original_docstring: 
      Loads training or test data without appyling any processing.
  
      If the classes kwarg is not none assumed to be loading labelled train
      data and returns two np objs:
          * data - list of image matrices
          * labels - vector of labels
  
      if classes kwarg is none, data will be loaded as test data and just return
          * data - list of image matrices
      
docstring: Loads training or test data without appyling any processing.
  If the classes kwarg is not none assumed to be loading labelled train
  data and returns two np objs:
  data - list of image matrices
  labels - vector of labels
  
  if classes kwarg is none, data will be loaded as test data and just return
  data - list of image matrices
docstring_tokens: Loads training or test data without appyling any processing . If the classes kwarg is not none assumed to be loading labelled train data and returns two np objs : data - list of image matrices labels - vector of labels if classes kwarg is none data will be loaded as test data and just return data - list of image matrices
code: def load_rawdata(image_fname_dict, classes=None, verbose=False):
      """
      Loads training or test data without appyling any processing.
  
      If the classes kwarg is not none assumed to be loading labelled train
      data and returns two np objs:
          * data - list of image matrices
          * labels - vector of labels
  
      if classes kwarg is none, data will be loaded as test data and just return
          * data - list of image matrices
      """
  
      # initialise lists
      data = []
  
      # e.g. labelled training data
      if classes:
          labels = []
  
          for class_index, class_name in enumerate(classes):
              if verbose:
                  print("class: {0} of 120: {1}".format(class_index, class_name))
  
              fpaths = image_fname_dict['train'][class_name]
  
              # Load the data and add to list
              data += [skimage.io.imread(fpath) for fpath in fpaths]
  
              # generate the class labels and add them to the list
              labels += len(fpaths) * [class_name]
  
          return data, np.array(labels)
  
      # e.g. test data
      else:
          data = [skimage.io.imread(fpath) for fpath in image_fname_dict['test']]
          names = [os.path.basename(fpath) for fpath in image_fname_dict['test']]
          return data, names
code_tokens: def load_rawdata ( image_fname_dict , classes = None , verbose = False ) : data = [ ] if classes : labels = [ ] for class_index , class_name in enumerate ( classes ) : if verbose : print ( "class: {0} of 120: {1}" . format ( class_index , class_name ) ) fpaths = image_fname_dict [ 'train' ] [ class_name ] data += [ skimage . io . imread ( fpath ) for fpath in fpaths ] labels += len ( fpaths ) * [ class_name ] return data , np . array ( labels ) else : data = [ skimage . io . imread ( fpath ) for fpath in image_fname_dict [ 'test' ] ] names = [ os . path . basename ( fpath ) for fpath in image_fname_dict [ 'test' ] ] return data , names
short_docstring: Loads training or test data without appyling any processing.
short_docstring_tokens: Loads training or test data without appyling any processing .
comment: """
      Loads training or test data without appyling any processing.
  
      If the classes kwarg is not none assumed to be loading labelled train
      data and returns two np objs:
          * data - list of image matrices
          * labels - vector of labels
  
      if classes kwarg is none, data will be loaded as test data and just return
          * data - list of image matrices
      """$SEP$# initialise lists$SEP$# e.g. labelled training data$SEP$# Load the data and add to list$SEP$# generate the class labels and add them to the list$SEP$# e.g. test data