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How to use a_max method in pandera

Best Python code snippet using pandera_python

shermann.py

Source:shermann.py

1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4import time5method = 'HybridUCB'6class HybridUCB:7    def __init__(self):8        self.article_features = {}9        # upper bound coefficient10        self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)11        r1 = 20.12        r0 = -0.513        self.r = (r0, r1)14        # dimension of user features = d15        self.d = 516        # dimension of article features = k17        self.k = self.d*self.d18        # A0 : matrix to compute hybrid part, k*k19        self.A0 = np.identity(self.k)20        self.A0I = np.identity(self.k)21        # b0 : vector to compute hybrid part, k22        self.b0 = np.zeros((self.k, 1))23        # Aa : collection of matrix to compute disjoint part for each article a, d*d24        self.Aa = {}25        # AaI : collection of matrix to compute disjoint part for each article a, d*d26        self.AaI = {}27        # Ba : collection of matrix to compute hybrid part, d*k28        self.Ba = {}29        # BaT : collection of matrix to compute hybrid part, d*k30        self.BaT = {}31        # ba : collection of vectors to compute disjoin part, d*132        self.ba = {}33        self.updateCycle = 1034        self.lastUp = 035        # other dicts to speed up computation36        self.AaIba = {}37        self.AaIBa = {}38        self.BaTAaI = {}39        self.theta = {}40        #additional stuff that is computated in the update function instead of recommend41	self.A0IBaTAaI = {}42        self.A0IBaA0IBaTAaI = {}43        self.lastart = []44        self.beta = np.zeros((self.k, 1))45        self.index = {}46        self.a_max = 047        self.z = None48        self.zT = None49        self.xaT = None50        self.xa = None51    # Evaluator will call this function and pass the article features.52    # Check evaluator.py description for details.53    def set_articles(self, art):54        # init collection of matrix/vector Aa, Ba, ba55        i = 056        art_len = len(art)57        self.art = art58        self.article_features = np.zeros((art_len, 1, self.d))59        self.Aa = np.zeros((art_len, self.d, self.d))60        self.AaI = np.zeros((art_len, self.d, self.d))61        self.Ba = np.zeros((art_len, self.d, self.k))62        self.BaT = np.zeros((art_len, self.k, self.d))63        self.ba = np.zeros((art_len, self.d, 1))64        self.AaIba = np.zeros((art_len, self.d, 1))65        self.AaIBa = np.zeros((art_len, self.d, self.k))66        self.BaTAaI = np.zeros((art_len, self.k, self.d))67        self.A0IBaTAaI = np.zeros((art_len, self.k, self.d))68        self.A0IBaA0IBaTAaI = np.zeros((art_len, self.d, self.d))69        self.theta = np.zeros((art_len, self.d, 1))70        for key in art:71            self.index[key] = i72            self.article_features[i] = art[key]73            self.Aa[i] = np.identity(self.d)74            self.AaI[i] = np.identity(self.d)75            self.Ba[i] = np.zeros((self.d, self.k))76            self.BaT[i] = np.zeros((self.k, self.d))77            self.ba[i] = np.zeros((self.d, 1))78            self.AaIba[i] = np.zeros((self.d, 1))79            self.AaIBa[i] = np.zeros((self.d, self.k))80            self.BaTAaI[i] = np.zeros((self.k, self.d))81            self.A0IBaTAaI[i] = np.zeros((self.k, self.d))82            self.A0IBaA0IBaTAaI[i] = np.zeros((self.d, self.d))83            self.theta[i] = np.zeros((self.d, 1))84            i += 185    # This function will be called by the evaluator.86    # Check task description for details.87    def update(self, reward):88        global ttime89        #print reward90        if reward == -1:91             pass92        else:93            r = self.r[reward]94            self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])95            self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])96            self.Aa[self.a_max] += np.dot(self.xa, self.xaT)97            B = np.divide(98                np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),99                (1 + np.dot(np.dot(self.xaT, self.AaI[self.a_max]), self.xa)))100            self.AaI[self.a_max] = self.AaI[self.a_max] - B101            self.Ba[self.a_max] += np.dot(self.xa, self.zT)102            self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])103            self.ba[self.a_max] += r * self.xa104            self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])105            self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])106            self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])107            self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])108            self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))109            #change to LSG?110            self.A0I = linalg.inv(self.A0)111            self.beta = np.dot(self.A0I, self.b0)112            #self.beta = linalg.solve(self.A0, self.b0)113            # if not np.array_equal(beta1, self.beta.all):114            #     print115            self.A0IBaTAaI[self.a_max] = np.dot(self.A0I,self.BaTAaI[self.a_max])116            self.A0IBaA0IBaTAaI[self.a_max] = np.dot(self.AaIBa[self.a_max],self.A0IBaTAaI[self.a_max])117            #do this here !!!!!118            #self.lastUp = self.lastUp + 1119            #if self.lastUp > self.updateCycle:120                 #self.matrixUpdate()121                 #self.lastUp = 0122            123            self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))124            self.lastart = []125    def matrixUpdate(self):126          #global ttime127          #ttime = ttime - time.time()128          for ind in range(self.BaTAaI.shape[0]):129                #print self.A0IBaTAaI130                self.A0IBaTAaI[ind] = np.dot(self.A0I,self.BaTAaI[ind])131                self.A0IBaA0IBaTAaI[ind] = np.dot(self.AaIBa[ind],self.A0IBaTAaI[ind])132          #ttime = ttime + time.time()133    # This function will be called by the evaluator.134    # Check task description for details.135	# Use vectorized code to increase speed136    def recommend(self, timestamp, user_features, articles):137        global ttime138        article_len = len(articles)139        # za : feature of current user/article combination, k*1140        self.xaT = np.array([user_features])141        self.xa = np.transpose(self.xaT)142        # recommend using hybrid ucb143        # fast vectorized for loops144        index = [self.index[article] for article in articles]145        ttime = ttime - time.time()146        if cmp(self.lastart,index)!=0:147            self.article_features_tmp = self.article_features[index]148            #preprocess matrices149            self.preA0IBaA0IBaTAaI=self.A0IBaA0IBaTAaI[index]150            self.preAaI = self.AaI[index]151            self.preA0IBaTAaI = self.A0IBaTAaI[index]152            self.preTheta = self.theta[index]153            self.lastart = index154            155        ttime = ttime + time.time()156        zaT_tmp = np.einsum('i,j', self.article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)157	#print zaT_tmp.shape158        za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))159        A0IBaTAaIxa_tmp = np.dot(self.preA0IBaTAaI, self.xa)160        #looks good :)161        #print A0IBaTAaIxa_tmp1-A0IBaTAaIxa_tmp162        A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)163        A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp164        sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)165        #ttime = ttime - time.time()166        AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.preAaI, self.xa) + np.dot(self.preA0IBaA0IBaTAaI,self.xa)167        #ttime = ttime + time.time()168	#print AaIxa_add_AaIBaA0IBaTAaIxa_tmp - AaIxa_add_AaIBaA0IBaTAaIxa_tmp1169	sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))170        sa_tmp = sa_1_tmp + sa_2_tmp171        xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.preTheta,(0,2,1)),self.xa),(0,2,1))172        max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))173        self.z = za_tmp[max_index]174        self.zT = zaT_tmp[max_index]175        art_max = index[max_index]176        # article index with largest UCB177        # global a_max, entries178        self.a_max = art_max179       # return np.random.choice(articles)180        return articles[max_index]181algorithms = {182    'HybridUCB': HybridUCB()183}184algorithm = algorithms[method]185set_articles = algorithm.set_articles186update = algorithm.update187ttime = 0...

example.py

Source:example.py

1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4method = 'HybridUCB'5combcut = 06artcut = 07usercut = 08class HybridUCB:9    def __init__(self):10        self.article_features = {}11        # upper bound coefficient12        self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)13        r1 = 20.14        r0 = -0.515        self.r = (r0, r1)16        # dimension of user features = d17        self.d = 618        # dimension of article features = k19        self.k = self.d*self.d-combcut20	print self.k21	#select subset of features22	ind = np.arange(self.d*self.d)23	np.random.shuffle(ind)24	self.scols = ind[:self.k]25	print self.scols26        # A0 : matrix to compute hybrid part, k*k27        self.A0 = np.identity(self.k)28        self.A0I = np.identity(self.k)29        # b0 : vector to compute hybrid part, k30        self.b0 = np.zeros((self.k, 1))31        # Aa : collection of matrix to compute disjoint part for each article a, d*d32        self.Aa = {}33        # AaI : collection of matrix to compute disjoint part for each article a, d*d34        self.AaI = {}35        # Ba : collection of matrix to compute hybrid part, d*k36        self.Ba = {}37        # BaT : collection of matrix to compute hybrid part, d*k38        self.BaT = {}39        # ba : collection of vectors to compute disjoin part, d*140        self.ba = {}41        # other dicts to speed up computation42        self.AaIba = {}43        self.AaIBa = {}44        self.BaTAaI = {}45        self.theta = {}46        self.beta = np.zeros((self.k, 1))47        self.index = {}48        self.a_max = 049        self.z = None50        self.zT = None51        self.xaT = None52        self.xa = None53    # Evaluator will call this function and pass the article features.54    # Check evaluator.py description for details.55    def set_articles(self, art):56        # init collection of matrix/vector Aa, Ba, ba57        i = 058        art_len = len(art)59        self.article_features = np.zeros((art_len, 1, self.d))60        self.Aa = np.zeros((art_len, self.d, self.d))61        self.AaI = np.zeros((art_len, self.d, self.d))62        self.Ba = np.zeros((art_len, self.d, self.k))63        self.BaT = np.zeros((art_len, self.k, self.d))64        self.ba = np.zeros((art_len, self.d, 1))65        self.AaIba = np.zeros((art_len, self.d, 1))66        self.AaIBa = np.zeros((art_len, self.d, self.k))67        self.BaTAaI = np.zeros((art_len, self.k, self.d))68        self.theta = np.zeros((art_len, self.d, 1))69        for key in art:70            self.index[key] = i71            self.article_features[i] = art[key]72            self.Aa[i] = np.identity(self.d)73            self.AaI[i] = np.identity(self.d)74            self.Ba[i] = np.zeros((self.d, self.k))75            self.BaT[i] = np.zeros((self.k, self.d))76            self.ba[i] = np.zeros((self.d, 1))77            self.AaIba[i] = np.zeros((self.d, 1))78            self.AaIBa[i] = np.zeros((self.d, self.k))79            self.BaTAaI[i] = np.zeros((self.k, self.d))80            self.theta[i] = np.zeros((self.d, 1))81            i += 182    # This function will be called by the evaluator.83    # Check task description for details.84    def update(self, reward):85        #print reward86        if reward == -1:87             pass88        else:89            r = self.r[reward]90            self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])91            self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])92            self.Aa[self.a_max] += np.dot(self.xa, self.xaT)93            B = np.divide(94                np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),95                (1 + np.dot(np.dot(self.xaT,self.AaI[self.a_max]),self.xa)))96            self.AaI[self.a_max] = self.AaI[self.a_max] - B97            #A = linalg.inv(self.Aa[self.a_max])98            self.Ba[self.a_max] += np.dot(self.xa, self.zT)99            self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])100            self.ba[self.a_max] += r * self.xa101            self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])102            self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])103            self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])104            self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])105            self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))106            self.A0I = linalg.inv(self.A0)107            self.beta = np.dot(self.A0I, self.b0)108            self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))109    # This function will be called by the evaluator.110    # Check task description for details.111	# Use vectorized code to increase speed112    def recommend(self, timestamp, user_features, articles):113        article_len = len(articles)114        # za : feature of current user/article combination, k*1115        self.xaT = np.array([user_features])116        self.xa = np.transpose(self.xaT)117        # recommend using hybrid ucb118        # fast vectorized for loops119        index = [self.index[article] for article in articles]120        article_features_tmp = self.article_features[index]121        #zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)122	#use different features123	#print "test"124	#print article_features_tmp.shape125	#print self.scols126	zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.d*self.d)[:,:,self.scols]127	#print zaT_tmp.shape128	za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))129        #np.dot(self.A0I, np.dot(BaTAaI_tmp, self.xa)) (20, 36, 1)130        A0IBaTAaIxa_tmp = np.transpose(np.dot(np.transpose(np.dot(self.BaTAaI[index], self.xa), (0,2,1)), np.transpose(self.A0I)), (0,2,1))131        A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)132        A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp133        # np.dot(zaT_tmp, A0Iza_diff_2A0IBaTAaIxa_tmp), (20, 1, 1)134        sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)135        # np.dot(AaIBa_tmp, A0IBaTAaIxa_tmp)136        AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.AaI[index], self.xa) + np.sum(np.transpose(self.AaIBa[index], (0,2,1)).reshape(article_len, self.k,self.d,1)*A0IBaTAaIxa_tmp.reshape(article_len,self.k,1,1),-3)137        sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))138        sa_tmp = sa_1_tmp + sa_2_tmp139        # np.dot(self.xaT, self.theta[article])140        xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.theta[index],(0,2,1)),self.xa),(0,2,1))141        max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))142        self.z = za_tmp[max_index]143        self.zT = zaT_tmp[max_index]144        art_max = index[max_index]145        # article index with largest UCB146        # global a_max, entries147        self.a_max = art_max148       # return np.random.choice(articles)149        return articles[max_index]150algorithms = {151    'HybridUCB': HybridUCB()152}153algorithm = algorithms[method]154set_articles = algorithm.set_articles155update = algorithm.update...

shermannold.py

Source:shermannold.py

1#!/usr/bin/env python2.72import numpy as np3from numpy import linalg4method = 'HybridUCB'5class HybridUCB:6    def __init__(self):7        self.article_features = {}8        # upper bound coefficient9        self.alpha = 3 #1 + np.sqrt(np.log(2/delta)/2)10        r1 = 20.11        r0 = -0.512        self.r = (r0, r1)13        # dimension of user features = d14        self.d = 615        # dimension of article features = k16        self.k = self.d*self.d17        # A0 : matrix to compute hybrid part, k*k18        self.A0 = np.identity(self.k)19        self.A0I = np.identity(self.k)20        # b0 : vector to compute hybrid part, k21        self.b0 = np.zeros((self.k, 1))22        # Aa : collection of matrix to compute disjoint part for each article a, d*d23        self.Aa = {}24        # AaI : collection of matrix to compute disjoint part for each article a, d*d25        self.AaI = {}26        # Ba : collection of matrix to compute hybrid part, d*k27        self.Ba = {}28        # BaT : collection of matrix to compute hybrid part, d*k29        self.BaT = {}30        # ba : collection of vectors to compute disjoin part, d*131        self.ba = {}32        # other dicts to speed up computation33        self.AaIba = {}34        self.AaIBa = {}35        self.BaTAaI = {}36        self.theta = {}37        self.beta = np.zeros((self.k, 1))38        self.index = {}39        self.a_max = 040        self.z = None41        self.zT = None42        self.xaT = None43        self.xa = None44    # Evaluator will call this function and pass the article features.45    # Check evaluator.py description for details.46    def set_articles(self, art):47        # init collection of matrix/vector Aa, Ba, ba48        i = 049        art_len = len(art)50        self.article_features = np.zeros((art_len, 1, self.d))51        self.Aa = np.zeros((art_len, self.d, self.d))52        self.AaI = np.zeros((art_len, self.d, self.d))53        self.Ba = np.zeros((art_len, self.d, self.k))54        self.BaT = np.zeros((art_len, self.k, self.d))55        self.ba = np.zeros((art_len, self.d, 1))56        self.AaIba = np.zeros((art_len, self.d, 1))57        self.AaIBa = np.zeros((art_len, self.d, self.k))58        self.BaTAaI = np.zeros((art_len, self.k, self.d))59        self.theta = np.zeros((art_len, self.d, 1))60        for key in art:61            self.index[key] = i62            self.article_features[i] = art[key]63            self.Aa[i] = np.identity(self.d)64            self.AaI[i] = np.identity(self.d)65            self.Ba[i] = np.zeros((self.d, self.k))66            self.BaT[i] = np.zeros((self.k, self.d))67            self.ba[i] = np.zeros((self.d, 1))68            self.AaIba[i] = np.zeros((self.d, 1))69            self.AaIBa[i] = np.zeros((self.d, self.k))70            self.BaTAaI[i] = np.zeros((self.k, self.d))71            self.theta[i] = np.zeros((self.d, 1))72            i += 173    # This function will be called by the evaluator.74    # Check task description for details.75    def update(self, reward):76        #print reward77        if reward == -1:78             pass79        else:80            r = self.r[reward]81            self.A0 += np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])82            self.b0 += np.dot(self.BaTAaI[self.a_max], self.ba[self.a_max])83            self.Aa[self.a_max] += np.dot(self.xa, self.xaT)84            B = np.divide(85                np.dot(np.dot(self.AaI[self.a_max], np.dot(self.xa, self.xaT)), self.AaI[self.a_max]),86                (1 + np.dot(np.dot(self.xaT, self.AaI[self.a_max]), self.xa)))87            self.AaI[self.a_max] = self.AaI[self.a_max] - B88            self.Ba[self.a_max] += np.dot(self.xa, self.zT)89            self.BaT[self.a_max] = np.transpose(self.Ba[self.a_max])90            self.ba[self.a_max] += r * self.xa91            self.AaIba[self.a_max] = np.dot(self.AaI[self.a_max], self.ba[self.a_max])92            self.AaIBa[self.a_max] = np.dot(self.AaI[self.a_max], self.Ba[self.a_max])93            self.BaTAaI[self.a_max] = np.dot(self.BaT[self.a_max], self.AaI[self.a_max])94            self.A0 += np.dot(self.z, self.zT) - np.dot(self.BaTAaI[self.a_max], self.Ba[self.a_max])95            self.b0 += r * self.z - np.dot(self.BaT[self.a_max], np.dot(self.AaI[self.a_max], self.ba[self.a_max]))96            #change to LSG?97            self.A0I = linalg.inv(self.A0)98            self.beta = np.dot(self.A0I, self.b0)99            #self.beta = linalg.solve(self.A0, self.b0)100            # if not np.array_equal(beta1, self.beta.all):101            #     print102            self.theta = self.AaIba - np.dot(self.AaIBa, self.beta)#self.AaI[article].dot(self.ba[article] - self.Ba[article].dot(self.beta))103    # This function will be called by the evaluator.104    # Check task description for details.105	# Use vectorized code to increase speed106    def recommend(self, timestamp, user_features, articles):107        article_len = len(articles)108        # za : feature of current user/article combination, k*1109        self.xaT = np.array([user_features])110        self.xa = np.transpose(self.xaT)111        # recommend using hybrid ucb112        # fast vectorized for loops113        index = [self.index[article] for article in articles]114        article_features_tmp = self.article_features[index]115        zaT_tmp = np.einsum('i,j', article_features_tmp.reshape(-1), user_features).reshape(article_len, 1, self.k)116        za_tmp = np.transpose(zaT_tmp, (0,2,1))#np.transpose(zaT_tmp,(0,2,1))117        #np.dot(self.A0I, np.dot(BaTAaI_tmp, self.xa)) (20, 36, 1)118        A0IBaTAaIxa_tmp = np.transpose(np.dot(np.transpose(np.dot(self.BaTAaI[index], self.xa), (0,2,1)), np.transpose(self.A0I)), (0,2,1))119        A0Iza_tmp = np.transpose(np.dot(zaT_tmp, np.transpose(self.A0I)), (0,2,1)) # (20, 36, 1)120        A0Iza_diff_2A0IBaTAaIxa_tmp = A0Iza_tmp - 2*A0IBaTAaIxa_tmp121        # np.dot(zaT_tmp, A0Iza_diff_2A0IBaTAaIxa_tmp), (20, 1, 1)122        sa_1_tmp = np.sum(za_tmp.reshape(article_len,self.k,1,1)*A0Iza_diff_2A0IBaTAaIxa_tmp.reshape(article_len, self.k,1,1),-3)123        # np.dot(AaIBa_tmp, A0IBaTAaIxa_tmp)124        AaIxa_add_AaIBaA0IBaTAaIxa_tmp = np.dot(self.AaI[index], self.xa) + np.sum(np.transpose(self.AaIBa[index], (0,2,1)).reshape(article_len, self.k,self.d,1)*A0IBaTAaIxa_tmp.reshape(article_len,self.k,1,1),-3)125        sa_2_tmp = np.transpose(np.dot(np.transpose(AaIxa_add_AaIBaA0IBaTAaIxa_tmp,(0,2,1)),self.xa),(0,2,1))126        sa_tmp = sa_1_tmp + sa_2_tmp127        # np.dot(self.xaT, self.thea[article])128        xaTtheta_tmp = np.transpose(np.dot(np.transpose(self.theta[index],(0,2,1)),self.xa),(0,2,1))129        max_index = np.argmax(np.dot(zaT_tmp, self.beta) + xaTtheta_tmp + self.alpha * np.sqrt(sa_tmp))130        self.z = za_tmp[max_index]131        self.zT = zaT_tmp[max_index]132        art_max = index[max_index]133        # article index with largest UCB134        # global a_max, entries135        self.a_max = art_max136       # return np.random.choice(articles)137        return articles[max_index]138algorithms = {139    'HybridUCB': HybridUCB()140}141algorithm = algorithms[method]142set_articles = algorithm.set_articles143update = algorithm.update...

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