quantile.py 文件源码

def toy_data_quantile(n_samples=50, probs=0.5, pattern=SinePattern(),
                      noise=(1., .2, 0., 1.5), random_state=None):
    """Sine wave toy dataset.

    The target y is computed as a sine curve at of modulated by a sine envelope
    with some period (default 1/3Hz) and mean (default 1).  Moreover, this
    pattern is distorted with a random Gaussian noise with mean 0 and a
    linearly decreasing standard deviation (default from 1.2 at X = 0 to 0.2 at
    X = 1 .  5).

    Parameters
    ----------
    n_samples : int
        Number of samples to generate.

    probs : list or float, shape = [n_quantiles], default=0.5
        Probabilities (quantiles levels).

    pattern : callable, default = SinePattern()
        Callable which generates n_sample 1D data (inputs and targets).

    noise : :rtype: (float, float, float, float)
        Noise parameters (variance, shift, support_min, support_max).

    Returns
    -------
    X : array, shape = [n_samples, 1]
        Input data.

    y : array, shape = [n_sample, 1]
        Targets.

    quantiles : array, shape = [n_samples x n_quantiles]
        True conditional quantiles.
    """
    probs = array(probs, ndmin=1)
    noise_var, noise_shift, noise_min, noise_max = noise

    random_state = check_random_state(random_state)
    pattern.random_state = random_state
    inputs, targets = pattern(n_samples)

    # Noise decreasing std (from noise+0.2 to 0.2)
    noise_std = noise_shift + (noise_var * (noise_max - inputs) /
                               (noise_max - noise_min))
    # Gaussian noise with decreasing std
    add_noise = noise_std * random_state.randn(n_samples, 1)
    observations = targets + add_noise
    quantiles = [targets + array([norm.ppf(p, loc=0., scale=abs(noise_c))
                                  for noise_c in noise_std]) for p in probs]
    return inputs, observations.ravel(), quantiles