コード例 #1
0
	def KMeansPredict(self,details,vec):
		# Details: centers , xtoc , distances
		return  Kmeans_l1.nearestcentres(X = [vec], centres = details[0], metric="cosine")
コード例 #2
0
ファイル: RHKMeans.py プロジェクト: sharathvignesh/PingMyFood
mv = len(model.vocab)
level = 1
while threshold < mv:
    # KMeans
    print()
    print()
    print(str(numInter) + " more iterations left ...")
    print()
    t0 = time.time()

    curVecs = list(set(curVecs) - set(Rseeds))

    #L2# kmeans = cluster.KMeans(n_clusters=len(Rseeds), init=np.array(Rseeds), max_iter = 1)
    #kmeans.fit(np.array(curVecs))
    centers, xtoc, distances = Kmeans_l1.kmeans(np.array(curVecs),
                                                np.array(Rseeds),
                                                maxiter=1,
                                                metric='cosine')
    #print(kmeans)
    print(str(time.time() - t0))
    #print("Level "+ str(level) + "\nIntertia  = " + str(kmeans.inertia_))
    print("Level " + str(level) + " Done.")
    kmeans = [centers, xtoc]
    save_obj(kmeans, path + "Level" + str(level))

    print("Mapping Centroids to Labels")
    centroid2Word = dict()
    for c in Rseeds:
        w = words[allseeds.index(tuple(c))]
        centroid2Word[Rseeds.index(c)] = w
    save_obj(centroid2Word, path + "Level" + str(level) + "c2w")
コード例 #3
0
ファイル: RHKMeans.py プロジェクト: suddu16/PingMyFood
threshold = len(Rseeds)
mv = len(model.vocab)
level = 1
while threshold < mv:
    # KMeans
    print()
    print()
    print(str(numInter) + " more iterations left ...")
    print()
    t0 = time.time()

    curVecs = list(set(curVecs) - set(Rseeds))

    #L2# kmeans = cluster.KMeans(n_clusters=len(Rseeds), init=np.array(Rseeds), max_iter = 1)
    #kmeans.fit(np.array(curVecs))
    centers , xtoc , distances = Kmeans_l1.kmeans(np.array(curVecs), np.array(Rseeds), maxiter=1, metric='cosine')
    #print(kmeans)
    print(str(time.time()-t0))
    #print("Level "+ str(level) + "\nIntertia  = " + str(kmeans.inertia_))
    print("Level "+ str(level) + " Done.")
    kmeans = [centers,xtoc]
    save_obj(kmeans,path+"Level"+str(level))

    print("Mapping Centroids to Labels")
    centroid2Word = dict()
    for c in Rseeds:
        w = words[allseeds.index(tuple(c))]
        centroid2Word[Rseeds.index(c)] = w
    save_obj(centroid2Word,path+"Level"+str(level)+"c2w")

    print()