From 925176b50390c5eba974d3609abc203527ae8ba6 Mon Sep 17 00:00:00 2001
From: Vasil Zlatanov
 <vasko@e4-pattern-vm.europe-west4-a.c.electric-orbit-223819.internal>
Date: Wed, 5 Dec 2018 18:23:40 +0000
Subject: Move to lib folder and fix kmeans

---
 kmean.py | 62 --------------------------------------------------------------
 1 file changed, 62 deletions(-)
 delete mode 100644 kmean.py

(limited to 'kmean.py')

diff --git a/kmean.py b/kmean.py
deleted file mode 100644
index 4e9d03e..0000000
--- a/kmean.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-import sys
-import random
-import os
-import json
-import scipy.io
-from random import randint
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.neighbors import DistanceMetric
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import accuracy_score
-import argparse
-import numpy as np
-from numpy import genfromtxt
-from numpy import linalg as LA
-from timeit import default_timer as timer
-from scipy.spatial.distance import cdist
-from rerank import re_ranking
-
-def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId):
-    for i in range(gallery_idx.size):
-        cam = camId[gallery_idx[i]] - 1
-        gallery[cam].append(feature_vectors[gallery_idx[i]])
-        gallerylab[cam].append(labels[gallery_idx[i]])
-            
-    
-    train = np.array(gallery)
-    tlabel = np.array(gallerylab)
-
-    for i in range(2):
-        km_train_data[i] =  KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train[i])
-        km_labels[i] = np.zeros(int(np.max(labels))) # clusters size
-        km_idx[i] = km_train_data[i].labels_
-
-        for j in range(np.max(labels)):
-            class_vote = np.zeros(np.max(labels))
-            for q in range(km_idx[i].size):
-                if km_idx[i][q]==j:
-                    class_vote[int(tlabel[i][q])-1] += 1
-            km_labels[i][j] = np.argmax(class_vote) + 1
-        
-    #MERGE CLUSTERS
-    cl = []
-    cllab = []
-    clustercam[0] = np.ones(km_labels_1.size)
-    clustercam[1] = np.add(np.ones(km_labels_2.size), 1)
-    for j in range(2):
-        for j in range(km_labels_1.size):
-            cl.append(km_train_data[i].cluster_centers_[j])
-            cllab.append(km_labels[i][j])
-
-    train_data = np.array(cl)
-    train_label = np.array(cllab)
-    train_cam = np.concatenate(clustercam, axis=1)
-    return train_data, train_label, train_cam
-        
-- 
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