如何从训练数据中删除“0”值 Python

如何解决如何从训练数据中删除“0”值 Python

我正在使用 Python 对时尚 mnist 数据集进行分类。我想要构建的分类器是朴素贝叶斯，在运行我构建的代码时，我下载的数据似乎具有零值，并且在某些数学计算中，变量除以这些零值，我不希望这样即将发生。所以我需要一种方法来从我想要分类的数据中“删除”这些零值。数据的示例：

[[0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]
 ...
 [0.         0.         0.         ... 0.27450982 0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]]

该示例仅包含 10 行 60000 条数据。

我试图用这个删除零：

Xtrain,ytrain),(Xtest,ytest) = fashion_mnist.load_data() # loading data

Xtrain = Xtrain.astype('float32') # I want from this to delete zero values 
Xtest = Xtest.astype('float32')   # and from that
Xtrain=Xtrain/255.0
Xtest=Xtest/255.0


    #Reshaping data 
Xtest=Xtest.reshape(Xtest.shape[0],Xtest.shape[1] * Xtest.shape[2])
Xtrain =Xtrain.reshape(Xtrain.shape[0],Xtrain.shape[1] * Xtrain.shape[2])

naive_bayes = NaiveBayes()
# So before i send this datas to fit function in order to run the classification i am trying 
# with not working way to delete thouse values 
Xtrain_non_zero = []
for i in range(len(Xtrain)):
    for j in range(len(Xtrain)):
        if Xtrain[i][j]!=0:
            Xtrain_non_zero.append(Xtrain[i][j])
print(Xtrain_non_zero)