Function get_xor

supervised_class/util.py:23–30

()

    return X, Y

def get_xor():
    X = np.zeros((200, 2))
    X[:50] = np.random.random((50, 2)) / 2 + 0.5 # (0.5-1, 0.5-1)
    X[50:100] = np.random.random((50, 2)) / 2 # (0-0.5, 0-0.5)
    X[100:150] = np.random.random((50, 2)) / 2 + np.array([[0, 0.5]]) # (0-0.5, 0.5-1)
    X[150:] = np.random.random((50, 2)) / 2 + np.array([[0.5, 0]]) # (0.5-1, 0-0.5)
    Y = np.array([0]*100 + [1]*100)
    return X, Y

def get_donut():
    N = 200