parameters: (self, features, label_names, valid_classes, parameters)
docstring: Create new Dataset from the given features and label names and parameters.  The parameters
          can be any tuple that is application specific.  For example, in the case of audio one might
          want to store the sample rate, window size and other audio specific parameters associated
          with the featurized data.  The valid_classes list all possible labels and label_names might only
          mention a subset of these.
docstring_summary: Create new Dataset from the given features and label names and parameters.  The parameters
          can be any tuple that is application specific.  For example, in the case of audio one might
          want to store the sample rate, window size and other audio specific parameters associated
          with the featurized data.  The valid_classes list all possible labels and label_names might only
          mention a subset of these.
docstring_tokens: Create new Dataset from the given features and label names and parameters . The parameters can be any tuple that is application specific . For example in the case of audio one might want to store the sample rate window size and other audio specific parameters associated with the featurized data . The valid_classes list all possible labels and label_names might only mention a subset of these .
function: def __init__(self, features, label_names, valid_classes, parameters):
          """
          Create new Dataset from the given features and label names and parameters.  The parameters
          can be any tuple that is application specific.  For example, in the case of audio one might
          want to store the sample rate, window size and other audio specific parameters associated
          with the featurized data.  The valid_classes list all possible labels and label_names might only
          mention a subset of these.
          """
  
          # Keep rows with finite entries (there are lots of NaNs initially)
          good_rows = np.isfinite(features).all(axis=1)
          features = features[good_rows]
          label_names = label_names[good_rows]
  
          self.file_name = None
          self.features = features
          self.labels = None  # computable labels ([0,1] for binary classification, 1-hot vector for multiclass)
          self.raw_labels = None  # numeric labels ([0, num_classes])
          self.label_names = label_names  # textual labels
          self.categories = None  # map from textual -> numeric label
          self.category_names = None  # map from numeric -> textual label
          self.class_weights = None
          self.class_distribution = None
          self.parameters = parameters
          self.valid_classes = valid_classes
  
          self._init()
function_tokens: def __init__ ( self , features , label_names , valid_classes , parameters ) : # Keep rows with finite entries (there are lots of NaNs initially) good_rows = np . isfinite ( features ) . all ( axis = 1 ) features = features [ good_rows ] label_names = label_names [ good_rows ] self . file_name = None self . features = features self . labels = None # computable labels ([0,1] for binary classification, 1-hot vector for multiclass) self . raw_labels = None # numeric labels ([0, num_classes]) self . label_names = label_names # textual labels self . categories = None # map from textual -> numeric label self . category_names = None # map from numeric -> textual label self . class_weights = None self . class_distribution = None self . parameters = parameters self . valid_classes = valid_classes self . _init ( )