def genModel(artist, song, model, embedDim, interval, distance):
XTrain, yTrain, XPredict, mean, var = pp.fprocess(artist, song, embedDim,
interval, distance)
yPredict = model.train(XTrain, yTrain, XPredict)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
return yPredict
def shortSongsPredict(shortSongsList, embedDim, interval):
yPredictSum = np.zeros(60)
for song in shortSongsList:
array, mean, var = pp.fprocess(song)
yPredict = itergbrtModel.train(array, embedDim, interval)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
yPredictSum += yPredict
return yPredictSum
def shortSongsPredict(shortSongsList, embedDim, interval):
yPredictSum = np.zeros(60)
for song in shortSongsList:
array, mean, var = pp.fprocess(song)
yPredict = itergbrtModel.train(array, embedDim, interval)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
yPredictSum += yPredict
return yPredictSum
def makeTraceList(clusterSongList): # 提取歌曲的trace
tracelist = []
meanList = []
varList = []
for song in clusterSongList:
array, mean, var = pp.fprocess(song)
tracelist.append(copy(array))
meanList.append(copy(mean))
varList.append(copy(var))
return tracelist, meanList, varList
def makeTraceList(clusterSongList): # 提取歌曲的trace
tracelist = []
meanList = []
varList = []
for song in clusterSongList:
array, mean, var = pp.fprocess(song)
tracelist.append(copy(array))
meanList.append(copy(mean))
varList.append(copy(var))
return tracelist, meanList, varList
def genModel(artist, song, model, embedDim, interval):
array, mean, var = pp.fprocess(artist, song)
yPredict = model.train(array, embedDim, interval)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
return yPredict
def genModel(artist, song, model, embedDim, interval):
array, mean, var = pp.fprocess(artist, song)
yPredict = model.train(array, embedDim, interval)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
return yPredict
def genModel(artist, song, model, embedDim, interval, distance):
XTrain, yTrain, XPredict, mean, var = pp.fprocess(artist, song, embedDim, interval, distance)
yPredict = model.train(XTrain, yTrain, XPredict)
yPredict = yPredict * var + mean
yPredict[yPredict < 0] = 0 # 预测值出现负数直接归零
return yPredict
