MaxAbsScaler
To bring values into range [-1, 1] we have to use the next formula:
Here we have the following values:
- x_scaled - normalized feature element,
- x - unnormalized feature element,
- max(x) -- maximum feature element.
There is a function in the sklearn library that normalizes data according to the formula given above: MaxAbsScaler(). In order to work with this function, it must first be imported in such a way:
1from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
12345678910from sklearn.preprocessing import MaxAbsScaler data = [[10, 5, -6],[11, -9, 4],[-10, 0, 1]] # Normalizer initialization scaler = MaxAbsScaler() # Dataset transfer and transformation scaler.fit(data) scaled_data = scaler.transform(data) print('Data before normalization', data) print('Data after normalization', scaled_data)
If you run this code you will get two different arrays: before and after normalization. And this function really works, because you can make sure that data after using MaxAbsScaler() function really lie within an interval [-1, 1]. Look below.v
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You have a numpy array. Please, normalize this array into range [-1, 1].
Lösung
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To bring values into range [-1, 1] we have to use the next formula:
Here we have the following values:
- x_scaled - normalized feature element,
- x - unnormalized feature element,
- max(x) -- maximum feature element.
There is a function in the sklearn library that normalizes data according to the formula given above: MaxAbsScaler(). In order to work with this function, it must first be imported in such a way:
1from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
12345678910from sklearn.preprocessing import MaxAbsScaler data = [[10, 5, -6],[11, -9, 4],[-10, 0, 1]] # Normalizer initialization scaler = MaxAbsScaler() # Dataset transfer and transformation scaler.fit(data) scaled_data = scaler.transform(data) print('Data before normalization', data) print('Data after normalization', scaled_data)
If you run this code you will get two different arrays: before and after normalization. And this function really works, because you can make sure that data after using MaxAbsScaler() function really lie within an interval [-1, 1]. Look below.v
It's time to practice!
Swipe to start coding
You have a numpy array. Please, normalize this array into range [-1, 1].
Lösung
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Awesome!
Completion rate improved to 12.5single
