generate three train datasets

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

from sklearn.model_selection import train_test_split

from sklearn.datasets import make_moons, make_circles, make_classification

def generate_datasets():  

    """

    generates three different datasets which are splitted in samples and labels.

    returns:

        a tuple of (X_train,y_train),each containing a list of the samples 

        respective the labels for the datasets at the corresponding index

    """

    samples, labels = make_classification(n_features=2, n_redundant=0, n_informative=2,

                            random_state=1, n_clusters_per_class=1)

    rng = np.random.RandomState(2)

    samples+= 2 * rng.uniform(size=samples.shape)

    linearly_separable = (samples,labels)

    datasets = [make_moons(noise=0.3, random_state=0),

                make_circles(noise=0.2, factor=0.5, random_state=1),

                linearly_separable

                ]

    X_train = [ sample for sample in datasets[0]]

    y_train = [ label  for label in datasets[1] ]

    return (X_train,y_train)

if __name__ =="__main__":

    generate_datasets()

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