Challenger Guangzhou 2 stats & predictions
Welcome to the Ultimate Guide for Tennis Challenger Guangzhou 2 China
The Tennis Challenger Guangzhou 2 China is a pivotal event in the world of professional tennis, showcasing emerging talents and seasoned players alike. This guide provides an in-depth look at what makes this tournament a must-watch for tennis enthusiasts and bettors. With daily updates on fresh matches and expert betting predictions, you’ll never miss a moment of the action.
China
Challenger Guangzhou 2
- 05:10 (1st Set’) Castelnuovo, Luca vs Sharipov, Marat 0-0Tie Break in 1st Set (No): 82.10%
- 05:25 (1st Set’) Evans, Daniel vs Crawford, Oliver 0-0Tie Break in 1st Set (No): 89.90%
- 05:25 (FT) Harris, Billy vs Yevseyev,Denis
- 06:40 O'Connell, Christopher vs Eubanks, Christopher -Tie Break in 1st Set (No): 82.60%Odd: Make Bet
- 05:10 (FT) Petr Bar, Biryukov vs Cerundolo, Juan Manuel
Overview of the Tournament
The Tennis Challenger Guangzhou 2 China is part of the ATP Challenger Tour, offering players a platform to compete against top-tier talent and improve their world rankings. Held annually in Guangzhou, this tournament attracts both local and international players eager to make their mark on the global stage.
What to Expect
- Daily Matches: Stay updated with daily match schedules, ensuring you never miss an exciting showdown.
- Expert Analysis: Gain insights from seasoned analysts who provide comprehensive breakdowns of each match.
- Betting Predictions: Benefit from expert betting predictions to enhance your wagering strategy.
Key Features of the Tournament
The tournament is renowned for its competitive atmosphere and high-quality matches. Here are some key features that make it stand out:
- Diverse Playing Styles: Witness a variety of playing styles as athletes from different backgrounds clash on the court.
- High Stakes: With significant points and prize money on offer, every match is played with intensity and passion.
- Emerging Talent: Keep an eye out for rising stars who could become future champions.
Understanding Betting Predictions
Betting on tennis can be both thrilling and rewarding if approached with the right knowledge. Here’s how expert predictions can guide your betting decisions:
- Player Form: Analyze recent performances to gauge a player’s current form.
- Historical Data: Consider past head-to-head records to predict potential outcomes.
- Surface Preferences: Understand which surfaces favor certain players to make informed bets.
Daily Match Highlights
Each day brings new excitement as players vie for victory. Here’s what you can expect from the daily highlights:
- Match Summaries: Get concise summaries of each match, highlighting key moments and turning points.
- Player Performances: Detailed analysis of standout performances and surprising upsets.
- Statistical Insights: Access in-depth statistics to understand the dynamics of each match.
The Importance of Strategy in Tennis Betting
Successful tennis betting requires more than just luck; it demands strategy. Here are some tips to refine your approach:
- Diversify Bets: Spread your bets across different matches to minimize risk.
- Analyze Odds: Compare odds from various bookmakers to find the best value bets.
- Stay Informed: Keep up-to-date with news and developments that could impact match outcomes.
Frequently Asked Questions (FAQs)
What is the ATP Challenger Tour?
The ATP Challenger Tour is a series of professional tennis tournaments organized by the Association of Tennis Professionals (ATP). It serves as a stepping stone for players aiming to climb the ranks in professional tennis.
How can I follow live scores?
You can follow live scores through official tournament websites, sports apps, or dedicated tennis news platforms that provide real-time updates.
What should I consider when placing bets?
Consider factors such as player form, head-to-head records, surface preferences, and expert predictions to make informed betting decisions.
The Role of Emerging Players
The Tennis Challenger Guangzhou 2 China is a breeding ground for emerging talent. Young players often use this platform to showcase their skills and gain valuable experience against tougher competition. Here’s why keeping an eye on these players is crucial:
- Innovative Techniques: Emerging players often bring fresh techniques and strategies to the court, adding excitement to matches.
- Potential Future Stars: Today’s challengers could be tomorrow’s champions. Watching their progress can be both rewarding and insightful.
- Rising Rivalries: New rivalries are born as young players compete against each other, creating compelling narratives throughout the tournament.
Tips for Engaging with the Tournament
To get the most out of your experience with the Tennis Challenger Guangzhou 2 China, consider these tips:
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<|repo_name|>HuiFangXu/Practical-Data-Science<|file_sep|>/README.md
# Practical-Data-Science
## Project Description
This project is about creating two models that can predict whether an image contains a cat or not.
The first model will be a convolutional neural network implemented using TensorFlow.
The second model will use transfer learning with InceptionV3.
## Project Structure
### Part 1 - Building Convolutional Neural Network using TensorFlow
#### Step 1 - Prepare Data
Firstly, we prepare our dataset by downloading images from Google Images. We have two classes: cat images and non-cat images. For each class we have about 2000 images.
After downloading images from Google Images we then use OpenCV library to resize them so that all images have size 64x64 pixels.
Then we split our data into train set (70%), validation set (15%) and test set (15%).
#### Step 2 - Build Model
We build our model using TensorFlow library. The structure of our model is below:
We then train our model with training data using Adam Optimizer with learning rate 0.001.
#### Step 3 - Evaluate Model
We evaluate our model using validation data set which has not been used during training process.
Finally we test our model using test data set which also has not been used during training process.
### Part 2 - Transfer Learning with InceptionV3
#### Step 1 - Prepare Data
We use same dataset as Part 1.
#### Step 2 - Build Model
We build our model using Keras library with InceptionV3 pre-trained model.
The structure of our model is below:
We then train our model with training data using Adam Optimizer with learning rate 0.0001.
#### Step 3 - Evaluate Model
We evaluate our model using validation data set which has not been used during training process.
Finally we test our model using test data set which also has not been used during training process.
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Dec 13 10:53:48 2018
@author: hxfan
"""
import os
import numpy as np
from PIL import Image
import cv2
import random
from sklearn.model_selection import train_test_split
def load_data(path):
# read images into numpy arrays
# create two lists containing image paths
# create two lists containing labels
# concatenate image paths list
# concatenate labels list
# shuffle lists together
# create two numpy arrays containing shuffled image paths and labels
# read image files into numpy arrays
# resize images
# normalize pixel values
# split into train/validation/test sets
# return all sets
def read_images(paths):
# create numpy array for storing all images
# loop through all image paths
# read each image file into numpy array
# resize image if necessary
# append image array into image array list
# return image array list
def load_image(path):
# read image file into numpy array
# resize image if necessary
# normalize pixel values
# return resized image array
def split_data(X,y,test_size=0.15):
# split data into train/validation/test sets
# return all sets
if __name__ == '__main__':
# path = 'C:/Users/hxfan/Desktop/DataScience/Project/Dataset/'
# X_train, y_train, X_val, y_val, X_test, y_test = load_data(path)
# print('Train Set:', X_train.shape)
# print('Train Labels:', y_train.shape)
# print('Validation Set:', X_val.shape)
# print('Validation Labels:', y_val.shape)
# print('Test Set:', X_test.shape)
# print('Test Labels:', y_test.shape)
# im = load_image(path+'cat/cat_00001.jpg')
# print(im.shape)
# im = cv2.imread(path+'cat/cat_00001.jpg')
# print(im.shape)<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Dec 13 13:53:45 2018
@author: hxfan
"""
import os
import numpy as np
from PIL import Image
import cv2
import random
def load_data(path):
# read images into numpy arrays
# create two lists containing image paths
# create two lists containing labels
# concatenate image paths list
# concatenate labels list
# shuffle lists together
# create two numpy arrays containing shuffled image paths and labels
# read image files into numpy arrays
# resize images
# normalize pixel values
# split into train/validation/test sets
cat_paths = []
noncat_paths = []
for root, dirs, files in os.walk(path+'cat/'):
for file in files:
cat_paths.append(os.path.join(root,file))
for root, dirs, files in os.walk(path+'noncat/'):
for file in files:
noncat_paths.append(os.path.join(root,file))
cat_labels = [1]*len(cat_paths)
noncat_labels = [0]*len(noncat_paths)
all_paths = cat_paths + noncat_paths
all_labels = cat_labels + noncat_labels
c = list(zip(all_paths,all_labels))
random.shuffle(c)
all_paths[:],all_labels[:] = zip(*c)
all_paths = np.array(all_paths)
all_labels = np.array(all_labels)
X = []
for path in all_paths:
im = cv2.imread(path)
im = cv2.resize(im,(64,64))
X.append(im)
X = np.array(X,dtype='float32')/255.
X_train,X_test,y_train,y_test = train_test_split(X,
all_labels,
test_size=0.15,
random_state=42,
stratify=all_labels)
<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Dec 13 13:47:18 2018
@author: hxfan
"""
import os
import numpy as np
from PIL import Image
import cv2
import random
def load_data(path):
# read images into numpy arrays
# create two lists containing image paths
# create two lists containing labels
# concatenate image paths list
# concatenate labels list
# shuffle lists together
# create two numpy arrays containing shuffled image paths and labels
# read image files into numpy arrays
# resize images
# normalize pixel values
# split into train/validation/test sets
if __name__ == '__main__':
path = 'C:/Users/hxfan/Desktop/DataScience/Project/Dataset/'
X_train,y_train,X_val,y_val,X_test,y_test = load_data(path)
print('Train Set:', X_train.shape)
print('Train Labels:', y_train.shape)
print('Validation Set:', X_val.shape)
print('Validation Labels:', y_val.shape)
print('Test Set:', X_test.shape)
print('Test Labels:', y_test.shape)<|repo_name|>moroboshi/stripe-ruby-mock<|file_sep|>/lib/stripe_mock/error.rb
module StripeMock
class ErrorExceptionClassNotFoundInMappingError <
RuntimeError; end
end<|repo_name|>moroboshi/stripe-ruby-mock<|file_sep|>/lib/stripe_mock/rails_helper.rb
module StripeMock
module RailsHelper
def self.included(base)
base.extend(ClassMethods)
end
module ClassMethods
def stripe_error(exception_class_name)
exception_class_name.constantize.new("Stripe error")
end
def strip_mock_from_stripe_params(stripe_params)
stripe_params.tap do |params|
params.delete(:stripe_account) if params.has_key?(:stripe_account)
params.delete(:stripe_version) if params.has_key?(:stripe_version)
end
end
def stripe_request_logger(request_type,&block)
block.call.tap do |response|
logger.info "[#{request_type}] #{response.status} #{response.request_method} #{response.request_url} (#{response.headers.to_s})n#{response.body}"
end
end
end
end<|file_sep|># encoding: UTF-8
require 'spec_helper'
require 'webmock/rspec'
require 'stripe_mock'
describe StripeMock::StripeMock do
let(:mock) { StripeMock.mock }
describe "mock" do
it "should mock Stripe" do
expect(Stripe::Customer).to receive(:create).and_return(double(:customer))
mock do |app|
app.get "/customers" do |env|
customer_id = env['HTTP_STRIPE_ID']
customer_id ||= "cus_123"
Stripe::Customer.create :id => customer_id,
:description => "test customer"
end
end
get "/customers", {}, {"HTTP_STRIPE_ID" => "cus_234"}
end
it "should call block when given" do
expect(mock).to receive(:call).and_yield("test").once.and_call_original
mock { |app|
app.get "/test" do |env|
app.call env.merge("foo" => "bar")
end
}
get "/test"
end
it "should yield app object" do
expect(mock).to receive(:call).with(instance_of(Proc)).once.and_call_original
mock { |app|
app.get "/test" do |env|
app.call env.merge("foo" => "bar")
end
}
get "/test"
end
it "should pass along env" do
expect(mock).to receive(:call).with(instance_of(Proc), kind_of(Hash)).once.and_call_original
mock { |app|
app.get "/test" do |env|
app.call env.merge("foo" => "bar")
end
}
get "/test"
end
it "should pass along rack environment" do
expect(mock).to receive(:call).with(instance_of(Proc), kind_of(Hash)).once.and_call_original
mock { |app|
app.get "/test" do |env|
app.call env.merge("foo" => "bar")
end
}
get "/test", {}, {"HTTP_STRIPE_ID" => "cus_123"}
end
it "should return response from block call" do
response_body = mock_response_body()
mock { |app|
app.get "/test" do |env|
[200,"OK",{}]
end
}
response = get "/test"
expect(response.body).to eq(response_body)
end
it "should allow specifying custom request methods" do
response_body = mock_response_body()
mock { |app|
app.post "/customers", :params => { :email => :required } do |env|
[200,"OK",{}]
end
}
post "/customers", :email => "[email protected]"
response_body.should == body()
end
it "should allow specifying multiple request methods" do
response_body = mock_response_body()
mock { |app|
app.post "/customers", :params => { :email => :required } do |env|
[200,"OK",{}]
end
app.put "/customers/:id", :params => { :email => :optional } do |env|
[200,"OK",{}]
end
}
post "/customers", :email => "[email protected]"
put "/customers/cus_1234"
response_body.should == body()
end
def body()
"nn
