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- from sklearn.preprocessing import StandardScaler
- def standardize_data(data, training_validation_split=0.2):
- """
- standardize features by removing the mean and scaling to unit variance
- :param data: the data desired to be scaled
- :param training_validation_split: The percentage of data to save for validation
- :return: the standardized features and scalar in the form (training, validation, scalar)
- """
- np.random.shuffle(data)
- split = int(data.shape[0]*(1 - training_validation_split))
- training, validation = data[:split, :], data[split:, :]
- scalar = StandardScaler()
- scalar.fit(training)
- return (scalar.transform(training), scalar.transform(validation), scalar)
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