Python stats.skewness函数代码示例

def test_exponential():
    rate = Symbol('lambda', positive=True, real=True, finite=True)
    X = Exponential('x', rate)

    assert E(X) == 1/rate
    assert variance(X) == 1/rate**2
    assert skewness(X) == 2
    assert skewness(X) == smoment(X, 3)
    assert smoment(2*X, 4) == smoment(X, 4)
    assert moment(X, 3) == 3*2*1/rate**3
    assert P(X > 0) == S(1)
    assert P(X > 1) == exp(-rate)
    assert P(X > 10) == exp(-10*rate)

    assert where(X <= 1).set == Interval(0, 1)

def test_multiple_normal():
    X, Y = Normal('x', 0, 1), Normal('y', 0, 1)

    assert E(X + Y) == 0
    assert variance(X + Y) == 2
    assert variance(X + X) == 4
    assert covariance(X, Y) == 0
    assert covariance(2*X + Y, -X) == -2*variance(X)
    assert skewness(X) == 0
    assert skewness(X + Y) == 0
    assert correlation(X, Y) == 0
    assert correlation(X, X + Y) == correlation(X, X - Y)
    assert moment(X, 2) == 1
    assert cmoment(X, 3) == 0
    assert moment(X + Y, 4) == 12
    assert cmoment(X, 2) == variance(X)
    assert smoment(X*X, 2) == 1
    assert smoment(X + Y, 3) == skewness(X + Y)
    assert E(X, Eq(X + Y, 0)) == 0
    assert variance(X, Eq(X + Y, 0)) == S.Half

def test_exponential():
    rate = Symbol('lambda', positive=True, real=True, bounded=True)
    X = Exponential('x', rate)

    assert E(X) == 1/rate
    assert variance(X) == 1/rate**2
    assert skewness(X) == 2
    assert P(X > 0) == S(1)
    assert P(X > 1) == exp(-rate)
    assert P(X > 10) == exp(-10*rate)

    assert where(X <= 1).set == Interval(0, 1)

def test_binomial_symbolic():
    n = 10  # Because we're using for loops, can't do symbolic n
    p = symbols('p', positive=True)
    X = Binomial('X', n, p)
    assert simplify(E(X)) == n*p == simplify(moment(X, 1))
    assert simplify(variance(X)) == n*p*(1 - p) == simplify(cmoment(X, 2))
    assert cancel((skewness(X) - (1-2*p)/sqrt(n*p*(1-p)))) == 0

    # Test ability to change success/failure winnings
    H, T = symbols('H T')
    Y = Binomial('Y', n, p, succ=H, fail=T)
    assert simplify(E(Y) - (n*(H*p + T*(1 - p)))) == 0

def test_binomial_symbolic():
    n = 10  # Because we're using for loops, can't do symbolic n
    p = symbols("p", positive=True)
    X = Binomial("X", n, p)
    assert simplify(E(X)) == n * p == simplify(moment(X, 1))
    assert simplify(variance(X)) == n * p * (1 - p) == simplify(cmoment(X, 2))
    assert factor(simplify(skewness(X))) == factor((1 - 2 * p) / sqrt(n * p * (1 - p)))

    # Test ability to change success/failure winnings
    H, T = symbols("H T")
    Y = Binomial("Y", n, p, succ=H, fail=T)
    assert simplify(E(Y)) == simplify(n * (H * p + T * (1 - p)))

def test_binomial_numeric():
    nvals = range(5)
    pvals = [0, S(1)/4, S.Half, S(3)/4, 1]

    for n in nvals:
        for p in pvals:
            X = Binomial('X', n, p)
            assert E(X) == n*p
            assert variance(X) == n*p*(1 - p)
            if n > 0 and 0 < p < 1:
                assert skewness(X) == (1 - 2*p)/sqrt(n*p*(1 - p))
            for k in range(n + 1):
                assert P(Eq(X, k)) == binomial(n, k)*p**k*(1 - p)**(n - k)

def test_binomial_numeric():
    nvals = range(5)
    pvals = [0, S(1) / 4, S.Half, S(3) / 4, 1]

    for n in nvals:
        for p in pvals:
            X = Binomial("X", n, p)
            assert Eq(E(X), n * p)
            assert Eq(variance(X), n * p * (1 - p))
            if n > 0 and 0 < p < 1:
                assert Eq(skewness(X), (1 - 2 * p) / sqrt(n * p * (1 - p)))
            for k in range(n + 1):
                assert Eq(P(Eq(X, k)), binomial(n, k) * p ** k * (1 - p) ** (n - k))

def test_hypergeometric_numeric():
    for N in range(1, 5):
        for m in range(0, N + 1):
            for n in range(1, N + 1):
                X = Hypergeometric('X', N, m, n)
                N, m, n = map(sympify, (N, m, n))
                assert sum(density(X).values()) == 1
                assert E(X) == n * m / N
                if N > 1:
                    assert variance(X) == n*(m/N)*(N - m)/N*(N - n)/(N - 1)
                # Only test for skewness when defined
                if N > 2 and 0 < m < N and n < N:
                    assert skewness(X) == simplify((N - 2*m)*sqrt(N - 1)*(N - 2*n)
                        / (sqrt(n*m*(N - m)*(N - n))*(N - 2)))

def test_binomial_symbolic():
    n = 2  # Because we're using for loops, can't do symbolic n
    p = symbols('p', positive=True)
    X = Binomial('X', n, p)
    t = Symbol('t')

    assert simplify(E(X)) == n*p == simplify(moment(X, 1))
    assert simplify(variance(X)) == n*p*(1 - p) == simplify(cmoment(X, 2))
    assert cancel((skewness(X) - (1 - 2*p)/sqrt(n*p*(1 - p)))) == 0
    assert characteristic_function(X)(t) == p ** 2 * exp(2 * I * t) + 2 * p * (-p + 1) * exp(I * t) + (-p + 1) ** 2
    assert moment_generating_function(X)(t) == p ** 2 * exp(2 * t) + 2 * p * (-p + 1) * exp(t) + (-p + 1) ** 2

    # Test ability to change success/failure winnings
    H, T = symbols('H T')
    Y = Binomial('Y', n, p, succ=H, fail=T)
    assert simplify(E(Y) - (n*(H*p + T*(1 - p)))) == 0

def test_dice():
    # TODO: Make iid method!
    X, Y, Z = Die('X', 6), Die('Y', 6), Die('Z', 6)
    a, b = symbols('a b')

    assert E(X) == 3 + S.Half
    assert variance(X) == S(35)/12
    assert E(X + Y) == 7
    assert E(X + X) == 7
    assert E(a*X + b) == a*E(X) + b
    assert variance(X + Y) == variance(X) + variance(Y) == cmoment(X + Y, 2)
    assert variance(X + X) == 4 * variance(X) == cmoment(X + X, 2)
    assert cmoment(X, 0) == 1
    assert cmoment(4*X, 3) == 64*cmoment(X, 3)
    assert covariance(X, Y) == S.Zero
    assert covariance(X, X + Y) == variance(X)
    assert density(Eq(cos(X*S.Pi), 1))[True] == S.Half
    assert correlation(X, Y) == 0
    assert correlation(X, Y) == correlation(Y, X)
    assert smoment(X + Y, 3) == skewness(X + Y)
    assert smoment(X, 0) == 1
    assert P(X > 3) == S.Half
    assert P(2*X > 6) == S.Half
    assert P(X > Y) == S(5)/12
    assert P(Eq(X, Y)) == P(Eq(X, 1))

    assert E(X, X > 3) == 5 == moment(X, 1, 0, X > 3)
    assert E(X, Y > 3) == E(X) == moment(X, 1, 0, Y > 3)
    assert E(X + Y, Eq(X, Y)) == E(2*X)
    assert moment(X, 0) == 1
    assert moment(5*X, 2) == 25*moment(X, 2)

    assert P(X > 3, X > 3) == S.One
    assert P(X > Y, Eq(Y, 6)) == S.Zero
    assert P(Eq(X + Y, 12)) == S.One/36
    assert P(Eq(X + Y, 12), Eq(X, 6)) == S.One/6

    assert density(X + Y) == density(Y + Z) != density(X + X)
    d = density(2*X + Y**Z)
    assert d[S(22)] == S.One/108 and d[S(4100)] == S.One/216 and S(3130) not in d

    assert pspace(X).domain.as_boolean() == Or(
        *[Eq(X.symbol, i) for i in [1, 2, 3, 4, 5, 6]])

    assert where(X > 3).set == FiniteSet(4, 5, 6)

    def __init__(self):
        mu1 = Symbol('mu1', positive=True, real=True, bounded=True)
        s1 = Symbol('s1', positive=True, real=True, bounded=True)
        mu2 = Symbol('mu2', positive=True, real=True, bounded=True)
        s2 = Symbol('s2', positive=True, real=True, bounded=True)

        N1 = Normal('N1', mu1, s1)
        N2 = Normal('N2', mu2, s2)
        NN = N1 * N2

        self.MeanNN = E(NN)
        self.VarNN = variance(NN)
        self.StdevNN = SQRT(self.VarNN)
        self.SkewNN = skewness(NN)

        self.meanNN = lambdify([mu1, s1, mu2, s2], self.MeanNN)
        self.varNN = lambdify([mu1, s1, mu2, s2], self.VarNN)
        self.stdevNN = lambdify([mu1, s1, mu2, s2], self.StdevNN)
        self.skewNN = lambdify([mu1, s1, mu2, s2], self.SkewNN)

def test_discreteuniform():
    # Symbolic
    a, b, c = symbols("a b c")
    X = DiscreteUniform([a, b, c])

    assert E(X) == (a + b + c) / 3
    assert variance(X) == (a ** 2 + b ** 2 + c ** 2) / 3 - (a / 3 + b / 3 + c / 3) ** 2
    assert P(Eq(X, a)) == P(Eq(X, b)) == P(Eq(X, c)) == S("1/3")

    Y = DiscreteUniform(range(-5, 5))

    # Numeric
    assert E(Y) == S("-1/2")
    assert variance(Y) == S("33/4")
    assert skewness(Y) == 0
    for x in range(-5, 5):
        assert P(Eq(Y, x)) == S("1/10")
        assert P(Y <= x) == S(x + 6) / 10
        assert P(Y >= x) == S(5 - x) / 10

    assert density(Die(6)) == density(DiscreteUniform(range(1, 7)))

def test_discreteuniform():
    # Symbolic
    a, b, c = symbols('a b c')
    X = DiscreteUniform('X', [a,b,c])

    assert E(X) == (a+b+c)/3
    assert variance(X) == (a**2+b**2+c**2)/3 - (a/3+b/3+c/3)**2
    assert P(Eq(X, a)) == P(Eq(X, b)) == P(Eq(X, c)) == S('1/3')

    Y = DiscreteUniform('Y', range(-5, 5))

    # Numeric
    assert E(Y) == S('-1/2')
    assert variance(Y) == S('33/4')
    assert skewness(Y) == 0
    for x in range(-5, 5):
        assert P(Eq(Y, x)) == S('1/10')
        assert P(Y <= x) == S(x+6)/10
        assert P(Y >= x) == S(5-x)/10

    assert density(Die('D', 6)) == density(DiscreteUniform('U', range(1,7)))