ML之Kmeans：利用自定義Kmeans函數(shù)實現(xiàn)對多個坐標點(自定義四個點)進行自動(最多迭代10次)分類

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核心代碼

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輸出結(jié)果

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核心代碼

#!/usr/bin/python # -*- coding:utf-8 -*-import numpy as np #ML之Kmeans：利用自定義Kmeans函數(shù)實現(xiàn)對多個坐標點(自定義四個點)進行自動(最多迭代10次)分類def kmeans(X, k, maxIt): numPoints, numDim = X.shape dataSet = np.zeros((numPoints, numDim + 1)) dataSet[:, :-1] = X centroids = dataSet[np.random.randint(numPoints, size = k), :] #centroids = dataSet[0:2, :] #Randomly assign labels to initial centorid給初始中心隨機分配標簽centroids[:, -1] = range(1, k +1) iterations = 0 oldCentroids = None # Run the main k-means algorithmwhile 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 centroidsupdateLabels(dataSet, centroids) # Assign centroids based on datapoint labelscentroids = getCentroids(dataSet, k) # We can get the labels too by calling getLabels(dataSet, centroids)return dataSet # 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 Truereturn np.array_equal(oldCentroids, centroids) # 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# 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 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)

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ML之Kmeans：利用自定義Kmeans函數(shù)實現(xiàn)對多個坐標點(自定義四個點)進行自動(最多迭代10次)分類

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