K-Means 聚类算法代码示例——简单的K-Means算法实现

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算法主体

import numpy as np

# Function: K Means
# -------------
# K-Means is an algorithm that takes in a dataset and a constant
# k and returns k centroids (which define clusters of data in the
# dataset which are similar to one another).

def kmeans(X, k, maxIt):
    
    numPoints, numDim = X.shape
    
    dataSet = np.zeros((numPoints, numDim + 1))
    dataSet[:, :-1] = X
    
    # Initialize centroids randomly
    centroids = dataSet[np.random.randint(numPoints, size = k), :]
    
    #Randomly assign labels to initial centorid
    centroids[:, -1] = range(1, k +1)
    
    # Initialize book keeping vars.
    iterations = 0
    oldCentroids = None
    
    # Run the main k-means algorithm
    while not shouldStop(oldCentroids, centroids, iterations, maxIt):
        print ("iteration: \n", iterations)
        print ("dataSet: \n", dataSet)
        print ("centroids: \n", centroids)
        # Save old centroids for convergence test. Book keeping.
        oldCentroids = np.copy(centroids)
        iterations += 1
        
        # Assign labels to each datapoint based on centroids
        updateLabels(dataSet, centroids)
        
        # Assign centroids based on datapoint labels
        centroids = getCentroids(dataSet, k)
        
    # We can get the labels too by calling getLabels(dataSet, centroids)
    return dataSet

终止条件

# Function: Should Stop
# -------------
# Returns True or False if k-means is done. K-means terminates either
# because it has run a maximum number of iterations OR the centroids
# stop changing.

def shouldStop(oldCentroids, centroids, iterations, maxIt):
    if iterations > maxIt:
        return True
    return np.array_equal(oldCentroids, centroids)  

更新标记

# Function: Get Labels
# -------------
# Update a label for each piece of data in the dataset. 

def updateLabels(dataSet, centroids):
    # For each element in the dataset, chose the closest centroid. 
    # Make that centroid the element's label.
    numPoints, numDim = dataSet.shape
    for i in range(0, numPoints):
        dataSet[i, -1] = getLabelFromClosestCentroid(dataSet[i, :-1], centroids)
 
def getLabelFromClosestCentroid(dataSetRow, centroids):
    label = centroids[0, -1];
    minDist = np.linalg.norm(dataSetRow - centroids[0, :-1])
    for i in range(1 , centroids.shape[0]):
        dist = np.linalg.norm(dataSetRow - centroids[i, :-1])
        if dist < minDist:
            minDist = dist
            label = centroids[i, -1]
    print ("minDist:", minDist)
    return label

重新计算中心点

# Function: Get Centroids
# -------------
# Returns k random centroids, each of dimension n.

def getCentroids(dataSet, k):
    # Each centroid is the geometric mean of the points that
    # have that centroid's label. Important: If a centroid is empty (no points have
    # that centroid's label) you should randomly re-initialize it.
    result = np.zeros((k, dataSet.shape[1]))
    for i in range(1, k + 1):
        oneCluster = dataSet[dataSet[:, -1] == i, :-1]
        result[i - 1, :-1] = np.mean(oneCluster, axis = 0)
        result[i - 1, -1] = i
    
    return result

简单测试

x1 = np.array([1, 1])
x2 = np.array([2, 1])
x3 = np.array([4, 3])
x4 = np.array([5, 4])
testX = np.vstack((x1, x2, x3, x4))

result = kmeans(testX, 2, 10)
print ("final result:")
print (result)

测试结果

iteration: 
 0
dataSet: 
 [[ 1.  1.  0.]
 [ 2.  1.  0.]
 [ 4.  3.  0.]
 [ 5.  4.  0.]]
centroids: 
 [[ 5.  4.  1.]
 [ 1.  1.  2.]]
minDist: 0.0
minDist: 1.0
minDist: 1.41421356237
minDist: 0.0
iteration: 
 1
dataSet: 
 [[ 1.  1.  2.]
 [ 2.  1.  2.]
 [ 4.  3.  1.]
 [ 5.  4.  1.]]
centroids: 
 [[ 4.5  3.5  1. ]
 [ 1.5  1.   2. ]]
minDist: 0.5
minDist: 0.5
minDist: 0.707106781187
minDist: 0.707106781187
final result:
[[ 1.  1.  2.]
 [ 2.  1.  2.]
 [ 4.  3.  1.]
 [ 5.  4.  1.]]