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