def dataset_fixed_cov():
 (n, dim) = (300, 2)
 np.random.seed(0)
 C = np.array([[0.0, (-0.23)], [0.83, 0.23]])
 X = np.r_[(np.dot(np.random.randn(n, dim), C), (np.dot(np.random.randn(n, dim), C) + np.array([1, 1])))]
 y = np.hstack((np.zeros(n), np.ones(n)))
 return (X, y)

def dataset_fixed_cov ( ) : ( n , dim ) = ( 300 , 2 ) np . random . seed ( 0 ) C = np . array ( [ [ 0.0 , ( - 0.23 ) ] , [ 0.83 , 0.23 ] ] ) X = np . r_ [ ( np . dot ( np . random . randn ( n , dim ) , C ) , ( np . dot ( np . random . randn ( n , dim ) , C ) + np . array ( [ 1 , 1 ] ) ) ) ] y = np . hstack ( ( np . zeros ( n ) , np . ones ( n ) ) ) return ( X , y )
generate 2 gaussians samples with the same covariance matrix .