def myclip(_province,_kind,_terrain): clips.Reset() rules = [rule.strip() for rule in readfile.readfile('rules.txt')] provinces = [province.strip() for province in readfile.readfile('province.txt')] for province in provinces: clips.SendCommand(province.strip()) clips.Assert("(province {0})".format(_province)) clips.Run() for rule in rules: clips.SendCommand(rule.strip()) clips.Assert("(kind {0})".format(_kind)) if _terrain is not "": clips.FactList()[3].Retract() clips.Assert("(terrain {0})".format(_terrain)) #BEFORE bf = len(clips.FactList()) #RUN clips.Run() #AFTER af = len(clips.FactList()) for i in range(af - (af - bf)): clips.FactList()[0].Retract() #clips.PrintRules() return [i.PPForm()[8+6:-1] for i in clips.FactList()]
def diffdata(files): # Error: can only use 2 files to diff if len(files) != 2: sys.exit(3) (t1, w1, a1) = readfile(files.pop(0)) (t2, w2, a2) = readfile(files.pop(0)) # Error: time or wavelength mismatch if list(t1) != list(t2) or list(w1) != list(w2): sys.exit(2) return t1, w1, a1 - a2
def avgdata(files): nfiles = float(len(files)) (t, w, a) = readfile(files.pop(0)) for file in files: (t1, w1, a1) = readfile(file) # Error: time or wavelength mismatch if list(t1) != list(t) or list(w1) != list(w): sys.exit(2) a += a1 a = a / nfiles return t, w, a
def kmeans(): nr_of_clusters = 5 blognames, words, data = readfile() kclust = kmeanscluster(data, k=5, max_iter=5) centroid_0 = [] centroid_1 = [] centroid_2 = [] centroid_3 = [] centroid_4 = [] centroids = [] for r in kclust[0]: centroid_0.append(blognames[r]) for i in kclust[1]: centroid_1.append(blognames[i]) for j in kclust[2]: centroid_2.append(blognames[j]) for k in kclust[3]: centroid_3.append(blognames[k]) for l in kclust[4]: centroid_4.append(blognames[l]) centroids = ((centroid_0, len(centroid_0)), (centroid_1, len(centroid_1)), (centroid_2, len(centroid_2)), (centroid_3, len(centroid_3)), (centroid_4, len(centroid_4))) #print centroids return render_template('kmeans.html', kclust=centroids, noc=nr_of_clusters)
def main(): depots, stations, customers, demand, time_win, tolerate_time_win = readfile.readfile( "4depot_10station_40customer3.csv", depot_num=4, station_num=10, customer_num=40) dis_dep_cus = readfile.get_dis_matrix(depots, customers) #depot到customer的距离矩阵 dis_sta_cus = readfile.get_dis_matrix(stations, customers) #station到customer的距离矩阵 dis_cus_cus = readfile.get_dis_matrix(customers, customers) #customer之间的距离矩阵 min_dis_depot = readfile.get_min_dis( dis_dep_cus) #获取距离每个customer最近的depot及距离 min_dis_station = readfile.get_min_dis( dis_sta_cus) #获取距离每个customer最近的station及距离 mans = 40 rows = 100 times = 300 # line = [3, 29, 14, 39, 19, 7, 35, 5, 21, 6, 33, 4, 26, 31, 11, 18, 20, 1, 30, 36, 15, 24, 23, 32, 34, 40, 37, 9, 22, 16, 8, 38, 12, 13, 10, 28, 2, 17, 25, 27] # result = calFitness.fitness(dis_cus_cus, dis_sta_cus, min_dis_depot, min_dis_station, time_win, tolerate_time_win, demand, line, True) # print result # genetic = GeneticAlgorithm(dis_dep_cus, dis_sta_cus, dis_cus_cus, min_dis_depot, min_dis_station, demand, time_win, tolerate_time_win, mans, rows, times) genetic.run()
def polygon_intersect(file): # Read the input file in the correct format. Use the plot_boundary function # to plot the polygons result = readfile.readfile(file) P1 = result['poly1'] P2 = result['poly2'] plt.cla() plt.axis('equal') plt.grid('on') plt.hold(True) plot_boundary(P2) plot_boundary(P1) # Find the intersections of the polygons and compose the new polygon result = find_intersections(P1,P2) P3 = compose_new_polygon(result) P3 = [[int(float(j)) for j in i] for i in P3] # Use the plot_boundary and flood_fill functions to plot the new polygon poly = P3 px = [poly[i][0] for i in range(len(poly))] py = [poly[i][1] for i in range(len(poly))] x_sorted = np.sort(px) # replace!!! y_sorted = np.sort(py) # replace!!! colors = plot_boundary(poly) plt.plot(int(np.median(px)),int(np.median(py)), marker='.', markersize = 15, markerfacecolor = 'g') flood_fill(int(np.median(px)),int(np.median(py)), 'w', 'g', x_sorted[0], y_sorted[0], colors) plt.show(block=False)
def __init__(self,): tablist=readfile() self.values=[] for i in range(3): value=tuple(tablist[0]) del tablist[0] self.values.append(value) self.pfdict=dict(tablist)
def main(): fabdict=dict(f.readfile("fabtest.csv")) localpath=fabdict['localpath'] remotepath=fabdict['remotepath'] #putfile(localpath,remotepath) #check_file(localpath,remotepath) untar(remotepath)
def top(path, f_type, hang, lie): f_list = readfile.readfile(path, f_type) for i in f_list: extract.extract(i, hang, lie) return
def __init__(self, ): tablist = readfile() self.values = [] for i in range(3): value = tuple(tablist[0]) del tablist[0] self.values.append(value) self.pfdict = dict(tablist)
def run(path=resource_manager.Properties.getDefaultDataFold()+"txt"+resource_manager.getSeparator()+"build.txt",sep=' '): ''' return cluster id i,j distance ''' (dist,xxdist,ND,N) = readfile(path, dimensions = 2, sep=sep) XY, eigs = mds(dist) (rho,delta,ordrho,dc,nneigh) = rhodelta(dist, xxdist, ND, N, percent = 2.0) DCplot(dist, XY, ND, rho, delta,ordrho,dc,nneigh,17,0.1)
def db(request): import sys,readfile from rice.models import Rice rules = [rule.strip() for rule in readfile.readfile("kem.csv")] for i in rules: i = i.split(',') r = Rice(name=i[0],kind=i[1],number=i[2],area=i[3],terrain=i[4],character=i[5],seed=i[6],detail=i[7],pic=i[8],ref=i[9]) r.save() return HttpResponse("GG")
def run(*args, **kwargs): ''' return cluster id ''' file = kwargs.get('fi') sep = kwargs.get('sep',' ') ######## (dist,xxdist,ND,N) = readfile(file, dimensions = 2, sep=sep) XY, eigs = mds(dist) (rho,delta,ordrho,dc,nneigh) = rhodelta(dist, xxdist, ND, N, percent = 2.0) DCplot(dist, XY, ND, rho, delta,ordrho,dc,nneigh,17,0.1,31)
def ___init__(filename): file=readfile.readfile() file.getvaluefromconfig() firstline=file.firstline lastline=file.lastline linenum=file.linenum f.getvaluefromfile() ffirstline=file.firstline() flastline=file.lastline() flinenum=file.linenum()
def done(self): # Dictionary containing {filename:configuration} pairs results = {} i=0 for file in self.filenames: results.update({self.trim_filename(self.filenames[i]):readfile(file)}) i = i + 1 openwindow(root, results) openwindow2(root, results)
def draw_table_price(): pl.ioff() contents = readfile.readfile("000007.csv") high, low = readfile.get_prices(contents) ranges = range(len(contents)) prices = [ content[2] for content in contents ] pl.plot(ranges, prices, color="red") #pl.plot([(float(low) - float(low)*0.02), (float(high) + float(high)*0.02)]) pl.plot([float(low), float(high)]) pl.title("Hello world") pl.show()
def main(): fname = sys.argv[1] X, Y = readfile.readfile(fname) X, Y = np.array(X), np.array(Y) times = [] np.random.seed(1208) for i in range(1126): w, updates = PLA(X, Y) times.append(updates) print(w, updates) print('average update steps : {}'.format(np.average(times))) plt.hist(np.array(times), bins=range(min(times), max(times),1)) plt.xlabel('Update counts') plt.ylabel('Counts') plt.savefig('hw1_7.png')
def convex_hull_plot(file): # Format input file and plot data result = readfile.readfile(file) points = result['points'] plt.cla() plt.axis('equal') plt.grid('on') plt.hold(True) # Find convex hull and plot it using plot_boundary hull = convex_hull(points) plt.scatter(*zip(*points)) plot_boundary(hull) plt.show(block=False)
def main(): depots, stations, customers, demand, time_win, tolerate_time_win = readfile.readfile( "4depot_10station_40customer3.csv", depot_num=4, station_num=10, customer_num=40) dis_dep_cus = readfile.get_dis_matrix(depots, customers) #depot到customer的距离矩阵 dis_sta_cus = readfile.get_dis_matrix(stations, customers) #station到customer的距离矩阵 dis_cus_cus = readfile.get_dis_matrix(customers, customers) #customer之间的距离矩阵 min_dis_depot = readfile.get_min_dis( dis_dep_cus) #获取距离每个customer最近的depot及距离 min_dis_station = readfile.get_min_dis( dis_sta_cus) #获取距离每个customer最近的station及距离 customer_means = [] cus1 = [] cus2 = [] cus3 = [] cus4 = [] for i in range(0, len(min_dis_depot)): if min_dis_depot[i][0] == 0: cus1.append(i + 1) if min_dis_depot[i][0] == 1: cus2.append(i + 1) if min_dis_depot[i][0] == 2: cus3.append(i + 1) if min_dis_depot[i][0] == 3: cus4.append(i + 1) customer_means.append(cus1) customer_means.append(cus2) customer_means.append(cus3) customer_means.append(cus4) for i in range(len(customer_means)): customers = customer_means[i] mans = len(customers) dep_num = i + 1 + 40 rows = 100 times = 300 genetic = GeneticAlgorithm(dep_num, customers, dis_dep_cus[i], dis_sta_cus, dis_cus_cus, min_dis_station, demand, time_win, tolerate_time_win, mans, rows, times) genetic.run()
def draw_table_vol(): pl.ioff() contents = readfile.readfile("000007.csv") high, low = readfile.get_prices(contents) ranges = range(len(contents)) prices = [content[2] for content in contents] table_x = list() table_y = list() high, low = readfile.get_vol(contents) for x, content in enumerate(contents): table_x.append(x) table_y.append(float(content[1])) pl.plot(table_x, table_y, "-") pl.show()
def tests(net, tims=-1, aimat=float(0.0), readf=readfile.readfile(), logs=None): aimat = float(aimat) net.eval() correct = 0 total = 0 tot = 0 for data in readf.testsetsmallloader: images, labels = data outputs = net(Variable(images, volatile=True)) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() if total > 100: break print('现实 : %d %%' % (100 * correct / total)) logs.refline((100 * correct / total), "现实正确率") if float(100 * correct / total) > float(aimat): aimat = float(aimat) aimat = float(100 * correct / total) print("正确率" + str(float(aimat)) + "刷新数据") torch.save(net, "./mod2/handalexnetmax34" + str(tims)) # net.state_dict(), correct = 0 total = 0 for data in readf.testselfloader: tot += 1 images, labels = data outputs = net(Variable(images, volatile=True)) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() if tot > 100: break print('自身 : %d %%' % (100 * correct / total)) logs.refline((100 * correct / total), "自身正确率") return float(aimat)
def __init__(self,): tablist=readfile() self.values=[] for i in range(3): value=tuple(tablist[0]) del tablist[0] self.values.append(value) self.pfdict=dict(tablist) try: self.conn=pymysql.connect(host=self.pfdict["host"], port=int(self.pfdict['port']), user=self.pfdict['user'], passwd=self.pfdict['passwd'], db=self.pfdict['db'], charset=self.pfdict['charset']) self.cur=self.conn.cursor() except: print("连接数据库失败!") else: print("连接数据库成功!")
def polygon_color(file): # Read in input file and arrange points in desired format result = readfile.readfile(file) poly = result['poly1'] px = [poly[i][0] for i in range(len(poly))] py = [poly[i][1] for i in range(len(poly))] # Sort the x and y coordinates x_sorted = np.sort(px) y_sorted = np.sort(py) # Call functions and plot plt.cla() plt.axis('equal') plt.grid('on') plt.hold(True) colors = plot_boundary(poly) flood_fill(int(np.median(px)), int(np.median(py)), 'w', 'g', x_sorted[0], y_sorted[0], colors) plt.show(block=False)
def __init__(self, ): tablist = readfile() self.values = [] for i in range(3): value = tuple(tablist[0]) del tablist[0] self.values.append(value) self.pfdict = dict(tablist) try: self.conn = pymysql.connect(host=self.pfdict["host"], port=int(self.pfdict['port']), user=self.pfdict['user'], passwd=self.pfdict['passwd'], db=self.pfdict['db'], charset=self.pfdict['charset']) self.cur = self.conn.cursor() except: print("连接数据库失败!") else: print("连接数据库成功!")
def test_readcsv(capsys): dirpath = Path("C:/code/cohort4/python-IO") filename = "Census_by_Community_2019.csv" hdr = "************************************************************\n" hdr += "************** Calgary Public Data Summary ***************\n" hdr += "************************************************************\n" content1 = "Residential and SOUTH: 230129" content2 = "Industrial and NORTH: 0" content3 = "Overall Total: 1283177" content4 = "Total number of records: 307" ftr = "************************************************************\n" ftr += "*************** End of Report Summary ********************\n" ftr += "************************************************************\n" result = readcsv(dirpath, filename) captured = capsys.readouterr() reportdata = readfile(dirpath, "report.txt") assert result == { "keyvaluepair": { "RES_CNT": 10, "CLASS": 1, "SECTOR": 5 }, "linenum": 308 } assert hdr in captured.out assert ftr in captured.out assert content1 in captured.out assert content2 in captured.out assert content3 in captured.out assert content4 in captured.out assert os.path.isfile(os.path.join(dirpath, "report.txt")) == True assert content1 in reportdata assert content2 in reportdata assert content3 in reportdata assert content4 in reportdata
'pricing': 0.05, 'blog': 0.5, 'payment': 1.0 }, } id_to_states = { 0: 'Zero', 1: 'Aware', 2: 'Considering', 3: 'Experiencing', 4: 'Ready', 5: 'Lost', 6: 'Satisfied' } obs_seq, obs_seq_id = readfile("hmm_customer_1586733275338.txt") new_emmi_prop = new_emmi_prop(emit_p, obs_seq) tran_prob = convert_to_list(trans_p) emmi_prob = convert_to_list(new_emmi_prop) init_prob = np.array(list(start_p.values())) hidden_s, max_prob = viterbi(tran_prob, emmi_prob, init_prob, obs_seq_id) opt_seq = [] for data in hidden_s: opt_seq.append(id_to_states[data]) print("Observation sequence : " + str(obs_seq)) print() print("Transition Probability: " + str(trans_p)) print()
def chat(): # keyword conditions condnext = False condweather = False condtime = False condlocation = False condtemp = False condkey = False condresponse = False foundinfo = False condtrain = False condcountry = False condspellcheck = True # global variables conversation = [] location = '' prevlocation = location time = 'today' key = '' keytemplate = [] fulltime = '' numdays = '' logstr = '' printstr = '' responsedict = {} # Dictionary to hold all inputs without predefined responses. This dictionary will be written into predefined_responses.txt before exiting the program. # read data files citylist = readfile.readfile('cities.txt') keylist = readfile.readfile('keywords.txt') timelist = readfile.readfile('time.txt') condlist = readfile.readfile('conditions.txt') numlist = readfile.readfile('numbers.txt') countrylist = readfile.readfile('countries.txt') exitlist = ['exit', 'quit', 'bye', 'ok'] # Greeting message printstr = 'Hello! You can ask me questions about the weather in any major city in the world. What would you like to know?' print printstr logstr += '\n\n' + printstr # Start main loop while True : foundinfo = False condtrain = False condcountry = False # read input from user input = raw_input('\nMe > ') logstr += '\nMe > ' + input + '\nBot > ' if input in exitlist: if input == 'ok': exitans = raw_input("Do you want to quit? (y/n)") if exitans in ('y','Y','Yes','YES','yes'): break else: continue break if input == 'disable spellcheck': condspellcheck = False continue if input == 'enable spellcheck': condspellcheck = True continue condcorrected = False if condspellcheck: corrected_input = '' for i in input.split(): str = spellcheck.correct(i) if str != i: condcorrected = True corrected_input += str + ' ' if condcorrected: print 'did you mean: \"' + corrected_input + '\"?' input = corrected_input currentstring = input.split() conversation.append(currentstring) # Start searching input for each of the keywords if input == 'train': condtrain = True printstr = 'Entering training mode. Enter input and response seperated by a "|": input|response. Type "exit" to quit training mode' print printstr logstr += '\n' + printstr + '\n' while True: traininput = raw_input('>') if traininput == 'exit': break if traininput.find('|') < 0: printstr = 'Format error: use input|response' print printstr logstr += '\n' + printstr + '\n' continue traininput = traininput.split('|') responsedict[traininput[0]] = traininput[1] if condtrain: continue for i in countrylist: for j in currentstring: if lower(i[0]) == lower(j): printstr = 'Which city in ' + i[0] + '?' condcountry = True foundinfo = True break if condcountry: print printstr logstr += printstr continue if 'next' in input: foundinfo = True condnext = True condtime = False numdays = currentstring[currentstring.index('next') + 1] for i in numlist: if numdays == i[0]: numdays = i[1] break if re.match('[0-9]*$',numdays): numdays = int(numdays) else: numdays = '' if 'weather' in input: foundinfo = True condweather = True condkey = False condtemp = False key = '' keytemplate = [] # get key from input for i in keylist: if i[0] in input: if 'sunday' in lower(input) and i[0] == 'sun': break else: foundinfo = True condkey = True condweather = False condtemp = False key = i[0] keytemplate = i break # get time from input for i in timelist: if lower(i[0]) in input: foundinfo = True condtime = True numdays = '' if lower(i[0]) != 'today' and lower(i[0]) != 'tomorrow': time = i[1] fulltime = i[0] break else: time = i[0] fulltime = time break if fulltime == '': fulltime = time if numdays != '': condtime = True if numdays > 4: printstr = 'Forecast is available only for the next 4 days.' print printstr logstr += '\n' + printstr + '\n' else: time = '' fulltime = '' count = numdays # get location from input for i in citylist: if lower(i[0]) in input: foundinfo = True condlocation = True location = i[0] break # find if a new location has been mentioned. if not, don't fetch data again if location != prevlocation: newlocation = True condlocation = True prevlocation = location else: newlocation = False if location is '': if prevlocation is '': condlocation = False else: location = prevlocation newlocation = False location = location.replace(' ','-') #Google requires a '-' in 2-word city names result = False # get temperature from input if 'temperature' in input: foundinfo = True condtemp = True # User gave no infomation about weather. Switching to general predefined response based chat if not foundinfo: response = predefined_responses.respond(input, responsedict) if response == '': printstr = "I don't know what that means. If I asked you the same question, what would you reply?" print printstr logstr += printstr responseinput = raw_input('Me > ') logstr += '\nMe > ' + responseinput if not responseinput in ('exit', 'quit'): responsedict[input] = responseinput print 'response learnt' else: printstr = response print printstr logstr += printstr continue if condlocation: if newlocation: #If location hasn't changed, don't fetch data again. It's already available printstr = 'Fetching weather information from Google...' print printstr logstr += printstr # Call Google weather to get current weather conditions google_result = weather.get_weather(location) if google_result == {}: print 'Could not get data from google.' continue # We have a valid location. Get further information # User has asked about temperature. Return temperature information and continue if condtemp: printstr = temperature.temperature(google_result, time) print printstr logstr += printstr continue # User has asked about a specific weather condition. Print information. There are 2 possibilities: # 1. Find the condition in the next n days # 2. Find the condition in a specified day if condkey: # 1. User has asked about a specific condition in the 'next x days'. Return appropriate response printstr = '' timecounter = 0 day_of_week = '' condition = '' if numdays != '': for i in google_result['forecasts']: count -= 1 if count < 0: break if key in lower(i['condition']): result = True day_of_week = i['day_of_week'] condition = i['condition'] break for i in timelist: if i[0] != 'today' and i[0] != 'tomorrow': if i[1] == day_of_week: fulltime = i[0] break if result: printstr = keytemplate[3] + keytemplate[0] + ' on ' + fulltime else: printstr = keytemplate[4] + keytemplate[0] + ' in the next ' + str(numdays) + ' days.' print printstr logstr += printstr continue # 2. User has asked about a particular condition on a particular day. Return appropriate response if time != 'today' and time != 'tomorrow': for i in google_result['forecasts']: if i['day_of_week'] == time: if key in lower(i['condition']): printstr = keytemplate[3] + keytemplate[0] + ' on' else: printstr = keytemplate[4] + keytemplate[0] + ' on' elif time == 'today': fulltime = time if key in lower(google_result['current_conditions']['condition']): printstr = keytemplate[1] + keytemplate[0] else: printstr = keytemplate[2] + keytemplate[0] elif time == 'tomorrow': fulltime = time if key in lower(google_result['forecasts'][1]['condition']): printstr = keytemplate[3] + keytemplate[0] else: printstr = keytemplate[4] + keytemplate[0] printstr = printstr + ' ' + fulltime print printstr logstr += printstr continue # User is asking about today's weather. Print details elif time == '' or time == 'today' : printstr = sentence.sentence(google_result['current_conditions']['condition'], time) printstr += ' ' + fulltime + '. ' + google_result['current_conditions']['humidity'] + ' ' if google_result['current_conditions'].has_key('wind_condition'): printstr += google_result['current_conditions']['wind_condition'] print printstr logstr += printstr continue # User is asking about weather of a particular day. Print details elif time == 'tomorrow': printstr = sentence.sentence(google_result['forecasts'][1]['condition'], time) printstr += ' ' + fulltime print printstr logstr += printstr else: found = False for i in range(4): if google_result['forecasts'][i]['day_of_week'] == time: printstr = sentence.sentence(google_result['forecasts'][i]['condition'], time) printstr += " on" + ' ' + fulltime print printstr logstr += printstr found = True if not found: printstr = "Forecast for " + time + " is not available currently." print printstr logstr += printstr continue else: printstr = 'What\'s the location?' print printstr logstr += printstr # End of outermost while loop. # Print message before exiting program dictcount = 0 for i in responsedict: dictcount += 1 if dictcount > 0: printstr = 'Writing new entries to database...' print printstr logstr += printstr datafile = file('predefined_responses.txt', 'a') for i in responsedict.keys(): trimmedi = re.sub('[^a-zA-Z0-9 ]+','', i) string = trimmedi + '|' + responsedict[i] + '\n' datafile.write(string) log.log(logstr) print 'Ending the program...' print 'Bye!' # End of function chat()
#!/usr/bin/env python #python import logsend import readfile import configure import judge configure = configure.Configure() first = configure.readoption judge = judge.Judge(filename) filename = 'aa.txt' readfile.readfile(filename)
if __name__ == "__main__": fileNames = [ "a_example.txt", "b_read_on.txt", "c_incunabula.txt", "d_tough_choices.txt", "e_so_many_books.txt", "f_libraries_of_the_world.txt" ] for fileName in fileNames: Data.nBooks = 0 Data.nLibraries = 0 Data.nScanningDays = 0 Data.bookScores = [] Data.libraries = [] Running.currentProcessing = None # The current library being processed Running.daysLeft = 0 # How many days are left for the current library to be processed Running.librariesLeft = [] # The libraries still to be processed Running.processed = [] # Librarues that have been processed Running.numberProcessed = 0 Running.totalScore = 0 # The total score readfile("data/{}".format(fileName)) sortLibraries() for lib in Data.libraries: lib.sortBooks() print("Read/sort files") simulate(Data.libraries, Data.nScanningDays, "out/out_{}.txt".format(fileName[0]))
# from mtranslate import translate from writefile import writefile from readfile import readfile from fileLocation import fileLocation from languages import LANGUAGES import urllib from googleapiclient.discovery import build # translator = Translator() # print(LANGUAGES) intents = readfile('en/intent.txt') # print(intents) service = build('translate', 'v2', developerKey='AIzaSyCN5X2fEWsdO4jRSgwB_PZ3v_0A4HbmXCY') for key, value in LANGUAGES.items(): print (key, value) list = [] translated = service.translations().list( source='en', target=key, q=intents ).execute() for tranlation in translated['translations']: list.append(tranlation['translatedText']) # break writefile(key, list) # print(key + ' : ' , list) # break
import sys from readfile import readfile fileA = sys.argv[1] fileB = sys.argv[2] mA = readfile(fileA) mb = readfile(fileB) def guass(a, b): n = len(a) for k in range(0, n - 1): for i in range(k + 1, n): if a[i][k] != 0.0: lam = a[i][k] / a[k][k] print(lam) #a[i,k+1:n] = a[i, k+1:n] - lam*a[k,k+1:n] #b[i] = b[i] - lam*b[k] guass(mA, mb)
#coding=utf-8 ''' Created on 2016年8月16日 @author: admin ''' from fabric.api import * from readfile import readfile condict = dict(readfile()) env.host_string = condict["connext"] env.password = condict["passwd"] run("rm -rf /taokey") run("yum -y remove mysql-libs*") run("yum -y remove mysql-libs") run("yum install -y perl-Module-Install.noarch") run("yum install -y libaio") run("mkdir -p /taokey/tools/") with cd("/taokey/tools/"): run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-server-5.6.21-1.rhel5.x86_64.rpm" ) run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-devel-5.6.21-1.rhel5.x86_64.rpm" ) run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-client-5.6.21-1.rhel5.x86_64.rpm" ) rpmlist = run("ls ./").split() for rpm in rpmlist:
def __init__(self): self.tab=dict(readfile())
def bot(): conversation = [] location = '' time = 'today' key = '' keytemplate = [] fulltime = '' numdays = '' citylist = readfile.readfile('cities.txt') keylist = readfile.readfile('keywords.txt') timelist = readfile.readfile('time.txt') condlist = readfile.readfile('conditions.txt') numlist = readfile.readfile('numbers.txt') exitlist = ['exit', 'quit', 'bye', 'ok'] print 'Hello! You can ask me questions about the weather in any major city in the world. What would you like to know?' while True : input = raw_input('Me > ') if input in exitlist: break currentstring = input.split() conversation.append(currentstring) if 'next' in currentstring: numdays = currentstring[currentstring.index('next') + 1] for i in numlist: if numdays == i[0]: numdays = i[1] break if re.match('[0-9]*$',numdays): numdays = int(numdays) else: numdays = '' if 'weather' in currentstring: key = '' keytemplate = [] # get key from input for i in keylist: if i[0] in input: key = i[0] keytemplate = i break # get time from input for i in timelist: if lower(i[0]) in input: numdays = '' if lower(i[0]) != 'today' and lower(i[0]) != 'tomorrow': time = i[1] fulltime = i[0] break else: time = i[0] fulltime = time break if fulltime == '': fulltime = time if numdays != '': if numdays > 4: print 'Forecast is available only for the next 4 days.' else: time = '' fulltime = '' count = numdays prevlocation = location #We store previous location to avoid re-fetching data if the location hasn't been changed # Below, we check if any token in the input matches a city name, and if so, set location to that city newlocation = False # get location from input foundLocation = False for i in citylist: if lower(i[0]) in input: location = i[0] foundLocation = True break #if not foundLocation: #if location != '': #print "I didn't find any city name in your input. I'll get you information about " + location # find if a new location has been mentioned. if not, don't fetch data again if location is not prevlocation: newlocation = True if location is '': if prevlocation is '': print 'City not found' else: location = prevlocation newlocation = False location = location.replace(' ','-') #Google requires a '-' in 2-word city names result = False if location is not '': if newlocation: #If location hasn't changed, don't fetch data again. It's already available print 'Fetching weather information from Google...' # Call Google weather to get current weather conditions google_result = weather.get_weather(location) if 'temperature' in currentstring: print temperature.temperature(google_result, time) continue printed = False if key is not '': printstring = '' timecounter = 0 day_of_week = '' condition = '' if numdays != '': for i in google_result['forecasts']: count -= 1 if count < 0: break if key in lower(i['condition']): result = True day_of_week = i['day_of_week'] condition = i['condition'] break for i in timelist: if i[0] != 'today' and i[0] != 'tomorrow': if i[1] == day_of_week: fulltime = i[0] break if result: printstring = keytemplate[3] + keytemplate[0] + ' on ' + fulltime else: printstring = keytemplate[4] + keytemplate[0] + ' in the next ' + str(numdays) + ' days.' print printstring printed = True if not printed: if time != 'today' and time != 'tomorrow': for i in google_result['forecasts']: if i['day_of_week'] == time: if key in lower(i['condition']): printstring = keytemplate[3] + keytemplate[0] + ' on' else: printstring = keytemplate[4] + keytemplate[0] + ' on' elif time == 'today': fulltime = time if key in lower(google_result['current_conditions']['condition']): printstring = keytemplate[1] + keytemplate[0] else: printstring = keytemplate[2] + keytemplate[0] elif time == 'tomorrow': fulltime = time if key in lower(google_result['forecasts'][1]['condition']): printstring = keytemplate[3] + keytemplate[0] else: printstring = keytemplate[4] + keytemplate[0] print printstring, fulltime elif time == '' or time == 'today' : printstring = sentence.sentence(google_result['current_conditions']['condition'], time) print printstring, fulltime, google_result['current_conditions']['humidity'], google_result['current_conditions']['wind_condition'] else : if time == 'tomorrow': printstring = sentence.sentence(google_result['forecasts'][1]['condition'], time) print printstring, fulltime else: found = False for i in range(4): if google_result['forecasts'][i]['day_of_week'] == time: printstring = sentence.sentence(google_result['forecasts'][i]['condition'], time) print printstring, "on", fulltime found = True if not found: print "Forecast for " + time + " is not available currently." else: print 'What\'s the location?' #end of outermost while loop print 'ending the program...' print 'bye!'
#!/usr/bin/python3 from readfile import readfile from emailsetting import sendmail """ Buscar por nombre y estado""" search = 'Cassandra' enable = 'Activo' """ Lectura de datos """ query = 'SELECT * FROM test' """ Obteniendo informacion de lectura """ file = readfile(1, query) """ Realizando busqueda """ file = file[file['name'].str.contains(search)] """ Enviar corrreo """ for reg in file.itertuples(): name = reg[1] receiver_mail = reg[2] mail2 = reg[3] subject = reg[4] body = reg[5] attachment = reg[8] filemail = reg[9] sendmail('*****@*****.**', receiver_mail, name, subject, body, attachment, filemail)
from readfile import readfile import linecache import string OCCURENCE_LIST_PATH = "./Occurence_List.dat" ## occurence list save address if __name__ == '__main__': keyTrie = readfile() print("FILE LOAD FINISH.") print("##################################") print("INPUT -h FOR HELP") print("INPUT -q TO QUIT THE SEARCH ENGINE") print( "This search engine support multiple search. If you want to search multiple keyword, you should use space to separate the keywords." ) print("Ex: 'nltk data'") print("This search engine also support prefix search.") print( "Ex: We have 'data', 'database' and 'dat' in trie. We input 'da', algorithm will return the most frequently in data, database and dat." ) print("##################################\n") lastSearchKey = "" lastSearchResult = [] while (True): inputString = input("\nWhat key word you want to search now: ") inputString = inputString.strip() ###### INSTRUCTION ###### if inputString == "-h" or inputString == "-H" or inputString == "-help": print("------- HELP LIST: -------") print("-h -H -help ======> HELP") print("-q -Q -quit ======> TO QUIT THE SEARCH ENGINE")
import clips,readfile,sys rules = [rule.strip() for rule in readfile.readfile(sys.argv[1])] provinces = [province.strip() for province in readfile.readfile(sys.argv[2])] for province in provinces: clips.SendCommand(province) clips.Assert("(province Bangkok)") clips.Run() for rule in rules: clips.SendCommand(rule) clips.Assert("(kind round shaped rice)") print "condition : " clips.PrintFacts() #BEFORE bf = len(clips.FactList()) #RUN clips.Run() #AFTER af = len(clips.FactList()) for i in range(af - (af - bf)): #print "retract Fact",i clips.FactList()[0].Retract()
#-------------入口函数,开始执行----------------------------- """ 输入参数的的意义依次为 self.rows = rows #排列个数 self.times = times #迭代次数 self.mans = mans #客户数量 self.cars = cars #车辆总数 self.tons = tons #车辆载重 self.distance = distance #车辆一次行驶的最大距离 self.PW = PW #当生成一个不可行路线时的惩罚因子 """ d, q, et, lt, eet, llt = readfile("sss.csv") ga = GeneticAlgorithm(dist=d, dema=q, e_time=et, l_time=lt, ee_time=eet, ll_time=llt, rows=5, times=5, depots=3, mans=34, cars=999, tons=100, distance=150, server=10, PW=10000)
import numpy as np from gridworld import grid_mat, print_policy ,print_values, all_actions_ingrid, all_rewards_ingrid from value_iteration_p3 import value_iteration from policy_iteration_p3 import policy_iteration from readfile import readfile import time if __name__ == '__main__': # Enter the file name for which you want to excute value and policy iteration. gamma, noise, gridworld = readfile("Input/i3.txt") gridworld = np.asarray(gridworld) all_actions = all_actions_ingrid(gridworld) all_rewards = all_rewards_ingrid(gridworld) grid = grid_mat(all_actions, all_rewards) #Value Iteration start = time.time() V,policy = value_iteration(grid,gamma,noise) end = time.time() print("Values:") print_values(V, gridworld.shape) print("Policy:") print_policy(policy, gridworld.shape) print("Runtime of Value iteration", end-start) #Policy Iteration start = time.time()
#coding=utf-8 ''' Created on 2016年8月16日 @author: admin ''' from fabric.api import * from readfile import readfile import sys condict=dict(readfile()) env.host_string=condict["connext"] env.password=condict["passwd"] file="linux_mysql_install.log" path_file=condict["log_path"]+file f=open(path_file,"w+",1) sys.stdout=f def mysql_install(): run("rm -rf /taokey") run("yum -y remove mysql-libs*") run("yum -y remove mysql-libs") run("yum install -y perl-Module-Install.noarch") run("yum install -y libaio") run("mkdir -p /taokey/tools/") with cd("/taokey/tools/"): run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-server-5.6.21-1.rhel5.x86_64.rpm") run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-devel-5.6.21-1.rhel5.x86_64.rpm") run("wget http://dev.mysql.com/Downloads/MySQL-5.6/MySQL-client-5.6.21-1.rhel5.x86_64.rpm")
def trains(self, net): self.logs = logger.logger() # logs.refline(1,"2334",1) # logs.refline(4,"2334",5) # logs.refline(1,"2334",6) # logs.refline() readf = readfile.readfile() aimat = float(0.0) # test.tests(net, 0, float(aimat), readf, self.logs) net.train(mode=True) torch.set_num_threads(8) criterion = nn.CrossEntropyLoss( ) # use a Classification Cross-Entropy loss optimizer = optim.SGD([{ 'params': net.features.parameters(), 'lr': 0.00001 }, { 'params': net.lin.parameters(), 'lr': 0.001 }, { 'params': net.classifier.parameters(), 'lr': 0.0005 }], momentum=0.9) # scheduler = MultiStepLR(optimizer, milestones=[10, 80], gamma=0.1) tot = 0 for epoch in range(2000): # loop over the dataset multiple times # scheduler.step() running_loss = 0.0 net.train(mode=True) for i, data in enumerate(readf.dataloader, 0): tot += 1 # get the inputs inputs, labels = data # wrap them in Variable inputs, labels = Variable(inputs), Variable(labels) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.data[0] if i % 25 == 24: # print every 2000 mini-batches print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 25)) self.logs.defx = tot # print(self.logs.defx) self.logs.refline(running_loss / 25 * 100, "loss") running_loss = 0.0 aimat = float(aimat) aimat = test.tests(net, epoch, float(aimat), readf, self.logs) net.train(mode=True) torch.save(net, "./mod2/handalexnet34") print("saved") print('Finished Training')
import numpy as np import preprocess_data as p import readfile as r import cal_probability as cd import naivebayes as nb data_train = r.readfile("Text_data_for_Project1_train_data.txt") data_test = r.readfile("Text_Data_for_Project1_test_data.txt") alabel_train, clabel_train, adata_train, cdata_train = p.p_traindata( data_train) alabel_test, adata_test = p.p_testdata(data_test) atraintestmix = np.vstack((adata_train, adata_test)) att_num = np.shape(adata_train)[1] dat_num = np.shape(adata_train)[0] adata_traintest_dummy, a_label = p.indexing(atraintestmix, att_num, dat_num, key="test") adata_train_dummy = adata_traintest_dummy[:-1, :] adata_test_dummy = np.array([adata_traintest_dummy[-1, :]]) cdata_train_dummy, c_label = p.indexing(cdata_train, 1, dat_num, key="test") m, p = 0, 0 prior_probability = cd.cal_prior(adata_train_dummy, cdata_train_dummy, a_label, c_label, dat_num, m, p)
from Block import Block from Cube import Cube from Drone import Drone from readfile import readfile import math import sys filename = sys.argv[1] cube = readfile(filename) size = cube.size sizec = size**3 mode = 1 stop = 0 storHop = int(math.sqrt(sizec) / 2) drone = Drone(storHop, cube, 0, 0, size) print(cube) drone.hop() x_limit = size - 1 y_limit = size - 1 def mover(mode, x_limit, y_limit, stop): if (mode == 1): drone.MoveRight() if (drone.x == x_limit): mode = 3 elif (mode == 2): drone.MoveLeft() if (drone.x == size - (x_limit + 1)): mode = -1 elif (mode == 3):
for v in range(voca_size): fout.write(str(beta[i][v]) + ' '); fout.write('\n'); def train(max_iter): global alpha,beta,Gamma,Phi,doc,doc_cnt; for i in range(max_iter): now = mle(); print(now); print('Estep'); for d in range(doc_size): Estep(d, 20); # the e step of em algorithm if (d % 100 == 0): print('*'); now = mle(); print(now); print('Mstep'); Mstep(20); # the m step of em algorith print(alpha); savemodel(i); if __name__ == '__main__': fin = readfile('ap.dat'); doc, voca, doc_cnt = fin.read(); voca_size = len(voca); doc_size = len(doc); init(); train(10);
from KEM import settings from django.core.management import setup_environ setup_environ(settings) from rice.models import Rice import sys,readfile rules = [rule.strip() for rule in readfile.readfile("kem.csv")] for i in rules: i = i.split(',') r = Rice(name=i[0],kind=i[1],number=i[2],area=i[3],terrain=i[4],character=i[5],seed=i[6],detail=i[7],pic=i[8],ref=i[9]) r.save() print Rice.objects.all()
ls_nsub = [] ls_subset = [] ls_rel = [] ls_times = [] lbound = [] nelements = [] nsubsets = [] means = ['Mean'] # The first two values printed on the list correspond to the total cost and # the numbers of subsets chosen respectively for name in files: # Read file path = os.path.join(datasets_path, name) df, costs = readfile(path) nelements.append(df.shape[0]) nsubsets.append(df.shape[1]) # Lower bound lb = lowerbound(df, costs) lbound.append(lb) # VND print('GA') start_ga = time.perf_counter() ga_cost, ga_subsets = GA(df, costs, npop, maxtime, nchilds, pmut) time_ga = time.perf_counter() - start_ga print('C:\t', [ga_cost, len(ga_subsets)] + ga_subsets, '\t', time_ga) ga_scores.append(ga_cost) ga_rel.append(np.float32(np.round(ga_cost / lb, 3)))
import sys,readfile class rule(object): def __init__(self,name,kind,terrain,area,count): self.name = name self.terrain = terrain self.area = area self.kind = kind self.count = count def getrule(self): return "(defrule {0}.{4} \"{0}\" (kind {1}) (terrain {2}) (area {3}) => (assert (rice {0})))\n".format(self.name,self.kind,self.terrain,self.area,self.count) data = [i.strip() for i in readfile.readfile(sys.argv[2])] lst = [] for i in data: x = i.split(",") lst.append([x[0],x[1],x[3],x[4]]) print lst for i in lst: with open(sys.argv[1],"a") as data: name = i[0] kind = i[1] area = i[2] terrain = i[3] count = 0 for k in area.split("/"): for j in terrain.split("/"): tmp_rule = rule(name,kind,j.lower(),k.title(),count) count+=1 print tmp_rule.getrule() data.write(tmp_rule.getrule())