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来源：花匠小妙招时间：2024-11-06 06:51

文章目录 Iris SpeciesKMeans核心算法花卉识别原始数据KMeans 算法的应用

Iris Species

KMeans核心算法

import numpy as np import pandas as pd import sklearn as sl class Kmeans: def __init__(self,data,k): self.data = data self.k = k #开始训练,核心是指定迭代次数 def train(self,max_iterations): #随机选k个质心 centroids = Kmeans.centroids_init(self.data,self.k) #开始训练 num_examples = self.data.shape[0] closest_centorids_ids = np.empty((num_examples,1)) #算距离 for _ in range(max_iterations): # 算每个点到质心的最近距离 closest_centorids_ids = Kmeans.centroids_find_closest(self.data,centroids) # 更新中心点位置 centroids = Kmeans.centroids_compute(self.data,closest_centorids_ids,self.k) return centroids,closest_centorids_ids @staticmethod def centroids_init(data,k): #指定数据源 num_examples = data.shape[0] #permutation不会洗牌,不改变原始数据.shuffle会改变 # random_ids = np.random.shuffle(num_examples) random_ids = np.random.permutation(num_examples) #定义中心点 centroids = data[random_ids[:k],:] return centroids @staticmethod def centroids_find_closest(data,centroids): #导入数据 num_examples = data.shape[0] num_centroids = centroids.shape[0] #初始化 closest_centroids_ids = np.zeros((num_examples,1)) for example_index in range(num_examples): #定义多少个distance,和类别个数一样多 distance = np.zeros((num_centroids, 1)) #每一个k的位置 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 #计算质心 @staticmethod def centroids_compute(data,closest_centroids_ids,k): num_features = data.shape[1] centroids = np.zeros((k,num_features)) for centroid_id in range (k): closest_ids = closest_centroids_ids == centroid_id # 返回每一个簇的质心 centroids[centroid_id] = np.mean(data[closest_ids.flatten(),:],axis=0) return centroids 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263

花卉识别

原始数据

数据下载地址:
https://www.kaggle.com/uciml/iris

import numpy as np import pandas as pd import matplotlib.pyplot as plt from KMeans import KMeans data = pd.read_csv('./archive/Iris.csv') iris_types = ['Iris-setosa','Iris-versicolor','Iris-virginica'] x_axis = 'PetalLengthCm' y_axis = 'PetalWidthCm' plt.figure(figsize=(12,5)) #生成一个一行两列的图,激活第一个 plt.subplot(1,2,1) # 根据已经存在的数据类别,画散点图 for iris_type in iris_types: plt.scatter(data[x_axis][data['Species']==iris_type], data[y_axis][data['Species']==iris_type], label=iris_type) plt.title('known') plt.legend() #生成一个一行两列的图,激活第二个 plt.subplot(1,2,2) # 画一个没有分类的图 plt.scatter(data[x_axis][:],data[y_axis][:]) plt.title('unknown') plt.show() 12345678910111213141516171819202122232425262728

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KMeans 算法的应用

import numpy as np import pandas as pd import matplotlib.pyplot as plt from KMeans import Kmeans data = pd.read_csv('./archive/Iris.csv') iris_types = ['Iris-setosa','Iris-versicolor','Iris-virginica'] x_axis = 'PetalLengthCm' y_axis = 'PetalWidthCm' num_examples = data.shape[0] x_train = data[[x_axis,y_axis]].values.reshape(num_examples,2) #指定好训练所需要的参数 k = 3 max_itritions = 1000 k_means = Kmeans(x_train,k) centroids,closest_centroids_ids = k_means.train(max_itritions) print('centroids,closest_centroids_ids',centroids,closest_centroids_ids) #对比结果 plt.figure(figsize=(12,5)) #生成一个一行两列的图,激活第一个 plt.subplot(1,2,1) # 根据已经存在的数据类别,画散点图 for iris_type in iris_types: plt.scatter(data[x_axis][data['Species']==iris_type], data[y_axis][data['Species']==iris_type], label=iris_type) plt.title('known') plt.legend() # 画kmeans分类后的 plt.subplot(1,2,2) for centroid_id ,centroid in enumerate(centroids): # 分类别 current_examples_index = (closest_centroids_ids == centroid_id).flatten() plt.scatter(data[x_axis][current_examples_index], data[y_axis][current_examples_index], label=iris_types[centroid_id]) for centroid_id,centroid in enumerate(centroids): plt.scatter(centroid[0],centroid[1],color='black',marker='o') plt.legend() plt.title('KMeans') plt.show() 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051

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