#矢吹奈子[超话]##矢吹奈子未来可期#

【资讯】240115 ichigosan_saga更新奈子相关

从今天1月15日“草莓日”开始
【草莓小姐如何2024】活动开始
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那样的今天，迎接了矢吹奈子小姐作为客人
举办了发表会活动
@/75_yabuki@/yabukinako_official75
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今年是第三年的“草莓小姐如何”
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参加店铺是过去最大的10家店铺
共计16种丰富的
“草莓小姐”菜单登场了
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第一张【草莓小姐的姿势】
是由矢吹小姐设计的
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草莓小姐的“さん”谐音
竖起三根手指拿着草莓
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去店里玩的时候
请一定要模仿草莓小姐的姿势
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然后@/ichigosan_和#草莓小姐如何”
如果能投稿的话我会很高兴的
⁡以下表参道十家咖啡店
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①キル フェ ボン 青山
@/quilfaitbon_official
2024/1/15～2/15
輪花型 佐賀県産「いちごさん」のフリルクリームのタルト
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②i2 cafe
@/i2.cafe
2024/1/15～2/15
佐賀県産「いちごさん」プレミアムダッチベイビー
※いちごの仕入れ価格により変動する可能性あり
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③ラ・ロシェル南青山
@/larochelleminamiaoyama
2024/1/15～2/29
佐賀県産「いちごさん」のシブーストサンド　香りと苦味をドームで閉じ込めて
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④YONA YONA BEER WORKS 青山店
@/yona_yona_beer_works
2024/1/15～2/29
佐賀県産「いちごさん」しゅわしゅわ
佐賀県産「いちごさん」カプレーゼ
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⑤アフタヌーンティー・ラブアンドテーブル 表参道
@/afternoontea_loveandtable
2024/1/15～3/3
佐賀県産「いちごさん」の苺のミルクレープ　※お茶付き
つぶつぶ「いちごさん」ソーダ　 �
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⑥パステル 表参道店
@/pastel_npudding
2024/1/15～3/15
佐賀県産「いちごさん」使用 いちごさんどア・ラ・モード
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⑦資生堂パーラー ザ・ハラジュク
@/shiseido_parlour
2024/2/1～3/31
《平日限定》 佐賀県産「いちごさん」づくしのアフタヌーンティー
佐賀県産「いちごさん」プレミアムパフェ
佐賀県産「いちごさん」 ハートを愛でる いちご♡さんど
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⑧Spiral Café（スパイラルカフェ）
@/spiral_jp
2024/2/1～3/1
佐賀県産「いちごさん 」ヌガーグラッセ フレッシュいちごさんソース
佐賀県産「いちごさん」 いちごさんどヴェリーヌ
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⑨DEK青山
@/dek_aoyama
2024/2/15～3/15
佐賀県産「いちごさん」のピスタチオビスキュイサンド
佐賀県産「いちごさん」のパフェ
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⑩lohasbeans coffee
@/lohasbeanscoffee
2024/2/15～3/15
佐賀県産「いちごさん」ナポレオンパイ
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活用了“草莓小姐”中的“红色”特征
断面鲜艳的“草莓小姐”甜点
“草莓小姐”温柔的甜味和酸味的味道
凸显出来的饮料等
⁡开发了各种各样的菜单✨
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请一定要在这个期间好好享受哦

搬运：雪
小鸟 nako_yabuki_75
Ig 75_yabuki
【群 806891297】
【招新 日翻/韓翻/軸/微管/資源】

【̲申̲有̲娜̲2̲0̲2̲3̲个̲̲̲资̲̲̲断̲̲̲层̲̲̲/̲皇̲族̲澄̲清̲】̲
说个资只会说cake回归期，全年个资比申有娜多还跑来倒打一耙[哈欠]
̲真̲是̲低̲估̲了̲皇̲̲族̲妈̲不̲要̲脸̲的̲程̲度̲，̲关̲于̲近̲期̲d̲y̲f̲所̲说̲申̲有̲娜̲为̲“̲皇̲族̲”̲一̲论̲，̲并̲且̲在̲各̲个̲平̲台̲广̲发̲洗̲脑̲包̲，̲以̲下̲是̲塞̲破̲整̲理̲的̲2̲0̲2̲3̲全̲年̲个̲̲̲资̲̲̲（̲有̲不̲准̲可̲提̲出̲来̲）̲c̲r̲@sktayor
̲众̲所̲周̲知̲在̲申̲有̲娜̲u̲g̲g̲大̲出̲圈̲之̲后̲，̲到̲c̲a̲k̲e̲回̲归̲前̲，̲申̲有̲娜̲是̲唯̲一̲0⃣️个̲资̲成̲员̲，̲但̲是̲回̲归̲之̲后̲，̲综̲艺̲处̲处̲都̲c̲u̲e̲到̲u̲g̲g̲，̲可̲见̲出̲圈̲程̲度̲
̲还̲记̲得̲c̲a̲k̲e̲刚̲出̲时̲，̲某̲队̲友̲粉̲丝̲因̲为̲申̲有̲娜̲e̲n̲d̲i̲n̲g̲c̲没̲被̲骂̲，̲而̲自̲担̲被̲骂̲了̲到̲处̲点̲̲̲炮̲̲̲卖̲̲̲惨̲̲̲，̲想̲让̲大̲家̲骂̲̲̲申̲有̲娜̲，̲但̲是̲申̲有̲娜̲蛋̲糕̲p̲a̲r̲t̲为̲倒̲一̲，̲e̲n̲d̲i̲n̲g̲也̲是̲很̲正̲常̲的̲大̲家̲都̲看̲镜̲头̲，̲反̲观̲柴̲郡̲猫̲e̲n̲d̲i̲n̲g̲是̲l̲e̲g̲e̲n̲d̲级̲别̲的̲，̲试̲问̲队̲友̲粉̲为̲何̲如̲此̲双̲̲̲标̲̲̲？̲
̲c̲a̲k̲e̲回̲归̲试̲听̲视̲频̲，̲也̲是̲大̲家̲闹̲̲̲的̲很̲大̲的̲一̲次̲，̲相̲当̲于̲是̲1̲3̲拍̲的̲舞̲蹈̲视̲频̲，̲这̲时̲候̲怎̲么̲不̲卖̲̲̲惨̲̲̲了̲？̲
̲申̲有̲娜̲在̲这̲个̲团̲镶̲边̲了̲四̲年̲，̲p̲a̲r̲t̲倒̲数̲镜̲头̲倒̲数̲了̲四̲年̲多̲，̲我̲试̲问̲就̲算̲后̲面̲真̲的̲捧̲̲̲了̲申̲有̲娜̲，̲又̲如̲何̲？̲
̲关̲于̲s̲o̲l̲o̲舞̲台̲，̲这̲是̲我̲最̲佩̲服̲d̲y̲f̲不̲̲̲要̲̲̲脸̲̲̲程̲度̲的̲一̲次̲，̲申̲有̲娜̲一̲个̲u̲g̲g̲可̲以̲抵̲你̲担̲一̲年̲的̲热̲度̲，̲拥̲有̲了̲第̲二̲次̲机̲会̲，̲难̲道̲不̲是̲再̲正̲常̲不̲过̲的̲事̲吗̲，̲今̲年̲不̲止̲申̲有̲娜̲一̲个̲人̲有̲s̲o̲l̲o̲舞̲台̲❗̲可̲是̲团̲队̲f̲l̲o̲p̲之̲后̲唯̲一̲出̲圈̲的̲确̲是̲申̲有̲娜̲
̲申̲有̲娜̲就̲算̲是̲主̲̲̲捧̲̲̲，̲是̲皇̲̲̲族̲̲̲，̲也̲是̲应̲该̲的̲，̲这̲四̲年̲多̲的̲贫̲民̲，̲队̲友̲粉̲想̲要̲就̲互̲换̲
̲常̲驻̲导̲师̲这̲种̲资̲源̲又̲凭̲什̲么̲和̲油̲管̲化̲妆̲以̲及̲油̲管̲综̲艺̲那̲种̲2̲0̲+̲分̲钟̲并̲列̲？？̲
申有娜发布MV之后，跑来超话反间，到处卖惨没有依据说只有劳务的MV最费钱最上心https://t.cn/A6lnvtRe，只能说你担不上心和申有娜无关
̲综̲合̲算̲起̲来̲，̲申̲有̲娜̲个̲资̲并̲不̲是̲今̲年̲最̲多̲的̲，̲甚̲至̲也̲不̲是̲第̲二̲，̲我̲请̲问̲，̲个̲̲̲资̲̲̲p̲a̲r̲t̲比̲申̲有̲娜̲多̲的̲人̲又̲凭̲什̲么̲反̲过̲来̲说̲申̲有̲娜̲是̲皇̲̲̲族̲̲̲�̲�̲
̲还̲是̲那̲句̲话̲，̲娜̲妈̲不̲管̲在̲哪̲个̲平̲台̲看̲见̲d̲y̲f̲发̲洗̲̲̲脑̲̲̲包̲̲̲请̲一̲定̲要̲澄̲清̲❗̲❗̲❗̲不̲要̲任̲由̲洗̲脑̲包̲扩̲散̲
̲最̲后̲，̲接̲队̲友̲粉̲口̲中̲的̲皇̲̲̲族̲̲̲
⭕̲评̲本̲博̲（sj标准是），̲c̲h̲o̲u̲一̲杯̲抹̲茶̲瑞̲纳̲冰̲（̲折̲现̲）̲ ̲
̲@甜娜我的兔 加̲码̲一̲个̲月̲娜̲泡̲
@请问谁看到我家兔兔狗了 加码抽两个人每人打20r
@神佑小拿 加码12.9r
@我豹豹是刘备 加码p5
12.31晚上8点开

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()