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How to use operations_index method in localstack

Best Python code snippet using localstack_python

tradeoff_functions.py

Source:tradeoff_functions.py

1from functions import functions_util, inference_fcns2import math, sys3def cost_est(model,bits,**kwargs):4    content_ac = model[0]5    cost_add_flt={64:32,32:16,16:8,8:4}6    cost_mult_flt={64:120,32:25,16:5,8:1}7    cost_reg={64:40,32:20,16:10,8:5}8    cost_sram={64:200,32:100,16:50,8:25}9    par_num = sum([1 for ac in content_ac if 'L' in ac]) #Number of parameters10    add_num = sum([1*len(ac.split(' '))-2-1+1 for ac in content_ac  if 'O' in ac]) #Number of multiplications11    mult_num = sum([1*len(ac.split(' '))-3-1+1 for ac in content_ac if 'A' in ac]) #Number of additions12    #Feature and cost sensor are 0 by default13    cost_sensors=014    cost_features=015    #Estimate savings from feature-pruning induced model reduction16    print '\n'17    if kwargs:18        if 'pruned_feature' in kwargs:19            #How much would we save if we pruned features in kwargs20            indicator_dict = model[5]21            variable_list = model[6]22            n_mult = len(indicator_dict[variable_list[kwargs['pruned_feature']] + '_0']) + len(23                indicator_dict[variable_list[kwargs['pruned_feature']] + '_1'])24            n_add = n_mult / 225            n_params = len(indicator_dict[variable_list[kwargs['pruned_feature']] + '_0']) + len(26                indicator_dict[variable_list[kwargs['pruned_feature']] + '_1'])27            saved_mul=n_mult28            saved_add=n_add29            saved_params=n_params30            par_num-=saved_params31            add_num-=saved_add32            mult_num-=saved_mul33            print 'Cost savings', saved_params, saved_mul, saved_add34        if 'sensor_dict' in kwargs:35            print 'Sensor costs!'36            sens_dict=kwargs['sensor_dict']37            sensor_c = kwargs['max_cost']*0.1   #Sensor cost is 10% of most expensive AC comp. cost38            feature_set=kwargs['feature_set']39            sensor_occurrence = [sens_dict[f] for f in feature_set if f in feature_set] #Which sensors are active?40            nsensors = len(set(sensor_occurrence))41            print set(sensor_occurrence)42            print 'Sensors number ', nsensors43            cost_sensors=sensor_c*nsensors44            cost_features=len(feature_set)*((30*cost_add_flt[32])+(30*cost_mult_flt[32])) #Each feature requires 30 MAC, features are extracted at 32 bits45    out_cost=(add_num * cost_add_flt[bits]) + (mult_num * cost_mult_flt[bits]) + ((add_num + mult_num) * cost_reg[bits] ) + (par_num * cost_sram[bits])+cost_sensors+cost_features46    #Return list with [total cost with sensors and features, total cost without sensors or features, sensor cost, feature cost]47    return out_cost48def accuracy_estimation(validation_set,indicator_dict,variable_list,obs_var_den,init_weight,49                        operations_index,operation,content_ac,classes,obs_var_num,thres,**kwargs):50    correct_count=051    all_ratios=[]52    for test in validation_set:53        if test:54            observation = [int(t) for t in test.split(',')]55            gt = observation[-1]56            #Set indicator values according to observation57            position_neg_den = [indicator_dict[variable_list[var] + '_' + str(value)] for value, var in58                                zip(observation, variable_list) if var in obs_var_den ]59            psn_den = [pos for position in position_neg_den for pos in position]60            init_weight_den = [w for w in init_weight]61            for psden in psn_den:62                init_weight_den[psden] = 063            wmc_den, w_den= inference_fcns.performWMC(operations_index, operation, init_weight_den,content_ac)64            w_den+= sys.float_info.min  #Avoid division by 065            #Collect num/den ratio66            ratios = []67            nn=[]68            for classv in classes:69                # chnage observation value of class variable70                observation[-1] = classv71                position_neg_num = [indicator_dict[variable_list[var] + '_' + str(value)] for value, var in zip(observation, variable_list) if var in obs_var_num]72                psn_num = [pos for position in position_neg_num for pos in position]73                init_weight_num = [w for w in init_weight]74                for psnum in psn_num:75                    init_weight_num[psnum] = 076                wmc_num, w_num= inference_fcns.performWMC(operations_index, operation, init_weight_num,content_ac)77                nn.append(w_num)78                rati = w_num / w_den79                ratios.append(rati)80            all_ratios.append(ratios)81            conditions = [loc for loc, num in enumerate(ratios) if num >= thres]82            if len(conditions) == 1:83                if int(conditions[0]) == gt:84                    correct_count += 185            elif len(conditions) > 1:86                if int(conditions.index(max(conditions))) == gt:87                    correct_count += 188    accuracy=float(correct_count) / (len(validation_set))89    return accuracy90def metric_est(bench_name,model,dataset,observed_features,bits,sensor_cost,**kwargs):91    content_ac=model[0]92    ce=cost_est(content_ac,64)93    lmap_id=model[1]94    lmap_w=model[2]95    indicator_dict = model[5]96    variable_list=model[6]97    class_num = len(dataset[0].split(','))98    thres=0.599    NodesEv = observed_features+[class_num]100    obs_var_num = NodesEv101    obs_var_den = observed_features102    classes = [0, 1]103    # Initialize weight vector104    init_weight = inference_fcns.init_weight(content_ac, lmap_id, lmap_w)105    operations_index, operation = inference_fcns.extract_operations(content_ac)106    #To verify WMC: w should be 1107    # wmc, w = inference_fcns.performWMC(operations_index, operation, init_weight, content_ac)108    # print 'w is ', w109    acc=accuracy_estimation(dataset, indicator_dict, variable_list, obs_var_den, init_weight,110                        operations_index, operation, content_ac, classes, obs_var_num, thres)111    if kwargs:112        if 'featp' in kwargs:113            cost=cost_est(model,bits,pruned_feature=kwargs['featp'])114    elif not kwargs:115        cost = cost_est(model, bits)116    # print 'acc is ', acc117    # print 'cost is ', cost...

inference_fcns.py

Source:inference_fcns.py

1import operator2def prod(factors):3    return reduce(operator.mul, factors, 1)4def init_weight(content_ac,content_lmap_parsed_indeces,content_lmap_parsed_weights):5    weight_ac = [None] * len(content_ac)6    for i, ac in enumerate(content_ac):7        if ac[0] == 'L':8            index = int(ac[2:len(ac)])9            # index_weight = indices(content_lmap_parsed_indeces, lambda x: x == index)10            index_weight=[ii for ii,con in enumerate(content_lmap_parsed_indeces) if con==index]11            weight_ac[i] = content_lmap_parsed_weights[index_weight[0]]12        else:13            weight_ac[i] = 014    return (weight_ac)15def extract_operation_numbers(content):16    # spaces = indices(content, lambda x: x.isspace())17    spaces=[ii for ii,con in enumerate(content) if con.isspace()]18    ex_op=[]19    if content[0] == 'A':20        for sp in range(len(spaces) - 1):21            if sp == len(spaces) - 2:22                ex_op.append(content[spaces[sp + 1] + 1:])23            else:24                ex_op.append(content[spaces[sp + 1] + 1:spaces[sp + 2]])25    else:26        for sp in range(len(spaces) - 2):27            if sp == len(spaces) - 3:28                ex_op.append(content[spaces[sp + 2] + 1:])29            else:30                ex_op.append(content[spaces[sp + 2] + 1:spaces[sp + 3]])31    return ex_op,spaces32def generate_operation_set(operations_index,content_ac):33    operation = [[] for _ in range(len(operations_index))]34    for i, op in enumerate(operations_index):35        content=content_ac[operations_index[i]]36        (extracted_op,sp)=extract_operation_numbers(content)37        operation[i]=extracted_op38    return operation39def extract_operations(args):40    k=141    for i, ac in enumerate(args):42        if ac[0]=='L':43            k=k+144    operations_index=[0]*(len(args)-k+1)45    operation_wmc=[0]*(len(args))46    j=047    for i, ac in enumerate(args):48        res=[]49        if ac[0] != 'L':50            operations_index[j]=i51            j=j+152            (op_ex, spaces) = extract_operation_numbers(ac)53            if ac[0]=='O':54                res.append(ac[0:spaces[2]])55            if ac[0]=='A':56                res.append(ac[0:spaces[1]])57            lis=list(map(int, op_ex))58            for l in lis:59                res.append(l)60            operation_wmc[i]=res61        else:62            operation_wmc[i]=ac63    operation=generate_operation_set(operations_index,args)64    return operations_index, operation65def performWMC(operations_index, operation, weight_ac_original, content_ac):66    weight_ac=[w for w in weight_ac_original]67    for i, op in enumerate(operations_index):68        temp = []69        for j in range(len(operation[i])):70            temp.append(weight_ac[int(operation[i][j])])71        if 'A' in content_ac[operations_index[i]][0]:72            weight_ac[operations_index[i]] = prod(temp)73        elif 'O' in content_ac[operations_index[i]][0]:74            weight_ac[operations_index[i]] = sum(temp)75    if content_ac[len(content_ac) - 1][0] != 'L':76        wc = weight_ac[len(content_ac) - 1]77    else:78        for w in range(len(content_ac) - 1, -1, -1):79            if content_ac[w][0] != 'L':80                wc = weight_ac[w]81                break...

urls.py

Source:urls.py

1__author__ = 'atulmala'2from django.conf.urls import url3from operations import views4urlpatterns = [5    url(r'^$', views.operations_index, name='operations_index'),6    url(r'^att_summary_school/$', views.att_summary_school, name='att_summary_school'),7    url(r'^att_summary_school_device/$', views.att_summary_school_device, name='att_summary_school_device'),8    url(r'^att_register_class/$', views.AttRegisterClass.as_view(), name='att_register_class'),9    url(r'^test_results/$', views.test_result, name='test_results'),10    url(r'^result_sms/$', views.result_sms, name='test_sms'),11    url(r'^send_message/(?P<school_id>\w+)/$', views.send_message, name='send_message'),12    url(r'^parents_communication_report/$', views.ParentCommunicationReport.as_view(),13        name='parents_communication_report'),14    url(r'^monthly_sms_report/$', views.MonthlySMSReport.as_view(), name='sms_summmary_school'),15    url(r'send_bulk_sms/$', views.send_bulk_sms, name='send_bulk_sms'),16    url(r'commit_bulk_sms/$', views.CommitBulkSMS.as_view(), name='commit_bulk_sms'),17    url(r'commit_failed_sms/$', views.CommitFailedSMS.as_view(), name='commit_failed'),18    url(r'retrieve_sms_history/(?P<parent_mobile>\w+)/$', views.SMSHistoryList.as_view()),19    url(r'send_welcome_sms/$', views.send_welcome_sms, name='send_welcome_sms'),...

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