def fit(self, X, y):
'''X and y are as in sklearn classifier.fit expected arguments
Creates a decision tree
'''
"*** YOUR CODE HERE AS NEEDED***"
self.clf = self.makeTree(X, y, self.attribute_list, self.attrib_dict, self.default_value)
mlUtil.raiseNotDefined()
def fit(self, X, y):
'''X and y are as in sklearn classifier.fit expected arguments
Creates a decision tree
'''
"*** YOUR CODE HERE AS NEEDED***"
self.clf = self.makeTree(X, y, self.attribute_list, self.attrib_dict,
self.default_value)
mlUtil.raiseNotDefined()
def classify(self, x, attributes, default_value):
'''
return the value for the given data
the input will be:
x - an object to classify - [v1, v2, ..., vn]
attributes - the names of all the attributes
'''
"*** YOUR CODE HERE ***"
mlUtil.raiseNotDefined()
def classify(self, x, attributes, default_value):
'''
return the value for the given data
the input will be:
x - an object to classify - [v1, v2, ..., vn]
attributes - the names of all the attributes
'''
"*** YOUR CODE HERE ***"
mlUtil.raiseNotDefined()
def __init__(self, attrib_d=None, attribs=None, default_v=None):
''' initialize classifier
'''
if not attribs:
attribs = []
if attrib_d:
self.attrib_dict = attrib_d
else:
self.attrib_dict = {}
self.attribute_list = attribs
self.default_value = default_v
"*** YOUR CODE HERE AS NEEDED ***"
mlUtil.raiseNotDefined()
def __init__(self, attrib_d=None, attribs=None, default_v=None):
''' initialize classifier
'''
if not attribs:
attribs = []
if attrib_d:
self.attrib_dict = attrib_d
else:
self.attrib_dict = {}
self.attribute_list = attribs
self.default_value = default_v
"*** YOUR CODE HERE AS NEEDED ***"
mlUtil.raiseNotDefined()
def predict(self, X):
''' Return a class label using the decision tree created by the fit method
'''
"*** YOUR CODE HERE AS NEEDED***"
#call recursive classify method on the learned tree for each x in X
mlUtil.raiseNotDefined()
def makeTree(self, dataset, labels, attributes, attrib_dict, defaultValue):
''' Helper recursive function for creating a tree
'''
"*** YOUR CODE HERE ***"
mlUtil.raiseNotDefined()
def predict(self, X):
''' Return a class label using the decision tree created by the fit method
'''
"*** YOUR CODE HERE AS NEEDED***"
#call recursive classify method on the learned tree for each x in X
mlUtil.raiseNotDefined()
def makeTree(self, dataset, labels, attributes, attrib_dict, defaultValue):
''' Helper recursive function for creating a tree
'''
"*** YOUR CODE HERE ***"
mlUtil.raiseNotDefined()