接上篇，test预测值不对的问题

修改了代码

#库的导入
import numpy as np
import pandas as pd
import math
from tensorflow.keras.losses import BinaryCrossentropy
losscc = BinaryCrossentropy()

#小波基函数
def wavelet(x):
    return (math.cos(1.75*x)) * (np.exp((x**2)/(-2)))
#激活函数tanh
def tanh(x):
    return (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))

#数据输入
data=pd.read_pickle('ICC_rms.pkl')
df=pd.DataFrame(data)
X = df.iloc[:, 0:510].values #所有样本的x值，0-510列 矩阵（1544，510）由此得出样本个数1544个，特征510
y = df.iloc[:, 511].values #所有样本的标签，511列 矩阵（1544，）
#把y转成1-0形式，Neurons对应0，Astrocytes对应1
Y=np.array([-1.0] * 1544)
for i in range(len(y)):
    if y[i] =='Neurons':
        Y[i]=0
    if y[i] =='Astrocytes':
        Y[i]=1

#输入数据的导入，用于测试数据的归一化与返归一化
df = pd.read_csv("train.csv")
df.columns = ["Co", "Cr", "Mg", "Pb", "Ti"]
Co = df["Co"]
Co = np.array(Co)
Cr = df["Cr"]
Cr = np.array(Cr)
Mg=df["Mg"]
Mg=np.array(Mg)
Pb = df["Pb"]
Pb =np.array(Pb)
Ti = df["Ti"]
Tisample = np.array(Ti)
sampleinsample = np.mat([Co,Cr,Mg,Pb])
Ti = np.array(Y)
samplein = np.mat(X.T)
sampleinminmax = np.array([samplein.min(axis=1).T.tolist()[0],samplein.max(axis=1).T.tolist()[0]]).transpose()#对应最大值最小值
sampleout = np.mat([Ti])
sampleoutminmax = np.array([sampleout.min(axis=1).T.tolist()[0],sampleout.max(axis=1).T.tolist()[0]]).transpose()#对应最大值最小值

#导入WNN.py训练好的参数
w1=np.load('w1.npy')
b=np.load('b.npy')
a=np.load('a.npy')
w2=np.load('w2.npy')
w1 = np.mat(w1)
w2 = np.mat(w2)
b = np.mat(b)
a = np.mat(a)

#隐含层节点数
hiddenunitnum = 8
#测试数据数量
testnum = 24


#测试数据的导入
df = pd.read_csv("test.csv")
df.columns = ["Co", "Cr", "Mg", "Pb", "Ti"]
Co = df["Co"]
Co = np.array(Co)
Cr = df["Cr"]
Cr = np.array(Cr)
Mg=df["Mg"]
Mg=np.array(Mg)
Pb = df["Pb"]
Pb =np.array(Pb)
Ti = df["Ti"]
# Ti = np.array(Ti)
# input=np.mat([Co,Cr,Mg,Pb])
Ti = np.array(Y)
# inputtest = np.mat(X.T) 
inputtest=np.mat(X)

#测试数据中输入数据的归一化
# inputnorm=(np.array(input.T)-sampleinminmax.transpose()[0])/(sampleinminmax.transpose()[1]-sampleinminmax.transpose()[0])
#hidden_out2用于保存隐含层输出
hidden_out = np.mat(np.zeros((testnum,hiddenunitnum)))
#计算隐含层输出
for m in range(testnum):
    for j in range(hiddenunitnum):
        # d = ((inputnorm[m, :] * w1[j, :].T) - b[j, :]) * (a[j, :] ** (-1))
        d = ((inputtest[m, :] * w1[j, :].T) - b[j, :]) * (a[j, :] ** (-1))
        hidden_out[m, j] = wavelet(d)
#计算输出层输出
output = tanh(hidden_out * w2 )
#对输出结果进行反归一化
# diff = sampleoutminmax[:,1]-sampleoutminmax[:,0]
# networkout2 = output*diff+sampleoutminmax[0][0]
networkout2 = output+sampleoutminmax[0][0]
networkout2 = np.array(networkout2).transpose()
output1=networkout2.flatten()#降成一维数组
output1=output1.tolist()
for i in range(testnum):
    output1[i] = float('%.2f'%output1[i])
print("the prediction is:",output1)

# #将输出结果与真实值进行对比，计算误差
# output=Ti
# rmse = (np.sum(np.square(output-output1))/len(output)) ** 0.5
# mae = np.sum(np.abs(output-output1))/len(output)
# #average_loss1=np.sum(np.abs((output-output1)/output))/len(output)
# average_loss1=losscc(output,output1)
# mape="%.2f%%"%(average_loss1*100)
# f1 = 0
# for m in range(testnum):
#     f1 = f1 + np.abs(output[m]-output1[m])/((np.abs(output[m])+np.abs(output1[m]))/2)
# f2 = f1 / testnum
# smape="%.2f%%"%(f2*100)
# print("the MAE is :",mae)
# print("the RMSE is :",rmse)
# print("the MAPE is :",mape)
# print("the SMAPE is :",smape)

#计算预测值与真实值误差与真实值之比的分布
A=0
B=0
C=0
D=0
E=0
for m in range(testnum):
    y1 = np.abs(output[m]-output1[m])/np.abs(output[m])
    if y1 <= 0.1:
        A = A + 1
    elif y1 > 0.1 and y1 <= 0.2:
        B = B + 1
    elif y1 > 0.2 and y1 <= 0.3:
        C = C + 1
    elif y1 > 0.3 and y1 <= 0.4:
        D = D + 1
    else:
        E = E + 1
print("Ratio <= 0.1 :",A)
print("0.1< Ratio <= 0.2 :",B)
print("0.2< Ratio <= 0.3 :",C)
print("0.3< Ratio <= 0.4 :",D)
print("Ratio > 0.4 :",E)

修改了inputtest并且去掉了归一化，但是还是不对，预测值全变成了0

the prediction is: [0.0, 0.0, 0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0, 0.0, -0.0, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0, -0.0, 0.0]