Upcoming Tennis Matches in Slobozia, Romania: W50 Event
Get ready for an electrifying day of tennis as the W50 tournament in Slobozia, Romania, gears up for its matches tomorrow. This event is set to feature some of the most talented players in the women's circuit, promising intense competition and thrilling matches. Whether you're a die-hard tennis fan or a casual viewer, this tournament offers something for everyone. With expert betting predictions at your fingertips, you can make informed decisions and potentially enhance your viewing experience with a bit of friendly wagering.
Tournament Overview
The W50 tournament in Slobozia is a key fixture on the women's tennis calendar, attracting top-tier talent from across the globe. Known for its challenging conditions and passionate local support, the event has become a staple for players looking to make a mark in the competitive landscape of women's tennis. This year's lineup promises to deliver high-quality matches, with players battling it out on the court to secure their spot in the winner's circle.
Key Players to Watch
- Maria Sakkari: The Greek powerhouse is known for her aggressive play and powerful groundstrokes. With a strong track record in similar tournaments, Sakkari is expected to be a formidable opponent.
- Elise Mertens: The Belgian star brings consistency and versatility to her game. Her all-court skills make her a tough matchup for any opponent.
- Katie Boulter: Representing Great Britain, Boulter is known for her tenacity and ability to fight back from challenging positions. She will be looking to make an impact on home soil.
- Petra Martić: The Croatian player is renowned for her defensive prowess and strategic play. Martić often turns matches around with her resilience and tactical acumen.
Match Highlights
Tomorrow's schedule is packed with exciting matchups that promise to keep fans on the edge of their seats. Here are some of the key clashes to look out for:
- Maria Sakkari vs Elise Mertens: A clash between two of the most consistent players in women's tennis. Both are known for their mental toughness and ability to perform under pressure.
- Katie Boulter vs Petra Martić: This match features contrasting styles, with Boulter's aggressive baseline play against Martić's defensive expertise. It will be fascinating to see how this tactical battle unfolds.
- Serena Williams vs Young Challenger: Although not officially announced, rumors suggest that Serena Williams might make a surprise appearance. Her presence would undoubtedly add an extra layer of excitement to the tournament.
Betting Predictions
For those interested in placing bets on tomorrow's matches, here are some expert predictions based on current form and historical performance:
- Maria Sakkari vs Elise Mertens: While both players are evenly matched, Sakkari's recent form gives her a slight edge. Consider backing Sakkari to win in straight sets.
- Katie Boulter vs Petra Martić: Martić's defensive skills could prove crucial in this matchup. A safe bet might be on Martić to win in three sets.
- Serena Williams Surprise Match: If Serena does appear, betting on her to win would be a popular choice among fans. However, keep an eye on her condition and any potential injuries.
Tournament Venue and Conditions
The W50 tournament is held at the Slobozia Tennis Complex, known for its fast courts and vibrant atmosphere. The weather forecast predicts sunny skies with mild temperatures, ideal conditions for outdoor tennis. Fans can expect an enthusiastic crowd supporting their favorite players throughout the day.
How to Watch
For those unable to attend in person, live streaming options are available through various sports networks and online platforms. Ensure you have your streaming service ready by early morning local time to catch all the action as it unfolds.
Tournament Schedule
| Time (Local) |
Matchup |
Round |
| 08:00 AM | Maria Sakkari vs Elise Mertens | Semifinals |
| 10:30 AM | Katie Boulter vs Petra Martić | Semifinals |
| 01:00 PM | Potential Serena Williams Matchup | Singles Quarterfinals |
| 03:30 PM | Semi-Final Winner Matchup | Finals |
Tips for Fans and Bettors
jimmygao2016/Chatbot-ML/README.md
# Chatbot-ML
Chatbot Machine Learning
## Libraries
* [Flask](http://flask.pocoo.org/)
* [NLTK](http://www.nltk.org/)
* [Tensorflow](https://www.tensorflow.org/)
* [Keras](https://keras.io/)
## Project
* [ChatterBot](https://github.com/gunthercox/ChatterBot)
* [seq2seq-chatbot](https://github.com/bentrevett/pytorch-seq2seq)
* [chatbot](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn/models/chatbot)
# ChatterBot
[](https://travis-ci.org/gunthercox/ChatterBot)
ChatterBot is a machine learning based conversational dialog engine built for creating chat bots.
It is written in Python and comes with a natural language processing pipeline that can be trained to generate different types of responses.
This repository contains both the source code for ChatterBot as well as documentation.
## Installation
### Python
#### Requirements
ChatterBot requires Python version `3.5` or higher.
#### Using Pip
The easiest way to install ChatterBot is using pip:
pip install chatterbot
#### From Source
You can also install ChatterBot directly from source using setup.py:
python setup.py install
### Node.js
#### Requirements
ChatterBot requires Node version `8.x` or higher.
#### Using NPM
The easiest way to install ChatterBot is using npm:
npm install chatterbot --save
#### From Source
You can also install ChatterBot directly from source:
npm install git+https://github.com/gunthercox/ChatterBot.git --save
## Getting Started
### Python
Here is an example of how you might create a simple bot:
python
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
chatbot = ChatBot('My Bot')
trainer = ChatterBotCorpusTrainer(chatbot)
trainer.train(
"chatterbot.corpus.english.greetings",
"chatterbot.corpus.english.conversations"
)
while True:
input_data = input("You: ")
response = chatbot.get_response(input_data)
print("My Bot:", response)
### Node.js
Here is an example of how you might create a simple bot:
javascript
var ChatBot = require('chatterbot');
var chatbot = new ChatBot();
chatbot.train([
'chatterbot.corpus.english.greetings',
'chatterbot.corpus.english.conversations'
]);
chatbot.on('message', function (message) {
console.log(message);
});
chatbot.startConversation();
var input = 'Hello';
chatbot.receive(input);
## Training
ChatterBot ships with several training data files included with it which contain conversations covering topics such as greetings and small talk.
These conversations are located inside of `chatterbot.corpus` package.
To use them you can simply pass them as arguments into the `train()` method.
python
trainer.train(
"chatterbot.corpus.english.greetings",
"chatterbot.corpus.english.conversations"
)
You can also use other corpus trainers such as `ListTrainer` which allows you train your bot with custom data.
Here is an example of how you might train your bot using custom data:
python
from chatterbot.trainers import ListTrainer
list_trainer = ListTrainer(chatbot)
conversation = [
"Hello",
"How are you?",
"I'm doing great.",
"Wonderful!"
]
list_trainer.train(conversation)
## Responses
When using `get_response()` method you can pass in either one or more strings as arguments.
If you pass multiple strings then ChatterBot will try match each string against all known statements before returning a response.
python
response = chatbot.get_response("What time is it?", "How old are you?")
If no suitable response was found then `None` will be returned.
If you want ChatterBot to return more than one possible response then pass `multiple=True` into the method call.
python
responses = chatbot.get_response("What time is it?", multiple=True)
The above call will return a list containing one or more responses.
## Customizing Your Bot
There are many ways that you can customize your bot including changing its name and logic adapters.
### Logic Adapters
Logic adapters determine how responses are selected when using `get_response()` method.
By default ChatterBot uses `BestMatch` logic adapter which returns the most likely response based on its confidence score.
You can change which logic adapter your bot uses by passing in a list of logic adapters when creating your bot:
python
from chatterbot.logic import BestMatch, TimeLogicAdapter
chatbot = ChatBot(
'name',
logic_adapters=[
{
'import_path': 'chatterbot.logic.BestMatch'
},
{
'import_path': 'chatterbot.logic.TimeLogicAdapter'
}
]
)
### Storage Adapters
Storage adapters determine where responses should be stored after they have been generated by your logic adapter(s).
By default ChatterBot uses SQLStorageAdapter which stores responses inside of an SQLite database file called `database.sqlite3`.
You can change which storage adapter your bot uses by passing in a storage adapter when creating your bot:
python
from chatterbot.storage import SQLStorageAdapter
chatbot = ChatBot(
'name',
storage_adapter='chatterbot.storage.SQLStorageAdapter'
)
## Contributing
Contributions are welcome! If you would like to contribute please read through our [Contributing Guide](CONTRIBUTING.md) first.
## License
Copyright (c) 2017-2019 Gunther Cox
Licensed under MIT License.
jimmygao2016/Chatbot-ML/chatspace/chat-space-backend-master/app.js
const express = require('express');
const bodyParser = require('body-parser');
const cors = require('cors');
const { ApolloServer } = require('apollo-server-express');
const typeDefs = require('./schema');
const resolvers = require('./resolvers');
// Create Express app
const app = express();
app.use(bodyParser.json());
app.use(cors());
// Create Apollo server instance
const server = new ApolloServer({
typeDefs,
resolvers,
// To resolve circular dependency errors we have defined context here instead of passing it while instantiating Apollo server.
context: ({ req }) => ({ req })
});
server.applyMiddleware({ app });
// Start server listening on port :4000
app.listen({ port: process.env.PORT || 4000 }, () =>
console.log(`🚀 Server ready at http://localhost:4000${server.graphqlPath}`)
);
jimmygao2016/Chatbot-ML/seq2seq-chat-bot-master/pytorch-seq2seq/utils.py
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 27 20:56:21 2018
@author: [email protected]
"""
import torch
def get_long_tensor(size):
"""
Create a long tensor initialized with -1.
Parameters:
size (tuple): size of tensor.
Returns:
long tensor initialized with -1.
"""
return torch.LongTensor(size).fill_(-1)
def get_float_tensor(size):
"""
Create a float tensor initialized with -1.
Parameters:
size (tuple): size of tensor.
Returns:
float tensor initialized with -1.
"""
return torch.FloatTensor(size).fill_(-1)
def repackage_hidden(h):
"""Wraps hidden states in new Variables,
to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)# -*- coding: utf-8 -*-
"""
Created on Sat Jul 28 16:37:55 2018
@author: [email protected]
"""
import random
class BeamSearchNode(object):
def __init__(self, hiddenstate, previousNode, wordId, logProb, length):
self.h = hiddenstate
self.prevNode = previousNode
self.wordid = wordId
self.logp = logProb
self.leng = length
def beam_decode(model,
src_seq_tensor,
src_lengths,
beam_size,
max_length,
n_best=1):
# Initialize start node
start_token_id = model.src_embedder.tokenizer.vocab['']
start_node = BeamSearchNode(hiddenstate=model.init_hidden(src_seq_tensor.size(0)),
previousNode=None,
wordId=start_token_id,
logProb=0,
length=1)
# put start node into queue
nodes_queue= []
nodes_queue.append(start_node)
# start beam search
end_nodes= []
while len(end_nodes) 0:
x= nodes_queue.pop()
h,x= model.forward(x.wordid.unsqueeze(0),
x.h,
src_seq_tensor,
src_lengths)
log_prob,y= x.log_softmax(dim=-1).data.topk(beam_size)
log_prob= log_prob.squeeze(0).tolist()
y= y.squeeze(0).tolist()
# put them into new queue
for new_k in range(beam_size):
decoded_t= y[new_k]
log_p= log_prob[new_k]
node= BeamSearchNode(hiddenstate=h,
previousNode=x,
wordId=decoded_t,
logProb=x.logProb+log_p,
length=x.length+1)
if decoded_t== model.src_embedder.tokenizer.vocab['']:
end_nodes.append(node)
if len(end_nodes) == n_best:
break
else:
candidates.append(node)
if len(end_nodes) == n_best:
break
# order by score
candidates.sort(key=lambda x:x.logProb/float(x.length), reverse=True)
nodes_queue= candidates[:beam_size]
# choose n-best paths
utterances= []
for node in end_nodes:
utterance= []
utterance.append(node.wordid.item())
while node.prevNode!= None:
node= node.prevNode
utterance.append(node.wordid.item())
utterance.reverse()
utterances.append(utterance)
return utterances[0]# -*- coding: utf-8 -*-
"""
Created on Sun Jul 29 09:06:56 2018
@author: [email protected]
"""
import os
def save_model(model_path,model):
if not os.path.exists(model_path):
os.makedirs(model_path)
torch.save(model.state_dict(),model_path+'/model.pth')
print('model saved at {}'.format(model_path))
def load_model(model_path,model):
model.load_state_dict(torch.load(model_path))
print('model loaded from {}'.format(model_path))jimmygao2016/Chatbot-ML/seq2seq-chat-bot-master/pytorch-seq2seq/chat.py
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 28 15:11:44 2018
@author: [email protected]
"""
from __future__ import print_function
import random
class Conversation(object):
def __init__(self,model,max_length=20):
self.model=model
self.max_length=max_length
#%%
if __name__ == '__main__':
conversation= Conversation(max_length=20)# -*- coding: utf-8 -*-
"""
Created on Fri Jul 27 20:54:42 2018
@author: [email protected]
"""
import numpy as np
def normalize(vectors):
norms=np.linalg.norm(vectors,axis=-1,keepdims=True)+1e-12
vectors=vectors/norms
return vectors# -*- coding:utf-8 -*-
"""
Author：王勇波（[email protected]）
"""
import os.path
class
