def calculate(self):
self.allPredicts = np.zeros((4, self.testSize))
bias = Bias(self.trainData, self.testData)
bias.calculateBias()
answers, predicts = bias.predict()
self.biasClass = bias
self.allPredicts[0, :] = predicts
#print("Bias: %f" % evaluationRMSE(answers, predicts))
similarity = Similarity(self.trainData, self.testData)
similarity.calculateBias()
similarity.calcSimiMatrix()
answers, predicts = similarity.predict()
self.similarityClass = similarity
self.allPredicts[1, :] = predicts
#print("Similarity: %f" % evaluationRMSE(answers, predicts))
svd = SVD(self.trainData, self.testData)
svd.generaterMat()
svd.calcSVD()
answers, predicts = svd.predict()
self.svdClass = svd
self.allPredicts[2, :] = predicts
#print("SVD: %f" % evaluationRMSE(answers, predicts))
matFactory = MatFactory(self.trainData, self.testData)
matFactory.train(10, 11)
answers, predicts = matFactory.predict()
self.matFactoryClass = matFactory
self.allPredicts[3, :] = predicts
#print("MatFactory: %f" % evaluationRMSE(answers, predicts))
pickleFile = open(predictsFile, 'wb')
pickle.dump(self.allPredicts, pickleFile)
def calculate(self):
self.allPredicts = np.zeros((4, self.testSize))
bias = Bias(self.trainData, self.testData)
bias.calculateBias()
answers, predicts = bias.predict()
self.biasClass = bias
self.allPredicts[0, :] = predicts
#print("Bias: %f" % evaluationRMSE(answers, predicts))
similarity = Similarity(self.trainData, self.testData)
similarity.calculateBias()
similarity.calcSimiMatrix()
answers, predicts = similarity.predict()
self.similarityClass = similarity
self.allPredicts[1, :] = predicts
#print("Similarity: %f" % evaluationRMSE(answers, predicts))
svd = SVD(self.trainData, self.testData)
svd.generaterMat()
svd.calcSVD()
answers, predicts = svd.predict()
self.svdClass = svd
self.allPredicts[2, :] = predicts
#print("SVD: %f" % evaluationRMSE(answers, predicts))
matFactory = MatFactory(self.trainData, self.testData)
matFactory.train(10, 11)
answers, predicts = matFactory.predict()
self.matFactoryClass = matFactory
self.allPredicts[3, :] = predicts
#print("MatFactory: %f" % evaluationRMSE(answers, predicts))
pickleFile = open(predictsFile, 'wb')
pickle.dump(self.allPredicts, pickleFile)
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
