我的代码如下
import scipy.io as sio
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
X=sio.loadmat('pyInput.mat')
y=sio.loadmat('pyTarget.mat')
X_train, X_test, y_train, y_test = train_test_split(X, y)
StandardScaler.fit(X_train)
X_train = StandardScaler.transform(X_train)
X_test = StandardScaler.transform(X_test)
mlp = MLPClassifier(hidden_layer_sizes=(10,10,10))
mlp.fit(X_train,y_train)
predictions = mlp.predict(X_test)
print(confusion_matrix(y_test,predictions))
print(classification_report(y_test,predictions))
当我在spyder中运行时，我的错误是
return [X[idx] for idx in indices]
我尝试通过在matlab中创建适当的分割矩阵来手动定义x_train，x_test，y_train和y_test。但是，我接到一个错误，说
fit() missing 1 required positional argument: 'X'\
用于StandardScaler.fit（X_train）部分。
如果我完全删除了StandardScaler部分，那么我的错误是
float() argument must be a string or a number, not 'dict'
该错误来自Anaconda软件包中的validation.py文件中的一行，特定行为402.
array=array.astype(np.float64)
我的矩阵（未手动分割时）为100x200，目标为100x1。