def predict(self, X):
"""
Make a prediction for a set of examples given as the rows of the matrix X.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
"""
Util.abstract()
def predict(self, X):
"""
Make a prediction for a set of examples given as the rows of the matrix X.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
"""
Util.abstract()
def learnModel(self, X, y):
"""
Learn a model for a set of examples given as the rows of the matrix X,
with corresponding labels given in the elements of 1D array y.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
:param y: A vector of labels
:type y: :class:`ndarray`
"""
Util.abstract()
def learnModel(self, X, y):
"""
Learn a model for a set of examples given as the rows of the matrix X,
with corresponding labels given in the elements of 1D array y.
:param X: A matrix with examples as rows
:type X: :class:`ndarray`
:param y: A vector of labels
:type y: :class:`ndarray`
"""
Util.abstract()
def complexity(self):
"""
Return a complexity measure of the current model.
"""
Util.abstract()
def __init__(self):
'''
Constructor
'''
Util.abstract()
def process(self, X):
Util.abstract()
def learn(self, X):
Util.abstract()
def __init__(self):
'''
Constructor
'''
Util.abstract()
def process(self, X):
Util.abstract()
def learn(self, X):
Util.abstract()
def complexity(self):
"""
Return a complexity measure of the current model.
"""
Util.abstract()
def learnModel(self, X, y):
Util.abstract()
def predict(self, X):
Util.abstract()
