title: How to set the min and max of the original array in sklearn.preprocessing.MinMaxScaler
body: <p>I am looking at </p>
  
  <pre><code>class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True
  </code></pre>
  
  <p>which has a description as follows:
  The standardization is given by:</p>
  
  <pre><code>X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
  X_scaled = X_std * (max - min) + min
  </code></pre>
  
  <p>As shown above, it assumes that the minimum of the original array is X.min(axis=0). Is there a way I can specify a different minimum w.r.t which I want the data to be scaled?</p>
  
  <p>For ex: I want the original range to be [0-255] and the final range to be [0-1]. Now if the original array does not contain the value 0, the least value will be taken as minimum which I don't want to consider.</p>