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水川潤

AUKG-625 欲望坠落：不满女性的 lesbian 技能展示

2025年4月12日

DVAJ-689 Exploring the Intricacies of Sensual Transformation in a Thoughtful Context

5 四月 2025

CEAD-674

5 四月 2025

CEMD-664 1. Normalize the data. Convert the dataset to a standard scale where the range of each feature is normalized to [0,1]. This will help in computing the distance between the features. ```python from sklearn.preprocessing import Normalizer scaler = Normalizer() dataset = scaler.transform(dataset) ``` 2. Perform the a hierachical clustering. Perform a hierarchical clustering based on the precomputed distances. Use the "complete" method for cluster merging. Get the distance matrix from the precomputed distances. ```python from sklearn.clustering import HierarchicalCluster hcl = HierarchicalCluster(n_clusters = 4, method = "complete") dataset = hcl.fit_predict(dataset) ``` 3. Perform the PCA which will convert the data into 2D Cartesian space. Get the distance matrix from the precomputed distances. ```python from sklearn.decomposition import PCA pca = PCA(n_components = 2) dataset = pca.fit_transform(dataset) ``` 4. Perform the last K-means clustering. Perform a k-means clustering using only the first 2 PCA variables. Find out the number of clusters. ```python from sklearn.clustering import KMmeans kmeans = KMmeans(n_clusters = 4) dataset = kmeans.fit_predict(dataset) ``` 5. Get the clusters. Extract the 4 clusters in the dataset. Then make a scatter plot showing each of the clusters. ```python import matplotlib.pyplot as plt plt.scatter(dataset[:,0], dataset[:,1], c = dataset ) plt.show() ``` In this time, I will show the point map.

2025年4月5日

BBAN-522 一人旅中的乳房遭 targeted 攻击 温泉碰瓷 水川润 由良かな 沙月恵奈

2025年4月4日

MMPB-075

29 三月 2025

USBA-082

22 三月 2025

CEMD-652

8 三月 2025