def productsKidsRequest(): oGetData = GetData() oUnisportData = oGetData.GetUnisportData() oSortData = SortData() oSortedData = oSortData.getDictsByStringInKeyValue(oUnisportData, "name", "Børn") #paginate the result to only show 10 pr page, #paginate the result to only show 10 pr page iPageVar=1 #paginate the result to only show 10 pr page oPage= oSortData.paginateList(oSortedData, iPageVar, 10) #convert the list back to json return json.dumps(oPage)
def productCreateRequest(): oGetData = GetData() oUnisportData = oGetData.GetUnisportData() if not request.json: return "json data not valid" oNewProduct = json.loads(request.json) oManipulateData = ManipulateData() oSortedData = oManipulateData.createProduct(oUnisportData, oNewProduct) #convert the list back to json return json.dumps(oSortedData)
def GetTestFeatures(): Data=GetData() TrainSet=Data[12] # print(TrainSet['cycle_life']) features=[] l=[] for i in TrainSet['summary']: if(i == "QD"): # print(i) # l.append(i) d=TrainSet['summary'][i] d=d.reshape(-1,1) min_max_scaler = preprocessing.MinMaxScaler() x_minmax = min_max_scaler.fit_transform(d) # x_minmax=d features.append(x_minmax.squeeze()) features=np.array(features) print(features.shape) features=features.T print(features.shape) # features=features.reshape((849,-1)) return features
def __init__(self, llwl='Brown', llNL=2, percen=80, NE = True, Col = True, Gram = True, Chu = True): ''' @param llwl:LogLikleyHood Corpa name ('Brown','AmE06','BE06') @param llNL:LogLikleyHood @param percen: Presision of output default = 20, 20% returned @param NE: Uses NE default True @param Col: Uses Collocation default True @param Gram: Uses N-Grams default True @param Chu: Uses Chunking default True ''' self.NEs = NE self.Col = Col self.Gram = Gram self.Chu = Chu self.p = percen print 'Starting to build ', llwl self.LL = LogLikelihood(wordlist=llwl, NLength=llNL) print 'LL Loaded' self.POS = POS() print 'POS Loaded' self.GD = GetData() print 'GD Loaded' self.Cu = Chunker(self.POS) print 'Cu Loaded' self.FL = Filter() print 'FL Loaded' self.CC = Collocation(self.POS) print 'CC Loaded' self.Ng = NGram() print 'Ng Loaded' self.S = Select(percentil=self.p) print 'S Loaded' self.To = Tokenize(self.FL) print 'To Loaded'
def productsRequest(): oGetData = GetData() oUnisportData = oGetData.GetUnisportData() oSortData = SortData() oSortedData = oSortData.sortByPrice(oUnisportData, False) iPageVar = str(request.args.get('page')) #test if the page value only contains numbers if not (iPageVar.isdigit()): iPageVar="1" #paginate the result to only show 10 pr page oPage= oSortData.paginateList(oSortedData, iPageVar, 10) #oPage = paginate.Page(oSortedData,page=iPageVar,items_per_page=10) #convert the list back to json return json.dumps(oPage)
def __init__(self, llwl='Brown', llNL=2, percen=80, NE=True, Col=True, Gram=True, Chu=True): ''' @param llwl:LogLikleyHood Corpa name ('Brown','AmE06','BE06') @param llNL:LogLikleyHood @param percen: Presision of output default = 20, 20% returned @param NE: Uses NE default True @param Col: Uses Collocation default True @param Gram: Uses N-Grams default True @param Chu: Uses Chunking default True ''' self.NEs = NE self.Col = Col self.Gram = Gram self.Chu = Chu self.p = percen print 'Starting to build ', llwl self.LL = LogLikelihood(wordlist=llwl, NLength=llNL) print 'LL Loaded' self.POS = POS() print 'POS Loaded' self.GD = GetData() print 'GD Loaded' self.Cu = Chunker(self.POS) print 'Cu Loaded' self.FL = Filter() print 'FL Loaded' self.CC = Collocation(self.POS) print 'CC Loaded' self.Ng = NGram() print 'Ng Loaded' self.S = Select(percentil=self.p) print 'S Loaded' self.To = Tokenize(self.FL) print 'To Loaded'
def request_handler(self, type): if type == 'GET': path = urlparse.urlparse(self.path) isparam = re.search('\?', self.path) querystr = path.query query = {} if isparam: for q in querystr.split('&'): key = q.split('=')[0] value = q.split('=')[1] query[key] = value self.send_response(200) self.send_header('Content-Type', 'application/json') self.end_headers() e = GetData() filejson = e.loadfilterdata(query, ServConf().initconf()) self.wfile.write(filejson) else: self.send_error(400) # else:
def __init__(self, botID: str): self.author = author self.version = version if len(botID) < 18 or len(botID) > 18: print(f"{botID} invalid") else: self.botID = botID self.data = GetData(botID) def getData(self): return self.data
def test(f, idx=14, filename="m1.h5"): model = load_model(filename) model.summary() features = GetFeatures(idx, f) testX, testY = BuildSet(features, step) testX = np.array(testX) testY = np.array(testY) testX = testX.reshape(testX.shape[0], step, -1) ans = model.predict(testX) plt.figure(1) Data = GetData() TrainSet = Data[idx] capacity = [] IR = [] for i in TrainSet['summary']: if (i in ["QD"]): d = TrainSet['summary'][i] capacity = d if (i in ["IR"]): d = TrainSet['summary'][i] IR = d # plt.subplot(311) # plt.plot(capacity,label="QD") # plt.xlabel("Cycle") # plt.ylabel("Discharge Capacity (Ah)") # plt.legend() # plt.subplot(312) # plt.plot(IR,label="IR") # plt.xlabel("Cycle") # plt.ylabel("Internal Resistance (Ohm)") # plt.legend() # plt.subplot(313) testY *= len(capacity) delta = [] for i in range(len(ans)): ans[i] = i / (1 - ans[i]) * ans[i] delta.append(ans[i] - testY[i]) x = np.arange(step, len(capacity) + 1) # plt.plot(x,ans,label="Remain Cycles Predict") plt.plot(x, delta) plt.xlabel("Last cycle") plt.ylabel("Cycle Error") plt.ylim((-100, 100)) plt.legend() plt.show()
class Bot: def __init__(self, pair, interval): self.pair = pair self.interval = interval self.df = GetData(pair, interval) self.df = self.df.getData() def run(self): pair = self.pair interval = self.interval df = self.df strat = Strategy(df, pair) strat.run() #run test the entire day and live goes live strat.output(True)
def main(): Data = GetData() Data = AddPos(Data) AddDate(Data) # The amount of twitters a = Sort_Dict(Counter(Data.date)) plt.plot([i[0] for i in a], [i[1] for i in a]) AddUserName(Data) move_list = GetAllMove_FasterVersion(Data) day_m = AggMoveListByTime(move_list, way='day') week_m = AggMoveListByTime(move_list, way='week') month_m = AggMoveListByTime(move_list, way='month') Save_Obj(day_m, './Data/day_move') Save_Obj(week_m, './Data/week_move') Save_Obj(month_m, './Data/month_move')
def submit(): global hsl if (ekspedisi.get() == 1): hsl = "JNE" if (ekspedisi.get() == 2): hsl = "JNT" if ((input1.get() != "") and (input2.get() != "") and (input3.get() != "") and (drop.get() != "")): dataTable.append( GetData(input1.get(), input2.get(), input3.get(), drop.get(), list(check.hasil()), hsl)) messagebox.showinfo("", "Data Berhasil Dimasukan") else: messagebox.showwarning("", "Data tidak lengkap") input1.delete(0, END) input2.delete(0, END) input3.delete(0, END) drop.delete(0, END)
def main(): #import data test_data = GetData(TEST_DIR) print(test_data.source_list) with tf.name_scope('inputs'): #create the model x=tf.placeholder(tf.float32,[Batch_SIZE,Img_depth,Img_rows,Img_cols,1],name='x_input') # Define loss and optimizer y_ = tf.placeholder(tf.int16, [Batch_SIZE,Img_depth, Img_rows,Img_cols,n_class],name='y__input') #define a global step global_step = tf.Variable(0,name="global_step") # Build the graph for the deep net network, outputs= network(x) dice_loss = dice_coef_loss(outputs,y_) with tf.name_scope('train'): train_step = tf.train.AdamOptimizer(1e-5).minimize(dice_loss) #add ops to save and restore all the variables saver = tf.train.Saver() #use only single CPU m_config = tf.ConfigProto() m_config.gpu_options.allow_growth = True with tf.Session(config=m_config) as sess: # sess.run(tf.global_variables_initializer()) #when continue training this model, should comment this line #first start to train the model, should comment these lines check_points_list = tf.train.latest_checkpoint(LOG_DIR) #return the filename of the lastest checkpoint print(len(check_points_list)) print(check_points_list) #is the name of this checkpoint saver.restore(sess,check_points_list) # global_step_value = sess.run(global_step) print("Last iteration:",global_step_value) for i in range(global_step_value+1,116001+1): #这里只是想让for循环跑一遍 last_point=0 for p in range(73): #根据原始NII图像分割出来的patch的数量而定 print(p) images_test=test_data.next_batch_order_2(Batch_SIZE,"mr_train_1019_boundingBox.nii.gz",64,16,last_point) dp_dict = tl.utils.dict_to_one(network.all_drop) #disable nosie layers when testing feed_dict_test = {x: images_test} feed_dict_test.update(dp_dict) output_image = sess.run(outputs,feed_dict=feed_dict_test) #use the test next_batch # output_image = outputs.eval(feed_dict=feed_dict_test) print(type(output_image)) print(np.shape(output_image)) # output_image = np.asarray(output_image) # output_image= outputs.eval(feed_dict={x:images}) for j in range(last_point,last_point+Batch_SIZE): input_Image=images_test[...,0] LVB = output_image[...,0] out_LVB = LVB[j-last_point,...] RVB = output_image[...,1] out_RVB = RVB[j-last_point,...] LAB = output_image[...,2] out_LAB = LAB[j-last_point,...] RAB = output_image[...,3] out_RAB = RAB[j-last_point,...] MLV = output_image[...,4] out_MLV = MLV[j-last_point,...] AA = output_image[...,5] out_AA = AA[j-last_point,...] PA = output_image[...,6] out_PA = PA[j-last_point,...] BACK = output_image[...,7] out_BACK = BACK[j-last_point,...] CreatNii_save(out_LVB,save_dir,"out_LVB" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_RVB,save_dir,"out_RVB" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_LAB,save_dir,"out_LAB" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_RAB,save_dir,"out_RAB" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_MLV,save_dir,"out_MLV" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_AA,save_dir,"out_AA" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_PA,save_dir,"out_PA" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(out_BACK,save_dir,"out_BACK" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) CreatNii_save(input_Image[j-last_point,...],save_dir,"Input_Test_Image" +str(i)+"_"+str(j)+ ".nii.gz",np.eye(4)) last_point = last_point+Batch_SIZE
#!/usr/bin/env python2 # -*- coding: utf-8 -*- import datetime from GetData import GetData from skfeature.utility.construct_W import construct_W from skfeature.function.similarity_based import lap_score from skfeature.function.sparse_learning_based import MCFS from EntropyBasedFeatureRanking import EntropyBasedFeatureRanking from skfeature.function.similarity_based import SPEC # initialization methodType = 0 dataSet = 0 data = GetData(dataSet) print "Data Preparation finished." timeStart = datetime.datetime.now() # feature selection if methodType == 0: # Laplacian Score kwrags_W = { "metric": "euclidean", "neighbor_mode": "knn", "weight_mode": "heat_kernel", "k": 5, "t": 1 } W = construct_W(data, **kwrags_W) result = lap_score.lap_score(data, W=W)
Last edited : 4:20pm 5/18/2020 """ from GetData import GetData from Utility import Util from Transform import Transform import numpy as np import seaborn as sn import matplotlib.pyplot as plt from sklearn import linear_model from sklearn.model_selection import cross_val_score #read config config = Util.read_config() #get data df = GetData.read_csv(GetData, config['data']['FuelConsumption']['filepath']) #get all numeric columns cols_numeric = df.select_dtypes([np.number]).columns #remove non-numeric columns df = df[cols_numeric] #find correlations between variables corrMatrix = df.corr() mask = np.zeros_like(corrMatrix) mask[np.triu_indices_from(mask)] = True with sn.axes_style("white"): f, ax = plt.subplots(figsize=(7, 5)) sn.heatmap(corrMatrix, vmin=-1, vmax=1, mask=mask, annot=True,
def main(): api = RiotAPI("RGAPI-ac961b5c-4740-4fd0-9f9b-585ec0b78924") gets = GetData(api) gets.run()
class runable(object): ''' Class for selecting keywords and extracting keywords from online contentent. ''' def __init__(self, llwl='Brown', llNL=2, percen=80, NE = True, Col = True, Gram = True, Chu = True): ''' @param llwl:LogLikleyHood Corpa name ('Brown','AmE06','BE06') @param llNL:LogLikleyHood @param percen: Presision of output default = 20, 20% returned @param NE: Uses NE default True @param Col: Uses Collocation default True @param Gram: Uses N-Grams default True @param Chu: Uses Chunking default True ''' self.NEs = NE self.Col = Col self.Gram = Gram self.Chu = Chu self.p = percen print 'Starting to build ', llwl self.LL = LogLikelihood(wordlist=llwl, NLength=llNL) print 'LL Loaded' self.POS = POS() print 'POS Loaded' self.GD = GetData() print 'GD Loaded' self.Cu = Chunker(self.POS) print 'Cu Loaded' self.FL = Filter() print 'FL Loaded' self.CC = Collocation(self.POS) print 'CC Loaded' self.Ng = NGram() print 'Ng Loaded' self.S = Select(percentil=self.p) print 'S Loaded' self.To = Tokenize(self.FL) print 'To Loaded' def Select(self, url, depth): ''' Determin the best keywords for a webpage. @param url: the base url to start sampaling from @param depth: the depth of the website to be sampled @return: the list of selected keywords, ordered with the highest rated words to the lower bownd of array. ''' #Get data from web page text = self.GD.getWebPage(url, depth) #Tokonize sentance and words tok = self.To.Tok(text) #POS tag the text pos = self.POS.POSTag(tok, 'tok') #Log Likly Hood log = self.LL.calcualte(tok) #Collocations if self.Col == True: col = self.CC.col(pos, tok) else: col = [] #NE Extraction if self.NEs == True: ne = self.Cu.Chunks(pos, nodes=['PERSON', 'ORGANIZATION', 'LOCATION']) else: ne = [] #Extract NP if self.Chu == True: chu = [self.Cu.parse(p) for p in pos] else: chu = [] #Creat N-gram if self.Gram == True: ga = self.Ng.Grams(pos, n=6) else: ga = [] return self.S.keywords(ne, ga , col , chu, log)
def __init__(self, pair, interval): self.pair = pair self.interval = interval self.df = GetData(pair, interval) self.df = self.df.getData()
from GetLinks import GetLinks from GetData import GetData from Register import Register from GetContacts import GetContacts from GetUniqueEmailPatterns import GetUniqueEmailPatterns from GenerateEmails import GenerateEmails if __name__ == '__main__': GetLinks.get_links() GetData().run() Register.register() GetContacts().run(4) print(GetUniqueEmailPatterns.get_unique_email_patterns()) input( 'Now you should write code for found email patterns. Enter when ready.' ) GenerateEmails().run()
class runable(object): ''' Class for selecting keywords and extracting keywords from online contentent. ''' def __init__(self, llwl='Brown', llNL=2, percen=80, NE=True, Col=True, Gram=True, Chu=True): ''' @param llwl:LogLikleyHood Corpa name ('Brown','AmE06','BE06') @param llNL:LogLikleyHood @param percen: Presision of output default = 20, 20% returned @param NE: Uses NE default True @param Col: Uses Collocation default True @param Gram: Uses N-Grams default True @param Chu: Uses Chunking default True ''' self.NEs = NE self.Col = Col self.Gram = Gram self.Chu = Chu self.p = percen print 'Starting to build ', llwl self.LL = LogLikelihood(wordlist=llwl, NLength=llNL) print 'LL Loaded' self.POS = POS() print 'POS Loaded' self.GD = GetData() print 'GD Loaded' self.Cu = Chunker(self.POS) print 'Cu Loaded' self.FL = Filter() print 'FL Loaded' self.CC = Collocation(self.POS) print 'CC Loaded' self.Ng = NGram() print 'Ng Loaded' self.S = Select(percentil=self.p) print 'S Loaded' self.To = Tokenize(self.FL) print 'To Loaded' def Select(self, url, depth): ''' Determin the best keywords for a webpage. @param url: the base url to start sampaling from @param depth: the depth of the website to be sampled @return: the list of selected keywords, ordered with the highest rated words to the lower bownd of array. ''' #Get data from web page text = self.GD.getWebPage(url, depth) #Tokonize sentance and words tok = self.To.Tok(text) #POS tag the text pos = self.POS.POSTag(tok, 'tok') #Log Likly Hood log = self.LL.calcualte(tok) #Collocations if self.Col == True: col = self.CC.col(pos, tok) else: col = [] #NE Extraction if self.NEs == True: ne = self.Cu.Chunks(pos, nodes=['PERSON', 'ORGANIZATION', 'LOCATION']) else: ne = [] #Extract NP if self.Chu == True: chu = [self.Cu.parse(p) for p in pos] else: chu = [] #Creat N-gram if self.Gram == True: ga = self.Ng.Grams(pos, n=6) else: ga = [] return self.S.keywords(ne, ga, col, chu, log)
def __init__(self, code): self.dicts = GetData(code).request_data() print self.dicts
def main(): #import data training_data = GetData(TRAINING_DIR) test_data = GetData(TEST_DIR) with tf.name_scope('inputs'): #create the model x = tf.placeholder(tf.float32, [Batch_SIZE, Img_depth, Img_rows, Img_cols, 1], name='x_input') # Define loss and optimizer y_ = tf.placeholder( tf.int16, [Batch_SIZE, Img_depth, Img_rows, Img_cols, n_class], name='y__input') #define a global step global_step = tf.Variable(0, name="global_step") # Build the graph for the deep net network, outputs = network(x) dice_loss = dice_coef_loss(outputs, y_) with tf.name_scope('train'): train_step = tf.train.AdamOptimizer(1e-5).minimize(dice_loss) #add ops to save and restore all the variables saver = tf.train.Saver() training_summary = tf.summary.scalar("training_loss", dice_loss) validation_summary = tf.summary.scalar("validation_loss", dice_loss) #use only single CPU m_config = tf.ConfigProto() m_config.gpu_options.allow_growth = True with tf.Session(config=m_config) as sess: summary_writer = tf.summary.FileWriter("log/", sess.graph) sess.run(tf.global_variables_initializer( )) #when continue training this model, should comment this line #first start to train the model, should comment these lines # check_points_list = tf.train.latest_checkpoint(LOG_DIR) #return the filename of the lastest checkpoint # print(len(check_points_list)) # print(check_points_list) #is the name of this checkpoint # saver.restore(sess,check_points_list) # global_step_value = sess.run(global_step) print("Last iteration:", global_step_value) for i in range(global_step_value + 1, 150000 + 1): images, labels = training_data.next_batch(Batch_SIZE) feed_dict_train = {x: images, y_: labels} feed_dict_train.update(network.all_drop) #enable noise layers train_step.run(feed_dict=feed_dict_train) if i % 50 == 0: print("iteration now:", i) train_loss, train_summ = sess.run( [dice_loss, training_summary], feed_dict=feed_dict_train) summary_writer.add_summary(train_summ, i) print('train loss %g' % train_loss) images_test, labels_test = test_data.next_batch(Batch_SIZE) dp_dict = tl.utils.dict_to_one( network.all_drop) #disable nosie layers when testing feed_dict_test = {x: images_test, y_: labels_test} feed_dict_test.update(dp_dict) # loss = dice_loss.eval(feed_dict=feed_dict) valid_loss, valid_summ = sess.run( [dice_loss, validation_summary], feed_dict=feed_dict_test) summary_writer.add_summary(valid_summ, i) print('test loss %g' % valid_loss) print('----------------------------------') if i % 5000 == 0: print("iteration now:", i) output_image = sess.run( outputs, feed_dict=feed_dict_test) #use the test next_batch # output_image = outputs.eval(feed_dict=feed_dict_test) print(type(output_image)) print(np.shape(output_image)) # output_image = np.asarray(output_image) # output_image= outputs.eval(feed_dict={x:images}) for j in range(Batch_SIZE): labels_test_union = labels_test[ ..., 0] * 500 + labels_test[..., 1] * 600 + labels_test[ ..., 2] * 420 + labels_test[..., 3] * 550 + labels_test[ ..., 4] * 205 + labels_test[ ..., 5] * 820 + labels_test[..., 6] * 850 input_Image = images_test[..., 0] LVB = output_image[..., 0] out_LVB = LVB[j, ...] RVB = output_image[..., 1] out_RVB = RVB[j, ...] LAB = output_image[..., 2] out_LAB = LAB[j, ...] RAB = output_image[..., 3] out_RAB = RAB[j, ...] MLV = output_image[..., 4] out_MLV = MLV[j, ...] AA = output_image[..., 5] out_AA = AA[j, ...] PA = output_image[..., 6] out_PA = PA[j, ...] BACK = output_image[..., 7] out_BACK = BACK[j, ...] #将heart单独的label存储下来,查看效果 CreatNii_save( out_LVB, save_dir, "out_LVB" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( out_RVB, save_dir, "out_RVB" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( out_LAB, save_dir, "out_LAB" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( out_RAB, save_dir, "out_RAB" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( out_MLV, save_dir, "out_MLV" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save(out_AA, save_dir, "out_AA" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save(out_PA, save_dir, "out_PA" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( out_BACK, save_dir, "out_BACK" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( input_Image[j, ...], save_dir, "Input_Test_Image" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) CreatNii_save( (labels_test_union[j, ...]).astype(np.float32), save_dir, "Test_Label" + str(i) + "_" + str(j) + ".nii.gz", np.eye(4)) if i % 1000 == 0: print("iteration now:", i) #注意global_step.assign()并不会改变global_step的值,只是创造了这么一个操作,只有运行它之后,global_step才会真正被赋值 global_step_op = global_step.assign( i ) #this line is necessary, if not the iteration number is always 0 print("global_step_value:", sess.run(global_step_op)) saver.save( sess, CHECKPOINT_FL, global_step=i ) #the "global_step" here is different from the one above print("================================") print("model is saved")
''' Created on Apr 22, 2017 @author: davidryv ''' from GetData import GetData as ReadData if __name__ == '__main__': GetUsers = ReadData() userList = GetUsers.getUserList() spaces = GetUsers.getSpaces() for space in spaces: print 'Deleting' + space['wiki_name'] users, role, id = GetUsers.getUsers(space['name']) for user in users: if user['login'].encode('utf-8') in userList: print user['login'].encode( 'utf-8') + " , Deleted from " + space['wiki_name'].encode( 'utf-8') GetUsers.DeleteUsersList(user['id'].encode('utf-8'), user['login'].encode('utf-8'), role, space['wiki_name'].encode('utf-8')) else: print '-----------Not deleted-----------' print user['login'].encode(