ceemdan-lstm代码

这里是基于Python和TensorFlow的Ceemdan-LSTM的代码示例：

import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn import LSTMCell

# 定义CEEMDAN函数
def ceemdan(x, max_imf):
    # 定义一些常量和变量
    x = np.array(x)
    N = len(x)
    H = x
    SD = 1.0
    t = 0
    imf = []
    # 进行CEEMDAN分解
    while (SD > 0.3 or t < 4) and t < max_imf:
        t += 1
        H1 = H
        SD1 = 2 * SD
        while SD1 > SD:
            H1 = H
            d = H1
            for i in range(t):
                # 计算均值
                mean = np.mean(d)
                # 构造上下限
                uppers = []
                lowers = []
                for j in range(len(d)):
                    if j == 0:
                        uppers.append((d[j] + d[j + 1]) / 2)
                        lowers.append((d[j] + d[N - 1]) / 2)
                    elif j == N - 1:
                        uppers.append((d[j] + d[0]) / 2)
                        lowers.append((d[j] + d[j - 1]) / 2)
                    else:
                        uppers.append((d[j] + d[j + 1]) / 2)
                        lowers.append((d[j] + d[j - 1]) / 2)
                # 构造均值为中心线的包络线
                upper_env = np.maximum(mean, uppers)
                lower_env = np.minimum(mean, lowers)
                # 计算平均包络线
                mean_env = (upper_env + lower_env) / 2
                # 计算d的振幅与平均包络线之差
                d = d - mean_env
            imf.append(d)
            H1 = H1 - d
            SD1 = np.std(H1)
        H = H1
        SD = np.std(H)
    imf.append(H)
    return imf

# 定义LSTM模型
class LSTMModel(object):
    def __init__(self, input_size, output_size, hidden_size, num_layers):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.build_model()

    def build_model(self):
        self.x = tf.placeholder(tf.float32, [None, None, self.input_size])
        self.y = tf.placeholder(tf.float32, [None, self.output_size])
        cell = LSTMCell(self.hidden_size, state_is_tuple=True)
        self.cell = tf.contrib.rnn.MultiRNNCell([cell] * self.num_layers, state_is_tuple=True)
        self.init_state = self.cell.zero_state(tf.shape(self.x)[0], tf.float32)
        self.outputs, self.final_state = tf.nn.dynamic_rnn(self.cell, self.x, initial_state=self.init_state, time_major=False)
        self.outputs = self.outputs[:, -1, :]
        self.W = tf.Variable(tf.truncated_normal([self.hidden_size, self.output_size], stddev=0.1))
        self.b = tf.Variable(tf.constant(0.1, shape=[self.output_size]))
        self.pred = tf.matmul(self.outputs, self.W) + self.b
        self.loss = tf.reduce_mean(tf.square(self.pred - self.y))
        self.train_op = tf.train.AdamOptimizer().minimize(self.loss)

    def train(self, train_x, train_y, test_x, test_y, epoch, batch_size):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            for i in range(epoch):
                start = 0
                while start < len(train_x):
                    end = start + batch_size
                    batch_x = train_x[start:end]
                    batch_y = train_y[start:end]
                    _, loss = sess.run([self.train_op, self.loss], feed_dict={self.x: batch_x, self.y: batch_y})
                    start += batch_size
                if i % 100 == 0:
                    print('Epoch ', i, ' loss: ', loss)
                    test_loss = sess.run(self.loss, feed_dict={self.x: test_x, self.y: test_y})
                    print('Test loss: ', test_loss)

            pred = sess.run(self.pred, feed_dict={self.x: test_x})
            return pred

# 加载数据
data = np.load('data.npy')
data = np.transpose(data)
data = np.reshape(data, [-1, 1])
data = np.array(ceemdan(data, 5))
data = np.transpose(data, [1, 2, 0])
train_x = data[:2000, :-1, :]
train_y = data[:2000, -1, :]
test_x = data[2000:, :-1, :]
test_y = data[2000:, -1, :]

# 训练模型并进行预测
model = LSTMModel(5, 1, 10, 2)
pred = model.train(train_x, train_y, test_x, test_y, 1000, 50)

其中，CEEMDAN函数实现了CEEMDAN分解，LSTMModel类实现了LSTM模型的定义和训练。