W75 Le Neubourg Predictions

Exciting Tennis Matches at Le Neubourg, France

The tennis community is abuzz with anticipation for the upcoming matches at Le Neubourg, France. As part of the W75 category, tomorrow's event promises thrilling encounters and showcases the talent of seasoned players. This article will delve into the matchups, provide expert betting predictions, and explore what fans can expect from this prestigious event.

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Overview of the W75 Category

The W75 category is a segment within women's tennis that features players aged 75 and above. It highlights the longevity and enduring skill of veteran athletes who continue to compete at high levels. This category not only celebrates their achievements but also offers fans a unique perspective on the sport's rich history.

Match Schedule for Tomorrow

Match 1: Player A vs. Player B - Starting at 10:00 AM
Match 2: Player C vs. Player D - Starting at 11:30 AM
Match 3: Player E vs. Player F - Starting at 1:00 PM
Semifinals: Winners of Match 1 vs. Match 2 and Match 3 - Starting at 3:00 PM
Final: Scheduled for 5:00 PM

Expert Betting Predictions

Betting enthusiasts are keenly analyzing the odds for tomorrow's matches. Here are some expert predictions:

Match 1: Player A is favored due to her recent form and experience in clay courts.
Match 2: Player D has been performing exceptionally well this season, making her a strong contender.
Match 3: Player F's aggressive playstyle might give her an edge over Player E.
Semifinals: Expect a closely contested match between the winners of Match 1 and Match 2.
Final: The final is anticipated to be a nail-biter, with both players having strengths that could tip the scales.

In-Depth Analysis of Key Players

Player A: A Veteran with Unmatched Skill

With decades of experience under her belt, Player A continues to dominate on the court. Her strategic play and mental fortitude make her a formidable opponent. Known for her precise serves and strong baseline game, she has consistently outperformed younger competitors.

In recent tournaments, Player A has shown remarkable adaptability, adjusting her game to counter different playing styles. Her ability to read the game and anticipate opponents' moves gives her a significant advantage.

Player D: Rising Star in the W75 Category

Player D has been making waves in the W75 category with her impressive performances. Her powerful forehand and quick footwork have caught the attention of fans and analysts alike. Despite being relatively new to this age category, she has quickly established herself as a top contender.

Her recent victories have been characterized by aggressive play and resilience under pressure. Player D's ability to maintain focus during critical points has been pivotal in her success.

Tournament Venue: Le Neubourg, France

Le Neubourg is renowned for its well-maintained courts and vibrant atmosphere during tennis events. The venue offers excellent facilities for both players and spectators, ensuring a memorable experience for all attendees.

The clay courts at Le Neubourg provide a unique challenge, testing players' endurance and tactical skills. The slow surface allows for extended rallies, making each match an engaging spectacle.

What Fans Can Expect from Tomorrow's Matches

Dramatic Rivalries and High Stakes

Tomorrow's matches are set to feature intense rivalries that have developed over years of competition. Fans can look forward to thrilling exchanges and strategic battles as players vie for victory.

The stakes are high, with each match contributing to the players' rankings and potential prize earnings. This adds an extra layer of excitement as athletes push themselves to their limits.

Spectator Experience: More Than Just Tennis

Beyond the matches, attendees will enjoy various activities organized by the tournament committee. From live commentary to interactive sessions with players, there is something for everyone.

The venue also offers excellent amenities, including food stalls featuring local cuisine, merchandise shops, and comfortable seating areas. This ensures that fans have a holistic experience while supporting their favorite players.

Tips for Betting Enthusiasts

Analyzing Players' Recent Performances

When placing bets, it's crucial to consider players' recent performances on similar surfaces. Analyzing head-to-head records can also provide insights into potential outcomes.

<|repo_name|>SeyedMohammad/ExData_Plotting1<|file_sep|>/plot1.R # reading data data <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE) # subsetting data data <- subset(data, Date == "1/2/2007" | Date == "2/2/2007") # converting date column data$Date <- as.Date(data$Date,"%d/%m/%Y") # converting numeric columns data$Global_active_power <- as.numeric(data$Global_active_power) data$Global_reactive_power <- as.numeric(data$Global_reactive_power) data$Voltage <- as.numeric(data$Voltage) data$Sub_metering_1 <- as.numeric(data$Sub_metering_1) data$Sub_metering_2 <- as.numeric(data$Sub_metering_2) data$Sub_metering_3 <- as.numeric(data$Sub_metering_3) # creating plot png("plot1.png", width=480,height=480) hist(data$Global_active_power, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)") dev.off() <|file_sep|># reading data data <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE) # subsetting data data <- subset(data, Date == "1/2/2007" | Date == "2/2/2007") # converting date column data$Date <- as.Date(data$Date,"%d/%m/%Y") # converting time column data$Time <- strptime(data$Time,"%H:%M:%S") # combining date and time columns datetime <- paste(as.Date(data$Date), data$Time) # converting numeric columns data$Global_active_power <- as.numeric(data$Global_active_power) data$Global_reactive_power <- as.numeric(data$Global_reactive_power) data$Voltage <- as.numeric(data$Voltage) data$Sub_metering_1 <- as.numeric(data$Sub_metering_1) data$Sub_metering_2 <- as.numeric(data$Sub_metering_2) data$Sub_metering_3 <- as.numeric(data$Sub_metering_3) # creating plot png("plot4.png", width=480,height=480) par(mfrow=c(2,2)) plot(datetime, data$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)") plot(datetime, data$Voltage, type="l", xlab="datetime", ylab="Voltage") plot(datetime, data$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering") lines(datetime,data$Sub_metering_2,col="red") lines(datetime,data$Sub_metering_3,col="blue") legend("topright", c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), lty=c(1,1),col=c("black","red","blue"),bty="n") plot(datetime, data$Global_reactive_power, type="l", xlab="datetime", ylab="Global_reactive_power") dev.off() <|repo_name|>SeyedMohammad/ExData_Plotting1<|file_sep|>/plot3.R # reading data data <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE) # subsetting data data <- subset(data, Date == "1/2/2007" | Date == "2/2/2007") # converting date column data$Date <- as.Date(data$Date,"%d/%m/%Y") # converting time column data$Time <- strptime(data$Time,"%H:%M:%S") # combining date and time columns datetime <- paste(as.Date(data$Date), data$Time) # converting numeric columns data$Global_active_power <- as.numeric(data$Global_active_power) data$Global_reactive_power <- as.numeric(data$Global_reactive_power) data$Voltage <- as.numeric(data$Voltage) data$Sub_metering_1 <- as.numeric(data$Sub_metering_1) data$Sub_metering_2 <- as.numeric(data$Sub_metering_2) data$Sub_metering_3 <- as.numeric(data$Sub_metering_3) # creating plot png("plot3.png", width=480,height=480) plot(datetime, data $ Sub_metering_1, type = "l", xlab = "", ylab = "Energy sub metering") lines(datetime,data $ Sub_metering_2,col = "red") lines(datetime,data $ Sub_metering_3,col = "blue") legend("topright", c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), lty=c(1,1),col=c("black","red","blue")) dev.off() <|file_sep|># reading data data <- read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE) # subsetting data data <- subset(data, Date == "1/2/2007" | Date == "2/2/2007") # converting date column data $ Date <- as.Date ( data $ Date,"% d / % m / % Y" ) # converting time column data $ Time <- strptime ( data $ Time,"% H : % M : % S" ) # combining date and time columns datetime<-paste(as.Date ( data $ Date ), data $ Time ) # converting numeric columns data $ Global_active_power<-as.numeric ( data $ Global_active_power ) # creating plot png ( "plot2 . png ", width =480 , height =480 ) plot ( datetime , data $ Global_active_power , type = "l ", xlab = "", ylab =" Global Active Power ( kilowatts ) " ) dev.off () <|file_sep|># -*- coding: utf-8 -*- import sys import os import json import logging import argparse import numpy as np from functools import partial from tqdm import tqdm from collections import defaultdict from torch.utils.data import DataLoader from sklearn.metrics import fbeta_score def get_args(): parser = argparse.ArgumentParser(description='Train', formatter_class=lambda prog: argparse.ArgumentDefaultsHelpFormatter(prog)) parser.add_argument('--batch_size', type=int, default=128) parser.add_argument('--max_len', type=int, default=256) parser.add_argument('--num_workers', type=int, default=8) parser.add_argument('--model_name', type=str, default='bert-base-cased') parser.add_argument('--model_path', type=str, default='/Users/miguelangel/padtec/data/models/bert-base-cased-padtec') parser.add_argument('--task', choices=['ner', 'pos'], default='ner') parser.add_argument('--output_path', type=str, default='/Users/miguelangel/padtec/data/models/bert-base-cased-padtec') parser.add_argument('--checkpoint', action='store_true') return parser.parse_args() def load_data(path): with open(path) as f: data = json.load(f)['sentences'] return [(s['text'], s['labels']) for s in data] def load_labels(path): with open(path) as f: labels = json.load(f)['labels'] return labels def get_label_map(labels): inv_label_map = {v:k for k,v in enumerate(labels)} label_map = {k:v for v,k in inv_label_map.items()} return label_map def get_examples_and_labels(sentences): xs = [] ys = [] for text,label_seq in sentences: tokens = tokenizer.tokenize(text)[:args.max_len-1] tokens.append('[SEP]') token_ids = tokenizer.convert_tokens_to_ids(tokens) label_ids = [label_map['O']] + [label_map[l] for l in label_seq[:args.max_len-1]] + [label_map['O']] xs.append(token_ids) ys.append(label_ids) return xs,np.array(ys) def batchify_fn(batch): xs,y_seq = zip(*batch) # batch size x max seq len max_len=max([len(x) for x in xs]) xs=[x+[0]*(max_len-len(x))for x in xs] y_seq=[y+[0]*(max_len-len(y))for y in y_seq] xs=np.array(xs,dtype=np.int64) # batch size x max seq len y_seq=np.array(y_seq,dtype=np.int64) # batch size x max seq len mask=np.where(xs!=0,np.ones_like(xs),np.zeros_like(xs)) return xs,y_seq,mask def evaluate(model,data_loader,label_map): model.eval() preds=[] golds=[] for batch in tqdm(list(iter(data_loader)),desc='Evaluation'): xs,y_seq,_=batch with torch.no_grad(): y_hat=model(xs.to(device)) y_hat=torch.argmax(y_hat,dim=-1).detach().cpu().numpy() preds.extend(y_hat.tolist()) golds.extend(y_seq.tolist()) macro_f_score=np.mean([ fbeta_score([golds[i][j]for i in range(len(golds))], [preds[i][j]for i in range(len(preds))], beta=beta, labels=list(range(len(label_map))), pos_label=label_map[label],average='macro') for j,label in enumerate(label_map.keys()) if label!='O']) print('Macro F-Score: {:.5f}'.format(macro_f_score)) return macro_f_score if __name__=='__main__': args=get_args() device=torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') os.makedirs(args.output_path+'/checkpoints/',exist_ok=True) logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO) logger=logging.getLogger(__name__) if args.task=='ner': label_path=os.path.join(args.model_path,'ner-labels.json') elif args.task=='pos': label_path=os.path.join(args.model_path,'pos-labels.json') sentences=load_data(os.path.join(args.model_path,'test.json')) labels=load_labels(label_path) if args.task=='ner': label_map=get_label_map(labels[0]) elif args.task=='pos': label_map=get_label_map(labels[0]['labels']) tokenizer=BertTokenizer.from_pretrained(args.model_name) # add 'cache/' if you want to cache downloaded models locally dataset_train=get_examples_and_labels(load_data(os.path.join(args.model_path,'train.json'))) dataset_test=get_examples_and_labels(sentences) if args.task=='ner': beta=[0] + [4]+[0]*(len(label_map)-4-1)+[4] elif args.task=='pos': beta=[0]*len(label_map) + [4] data_loader_train=torch.utils.data.DataLoader(dataset_train[0], dataset_train[1], batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=batchify_fn,) data_loader_test=torch.utils.data.DataLoader(dataset_test[0], dataset_test[1], batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=batchify_fn,) if args.task=='ner': model=BertForTokenClassification.from_pretrained(args.model_name, num_labels=len(label_map)) elif args.task=='pos': model=BertForTokenClassification.from_pretrained(args.model_name, num_labels=len(label_map)+1) model.to(device) num_epochs=args.epoch if not args.checkpoint else int(model.config.num_epoch_checkpointed)+args.epoch for epoch in range(num_epochs): model.train() total_loss=[] for batch in tqdm(list(iter(data_loader_train)),desc='Training'): xs,y_seq,_=batch # xs: batch size x max seq len y_seq=y_seq.to(device).long() model.zero_grad() output=model(xs.to(device),labels=y_seq) # output:(batch_size,max_seq_len,num_labels) output=output[0] mask=np.where(xs!=0,np.ones_like(xs),np.zeros_like(xs)) mask=torch.tensor(mask,dtype=torch.float32).to(device)