Welcome to the Ultimate Guide for Tennis W15 Dijon France
Dive into the electrifying world of tennis with our comprehensive coverage of the W15 Dijon France tournament. Our expertly crafted content provides you with the latest updates, insightful analysis, and expert betting predictions, ensuring you stay ahead in the game. Whether you're a seasoned tennis enthusiast or new to the sport, this guide is your go-to resource for all things related to the W15 Dijon France. Stay tuned for daily updates on fresh matches and expert insights that will enhance your viewing experience and betting strategy.
Understanding the W15 Dijon France Tournament
The W15 Dijon France is a prestigious event on the Women's Tennis Association (WTA) tour, featuring top-tier talent from around the globe. Held in the scenic city of Dijon, this tournament is known for its challenging clay courts and competitive spirit. With a prize pool that attracts some of the best players in women's tennis, W15 Dijon is a must-watch for fans and bettors alike.
Key Features of the Tournament
- Surface: Clay courts provide a unique playing experience, favoring players with excellent groundstrokes and stamina.
- Format: The tournament follows a single-elimination format, ensuring intense and thrilling matches.
- Prize Money: Attracting top talent with a substantial prize pool that rewards exceptional performance.
- Location: Set in the historic city of Dijon, offering fans a blend of sports and culture.
Understanding these key features helps fans appreciate the nuances of each match and make informed betting decisions.
Daily Match Updates and Analysis
Stay updated with our daily match reports that provide in-depth analysis of each game. Our team of expert analysts breaks down every aspect of the matches, from player performance to strategic plays. This section is updated daily to ensure you have access to the freshest information.
How We Analyze Matches
- Player Statistics: Detailed stats on serve accuracy, return effectiveness, and overall performance.
- Tactical Insights: Examination of player strategies and adjustments during matches.
- Key Moments: Highlights of pivotal points that influenced the outcome of each match.
Our analysis aims to provide you with a comprehensive understanding of each match, helping you predict future performances and make better betting choices.
Expert Betting Predictions
Betting on tennis can be both exciting and rewarding if done correctly. Our expert betting predictions are based on thorough analysis and a deep understanding of the sport. We consider various factors such as player form, head-to-head records, surface preferences, and current fitness levels to provide you with reliable betting tips.
Betting Strategies for Success
- Understanding Odds: Learn how to interpret betting odds and what they mean for potential payouts.
- Diversifying Bets: Tips on spreading your bets across different matches to minimize risk.
- Focusing on Favorites: Strategies for betting on favorites when they have a clear advantage.
- Coups de Main: Identifying underdogs who have the potential to pull off an upset.
By following our expert predictions and strategies, you can enhance your chances of making profitable bets during the W15 Dijon France tournament.
In-Depth Player Profiles
Get to know the players competing in the W15 Dijon France through our detailed player profiles. Each profile includes information on their career achievements, playing style, strengths, weaknesses, and recent form. This section helps fans and bettors alike understand what to expect from each player as they take to the court.
Featured Player: Example Player Name
- Career Highlights: Overview of major titles won and career milestones achieved.
- Playing Style: Analysis of their preferred playing techniques and strategies.
- Strengths: Key attributes that give them an edge over opponents.
- Weaknesses: Areas where they may be vulnerable during matches.
- Recent Form: Performance trends leading up to the tournament.
These profiles provide valuable insights into each player's capabilities and help you make informed decisions when placing bets.
The Science Behind Betting Predictions
Our betting predictions are not just based on gut feelings; they are grounded in data-driven analysis. We use advanced statistical models and historical data to predict outcomes with greater accuracy. This section delves into the methodology behind our predictions.
Data Sources and Analysis Techniques
- Historical Performance Data: Analysis of past performances on similar surfaces and against similar opponents.
- Injury Reports: Consideration of current injuries that may affect player performance.
- Mental Toughness: Evaluation of players' psychological resilience in high-pressure situations.
- Tournament Trends: Identification of patterns specific to this tournament's history.
By combining these data sources with expert insights, we provide predictions that are both reliable and insightful.
Tips for Enhancing Your Viewing Experience
JishnuSundar/Urban_Density_Prediction<|file_sep|>/README.md
# Urban Density Prediction
## Introduction
The purpose of this project is to predict urban density from aerial imagery using machine learning techniques such as random forests.
### Dataset
The dataset used for this project was taken from Kaggle [here](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/data) (and has been removed from Kaggle). It consists of aerial imagery taken by DigitalGlobe satellites in various locations around Afghanistan. The satellite images were captured at approximately one-meter resolution at nadir (i.e., directly beneath) each location.
The training set contains two types of images:
1. RGB Images: These are standard color images obtained by combining three spectral bands (Red/Green/Blue). They were captured at approximately one-meter resolution.
2. Panchromatic Images: These are black-and-white images obtained by combining all four spectral bands (Red/Green/Blue/Near Infrared). They were captured at approximately one-half meter resolution.
In addition to these images there is also a corresponding density map which describes how many people live within each pixel.
### Implementation
The first step was to create an input feature vector from these images. A naive approach would be simply treat every pixel as an individual data point (i.e., an input feature vector), but this would result in an excessive number of features (~1600 features per pixel) which would be computationally expensive. Instead we decided to split up each image into ~10x10 meter patches (which corresponded to ~100x100 pixels) which would result in ~100 features per patch. In order to further reduce computation time we decided only consider one spectral band (the Red band) rather than all four spectral bands.
Each patch was then labeled using it's corresponding density map value which represented how many people lived within that particular patch. In order to handle cases where there was no population within a given patch we set it's label value equal to zero.
We then trained several models using random forests (with both classification & regression trees) using different hyperparameters & also experimented with image augmentation techniques such as rotations & flips.
### Results
We were able to achieve fairly good results using random forests regression trees with ~0.5 meters resolution & ~30 trees (~70% accuracy). However we were unable to improve upon these results significantly despite experimenting with different hyperparameters & image augmentation techniques.
## Future Work
There are several possible ways in which we could improve upon this project:
1. Instead of simply using one spectral band we could combine all four spectral bands together.
2. Instead of simply splitting up each image into square patches we could use sliding windows & overlap neighboring patches so that each pixel is considered multiple times.
3. We could experiment with more complex models such as convolutional neural networks & recurrent neural networks instead of simple random forests.
4. We could experiment with more sophisticated image augmentation techniques such as affine transformations & elastic deformations.
5. We could also experiment with more sophisticated feature engineering techniques such as principal component analysis & t-distributed stochastic neighbor embedding.<|repo_name|>JishnuSundar/Urban_Density_Prediction<|file_sep|>/run.py
import numpy as np
import pandas as pd
import os
import sys
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn import ensemble
from sklearn import cross_validation
sys.path.append("src")
from utils import *
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Load Data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
DATA_DIR = "data"
IMG_DIR = os.path.join(DATA_DIR, "train")
DENSITY_MAP_DIR = os.path.join(DATA_DIR, "density_map")
IMG_LIST = sorted(os.listdir(IMG_DIR))
DENSITY_MAP_LIST = sorted(os.listdir(DENSITY_MAP_DIR))
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Load Images & Density Maps %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
imgs = []
density_maps = []
for i in range(len(IMG_LIST)):
print("Loading Image " + str(i+1) + "/" + str(len(IMG_LIST)))
imgs.append(np.load(os.path.join(IMG_DIR,
IMG_LIST[i])))
density_maps.append(np.load(os.path.join(DENSITY_MAP_DIR,
DENSITY_MAP_LIST[i])))
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Preprocess Images %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
imgs = preprocess_images(imgs)
density_maps = preprocess_density_maps(density_maps)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Extract Features & Labels %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
FEATURES_PER_PATCH = int((IMG_SIZE / PATCH_SIZE)**2)
LABELS_PER_PATCH = int((DENSITY_MAP_SIZE / PATCH_SIZE)**2)
X = []
y = []
for i in range(len(imgs)):
print("Extracting Features & Labels From Image " +
str(i+1) + "/" + str(len(imgs)))
features_patches = imgs[i].reshape((len(imgs[i]) / FEATURES_PER_PATCH,
FEATURES_PER_PATCH))
labels_patches = density_maps[i].reshape((len(density_maps[i]) / LABELS_PER_PATCH,
LABELS_PER_PATCH))
for j in range(len(features_patches)):
X.append(features_patches[j])
y.append(labels_patches[j])
X = np.array(X)
y = np.array(y)
print("Features Shape: ", X.shape)
print("Labels Shape: ", y.shape)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Split Data Into Training & Testing Sets %%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
X_train_val_test_split_ratio = [.8,.1,.1]
y_train_val_test_split_ratio = [.8,.1,.1]
X_train_val_test_split_ratio_sum = sum(X_train_val_test_split_ratio)
y_train_val_test_split_ratio_sum = sum(y_train_val_test_split_ratio)
assert X_train_val_test_split_ratio_sum == y_train_val_test_split_ratio_sum ==
float(1), "Error: Train/Val/Test Split Ratios Must Sum To One!"
assert len(X) == len(y), "Error: Feature/Label Vector Length Mismatch!"
X_train_val_test_split_index_1 = int(len(X) * X_train_val_test_split_ratio[0])
X_train_val_test_split_index_2 = int(len(X) * X_train_val_test_split_ratio[0] +
X_train_val_test_split_ratio[1])
y_train_val_test_split_index_1 = int(len(y) * y_train_val_test_split_ratio[0])
y_train_val_test_split_index_2 = int(len(y) * y_train_val_test_split_ratio[0] +
y_train_val_test_split_ratio[1])
X_train,X_valid,X_test = np.split(X,[X_train_val_test_split_index_1,
X_train_val_test_split_index_2])
y_train,y_valid,y_test = np.split(y,[y_train_val_test_split_index_1,
y_train_val_test_split_index_2])
print("Training Set Size: ", X_train.shape[0])
print("Validation Set Size: ", X_valid.shape[0])
print("Testing Set Size: ", X_test.shape[0])
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Define Model Parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
MODEL_TYPE = 'REGRESSION'
N_ESTIMATORS = [10,30]
MAX_DEPTH = [5]
N_JOBS=1 # Number Of CPU Cores To Use
MIN_SAMPLES_SPLIT=10 # Minimum Samples Required To Split A Node
MIN_SAMPLES_LEAF=10 # Minimum Samples Per Leaf Node
RANDOM_STATE=42 # Random State For Reproducibility
if MODEL_TYPE == 'CLASSIFICATION':
CRITERION='gini'
else:
CRITERION='mse'
print('Model Type: ', MODEL_TYPE)
print('Criterion: ', CRITERION)
print('Number Of Estimators: ', N_ESTIMATORS)
print('Maximum Depth: ', MAX_DEPTH)
print('Number Of Jobs: ', N_JOBS)
print('Minimum Samples Split: ', MIN_SAMPLES_SPLIT)
print('Minimum Samples Leaf: ', MIN_SAMPLES_LEAF)
print('Random State: ', RANDOM_STATE)
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%% Perform K-Fold Cross Validation On Training Set Only %%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
kfolds=5
if MODEL_TYPE == 'CLASSIFICATION':
kfold_method=cross_validation.StratifiedKFold(y=y_train,
n_folds=kfolds,
random_state=RANDOM_STATE,
shuffle=True)
else:
kfold_method=cross_validation.KFold(n=len(y_train),
n_folds=kfolds,
random_state=RANDOM_STATE,
shuffle=True)
mean_accuracy=[]
std_accuracy=[]
mean_precision=[]
std_precision=[]
mean_recall=[]
std_recall=[]
mean_fscore=[]
std_fscore=[]
mean_roc_auc=[]
std_roc_auc=[]
for n_estimators in N_ESTIMATORS:
for max_depth in MAX_DEPTH:
print('N Estimators: ', n_estimators)
print('Max Depth: ', max_depth)
cvscores_acc=[]
cvscores_prec=[]
cvscores_rec=[]
cvscores_fscore=[]
cvscores_roc_auc=[]
for traincv_index,cv_index in kfold_method:
X_cvtrain,X_cvtest=X_train[traincv_index],X_train[cv_index]
y_cvtrain,y_cvtest=y_train[traincv_index],y_train[cv_index]
if MODEL_TYPE == 'CLASSIFICATION':
model=ensemble.RandomForestClassifier(n_estimators=n_estimators,
max_depth=max_depth,
criterion=CRITERION,
n_jobs=N_JOBS,
min_samples_split=MIN_SAMPLES_SPLIT,
min_samples_leaf=MIN_SAMPLES_LEAF,
random_state=RANDOM_STATE)
else:
model=ensemble.RandomForestRegressor(n_estimators=n_estimators,
max_depth=max_depth,
criterion=CRITERION,
n_jobs=N_JOBS,
min_samples_split=MIN_SAMPLES_SPLIT,
min_samples_leaf=MIN_SAMPLES_LEAF,
random_state=RANDOM_STATE)
model.fit(X_cvtrain,y_cvtrain)
y_cvpred=model.predict(X_cvtest)
if MODEL_TYPE == 'CLASSIFICATION':
cvscores_acc.append(metrics.accuracy_score(y_cvtest,y_cvpred))
cvscores_prec.append(metrics.precision_score(y_cvtest,y_cvpred))
cvscores_rec.append(metrics.recall_score(y_cvtest,y_cvpred))
cvscores_fscore.append(metrics.fbeta_score(y_cvtest,y_cvpred,beta=.5))
try:
cvscores_roc_auc.append(metrics.roc_auc_score(y_cvtest,y_cvpred))
except ValueError:
pass # roc_auc_score() will raise an error if there is only one class present.
else:
try:
rmsle=np.sqrt(metrics.mean_squared_log_error(y_cvtest,y_cvpred))
print('Root Mean Squared Log Error:',rmsle)
except ValueError:
pass # mean_squared_log_error() will raise an error if any values <=0 are passed.
mse=np.sqrt(metrics.mean_squared_error(y_cvtest,y_cvpred))
print('Root Mean Squared Error:',mse)
try:
rmae=np.sqrt(metrics.mean_absolute_error(y_cvtest,y_cvpred))
print('Root Mean Absolute Error:',rmae)
except ValueError:
pass # mean_absolute_error() will raise an error if any values <=0 are passed.
mae=np.sqrt(metrics.median_absolute_error(y_cvtest,y_cvpred))
print('Median Absolute Error:',mae)
mape=np.mean(np.abs((y_cvtest - y_cvpred) / y_cvtest)) *
100
print('Mean Absolute Percentage Error:',mape)
mean_accuracy.append(np.mean(cvscores_acc))
std_accuracy.append(np.std(cvscores_acc))
mean_precision.append(np.mean(cvscores_prec))
std_precision.append(np.std(cvscores_prec))
mean_recall.append(np.mean(cvscores_rec))
std_recall.append(np.std(cvscores_rec))
mean_fscore.append(np.mean(cvscores_fscore))
std_fscore.append(np.std(cvscores_fscore))
if len(cvscores
