From 37a8050019aebcb5187fd3409af6d29e96a7d75b Mon Sep 17 00:00:00 2001 From: Valentina Zantedeschi Date: Fri, 10 Mar 2017 11:42:19 +0100 Subject: [PATCH] correct gamma tuning --- src/l3svms.py | 4 ++-- src/projection.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/l3svms.py b/src/l3svms.py index 0d068de..4bc861f 100644 --- a/src/l3svms.py +++ b/src/l3svms.py @@ -69,8 +69,8 @@ def learning(train_x,train_y,test_x,test_y,printf=print,CLUS=1,PCA_BOOL=False,LI t5 = time.time() printf("training time:",t5-t4,"s") - te_x = parallelized_projection(-1,test_x,landmarks,clusters=test_clusters,unit_vectors=u,linear=LIN) - # te_x = project(test_x,landmarks,clusters=test_clusters,unit_vectors=u,linear=LIN) + te_x = parallelized_projection(-1,test_x,landmarks,clusters=test_clusters,unit_vectors=u,linear=LIN,gamma=best_G) + # te_x = project(test_x,landmarks,clusters=test_clusters,unit_vectors=u,linear=LIN,gamma=best_G) p_label,p_acc,p_val = predict(test_y, te_x, model) t6 = time.time() diff --git a/src/projection.py b/src/projection.py index bc10e03..72a395a 100644 --- a/src/projection.py +++ b/src/projection.py @@ -10,7 +10,7 @@ from src.utils import array_to_dict -def project(x,landmarks,clusters=None,unit_vectors=None,linear=True): +def project(x,landmarks,clusters=None,unit_vectors=None,linear=True,gamma=1): # project on landmark space if unit_vectors is not None: @@ -22,7 +22,7 @@ def project(x,landmarks,clusters=None,unit_vectors=None,linear=True): if linear: projection = x.dot(landmarks.transpose()) else: - projection = pairwise.rbf_kernel(x,landmarks) + projection = pairwise.rbf_kernel(x,landmarks,gamma=gamma) if clusters is not None: assert len(clusters) == x.shape[0] @@ -42,7 +42,7 @@ def project_with_id(indexes,x,landmarks,clusters=None,unit_vectors=None,linear=T if linear: projection = a.dot(landmarks.transpose()) else: - projection = pairwise.rbf_kernel(a,landmarks) + projection = pairwise.rbf_kernel(a,landmarks,gamma=gamma) if clusters is not None: return array_to_dict(projection,clusters=clusters[indexes],land=landmarks.shape[0])