# What's the formula behind Normalization function?

sklearn.preprocessing.Normalizer(norm=l2)

I need to undone a normalization (Normalizer algorithm) within my dataset and I'd like to know which formula I should use for it. I've try both formulas (below) and didn't work out.

• z-score formula: $$X_{original} = (x_{normalized} \cdot \sigma) + \mu$$
• Min-Max formula: $$X_{original} = [x_{normalized} \cdot (X_{max} - X_{min})] + X_{min}$$

Thanks for your attention

So if $$x = (x_1, \ldots, x_p)$$ is a row of the data matrix, the normalizer changes it to $$(x_1 / \|x\|_2, \ldots, x_p/\|x\|_2)$$, where $$\|x\|_2 := \sqrt{x_1^2 + \cdots + x_p^2}$$.
If using norm='l1', then $$\|x\|_2$$ is replaced by $$\|x\|_1 = |x_1| + \cdots + |x_p|$$; if using norm='inf', then $$\|x\|_2$$ is replaced by $$\|x\|_\infty := \max\{|x_1|, \ldots, |x_p|\}$$.