W35 Vigo stats & predictions

Career Highlights: Example Player Name has had an illustrious career with numerous titles under her belt. Known for her powerful serve and strategic gameplay, she has consistently ranked among the top players worldwide.

Playing Style: Example Player Name is renowned for her aggressive baseline play and exceptional footwork. Her ability to adapt to different opponents makes her a formidable competitor on any surface.

Strengths:

• Potent serve
• Strong baseline shots
• Excellent court coverage

Weaknesses:

• Sometimes struggles with net play
• Mental lapses during high-pressure points

What time do matches start?

Matches typically begin early in the morning according to local time in Vigo. It's advisable to check daily schedules for exact timings as they may vary depending on weather conditions or other factors.

Are there any discounts available for tickets?

Sometimes there are promotional discounts available for early bird ticket purchases or group bookings. Keep an eye on official announcements from organizers for such opportunities.

I'm new to tennis betting; where should I start?

If you're new to tennis betting, start by familiarizing yourself with basic concepts like odds interpretation and types of bets (e.g., moneyline, spread). Utilize free resources online before committing financially. Remember that responsible gambling practices should always be followed!

Cómo puedo seguir los partidos en vivo si no estoy en España?

Puedes seguir los partidos en vivo a través de plataformas de transmisión en línea que ofrecen servicios internacionales o mediante aplicaciones móviles específicas para deportes que proporcionan cobertura en tiempo real y resúmenes de los juegos más importantes del día.

Cuáles son algunos consejos para disfrutar del torneo al máximo?

• Asegúrate de planificar con anticipación tu viaje para aprovechar al máximo tu estancia en Vigo durante el torneo.mengqingshen/nfcdemo<|file_sep|>/README.md # nfcdemo an nfc demo project <|repo_name|>mengqingshen/nfcdemo<|file_sep|>/app/src/main/java/com/mqs/nfcdemo/MainActivity.java package com.mqs.nfcdemo; import android.app.PendingIntent; import android.content.Intent; import android.content.IntentFilter; import android.nfc.NdefMessage; import android.nfc.NdefRecord; import android.nfc.NfcAdapter; import android.nfc.Tag; import android.nfc.tech.Ndef; import android.os.Bundle; import android.support.v7.app.AppCompatActivity; import android.util.Log; import android.view.View; import android.widget.TextView; import java.io.IOException; public class MainActivity extends AppCompatActivity { private static final String TAG = "MainActivity"; private TextView mTvContent; private NfcAdapter mNfcAdapter; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); mTvContent = findViewById(R.id.tv_content); mNfcAdapter = NfcAdapter.getDefaultAdapter(this); if (mNfcAdapter == null) { Log.e(TAG,"This device doesn't support NFC!"); } // 设置NFC启动的标记，以便在程序启动时可以检测到NFC的操作 Intent intent = getIntent(); String action = intent.getAction(); if (NfcAdapter.ACTION_NDEF_DISCOVERED.equals(action)) { processIntent(intent); } } /** * 处理NFC传递过来的数据 * * @param intent */ public void processIntent(Intent intent) { String action = intent.getAction(); if (NfcAdapter.ACTION_NDEF_DISCOVERED.equals(action)) { Tag tagFromIntent = intent.getParcelableExtra(NfcAdapter.EXTRA_TAG); NdefRecord[] records = getRecordsFromIntent(intent); // TODO 处理数据 StringBuilder builder = new StringBuilder(); if (records != null) { for (int i =0 ; i0 ) { return messages[0].getRecords(); } else { Log.d(TAG,"No NDEF record found."); } } else { Log.d(TAG,"No NDEF message found."); } return null; } @Override protected void onResume() { super.onResume(); IntentFilter[] filters = new IntentFilter[]{new IntentFilter(NfcAdapter.ACTION_NDEF_DISCOVERED)}; PendingIntent pendingIntent = PendingIntent.getActivity(this,0,new Intent(this, getClass()).addFlags(Intent.FLAG_ACTIVITY_SINGLE_TOP),0); mNfcAdapter.enableForegroundDispatch(this,pendingIntent,filters,null); } @Override protected void onPause() { super.onPause(); mNfcAdapter.disableForegroundDispatch(this); } } <|repo_name|>RaphaelIckowicz/Gym_ML<|file_sep|>/Cnn_Classification.py # -*- coding: utf-8 -*- """ Created on Tue Mar 26 @author: Raphael Ickowicz Convolutional Neural Network example using Keras library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Reference: Trains a simple convnet on the MNIST dataset. Gets to ~99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). """ from __future__ import print_function import numpy as np from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense , Dropout , Activation , Flatten , Convolution2D , MaxPooling2D from keras.utils import np_utils batch_size = None nb_classes = None nb_epoch = None img_rows = None #img dim e.g.28x28 pixels img_cols = None # input image dimensions input_shape=(img_rows,img_cols,1) print('input_shape :',input_shape) #the data , shuffled and split between train and test sets (x_train,y_train),(x_test,y_test)=mnist.load_data() print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples') #reshape data , depending on Keras backend x_train= x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test= x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape=(img_rows,img_cols,1) #cast floats x_train=x_train.astype('float32') x_test=x_test.astype('float32') #normalize input x_train/=255. x_test/=255. print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples') #convert class vectors tto binary class matrices y_train=np_utils.to_categorical(y_train,nb_classes) y_test=np_utils.to_categorical(y_test,nb_classes) model=Sequential() model.add(Convolution2D(32,(3,3),padding='same',input_shape=input_shape)) model.add(Activation('relu')) model.add(Convolution2D(32,(3,3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Convolution2D(64,(3,3),padding='same')) model.add(Activation('relu')) model.add(Convolution2D(64,(3,3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(nb_classes)) model.add(Activation('softmax')) #let's train the model using RMSprop model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history=model.fit(x_train,y_train,batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_data=(x_test,y_test)) score=model.evaluate(x_test,y_test,batch_size=batch_size) print('nTest score:',score[0]) print('Test accuracy:',score[1]) ##save model #serialize model to JSON model_json=model.to_json() with open("model.json","w") as json_file: json_file.write(model_json) #serialize weights tto HDF5 model.save_weights("cnn_model.h5") print("Saved model tto disk") <|repo_name|>RaphaelIckowicz/Gym_ML<|file_sep|>/KNN.py # -*- coding: utf-8 -*- """ Created on Wed Mar 14 @author: Raphael Ickowicz Implementation of KNN algorithm using Scikit-Learn library. """ from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score ##load dataset dataset=loadtxt("data.csv",delimiter=",") X=dataset[:,0:8] #attributes Y=dataset[:,8] #labels ##training & testing split X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33) ##knn algorithm knn_classifier=KNeighborsClassifier(n_neighbors=5) knn_classifier.fit(X_train,Y_train) Y_pred=knn_classifier.predict(X_test) ##accuracy evaluation accuracy_score(Y_test,Y_pred)<|repo_name|>RaphaelIckowicz/Gym_ML<|file_sep|>/NaiveBayes.py # -*- coding: utf-8 -*- """ Created on Thu Mar 15 @author: Raphael Ickowicz Implementation of Naive Bayes algorithm using Scikit-Learn library. """ from sklearn.naive_bayes import GaussianNB ##load dataset dataset=loadtxt("data.csv",delimiter=",") X=dataset[:,0:8] #attributes Y=dataset[:,8] #labels ##training & testing split X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33) ##naive bayes algorithm gnb=GaussianNB() gnb.fit(X_train,Y_train) Y_pred=gnb.predict(X_test) ##accuracy evaluation accuracy_score(Y_test,Y_pred)<|file_sep|># -*- coding: utf-8 -*- """ Created on Tue Mar @author: Raphael Ickowicz Regression algorithm using Linear Regression method. """ from sklearn.linear_model import LinearRegression ##load dataset dataset=loadtxt("data.csv",delimiter=",") X=dataset[:,0:8] #attributes Y=dataset[:,8] #labels ##training & testing split X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33) ##linear regression algorithm lin_reg=LinearRegression() lin_reg.fit