粒子群算法优化bp神经网络预测番茄产量python代码案例
时间: 2023-07-23 17:44:41 浏览: 65
好的,我会尝试回答您的问题。首先,需要说明的是,粒子群算法和BP神经网络是两个独立的概念,它们可以结合使用来进行预测。
以下是一个使用粒子群算法优化BP神经网络预测番茄产量的Python代码案例:
```python
import numpy as np
import matplotlib.pyplot as plt
# 设置随机种子,保证每次运行结果一致
np.random.seed(1)
# 定义BP神经网络类
class BPNeuralNetwork(object):
def __init__(self, input_size, hidden_size, output_size):
# 初始化神经网络参数
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.W1 = np.random.randn(self.input_size, self.hidden_size)
self.W2 = np.random.randn(self.hidden_size, self.output_size)
def sigmoid(self, x):
# sigmoid函数
return 1 / (1 + np.exp(-x))
def forward(self, X):
# 前向传播计算输出
self.z2 = np.dot(X, self.W1)
self.a2 = self.sigmoid(self.z2)
self.z3 = np.dot(self.a2, self.W2)
y_hat = self.sigmoid(self.z3)
return y_hat
def sigmoid_prime(self, x):
# sigmoid函数的导数
return np.exp(-x) / ((1 + np.exp(-x)) ** 2)
def cost_function(self, X, y):
# 计算损失函数值
self.y_hat = self.forward(X)
J = 0.5 * sum((y - self.y_hat) ** 2)
return J
def cost_function_prime(self, X, y):
# 计算损失函数的导数
self.y_hat = self.forward(X)
delta3 = np.multiply(-(y - self.y_hat), self.sigmoid_prime(self.z3))
dJdW2 = np.dot(self.a2.T, delta3)
delta2 = np.dot(delta3, self.W2.T) * self.sigmoid_prime(self.z2)
dJdW1 = np.dot(X.T, delta2)
return dJdW1, dJdW2
def get_params(self):
# 获取神经网络参数
params = np.concatenate((self.W1.ravel(), self.W2.ravel()))
return params
def set_params(self, params):
# 设置神经网络参数
W1_start = 0
W1_end = self.hidden_size * self.input_size
self.W1 = np.reshape(params[W1_start:W1_end], (self.input_size, self.hidden_size))
W2_end = W1_end + self.hidden_size * self.output_size
self.W2 = np.reshape(params[W1_end:W2_end], (self.hidden_size, self.output_size))
def compute_gradients(self, X, y):
# 计算梯度
dJdW1, dJdW2 = self.cost_function_prime(X, y)
return np.concatenate((dJdW1.ravel(), dJdW2.ravel()))
# 定义粒子类
class Particle(object):
def __init__(self, x0):
self.position = x0
self.velocity = np.random.rand(len(x0))
self.best_position = self.position
self.best_cost = -1
def move(self):
self.position = self.position + self.velocity
def compute_cost(self, cost_function):
self.cost = cost_function(self.position)
if self.best_cost < 0 or self.cost < self.best_cost:
self.best_cost = self.cost
self.best_position = self.position
# 定义粒子群类
class ParticleSwarmOptimizer(object):
def __init__(self, cost_function, x0, bounds, num_particles, maxiter):
self.cost_function = cost_function
self.bounds = bounds
self.num_particles = num_particles
self.maxiter = maxiter
self.swarm = []
for i in range(self.num_particles):
self.swarm.append(Particle(x0))
def run(self):
i = 0
while i < self.maxiter:
for j in range(self.num_particles):
# 计算粒子的代价
self.swarm[j].compute_cost(self.cost_function)
# 更新粒子群中最优解
if i == 0 or self.swarm[j].best_cost < self.best_cost:
self.best_position = self.swarm[j].best_position
self.best_cost = self.swarm[j].best_cost
for j in range(self.num_particles):
# 更新粒子的速度和位置
v = self.swarm[j].velocity + \
np.random.rand(len(x0)) * (self.swarm[j].best_position - self.swarm[j].position) + \
np.random.rand(len(x0)) * (self.best_position - self.swarm[j].position)
self.swarm[j].velocity = v
self.swarm[j].move()
# 限制粒子的位置在给定的边界内
for k in range(len(x0)):
if self.swarm[j].position[k] < self.bounds[k][0]:
self.swarm[j].position[k] = self.bounds[k][0]
if self.swarm[j].position[k] > self.bounds[k][1]:
self.swarm[j].position[k] = self.bounds[k][1]
i += 1
# 返回最优解
return self.best_position
# 加载数据
data = np.loadtxt('tomato_production.csv', delimiter=',')
# 数据预处理
X = data[:, :-1]
y = data[:, -1]
X /= np.max(X, axis=0)
y /= np.max(y)
# 定义BP神经网络模型
input_size = X.shape[1]
hidden_size = 4
output_size = 1
bpnn = BPNeuralNetwork(input_size, hidden_size, output_size)
# 定义代价函数和代价函数的导数
cost_function = lambda p: bpnn.cost_function(X, y)
grad_function = lambda p: bpnn.compute_gradients(X, y)
# 定义边界
bounds = [(-2, 2)] * (input_size * hidden_size + hidden_size * output_size)
# 定义粒子群优化器
num_particles = 10
maxiter = 100
ps_optimizer = ParticleSwarmOptimizer(cost_function, bpnn.get_params(), bounds, num_particles, maxiter)
# 运行粒子群算法进行优化
best_params = ps_optimizer.run()
# 设置优化后的参数
bpnn.set_params(best_params)
# 绘制预测结果图像
X_test = np.array([[0.5, 0.6, 0.7, 0.8]])
X_test /= np.max(X, axis=0)
y_pred = bpnn.forward(X_test)[0][0] * np.max(y)
plt.plot(y, 'b-', label='actual')
plt.plot(y_pred, 'r-', label='predicted')
plt.legend()
plt.show()
```
以上就是一个使用粒子群算法优化BP神经网络预测番茄产量的Python代码案例。其中,我们首先加载数据,然后对数据进行预处理,接着定义BP神经网络模型,并定义代价函数和代价函数的导数。然后,我们定义边界,并使用粒子群算法进行优化。最后,我们设置优化后的参数,绘制预测结果图像。