Python Classification.Moving_Mean示例

示例#1

def Test_ARPF():
    test_data = Cl.Moving_Mean(data, 5)
    real_result = np.load(way + "Unit_test_pro_ARPF.npy")

    if (np.allclose(test_data, real_result)):
        return ("Funguje")
    else:
        return ("Nefunguje")

示例#2

# HMM_klasifikace = GaussianHMM(pocet_stavu)
#
# #buď Unsupervised
# #HMM_klasifikace.fit(training_data)
#
# #nebo Supervised
# #HMM_klasifikace.fit(training_data, real_lables)
# #######################################
#
#
# #můžu zkusit klasifikovat testovací data
# states = HMM_klasifikace.predict(testing_data)
# print(states)

X = np.load(way + "Synteticka_data_sum_0.025.npy")
Y = CL.Moving_Mean(X, 5)
#[CL.aritmeticky_prumer_fce(X, x, 5) for x in range(len(X))]
Z = CL.Exp_Moving_Mean(X, 5)
#[CL.suma_zleva_fce(X, x, 5) for x in range(len(X))]
XX = np.vstack((Y, Z, X)).T
XX
# np.shape(XX)
# plt.scatter(Y,X)
# plt.show()

np.shape(Z)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(Y, Z, X)
plt.show()
km.Nakresly(XX, 2, "3d")