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author | nunzip <np.scarh@gmail.com> | 2018-12-10 16:32:37 +0000 |
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committer | nunzip <np.scarh@gmail.com> | 2018-12-10 16:32:37 +0000 |
commit | b9bc3e045e1244183b76682a5f4be2c3e693d517 (patch) | |
tree | 4776c26806bd0e69664911e03de5b046b42478a3 | |
parent | 4a6650f5e231b0d1a62feb87716fbca9f5ef2a80 (diff) | |
download | vz215_np1915-b9bc3e045e1244183b76682a5f4be2c3e693d517.tar.gz vz215_np1915-b9bc3e045e1244183b76682a5f4be2c3e693d517.tar.bz2 vz215_np1915-b9bc3e045e1244183b76682a5f4be2c3e693d517.zip |
Fix standard p-q and fix notation for comparison multrank
-rwxr-xr-x | evaluate.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/evaluate.py b/evaluate.py index 5948acc..7808c2e 100755 --- a/evaluate.py +++ b/evaluate.py @@ -42,8 +42,8 @@ parser.add_argument("-k", "--kmean", help="Perform Kmeans", action='store_true', parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0) parser.add_argument("-e", "--euclidean", help="Standard euclidean", 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 1 for Rerank", type=int, default = 11) -parser.add_argument("-q", "--rerankb", help="Parameter 2 for rerank", type=int, default = 3) +parser.add_argument("-p", "--reranka", help="Parameter 1 for Rerank", type=int, default = 9) +parser.add_argument("-q", "--rerankb", help="Parameter 2 for rerank", type=int, default = 5) parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances", type=float, default = 0.3) parser.add_argument("-n", "--neighbors", help="Number of neighbors", type=int, default = 1) parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true') @@ -249,9 +249,9 @@ def main(): plt.plot(test_table[:(args.multrank)], 100*accuracy[0]) if(args.comparison!=1): plt.plot(test_table[:(args.multrank)], 100*accuracy[1]) - plt.legend(['Baseline kNN', 'Improved metric'], loc='upper left') - plt.xlabel('k rank') - plt.ylabel('Recognition Accuracy (%)') + plt.legend(['Baseline NN', 'NN+Reranking'], loc='upper left') + plt.xlabel('Top k') + plt.ylabel('Identification Accuracy (%)') plt.grid(True) plt.show() |