def test_ceemdan_simpleRun(self):
T = np.linspace(0, 1, 100)
S = np.sin(2 * np.pi * T)
ceemdan = CEEMDAN(trials=10, max_imf=1)
ceemdan.EMD.FIXE_H = 5
ceemdan.ceemdan(S)
def test_ceemdan_simpleRun():
T = np.linspace(0, 1, 100)
S = np.sin(2*np.pi*T)
config = {"processes": 1}
ceemdan = CEEMDAN(trials=10, max_imf=1, **config)
ceemdan.EMD.FIXE_H = 5
ceemdan.ceemdan(S)
def test_ceemdan_simpleRun():
T = np.linspace(0, 1, 100)
S = np.sin(2 * np.pi * T)
config = {"processes": 1}
ceemdan = CEEMDAN(trials=10, max_imf=1, **config)
ceemdan.EMD.FIXE_H = 5
ceemdan.ceemdan(S)
def test_ceemdan_simpleRun(self):
T = np.linspace(0, 1, 100)
S = np.sin(2*np.pi*T)
config = {"processes": 1}
ceemdan = CEEMDAN(trials=10, max_imf=1, **config)
ceemdan.EMD.FIXE_H = 5
ceemdan.ceemdan(S)
self.assertTrue('processes' in ceemdan.__dict__)
def main():
full_data, scaler = load_data('data-569-1.csv')
training_set_split = int(len(full_data) * (0.8))
lookback_window = 2
global result
result = '\nEvaluation.'
# #数组划分为不同的数据集
data_regular = data_split(full_data, training_set_split, lookback_window)
data_regular_DL = data_split_LSTM(data_regular)
x_real = scaler.inverse_transform(data_regular[1].reshape(-1, 1)).reshape(
-1, )
y_real = scaler.inverse_transform(data_regular[3].reshape(-1, 1)).reshape(
-1, )
a = np.concatenate((x_real, y_real), axis=0)
predict_svr = Training_Prediction_ML(model_SVR(), a, scaler, data_regular,
'SVR')
predict_elm = Training_Prediction_ML(model_ELM(), a, scaler, data_regular,
'ELM')
predict_bp = Training_Prediction_ML(model_BPNN(), a, scaler, data_regular,
'BPNN')
predict_LSTM = Training_Prediction_DL(model_LSTM(lookback_window), a,
scaler, data_regular_DL, 'LSTM')
#################################################################EMD_LSTM
emd = EMD()
emd_imfs = emd.emd(full_data.reshape(-1), None, 8)
emd_imfs_prediction = []
# i = 1
# plt.rc('font', family='Times New Roman')
# plt.subplot(len(emd_imfs) + 1, 1, i)
# plt.plot(full_data, color='black')
# plt.ylabel('Signal')
# plt.title('EMD')
# for emd_imf in emd_imfs:
# plt.subplot(len(emd_imfs) + 1, 1, i + 1)
# plt.plot(emd_imf, color='black')
# plt.ylabel('IMF ' + str(i))
# i += 1
# plt.show()
test = np.zeros([len(full_data) - training_set_split - lookback_window, 1])
i = 1
for emd_imf in emd_imfs:
print('-' * 45)
print('This is ' + str(i) + ' time(s)')
print('*' * 45)
data_imf = data_split_LSTM(
data_split(imf_data(emd_imf, 1), training_set_split,
lookback_window))
test += data_imf[3]
model = EEMD_LSTM_Model(data_imf[0], data_imf[1], i)
# model.save('EEMD-LSTM-imf' + str(i) + '.h5')
emd_prediction_X = model.predict(data_imf[0])
emd_prediction_Y = model.predict(data_imf[2])
emd_imfs_prediction.append(
np.concatenate((emd_prediction_X, emd_prediction_Y), axis=0))
i += 1
emd_imfs_prediction = np.array(emd_imfs_prediction)
emd_prediction = [0.0 for i in range(len(a))]
emd_prediction = np.array(emd_prediction)
for i in range(len(a)):
emd_t = 0.0
for emd_imf_prediction in emd_imfs_prediction:
emd_t += emd_imf_prediction[i][0]
emd_prediction[i] = emd_t
emd_prediction = scaler.inverse_transform(emd_prediction.reshape(
-1, 1)).reshape(-1, )
result += '\n\nMAE_emd_lstm: {}'.format(MAE1(a, emd_prediction))
result += '\nRMSE_emd_lstm: {}'.format(RMSE1(a, emd_prediction))
result += '\nMAPE_emd_lstm: {}'.format(MAPE1(a, emd_prediction))
result += '\nR2_emd_lstm: {}'.format(R2(a, emd_prediction))
################################################################EEMD_LSTM
# eemd = EEMD()
# # eemd.noise_seed(12345)
# eemd_imfs = eemd.eemd(full_data.reshape(-1), None, 8)
# eemd_imfs_prediction = []
#
# # i = 1
# # plt.rc('font', family='Times New Roman')
# # plt.subplot(len(eemd_imfs) + 1, 1, i)
# # plt.plot(full_data, color='black')
# # plt.ylabel('Signal')
# # plt.title('EEMD')
# # for imf in eemd_imfs:
# # plt.subplot(len(eemd_imfs) + 1, 1, i + 1)
# # plt.plot(imf, color='black')
# # plt.ylabel('IMF ' + str(i))
# # i += 1
# #
# # # # plt.savefig('result_imf.png')
# # plt.show()
#
# test = np.zeros([len(full_data) - training_set_split - lookback_window, 1])
#
# i = 1
# for imf in eemd_imfs:
# print('-' * 45)
# print('This is ' + str(i) + ' time(s)')
# print('*' * 45)
#
# data_imf = data_split_LSTM(data_split(imf_data(imf, 1), training_set_split, lookback_window))
#
# test += data_imf[3]
#
# model = EEMD_LSTM_Model(data_imf[0], data_imf[1], i)
#
# eemd_prediction_X = model.predict(data_imf[0])
# eemd_prediction_Y = model.predict(data_imf[2])
# eemd_imfs_prediction.append(np.concatenate((eemd_prediction_X, eemd_prediction_Y), axis=0))
#
# i += 1
#
# eemd_imfs_prediction = np.array(eemd_imfs_prediction)
#
# eemd_prediction = [0.0 for i in range(len(a))]
# eemd_prediction = np.array(eemd_prediction)
# for i in range(len(a)):
# t = 0.0
# for imf_prediction in eemd_imfs_prediction:
# t += imf_prediction[i][0]
# eemd_prediction[i] = t
#
# eemd_prediction = scaler.inverse_transform(eemd_prediction.reshape(-1, 1)).reshape(-1, )
#
# result += '\n\nMAE_eemd_lstm: {}'.format(MAE1(a, eemd_prediction))
# result += '\nRMSE_eemd_lstm: {}'.format(RMSE1(a, eemd_prediction))
# result += '\nMAPE_eemd_lstm: {}'.format(MAPE1(a, eemd_prediction))
# result += '\nR2_eemd_lstm: {}'.format(R2(a, eemd_prediction))
################################################################CEEMDAN_LSTM
ceemdan = CEEMDAN()
ceemdan_imfs = ceemdan.ceemdan(full_data.reshape(-1), None, 8)
ceemdan_imfs_prediction = []
# i = 1
# plt.rc('font', family='Times New Roman')
# plt.subplot(len(ceemdan_imfs) + 1, 1, i)
# plt.plot(full_data,color= 'black')
# plt.ylabel('orignal')
# # plt.title('CEEMDAN')
# for imf in ceemdan_imfs:
# plt.subplot(len(ceemdan_imfs) + 1, 1, i + 1)
# plt.plot(imf, color='steelblue')
# plt.ylabel('IMF ' + str(i))
# i += 1
# # # plt.savefig('result_imf.png')
# plt.show()
test = np.zeros([len(full_data) - training_set_split - lookback_window, 1])
i = 1
for imf in ceemdan_imfs:
print('-' * 45)
print('This is ' + str(i) + ' time(s)')
print('*' * 45)
data_imf = data_split_LSTM(
data_split(imf_data(imf, 1), training_set_split, lookback_window))
test += data_imf[3]
model = EEMD_LSTM_Model(data_imf[0], data_imf[1],
i) # [X_train, Y_train, X_test, y_test]
prediction_X = model.predict(data_imf[0])
prediction_Y = model.predict(data_imf[2])
# ceemdan_imfs_prediction.append(prediction_Y)
ceemdan_imfs_prediction.append(
np.concatenate((prediction_X, prediction_Y), axis=0))
i += 1
ceemdan_imfs_prediction = np.array(ceemdan_imfs_prediction)
ceemdan_prediction = [0.0 for i in range(len(a))]
ceemdan_prediction = np.array(ceemdan_prediction)
for i in range(len(a)):
t = 0.0
for imf_prediction in ceemdan_imfs_prediction:
t += imf_prediction[i][0]
ceemdan_prediction[i] = t
ceemdan_prediction = scaler.inverse_transform(
ceemdan_prediction.reshape(-1, 1)).reshape(-1, )
result += '\n\nMAE_ceemdan_lstm: {}'.format(MAE1(a, ceemdan_prediction))
result += '\nRMSE_ceemdan_lstm: {}'.format(RMSE1(a, ceemdan_prediction))
result += '\nMAPE_ceemdan_lstm: {}'.format(MAPE1(a, ceemdan_prediction))
result += '\nR2_ceemdan_lstm: {}'.format(R2(a, ceemdan_prediction))
##################################################evaluation
print(result)
###===============画图===========================
# plt.rc('font', family='Times New Roman')
# plt.figure(1, figsize=(15, 5))
# plt.plot(a, 'black', linewidth=1, label='true',linestyle='-')
# plt.plot(predict_svr, 'tan', linewidth=1,label='SVR')
# plt.plot(predict_bp, 'indianred', linewidth=1,label='BP')
# plt.plot(predict_elm, 'khaki', linewidth=1,label='ELM')
# plt.plot(predict_LSTM, 'lightsteelblue', label='LSTM', linewidth=1)
# plt.plot(emd_prediction, 'seagreen', label='EMD-LSTM', linewidth=1)
# # plt.plot(eemd_prediction, 'r', linewidth=2.5, linestyle='--', marker='^', markersize=2)
# plt.plot(ceemdan_prediction, 'red',label='Proposed', linewidth=1, linestyle='-',marker='^', markersize=2)
# plt.grid(True, linestyle=':', color='gray', linewidth='0.5', axis='both')
# plt.xlabel('time(days)', fontsize=18)
# plt.ylabel('height(mm)', fontsize=18)
# # plt.title('551')
# plt.legend(loc='best')
#
# plt.show()
plt.rc('font', family='Times New Roman')
plt.figure(1, figsize=(15, 5))
plt.plot(a, 'black', linewidth=1, linestyle='-')
plt.plot(predict_svr, 'tan', linewidth=1)
plt.plot(predict_bp, 'indianred', linewidth=1)
plt.plot(predict_elm, 'khaki', linewidth=1)
plt.plot(predict_LSTM, 'lightsteelblue', linewidth=1)
plt.plot(emd_prediction, 'seagreen', linewidth=1)
# plt.plot(eemd_prediction, 'r', linewidth=2.5, linestyle='--', marker='^', markersize=2)
plt.plot(ceemdan_prediction,
'red',
linewidth=1,
linestyle='-',
marker='^',
markersize=2)
plt.grid(True, linestyle=':', color='gray', linewidth='0.5', axis='both')
plt.xlabel('time(days)', fontsize=18)
plt.ylabel('height(mm)', fontsize=18)
# plt.title('551')
plt.legend(loc='best')
plt.show()
tMin, tMax = 0, 1
T = np.linspace(tMin, tMax, N, dtype=DTYPE)
# S = 6*T +np.cos(8*np.pi**T)+0.5*np.cos(40*np.pi*T)
file = 'as_early_uw_8k.wav'
sig, fs = wav.read(file)
w = np.array(fs)
s = w/32767
S = s[0:8000]
cemdan = CEEMDAN()
cemdan.FIXE_H = 5
cemdan.nbsym = 1
cemdan.spline_kind = 'cubic'
cemdan.DTYPE = DTYPE
imfs = cemdan.ceemdan(S, T, maxImf)
imfNo = imfs.shape[0]
c = 1
r = np.ceil((imfNo+1)/c)
plt.figure(1)
plt.ioff()
plt.subplot(r, c, 1)
plt.plot(T, S, 'r')
plt.xlim((tMin, tMax))
plt.title("Original signal")
for num in range(imfNo):
plt.subplot(r,c,num+2)
plt.plot(T, imfs[num], 'g')
# eemd.noise_seed(12345)
# eemd_imfs = eemd.eemd(DO.reshape(-1),None,8)
# i = 1
# plt.subplot(len(eemd_imfs)+1,1,i)
# plt.plot(DO)
# for emd_imf in eemd_imfs:
# plt.subplot(len(eemd_imfs)+1, 1, i+1)
# plt.plot(emd_imf,color = 'black')
# i += 1
# # plt.plot(DO, "black")
# plt.show()
# ########################################################CEEMDAN
ceemdan = CEEMDAN()
# ceemdan.noise_seed(12345)
ceemdan_imfs = ceemdan.ceemdan(DO.reshape(-1), None, 8)
print("ceemdan_imfs", ceemdan_imfs)
i = 1
plt.rc('font', family='Times New Roman')
plt.subplot(len(ceemdan_imfs) + 1, 1, i)
plt.plot(DO)
plt.ylabel("Signal")
for emd_imf in ceemdan_imfs:
plt.subplot(len(ceemdan_imfs) + 1, 1, i + 1)
plt.plot(emd_imf, color='black')
plt.ylabel("IMF " + str(i))
i += 1
# plt.plot(DO, "black")
plt.show()
np.random.seed(0)
t = np.linspace(0, 1, 200)
sin = lambda x, p: np.sin(2 * np.pi * x * t + p)
S = 3 * sin(18, 0.2) * (t - 0.2) ** 2
S += 5 * sin(11, 2.7)
S += 3 * sin(14, 1.6)
S += 1 * np.sin(4 * 2 * np.pi * (t - 0.8) ** 2)
S += t ** 2.1 - t
# Execute EEMD on S
eemd = CEEMDAN(trials=1)
# eIMFs = eemd(s)
eIMFs = eemd.ceemdan(S, t)
nIMFs = eIMFs.shape[0]
print('number of IMFs:', nIMFs)
# Plot results
plt.figure(figsize=(12,9))
plt.subplot(nIMFs+2, 1, 1)
plt.plot(t, S, 'r')
plt.ylabel("Original")
reconstructed_points = np.sum(eIMFs, axis=0)
corr_data = []
for n in range(nIMFs):
plt.subplot(nIMFs+2, 1, n+2)
plt.plot(t, eIMFs[n], 'g')