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()
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()」
展开阅读网页
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：
-
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。
-
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。
-
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。
-
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。
-
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。
-
主程序：
- 在
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。
- 在
-
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。
- 使用
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 matplotlib 和 numpy。如果尚未安装，可以通过以下命令进行安装：
pip install matplotlib numpy