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M25 Bali stats & predictions

Upcoming M25 Tennis Matches in Bali, Indonesia: A Detailed Overview

Get ready for an electrifying day of tennis as Bali, Indonesia, gears up for the highly anticipated M25 matches scheduled for tomorrow. This event promises to showcase the best of emerging talent and thrilling gameplay. Whether you're a die-hard tennis fan or a newcomer to the sport, these matches offer something for everyone. With expert betting predictions and comprehensive coverage, you won't want to miss what promises to be an unforgettable day in the world of tennis.

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Match Schedule and Key Players

The M25 category is known for its fierce competition and up-and-coming stars. Tomorrow's schedule features several exciting matchups that will keep spectators on the edge of their seats. Here's a detailed look at what to expect:

  • Match 1: Player A vs. Player B - Known for their powerful serves and strategic play, both athletes are expected to deliver a nail-biting contest.
  • Match 2: Player C vs. Player D - With Player C's aggressive baseline game and Player D's exceptional net play, this match is anticipated to be a tactical battle.
  • Match 3: Player E vs. Player F - Watch for Player E's consistent performance and Player F's remarkable comeback ability, making this an unpredictable encounter.

Expert Betting Predictions

Betting enthusiasts can look forward to some insightful predictions from top analysts. Here are the expert insights for tomorrow's matches:

  • Player A vs. Player B: Analysts predict a close match, but Player A is favored due to recent form and home advantage.
  • Player C vs. Player D: With both players in top form, this match is considered a toss-up, though Player D has shown resilience in past encounters.
  • Player E vs. Player F: Experts lean towards Player E, citing superior consistency and mental toughness under pressure.

What Makes Bali the Perfect Venue?

Bali's stunning landscapes and vibrant atmosphere make it an ideal setting for tennis events. The island's rich cultural heritage and warm hospitality enhance the overall experience for players and spectators alike.

  • Natural Beauty: Surrounded by picturesque beaches and lush greenery, Bali provides a serene backdrop that enhances the enjoyment of the sport.
  • Cultural Experience: Visitors can immerse themselves in Balinese culture, exploring temples, traditional dance performances, and local cuisine.
  • Premier Facilities: State-of-the-art tennis courts ensure world-class playing conditions, allowing athletes to perform at their best.

Tips for Spectators

If you're planning to attend the matches in person, here are some tips to make the most of your experience:

  • Dress Appropriately: Bali's tropical climate can be hot and humid, so wear lightweight clothing and sunscreen.
  • Arrive Early: Get there ahead of time to secure good seats and soak in the atmosphere before the matches begin.
  • Explore Local Attractions: Take advantage of your visit by exploring nearby attractions such as Ubud Monkey Forest or Uluwatu Temple.

The Significance of the M25 Category

The M25 category plays a crucial role in the development of professional tennis players. It serves as a stepping stone for athletes aiming to climb the ranks and make their mark on the international stage.

  • Nurturing Talent: The M25 category provides a platform for young players to hone their skills against strong competition.
  • Fostering Competition: With a mix of seasoned players and fresh talent, the matches are highly competitive and entertaining.
  • Promoting Diversity: Players from various countries participate in M25 tournaments, promoting diversity and international camaraderie in tennis.

The Role of Technology in Modern Tennis

Technology continues to revolutionize tennis, enhancing both player performance and spectator experience. From advanced analytics to immersive viewing options, here's how tech is shaping the sport:

  • Data Analytics: Teams use data analytics to analyze player performance and develop strategies tailored to individual opponents.
  • Hawk-Eye Technology: This system provides accurate line-calling, reducing human error and ensuring fair play.
  • Virtual Reality (VR): VR offers fans a unique way to experience matches from different angles, bringing them closer to the action than ever before.

Sustainability Initiatives in Tennis

As environmental awareness grows, tennis organizations are implementing sustainability initiatives to reduce their ecological footprint. These efforts include:

  • Eco-Friendly Materials: Many tournaments now use biodegradable products and recycled materials for equipment and facilities.
  • Solar Energy: Solar panels are being installed at various venues to harness renewable energy sources.
  • Clean Transport Options: Encouraging public transportation and carpooling helps reduce carbon emissions associated with large events.

The Future of Tennis in Indonesia

yipengzhen/CS236-AI-Programming<|file_sep|>/src/Model.py import numpy as np import math class Model(object): # def __init__(self): # self.M = np.array([[0.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.], # [1.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.], # [0.,1.,0.,1.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.], # [0.,1.,1.,0.,1.,1.,1.,0.,0.,0.,0.,0.,0.,0.,0.], # [0.,1.,1.,1.,0.,1./3.*np.sqrt(3),1./3.*np.sqrt(3), # sqrt(3)/3*np.sqrt(3),sqrt(3)/3*np.sqrt(3),sqrt(3)/3*np.sqrt(3), # sqrt(3)/3*np.sqrt(3),sqrt(3)/3*np.sqrt(3),sqrt(3)/3*np.sqrt(3), # sqrt(3)/3*np.sqrt(3),sqrt(3)/3*np.sqrt(3)], # [0,1,np.sqrt(3)/3,np.sqrt(3)/3,np.sqrt(3)/3, # (5/9)*np.sqrt(5),(5/9)*np.sqrt(5),(5/9)*np.sqrt(5), # (5/9)*np.sqrt(5),(5/9)*np.sqrt(5),(5/9)*np.sqrt(5), # (5/9)*np.sqrt(5),(5/9)*np.sqrt(5),(5/9)*np.sqrt(5), # (5/9)*np.sqrt(5)], # [0,np.sqrt(3)/3,np.sqrt(4/9),np.sqrt(4/9),np.sqrt((4+4+7)/27), # np.sqrt((4+4+7)/27),np.sqrt((4+4+7)/27), # np.sqrt((4+4+7)/27),sqrt((4+4+7)/27), # np.sqrt((4+4+7)/27),sqrt((4+4+7)/27), # sqrt((4+4+7)/27),sqrt((4+4+7)/27), # sqrt((4+4+7)/27)], # [..]]) # init parameters self.alpha = .01 self.batch_size = None self.num_epochs = None self.verbose = True # training parameters self.params = None self.optimized = False # network parameters self.layers = [] self.L = len(self.layers) # initialize weights & biases #self.init_weights() # training history self.losses_train = [] self.losses_val = [] <|repo_name|>yipengzhen/CS236-AI-Programming<|file_sep|>/src/utils.py import numpy as np def one_hot(y): """ Convert class labels from scalars to one-hot vectors. Assumes that class labels take on values from {0,...K-1}. Input: y: numpy array of shape (N,) Output: onehot_y: numpy array of shape (N,K) """ N = y.shape[0] K = len(np.unique(y)) onehot_y = np.zeros([N,K]) onehot_y[np.arange(N),y] = np.ones(N) return onehot_y def load_data(): """ Load CIFAR-10 dataset from disk. Preprocessing steps: - Convert class labels from scalars to one-hot vectors. - Normalize pixel intensities. Input: None Output: trX: numpy array of shape (50000,3072) trY: numpy array of shape (50000,10) vaX: numpy array of shape (10000,3072) vaY: numpy array of shape (10000,10) """ from six.moves import cPickle as pickle trX = [] trY = [] for i in range(1,6): with open('../data/cifar-10-batches-py/data_batch_'+str(i),'rb') as fo: d = pickle.load(fo) trX.append(d['data']) trY.append(d['labels']) trX = np.concatenate(trX) trY = np.concatenate(trY) with open('../data/cifar-10-batches-py/batches.meta','rb') as fo: d_meta = pickle.load(fo) label_names = d_meta['label_names'] with open('../data/cifar-10-batches-py/test_batch','rb') as fo: d_test = pickle.load(fo) teX = d_test['data'] teY = d_test['labels'] vaX = trX[40000:] vaY = trY[40000:] trX= trX[:40000] trY= trY[:40000] trX -= np.mean(trX,axis=0) trX /= np.std(trX,axis=0) vaX -= np.mean(vaX,axis=0) vaX /= np.std(vaX,axis=0) trY_onehot = one_hot(trY) vaY_onehot = one_hot(vaY) return trX,trY_onehot,vaX,vayOneHot,label_names def display_cifar(images,cmap='gray'): """ Display CIFAR images. Input: images: numpy array of shape (N,C,H,W) where N is number of images, C is number of color channels (i.e. C=3 for RGB), H,W are height,width respectively. cmap: matplotlib colormap string. Default='gray' which works well if images only have one channel. Use 'rgb' if images have three channels. """ from matplotlib import pyplot as plt N,C,H,W=images.shape if C==1: cmap='gray' elif C==3: cmap='rgb' else: raise Exception('Invalid number of channels.') plt.figure(figsize=(10,N*10/N)) for i in range(N): plt.subplot(N,10,i+1) plt.imshow(images[i].reshape(H,W).T,cmap=cmap) plt.axis('off') plt.show() def evaluate(X,Y,model=None): """ Evaluate model accuracy on dataset X,Y. If no model provided then use best model found during training. Input: X: numpy array of shape (N,D) where each X[i] is a D-dimensional input. Y: numpy array of shape (N,) where Y[i] is scalar class label between {0,...K-1}. model: optional PyTorch model instance used for inference. Output: accuracy: scalar accuracy between [0,100] """ import torch if not model: model=model.best_model N,D=X.shape K=model.output_size X=torch.from_numpy(X).type(torch.FloatTensor).view(N,D) Y=torch.from_numpy(Y).type(torch.LongTensor).view(N) model.eval() # set model to evaluation mode with torch.no_grad(): # don't compute gradients since we're evaluating performance only y_pred=model.forward(X)[torch.arange(N)] accuracy=100*(y_pred==Y).float().mean().item() return accuracy def train(model,data,batch_size,num_epochs): """ Main training loop which performs mini-batch gradient descent using SGD optimization. Input: model: PyTorch model instance which contains NN architecture & hyperparameters data: tuple containing training/validation data (trX,trY_onehot,vaiX,vaiY_onehot,label_names). See utils.py::load_data() function documentation for details on data format. batch_size: mini-batch size used during SGD training num_epochs: number of epochs used during training Output: None; however training history is stored within `model` object. TODO: implement mini-batch gradient descent using SGD optimization with momentum. Store training history within `model` object after each epoch. TODO-BONUS: implement learning rate decay schedule using `num_epochs` & `batch_size`. You may wish to use `optim.lr_scheduler.StepLR` module from PyTorch library. TODO-BONUS: implement early stopping criteria based on validation loss not improving after certain epochs. TODO-BONUS(optional): implement model checkpointing which saves best model weights after each epoch. HINTS: - Create SGD optimizer object with desired hyperparameters using `optim.SGD` module from PyTorch library. - Create learning rate scheduler object with desired decay schedule using `optim.lr_scheduler.StepLR` module from PyTorch library. - Use `model.train()` method before each forward pass during training phase. - Use `model.train()` method before each forward pass during validation phase. - Use `torch.utils.data.DataLoader` module from PyTorch library with desired batch size & shuffling option which returns batches of data during each epoch. - Use `torch.nn.CrossEntropyLoss()` module from PyTorch library as loss function. - Use `.zero_grad()` method before computing loss & gradients during each mini-batch iteration. - Use `.backward()` method after computing loss during each mini-batch iteration. - Use `.step()` method after computing gradients during each mini-batch iteration. - Use `.item()` method after computing loss & accuracy during each epoch. - Use `model.eval()` method before evaluating model performance on validation set after each epoch. - Use `torch.no_grad()` context manager before evaluating model performance on validation set after each epoch. - Print out loss & accuracy values every epoch if verbose option is set True. - Append loss & accuracy values every epoch within `model.losses_train`, `model.losses_val` lists respectively. """ import torch.optim as optim import torch.utils.data as data_utils trX,trY_onehot,vaiX,vaiY_onehot,label_names=data model.train() # set model into training mode L=len(trX) dataset=data_utils.TensorDataset(torch.from_numpy(trX).float(),torch.from_numpy(trY_onehot).float()) dataloader=data_utils.DataLoader(dataset,batch_size=batch_size,num_workers=8) criterion=torch.nn.CrossEntropyLoss() scheduler=None if scheduler!=None: optimizer=optim.SGD(model.parameters(),lr=model.lr,momentum=.9,nesterov=True) else: optimizer=optim.SGD(model.parameters(),lr=model.lr,momentum=.9,nesterov=True) for e in range(num_epochs): running_loss=running_accu=epoch_loss=epoch_accu=correct_preds_total=preds_total=total_loss=total_correct_preds=total_preds=loss_sum=correct_preds_sum=preds_sum=epoch_start=time.time() # iterate over batches for i,(inputs,y_true)in enumerate(dataloader): inputs,y_true=inputs.float(),y_true.float() preds=model(inputs) loss=criterion(preds,y_true.argmax(dim=-1)) loss.backward() optimizer.step() optimizer.zero_grad() running_loss+=loss.item() _,preds_class=preds.max(dim=-1) correct_preds=(preds_class==y_true.argmax(dim=-1)).sum().item() running_accu+=correct_preds preds_total+=len(y_true) if i%20==19: print('[%d/%d][%d/%d]tLoss %.ftAccu %.f'%(e,num_epochs,i,len(dataloader)-1, running_loss/(i*batch_size), running_accu/preds_total)) running_loss=running_accu=epoch_loss=preds_total=total_correct_preds=preds_sum= correct_preds_sum= loss_sum= correct_preds_total= total_loss= total_correct_preds= total_preds= epoch_start= total_correct_preds+=correct_preds_total total