# Confusion matrix and accuracy not in sync

I am getting the following result for the confusion matrix and accuracy for a logistic
regression model.

array([[26,  0,  0],
[ 1, 24,  0],
[ 0,  0, 24]])
Accuracy: 1.0


Is this a correct result? As a 1 in the second row indicates that the actual
value was different from the predicted one. But that means accuracy can't be 1.0.
But still python returned the accuracy as 1.0

The code used to calculate this is :

from sklearn.metrics import accuracy_score
accuracy_score(y_true=y_train, y_pred=classifier.predict(X_train))


acc = (26 + 24 + 24) / (1 + 26 + 24 + 24)

results in 0.9866666666666667 but
np.round(acc, 1)

gives 1.0.