def testTrain():
#saveCombinationClass()
combination = loadCombinationClass()
combination.train(alpha = 0.01, iter = 10000)
tdata = "Data/100k/u1.test"
answers, predicts = combination.predict(np.loadtxt(tdata))
err = evaluationRMSE(answers, predicts)
print("Error : %f" % err)
def test_predict():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
bias = Bias(np.loadtxt(fdata), np.loadtxt(ftest))
bias.calculateBias()
answer, predicts = bias.predict()
err = evaluationRMSE(answer, predicts)
print("Err: %f" % (err))
def testTrain():
#saveCombinationClass()
combination = loadCombinationClass()
combination.train(alpha=0.01, iter=10000)
tdata = "Data/100k/u1.test"
answers, predicts = combination.predict(np.loadtxt(tdata))
err = evaluationRMSE(answers, predicts)
print("Error : %f" % err)
def test_predict():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
bias = Bias(np.loadtxt(fdata), np.loadtxt(ftest))
bias.calculateBias()
answer, predicts = bias.predict()
err = evaluationRMSE(answer, predicts)
print("Err: %f" % (err))
def test_SVD():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
svd = SVD(np.loadtxt(fdata), np.loadtxt(ftest))
svd.generaterMat()
svd.calcSVD()
answer, predicts = svd.predict()
err = evaluationRMSE(answer, predicts)
print("Error: %f" % err)
def test_knn():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
knn = KNN(np.loadtxt(fdata), np.loadtxt(ftest))
#knn.calcSimMatrix()
#knn.saveSim(100)
knn.loadSim(100)
answer, predicts = knn.predict(n = 15)
err = evaluationRMSE(answer, predicts)
print("Error: %f" % err)
def test_matFactory():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
matFactory = MatFactory(np.loadtxt(fdata), np.loadtxt(ftest))
factors = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200]
for i in factors:
matFactory.train(20, i)
answer, predicts = matFactory.predict()
err = evaluationRMSE(answer, predicts)
print("Factors: %d, Error: %f" % (i, err))
def test_matFactory():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
matFactory = MatFactory(np.loadtxt(fdata), np.loadtxt(ftest))
factors = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, 200]
for i in factors:
matFactory.train(20, i)
answer, predicts = matFactory.predict()
err = evaluationRMSE(answer, predicts)
print("Factors: %d, Error: %f" % (i, err))
def test():
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
simi = Similarity(fdata, ftest)
#simi.calcSimi()
#simi.calcSimiLocal()
#simi.saveSimi(1)
simi.calcSimiMatrix()
answer, predicts = simi.predict()
err = evaluationRMSE(answer, predicts)
print("Error: %f" % err)
def test():
from bias import Bias
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
simi = Similarity(np.loadtxt(fdata), np.loadtxt(ftest))
#simi.calcSimi()
simi.calcSimiMatrix()
#simi.saveSimi(10)
#simi.loadSimi(10)
answer, predicts = simi.predict()
err = evaluationRMSE(answer, predicts)
print("Error: %f" % err)
def test():
from bias import Bias
fdata = "Data/100k/u1.base"
ftest = "Data/100k/u1.test"
simi = Similarity(np.loadtxt(fdata), np.loadtxt(ftest))
#simi.calcSimi()
simi.calcSimiMatrix()
#simi.saveSimi(10)
#simi.loadSimi(10)
answer, predicts = simi.predict()
err = evaluationRMSE(answer, predicts)
print("Error: %f" % err)
def calAll(self):
self.errs = [0] * 5
bias = Bias(self.data, self.test)
bias.calculateBias()
answers, predicts = bias.predict()
err = evaluationRMSE(answers, predicts)
self.errs[0] = err
print("Bias: %f" % err)
similarity = Similarity(self.data, self.test)
similarity.calculateBias()
similarity.calcSimiMatrix()
answers, predicts = similarity.predict()
err = evaluationRMSE(answers, predicts)
self.errs[1] = err
print("Similarity: %f" % err)
svd = SVD(self.data, self.test)
svd.generaterMat()
svd.calcSVD()
answers, predicts = svd.predict()
err = evaluationRMSE(answers, predicts)
self.errs[2] = err
print("SVD: %f" % err)
matFactory = MatFactory(self.data, self.test)
matFactory.train(20, 35)
answers, predicts = matFactory.predict()
err = evaluationRMSE(answers, predicts)
self.errs[3] = err
print("MatFactory: %f" % evaluationRMSE(answers, predicts))
combination = Combination(self.data)
combination.separateData()
combination.calculate()
combination.train(alpha=0.01, iter=10000)
answers, predicts = combination.predict(self.test)
err = evaluationRMSE(answers, predicts)
self.errs[4] = err
print("Combination: %f" % err)
return self.errs
def calAll(self):
self.errs = [0] * 5
bias = Bias(self.data, self.test)
bias.calculateBias()
answers, predicts = bias.predict()
err = evaluationRMSE(answers, predicts)
self.errs[0] = err
print("Bias: %f" % err)
similarity = Similarity(self.data, self.test)
similarity.calculateBias()
similarity.calcSimiMatrix()
answers, predicts = similarity.predict()
err = evaluationRMSE(answers, predicts)
self.errs[1] = err
print("Similarity: %f" % err)
svd = SVD(self.data, self.test)
svd.generaterMat()
svd.calcSVD()
answers, predicts = svd.predict()
err = evaluationRMSE(answers, predicts)
self.errs[2] = err
print("SVD: %f" % err)
matFactory = MatFactory(self.data, self.test)
matFactory.train(20, 35)
answers, predicts = matFactory.predict()
err = evaluationRMSE(answers, predicts)
self.errs[3] = err
print("MatFactory: %f" % evaluationRMSE(answers, predicts))
combination = Combination(self.data)
combination.separateData()
combination.calculate()
combination.train(alpha = 0.01, iter = 10000)
answers, predicts = combination.predict(self.test)
err = evaluationRMSE(answers, predicts)
self.errs[4] = err
print("Combination: %f" % err)
return self.errs