Пример #1
0
def opt_q_uniform(target):
    rng = np.random.default_rng(123)
    x = hp.quniform("x", 1.01, 10, 1)
    return {
        "loss": (x - target)**2 + scope.normal(0, 1, rng=rng),
        "status": STATUS_OK,
    }
def opt_q_uniform(target):
    rng = np.random.RandomState(123)
    x = hp_quniform('x', 1.01, 10, 1)
    return {
        'loss': (x - target)**2 + scope.normal(0, 1, rng=rng),
        'status': STATUS_OK
    }
Пример #3
0
def test_repeatable():
    u = scope.uniform(0, 1)
    aa = as_apply(
        dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
    dd1 = sample(aa, np.random.RandomState(3))
    dd2 = sample(aa, np.random.RandomState(3))
    dd3 = sample(aa, np.random.RandomState(4))
    assert dd1 == dd2
    assert dd1 != dd3
Пример #4
0
def test_repeatable():
    u = scope.uniform(0, 1)
    aa = as_apply(dict(
                u = u,
                n = scope.normal(5, 0.1),
                l = [0, 1, scope.one_of(2, 3), u]))
    dd1 = sample(aa, np.random.RandomState(3))
    dd2 = sample(aa, np.random.RandomState(3))
    dd3 = sample(aa, np.random.RandomState(4))
    assert dd1 == dd2
    assert dd1 != dd3
Пример #5
0
def test_sample():
    u = scope.uniform(0, 1)
    aa = as_apply(
        dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
    print aa
    dd = sample(aa, np.random.RandomState(3))
    assert 0 < dd['u'] < 1
    assert 4 < dd['n'] < 6
    assert dd['u'] == dd['l'][3]
    assert dd['l'][:2] == (0, 1)
    assert dd['l'][2] in (2, 3)
Пример #6
0
def test_sample():
    u = scope.uniform(0, 1)
    aa = as_apply(
        dict(u=u, n=scope.normal(5, 0.1), l=[0, 1, scope.one_of(2, 3), u]))
    print(aa)
    dd = sample(aa, np.random.default_rng(3))
    assert 0 < dd["u"] < 1
    assert 4 < dd["n"] < 6
    assert dd["u"] == dd["l"][3]
    assert dd["l"][:2] == (0, 1)
    assert dd["l"][2] in (2, 3)
Пример #7
0
def test_sample():
    u = scope.uniform(0, 1)
    aa = as_apply(dict(
                u = u,
                n = scope.normal(5, 0.1),
                l = [0, 1, scope.one_of(2, 3), u]))
    print aa
    dd = sample(aa, np.random.RandomState(3))
    assert 0 < dd['u'] < 1
    assert 4 < dd['n'] < 6
    assert dd['u'] == dd['l'][3]
    assert dd['l'][:2] == (0, 1)
    assert dd['l'][2] in (2, 3)
Пример #8
0
def n_arms(N=2):
    """
    Each arm yields a reward from a different Gaussian.

    The correct arm is arm 0.

    """
    rng = np.random.RandomState(123)
    x = hp.choice('x', [0, 1])
    reward_mus = as_apply([-1] + [0] * (N - 1))
    reward_sigmas = as_apply([1] * N)
    return {'loss': scope.normal(reward_mus[x], reward_sigmas[x], rng=rng),
            'loss_variance': 1.0,
            'status': base.STATUS_OK}
Пример #9
0
def n_arms(N=2):
    """
    Each arm yields a reward from a different Gaussian.

    The correct arm is arm 0.

    """
    rng = np.random.default_rng(123)
    x = hp.choice("x", [0, 1])
    reward_mus = as_apply([-1] + [0] * (N - 1))
    reward_sigmas = as_apply([1] * N)
    return {
        "loss": scope.normal(reward_mus[x], reward_sigmas[x], rng=rng),
        "loss_variance": 1.0,
        "status": base.STATUS_OK,
    }
Пример #10
0
def gauss_wave2():
    """
    Variant of the GaussWave problem in which noise is added to the score
    function, and there is an option to either have no sinusoidal variation, or
    a negative cosine with variable amplitude.

    Immediate local max is to sample x from spec and turn off the neg cos.
    Better solution is to move x a bit to the side, turn on the neg cos and turn
    up the amp to 1.
    """

    rng = np.random.RandomState(123)
    var = .1
    x = hp.uniform('x', -20, 20)
    amp = hp.uniform('amp', 0, 1)
    t = (scope.normal(0, var, rng=rng) + 2 * scope.exp(-(old_div(x, 5.0)) ** 2))
    return {'loss': - hp.choice('hf', [t, t + scope.sin(x) * amp]),
            'loss_variance': var, 'status': base.STATUS_OK}
Пример #11
0
def gauss_wave2():
    """
    Variant of the GaussWave problem in which noise is added to the score
    function, and there is an option to either have no sinusoidal variation, or
    a negative cosine with variable amplitude.

    Immediate local max is to sample x from spec and turn off the neg cos.
    Better solution is to move x a bit to the side, turn on the neg cos and turn
    up the amp to 1.
    """

    rng = np.random.default_rng(123)
    var = 0.1
    x = hp.uniform("x", -20, 20)
    amp = hp.uniform("amp", 0, 1)
    t = scope.normal(0, var, rng=rng) + 2 * scope.exp(-((old_div(x, 5.0))**2))
    return {
        "loss": -hp.choice("hf", [t, t + scope.sin(x) * amp]),
        "loss_variance": var,
        "status": base.STATUS_OK,
    }
Пример #12
0
def opt_q_uniform(target):
    rng = np.random.RandomState(123)
    x = hp.quniform('x', 1.01, 10, 1)
    return {'loss': (x - target) ** 2 + scope.normal(0, 1, rng=rng),
            'status': STATUS_OK}
Пример #13
0
def opt_q_uniform(target):
    x = hp_quniform('x', 1.01, 10, 1)
    return {'loss': (x - target)**2 + scope.normal(0, 1)}
Пример #14
0
def opt_q_uniform(target):
    x = hp_quniform('x', 1.01, 10, 1)
    return {'loss': (x - target) ** 2 + scope.normal(0, 1)}