`pyts.datasets`.make_cylinder_bell_funnel¶

pyts.datasets.make_cylinder_bell_funnel(n_samples=30, weights=None, shuffle=True, random_state=None, return_params=False)[source]¶

Make a Cylinder-Bell-Funnel dataset.

The classes are coded with the following meaning:

0: Cylinder

1: Bell

2: Funnel

Parameters:

n_samples : int (default = 30): The number of time series.
weights : list of floats or None (default=None): The proportions of samples assigned to each class. If None, then classes are balanced. Note that if len(weights) == 2, then the last class weight is automatically inferred. More than n_samples samples may be returned if the sum of weights exceeds 1.
shuffle : bool (default = True): If True, shuffle the samples.
return_params : bool (default = False): If True, a dictionary containing the parameters used to make the time series is returned.
random_state : int, RandomState instance or None (default = None): Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls.

Returns:

X : array, shape = (n_samples, 128)

Generated time series.

y : array, shape = (n_samples,)

Generated class labels. Labels have the following meaning:

0: Cylinder
1: Bell
2: Funnel

params : dict

The parameters used to generate the data. The keys are ‘a’, ‘b’, ‘eta’ and ‘epsilon’ and the values are the corresponding values. Only returned if return_params=True.

Notes

The time series are generated from the following distributions:

$c(t) = (6 + \eta) \cdot 1_{[a, b]}(t) + \epsilon(t) b(t) = (6 + \eta) \cdot 1_{[a, b]}(t) \cdot (t - a) / (b - a) + \epsilon(t) f(t) = (6 + \eta) \cdot 1_{[a, b]}(t) \cdot (b - t) / (b - a) + \epsilon(t)$

where $t=1,\ldots,128$ , $a$ is an integer-valued uniform random variable on the inverval $[16, 32]$ , $b-a$ is an integer-valued uniform distribution on the inveral $[32, 96]$ , $\eta$ and $\epsilon(t)$ are standard normal variables, ${1}_{[a, b]}$ is the characteristic function on the interval $[a, b]$ . $c$ , $b$ , and $f$ stand for “cylinder”, “bell”, and “funnel” respectively.

References

[1]	N. Saito, “Local feature extraction and its application using a library of bases”. Ph.D. thesis, Department of Mathematics, Yale University, 1994.

Examples

>>> import numpy as np
>>> from pyts.datasets import make_cylinder_bell_funnel
>>> X, y = make_cylinder_bell_funnel()
>>> X.shape
(30, 128)
>>> print(np.bincount(y))
[10 10 10]