Minor changes

1 files changed, 3 insertions, 4 deletions

diff --git a/evaluate.py b/evaluate.py
index 208f517..642116f 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -18,7 +18,7 @@ from sklearn.neighbors import NearestNeighbors
 from sklearn.neighbors import DistanceMetric
 from sklearn.cluster import KMeans
 from sklearn.decomposition import PCA
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import confusion_matrix
@@ -42,7 +42,7 @@ parser.add_argument("-k", "--kmean_alt", help="Perform clustering with generaliz
 parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
 parser.add_argument("-e", "--euclidean", help="Use standard euclidean distance", action='store_true', default=0)
 parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
-parser.add_argument("-p", "--reranka", help="Parameter k1 for Rerank -p '$k1val' -ARGUMENT REQUIRED, default=9-", type=int, default = 9)
+parser.add_argument("-p", "--reranka", help="Parameter k1 for rerank -p '$k1val' -ARGUMENT REQUIRED, default=9-", type=int, default = 9)
 parser.add_argument("-q", "--rerankb", help="Parameter k2 for rerank -q '$k2val' -ARGUMENT REQUIRED, default=3-", type=int, default = 3)
 parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances(lambda) -l '$lambdaval' -ARGUMENT REQUIRED, default=0.3-", type=float, default = 0.3)
 parser.add_argument("-n", "--neighbors", help="Use customized ranklist size -n 'size' -ARGUMENT REQUIRED, default=1-", type=int, default = 1)
@@ -84,11 +84,10 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                                MemorySave = False, Minibatch = 2000)
     else:
         if args.mahalanobis:
-            # metric = 'jaccard' is also valid
             cov_inv = np.linalg.inv(np.cov(train_model.T))
             distances = np.zeros((probe_data.shape[0], gallery_data.shape[0]))
             for i in range(int(probe_data.shape[0]/10)):
-                print("Comupting from", i*10, "to", (i+1)*10-1)
+                debug("Comupting from", i*10, "to", (i+1)*10-1)
                 distances[i*10:(i+1)*10-1] = cdist(probe_data[i*10:(i+1)*10-1], gallery_data, 'mahalanobis', VI=cov_inv)
         else:
             distances = cdist(probe_data, gallery_data, 'euclidean')