# How to normalize test data according to the training data if the normalization on the training data is performed row wise?

I read in several places about the normalization of features in the machine learning method. But I normalize my training data row-wise as shown in the following code. I showed only two samples of training data. My question is that while performing the normalization on test data, should I choose the minimum and maximum value of each test sample to normalize each test data, or should I uses the minimum and maximum values from the training data? As an explanation in the first row -3 is one feature, -2 is second 0 is third and 3 is the fifth feature. And the second row is the second sample comprising of 5 features from -4 to 2. Similar to all other machine learning algorithms each row corresponds to one sample consisting of 5 features.

data = np.array([[-3,-2,0, 2,3],[-4,-1,0,3,2]])
print(data)

print(data.shape)
for i in range(len(data)):
print("i: ",i)
old_range = np.amax(data[i]) - np.amin(data[i])
new_range = 2
new_min = -1
data_norm = ((data[i] - np.amin(data[i])) / old_range)*new_range +
new_min
print(data_norm)


Result

[-1.         -0.66666667  0.          0.66666667  1.        ]
[-1.         -0.14285714  0.14285714  1.          0.71428571]

• Is there anything unclear in my question? Jun 21, 2019 at 2:59
• This might be helpful: stats.stackexchange.com/questions/175463/… Jun 21, 2019 at 6:19
• I don't understand your question: if you normalize row-wise, it means that each normalized instance depends only on the original instance and nothing else. Therefore each test instance should be normalized the same way, independently from the training set or the other instances in the test set. However if your min/max values are computed across the training set then it's not row-wise normalization and the answer is different. Jun 21, 2019 at 13:35