import numpy as np
from basemodel import BaseModel
from sklearn import svm, cross_validation
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.neighbors import KNeighborsClassifier


class SVM_QUANT(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = svm.SVC()


class DT_Quant(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = DecisionTreeClassifier(random_state=4)


class RandomForest(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = RandomForestClassifier(n_estimators=50)


class Adaboost(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = AdaBoostClassifier(base_estimator=
            RandomForestClassifier(100), n_estimators=100)


class Multinomial(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = KNeighborsClassifier()


class ExtraTrees(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = ExtraTreesClassifier(n_estimators=200)

21510265a27134984ca4dd70be2cc51b15bf4264
<mask token>


class Adaboost(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = AdaBoostClassifier(base_estimator=
            RandomForestClassifier(100), n_estimators=100)


class Multinomial(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = KNeighborsClassifier()


class ExtraTrees(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = ExtraTreesClassifier(n_estimators=200)

<mask token>


class RandomForest(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = RandomForestClassifier(n_estimators=50)


class Adaboost(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = AdaBoostClassifier(base_estimator=
            RandomForestClassifier(100), n_estimators=100)


class Multinomial(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = KNeighborsClassifier()


class ExtraTrees(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = ExtraTreesClassifier(n_estimators=200)

<mask token>


class DT_Quant(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = DecisionTreeClassifier(random_state=4)


class RandomForest(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = RandomForestClassifier(n_estimators=50)


class Adaboost(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = AdaBoostClassifier(base_estimator=
            RandomForestClassifier(100), n_estimators=100)


class Multinomial(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = KNeighborsClassifier()


class ExtraTrees(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = ExtraTreesClassifier(n_estimators=200)

import numpy as np
from basemodel import BaseModel
from sklearn import svm, cross_validation
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.neighbors import KNeighborsClassifier


class SVM_QUANT(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = svm.SVC()


class DT_Quant(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = DecisionTreeClassifier(random_state=4)


class RandomForest(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = RandomForestClassifier(n_estimators=50)


class Adaboost(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = AdaBoostClassifier(base_estimator=
            RandomForestClassifier(100), n_estimators=100)


class Multinomial(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = KNeighborsClassifier()


class ExtraTrees(BaseModel):

    def __init__(self, hyperParams=None):
        self.model = ExtraTreesClassifier(n_estimators=200)
