diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 52f8093c10867275018ac8c6e474b3fbd777fe89..03de9a0eda550753eda99a0dcff64d38dd1f9b37 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -246,7 +246,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.example_weights_.append(self.example_weights)
if self.random_start:
first_voter_index = self.random_state.choice(
- self.get_possible(y_kernel_matrix, y))
+ np.where(np.sum(y_kernel_matrix, axis=0)>0)[0])
else:
first_voter_index, _ = self._find_best_weighted_margin(
y_kernel_matrix)
@@ -374,8 +374,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
if self.c_bound_sol:
return np.sum(hypothese) > 0
else:
- # ones_matrix = np.zeros(y.shape)
- # ones_matrix[hypothese.reshape(y.shape) < 0] = 1
print(np.average(hypothese.reshape(y.shape), weights=self.example_weights))
quit()
weighted_margin = np.average(hypothese.reshape(y.shape), weights=self.example_weights)#ondes matrix, axis=0
@@ -392,10 +390,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
def _find_new_voter(self, y_kernel_matrix, y):
"""Here, we solve the two_voters_mincq_problem for each potential new voter,
and select the one that has the smallest minimum"""
- c_bounds = []
- possible_sols = []
- indices = []
- causes = []
m = y_kernel_matrix.shape[0]
weighted_previous_sum = np.multiply(y,
self.previous_vote.reshape((m, 1)))
@@ -432,7 +426,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
return sols[best_hyp_index], best_hyp_index
-
def make_masked_c_bounds(self, sols, bad_margins):
c_bounds = self.compute_c_bounds(sols)
trans_c_bounds = self.compute_c_bounds(sols + 1)
@@ -519,135 +512,3 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
interpretString += "\n\n The bound was not respected"
return interpretString
-
-
-# def _solve_one_weight_min_c(self, next_column, y, margin_old, m):
- # """Here we solve the min C-bound problem for two voters using one weight only and return the best weight
- # No precalc because longer ; see the "derivee" latex document for more precision"""
- # # zero_diag = np.ones((m, m)) - np.identity(m)
- # weighted_previous_sum = np.multiply(y,
- # self.previous_vote.reshape(
- # (m, 1)))
- # if self.c_bound_sol:
- # weighted_next_column = next_column.reshape((m, 1))
- # else:
- # weighted_next_column = np.multiply(next_column.reshape((m, 1)),
- # self.example_weights.reshape((m, 1)))
- #
- # self.B1 = np.sum(2 * weighted_next_column * weighted_previous_sum)
- # self.B0 = np.sum(weighted_previous_sum ** 2)
- # self.B2 = m
- #
- # # M2 = np.sum(np.multiply(
- # # np.matmul(weighted_next_column, np.transpose(weighted_next_column)),
- # # zero_diag))
- # # plif = np.sum(np.multiply(weighted_next_column, y))**2 - m
- # # print(M2, plif, "plouf")
- # # M1 = np.sum(np.multiply(np.matmul(weighted_previous_sum,
- # # np.transpose(weighted_next_column)) +
- # # np.matmul(weighted_next_column,
- # # np.transpose(weighted_previous_sum))
- # # , zero_diag))
- # # M0 = np.sum(np.multiply(np.matmul(weighted_previous_sum,
- # # np.transpose(weighted_previous_sum)),
- # # zero_diag))
- # # self.A2 = self.B2 + M2
- # # self.A1 = self.B1 + M1
- # # self.A0 = self.B0 + M0
- #
- # # C2 = (M1 * self.B2 - M2 * self.B1)
- # # C1 = 2 * (M0 * self.B2 - M2 * self.B0)
- # # C0 = M0 * self.B1 - M1 * self.B0
- #
- # margin_new = np.sum(weighted_next_column)
- # # margin_old = np.sum(weighted_previous_sum)
- #
- # self.A2 = margin_new**2
- # self.A1 = 2*margin_new*margin_old
- # self.A0 = margin_old**2
- #
- # C2 = (self.A1 * self.B2 - self.A2 * self.B1)
- # C1 = 2 * (self.A0 * self.B2 - self.A2 * self.B0)
- # C0 = self.A0 * self.B1 - self.A1 * self.B0
- #
- #
- # det = C1*C1-4*C2*C0
- #
- # if C2 == 0:
- # if C1 == 0:
- # return ['break', "the derivate was constant"]
- # else:
- # sols = np.array([float(C0) / C1])
- # elif det < 0:
- # return ['break', "no real roots"]
- # elif det == 0 :
- # sols = np.array([-C1/(2*C2)])
- # else:
- # # print('yes')
- # sols = np.array([(-C1 + math.sqrt(C1 * C1 - 4 * C2 * C0)) / (2 * C2)])
- #
- # is_acceptable, sol = self._analyze_solutions_one_weight(sols)
- # if is_acceptable:
- # return np.array([sol])
- # else:
- # return ["break", sol]
- #
- # def _analyze_solutions_one_weight(self, sols):
- # """"We just check that the solution found by np.roots is acceptable under our constraints
- # (real, a minimum and over 0)"""
- # if sols.shape[0] == 2:
- # best_sol = self._best_sol(sols)
- # if best_sol > 0:
- # if best_sol == sols[0]:
- # print("moins")
- # return True, best_sol
- # else:
- # return False, "sol < 0"
- # elif sols.shape[0] == 1:
- # if self._cbound(sols[0]) < self._cbound(sols[0] + 1) and sols[0] > 0:
- # return True, sols[0]
- # else:
- # return False, "sol was max"
- # else:
- # return False, "no sol"
-
- # elif sols.shape[0] == 2 and sols[0] > 0 and sols[1] > 1:
- # best_sol = self._best_sol(sols)
- # elif np.greater(sols, np.zeros(2)).any():
- # return self._analyze_solutions_one_weight(np.array([np.max(sols)]))
- # else:
- # return False, "no solution were found"
- #
- # if isinstance(best_sol, complex):
- # return False, "the sol was complex"
- # else:
- # return True, best_sol
-
- #
- # else:
- # for hypothese_index, hypothese in enumerate(
- # weighted_hypothesis.transpose()):
- # if (
- # hypothese_index not in self.chosen_columns_
- # or self.twice_the_same) \
- # and set(self.chosen_columns_) != {hypothese_index} \
- # and self._is_not_too_wrong(hypothese, y):
- # w = self._solve_one_weight_min_c(hypothese, y, margin_old, m)
- # if w[0] != "break":
- # c_bounds.append(self._cbound(w[0]))
- # possible_sols.append(w)
- # indices.append(hypothese_index)
- # else:
- # causes.append(w[1])
- # if not causes:
- # causes = ["no feature was better than random and acceptable"]
- # if c_bounds:
- # min_c_bound_index = ma.argmin(c_bounds)
- # self.c_bounds.append(c_bounds[min_c_bound_index])
- # selected_sol = possible_sols[min_c_bound_index]
- # self.margins.append(self.margin(selected_sol))
- # self.disagreements.append(self.disagreement(selected_sol))
- # selected_voter_index = indices[min_c_bound_index]
- # return selected_sol, selected_voter_index #selected_sol/(1+selected_sol)
- # else:
- # return "break", " and ".join(set(causes))
\ No newline at end of file