umbmid/ai/preprocessing.py
def normalize_samples(data):
    """Normalizes each sample in data to have unity maximum

    Parameters
    ----------
    data : array_like
        3D array of the features for each sample (assumes 2D features)

    Returns
    -------
    normalized_data : array_like
        Array of the features for each sample, normalized so that the
        max value is unity for each sample
    """

    # Assert that data must be 3D
    assert len(np.shape(data)) == 3, 'Error: data must have 3 dim'

    normalized_data = np.ones_like(data)  # Init array to return

    # For each sample
    for sample_idx in range(np.size(data, axis=0)):

        # Normalize to have max of unity
        normalized_data[sample_idx, :, :] = (data[sample_idx, :, :] /
                                             np.max(data[sample_idx, :, :]))

    return normalized_data
Normalizes each sample in data to have unity maximum

    Parameters
    ----------
    data : array_like
        3D array of the features for each sample (assumes 2D features)

    Returns
    -------
    normalized_data : array_like
        Array of the features for each sample, normalized so that the
        max value is unity for each sample
    
Normalizes each sample in data to have unity maximum
Normalizes each sample in data to have unity maximum
def normalize_samples(data):
    assert len(np.shape(data)) == 3, 'Error: data must have 3 dim'
    normalized_data = np.ones_like(data)  
    for sample_idx in range(np.size(data, axis=0)):
        normalized_data[sample_idx, :, :] = (data[sample_idx, :, :] /
                                             np.max(data[sample_idx, :, :]))
    return normalized_data
def normalize_samples ( data ) : assert len ( np . shape ( data ) ) == 3 , 'Error: data must have 3 dim' normalized_data = np . ones_like ( data ) for sample_idx in range ( np . size ( data , axis = 0 ) ) : normalized_data [ sample_idx , : , : ] = ( data [ sample_idx , : , : ] / np . max ( data [ sample_idx , : , : ] ) ) return normalized_data
Normalizes each sample in data to have unity maximum
Normalizes each sample in data to have unity maximum
"""Normalizes each sample in data to have unity maximum

    Parameters
    ----------
    data : array_like
        3D array of the features for each sample (assumes 2D features)

    Returns
    -------
    normalized_data : array_like
        Array of the features for each sample, normalized so that the
        max value is unity for each sample
    """ # Assert that data must be 3D
 # Init array to return
 # For each sample
 # Normalize to have max of unity

Array of the features for each sample, normalized so that the
max value is unity for each sample
Array of the features for each sample normalized so that the max value is unity for each sample
array_like

data
3D array of the features for each sample (assumes 2D features)
3D array of the features for each sample ( assumes 2D features )