コード例 #1
0
def test_fit():
    import random
    random.seed(1234)
    n = Normal.fit(Uniform.fit(Normal(2, 1.5).random(1000)))
    #print n.mean
    #print n.stddev
    assert abs(n.mean - 2) < 0.3
    assert abs(n.stddev - 1.5) < 0.3
コード例 #2
0
ファイル: normal.py プロジェクト: westurner/math
def normal_dist(mu=100, sigma=15):
    """
    mainfunc
    """
    dist = Normal(mu,sigma)
    dist_cdf = dist.cdf(x)
    sympy.Plot(dist_cdf)
    pass
コード例 #3
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def test_normal():
    dps, mp.dps = mp.dps, 20

    N = Normal(0, 1)
    assert N.mean == 0
    assert N.variance == 1
    assert N.probability(-1, 1) == erf(1 / sqrt(2))
    assert N.probability(-1, 0) == erf(1 / sqrt(2)) / 2
    N = Normal(2, 4)
    assert N.mean == 2
    assert N.variance == 16
    assert N.confidence(1) == (-oo, oo)
    assert N.probability(1, 3) == erf(1 / sqrt(32))
    assert N.pdf(1).evalf() == (exp(Rational(-1, 32)) /
                                (4 * sqrt(2 * pi))).evalf()
    for p in [0.1, 0.3, 0.7, 0.9, 0.995]:
        a, b = N.confidence(p)
        assert abs(float(N.probability(a, b).evalf()) - p) < 1e-10

    N = Normal(0, 2 / sqrt(2 * pi))
    assert N.pdf(0) == Rational(1, 2)
    mp.dps = dps
コード例 #4
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def compute_pref(mu1, mu2, sigma1_sq, sigma2_sq, margin):
    if mu1 < mu2:
        raise "mu1 must be greater than mu2"

    mu0 = mu1 - mu2
    sigma0_sq = sigma1_sq + sigma2_sq
    sigma0 = math.sqrt(sigma0_sq)

    N = Normal(mu0, sigma0)
    prob_win = N.probability(margin, oo).evalf()
    prob_lost = N.probability(-oo, 0).evalf()
    prob_draw = 1. - (prob_lost + prob_win)
    return prob_win, prob_draw, prob_lost
コード例 #5
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ファイル: test_statistics.py プロジェクト: tuhina/sympy
def test_fit():
    import random
    random.seed(1234)
    n = Normal.fit(Uniform.fit(Normal(2, 1.5).random(1000)))
    #print n.mean
    #print n.stddev
    assert abs(n.mean - 2) < 0.3
    assert abs(n.stddev - 1.5) < 0.3
    n = Normal.fit([1,2,3,4,5])
    assert n.mean == 3
    assert n.stddev == sqrt(2)
    n = Uniform.fit([1,2,3,4,5])
    assert n.mean == 3
    assert n.stddev == sqrt(2)
コード例 #6
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def getStdDev(distance, confidence, x=Normal(0, 1)):
    "Distance = end point - mean"
    confidenceDistance = x.confidence(confidence)[1]
    stddev = confidenceDistance * distance
    return stddev