def __init__(self, examples=None, attrs=None, attrnames=None, target=-<NUM_LIT:1>,<EOL>inputs=None, values=None, distance=mean_boolean_error,<EOL>name='<STR_LIT>', source='<STR_LIT>', exclude=()):
update(self, name=name, source=source, values=values, distance=distance)<EOL>if isinstance(examples, str):<EOL><INDENT>self.examples = parse_csv(examples)<EOL><DEDENT>elif examples is None:<EOL><INDENT>self.examples = parse_csv(DataFile(name+'<STR_LIT>').read())<EOL><DEDENT>else:<EOL><INDENT>self.examples = examples<EOL><DEDENT>if not attrs and self.examples:<EOL><INDENT>attrs = list(range(len(self.examples[<NUM_LIT:0>])))<EOL><DEDENT>self.attrs = attrs<EOL>if isinstance(attrnames, str):<EOL><INDENT>self.attrnames = attrnames.split()<EOL><DEDENT>else:<EOL><INDENT>self.attrnames = attrnames or attrs<EOL><DEDENT>self.setproblem(target, inputs=inputs, exclude=exclude)<EOL>
Accepts any of DataSet's fields.  Examples can also be a
        string or file from which to parse examples using parse_csv.
        Optional parameter: exclude, as documented in .setproblem().
        >>> DataSet(examples='1, 2, 3')
        <DataSet(): 1 examples, 3 attributes>