def make_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None):
 generator = check_random_state(random_state)
 X = generator.normal(loc=0, scale=1, size=(n_samples, n_features))
 y = generator.normal(loc=(((X[:, 0] + (2 * X[:, 1])) - (2 * X[:, 2])) - (1.5 * X[:, 3])), scale=np.ones(n_samples))
 return (X, y)

def make_sparse_uncorrelated ( n_samples = 100 , n_features = 10 , random_state = None ) : generator = check_random_state ( random_state ) X = generator . normal ( loc = 0 , scale = 1 , size = ( n_samples , n_features ) ) y = generator . normal ( loc = ( ( ( X [ : , 0 ] + ( 2 * X [ : , 1 ] ) ) - ( 2 * X [ : , 2 ] ) ) - ( 1.5 * X [ : , 3 ] ) ) , scale = np . ones ( n_samples ) ) return ( X , y )
generate a random regression problem with sparse uncorrelated design this dataset is described in celeux et al [1] .