diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py index b21a6ed80af1c9fad6d9d130a815f721f975076f..04717db38a12361b95c29cb28c78ecaab5c20703 100644 --- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py +++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py @@ -15,8 +15,7 @@ from ... import Metrics class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost): def __init__(self, n_max_iterations=None, estimators_generator=None, - random_state=42, self_complemented=True, twice_the_same=False, old_fashioned=False, - previous_vote_weighted=True, c_bound_choice = True, random_start = True, + random_state=42, self_complemented=True, twice_the_same=False, c_bound_choice = True, random_start = True, two_wieghts_problem=False, divided_ponderation=True, n_stumps_per_attribute=None, use_r=True, plotted_metric=Metrics.zero_one_loss): super(ColumnGenerationClassifierQar, self).__init__() @@ -29,7 +28,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost): self.random_state = random_state self.self_complemented = self_complemented self.twice_the_same = twice_the_same - self.previous_vote_weighted = previous_vote_weighted self.c_bound_choice = c_bound_choice self.random_start = random_start self.two_wieghts_problem = two_wieghts_problem @@ -39,13 +37,12 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost): self.n_stumps = n_stumps_per_attribute self.use_r = use_r self.printed_args_name_list = ["n_max_iterations", "self_complemented", "twice_the_same", - "previous_vote_weighted", "c_bound_choice", "random_start", + "c_bound_choice", "random_start", "two_wieghts_problem", "divided_ponderation", "n_stumps", "use_r"] def set_params(self, **params): self.self_complemented = params["self_complemented"] self.twice_the_same = params["twice_the_same"] - self.previous_vote_weighted = params["previous_vote_weighted"] self.c_bound_choice = params["c_bound_choice"] self.random_start = params["random_start"] self.two_wieghts_problem = params["two_wieghts_problem"] @@ -283,10 +280,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost): No precalc because longer ; see the "derivee" latex document for more precision""" m = next_column.shape[0] zero_diag = np.ones((m, m)) - np.identity(m) - if self.previous_vote_weighted: - weighted_previous_sum = np.multiply(np.multiply(y, self.previous_vote.reshape((m, 1))), self.example_weights.reshape((m,1))) - else: - weighted_previous_sum = np.multiply(y, self.previous_vote.reshape((m, 1))) + weighted_previous_sum = np.multiply(y, self.previous_vote.reshape((m, 1))) weighted_next_column = np.multiply(next_column.reshape((m,1)), self.example_weights.reshape((m,1))) self.B2 = np.sum(weighted_next_column ** 2) @@ -347,10 +341,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost): No precalc because longer""" m = next_column.shape[0] zero_diag = np.ones((m, m)) - np.identity(m) - if self.previous_vote_weighted: - weighted_previous_sum = np.multiply(np.multiply(y, self.previous_vote.reshape((m, 1))), self.example_weights.reshape((m,1))) - else: - weighted_previous_sum = np.multiply(y, self.previous_vote.reshape((m, 1))) + weighted_previous_sum = np.multiply(y, self.previous_vote.reshape((m, 1))) weighted_next_column = np.multiply(next_column.reshape((m,1)), self.example_weights.reshape((m,1))) self.B2 = np.sum((weighted_previous_sum - weighted_next_column) ** 2) diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py index 479cdb41ad2e346d8fe0f453be0eabc988012802..963ca6b35bf473c95f9d61442dd82310ed5c5498 100644 --- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py +++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py @@ -10,8 +10,6 @@ class QarBoostNC(ColumnGenerationClassifierQar, BaseMonoviewClassifier): random_state=random_state, self_complemented=True, twice_the_same=False, - old_fashioned=False, - previous_vote_weighted=False, c_bound_choice=True, random_start=False, two_wieghts_problem=False,