def testTp(self):
simCalMethod = paper.simPCC
sparseness = 20
fileNumbers = 10
for i in range(1, fileNumbers + 1):
#文件对象
print i,
euiFileName = 'throught/ipcc/euislopeone-%d-%d.txt' % (sparseness,
i)
pfEui = open(euiFileName, 'w')
#load data
trainFileName = r'throught/training%d-%d.txt' % (sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testFileName = r'throught/test%d-%d.txt' % (sparseness, i)
testArrayObj = paper.loadTest(testFileName)
#相似度矩阵数据
wsSimFileName = 'throught/ipcc/simArrayWs-%d-%d.txt' % (sparseness,
i)
wsSimArrayObj = paper.createSimArray(trainArrayObj.T, simCalMethod)
paper.save(wsSimArrayObj, wsSimFileName)
# wsSimArrayObj = paper.load(wsSimFileName)
#计算预测准确
print calMaeAndRmse(trainArrayObj, testArrayObj, wsSimArrayObj,
pfEui)
pfEui.close()
print 'ok'
def testTp(self):
for sparseness in [5, 10, 15, 20]:
for i in range(1, fileNumbers+1):
#文件对象
print i,
euiFileName = 'throught/weightedslopeone/euislopeone-%d-%d.txt' % (sparseness,i)
pfEui = open(euiFileName, 'w')
#load data
trainFileName = r'throught/training%d-%d.txt' % (sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testFileName = r'throught/test%d-%d.txt' % (sparseness, i)
testArrayObj = paper.loadTest(testFileName)
#计算预测准确
print calMaeAndRmse(trainArrayObj, testArrayObj, pfEui)
pfEui.close()
print 'ok'
def testRt(self):
sparseness = 5
fileNumbers = 10
for i in range(1, fileNumbers + 1):
#文件对象
print i,
euiFileName = 'rt/userMean/euislopeone-%d-%d.txt' % (sparseness, i)
pfEui = open(euiFileName, 'w')
#load data
trainFileName = r'rt/sparseness%d/training%d.txt' % (sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testFileName = r'rt/sparseness%d/test%d.txt' % (sparseness, i)
testArrayObj = paper.loadTest(testFileName)
#计算预测准确
print calMaeAndRmse(trainArrayObj, testArrayObj, pfEui)
pfEui.close()
print 'ok'
def testRt(self):
for sparseness in [5, 10, 15, 20]:
for i in range(1, fileNumbers + 1):
#文件对象
print i,
euiFileName = 'rt/mf/euislopeone-%d-%d.txt' % (sparseness, i)
pfEui = open(euiFileName, 'w')
#load data
trainFileName = r'rt/sparseness%d/training%d.txt' % (
sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testFileName = r'rt/sparseness%d/test%d.txt' % (sparseness, i)
testArrayObj = paper.loadTest(testFileName)
#计算预测准确
p, q = learningAddIndicateFunctionlfm(trainArrayObj)
maeAndRmseRt(p, q, testArrayObj, pfEui)
pfEui.close()
print 'ok'
def testTp(self):
for sparseness in [5, 10, 15, 20]:
for i in range(1, fileNumbers + 1):
#文件对象
print i,
euiFileName = 'throught/mf/euislopeone-%d-%d.txt' % (
sparseness, i)
pfEui = open(euiFileName, 'w')
#load data
trainFileName = r'throught/training%d-%d.txt' % (sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testFileName = r'throught/test%d-%d.txt' % (sparseness, i)
testArrayObj = paper.loadTest(testFileName)
#计算预测准确
trainArrayObj[trainArrayObj != NoneValue] = (
trainArrayObj[trainArrayObj != NoneValue] -
44.034) / 107.439
p, q = learningAddIndicateFunctionlfm(trainArrayObj)
maeAndRmseTp(p, q, testArrayObj, pfEui)
pfEui.close()
print 'ok'
def loadDataSet(trainFile, testFile):
import paper
trainArray = paper.createArrayObj(trainFile)
testObj = paper.loadTest(testFile)
return trainArray, testObj
if __name__ == '__main__':
simCalMethod = paper.simPCC
fileNumbers = 10
for sparseness in [5, 10, 15, 20]:
#文件对象
for i in range(1, fileNumbers + 1):
print i,
# euiFileName = 'rt/upcc/euislopeone-%d-%d.txt' % (sparseness,i)
euiFileName = 'throught/upcc/euislopeone-%d-%d.txt' % (sparseness,
i)
pfEui = open(euiFileName, 'w')
#load data
# trainFileName = r'rt/sparseness%d/training%d.txt' % (sparseness, i)
# testFileName = r'rt/sparseness%d/test%d.txt' % (sparseness, i)
#throught
trainFileName = r'throught/training%d-%d.txt' % (sparseness, i)
testFileName = r'throught/test%d-%d.txt' % (sparseness, i)
trainArrayObj = paper.createArrayObj(trainFileName)
testArrayObj = paper.loadTest(testFileName)
#相似度矩阵数据
# userSimFileName = 'rt/upcc/simArrayUser-%s-%d.txt' % (sparseness,i)
userSimFileName = 'throught/upcc/simArrayUser-%s-%d.txt' % (
sparseness, i)
userSimArrayObj = paper.createSimArray(trainArrayObj, simCalMethod)
paper.save(userSimArrayObj, userSimFileName)
# userSimArrayObj = paper.load(userSimFileName)
#计算预测准确
mae, rmse = calMaeAndRmse()
print mae, rmse
pfEui.close()
print 'ok'