def __init__(self, num_fourier_des=10):
ml_alg_base.__init__(self)
# self.reader = DatasetReader()
self.num_fourier_des = num_fourier_des
"""
The following classifier configurations has been selected by the grid search
These were the results of running the grid search for 5 times
{'kernel': 'rbf', 'C': 100, 'degree': 2}
0.955
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.98
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.96
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.98
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.965
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.955
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.97
"""
self.learning_model = svm.SVC(C=10, kernel='linear')
def __init__(self, num_fourier_des=10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
"""
The following classifier configurations has been selected by the grid search
These were the results of running the grid search for 5 times
{'penalty': 'l2', 'C': 10}
0.945
{'penalty': 'l1', 'C': 50}
0.965
{'penalty': 'l2', 'C': 50}
0.955
{'penalty': 'l2', 'C': 10}
0.935
{'penalty': 'l2', 'C': 50}
0.965
{'penalty': 'l2', 'C': 50}
0.94
{'penalty': 'l2', 'C': 100}
0.965
{'penalty': 'l2', 'C': 50}
0.97
{'penalty': 'l1', 'C': 10}
0.955
{'penalty': 'l1', 'C': 10}
0.97
"""
self.learning_model = LogisticRegression(multi_class='multinomial',
penalty="l2",
C=10,
solver="lbfgs")
def __init__(self, num_fourier_des = 10):
ml_alg_base.__init__(self)
# self.reader = DatasetReader()
self.num_fourier_des = num_fourier_des
"""
The following classifier configurations has been selected by the grid search
These were the results of running the grid search for 5 times
{'kernel': 'rbf', 'C': 100, 'degree': 2}
0.955
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.98
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.96
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.98
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.965
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.97
{'kernel': 'linear', 'C': 50, 'degree': 2}
0.955
{'kernel': 'linear', 'C': 10, 'degree': 2}
0.97
"""
self.learning_model = svm.SVC(C=10, kernel='linear')
def __init__(self):
'''
Constructor
'''
ml_alg_base.__init__(self)
self.dsr = DatasetReader()
self.learning_model = naive_bayes.GaussianNB()
def __init__(self, num_fourier_des=10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
self.learning_model = GradientBoostingClassifier(n_estimators=100,
learning_rate=1.0,
max_depth=5)
def __init__(self, num_fourier_des=10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
"""
The following classifier configurations has been selected by the grid search
These were the results of running the grid search for 5 times
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10}
0.945
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 25}
0.96
{'n_estimators': 100, 'criterion': 'entropy', 'max_depth': 20}
0.965
{'n_estimators': 80, 'criterion': 'entropy', 'max_depth': 25}
0.95
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10}
0.945
"""
self.learning_model = RandomForestClassifier(max_depth=10, n_estimators=100, criterion="gini")
def __init__(self, num_fourier_des = 10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
"""
The following classifier configurations has been selected by the grid search
These were the results of running the grid search for 5 times
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10}
0.945
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 25}
0.96
{'n_estimators': 100, 'criterion': 'entropy', 'max_depth': 20}
0.965
{'n_estimators': 80, 'criterion': 'entropy', 'max_depth': 25}
0.95
{'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10}
0.945
"""
self.learning_model = RandomForestClassifier(max_depth=10, n_estimators=100, criterion='gini')
def __init__(self, num_fourier_des = 10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
self.learning_model = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,max_depth=5)
def __init__(self, num_fourier_des = 10):
ml_alg_base.__init__(self)
self.num_fourier_des = num_fourier_des
self.learning_model = AdaBoostClassifier(n_estimators=100)
