Question

我是python的新手，我正在尝试学习如何使用我自己的数据使用Sklearn。我基本上只是从我的ARIMA模型得到了残差，并从我的离散小波变换得到了近似值，并尝试拟合它以获得更好的模型。但是，我收到了此错误消息：

File "C:\Users\Guilherme\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 330, in _fit
    X, y = self._validate_input(X, y, incremental)

  File "C:\Users\Guilherme\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 902, in _validate_input
    multi_output=True)

  File "C:\Users\Guilherme\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 531, in check_X_y
    check_consistent_length(X, y)

  File "C:\Users\Guilherme\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 181, in check_consistent_length
    " samples: %r" % [int(l) for l in lengths])

**ValueError: Found input variables with inconsistent numbers of samples: [2, 974]**

这就是我的输入的样子：

residual [-0.62259993  6.71457698  8.14639867 ...,  3.17906001 -7.54454573
  4.38012835]
approximation [ 59.91585806  61.33864282  64.3283577  ...,  43.82833793  44.61630255
  46.4758499 ]


X [[ 59.91585806  61.33864282  64.3283577  ...,  43.82833793  44.61630255
   46.4758499 ]
 [ -0.62262456   6.71456765   8.14639445 ...,  -4.5462162   -7.28155588
  -18.45525218]]

这是我的目标：

Y [ -0.62274596   6.71452528   8.14634609 ...,  -4.54623634  -7.28158029
 -18.4552741 ]

这是我的代码

    ## ANN
print('residual',len(residual))
A = A[4:978]
Residual = residual[0:974]
X = np.array([A,Residual])
print(X.shape)

y = np.array(residual[0:974])
print(y)


clf = MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto',
       beta_1=0.9, beta_2=0.999, early_stopping=False,
       epsilon=1e-08, hidden_layer_sizes=(15,), learning_rate='constant',
       learning_rate_init=0.001, max_iter=200, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
       warm_start=False)

clf.fit(X, y)                        

print(clf.fit(X, y))

顺便说一下，这是我的参考资料

Khandelwal，I.，Adhikari，R.，Verma，G。（2015）。基于DWT分解的混合ARIMA和ANN模型的时间序列预测。 Procedia Computer Science 48（2015）173 - 179

[神经网络模型（监督）1

ValueError：找到样本数不一致的输入变量[Sklearn和时间序列]

0 个答案: