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How to use ep_all method in localstack

Best Python code snippet using localstack_python

main.py

Source:main.py

1from Phase1.preprocess.persian_preprocessor import PersianPreprocessor as PP2from Phase2.preprocessor import Preprocessor as EP23from Phase1.preprocess.english_preprocessor import EnglishPreprocessor as EP4from Phase1.index.indexer import Indexer5from Phase1.preprocess.document_io import read_csv_file_as_list as read_english, \6    read_persian_xml_file_as_list as read_persian7from Phase2.document_io import read_csv_file as read_english28from Phase1.edit_query.edit_query import EditQuery as EQ9from Phase1.search import Searcher10from Phase2.my_tfidf_vectorizer import MyTfIdfVectorizer11from sklearn.svm import LinearSVC12from Phase2.svm import TfIdfClassifier as Classifier13import os14if __name__ == '__main__':15    # Classifier16    train_data = read_english2('../Phase2/source/phase2_train.csv')17    test_data = read_english2('../Phase2/source/phase2_test.csv')18    tfidf_vectorizer = MyTfIdfVectorizer(train_data['text'], EP2())19    best_model = LinearSVC(C=0.5)20    classifier = Classifier(train_data, test_data, tfidf_vectorizer, best_model)21    classifier.fit()22    ep_all = EP()23    pp_all = PP()24    eng_docs = ep_all.preprocess(read_english())25    per_docs = pp_all.preprocess(read_persian())26    indexer = Indexer()27    while True:28        print('Enter the section number:')29        section = input()30        print('Enter subsection number:')31        subsection = input()32        if section == '1':33            if subsection == '1':34                print('Enter the text:')35                txt = input()36                is_eng = EQ.is_english(query=txt)37                if is_eng:38                    norm_text = EP().preprocess([txt])39                else:40                    norm_text = PP().preprocess([txt])41                print(norm_text)42            elif subsection == '2':43                print('English Repetitive Words:')44                print(ep_all.get_high_accured_words())45                print('Persian Repetitive Words:')46                print(pp_all.get_high_accured_words())47        elif section == '2':48            if subsection == '1':49                for doc in eng_docs:50                    indexer.add_doc(doc)51                for doc in per_docs:52                    indexer.add_doc(doc)53                print('Indexing is done')54            elif subsection == '2':55                print('Enter the term:')56                term = input()57                ed_term = EQ(term, indexer, ep_all, pp_all).edit()58                print(indexer.get_posting(ed_term))59            elif subsection == '3':60                print('Enter the term:')61                term = input()62                ed_term = EQ(term, indexer, ep_all, pp_all).edit()63                print(indexer.get_pos_posting(ed_term))64        elif section == '3':65            indexer.save_dictionary(file_name='normal_dict.txt')66            print("normal dict size: " + str(os.path.getsize('normal_dict.txt')))67            if subsection == '1':68                indexer.save_dictionary(method='var', file_name='var_dict.txt')69                print("â«âªvariableâ¬â¬ â«âªbyteâ¬â¬ dict size: " + str(os.path.getsize('var_dict.txt')))70                indexer.load_dictionary(method='var', file_name='var_dict.txt')71                print('Indexer saved and loaded')72            elif subsection == '2':73                indexer.save_dictionary(method='gamma', file_name='gamma_dict.txt')74                print("gamma codeâ¬â¬ dict size: " + str(os.path.getsize('gamma_dict.txt')))75                # indexer.load_dictionary(method='gamma', file_name='gamma_dict.txt')76                print('Indexer saved and loaded')77        elif section == '4':78            if subsection == '1':79                print('Enter the query:')80                query = input()81                print(EQ(query, indexer, ep_all, pp_all).edit())82        elif section == '5':83            if subsection == '1':84                print('Enter the query:')85                query = input()86                print('Enter the subject (0 for none):')87                subject = int(input())88                if subject == 0:89                    subject = None90                ed_query = EQ(query, indexer, ep_all, pp_all).edit()91                print(Searcher(indexer, classifier).search(ed_query, subject))92            elif subsection == '2':93                print('Enter the query:')94                query = input()95                print('Enter the window size')96                size = int(input())97                print('Enter the subject (0 for none):')98                subject = int(input())99                if subject == 0:100                    subject = None101                ed_query = EQ(query, indexer, ep_all, pp_all).edit()102                print(Searcher(indexer, classifier).search_prox(ed_query, size, subject))103        elif section == '6':104            print("Classify all the English docs")105            tags = classifier.predict_docs(eng_docs)106            print(list(zip(range(len(eng_docs)), tags)))107        elif section == 'exit':108            break109        else:...

overall_EP.py

Source:overall_EP.py

1"""2RDX Overall EP3"""4def overall_EP(N, TIME, solutionM, Tset, Rlist, Slist, lg, expmatrixF, coeffmatrixF, expmatrixB, coeffmatrixB, targets, reduction_type, EPmin, graph):5    import numpy as np    6    from my_mech_obj import my_mech_obj7    from RDX_EP import local_error_propagation8    9    t_array = np.linspace(0,max(TIME), N)10    index = np.zeros((N))11    time = np.zeros((N))12    if reduction_type in ['species', 'S']:13        EP_all = np.zeros((len(Slist),N))14        DIC_ind = np.zeros((len(Slist)))15        DIC = np.zeros((len(Slist),len(Slist), N))16    17    else:18        EP_all = np.zeros((len(Rlist),N))19    20    ind_list = []21    ind_list_f = []22    coeffs = []23    #coeff_list = []24    #coeffs = np.zeros((len(Slist), 2, N))25    iterations = np.zeros((N))26    for i in range(N):27        a = 128        while TIME[a] < t_array[i] and a <= len(TIME):29            a = a+130        index[i] = a31        time[i] = TIME[int(a)]32 33    for i in range(len(Slist)):34        ind_list_f.append([])35 36    37    for i in range(N):38        39        t = time[i]40    41        #sample = solutionM[int(index[i]),0:len(Slist)]42        sample = solutionM[int(index[i])][:len(Slist)]43        samplesign = sample >= 044        45        #if samplesign.all:46        if t > -100:47        48        #print(np.shape(sample))49            50            mechobj = my_mech_obj(sample, t, Tset, Rlist, Slist, lg, expmatrixF, coeffmatrixF, expmatrixB, coeffmatrixB)51        52        53            if reduction_type in ['species', 'S']:54                [EP_i, ind_listi, DIC_i, coeffs_i] = local_error_propagation(mechobj, sample, targets, reduction_type, EPmin)55                56                EP_all[:,i] = EP_i57                ind_list.append(ind_listi)58                DIC[:,:,i] = DIC_i[:-1,:]59                coeffs.append(coeffs_i[:,1:])60            elif reduction_type in ['reactions', 'R']:61                EP_i = local_error_propagation(mechobj, sample, targets, reduction_type, EPmin)62                EP_all[:,i] = EP_i63        64        65    EP = np.max(EP_all, axis = 1)66    if graph:67        if reduction_type in ['species', 'S']:68            for i in range(len(Slist)):69                if sum(EP[i] == EP_all[i,:]) == 1:70                    ind = np.where(EP[i] == EP_all[i,:])71                    ind_list_f[i] = ind_list[int(ind[0])][i]72                    DIC_ind[i] = int(ind[0])73        74                    #coeff_list.append(coeffs[int(ind[0])][i][1:])75                    76                elif sum(EP[i] == EP_all[i,:]) > 1:77                    print('Multiple Matches: ', Slist[i]['name'])78                    ind_list_f[i] = ind_list[N-1][i]79                    DIC_ind[i] = int(N-1)80                    #coeff_list.append(coeffs[N-1][i][1:])81                else:82                    print('Error: ', Slist[i]['name'])83        84    85        return [EP, mechobj, ind_list_f, DIC, DIC_ind, coeffs]86        87    else:88        89        return [EP, mechobj]90    '''91# Uncomment below if making graphs!  92    if reduction_type in ['species', 'S']:93        for i in range(len(Slist)):94            if sum(EP[i] == EP_all[i,:]) == 1:95                ind = np.where(EP[i] == EP_all[i,:])96                ind_list_f[i] = ind_list[int(ind[0])][i]97                DIC_ind[i] = int(ind[0])98    99                #coeff_list.append(coeffs[int(ind[0])][i][1:])100                101            elif sum(EP[i] == EP_all[i,:]) > 1:102                print('Multiple Matches: ', Slist[i]['name'])103                ind_list_f[i] = ind_list[N-1][i]104                DIC_ind[i] = int(N-1)105                #coeff_list.append(coeffs[N-1][i][1:])106            else:107                print('Error: ', Slist[i]['name'])108        109    110        return [EP, mechobj, ind_list_f, DIC, DIC_ind, coeffs]111    112    elif reduction_type in ['reactions', 'R']:113        return [EP, mechobj]...

fitdir.py

Source:fitdir.py

1"""2Point estimates of Dirichlet parameters, based on3Thomas P. Minka 2009, "Estimating a Dirichlet distribution"4http://research.microsoft.com/en-us/um/people/minka/papers/dirichlet/minka-dirichlet.pdf5"""6from __future__ import division7import numpy as np8def moment_match_ronning(pvecs):9  """Minka eq. 23. not extensively tested"""10  N,K = pvecs.shape11  if N < 10:12    print "warning, N={} is unreliable for moment matching".format(N)13  ep_all = pvecs.mean(0)14  vp_all = pvecs.var(0)15  vp_all[vp_all < 1e-5] = 1e-516  ep = ep_all[:(K-1)]17  vp = vp_all[:(K-1)]18  terms = np.log(ep*(1-ep)/vp - 1)19  sumalpha = np.exp( np.mean(terms) )20  return ep_all * sumalpha21fitdir = moment_match_ronning22if __name__=='__main__':23  import random24  C=1025  N=2026  K=527  alpha = C * np.ones(K) / K28  for outer in range(10):29    d = np.random.dirichlet(alpha, N)30    # bootstrap31    results = []32    for itr in range(10000):33      samp=np.array([random.randrange(N) for i in range(N)])34      a=fitdir(d[samp])35      results.append(a)36    r = np.array(results)37    print "Real ", alpha38    print "Estimates:"39    print "low  ",r.mean(0)-r.std(0)*240    print "mean ",r.mean(0)41    print "high ",r.mean(0)+r.std(0)*2...

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