神经网络

代码示例：:

from knowledge.KnowledgeSet import KnowledgeSet
from benchmark.sphere import Sphere
from utils.normalization import Normalization


ndim = 2
trainNum = 100
testNum = 10

x = np.ones((trainNum, ndim))
x[:, 0] = np.linspace(-10, 10.0, trainNum)
x[:, 1] = np.linspace(-10, 10.0, trainNum)

xt = np.ones((testNum, ndim))
xt[:, 0] = np.linspace(-10, 10.0, testNum)
xt[:, 1] = np.linspace(-10, 10.0, testNum)

s = Sphere()
trainSet = s(x)
testSet = s(xt)

# 获取知识
k = KnowledgeSet("C:\data\sphereMonoKnowledge1.xml", "C:\data\sphereMonoKnowledge2.xml")
knowList = k.readKnowledge()

# k.visualKnowledge()
x_test = testSet["input"]

# 对知识和数据进行归一化操作
n = Normalization()
trainSet, knowList = n(trainSet, knowList)
print(knowList)
xt = n.transform(xt)

nnModel = EDaKnow()

# nnModel.iterationTime = 10000

nnModel.setData(trainSet)  # 设置数据

nnModel.setKnowledge(knowList=knowList)  # 设置知识

nnModel.train()  # 训练

yp = nnModel.predict(xt)  # 预测
print(yp)

plt.plot(trainSet["input"][:, 0], trainSet["output"][:, 0], label="真实", color="red")
plt.plot(xt[:, 0], yp[:, 0], label="预测", color='green')
plt.legend()
plt.xlabel("x")
plt.ylabel("y")
plt.show()

# b = nnModel.score(testSet, index="RSME")  # 评价代理模型 ，"RSME", "R2", "MAE", "Confidence"
# print(b)

nnModel.save(r"C:\data\代理模型训练\高斯过程代理模型.pkl")  # 保存模型文件