51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
import numpy as np
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class KMeans:
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def __init__(self,data,num_clustres):
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self.data = data
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self.num_clustres = num_clustres
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def train(self,max_iterations):
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#1.先随机选择K个中心点
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centroids = KMeans.centroids_init(self.data,self.num_clustres)
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#2.开始训练
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num_examples = self.data.shape[0]
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closest_centroids_ids = np.empty((num_examples,1))
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for _ in range(max_iterations):
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#3得到当前每一个样本点到K个中心点的距离，找到最近的
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closest_centroids_ids = KMeans.centroids_find_closest(self.data,centroids)
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#4.进行中心点位置更新
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centroids = KMeans.centroids_compute(self.data,closest_centroids_ids,self.num_clustres)
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return centroids,closest_centroids_ids
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@staticmethod
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def centroids_init(data,num_clustres):
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num_examples = data.shape[0]
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random_ids = np.random.permutation(num_examples)
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centroids = data[random_ids[:num_clustres],:]
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return centroids
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@staticmethod
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def centroids_find_closest(data,centroids):
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num_examples = data.shape[0]
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num_centroids = centroids.shape[0]
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closest_centroids_ids = np.zeros((num_examples,1))
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for example_index in range(num_examples):
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distance = np.zeros((num_centroids,1))
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for centroid_index in range(num_centroids):
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distance_diff = data[example_index,:] - centroids[centroid_index,:]
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distance[centroid_index] = np.sum(distance_diff**2)
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closest_centroids_ids[example_index] = np.argmin(distance)
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return closest_centroids_ids
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@staticmethod
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def centroids_compute(data,closest_centroids_ids,num_clustres):
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num_features = data.shape[1]
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centroids = np.zeros((num_clustres,num_features))
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for centroid_id in range(num_clustres):
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closest_ids = closest_centroids_ids == centroid_id
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centroids[centroid_id] = np.mean(data[closest_ids.flatten(),:],axis=0)
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return centroids
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