt(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, False), Interval(1, s, False, True)) assert reduce_rational_inequalities( [[Lt(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, True), Interval(1, s, True, True)) assert reduce_rational_inequalities( [[Lt(x**2 - 2, 0), Ne(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, True), Interval(-1, 1, True, True), Interval(1, s, True, True))
def test_reduce_poly_inequalities_real_interval(): global_assumptions.add(Q.real(x)) global_assumptions.add(Q.real(y)) assert reduce_poly_inequalities([[Eq(x**2, 0)]], x, relational=False) == FiniteSet(0) assert reduce_poly_inequalities([[Le(x**2, 0)]], x, relational=False) == FiniteSet(0) assert reduce_poly_inequalities([[Lt(x**2, 0)]], x, relational=False) == S.EmptySet assert reduce_poly_inequalities([[Ge(x**2, 0)]], x, relational=False) == Interval(-oo, oo) assert reduce_poly_inequalities( [[Gt(x**2, 0)]], x, relational=False) == FiniteSet(0).complement assert reduce_poly_inequalities( [[Ne(x**2, 0)]], x, relational=False) == FiniteSet(0).complement assert reduce_poly_inequalities([[Eq(x**2, 1)]], x, relational=False) == FiniteSet(-1, 1) assert reduce_poly_inequalities([[Le(x**2, 1)]], x, relational=False) == Interval(-1, 1) assert reduce_poly_inequalities([[Lt(x**2, 1)]], x, relational=False) == Interval( -1, 1, True, True) assert reduce_poly_inequalities([[Ge(x**2, 1)]], x, relational=False) == Union( Interval(-oo, -1), Interval(1, oo)) assert reduce_poly_inequalities([[Gt(x**2, 1)]], x, relational=False) == Interval(-1, 1).complement assert reduce_poly_inequalities( [[Ne(x**2, 1)]], x, relational=False) == FiniteSet(-1, 1).complement assert reduce_poly_inequalities([[Eq(x**2, 1.0)]], x, relational=False) == FiniteSet( -1.0, 1.0).evalf() assert reduce_poly_inequalities([[Le(x**2, 1.0)]], x, relational=False) == Interval(-1.0, 1.0) assert reduce_poly_inequalities([[Lt(x**2, 1.0)]], x, relational=False) == Interval( -1.0, 1.0, True, True) assert reduce_poly_inequalities([[Ge(x**2, 1.0)]], x, relational=False) == Union( Interval(-inf, -1.0), Interval(1.0, inf)) assert reduce_poly_inequalities([[Gt(x**2, 1.0)]], x, relational=False) == Union( Interval(-inf, -1.0, right_open=True), Interval(1.0, inf, left_open=True)) assert reduce_poly_inequalities([[Ne(x**2, 1.0)]], x, relational=False) == FiniteSet( -1.0, 1.0).complement s = sqrt(2) assert reduce_poly_inequalities( [[Lt(x**2 - 1, 0), Gt(x**2 - 1, 0)]], x, relational=False) == S.EmptySet assert reduce_poly_inequalities( [[Le(x**2 - 1, 0), Ge(x**2 - 1, 0)]], x, relational=False) == FiniteSet(-1, 1) assert reduce_poly_inequalities( [[Le(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, False, False), Interval(1, s, False, False)) assert reduce_poly_inequalities( [[Le(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, False, True), Interval(1, s, True, False)) assert reduce_poly_inequalities( [[Lt(x**2 - 2, 0), Ge(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, False), Interval(1, s, False, True)) assert reduce_poly_inequalities( [[Lt(x**2 - 2, 0), Gt(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, True), Interval(1, s, True, True)) assert reduce_poly_inequalities( [[Lt(x**2 - 2, 0), Ne(x**2 - 1, 0)]], x, relational=False) == Union(Interval(-s, -1, True, True), Interval(-1, 1, True, True), Interval(1, s, True, True)) global_assumptions.remove(Q.real(x)) global_assumptions.remove(Q.real(y))