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