def get_algorithms(algorithm):
    """
    Gets two classifiers for each type of algorithm, and returns them.  First for predicting, second for cv error.
    type - one of util_functions.AlgorithmTypes
    """
    if algorithm == util_functions.AlgorithmTypes.classification:
        clf = sklearn.ensemble.GradientBoostingClassifier(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
        clf2=sklearn.ensemble.GradientBoostingClassifier(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
    else:
        clf = sklearn.ensemble.GradientBoostingRegressor(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
        clf2=sklearn.ensemble.GradientBoostingRegressor(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
    return clf, clf2
def get_algorithms(algorithm):
    """
    Gets two classifiers for each type of algorithm, and returns them.  First for predicting, second for cv error.
    type - one of util_functions.AlgorithmTypes
    """
    if algorithm == util_functions.AlgorithmTypes.classification:
        clf = sklearn.ensemble.GradientBoostingClassifier(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
        clf2=sklearn.ensemble.GradientBoostingClassifier(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
    else:
        clf = sklearn.ensemble.GradientBoostingRegressor(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
        clf2=sklearn.ensemble.GradientBoostingRegressor(n_estimators=100, learn_rate=.05,
            max_depth=4, random_state=1,min_samples_leaf=3)
    return clf, clf2
def get_algorithms ( algorithm ) : if algorithm == util_functions . AlgorithmTypes . classification : clf = sklearn . ensemble . GradientBoostingClassifier ( n_estimators = 100 , learn_rate = .05 , max_depth = 4 , random_state = 1 , min_samples_leaf = 3 ) clf2 = sklearn . ensemble . GradientBoostingClassifier ( n_estimators = 100 , learn_rate = .05 , max_depth = 4 , random_state = 1 , min_samples_leaf = 3 ) else : clf = sklearn . ensemble . GradientBoostingRegressor ( n_estimators = 100 , learn_rate = .05 , max_depth = 4 , random_state = 1 , min_samples_leaf = 3 ) clf2 = sklearn . ensemble . GradientBoostingRegressor ( n_estimators = 100 , learn_rate = .05 , max_depth = 4 , random_state = 1 , min_samples_leaf = 3 ) return clf , clf2
Gets two classifiers for each type of algorithm, and returns them.  First for predicting, second for cv error.
    type - one of util_functions.AlgorithmTypes
Gets two classifiers for each type of algorithm and returns them . First for predicting second for cv error . type - one of util_functions . AlgorithmTypes