【出战2024赛季BWF羽毛球超级300 泰国大师赛】
北京时间2024.01.30，2024赛季BWF羽毛球超级300 泰国大师赛即将开赛，参赛运动员如下：
男子运动员：
陈柏阳、程星、何济霆、雷兰曦、刘毅、任翔宇、谢浩楠、曾维瀚
女子运动员：
坑姝良、李汶妹、李怡婧、刘玄炫、罗徐敏、张驰
�第1比赛日，赛程如下： 男子双打1/16决赛
第7场 16:00 谢浩楠/曾维瀚ONG Yew Sin王耀新/TEO Ee Yi张御宇
第10场 陈柏阳/刘毅Andreas·SONDERGAARD安德烈亚斯·森德高/Jesper·TOFT杰斯帕·托夫特
女子双打1/16决赛
第7场 16:10 李怡婧/罗徐敏Meilysa Trias·PUSPITA SARI梅丽莎·普斯皮塔·萨里/Rachel Allessya·ROSE瑞秋·阿莱西娅·罗斯
第7场 李汶妹/刘玄炫Yuki FUKUSHIMA福岛由纪/Riko KIYOSE清濑璃子
第8场 坑姝良/张驰Jongkolphan·KITITHARAKUL宗空潘·基蒂塔拉库尔/Rawinda·PRAJONGJAI拉温达·巴宗哉。
以上信息截至发表前，以实际比赛情况为准。
北京时间2024.01.30，2024赛季BWF羽毛球超级300 泰国大师赛即将开赛，参赛运动员如下：
男子运动员：
陈柏阳、程星、何济霆、雷兰曦、刘毅、任翔宇、谢浩楠、曾维瀚
女子运动员：
坑姝良、李汶妹、李怡婧、刘玄炫、罗徐敏、张驰
�第1比赛日，赛程如下： 男子双打1/16决赛
第7场 16:00 谢浩楠/曾维瀚ONG Yew Sin王耀新/TEO Ee Yi张御宇
第10场 陈柏阳/刘毅Andreas·SONDERGAARD安德烈亚斯·森德高/Jesper·TOFT杰斯帕·托夫特
女子双打1/16决赛
第7场 16:10 李怡婧/罗徐敏Meilysa Trias·PUSPITA SARI梅丽莎·普斯皮塔·萨里/Rachel Allessya·ROSE瑞秋·阿莱西娅·罗斯
第7场 李汶妹/刘玄炫Yuki FUKUSHIMA福岛由纪/Riko KIYOSE清濑璃子
第8场 坑姝良/张驰Jongkolphan·KITITHARAKUL宗空潘·基蒂塔拉库尔/Rawinda·PRAJONGJAI拉温达·巴宗哉。
以上信息截至发表前，以实际比赛情况为准。
give some examples of how to use Python,machine learning and Bing API for Numerical weather prediction and numerical analysis
Here are some examples of how to use Python, machine learning and Bing API for numerical weather prediction and numerical analysis:
To perform weather prediction using machine learning in Python, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access weather data and forecasts from different sources. For example, you can use the following code to get the current weather conditions for a given location using Bing API:
import requests import json # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Construct the request URL url = f"https://t.cn/A6lLORhJ{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather information weather = data["resourceSets"][0]["resources"][0]["currentConditions"][0] # Print the weather information print(f"Location: {weather['observationArea']}") print(f"Date: {weather['localObservationDateTime']}") print(f"Temperature: {weather['temperature']} °C") print(f"Description: {weather['weatherText']}")
To perform numerical analysis using Python, you can use various libraries and frameworks such as NumPy, SciPy, SymPy, matplotlib, etc. You can also use Bing API to access mathematical data and formulas from different sources. For example, you can use the following code to plot the function
�(�)=sin(�)+cos(2�)f(x)=sin(x)+cos(2x)
using Bing API and matplotlib:
import requests import json import numpy as np import matplotlib.pyplot as plt # Replace with your Bing API key api_key = "YOUR_API_KEY" # Specify the function function = "sin(x)+cos(2x)" # Construct the request URL url = f"https://t.cn/A6lLORhI{function}&api-key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the function expression expression = data["expression"] # Create a range of x values x = np.linspace(-np.pi, np.pi, 100) # Evaluate the function expression y = eval(expression) # Plot the function plt.plot(x, y) plt.xlabel("x") plt.ylabel("f(x)") plt.title(f"Plot of {function}") plt.show()
To perform numerical weather prediction using Python, machine learning and Bing API, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access historical and real-time weather data and forecasts from different sources. For example, you can use the following code to train a neural network to predict the temperature for the next day based on the previous 10 days of weather data using Bing API and TensorFlow:
import requests import json import pandas as pd import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Specify the number of days to use as input input_days = 10 # Specify the number of days to predict as output output_days = 1 # Construct the request URL url = f"https://t.cn/A6lLORhx{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather data weather_data = data["resourceSets"][0]["resources"][0]["pastSearch"]["results"] # Convert the weather data to a pandas dataframe df = pd.DataFrame(weather_data) # Select the date and temperature columns df = df[["date", "temperature"]] # Convert the date column to datetime format df["date"] = pd.to_datetime(df["date"]) # Sort the dataframe by date df = df.sort_values(by="date") # Reset the index df = df.reset_index(drop=True) # Print the dataframe print(df) # Convert the dataframe to a numpy array data = df.to_numpy() # Normalize the data data = (data - data.mean()) / data.std() # Split the data into input and output sequences X = [] y = [] for i in range(len(data) - input_days - output_days + 1): X.append(data[i:i+input_days, 1]) y.append(data[i+input_days:i+input_days+output_days, 1]) X = np.array(X) y = np.array(y) # Reshape the input sequences to match the expected input shape of the neural network X = X.reshape(X.shape[0], X.shape[1], 1) # Split the data into training and testing sets train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # Define the neural network model model = keras.Sequential([ layers.LSTM(32, return_sequences=True, input_shape=(input_days, 1)), layers.LSTM(16), layers.Dense(output_days) ]) # Compile the model model.compile(optimizer="adam", loss="mse", metrics=["mae"]) # Train the model model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) # Evaluate the model model.evaluate(X_test, y_test) # Make predictions y_pred = model.predict(X_test) # Plot the actual and predicted temperatures plt.plot(df["date"][-len(y_test):], y_test * data.std() + data.mean(), label="Actual") plt.plot(df["date"][-len(y_test):], y_pred * data.std() + data.mean(), label="Predicted") plt.xlabel("Date") plt.ylabel("Temperature") plt.title(f"Temperature prediction for {location}") plt.legend() plt.show()
Here are some examples of how to use Python, machine learning and Bing API for numerical weather prediction and numerical analysis:
To perform weather prediction using machine learning in Python, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access weather data and forecasts from different sources. For example, you can use the following code to get the current weather conditions for a given location using Bing API:
import requests import json # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Construct the request URL url = f"https://t.cn/A6lLORhJ{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather information weather = data["resourceSets"][0]["resources"][0]["currentConditions"][0] # Print the weather information print(f"Location: {weather['observationArea']}") print(f"Date: {weather['localObservationDateTime']}") print(f"Temperature: {weather['temperature']} °C") print(f"Description: {weather['weatherText']}")
To perform numerical analysis using Python, you can use various libraries and frameworks such as NumPy, SciPy, SymPy, matplotlib, etc. You can also use Bing API to access mathematical data and formulas from different sources. For example, you can use the following code to plot the function
�(�)=sin(�)+cos(2�)f(x)=sin(x)+cos(2x)
using Bing API and matplotlib:
import requests import json import numpy as np import matplotlib.pyplot as plt # Replace with your Bing API key api_key = "YOUR_API_KEY" # Specify the function function = "sin(x)+cos(2x)" # Construct the request URL url = f"https://t.cn/A6lLORhI{function}&api-key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the function expression expression = data["expression"] # Create a range of x values x = np.linspace(-np.pi, np.pi, 100) # Evaluate the function expression y = eval(expression) # Plot the function plt.plot(x, y) plt.xlabel("x") plt.ylabel("f(x)") plt.title(f"Plot of {function}") plt.show()
To perform numerical weather prediction using Python, machine learning and Bing API, you can use various libraries and frameworks such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc. You can also use Bing API to access historical and real-time weather data and forecasts from different sources. For example, you can use the following code to train a neural network to predict the temperature for the next day based on the previous 10 days of weather data using Bing API and TensorFlow:
import requests import json import pandas as pd import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Replace with your Bing Maps API key api_key = "YOUR_API_KEY" # Specify the location location = "Tokyo" # Specify the number of days to use as input input_days = 10 # Specify the number of days to predict as output output_days = 1 # Construct the request URL url = f"https://t.cn/A6lLORhx{location}?key={api_key}" # Send the request and get the response response = requests.get(url) # Parse the response as JSON data = response.json() # Extract the weather data weather_data = data["resourceSets"][0]["resources"][0]["pastSearch"]["results"] # Convert the weather data to a pandas dataframe df = pd.DataFrame(weather_data) # Select the date and temperature columns df = df[["date", "temperature"]] # Convert the date column to datetime format df["date"] = pd.to_datetime(df["date"]) # Sort the dataframe by date df = df.sort_values(by="date") # Reset the index df = df.reset_index(drop=True) # Print the dataframe print(df) # Convert the dataframe to a numpy array data = df.to_numpy() # Normalize the data data = (data - data.mean()) / data.std() # Split the data into input and output sequences X = [] y = [] for i in range(len(data) - input_days - output_days + 1): X.append(data[i:i+input_days, 1]) y.append(data[i+input_days:i+input_days+output_days, 1]) X = np.array(X) y = np.array(y) # Reshape the input sequences to match the expected input shape of the neural network X = X.reshape(X.shape[0], X.shape[1], 1) # Split the data into training and testing sets train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = y[:train_size], y[train_size:] # Define the neural network model model = keras.Sequential([ layers.LSTM(32, return_sequences=True, input_shape=(input_days, 1)), layers.LSTM(16), layers.Dense(output_days) ]) # Compile the model model.compile(optimizer="adam", loss="mse", metrics=["mae"]) # Train the model model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) # Evaluate the model model.evaluate(X_test, y_test) # Make predictions y_pred = model.predict(X_test) # Plot the actual and predicted temperatures plt.plot(df["date"][-len(y_test):], y_test * data.std() + data.mean(), label="Actual") plt.plot(df["date"][-len(y_test):], y_pred * data.std() + data.mean(), label="Predicted") plt.xlabel("Date") plt.ylabel("Temperature") plt.title(f"Temperature prediction for {location}") plt.legend() plt.show()
Metacritic 公布 2021 年度十大影集排行榜，风靡全球的《鱿鱼游戏 Squid Game》仅排在第 10 名
1⃣️《继承之战 Succession》117 分
2⃣️《地下铁道 The Underground Railroad》75.5 分
3⃣️《白莲花大饭店 The White Lotus》72 分
4⃣️《东城奇案 Mare of Easttown》68 分
5⃣️《救赎犬 Reservation Dogs》58 分
6⃣️《天后与草莓 Hacks》57 分
7⃣️《汪达幻视 WandaVision》54 分
8⃣️《破案三人行 Only Murders in the Building》53.5 分
9⃣️《这是罪 It’s a Sin》44 分
��《鱿鱼游戏 Squid Game》41.5 分
十部当中，你看过几部 [太开心]
1⃣️《继承之战 Succession》117 分
2⃣️《地下铁道 The Underground Railroad》75.5 分
3⃣️《白莲花大饭店 The White Lotus》72 分
4⃣️《东城奇案 Mare of Easttown》68 分
5⃣️《救赎犬 Reservation Dogs》58 分
6⃣️《天后与草莓 Hacks》57 分
7⃣️《汪达幻视 WandaVision》54 分
8⃣️《破案三人行 Only Murders in the Building》53.5 分
9⃣️《这是罪 It’s a Sin》44 分
��《鱿鱼游戏 Squid Game》41.5 分
十部当中，你看过几部 [太开心]
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