mvpa2.datasets.sources.skl_data.skl_gaussian_quantiles

mvpa2.datasets.sources.skl_data.skl_gaussian_quantiles(mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3, shuffle=True, random_state=None)

Generate isotropic Gaussian and label samples by quantile

This classification dataset is constructed by taking a multi-dimensional standard normal distribution and defining classes separated by nested concentric multi-dimensional spheres such that roughly equal numbers of samples are in each class (quantiles of the \chi^2 distribution).

Parameters:

mean : array of shape [n_features], optional (default=None)

The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, ...).

cov : float, optional (default=1.)

The covariance matrix will be this value times the unit matrix. This dataset only produces symmetric normal distributions.

n_samples : int, optional (default=100)

The total number of points equally divided among classes.

n_features : int, optional (default=2)

The number of features for each sample.

n_classes : int, optional (default=3)

The number of classes

shuffle : boolean, optional (default=True)

Shuffle the samples.

random_state : int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Returns:

X : array of shape [n_samples, n_features]

The generated samples.

y : array of shape [n_samples]

The integer labels for quantile membership of each sample.

Notes

This function has been auto-generated by wrapping make_gaussian_quantiles() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.

References

[R31]
  1. Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.