17 lines
510 B
Python
17 lines
510 B
Python
|
import numpy as np
|
||
|
|
from sklearn.datasets import make_classification
|
||
|
|
from sklearn.model_selection import train_test_split
|
||
|
|
|
||
|
|
|
||
|
|
def generate_dataset():
|
||
|
|
x, y = make_classification(n_samples=500, n_features=2, n_redundant=0,
|
||
|
|
n_informative=2, random_state=0, n_clusters_per_class=1)
|
||
|
|
random = np.random.RandomState(2)
|
||
|
|
x += 2.5 * random.uniform(size=x.shape)
|
||
|
|
return x, y
|
||
|
|
|
||
|
|
|
||
|
|
def split_dataset(x, y):
|
||
|
|
return train_test_split(
|
||
|
|
x, y, test_size=.05, random_state=42)
|