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import numpy as np import matplotlib.pyplot as plt import sklearn.datasets as datasets def create_data(): X,y = datasets.make_blobs(n_samples=1000,n_features=2,centers=[[1,0],[5,4],[2,3],[10,8],[7,4]]) return X,y def init_centers(data,k): m, n =data.shape center_ids = np.random.choice(m,k) centers = data[center_ids] return centers def cal_dist(ptA,ptB): return np.linalg.norm(ptA-ptB) def kmeans_process(data,k): centers = init_centers(data, k) m, n = data.shape keep_changing = True pred_y = np.zeros((m,)) pred_idlist=[] while keep_changing: keep_changing = False count=1 for i in range(m): min_distance = np.inf for center in range(k): distance = cal_dist(data[i,:],centers[center,:]) if distance<min_distance: min_distance = distance idx = center pred_idlist.append(idx) if pred_y[i] != idx: keep_changing = True pred_y[i] = idx for center in range(k): cluster_data = data[pred_y==center] centers[center,:] = np.mean(cluster_data, axis=0) return centers, pred_y,pred_idlist if __name__ == '__main__': X, y = create_data() print(X.shape) centers , pred_y,pred_idlist = kmeans_process(data=X, k=5) plt.scatter(X[:,0], X[:,1], s=3, c=pred_y) plt.scatter(centers[:,0], centers[:,1], s=10, c='k') plt.show()

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理解问题import numpy as np import matplotlib.pyplot as plt import sklearn.datasets as datasets def create_data(): X,y = datasets.make_blobs(n_samples=1000,n_features=2,centers=[[1,0],[5,4],[2,3],[10,8],[7,4]]) return X,y def init_centers(data,k): m, n =data.shape center_ids = np.random.choice(m,k) centers = data[center_ids] return centers def cal_dist(ptA,ptB): return np.linalg.norm(ptA-ptB) def kmeans_process(data,k): centers = init_centers(data, k) m, n = data.shape keep_changing = True pred_y = np.zeros((m,)) pred_idlist=[] while keep_changing: keep_changing = False count=1 for i in range(m): min_distance = np.inf for center in range(k): distance = cal_dist(data[i,:],centers[center,:]) if distance<min_distance: min_distance = distance idx = center pred_idlist.append(idx) if pred_y[i] != idx: keep_changing = True pred_y[i] = idx for center in range(k): cluster_data = data[pred_y==center] centers[center,:] = np.mean(cluster_data, axis=0) return centers, pred_y,pred_idlist if __name__ == '__main__': X, y = create_data() print(X.shape) centers , pred_y,pred_idlist = kmeans_process(data=X, k=5) plt.scatter(X[:,0], X[:,1], s=3, c=pred_y) plt.scatter(centers[:,0], centers[:,1], s=10, c='k') plt.show()

已完成理解import numpy as np import matplotlib.pyplot as plt import sklearn.datasets as datasets def create_data(): X,y = datasets.make_blobs(n_samples=1000,n_features=2,centers=[[1,0],[5,4],[2,3],[10,8],[7,4]]) return X,y def init_centers(data,k): m, n =data.shape center_ids = np.random.choice(m,k) centers = data[center_ids] return centers def cal_dist(ptA,ptB): return np.linalg.norm(ptA-ptB) def kmeans_process(data,k): centers = init_centers(data, k) m, n = data.shape keep_changing = True pred_y = np.zeros((m,)) pred_idlist=[] while keep_changing: keep_changing = False count=1 for i in range(m): min_distance = np.inf for center in range(k): distance = cal_dist(data[i,:],centers[center,:]) if distance<min_distance: min_distance = distance idx = center pred_idlist.append(idx) if pred_y[i] != idx: keep_changing = True pred_y[i] = idx for center in range(k): cluster_data = data[pred_y==center] centers[center,:] = np.mean(cluster_data, axis=0) return centers, pred_y,pred_idlist if __name__ == '__main__': X, y = create_data() print(X.shape) centers , pred_y,pred_idlist = kmeans_process(data=X, k=5) plt.scatter(X[:,0], X[:,1], s=3, c=pred_y) plt.scatter(centers[:,0], centers[:,1], s=10, c='k') plt.show()

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回答简洁
更专业一些

您提供的代码是一个使用 K-means 聚类算法的 Python 脚本。以下是对代码的简要解释和分析:

  1. 导入必要的库

    • numpy 用于数学运算。
    • matplotlib.pyplot 用于绘图。
    • sklearn.datasets 用于生成数据集。
  2. 创建数据集

    • create_data 函数使用 sklearn.datasets.make_blobs 生成具有五个中心的数据集,每个中心有 1000 个样本点,每个样本点有两个特征。
  3. 初始化中心点

    • init_centers 函数随机选择 k 个数据点作为初始中心点。
  4. 计算两点之间的距离

    • cal_dist 函数使用 numpy.linalg.norm 计算两点之间的欧氏距离。
  5. K-means 聚类过程

    • kmeans_process 函数实现了 K-means 算法。它首先初始化中心点,然后迭代地更新聚类结果和中心点,直到中心点不再改变。
  6. 主程序

    • if __name__ == '__main__': 块中,首先生成数据集,然后调用 kmeans_process 函数进行聚类,最后使用 matplotlib 绘制聚类结果。
  7. 绘图

    • 使用 plt.scatter 绘制原始数据点和聚类中心点。其中,原始数据点的颜色由聚类结果 pred_y 决定,中心点用黑色表示。

请注意,您的代码中存在一个小错误:在调用 kmeans_process 函数时,应该使用 X 而不是 data 作为参数。以下是修正后的代码行:

centers , pred_y, pred_idlist = kmeans_process(data=X, k=5)

此外,根据您的代码,您需要确保已经安装了 matplotlibnumpy。如果尚未安装,可以通过以下命令进行安装:

pip install matplotlib numpy

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