File "C:\Users\dell\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\utils\extmath.py", line 173, in safe_sparse_dot
return np.dot(a, b)
ValueError: shapes (4,9) and (10,10) not aligned: 9 (dim 1) != 10 (dim 0)
我看到类似这样的队列试图修复矩阵不起作用！
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn import metrics
data_frame = pd.read_csv("dataset.csv")
feature_column_names = [0,1,2,3]
predicted_class_name = [4]
print("Enter KFold Cross Validation number:")
number = int(input(""))
k_fold = KFold(n_splits = number)
sum = 0
for train, test in k_fold.split(data_frame):
x1 = np.array(data_frame.values[train])
y1 =np.array(data_frame.values[test])
train_test_size = len(y1)
data = np.concatenate((x1,y1), axis = 0)
dt = pd.DataFrame(data)
x = dt[feature_column_names].values
y = dt[predicted_class_name].values
train_column,test_column,train_class,test_class = train_test_split(x,y,test_size = train_test_size,random_state = 42)
neural_net = MLPClassifier(activation = 'relu', solver = 'lbfgs', alpha=1e-05, hidden_layer_sizes = (10,5))
train_column = [dict(enumerate(train_columns)) for train_columns in train_column]
test_column = [dict(enumerate(test_columns)) for test_columns in test_column]
vect = DictVectorizer(sparse = False)
a = vect.fit_transform(train_column)
b = vect.fit_transform(test_column)
neural_net.fit(a, train_class.ravel())
prediction = neural_net.predict(b)
accuracy = metrics.accuracy_score(test_class,prediction)
sum += accuracy
print(accuracy)
print("Accuracy is: ", sum/number)