def folder_loop_bigram(folder,data_map): tracks = glob.glob(folder) for setlist in tracks: res = track_maps.readSetClusters(setlist) clusters = res["clusters"] for i in range(0,len(clusters)-2,3): tup = ((int(clusters[i]),int(clusters[i+1])),int(clusters[i+2])) data_map[str(tup)] = data_map[str(tup)] + 1
def folder_loop(folder,data_map): tracks = glob.glob(folder) for setlist in tracks: res = track_maps.readSetClusters(setlist) clusters = res["clusters"] for i in range(0,len(clusters)-1,2): start = int(clusters[i]) end = int(clusters[i+1]) data_map[start][end] = data_map[start][end] + 1
def folder_loop(folder,km,dataMap,clusters,test_list,feature_list):
tracks = glob.glob(folder)
for setlist in tracks:
res = track_maps.readSetClusters(setlist)
newls = []
ls = res["tracks"]
points = []
for tup in ls:
point = dataMap[tup]
cluster = kmeans_clustering.getCluster(clusters,point)
newls.append(cluster)
points.append(point)
track_maps.writeSetClusters(newls,ls,setlist)
if test_list != None:
test_list.append(newls)
feature_list.append(points)
def folder_loop(set_loc,unique_songs,i): mostSongs = -1*sys.maxint leastSongs = sys.maxint numSongs = 0 numSets = 0 all_sets = glob.glob(set_loc) for setlist in all_sets: m_cluster_tracks = track_maps.readSetClusters(setlist) ls = m_cluster_tracks["tracks"] unique_songs[i].extend(ls) num = len(ls) if num > mostSongs: mostSongs = num if num < leastSongs: leastSongs = num numSongs = numSongs + num numSets = numSets + 1 return (mostSongs,leastSongs,numSongs,numSets)
