def test_doit(): from sympy import Symbol p = Symbol('p', positive=True) n = Symbol('n', negative=True) np = Symbol('np', nonpositive=True) nn = Symbol('nn', nonnegative=True) assert Gt(p, 0).doit() is S.true assert Gt(p, 1).doit() == Gt(p, 1) assert Ge(p, 0).doit() is S.true assert Le(p, 0).doit() is S.false assert Lt(n, 0).doit() is S.true assert Le(np, 0).doit() is S.true assert Gt(nn, 0).doit() == Gt(nn, 0) assert Lt(nn, 0).doit() is S.false assert Eq(x, 0).doit() == Eq(x, 0)
def test_reduce_abs_inequalities(): e = abs(x - 5) < 3 ans = And(Lt(2, x), Lt(x, 8)) assert reduce_inequalities(e) == ans assert reduce_inequalities(e, x) == ans assert reduce_inequalities(abs(x - 5)) == Eq(x, 5) assert reduce_inequalities( abs(2*x + 3) >= 8) == Or(And(Le(Rational(5, 2), x), Lt(x, oo)), And(Le(x, Rational(-11, 2)), Lt(-oo, x))) assert reduce_inequalities(abs(x - 4) + abs( 3*x - 5) < 7) == And(Lt(S.Half, x), Lt(x, 4)) assert reduce_inequalities(abs(x - 4) + abs(3*abs(x) - 5) < 7) == \ Or(And(S(-2) < x, x < -1), And(S.Half < x, x < 4)) nr = Symbol('nr', extended_real=False) raises(TypeError, lambda: reduce_inequalities(abs(nr - 5) < 3)) assert reduce_inequalities(x < 3, symbols=[x, nr]) == And(-oo < x, x < 3)
def pmf(self, x): x = sympify(x) if not (x.is_number or x.is_Symbol or is_random(x)): raise ValueError( "'x' expected as an argument of type 'number', 'Symbol', or " "'RandomSymbol' not %s" % (type(x))) cond = Ge(x, 1) & Le(x, self.sides) & Contains(x, S.Integers) return Piecewise((S.One / self.sides, cond), (S.Zero, True))
def pmf(self, x): x = sympify(x) if not (x.is_number or x.is_Symbol or is_random(x)): raise ValueError("'x' expected as an argument of type 'number', 'Symbol', or " "'RandomSymbol' not %s" % (type(x))) cond1 = Eq(x, 1) & x.is_integer cond2 = Ge(x, 1) & Le(x, self.k) & x.is_integer return Piecewise((1/self.k, cond1), (1/(x*(x - 1)), cond2), (S.Zero, True))
def pmf(self, x): n, p = self.n, self.p x = sympify(x) if not (x.is_number or x.is_Symbol or is_random(x)): raise ValueError("'x' expected as an argument of type 'number', 'Symbol', or " "'RandomSymbol' not %s" % (type(x))) cond = Ge(x, 0) & Le(x, n) & Contains(x, S.Integers) return Piecewise((binomial(n, x) * p**x * (1 - p)**(n - x), cond), (S.Zero, True))
def test_as_poly(): from sympy.polys.polytools import Poly # Only Eq should have an as_poly method: assert Eq(x, 1).as_poly() == Poly(x - 1, x, domain='ZZ') raises(AttributeError, lambda: Ne(x, 1).as_poly()) raises(AttributeError, lambda: Ge(x, 1).as_poly()) raises(AttributeError, lambda: Gt(x, 1).as_poly()) raises(AttributeError, lambda: Le(x, 1).as_poly()) raises(AttributeError, lambda: Lt(x, 1).as_poly())
def test_evaluate(): assert str(Eq(x, x, evaluate=False)) == "Eq(x, x)" assert Eq(x, x, evaluate=False).doit() == S.true assert str(Ne(x, x, evaluate=False)) == "Ne(x, x)" assert Ne(x, x, evaluate=False).doit() == S.false assert str(Ge(x, x, evaluate=False)) == "x >= x" assert str(Le(x, x, evaluate=False)) == "x <= x" assert str(Gt(x, x, evaluate=False)) == "x > x" assert str(Lt(x, x, evaluate=False)) == "x < x"
def test_evaluate(): assert str(Eq(x, x, evaluate=False)) == 'Eq(x, x)' assert Eq(x, x, evaluate=False).doit() == S.true assert str(Ne(x, x, evaluate=False)) == 'Ne(x, x)' assert Ne(x, x, evaluate=False).doit() == S.false assert str(Ge(x, x, evaluate=False)) == 'x >= x' assert str(Le(x, x, evaluate=False)) == 'x <= x' assert str(Gt(x, x, evaluate=False)) == 'x > x' assert str(Lt(x, x, evaluate=False)) == 'x < x'
def test_python_relational(): assert python(Eq(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = Eq(x, y)" assert python(Ge(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x >= y" assert python(Le(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x <= y" assert python(Gt(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x > y" assert python(Lt(x, y)) == "x = Symbol('x')\ny = Symbol('y')\ne = x < y" assert python(Ne(x / (y + 1), y**2)) in [ "x = Symbol('x')\ny = Symbol('y')\ne = Ne(x/(1 + y), y**2)", "x = Symbol('x')\ny = Symbol('y')\ne = Ne(x/(y + 1), y**2)" ]
def pmf(self, x): x = sympify(x) if not (x.is_number or x.is_Symbol or is_random(x)): raise ValueError( "'x' expected as an argument of type 'number', 'Symbol', or " "'RandomSymbol' not %s" % (type(x))) cond1 = Eq(x, 1) & x.is_integer cond2 = Ge(x, 1) & Le(x, self.k) & x.is_integer rho = Piecewise((Rational(1, self.k), cond1), (Rational(1, x * (x - 1)), cond2), (S.Zero, True)) cond1 = Ge(x, 1) & Le(x, round(self.k / self.R) - 1) cond2 = Eq(x, round(self.k / self.R)) tau = Piecewise((self.R / (self.k * x), cond1), (self.R * log(self.R / self.delta) / self.k, cond2), (S.Zero, True)) return (rho + tau) / self.Z
def __le__(self, other): if self.is_extended_real: other = _sympify(other) # Check if other < 0 if other.is_extended_negative: return S.false # Check if other >= 1 res = self._value_one_or_more(other) if res is not None: return res return Le(self, other, evaluate=False)
def test_Interval_as_relational(): x = Symbol('x') assert Interval(-1, 2, False, False).as_relational(x) == \ And(Le(-1, x), Le(x, 2)) assert Interval(-1, 2, True, False).as_relational(x) == \ And(Lt(-1, x), Le(x, 2)) assert Interval(-1, 2, False, True).as_relational(x) == \ And(Le(-1, x), Lt(x, 2)) assert Interval(-1, 2, True, True).as_relational(x) == \ And(Lt(-1, x), Lt(x, 2)) assert Interval(-oo, 2, right_open=False).as_relational(x) == And( Lt(-oo, x), Le(x, 2)) assert Interval(-oo, 2, right_open=True).as_relational(x) == And( Lt(-oo, x), Lt(x, 2)) assert Interval(-2, oo, left_open=False).as_relational(x) == And( Le(-2, x), Lt(x, oo)) assert Interval(-2, oo, left_open=True).as_relational(x) == And( Lt(-2, x), Lt(x, oo)) assert Interval(-oo, oo).as_relational(x) == And(Lt(-oo, x), Lt(x, oo)) x = Symbol('x', real=True) y = Symbol('y', real=True) assert Interval(x, y).as_relational(x) == (x <= y) assert Interval(y, x).as_relational(x) == (y <= x)
def __le__(self, other): other = S(other) if self.args[0].is_real: if other.is_integer: return self.args[0] <= other if other.is_number and other.is_real: return self.args[0] <= floor(other) if self.args[0] == other and other.is_real: return S.false if other is S.Infinity and self.is_finite: return S.true return Le(self, other, evaluate=False)
def test_inequalities_symbol_name_same(): """Using the operator and functional forms should give same results.""" # We test all combinations from a set # FIXME: could replace with random selection after test passes A = (x, y, S.Zero, S.One / 3, pi, oo, -oo) for a in A: for b in A: assert Gt(a, b) == (a > b) assert Lt(a, b) == (a < b) assert Ge(a, b) == (a >= b) assert Le(a, b) == (a <= b) for b in (y, S.Zero, S.One / 3, pi, oo, -oo): assert Gt(x, b, evaluate=False) == (x > b) assert Lt(x, b, evaluate=False) == (x < b) assert Ge(x, b, evaluate=False) == (x >= b) assert Le(x, b, evaluate=False) == (x <= b) for b in (y, S.Zero, S.One / 3, pi, oo, -oo): assert Gt(b, x, evaluate=False) == (b > x) assert Lt(b, x, evaluate=False) == (b < x) assert Ge(b, x, evaluate=False) == (b >= x) assert Le(b, x, evaluate=False) == (b <= x)
def test_inequalities_symbol_name_same_complex(): """Using the operator and functional forms should give same results. With complex non-real numbers, both should raise errors. """ # FIXME: could replace with random selection after test passes for a in (x, S.Zero, S.One / 3, pi, oo, Rational(1, 3)): raises(TypeError, lambda: Gt(a, I)) raises(TypeError, lambda: a > I) raises(TypeError, lambda: Lt(a, I)) raises(TypeError, lambda: a < I) raises(TypeError, lambda: Ge(a, I)) raises(TypeError, lambda: a >= I) raises(TypeError, lambda: Le(a, I)) raises(TypeError, lambda: a <= I)
def as_relational(self, symbol): """Rewrite an interval in terms of inequalities and logic operators. """ from sympy.core.relational import Lt, Le if not self.is_left_unbounded: if self.left_open: left = Lt(self.start, symbol) else: left = Le(self.start, symbol) if not self.is_right_unbounded: if self.right_open: right = Lt(symbol, self.right) else: right = Le(symbol, self.right) if self.is_left_unbounded and self.is_right_unbounded: return True # XXX: Contained(symbol, Floats) elif self.is_left_unbounded: return right elif self.is_right_unbounded: return left else: return And(left, right)
def test_inequalities_symbol_name_same(): """Using the operator and functional forms should give same results.""" # currently fails because rhs reduces to bool but the lhs does not assert Lt(x, oo) == (x < oo) # We test all combinations from a set # FIXME: could replace with random selection after test passes A = (x, y, S(0), S(1) / 3, pi, oo, -oo) for a in A: for b in A: assert Gt(a, b) == (a > b) assert Lt(a, b) == (a < b) assert Ge(a, b) == (a >= b) assert Le(a, b) == (a <= b)
def test_bool(): assert Eq(0, 0) is True assert Eq(1, 0) is False assert Ne(0, 0) is False assert Ne(1, 0) is True assert Lt(0, 1) is True assert Lt(1, 0) is False assert Le(0, 1) is True assert Le(1, 0) is False assert Le(0, 0) is True assert Gt(1, 0) is True assert Gt(0, 1) is False assert Ge(1, 0) is True assert Ge(0, 1) is False assert Ge(1, 1) is True assert Eq(I, 2) is False assert Ne(I, 2) is True assert Gt(I, 2) not in [True, False] assert Ge(I, 2) not in [True, False] assert Lt(I, 2) not in [True, False] assert Le(I, 2) not in [True, False] a = Float('.000000000000000000001', '') b = Float('.0000000000000000000001', '') assert Eq(pi + a, pi + b) is False
def test_bool(): assert Eq(0, 0) is S.true assert Eq(1, 0) is S.false assert Ne(0, 0) is S.false assert Ne(1, 0) is S.true assert Lt(0, 1) is S.true assert Lt(1, 0) is S.false assert Le(0, 1) is S.true assert Le(1, 0) is S.false assert Le(0, 0) is S.true assert Gt(1, 0) is S.true assert Gt(0, 1) is S.false assert Ge(1, 0) is S.true assert Ge(0, 1) is S.false assert Ge(1, 1) is S.true assert Eq(I, 2) is S.false assert Ne(I, 2) is S.true raises(TypeError, lambda: Gt(I, 2)) raises(TypeError, lambda: Ge(I, 2)) raises(TypeError, lambda: Lt(I, 2)) raises(TypeError, lambda: Le(I, 2)) a = Float('.000000000000000000001', '') b = Float('.0000000000000000000001', '') assert Eq(pi + a, pi + b) is S.false
def test_wrappers(): e = x + x**2 res = Relational(y, e, '==') assert Rel(y, x + x**2, '==') == res assert Eq(y, x + x**2) == res res = Relational(y, e, '<') assert Lt(y, x + x**2) == res res = Relational(y, e, '<=') assert Le(y, x + x**2) == res res = Relational(y, e, '>') assert Gt(y, x + x**2) == res res = Relational(y, e, '>=') assert Ge(y, x + x**2) == res res = Relational(y, e, '!=') assert Ne(y, x + x**2) == res
def test_reversedsign_property(): eq = Eq(x, y) assert eq.reversedsign == Eq(-x, -y) eq = Ne(x, y) assert eq.reversedsign == Ne(-x, -y) eq = Ge(x + y, y - x) assert eq.reversedsign == Le(-x - y, x - y) for f in (Eq, Ne, Ge, Gt, Le, Lt): assert f(x, y).reversedsign.reversedsign == f(x, y) for f in (Eq, Ne, Ge, Gt, Le, Lt): assert f(-x, y).reversedsign.reversedsign == f(-x, y) for f in (Eq, Ne, Ge, Gt, Le, Lt): assert f(x, -y).reversedsign.reversedsign == f(x, -y) for f in (Eq, Ne, Ge, Gt, Le, Lt): assert f(-x, -y).reversedsign.reversedsign == f(-x, -y)
def test_wrappers(): e = x + x ** 2 res = Relational(y, e, "==") assert Rel(y, x + x ** 2, "==") == res assert Eq(y, x + x ** 2) == res res = Relational(y, e, "<") assert Lt(y, x + x ** 2) == res res = Relational(y, e, "<=") assert Le(y, x + x ** 2) == res res = Relational(y, e, ">") assert Gt(y, x + x ** 2) == res res = Relational(y, e, ">=") assert Ge(y, x + x ** 2) == res res = Relational(y, e, "!=") assert Ne(y, x + x ** 2) == res
def test_reduce_poly_inequalities_complex_relational(): 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) == \ And(Gt(x, -oo), Lt(x, oo), Ne(x, 0)) assert reduce_rational_inequalities( [[Ne(x**2, 0)]], x, relational=True) == \ And(Gt(x, -oo), Lt(x, oo), Ne(x, 0)) for one in (S.One, S(1.0)): inf = one * oo assert reduce_rational_inequalities( [[Eq(x**2, one)]], x, relational=True) == \ Or(Eq(x, -one), Eq(x, one)) assert reduce_rational_inequalities( [[Le(x**2, one)]], x, relational=True) == \ And(And(Le(-one, x), Le(x, one))) assert reduce_rational_inequalities( [[Lt(x**2, one)]], x, relational=True) == \ And(And(Lt(-one, x), Lt(x, one))) assert reduce_rational_inequalities( [[Ge(x**2, one)]], x, relational=True) == \ And(Or(And(Le(one, x), Lt(x, inf)), And(Le(x, -one), Lt(-inf, x)))) assert reduce_rational_inequalities( [[Gt(x**2, one)]], x, relational=True) == \ And(Or(And(Lt(-inf, x), Lt(x, -one)), And(Lt(one, x), Lt(x, inf)))) assert reduce_rational_inequalities( [[Ne(x**2, one)]], x, relational=True) == \ Or(And(Lt(-inf, x), Lt(x, -one)), And(Lt(-one, x), Lt(x, one)), And(Lt(one, x), Lt(x, inf)))
def test_issue_8449(): p = Symbol('p', nonnegative=True) assert Lt(-oo, p) assert Ge(-oo, p) is S.false assert Gt(oo, -p) assert Le(oo, -p) is S.false
def test_simplify_relational(): assert simplify(x * (y + 1) - x * y - x + 1 < x) == (x > 1) assert simplify(x * (y + 1) - x * y - x - 1 < x) == (x > -1) assert simplify(x < x * (y + 1) - x * y - x + 1) == (x < 1) q, r = symbols("q r") assert (((-q + r) - (q - r)) <= 0).simplify() == (q >= r) root2 = sqrt(2) equation = ((root2 * (-q + r) - root2 * (q - r)) <= 0).simplify() assert equation == (q >= r) r = S.One < x # canonical operations are not the same as simplification, # so if there is no simplification, canonicalization will # be done unless the measure forbids it assert simplify(r) == r.canonical assert simplify(r, ratio=0) != r.canonical # this is not a random test; in _eval_simplify # this will simplify to S.false and that is the # reason for the 'if r.is_Relational' in Relational's # _eval_simplify routine assert simplify(-(2**(pi * Rational(3, 2)) + 6**pi)**(1 / pi) + 2 * (2**(pi / 2) + 3**pi)**(1 / pi) < 0) is S.false # canonical at least assert Eq(y, x).simplify() == Eq(x, y) assert Eq(x - 1, 0).simplify() == Eq(x, 1) assert Eq(x - 1, x).simplify() == S.false assert Eq(2 * x - 1, x).simplify() == Eq(x, 1) assert Eq(2 * x, 4).simplify() == Eq(x, 2) z = cos(1)**2 + sin(1)**2 - 1 # z.is_zero is None assert Eq(z * x, 0).simplify() == S.true assert Ne(y, x).simplify() == Ne(x, y) assert Ne(x - 1, 0).simplify() == Ne(x, 1) assert Ne(x - 1, x).simplify() == S.true assert Ne(2 * x - 1, x).simplify() == Ne(x, 1) assert Ne(2 * x, 4).simplify() == Ne(x, 2) assert Ne(z * x, 0).simplify() == S.false # No real-valued assumptions assert Ge(y, x).simplify() == Le(x, y) assert Ge(x - 1, 0).simplify() == Ge(x, 1) assert Ge(x - 1, x).simplify() == S.false assert Ge(2 * x - 1, x).simplify() == Ge(x, 1) assert Ge(2 * x, 4).simplify() == Ge(x, 2) assert Ge(z * x, 0).simplify() == S.true assert Ge(x, -2).simplify() == Ge(x, -2) assert Ge(-x, -2).simplify() == Le(x, 2) assert Ge(x, 2).simplify() == Ge(x, 2) assert Ge(-x, 2).simplify() == Le(x, -2) assert Le(y, x).simplify() == Ge(x, y) assert Le(x - 1, 0).simplify() == Le(x, 1) assert Le(x - 1, x).simplify() == S.true assert Le(2 * x - 1, x).simplify() == Le(x, 1) assert Le(2 * x, 4).simplify() == Le(x, 2) assert Le(z * x, 0).simplify() == S.true assert Le(x, -2).simplify() == Le(x, -2) assert Le(-x, -2).simplify() == Ge(x, 2) assert Le(x, 2).simplify() == Le(x, 2) assert Le(-x, 2).simplify() == Ge(x, -2) assert Gt(y, x).simplify() == Lt(x, y) assert Gt(x - 1, 0).simplify() == Gt(x, 1) assert Gt(x - 1, x).simplify() == S.false assert Gt(2 * x - 1, x).simplify() == Gt(x, 1) assert Gt(2 * x, 4).simplify() == Gt(x, 2) assert Gt(z * x, 0).simplify() == S.false assert Gt(x, -2).simplify() == Gt(x, -2) assert Gt(-x, -2).simplify() == Lt(x, 2) assert Gt(x, 2).simplify() == Gt(x, 2) assert Gt(-x, 2).simplify() == Lt(x, -2) assert Lt(y, x).simplify() == Gt(x, y) assert Lt(x - 1, 0).simplify() == Lt(x, 1) assert Lt(x - 1, x).simplify() == S.true assert Lt(2 * x - 1, x).simplify() == Lt(x, 1) assert Lt(2 * x, 4).simplify() == Lt(x, 2) assert Lt(z * x, 0).simplify() == S.false assert Lt(x, -2).simplify() == Lt(x, -2) assert Lt(-x, -2).simplify() == Gt(x, 2) assert Lt(x, 2).simplify() == Lt(x, 2) assert Lt(-x, 2).simplify() == Gt(x, -2) # Test particulat branches of _eval_simplify m = exp(1) - exp_polar(1) assert simplify(m * x > 1) is S.false # These two tests the same branch assert simplify(m * x + 2 * m * y > 1) is S.false assert simplify(m * x + y > 1 + y) is S.false
def __le__(self, other): if self.args[0] == other and other.is_real: return S.true return Le(self, other, evaluate=False)
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(S(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, ImmutableMatrix) # 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() == ImmutableMatrix( [[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 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_new_relational(): x = Symbol('x') assert Eq(x, 0) == Relational(x, 0) # None ==> Equality assert Eq(x, 0) == Relational(x, 0, '==') assert Eq(x, 0) == Relational(x, 0, 'eq') assert Eq(x, 0) == Equality(x, 0) assert Eq(x, 0) != Relational(x, 1) # None ==> Equality assert Eq(x, 0) != Relational(x, 1, '==') assert Eq(x, 0) != Relational(x, 1, 'eq') assert Eq(x, 0) != Equality(x, 1) assert Eq(x, -1) == Relational(x, -1) # None ==> Equality assert Eq(x, -1) == Relational(x, -1, '==') assert Eq(x, -1) == Relational(x, -1, 'eq') assert Eq(x, -1) == Equality(x, -1) assert Eq(x, -1) != Relational(x, 1) # None ==> Equality assert Eq(x, -1) != Relational(x, 1, '==') assert Eq(x, -1) != Relational(x, 1, 'eq') assert Eq(x, -1) != Equality(x, 1) assert Ne(x, 0) == Relational(x, 0, '!=') assert Ne(x, 0) == Relational(x, 0, '<>') assert Ne(x, 0) == Relational(x, 0, 'ne') assert Ne(x, 0) == Unequality(x, 0) assert Ne(x, 0) != Relational(x, 1, '!=') assert Ne(x, 0) != Relational(x, 1, '<>') assert Ne(x, 0) != Relational(x, 1, 'ne') assert Ne(x, 0) != Unequality(x, 1) assert Ge(x, 0) == Relational(x, 0, '>=') assert Ge(x, 0) == Relational(x, 0, 'ge') assert Ge(x, 0) == GreaterThan(x, 0) assert Ge(x, 1) != Relational(x, 0, '>=') assert Ge(x, 1) != Relational(x, 0, 'ge') assert Ge(x, 1) != GreaterThan(x, 0) assert (x >= 1) == Relational(x, 1, '>=') assert (x >= 1) == Relational(x, 1, 'ge') assert (x >= 1) == GreaterThan(x, 1) assert (x >= 0) != Relational(x, 1, '>=') assert (x >= 0) != Relational(x, 1, 'ge') assert (x >= 0) != GreaterThan(x, 1) assert Le(x, 0) == Relational(x, 0, '<=') assert Le(x, 0) == Relational(x, 0, 'le') assert Le(x, 0) == LessThan(x, 0) assert Le(x, 1) != Relational(x, 0, '<=') assert Le(x, 1) != Relational(x, 0, 'le') assert Le(x, 1) != LessThan(x, 0) assert (x <= 1) == Relational(x, 1, '<=') assert (x <= 1) == Relational(x, 1, 'le') assert (x <= 1) == LessThan(x, 1) assert (x <= 0) != Relational(x, 1, '<=') assert (x <= 0) != Relational(x, 1, 'le') assert (x <= 0) != LessThan(x, 1) assert Gt(x, 0) == Relational(x, 0, '>') assert Gt(x, 0) == Relational(x, 0, 'gt') assert Gt(x, 0) == StrictGreaterThan(x, 0) assert Gt(x, 1) != Relational(x, 0, '>') assert Gt(x, 1) != Relational(x, 0, 'gt') assert Gt(x, 1) != StrictGreaterThan(x, 0) assert (x > 1) == Relational(x, 1, '>') assert (x > 1) == Relational(x, 1, 'gt') assert (x > 1) == StrictGreaterThan(x, 1) assert (x > 0) != Relational(x, 1, '>') assert (x > 0) != Relational(x, 1, 'gt') assert (x > 0) != StrictGreaterThan(x, 1) assert Lt(x, 0) == Relational(x, 0, '<') assert Lt(x, 0) == Relational(x, 0, 'lt') assert Lt(x, 0) == StrictLessThan(x, 0) assert Lt(x, 1) != Relational(x, 0, '<') assert Lt(x, 1) != Relational(x, 0, 'lt') assert Lt(x, 1) != StrictLessThan(x, 0) assert (x < 1) == Relational(x, 1, '<') assert (x < 1) == Relational(x, 1, 'lt') assert (x < 1) == StrictLessThan(x, 1) assert (x < 0) != Relational(x, 1, '<') assert (x < 0) != Relational(x, 1, 'lt') assert (x < 0) != StrictLessThan(x, 1) # finally, some fuzz testing from sympy.core.random import randint for i in range(100): while 1: strtype, length = (chr, 65535) if randint(0, 1) else (chr, 255) relation_type = strtype(randint(0, length)) if randint(0, 1): relation_type += strtype(randint(0, length)) if relation_type not in ('==', 'eq', '!=', '<>', 'ne', '>=', 'ge', '<=', 'le', '>', 'gt', '<', 'lt', ':=', '+=', '-=', '*=', '/=', '%='): break raises(ValueError, lambda: Relational(x, 1, relation_type)) assert all(Relational(x, 0, op).rel_op == '==' for op in ('eq', '==')) assert all( Relational(x, 0, op).rel_op == '!=' for op in ('ne', '<>', '!=')) assert all(Relational(x, 0, op).rel_op == '>' for op in ('gt', '>')) assert all(Relational(x, 0, op).rel_op == '<' for op in ('lt', '<')) assert all(Relational(x, 0, op).rel_op == '>=' for op in ('ge', '>=')) assert all(Relational(x, 0, op).rel_op == '<=' for op in ('le', '<='))
def test_rich_cmp(): assert (x < y) == Lt(x, y) assert (x <= y) == Le(x, y) assert (x > y) == Gt(x, y) assert (x >= y) == Ge(x, y)