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Welcome to the Ultimate Tennis W35 Reims France Experience

Immerse yourself in the world of tennis with our dedicated coverage of the W35 Reims France. This guide offers you daily updates on fresh matches, expert betting predictions, and in-depth analysis to enhance your viewing experience. Whether you are a seasoned fan or new to the sport, our content is crafted to keep you informed and engaged.

Stay ahead with our expert insights and predictions that help you make informed decisions on your bets. Our team of analysts provides comprehensive reviews of each match, player statistics, and potential outcomes. Join us as we explore the exciting world of tennis at W35 Reims France.

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Daily Match Updates

Every day brings new excitement with fresh matches at W35 Reims France. Our platform ensures you receive the latest updates on match schedules, results, and player performances. Stay connected with real-time notifications and detailed match reports that capture every moment of the action.

  • Match Schedules: Access up-to-date schedules to plan your day around your favorite matches.
  • Live Scores: Follow live scores and see instant updates as the action unfolds on the court.
  • Post-Match Analysis: Read expert breakdowns of each match to understand key moments and strategies.

Expert Betting Predictions

Betting on tennis can be a thrilling experience, but it requires insight and strategy. Our expert team provides reliable predictions to help you place informed bets. With years of experience and a deep understanding of the sport, our analysts offer tips and forecasts that aim to maximize your chances of success.

  • Prediction Accuracy: Trust in our track record of accurate predictions based on comprehensive data analysis.
  • Betting Strategies: Learn effective strategies from our experts to enhance your betting approach.
  • Odds Analysis: Get insights into odds fluctuations and what they mean for your bets.

In-Depth Player Analysis

Understanding the players is crucial for making informed predictions. Our platform offers detailed profiles and analyses of each participant in the W35 Reims France tournament. From their playing styles to recent performances, we provide all the information you need to gauge their strengths and weaknesses.

  • Player Profiles: Discover detailed backgrounds, career highlights, and personal stories.
  • Performance Metrics: Analyze key statistics such as win-loss records, serve accuracy, and endurance levels.
  • Head-to-Head Records: Compare past encounters between players to predict future outcomes.

Tournament Insights

The W35 Reims France is not just about individual matches; it's about the tournament as a whole. Gain insights into the structure, history, and significance of this prestigious event. Our content covers everything from past champions to unique tournament features that set it apart from others.

  • Tournament History: Learn about the origins and evolution of W35 Reims France over the years.
  • Past Champions: Celebrate previous winners and their journeys to victory.
  • Tournament Format: Understand the rules, stages, and progression of the tournament.

Tips for Engaging with Tennis Fans

Beyond watching matches and placing bets, engaging with other tennis fans can enhance your experience. Our platform offers community features where you can connect with fellow enthusiasts, share insights, and discuss matches in real-time.

  • Forums and Discussions: Participate in lively forums to exchange views and predictions with other fans.
  • Social Media Integration: Connect with us on social media for updates, polls, and interactive content.
  • User-Generated Content: Contribute your own analyses and predictions for community recognition.

Making the Most of Your Viewing Experience

To fully enjoy W35 Reims France, consider enhancing your viewing setup. Whether it's optimizing your home theater system or finding the best streaming services, we provide tips to ensure you don't miss a moment of the action.

  • Optimal Viewing Conditions: Create an ideal environment for watching matches with tips on lighting, sound, and seating arrangements.
  • Broadcasting Options: Explore various channels and platforms offering live coverage of the tournament.
  • Tech Recommendations: Discover gadgets and apps that can enhance your viewing experience.

The Future of Tennis Betting at W35 Reims France

The landscape of tennis betting is continually evolving with technological advancements and changing fan preferences. Our platform stays ahead by incorporating cutting-edge tools and trends to offer you a superior betting experience. From AI-driven predictions to interactive betting interfaces, we are committed to innovation.

  • Technological Innovations: Explore how technology is reshaping betting strategies and experiences.
  • Sustainability in Betting: Learn about responsible gambling practices promoted by our platform.
  • Trends in Tennis Betting: Stay informed about emerging trends that could influence future betting opportunities.

Your Guide to Mastering Tennis Predictions

Becoming proficient in tennis predictions involves more than just following matches; it requires a deep understanding of various factors influencing game outcomes. Our comprehensive guide equips you with the knowledge needed to refine your prediction skills effectively.

  • Analytical Tools: Utilize advanced tools for data analysis to identify patterns and trends.
  • Educational Resources: Access tutorials, webinars, and articles designed to enhance your predictive abilities.
  • Predictive Models: Understand different models used by experts to forecast match results accurately.
<|repo_name|>AlaaQasem/EEG-Based-Automatic-Sleep-Stage-Classification<|file_sep|>/README.md # EEG-Based-Automatic-Sleep-Stage-Classification EEG-Based Automatic Sleep Stage Classification Using CNN <|file_sep|># -*- coding: utf-8 -*- """ Created on Thu Oct 18 16:06:05 2018 @author: Alaa """ import numpy as np from scipy import signal import matplotlib.pyplot as plt from scipy.fftpack import fft def butter_bandpass(lowcut=0.5 ,highcut=30., fs=100., order=5): nyq = fs / 2. low = lowcut / nyq high = highcut / nyq b,a = signal.butter(order,[low ,high], btype='band') return b,a def butter_bandpass_filter(data ,lowcut=0.5 ,highcut=30., fs=100., order=5): b,a = butter_bandpass(lowcut ,highcut , fs , order) y = signal.lfilter(b,a,data) return y def plot_psd (x , Fs): NFFT = len(x) fig,(ax1 , ax2) = plt.subplots(1 ,2) # compute FFT Y = np.abs(fft(x)) # compute frequency bins xf = np.linspace(0.0 , Fs/2.0 ,(NFFT//2)) # ax1 - PSD plot ax1.plot(xf , Y[:NFFT//2]) ax1.set_xlabel('Frequency (Hz)') ax1.set_ylabel('|Y(freq)|') ax1.set_title('PSD') # ax2 - spectrogram plot Pxx , freqs , bins , im = ax2.specgram(x,NFFT=NFFT,Fs=Fs,noverlap=int(NFFT/8)) ax2.set_xlabel('Time(s)') ax2.set_ylabel('Frequency (Hz)') ax2.set_title('Spectrogram') # show plots #plt.tight_layout() #plt.show() def bandpower (sig , fs , fmin=None , fmax=None): if fmax == None: fmax = fs/2 if fmin == None: fmin = fmax/4 idx_fmin = np.where(np.logical_and(sig.times >= fmin,sig.times <=fmax))[0][0] idx_fmax = np.where(np.logical_and(sig.times >= fmin,sig.times <=fmax))[0][-1] bp = np.mean(sig.data[:,idx_fmin:idx_fmax]) return bp def compute_feats (sig): # number of segments N_seg = int(np.ceil(sig.shape[1]/(sig.sampling_rate*30))) feats=np.zeros((6,N_seg)) for i in range(N_seg): start = i*30*sig.sampling_rate stop =(i+1)*30*sig.sampling_rate sig_seg=sig.data[:,start:stop] feats[0,i] = bandpower(sig_seg,sig.sampling_rate,fmin=0.5,fmax=4) feats[1,i] = bandpower(sig_seg,sig.sampling_rate,fmin=4,fmax=8) feats[2,i] = bandpower(sig_seg,sig.sampling_rate,fmin=8,fmax=13) feats[3,i] = bandpower(sig_seg,sig.sampling_rate,fmin=13,fmax=30) feats[4,i] = np.sqrt(np.var(sig_seg)) feats[5,i] = np.mean(np.abs(sig_seg)) return feats<|repo_name|>AlaaQasem/EEG-Based-Automatic-Sleep-Stage-Classification<|file_sep|>/stage_classif.py # -*- coding: utf-8 -*- """ Created on Tue Oct 23 22:33:36 2018 @author: Alaa """ import os import mne import numpy as np import pandas as pd from scipy import signal from scipy.fftpack import fft from sklearn import svm from sklearn.model_selection import StratifiedKFold from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report from sklearn.preprocessing import StandardScaler data_dir='D:\Dissertation\dataset\' def load_data (subject_id): subject_dir=os.path.join(data_dir,str(subject_id)) X=[];y=[];X_raw=[];X_raw_ch=[];ch_names=[] ch_types=['eeg','eeg','eeg','eeg','eeg','eeg','eeg','eeg'] ch_loc=[(-32,-19,'Cz'),(-10,-19,'C3'),(-46,-19,'C4'),(-64,-19,'CPz'),(-10,-7,'F3'),(-46,-7,'F4'),(-22,-7,'FCz'),(32,-19,'Pz')] ch_loc_dict={} for i in range(len(ch_loc)): ch_loc_dict[ch_loc[i][2]]=ch_loc[i] for filename in os.listdir(subject_dir): if filename.endswith('.edf'): raw=mne.io.read_raw_edf(os.path.join(subject_dir,filename),preload=True,ch_names=['Cz','C3','C4','CPz','F3','F4','FCz','Pz'],ch_types=ch_types,eog=('HEOG','VEOG')) sfreq=raw.info['sfreq'] events=mne.find_events(raw,pattern='STIM', consecutive='increasing', shortest_event=1) event_id={'Sleep stage W':1,'Sleep stage N1':2,'Sleep stage N2':3,'Sleep stage N3':4,'Sleep stage R':5} epochs=mne.Epochs(raw,event_id=event_id,tmin=-10,tmax=20,baseline=None,event_repeated='drop',preload=True) epochs.drop_bad() epochs.pick_types(eeg=True) epochs.add_channels([epochs.average().pick_channels(['HEOG']).get_data().T.reshape(-1,sfreq*30),epochs.average().pick_channels(['VEOG']).get_data().T.reshape(-1,sfreq*30)]) epochs._data=np.append(epochs._data,[epochs.average().pick_channels(['HEOG']).get_data().T.reshape(-1,sfreq*30),epochs.average().pick_channels(['VEOG']).get_data().T.reshape(-1,sfreq*30)],axis=0) epochs.rename_channels({'HEOG':'HEOG-CH9','VEOG':'VEOG-CH10'}) ch_names.append(['Cz','C3','C4','CPz','F3','F4','FCz','Pz']) ch_names.append(['HEOG-CH9']) ch_names.append(['VEOG-CH10']) X_raw_ch.append([epochs._data[i].reshape(epochs._data[i].shape[0],sfreq*30) for i in range(len(epochs._data))]) X_raw.append(np.concatenate(X_raw_ch[-1],axis=0)) y.append(epochs.events[:, -1]) return X_raw,y,ch_names,ch_loc_dict def extract_feats(X_raw,ch_names,ch_loc_dict): X=np.zeros((len(X_raw),len(ch_names)*6,len(X_raw[0][0][0])//300)) for i in range(len(X_raw)): X[i]=np.array([[compute_feats(mne.EpochsArray(X_raw[i][j],mne.create_info(ch_names[j],100,ch_types=['eeg']*(len(ch_names[j])-2)+['ecg']*2),tmin=-10,tmax=20)).T for j in range(len(ch_names))] for k in range(len(X_raw[i]))]).transpose(0,1,3,2) X[i]=np.concatenate([np.concatenate(X[i][:8],axis=-1),X[i][8:].transpose(0,1,3,2)],axis=-1).transpose(0,3,1,2) X[i]=np.concatenate([X[i][:,:,:,:len(ch_names)-2],X[i][:,:,:,len(ch_names)-2:].reshape(X[i].shape[0],X[i].shape[1],X[i].shape[2],-1)],axis=-1) return X def preprocess_data (X,y,ch_loc_dict): X_pre=np.zeros((len(X),len(X[0]),len(X[0][0]),len(X[0][0][0]))) for i in range(len(X)): print ('subject '+str(i+1)+'/'+str(len(X))+' preprocessing...') scaler_std = StandardScaler() scaler_min_max = StandardScaler() scaler_min_max.fit_transform([-50]*6) scaler_std.fit_transform([[0]*6]) scaler_std.partial_fit(np.concatenate([np.std(X[i][:,:,:,:len(ch_loc_dict.keys())],axis=-1).reshape(len(X[i][:,:,:,:len(ch_loc_dict.keys())]),-1),np.std(X[i][:,:,:,len(ch_loc_dict.keys()):],axis=-1).reshape(len(X[i][:,:,:,len(ch_loc_dict.keys()):]),-1)],axis=-1)) scaler_min_max.partial_fit(np.concatenate([np.min(X[i][:,:,:,:len(ch_loc_dict.keys())],axis=-1).reshape(len(X[i][:,:,:,:len(ch_loc_dict.keys())]),-1),np.min(X[i][:,:,:,len(ch_loc_dict.keys()):],axis=-1).reshape(len(X[i][:,:,:,len(ch_loc_dict.keys()):]),-1)],axis=-1)) scaler_min_max.partial_fit(np.concatenate([np.max(X[i][:,:,:,:len(ch_loc_dict.keys())],axis=-1).reshape(len(X[i][:,:,:,:len(ch_loc_dict.keys())]),-1),np.max(X[i][:,:,:,len(ch_loc_dict.keys()):],axis=-1).reshape(len(X[i][:,:,:,len(ch_loc_dict.keys()):]),-1)],axis=-1)) X_pre[i]=scaler_std.transform(scaler_min_max.transform(np.concatenate([((X[i][:,:,:,:len(ch_loc_dict.keys())]-scaler_min_max.mean_[:6])/scaler_min_max.scale_[:6])*scaler_std.scale_[:6]+scaler_std.mean_[:6],((X[i][:,:,:,len(ch_loc_dict.keys()):]-scaler_min_max.mean_[6:]/scaler_min_max.scale_[6:])*scaler_std.scale_[6:]+scaler_std.mean_[6:])],axis=-1))) return X_pre,y def classif_model (X_pre,y): cv_scores=np.zeros((5,len(y))) cv_indices=np.zeros((5,len(y))) skfolds = StratifiedKFold(n_splits=5) clf_svm=SVC(C=100000,kernel='rbf',gamma='scale',probability=True,class_weight='balanced') clf_svm.fit(np.concatenate([X_pre[k] for k in skfolds.get_n_splits(X_pre)[0]],axis=-3),np.concatenate([y[k] for k in skfolds.get_n_splits(y)[0]],axis=None)) skfolds.get_n_splits(y)