def eaData3Classify(m=1000):
distance = 8
np.random.seed(1)
X1 = 2 * np.random.randn(2, m)
Y1 = np.zeros((3, m))
Y1[0] = 1
type1 = np.zeros((1, m))
X2 = 2 * np.random.randn(2, m)
X2[0] += distance
Y2 = np.zeros((3, m))
Y2[1] = 1
type2 = 1 + np.zeros((1, m))
X3 = 2 * np.random.randn(2, m)
X3[0] += distance / 2
X3[1] += distance * math.sin(math.pi / 3)
Y3 = np.zeros((3, m))
Y3[2] = 1
type3 = 2 + np.zeros((1, m))
X = np.c_[X1, X2, X3]
Y = np.c_[Y1, Y2, Y3]
Type = np.c_[type1, type2, type3]
eaData = Ea()
eaData.X = X
eaData.Y = Y
eaData.Type = Type
return eaData
def eaDataTFRing(m=1000):
# np.random.seed(...) 保证每次输出结果不变。
np.random.seed(1)
X = 2 * np.random.randn(2, m)
# 很方便的求 2-范数 的方法（相当于求半径）
R = np.linalg.norm(X, ord=2, axis=0).reshape(1, m)
# 增加一个噪音
np.random.seed(2)
noise = .3 * np.random.normal(size=R.shape)
R = R + noise
# 二值化输出
Y = np.int32(np.array(R < 2.5))
# print("dataTF X.shape",X.shape,"Y.shape",Y.shape)
# 合并输入输出数据 （学会用 np.r_[...] 和 np.c_[...])
eaData = Ea()
eaData.X = X
eaData.Y = Y
eaData.Type = Y
return eaData