我在 stackoverflow 上查找了这个错误并找到了几篇帖子，但没有人解决这种特定情况。
我有以下数据框：
输入变量和输出变量在此代码中定义：
xcol=["h","o","p","d","ddlt","devdlt","sl","lt"]
ycol=["Q","r"]
x=df[xcol].values
y=df[ycol].values
我的目标是根据输入 (x) 猜测输出值 Q & r。我尝试了两种方法，但都失败了。对于第一个，我尝试了一个多输出回归器。
我首先将测试数据和训练数据中的数据分开：
import numpy as np
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
y_train = y_train.ravel()
y_test = y_test.ravel()
然后导入函数：
from sklearn.multioutput import MultiOutputRegressor
然后尝试预测 Q & r：
reg= MultiOutputRegressor(estimator=100, n_jobs=None)
reg=reg.predict(X_train, y_train)
这给了我错误：
TypeError: predict() takes 2 positional arguments but 3 were given
我做错了什么，我该如何解决？
接下来我尝试的是神经网络。分配 x 和 y 列后，我制作了神经网络：
# neural network class definition
class neuralNetwork:
#Step 1:
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
#set number of nodes in each input, hidden, output layer
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
#link weight matrices, wih and who (weights in hidden en output layers),
# we are going to create matrices for the multiplication of it to get an
# output
# weights inside the arrays (matrices) are w_i_j, where link is from node
# i to node j in the next layer
#w11 w21
#w12 w22 etc
self.wih = numpy.random.normal(0.0,pow(self.inodes,-0.5),( self.hnodes,
self.inodes))
self.who = numpy.random.normal(0.0,pow(self.hnodes,-0.5),( self.onodes,
self.hnodes))
# setting the learning rate
self.lr = learningrate
# activation function is the sigmoid function
self.activation_function = lambda x: scipy.special.expit(x)
pass
#Step 2:
def train(self, inputs_list, targets_list):
#convert input lists to 2d array (matrice)
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
#calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
#calculate signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
#calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
#calculate signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
# output layer error is the (target-actual)
output_errors = targets -final_outputs
#hidden layer error is the output_errors, split by weights, recombined
at hidden nodes
hidden_errors = numpy.dot(self.who.T, output_errors)
#update the weights for the links between the hidden and output layers
self.who += self.lr * numpy.dot((output_errors*final_outputs * (1.0-
final_outputs)),numpy.transpose(hidden_outputs))
# update the weights for the links between the input and hidden layers
self.wih += self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-
hidden_outputs)),numpy.transpose(inputs))
pass
#Step 3
def query(self, inputs_list):
#convert input lists to 2d array (matrice)
inputs = numpy.array(inputs_list, ndmin=2).T
#calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
#calculate signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
#calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
#calculate signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
return final_outputs
然后我创建了一个神经网络的实例：
#Creating instance of neural network
#number of input, hidden and output nodes
input_nodes = 8
hidden_nodes = 100
output_nodes = 2
#learning rate is 0.8
learning_rate = 0.8
#create instance of neural network
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
我得到了需要预测的 8 个输入和 2 个输出。
然后我训练了神经网络：
# train the neural network
# go through all records in the training data set
for record in df:
#scale and shift te inputs
inputs = x
#create the target output values
targets = y
n.train(inputs, targets)
pass
然后我想查询猜测的输出，现在它出错了：
所以我想用 Q (Q*) & r (r*) 的猜测在数据框中添加 2 个额外的列：
df["Q*","r*"] = n.query(x)
我真的不知道如何正确地做到这一点。上面的代码给了我错误：
ValueError: Length of values does not match length of index
任何帮助表示赞赏。
史蒂文