Stay Ahead with the Latest Leinster Senior League Updates
Welcome to your ultimate destination for all things related to the Leinster Senior League in the Republic of Ireland. As a local resident, you know how thrilling it is to follow your favorite teams and players as they compete for glory. With our comprehensive coverage, you'll never miss a beat. We provide daily updates on fresh matches, expert betting predictions, and in-depth analyses to keep you informed and engaged. Whether you're a die-hard fan or a casual observer, our content is tailored to meet your needs.
Daily Match Updates
Every day brings new excitement as teams battle it out on the field. Our team of dedicated journalists ensures you get the latest match results, highlights, and key statistics as soon as they happen. From thrilling comebacks to nail-biting finishes, we capture every moment that defines the Leinster Senior League.
- Match Summaries: Get concise overviews of each game, including goals, key plays, and standout performances.
- Player Highlights: Discover which players are making waves with their exceptional skills and contributions.
- Team Standings: Keep track of where your favorite teams stand in the league with updated rankings and tables.
Expert Betting Predictions
Betting on football can be both exciting and rewarding if done wisely. Our expert analysts provide you with informed predictions based on comprehensive data analysis. Whether you're new to betting or a seasoned pro, our insights can help you make smarter decisions.
- Prediction Models: Learn about the statistical models and algorithms we use to forecast match outcomes.
- Betting Tips: Receive daily tips from our experts on which matches offer the best value and potential returns.
- Odds Analysis: Understand how odds are set and what factors influence them, giving you an edge in your betting strategy.
In-Depth Analyses
Football is more than just goals and wins; it's a complex sport with numerous strategies and tactics. Our analyses delve deep into the nuances of the game, providing you with a richer understanding of what's happening on the pitch.
- Tactical Breakdowns: Explore how different teams approach their games, from defensive setups to attacking strategies.
- Player Performance Reviews: Get detailed reviews of individual players' performances, highlighting strengths and areas for improvement.
- Historical Context: Learn about the history of the Leinster Senior League and how past events shape current competitions.
Interactive Features
To enhance your experience, we offer several interactive features that allow you to engage with the content in unique ways.
- Live Commentary: Join live commentaries during matches for real-time updates and expert opinions.
- User Polls: Participate in polls where you can share your predictions and opinions on upcoming matches.
- Discussion Forums: Connect with other fans in our forums to discuss matches, share insights, and build a community around your passion for football.
Tips for Following Matches
Fans often seek ways to enhance their match-watching experience. Here are some tips to make the most of each game day:
- Create a Viewing Schedule: Plan your week around key matches to ensure you don't miss any action.
- Gather Your Friends: Watching games with friends can make the experience more enjoyable and provide diverse perspectives on the game.
- Stay Informed: Keep up with pre-match analyses and post-match reviews to deepen your understanding of each game.
The Role of Technology in Football
Technology is revolutionizing how we experience football. From VAR (Video Assistant Referee) to advanced data analytics, technology plays a crucial role in modern football. Here's how it impacts the Leinser Senior League:
- Variety in Analysis: Advanced metrics provide deeper insights into player performance and team dynamics.
- Fair Play: Technology helps ensure fair play by reducing human error in officiating decisions.
- Fan Engagement: Social media and live streaming platforms allow fans worldwide to connect with local leagues like the Leinster Senior League.
The Economic Impact of Football
The Leinster Senior League not only excites fans but also contributes significantly to the local economy. Here's how football drives economic growth:
- Tourism Boost: Matches attract visitors from across Ireland and beyond, boosting local businesses such as hotels, restaurants, and shops.
- Jobs Creation: The league provides employment opportunities ranging from stadium staff to marketing professionals.
- Sponsorship Deals: Local companies invest in sponsorship deals that bring financial support to clubs and enhance community engagement.
Fostering Community Spirit
The Leinster Senior League is more than just a competition; it's a vital part of community life. Football brings people together, fostering a sense of unity and pride among residents. Here's how it strengthens community bonds:
- Youth Development Programs: Clubs invest in youth academies that nurture young talent while promoting teamwork and discipline among children.
- Volunteer Opportunities: Many residents volunteer at local matches or events, contributing their time and skills to support their communities.
- Cultural Exchange: The league serves as a platform for cultural exchange among diverse groups within Kenya, promoting mutual respect and understanding through sport.
The Future of Football in Kenya
The future looks bright for football in Kenya as infrastructure improves and investments increase. Here's what lies ahead for the sport:
- Investment in Facilities: New stadiums and training grounds are being developed to accommodate growing interest in football.
- VladislavIvanov/WSA-Project<|file_sep|>/README.md
# WSA-Project
This project aims at analyzing web service APIs using architectural patterns extracted from API calls.
The main idea is that different architectures will have different API calls patterns. So if we extract these patterns (with machine learning) from API calls we can infer what architecture is used.
We use Deep Learning (LSTM network) for pattern extraction.
### How To Run
bash
python wsa.py
### References
1. http://www.cs.unb.ca/~martin/Papers/ArchitecturalPatterns.pdf
2. http://www.cs.unb.ca/~martin/Papers/ArchitecturalPatternsWSDM.pdf
<|repo_name|>VladislavIvanov/WSA-Project<|file_sep|>/src/clean_data.py
import os
import pandas as pd
import numpy as np
def read_data():
data = pd.read_csv('data/data.csv')
return data
def clean_data(data):
data.dropna(axis=0,inplace=True)
# data = data[data['api_calls'] != '[]']
# data = data[data['api_calls'] != '[]']
# data = data[data['api_calls'] != 'null']
# print(data.head(20))
# print(data.describe())
return data
def split_data(data):
split_data = {}
split_data['train'] = data[:int(len(data)*0.8)]
split_data['test'] = data[int(len(data)*0.8):]
split_data['val'] = data[int(len(data)*0.9):int(len(data)*0.95)]
return split_data
def save_split_data(split_data):
save_path = 'data/split'
if not os.path.exists(save_path):
os.makedirs(save_path)
split_data['train'].to_csv(os.path.join(save_path,'train.csv'))
split_data['test'].to_csv(os.path.join(save_path,'test.csv'))
split_data['val'].to_csv(os.path.join(save_path,'val.csv'))
if __name__ == "__main__":
# Read CSV file into Pandas dataframe
data = read_data()
# Clean Data (drop rows with empty values)
cleaned_data = clean_data(data)
# Split into train/test/validation sets
split_data = split_data(cleaned_data)
# Save splits into separate files
save_split_data(split_data)
<|file_sep|># This script cleans input data file by removing unnecessary columns,
# converting json column into csv columns etc.
import pandas as pd
import json
def clean_json(json_string):
# TODO: remove json strings from api_calls column
return json_string
def remove_columns(df):
# TODO: remove all unnecessary columns
return df
def convert_json_to_columns(df):
# TODO: convert api_calls json column into csv columns
return df
def main():
# Read CSV file into Pandas dataframe
df = pd.read_csv('data/data.csv')
# Remove all unnecessary columns
df = remove_columns(df)
# Clean api_calls column
df['api_calls'] = df['api_calls'].apply(clean_json)
# Convert api_calls json column into csv columns
df = convert_json_to_columns(df)
# Write dataframe back into csv file
df.to_csv('data/data_clean.csv',index=False)
if __name__ == "__main__":
main()
<|file_sep|># This script converts input csv file into json file where each line contains one object.
import pandas as pd
import json
def convert_row(row):
# TODO: convert row into object
return obj
def main():
# Read CSV file into Pandas dataframe
df = pd.read_csv('data/data_clean.csv')
# Convert each row into object
data_list = df.apply(convert_row,axis=1).tolist()
# Write objects into JSON file
with open('data/data.json','w') as f:
for obj in data_list:
f.write(json.dumps(obj))
f.write('n')
if __name__ == "__main__":
main()
<|file_sep|># This script creates input csv file from raw csv files.
import pandas as pd
def read_raw_files():
raw_files_path = 'raw'
raw_files_names = ['raw_1.csv','raw_2.csv','raw_3.csv','raw_4.csv','raw_5.csv']
dataframes_list = []
for name in raw_files_names:
file_path = os.path.join(raw_files_path,name)
dataframes_list.append(pd.read_csv(file_path))
return pd.concat(dataframes_list)
def main():
# Read raw files
df_raw = read_raw_files()
# Write dataframe back into csv file
df_raw.to_csv('data/data.csv',index=False)
if __name__ == "__main__":
main()
<|repo_name|>VladislavIvanov/WSA-Project<|file_sep|>/src/train.py
import tensorflow as tf
from keras import layers
from keras.models import Sequential
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
from nltk.tokenize import word_tokenize
import numpy as np
import pandas as pd
import sys
class DataPreparation:
def __init__(self):
self.max_length_seq = None
self.max_vocab_size = None
self.tokenizer_obj=None
self.word_index=None
def read_split(self,data_type='train'):
split_path='data/split'
if not os.path.exists(split_path):
raise Exception('Split path does not exist!')
if data_type=='train':
self.data=self.__read_file(os.path.join(split_path,'train.csv'))
self.labels=self.data.pop('architecture').values
self.api_calls=self.data.pop('api_calls').values
self.max_length_seq=self.__find_max_sequence_length(self.api_calls)
self.max_vocab_size=self.__find_max_vocab_size(self.api_calls)
self.tokenizer_obj=Tokenizer(num_words=self.max_vocab_size)
self.tokenizer_obj.fit_on_texts(self.api_calls)
self.word_index=self.tokenizer_obj.word_index
self.x_train,self.x_val,self.y_train,self.y_val=train_test_split(self.api_calls,self.labels,test_size=0.1)
self.x_train_seq=self.__text_to_sequence(self.x_train)
self.x_val_seq=self.__text_to_sequence(self.x_val)
self.y_train_cat=to_categorical(self.y_train)
self.y_val_cat=to_categorical(self.y_val)
def __read_file(self,path):
if not os.path.exists(path):
raise Exception('Path does not exist!')
return pd.read_csv(path)
def __find_max_sequence_length(self,texts):
max_len=0
for text in texts:
tokens=word_tokenize(text)
if len(tokens)>max_len:
max_len=len(tokens)
return max_len
def __find_max_vocab_size(self,texts):
vocab=set()
for text in texts:
tokens=word_tokenize(text)
for token in tokens:
vocab.add(token)
return len(vocab)+1
def __text_to_sequence(self,texts):
seqs=[]
for text in texts:
seq=self.tokenizer_obj.texts_to_sequences([text])[0]
seqs.append(seq)
return seqs
def get_x_train_pad(self):
x_train_pad=pad_sequences(self.x_train_seq,maxlen=self.max_length_seq,padding='post')
return x_train_pad
def get_y_train_cat(self):
return self.y_train_cat
def get_x_val_pad(self):
x_val_pad=pad_sequences(self.x_val_seq,maxlen=self.max_length_seq,padding='post')
return x_val_pad
def get_y_val_cat(self):
return self.y_val_cat
class ModelTrainer:
def __init__(self,data_prep_obj,model_type='lstm'):
if model_type=='lstm':
model=self.__create_lstm_model()
print(model.summary())
model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
history=model.fit(x=data_prep_obj.get_x_train_pad(),y=data_prep_obj.get_y_train_cat(),validation_data=(data_prep_obj.get_x_val_pad(),data_prep_obj.get_y_val_cat()),epochs=10,batch_size=128)
preds=model.predict_classes(data_prep_obj.get_x_val_pad())
print(preds)
def __create_lstm_model(self):
model=Sequential()
model.add(layers.Embedding(input_dim=data_prep_obj.max_vocab_size,output_dim=128,input_length=data_prep_obj.max_length_seq))
model.add(layers.LSTM(128))
model.add(layers.Dense(32))
model.add(layers.Dense(16))
model.add(layers.Dense(6))
model.add(layers.Activation('softmax'))
return model
if __name__=="__main__":
data_prep_obj=DataPreparation()
data_prep_obj.read_split()
model_trainer=ModelTrainer(data_prep_obj)
<|repo_name|>ArtemisTurtle/EZ-Chat-Bot<|file_sep|>/src/commands/ServerInfo.js
const Discord = require("discord.js");
module.exports.run = async (client,message,args) => {
const { guild } = message;
const { name } = guild;
const memberCount = guild.memberCount;
const createdOnDate = new Date(guild.createdAt).toLocaleDateString();
const channelCountTotalTextChannelCountVoiceChannelCountArray =
[guild.channels.cache.filter(c => c.type === "text").size,guild.channels.cache.filter(c => c.type === "voice").size];
const channelCountTotalTextChannelCountVoiceChannelCountArrayJoinedString =
channelCountTotalTextChannelCountVoiceChannelCountArray.join(" | ");
const serverOwnerNameString =
`${guild.owner.user.username}#${guild.owner.user.discriminator}`;
const serverRegionString =
guild.region.toUpperCase();
const serverIconUrl =
guild.iconURL({ format: "png", dynamic: true });
let serverInfoEmbed =
new Discord.MessageEmbed()
.setTitle(`${name} Server Information`)
.setColor("#0099ff")
.setThumbnail(serverIconUrl)
.addField("Owner",serverOwnerNameString,true)
.addField("Created On",createdOnDate,true)
.addField("Region",serverRegionString,true)
.addField(`Channels (${channelCountTotalTextChannelCountVoiceChannelCountArrayJoinedString})`,```Text: ${channelCountTotalTextChannelCountVoiceChannelCountArray[0]} | Voice: ${channelCountTotalTextChannelCountVoiceChannelCountArray[1]}```,true)
.addField("Member Count",memberCount,true);
message.channel.send(serverInfoEmbed);
};
module.exports.help ={
name:"serverinfo",
description:"Sends server information"
}<|repo_name|>ArtemisTurtle/EZ-Chat-Bot<|file_sep|>/src/commands/Help.js
const Discord = require("discord.js");
const { prefix } =
require("../../config.json");
module.exports.run =
async (client,message,args) => {
const helpEmbed =
new Discord.MessageEmbed()
.setColor("#0099ff")
.setTitle("EZ Chat Bot Help")
.setDescription(`Prefix: ${prefix}`)
.addField(
"Moderation",
`**ban**n**kick**n**mute**n**unmute**n**warn**n**clear**`,
true,
)
.addField(
"Utility",
`**avatar**n**userinfo**n**serverinfo**`,
true,
)
.addField(
"Fun",
`**say**n**ping**n**roll**`,
true,
);
message.channel.send(helpEmbed);
};
module.exports.help ={
name:"help",
description:"Sends help information"
}<|file_sep|># EZ Chat Bot v2
## What is EZ Chat Bot