Kmean fixed

1 files changed, 29 insertions, 11 deletions

diff --git a/evaluate.py b/evaluate.py
index 6561b81..ebfc34e 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -14,6 +14,7 @@ import json
 import scipy.io
 from random import randint
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neighbors import NearestNeighbors
 from sklearn.neighbors import DistanceMetric
 from sklearn.cluster import KMeans
 from sklearn.decomposition import PCA
@@ -41,8 +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 = 20)
-parser.add_argument("-q", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
+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("-l", "--rerankl", help="Coefficient to combine distances", type=int, 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')
@@ -53,6 +54,7 @@ parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes e
 parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and Improved metric", type=int, default=1)
 parser.add_argument("--data", help="Data folder with features data", default='data')
 parser.add_argument("-V", "--validation", help="Validation Mode", action='store_true')
+parser.add_argument("-K", "--kmean_alt", help="Validation Mode", action='store_true')
 
 
 
@@ -98,7 +100,6 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
     nneighbors = np.zeros((ranklist.shape[0],nsize))
     nnshowrank = (np.zeros((ranklist.shape[0],nsize))).astype(object)
 
-
     for i in range(args.multrank):
         if args.multrank!= 1:
             args.neighbors = test_table[i]
@@ -192,17 +193,34 @@ def main():
 
     if (args.normalise):
         debug("Normalising data")
-        train_data = np.divide(train_data,LA.norm(train_data, axis=0))
-        test_data = np.divide(test_data, LA.norm(test_data, axis=0))
+        train_data = np.divide(train_data,LA.norm(train_data,axis=0))
+        test_data = np.divide(test_data, LA.norm(test_data,axis=0))
     if(args.kmean):
         debug("Using Kmeans")
         train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId)
-    for q in range(args.comparison):
-        target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
-        for i in range(args.multrank):
-            accuracy[q][i] = draw_results(test_label, target_pred[i])
-        args.rerank = True
-        args.neighbors = 1
+     
+    if args.kmean_alt:
+        kmeans = KMeans(n_clusters=10, random_state=0).fit(train_data)
+        neigh = NearestNeighbors(n_neighbors=1)
+        neigh.fit(kmeans.cluster_centers_)
+        neighbors = neigh.kneighbors(test_data, return_distance=False)
+        target_pred = np.zeros(test_data.shape[0])
+        for i in range(10):
+            print(train_label[np.where(kmeans.labels_==i)].shape)
+        for i in range(test_data.shape[0]):
+            td = test_data[i].reshape(1,test_data.shape[1])
+            tc = np.array([test_cam[i]])
+            tl = np.array([test_label[i]])
+            target_pred[i] = (test_model(train_data[np.where(kmeans.labels_==neighbors[i])], td, train_label[np.where(kmeans.labels_==neighbors[i])], tl, train_cam[np.where(kmeans.labels_==neighbors[i])], tc, showfiles_train[np.where(kmeans.labels_==neighbors[i])], showfiles_test[i], args))
+         
+        accuracy[0] = draw_results(test_label, target_pred)
+    else:
+        for q in range(args.comparison):
+            target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
+            for i in range(args.multrank):
+                accuracy[q][i] = draw_results(test_label, target_pred[i])
+            args.rerank = True
+            args.neighbors = 1
 
     if(args.multrank != 1):
         plt.plot(test_table[:(args.multrank)], 100*accuracy[0])