def multilayer_perceptron(params=None):
'''Feedforward artificial neural network, using backpropagation to classify instances
:param params: parameters in textual form to pass to the MultilayerPerceptron Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.functions.MultilayerPerceptron')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def random_tree(params=None):
'''A tree that considers K randomly chosen attributes at each node, and performs no pruning
:param params: parameters in textual form to pass to the RandomTree Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.trees.RandomTree')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def random_forest(params=None):
'''Random Forest learner by Weka
:param params: parameters in textual form to pass to the RandomForest Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.trees.RandomForest')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def k_star(params=None):
'''Instance-Based learner K* by Weka
:param params: parameters in textual form to pass to the KStar Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.lazy.KStar')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def rep_tree(params=None):
'''A REP Tree, which is a fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning
:param params: parameters in textual form to pass to the REPTree Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.trees.REPTree')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def rules_zeror(params=None):
'''Weka's rulesZeroR classifier: predicts the mean (for a numeric class) or the mode (for a nominal class).
:param params: parameters in textual form to pass to the rulesZeroR Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.rules.ZeroR')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def naive_bayes(params=None):
'''Naive Bayes classifier provided by Weka. Naive Bayes is a simple probabilistic classifier based on applying the Bayes' theorem.
:param params: parameters in textual form to pass to the NaiveBayes Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.bayes.NaiveBayes')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def rules_jrip(params=None):
'''The RIPPER rule learner by Weka
:param params: parameters in textual form to pass to the JRip Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.rules.JRip')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def ibk(params=None):
'''K-nearest neighbours classifier by Weka
:param params: parameters in textual form to pass to the IBk Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.lazy.IBk')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def logistic(params=None):
'''Logistic regression by Weka
:param params: parameters in textual form to pass to the Logistic Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.functions.Logistic')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def smo(params=None):
'''A support vector classifier, trained using the Sequential Minimal Optimization (SMO) algorithm
:param params: parameters in textual form to pass to the SMO Weka class
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.functions.SMO')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))
def j48(params=None):
'''Weka decision tree learner J48
:param params: parameters in textual form to pass to the J48 Weka class (e.g. "-C 0.25 -M 2")
:return: a WekaClassifier object
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
model = jp.JClass('weka.classifiers.trees.J48')()
model.setOptions(common.parse_options(params))
return WekaClassifier(common.serialize_weka_object(model))