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if scale_cols:
    # Scale year and week no but within (0,1)
    new_data[scale_cols] = MinMaxScaler(feature_range=(0, 1)).fit(train_scale[scale_cols]).transform(
        new_data[scale_cols])

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unnormalized_train_data = extract_data(path_to_train_file)
normalized_train_data, train_scale = preproc_data(unnormalized_train_data, norm_cols, scale_cols)
// Create and train model
unnormalized_test_data = extract_data(path_to_test_file)
normalized_test_data, _ = preproc_data(unnormalized_test_data, norm_cols, scale_cols, train_scale)

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def plot_confusion_matrix(cm, class_names=class_names):
    """
    Returns a matplotlib figure containing the plotted confusion matrix.

    Args:
    cm (array, shape = [n, n]): a confusion matrix of integer classes
    class_names (array, shape = [n]): String names of the integer classes
    """
    figure = plt.figure(figsize=(8, 8))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

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# Load the TensorBoard notebook extension
%load_ext tensorboard

# Clear out any prior log data. (optional)
!rm -rf logs

import datetime
import io
import itertools
import numpy as np

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export const DicomDictionary = {
  '00000000': 'Command Group Length',
  '00000001': 'Command Length to End',
  '00000002': 'Affected SOP Class UID',
  '00000003': 'Requested SOP Class UID',
  '00000010': 'Command Recognition Code',
  '00000100': 'Command Field',
  '00000110': 'Message ID',
  '00000120': 'Message ID Being Responded To',
  '00000200': 'Initiator',