CODEWITHSUNDEEP
numpy
user9703439
user9703439

Reputation:

Array functions in numpy

I have a numpy array:

a = np.arange(500).reshape(100,5)

I can normalize it using the function below:

def normalizer(X, mini, maxi):
    X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
    X_scaled = X_std * (maxi - mini) + mini
    return X_scaled

normalized = normalizer(X, -1, +1)

Now, I want to denormalize it, I mean get original array. What function should I write?

 def denormalizer():

denormalized = denormalizer()

Upvotes: 1

Views: 63

Answers (1)

York's
York's

Reputation: 164

I would agree in my answer with @Matt. One thing you can do is save all parameters of the normalisation (X_min, X_max, mini, maxi) and reverse all mathematical operations, for example:

 def normalizer(X, mini, maxi):                         
     X_min = X.min(axis=0)                                  
     X_max = X.max(axis=0)               
     X_std = (X - X_min) / (X_max - X_min)
     X_scaled = X_std * (maxi - mini) + mini                
     return X_scaled, {'x_min': X_min, 
                       'x_max': X_max, 
                       'min': mini, 
                       'max': maxi}


 def denormalizer(X_scaled, params):
     X_min = params['x_min']
     X_max = params['x_max']
     mini = params['min']
     maxi = params['max']
     X_std = (X_scaled - mini) / (maxi - mini)           
     X = X_std * (X_max - X_min) + X_min 
     return X


a = np.arange(500).reshape(100,5)
a_scaled, params = normalizer(a, -1, 1)
a_restored = denormalizer(a_scaled, params)
print(a - a_restored)

Upvotes: 2

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