PSO-LSTM python代码

以下是一个简单的 PSO-LSTM 的 Python 代码实现：

import numpy as np
import random

class Particle:
    def __init__(self, x0):
        self.position_i = []
        self.velocity_i = []
        self.pos_best_i = []
        self.err_best_i = -1
        self.err_i = -1

        for i in range(0, num_dimensions):
            self.velocity_i.append(random.uniform(-1, 1))
            self.position_i.append(x0[i])

    def evaluate(self, costFunc):
        self.err_i = costFunc(self.position_i)

        if self.err_i < self.err_best_i or self.err_best_i == -1:
            self.pos_best_i = self.position_i
            self.err_best_i = self.err_i

    def update_velocity(self, pos_best_g):
        w = 0.5
        c1 = 1
        c2 = 2

        for i in range(0, num_dimensions):
            r1 = random.random()
            r2 = random.random()

            vel_cognitive = c1 * r1 * (self.pos_best_i[i] - self.position_i[i])
            vel_social = c2 * r2 * (pos_best_g[i] - self.position_i[i])
            self.velocity_i[i] = w * self.velocity_i[i] + vel_cognitive + vel_social

    def update_position(self, bounds):
        for i in range(0, num_dimensions):
            self.position_i[i] = self.position_i[i] + self.velocity_i[i]

            # adjust maximum position if necessary
            if self.position_i[i] > bounds[i][1]:
                self.position_i[i] = bounds[i][1]

            # adjust minimum position if necessary
            if self.position_i[i] < bounds[i][0]:
                self.position_i[i] = bounds[i][0]

class PSO:
    def __init__(self, costFunc, x0, bounds, num_particles, maxiter):
        global num_dimensions

        num_dimensions = len(x0)
        err_best_g = -1
        pos_best_g = []

        swarm = []
        for i in range(0, num_particles):
            swarm.append(Particle(x0))

        i = 0
        while i < maxiter:
            for j in range(0, num_particles):
                swarm[j].evaluate(costFunc)

                if swarm[j].err_i < err_best_g or err_best_g == -1:
                    pos_best_g = list(swarm[j].position_i)
                    err_best_g = float(swarm[j].err_i)

            for j in range(0, num_particles):
                swarm[j].update_velocity(pos_best_g)
                swarm[j].update_position(bounds)
            i += 1

        print('FINAL:')
        print(pos_best_g)
        print(err_best_g)

def lstm_predict(X_train, y_train, X_test, particle):
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.layers import LSTM

    X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
    X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))

    model = Sequential()
    model.add(LSTM(particle[0], input_shape=(1, X_train.shape[2]), activation='relu'))
    model.add(Dense(1))

    model.compile(loss='mean_squared_error', optimizer='adam')
    model.fit(X_train, y_train, epochs=particle[1], batch_size=1, verbose=2)

    y_pred = model.predict(X_test)
    return y_pred

def costFunc(particle):
    y_pred = lstm_predict(X_train, y_train, X_test, particle)
    error = np.mean(np.abs(y_pred - y_test))
    return error

# Input data
X_train = np.random.rand(100, 10)
y_train = np.random.rand(100, 1)
X_test = np.random.rand(10, 10)
y_test = np.random.rand(10, 1)

# Define bounds for PSO
bounds = [(4, 256), (1, 20)]

# Run PSO
PSO(costFunc, [128, 10], bounds, num_particles=15, maxiter=30)

这个代码实现的是一个简单的 PSO-LSTM 模型，其中 `Particle` 类代表每个粒子，`PSO` 类代表整个 PSO 优化算法，`lstm_predict` 函数是用于预测的 LSTM 模型，`costFunc` 函数是 PSO 的成本函数。