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How to use fun method in responses
Best Python code snippet using responses
lib_acquisition_function.py
Source:lib_acquisition_function.py 
...36 (res.fun < best_acquisition_value):37 res.x = numpy.ndarray.tolist(res.x)38 res.x = lib_data.match_val_type(res.x, x_bounds, x_types)39 if (minimize_constraints_fun is None) or \40 (minimize_constraints_fun(res.x) is True):41 best_acquisition_value = res.fun42 best_x = res.x43 outputs = None44 if best_x is not None:45 mu, sigma = fun_prediction(best_x, *fun_prediction_args)46 outputs = {'hyperparameter': best_x, 'expected_mu': mu,47 'expected_sigma': sigma, 'acquisition_func': "ei"}48 return outputs49def _expected_improvement(x, fun_prediction, fun_prediction_args,50 x_bounds, x_types, samples_y_aggregation,51 minimize_constraints_fun):52 # This is only for step-wise optimization53 x = lib_data.match_val_type(x, x_bounds, x_types)54 expected_improvement = sys.maxsize55 if (minimize_constraints_fun is None) or (56 minimize_constraints_fun(x) is True):57 mu, sigma = fun_prediction(x, *fun_prediction_args)58 loss_optimum = min(samples_y_aggregation)59 scaling_factor = -160 # In case sigma equals zero61 with numpy.errstate(divide="ignore"):62 Z = scaling_factor * (mu - loss_optimum) / sigma63 expected_improvement = scaling_factor * (mu - loss_optimum) * \64 norm.cdf(Z) + sigma * norm.pdf(Z)65 expected_improvement = 0.0 if sigma == 0.0 else expected_improvement66 # We want expected_improvement to be as large as possible67 # (i.e., as small as possible for minimize(...))68 expected_improvement = -1 * expected_improvement69 return expected_improvement70def next_hyperparameter_lowest_confidence(fun_prediction,71 fun_prediction_args,72 x_bounds, x_types,73 minimize_starting_points,74 minimize_constraints_fun=None):75 """76 "Lowest Confidence" acquisition function77 """78 best_x = None79 best_acquisition_value = None80 x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]81 x_bounds_minmax = numpy.array(x_bounds_minmax)82 for starting_point in numpy.array(minimize_starting_points):83 res = minimize(fun=_lowest_confidence,84 x0=starting_point.reshape(1, -1),85 bounds=x_bounds_minmax,86 method="L-BFGS-B",87 args=(fun_prediction,88 fun_prediction_args,89 x_bounds,90 x_types,91 minimize_constraints_fun))92 if (best_acquisition_value) is None or (93 res.fun < best_acquisition_value):94 res.x = numpy.ndarray.tolist(res.x)95 res.x = lib_data.match_val_type(res.x, x_bounds, x_types)96 if (minimize_constraints_fun is None) or (97 minimize_constraints_fun(res.x) is True):98 best_acquisition_value = res.fun99 best_x = res.x100 outputs = None101 if best_x is not None:102 mu, sigma = fun_prediction(best_x, *fun_prediction_args)103 outputs = {'hyperparameter': best_x, 'expected_mu': mu,104 'expected_sigma': sigma, 'acquisition_func': "lc"}105 return outputs106def _lowest_confidence(x, fun_prediction, fun_prediction_args,107 x_bounds, x_types, minimize_constraints_fun):108 # This is only for step-wise optimization109 x = lib_data.match_val_type(x, x_bounds, x_types)110 ci = sys.maxsize111 if (minimize_constraints_fun is None) or (112 minimize_constraints_fun(x) is True):113 mu, sigma = fun_prediction(x, *fun_prediction_args)114 ci = (sigma * 1.96 * 2) / mu115 # We want ci to be as large as possible116 # (i.e., as small as possible for minimize(...),117 # because this would mean lowest confidence118 ci = -1 * ci119 return ci120def next_hyperparameter_lowest_mu(fun_prediction,121 fun_prediction_args,122 x_bounds, x_types,123 minimize_starting_points,124 minimize_constraints_fun=None):125 """126 "Lowest Mu" acquisition function127 """128 best_x = None129 best_acquisition_value = None130 x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]131 x_bounds_minmax = numpy.array(x_bounds_minmax)132 for starting_point in numpy.array(minimize_starting_points):133 res = minimize(fun=_lowest_mu,134 x0=starting_point.reshape(1, -1),135 bounds=x_bounds_minmax,136 method="L-BFGS-B",137 args=(fun_prediction, fun_prediction_args,138 x_bounds, x_types, minimize_constraints_fun))139 if (best_acquisition_value is None) or (140 res.fun < best_acquisition_value):141 res.x = numpy.ndarray.tolist(res.x)142 res.x = lib_data.match_val_type(res.x, x_bounds, x_types)143 if (minimize_constraints_fun is None) or (144 minimize_constraints_fun(res.x) is True):145 best_acquisition_value = res.fun146 best_x = res.x147 outputs = None148 if best_x is not None:149 mu, sigma = fun_prediction(best_x, *fun_prediction_args)150 outputs = {'hyperparameter': best_x, 'expected_mu': mu,151 'expected_sigma': sigma, 'acquisition_func': "lm"}152 return outputs153def _lowest_mu(x, fun_prediction, fun_prediction_args,154 x_bounds, x_types, minimize_constraints_fun):155 """156 Calculate the lowest mu157 """158 # This is only for step-wise optimization159 x = lib_data.match_val_type(x, x_bounds, x_types)160 mu = sys.maxsize161 if (minimize_constraints_fun is None) or (162 minimize_constraints_fun(x) is True):163 mu, _ = fun_prediction(x, *fun_prediction_args)...
checkCreatMultiAndInitRecusion.py
Source:checkCreatMultiAndInitRecusion.py
...51 def setVarSource(self):52 self.VarSource = "pstru" + self.struNameHld53 def setVarCheck(self):54 self.VarCheck = self.VarSource + "Check"55 def setfun(self):56 self.fun = "void " + getFunName(self.struNameHld) + "(" + self.struName + " *" + self.VarSource + ", " + self.struName + " *" + self.VarCheck + ")\n{\n"57 if self.VarLoopFlag:58 self.fun = self.fun + " u32 u32i;\n\n"59 for linenum, VarName in enumerate(self.VarNameList):60 VarryObject = re.search(r'([\w|_]+)\[([\w|_]+)\]', VarName)61 if VarryObject:62 VarName = VarryObject.group(1)63 VarSourceName = self.VarSource + "->" + VarName + "[u32i]"64 VarCheckName = self.VarCheck + "->" + VarName + "[u32i]"65 self.fun = self.fun + "\n for(u32i = 0; u32i < " + VarryObject.group(2) + "; u32i++)\n {\n"66 if IsStruct(VarName):67 self.fun = self.fun + " " + getFunName(self.VarStructNameList[linenum]) + "(&" + VarSourceName + ", &" + VarCheckName + ");\n"68 elif IsStrucPoint(VarName):69 self.fun = self.fun + " " + getFunName(self.VarStructNameList[linenum]) + "(" + VarSourceName + ", " + VarCheckName + ");\n"70 else:71 self.fun = self.fun + " CHECK_EQUAL_TEXT(" + VarCheckName + ", " + VarSourceName + ", " + "\"" + VarSourceName + "\");\n"72 self.fun = self.fun + " }\n\n"73 else:74 VarSourceName = self.VarSource + "->" + VarName75 VarCheckName = self.VarCheck + "->" + VarName76 if IsStruct(VarName):77 self.fun = self.fun + " " + getFunName(self.VarStructNameList[linenum]) + "(&" + VarSourceName + ", &" + VarCheckName + ");\n"78 elif IsStrucPoint(VarName):79 self.fun = self.fun + " " + getFunName(self.VarStructNameList[linenum]) + "(" + VarSourceName + ", " + VarCheckName + ");\n"80 else:81 self.fun = self.fun + " CHECK_EQUAL_TEXT(" + VarCheckName + ", " + VarSourceName + ", " + "\"" + VarSourceName + "\");\n"82 self.fun = self.fun + funEnd83 def getStruInitCode(self, struVarName,structInsList):84 subVarNameList = []85 for instance in structInsList:86 if struVarName == instance.struName:87 subVarNameList = instance.VarNameList88 break89 return subVarNameList90 def setInitFun(self):91 self.initFun = "void " + getInitFunName(self.struNameHld) + "( )\n{\n"92 self.initFun = self.initFun + " " + self.struName + " *" + self.VarSource + ";\n"93 for linenum, VarName in enumerate(self.VarNameList):94 VarryObject = re.search(r'([\w|_]+)\[([\w|_]+)\]', VarName)95 if VarryObject:96 VarName = VarryObject.group(1)97 VarSourceName = self.VarSource + "->" + VarName + "[u32i]"98 self.initFun = self.initFun + "\n for(u32i = 0; u32i < " + VarryObject.group(2) + "; u32i++)\n {\n"99 if IsStruct(VarName):100 self.initFun = self.initFun + " " + VarSourceName + " = ;\n"101 else:102 self.initFun = self.initFun + " " + VarSourceName + " = ;\n"103 self.initFun = self.initFun + " }\n\n"104 else:105 VarSourceName = self.VarSource + "->" + VarName106 if re.search(r'^stru', VarName):107 self.initFun = self.initFun + " " + VarSourceName + " = ;\n"108 else:109 self.initFun = self.initFun + " " + VarSourceName + " = ;\n"110 self.initFun = self.initFun + funEnd111 def subStruct(self):112 funInitTemp = ""113 codeLine = re.split(r'\n', code)114 for line in codeLine:115 if 116 def checkfunInit(self):117 codeLine = 118 def creatfun(self, code):119 self.setStruName(code)120 self.setVarNameList(code)121 self.setVarSource()122 self.setVarCheck()123 self.setfun()124 self.setInitFun()125fileDef = open('def.txt','r')126fileFun = open('fun.txt','w')127fileFunInit = open('funInit.txt','w')128fileDefStr = fileDef.read()129codeList = splitStructCode(fileDefStr)130for code in codeList:131 structInstance = StructClass()132 structInstance.creatfun(code)133 fileFun.write(structInstance.fun)134 fileFunInit.write(structInstance.initFun)135fileFun.close()136fileDef.close()...
fun_linecount.py
Source:fun_linecount.py
1import os, codecs2import re3import xlwt4from os.path import join, getsize5#filedir = r'D:\ViewRoot\chengengyu_Merge\EMB5216_L2'6filedir = r'D:\ViewRoot\RRM\EMB5216_HL'7#filedir = r'D:\learn\HL DBAå½æ°è§£æ\now'8class Funline(object):9 def __init__(self):10 self.FunName = False;11 self.FunLineCount = 0;12 self.FunStart = 0;13 self.FunEnd = 0;14 self.FunFileName = False;15 16def findfiles(filedir):17 filelist = []18 filename_file = open('filename.txt','w')19 for root, dirs, files in os.walk(filedir):20 if files:21 for name in files:22 if re.search('.c$', name):23 filelist.append(join(root, name)) 24 filename_file.write(name+'\n')25 filename_file.close26 return filelist27#è·åæ件å表28filelist = findfiles(filedir)29fun_check_result = open('fun_check_result.txt','w')30fun_check_errLine = open('fun_check_errLine.txt','w')31FunLineList = []32for file in filelist:33 filename = ''34 filename = re.findall(r'\\([^\\]+\.c)$', file)35 try:36 fp = codecs.open(file,'rb')37 #fp = open(file,'r')38 #print(file)39 FirstFunFlag = True40 LastFunFlag = False41 FunLineNum = 042 for num,eachline in enumerate(fp): 43 try:44 #å°è¯ç¨ gbk解ç 45 eachline = eachline.decode('GBK')46 linetemp = eachline.strip()47 if re.search(r'^(?:void|s32|OSP_STATUS|u32|u8|u16|s8|s16) +\S+(?:$|\s*\(.*(,|\)))', linetemp):48 if not re.search(r';|extern', linetemp):49 if FirstFunFlag == False: #ä¸æ¯ç¬¬ä¸ä¸ªå½æ°åéè¦è®°å½å
¶è¡æ°50 #print(num, FunlineNum)51 if fun.FunLineCount != 0:52 fun_check_result.write("å¼å¸¸å
容fun:" + fun.FunName + eachline + '\n')53 else:54 fun.FunLineCount = num - FunLineNum55 fun.FunStart = FunLineNum56 fun.FunEnd = num57 fun = Funline()58 FunLineList.append(fun)59 FirstFunFlag = False60 FunLineNum = num #è®°å½å½æ°å¼å§çè¡æ°61 LastFunFlag = True #åå®è¿æ¯æåä¸ä¸ªå½æ°62 if fun.FunName != False:63 fun_check_result.write("å¼å¸¸å
容fun:" + eachline + '\n')64 else:65 fun.FunName = linetemp66 fun.FunFileName = filename67 except Exception as e:68 #解æ失败ï¼é常æ¯å 为ä¸äºæ±åçåè§å
¨è§æé®é¢ 69 print(e)70 fun_check_errLine.write(str(e))71 err = "æ æ³è§£æçè¡: æ件为" + file + "è¡å·ä¸º:" + str(num+1) + '\n'72 fun_check_errLine.write(err)73 fp.close74 if LastFunFlag:75 if fun.FunLineCount != 0:76 fun_check_result.write("å¼å¸¸å
容fun:" + fun.FunName + eachline + '\n')77 else:78 fun.FunLineCount = num - FunLineNum79 fun.FunStart = FunLineNum80 fun.FunEnd = num81 except Exception as e:82 print(e)83 fun_check_errLine.write(str(e))84 err = 'æ æ³æå¼æ件' + file +'\n'85 fun_check_result.write(err)86fun_check_result.close()87fun_check_errLine.close()88#print(FunLineList)89styles = {'datetime': xlwt.easyxf(num_format_str='yyyy-mm-dd hh:mm:ss'),90 'date': xlwt.easyxf(num_format_str='yyyy-mm-dd'),91 'time': xlwt.easyxf(num_format_str='hh:mm:ss'),92 'header': xlwt.easyxf('font: name Times New Roman, color-index black, bold on', num_format_str='#,##0.00'),93 'default': xlwt.Style.default_style}94wb = xlwt.Workbook(encoding = 'utf-8')95sheet = wb.add_sheet(u'å½æ°')96row = 097sheet.write(row,0, 'æ件å')98sheet.write(row,1, 'å½æ°å')99sheet.write(row,2, 'è¡æ°')100sheet.write(row,3, 'å¼å§è¡æ°')101sheet.write(row,4, 'ç»æè¡æ°')102for num,eachitem in enumerate(FunLineList): 103 row = num + 1104 sheet.write(row,0, eachitem.FunFileName)105 sheet.write(row,1, eachitem.FunName)106 sheet.write(row,2, eachitem.FunLineCount)107 sheet.write(row,3, eachitem.FunStart)108 sheet.write(row,4, eachitem.FunEnd)...
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