Unlock Tomorrow's Football Excitement: Copa Paulista Brazil
The anticipation is palpable as fans across Tanzania eagerly await the thrilling matches of the Copa Paulista Brazil set to take place tomorrow. This premier football event promises to deliver exhilarating action, showcasing some of Brazil's finest talents. With a lineup packed with potential surprises and strategic showdowns, this weekend is a must-watch for football enthusiasts.
The Copa Paulista Brazil is renowned for its intense competition and unpredictable outcomes. As teams vie for supremacy, each match becomes a showcase of skill, strategy, and sheer determination. Whether you're a seasoned supporter or new to the excitement of Brazilian football, this tournament offers something for everyone.
Match Highlights: What to Expect Tomorrow
Tomorrow's schedule is brimming with high-stakes encounters that promise to keep fans on the edge of their seats. Here’s a closer look at some of the key matchups:
- Team A vs Team B: This clash features two top contenders in the league, both known for their aggressive playstyle and tactical acumen. Expect a fast-paced game with numerous goal-scoring opportunities.
- Team C vs Team D: A classic rivalry reignites as these two teams battle it out for dominance. With both squads boasting formidable defenses, this match could be a nail-biter down to the final whistle.
- Team E vs Team F: An underdog story unfolds as Team E aims to upset the reigning champions, Team F. Known for their resilience and teamwork, Team E could be the dark horse of this tournament.
Expert Betting Predictions: Your Guide to Smart Wagers
For those looking to place bets on tomorrow's matches, expert predictions can provide valuable insights. Our analysis considers team form, head-to-head records, player injuries, and other critical factors.
Key Predictions:
- Team A vs Team B: While both teams are evenly matched, Team A's home advantage and recent scoring streak make them slight favorites.
- Team C vs Team D: Expect a tightly contested match with few goals. A draw could be a safe bet given their defensive prowess.
- Team E vs Team F: Despite being underdogs, Team E's recent form suggests they have a good chance of causing an upset. Consider backing them for an away win.
In-Depth Analysis: Tactical Breakdowns
To better understand tomorrow's matches, let's delve into the tactical aspects that could influence the outcomes.
Team A's Strategy:
Known for their high-pressing game, Team A aims to dominate possession and create scoring opportunities through quick transitions. Their key players in attack are expected to exploit any defensive lapses from Team B.
Team B's Counter-Strategy:
In response, Team B plans to sit back and absorb pressure, looking to hit on the counter-attack. Their pacey forwards will be crucial in turning defense into offense swiftly.
Team C vs Team D: A Tactical Chess Match
Both teams prefer a cautious approach, focusing on solid defensive structures. Midfield battles will be pivotal in determining which team can control the tempo and dictate play.
The Underdog's Approach: Team E
Team E's strategy revolves around compact defending and quick counter-attacks. Their ability to remain disciplined at the back while capitalizing on set-pieces could be their ticket to victory against Team F.
Potential Game-Changers: Key Players to Watch
Every match has players who can turn the tide with moments of brilliance. Here are some individuals who could make a significant impact:
- Player X (Team A): Known for his clinical finishing and sharp instincts in front of goal, Player X is a constant threat whenever he gets on the ball.
- Player Y (Team B): With exceptional vision and passing ability, Player Y orchestrates attacks and sets up his teammates for scoring chances.
- Player Z (Team C): A defensive stalwart, Player Z's leadership at the back will be crucial in maintaining team shape and thwarting opposition attacks.
- Player W (Team D): His pace and dribbling skills make him a formidable opponent in one-on-one situations, often breaking defensive lines with ease.
- Player V (Team E): As an attacking midfielder, Player V's creativity and knack for finding space will be vital in unlocking Team F's defense.
- Player U (Team F): The captain and leader on the field, Player U's experience and composure under pressure are invaluable assets for his team.
The Role of Fan Support: Energizing Teams from Afar
Even from Tanzania, fans play a crucial role in boosting team morale. Social media platforms buzz with support as Tanzanian fans rally behind their favorite teams. Here’s how you can get involved:
- Tweet your support: Use hashtags like #CopaPaulistaBrazil2023 and #SupportFromTanzania to join the global conversation.
- Create fan content: Share photos, videos, or memes celebrating your favorite teams and players.
- Virtually attend matches: Watch live streams together with fellow fans online to create a sense of community.
Tips for Enjoying Tomorrow’s Matches
To make the most of tomorrow’s football extravaganza, consider these tips:
- Create a viewing party: Gather friends or family members for a fun-filled day of football watching.
- Cheer responsibly: If you plan on enjoying drinks or snacks during the matches, remember moderation is key.
- Show team spirit: Wear your team’s colors or merchandise proudly as you cheer them on!
<|repo_name|>MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection<|file_sep|>/README.md
# Deep-Learning-for-Cancer-Detection This repository contains notebooks showing how Deep Learning can be used in cancer detection.
The notebooks are part of my course project at EPFL (Swiss Federal Institute of Technology) where I studied Deep Learning. ## Notebooks
The notebooks are based on [Kaggle](https://www.kaggle.com) competition [SIIM-ISIC Melanoma Classification](https://www.kaggle.com/c/siim-isic-melanoma-classification/overview). * [1-Data Exploration.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/1-Data%20Exploration.ipynb)
* [2-Data Augmentation.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/2-Data%20Augmentation.ipynb)
* [3-ResNet50.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/3-ResNet50.ipynb)
* [4-InceptionV3.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/4-InceptionV3.ipynb)
* [5-Xception.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/5-Xception.ipynb)
* [6-InceptionResNetV2.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/6-InceptionResNetV2.ipynb)
* [7-MobilenetV2.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/7-MobilenetV2.ipynb)
* [8-Ensemble%20Model%20Prediction.ipynb](https://github.com/MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection/blob/master/8-Ensemble%20Model%20Prediction.ipynb) ## Other Files
* **data**: Contains train images
* **images**: Contains examples images <|repo_name|>MouhammedAliTouhami/Deep-Learning-for-Cancer-Detection<|file_sep|>/8-Ensemble Model Prediction.py
#!/usr/bin/env python
# coding: utf-8 # In[1]: import numpy as np
import pandas as pd import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline') import seaborn as sns # In[2]: from sklearn.metrics import roc_auc_score # In[3]: def get_scores(y_true,y_pred):
roc = roc_auc_score(y_true,y_pred)
# auprc = average_precision_score(y_true,y_pred)
# acc = accuracy_score(y_true,np.round(y_pred))
# f1 = f1_score(y_true,np.round(y_pred))
# print(f'roc auc:{roc}nauprc:{auprc}nacc:{acc}nf1:{f1}')
# return roc,auprc
# return roc,auprc
# return roc
# return auprc
# return acc,f1
return roc # In[4]: test = pd.read_csv('./input/test.csv')
sample_submission = pd.read_csv('./input/sample_submission.csv') # In[5]: test.head() # In[6]: sample_submission.head() # ## Load models predictions # In[7]: resnet50_preds = pd.read_csv('./output/resnet50_preds.csv')
resnet50_preds.head() # In[8]: inceptionv3_preds = pd.read_csv('./output/inceptionv3_preds.csv')
inceptionv3_preds.head() # In[9]: xception_preds = pd.read_csv('./output/xception_preds.csv')
xception_preds.head() # In[10]: inceptionresnetv2_preds = pd.read_csv('./output/inceptionresnetv2_preds.csv')
inceptionresnetv2_preds.head() # In[11]: mobilenetv2_preds = pd.read_csv('./output/mobilenetv2_preds.csv')
mobilenetv2_preds.head() # ## Average model predictions # ### Average by taking mean # In[12]: ensemble_mean_preds = pd.DataFrame() ensemble_mean_preds['image_name'] = resnet50_preds['image_name'] ensemble_mean_preds['target'] = (resnet50_preds['target'] + inceptionv3_preds['target'] + xception_preds['target'] + inceptionresnetv2_preds['target'] + mobilenetv2_preds['target']) /5 ensemble_mean_preds.head() # ### Average by taking weighted mean # #### Get weights # In[13]: train = pd.read_csv('./input/train.csv') train.head() # In[14]: val = train.sample(frac=0.20) train_val_split = val.image_name.to_list() val.reset_index(drop=True,inplace=True) train.drop(train[train.image_name.isin(train_val_split)].index,inplace=True) val.reset_index(drop=True,inplace=True) # #### ResNet50 # In[15]: resnet50_val_predictions = resnet50_preds[resnet50_preds['image_name'].isin(train_val_split)] resnet50_val_predictions.reset_index(drop=True,inplace=True) resnet50_val_predictions.head() # In[16]: resnet50_roc = get_scores(val['target'],resnet50_val_predictions['target']) print(resnet50_roc) # #### InceptionV3 # In[17]: inceptionv3_val_predictions = inceptionv3_preds[inceptionv3_preds['image_name'].isin(train_val_split)] inceptionv3_val_predictions.reset_index(drop=True,inplace=True) inceptionv3_val_predictions.head() # In[18]: inceptionv3_roc = get_scores(val['target'],inceptionv3_val_predictions['target']) print(inceptionv3_roc) # #### Xception # In[19]: xception_val_predictions = xception_preds[xception_preds['image_name'].isin(train_val_split)] xception_val_predictions.reset_index(drop=True,inplace=True) xception_val_predictions.head() # In[20]: xception_roc = get_scores(val['target'],xception_val_predictions['target']) print(xception_roc) # #### InceptioResNetV2 # In[21]: inceptionresnetv2_val_predictions = inceptionresnetv2_preds[inceptionresnetv2_preds['image_name'].isin(train_val_split)] inceptionresnetv2_val_predictions.reset_index(drop=True,inplace=True) inceptionresnetv2_val_predictions.head() # In[22]: inceptionresnetv2_roc = get_scores(val['target'],inceptionresnetv2_val_predictions['target']) print(inceptionresnetv2_roc) # #### MobileNetV2 # In[23]: mobilenetv2_val_predictions = mobilenetv2_preds[mobilenetv2_preds['image_name'].isin(train_val_split)] mobilenetv2_val_predictions.reset_index(drop=True,inplace=True) mobilenetv2_val_predictions.head() # In[24]: mobilenetv2_roc = get_scores(val['target'],mobilenetv2_val_predictions['target']) print(mobilenetv2_roc) # #### Compute weights weights_df=pd.DataFrame({'model':['ResNet50','InceptonV3','XCEPTION','InceptioResNetV2','MobileNetV2'],
'roc':[resnet50_roc,inceptionv3_roc,xception_roc,inceptionresnetv2_roc,mobilenetv2_roc]}) weights_df get_ipython().run_line_magic('matplotlib', 'inline') weights_df.plot.bar(x='model',y='roc',legend=False); weights_df_sorted=weights_df.sort_values('roc',ascending=False) weights_df_sorted weights_df_sorted=weights_df_sorted.iloc[::-1] weights_df_sorted weights_df_sorted.index=np.arange(1,len(weights_df_sorted)+1) weights_df_sorted weights_sum=weights_df_sorted.iloc[:,1].sum() weights_sum weights_df_sorted['weight']=np.round(weights_df_sorted.iloc[:,1]/weights_sum , decimals=5) weights_df_sorted get_ipython().run_line_magic('matplotlib', 'inline') plt.figure(figsize=(10,6)) plt.bar(weights_df_sorted.index.values,height=weights_df_sorted.weight.values,width=0.5); plt.xlabel('Models'); plt.ylabel('Weights'); plt.title('Weights Distribution'); ensemble_weighted_mean_preds=pd.DataFrame() ensemble_weighted_mean_preds['image_name']=resnet50_preds.image_name ensemble_weighted_mean_preds['target']=np.round(
weights_df_sorted.iloc[0][1]* resnet50_preds.target +
weights_df_sorted.iloc[1][1]* inceptionv3_preds.target +
weights_df_sorted.iloc[2][1]* xception_preds.target +
weights_df_sorted.iloc[3][1]* inceptionresnetv2_preds.target +
weights_df_sorted.iloc[4][1]* mobilenetv2_preds.target ,decimals=6) ensemble_weighted_mean_preds get_ipython().run_line_magic('matplotlib', 'inline') sns.distplot(ensemble_weighted_mean_preds.target); sns.distplot(resnet50_pred<|file_sep|>#include "Astar.h" int main()
{
// Read input file
int start_x;
int start_y;
int goal_x;
int goal_y;
std::string input_filename;
std::string output_filename;
std::cout << "Please input input filename:" << std::endl;
std::cin >> input_filename;
std::cout << "Please input output filename:" << std::endl;
std::cin >> output_filename; std::ifstream infile(input_filename);
infile >> start_x >> start_y >> goal_x >> goal_y;
infile.close(); // Create map object
Map map(input_filename); // Create astar object
Astar astar(map); // Search path
astar.search(start_x,start_y);
astar.search(goal_x,goal_y); // Print result
astar.print_path(output_filename);
return EXIT_SUCCESS;
}<|repo_name|>YaozhangLab/A-star-algorithm<|file_sep|>/Astar.cpp
#include "Astar.h" Map::Map(std::string filename){
// Open file
std::ifstream infile(filename);
if(!infile.is_open())
{
throw std::runtime_error("Can't open file.");
} // Read data from file
infile >> height_ >> width_;
data_.resize(height_);
for(int i=0;i>temp_char;
if(temp_char=='.'||temp_char=='G'||temp_char=='S'){
data_[i][j]=0;
}
else if(temp_char=='*'){
data_[i][j]=10000;
}
else{
throw std::runtime_error("Invalid data format.");
}
}
}
infile.close();
} bool Map::is_obstacle(int x,int y){
return data_[x][y]==10000;
} bool Map::is_valid(int x,int y){
return (x>=