From 79a007bf57ac4ae546c16b206a218c9caa54a7a8 Mon Sep 17 00:00:00 2001 From: DinoWhite Date: Sun, 11 Oct 2020 00:27:31 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E5=86=99kmeans=20=E9=B8=A2=E5=B0=BE?= =?UTF-8?q?=E8=8A=B1=E5=88=86=E6=9E=90=E6=96=B9=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 3_kmeans/1-k-means.ipynb | 550 ++++++++++++++++----------------------- 3_kmeans/1-k-means_EN.ipynb | 620 +++++++++++++++++++------------------------- 2 files changed, 501 insertions(+), 669 deletions(-) diff --git a/3_kmeans/1-k-means.ipynb b/3_kmeans/1-k-means.ipynb index 27c5cbf..680285a 100644 --- a/3_kmeans/1-k-means.ipynb +++ b/3_kmeans/1-k-means.ipynb @@ -211,82 +211,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { + "output_type": "execute_result", "data": { - "text/html": [ - "
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05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "execution_count": 1 } ], "source": [ @@ -307,312 +237,288 @@ "# If you are using an old version of IPython, try using '%pylab inline' instead.\n", "%matplotlib inline\n", "\n", - "# import librarys\n", - "from numpy import *\n", - "import matplotlib.pyplot as plt\n", + "# 导入所需库\n", "import pandas as pd\n", + "import numpy as np\n", "import random\n", + "from matplotlib import pyplot as plt\n", + "\n", "\n", - "# Load dataset\n", - "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n", - "dataset = pd.read_csv(\"iris.csv\", header=0, index_col=0)\n", - "dataset.head()\n" + "# 1 读取鸢尾花iris.csv\n", + "iris_df = pd.read_csv('./iris.csv', header=0, index_col=0)\n", + "iris_df.head()\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { - "ename": "TypeError", - "evalue": "list indices must be integers or slices, not Series", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'Iris-setosa'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'Iris-versicolor'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'class'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'Iris-virginica'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not Series" - ] + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "145 2\n", + "146 2\n", + "147 2\n", + "148 2\n", + "149 2\n", + "Name: class, Length: 150, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 2 } ], "source": [ - "#对类别进行编码,3个类别分别赋值0,1,2\n", + "# 2 对类别进行编码,3个类别分别赋值0,1,2\n", "\n", - "dataset['class'][dataset['class']=='Iris-setosa']=0\n", - "dataset['class'][dataset['class']=='Iris-versicolor']=1\n", - "dataset['class'][dataset['class']=='Iris-virginica']=2" + "iris_df['class'].replace(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], [0, 1, 2], inplace=True)\n", + "iris_df['class']" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], - "source": [ - "def originalDatashow(dataSet):\n", - " #绘制原始的样本点\n", - " num,dim=shape(dataSet)\n", - " marksamples=['ob'] #Sample graphic marking\n", - " for i in range(num):\n", - " plt.plot(datamat.iat[i,0],datamat.iat[i,1],marksamples[0],markersize=5)\n", - " plt.title('original dataset')\n", - " plt.xlabel('sepal length')\n", - " plt.ylabel('sepal width') \n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "lines_to_end_of_cell_marker": 2, - "scrolled": true - }, "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } } ], "source": [ - "#获取样本数据\n", - "datamat = dataset.loc[:, ['sepal-length', 'sepal-width']]\n", - "# 真实的标签\n", - "labels = dataset.loc[:, ['class']]\n", - "#原始数据显示\n", - "originalDatashow(datamat)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", + "# 2 可视化信息\n", + "def visualizeIris(iris_df, feature='sepal', form='ro', mark_size=5):\n", + " plt.plot(iris_df[feature+'-length'], iris_df[feature+'-width'], form, markersize=mark_size)\n", + " plt.title('original dataset')\n", + " plt.xlabel(feature+'-length')\n", + " plt.ylabel(feature+'-width')\n", + " plt.show()\n", "\n", - "def randChosenCent(dataSet,k):\n", - " \"\"\"初始化聚类中心:通过在区间范围随机产生的值作为新的中心点\"\"\"\n", - "\n", - " # 样本数\n", - " m=shape(dataSet)[0]\n", - " # 初始化列表\n", - " centroidsIndex=[]\n", - " \n", - " #生成类似于样本索引的列表\n", - " dataIndex=list(range(m))\n", - " if False:\n", - " for i in range(k):\n", - " #生成随机数\n", - " randIndex=random.randint(0,len(dataIndex))\n", - " #将随机产生的样本的索引放入centroidsIndex\n", - " centroidsIndex.append(dataIndex[randIndex])\n", - " #删除已经被抽中的样本\n", - " del dataIndex[randIndex]\n", - " else:\n", - " random.shuffle(dataIndex)\n", - " centroidsIndex = dataIndex[:k]\n", - " \n", - " #根据索引获取样本\n", - " centroids = dataSet.iloc[centroidsIndex]\n", - " return mat(centroids)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ "\n", - "def distEclud(vecA, vecB):\n", - " \"\"\"算距离, 两个向量间欧式距离\"\"\"\n", - " return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)\n", - "\n", - "\n", - "def kMeans(dataSet, k):\n", - " # 样本总数\n", - " m = shape(dataSet)[0]\n", - " # 分配样本到最近的簇:存[簇序号,距离的平方] (m行 x 2 列)\n", - " clusterAssment = mat(zeros((m, 2)))\n", - "\n", - " # step1: 通过随机产生的样本点初始化聚类中心\n", - " centroids = randChosenCent(dataSet, k)\n", - " print('最初的中心=', centroids)\n", - "\n", - " # 标志位,如果迭代前后样本分类发生变化值为True,否则为False\n", - " clusterChanged = True\n", - " # 查看迭代次数\n", - " iterTime = 0\n", - " \n", - " # 所有样本分配结果不再改变,迭代终止\n", - " while clusterChanged:\n", - " clusterChanged = False\n", - " \n", - " # step2:分配到最近的聚类中心对应的簇中\n", - " for i in range(m):\n", - " # 初始定义距离为无穷大\n", - " minDist = inf;\n", - " # 初始化索引值\n", - " minIndex = -1\n", - " # 计算每个样本与k个中心点距离\n", - " for j in range(k):\n", - " # 计算第i个样本到第j个中心点的距离\n", - " distJI = distEclud(centroids[j, :], dataSet.values[i, :])\n", - " # 判断距离是否为最小\n", - " if distJI < minDist:\n", - " # 更新获取到最小距离\n", - " minDist = distJI\n", - " # 获取对应的簇序号\n", - " minIndex = j\n", - " # 样本上次分配结果跟本次不一样,标志位clusterChanged置True\n", - " if clusterAssment[i, 0] != minIndex:\n", - " clusterChanged = True\n", - " clusterAssment[i, :] = minIndex, minDist ** 2 # 分配样本到最近的簇\n", - " \n", - " iterTime += 1\n", - " sse = sum(clusterAssment[:, 1])\n", - " print('the SSE of %d' % iterTime + 'th iteration is %f' % sse)\n", - " \n", - " # step3:更新聚类中心\n", - " for cent in range(k): # 样本分配结束后,重新计算聚类中心\n", - " # 获取该簇所有的样本点,nonzero[0]表示A == cent的元素所在的行,如果没有[0],列也会表示\n", - " ptsInClust = dataSet.iloc[nonzero(clusterAssment[:, 0].A == cent)[0]]\n", - " # 更新聚类中心:axis=0沿列方向求均值。\n", - " centroids[cent, :] = mean(ptsInClust, axis=0)\n", - " return centroids, clusterAssment\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'kMeans' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 进行k-means聚类\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;31m# 用户定义聚类数\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmycentroids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclusterAssment\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkMeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatamat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'kMeans' is not defined" - ] - } - ], - "source": [ - "# 进行k-means聚类\n", - "k = 3 # 用户定义聚类数\n", - "mycentroids, clusterAssment = kMeans(datamat, k)" + "visualizeIris(iris_df, feature='sepal', form='ro')\n", + "visualizeIris(iris_df, feature='petal', form='bo')\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "def datashow(dataSet, k, centroids, clusterAssment): # 二维空间显示聚类结果\n", - " from matplotlib import pyplot as plt\n", - " num, dim = shape(dataSet) # 样本数num ,维数dim\n", - "\n", - " if dim != 2:\n", - " print('sorry,the dimension of your dataset is not 2!')\n", - " return 1\n", - " marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', ' len(marksamples):\n", - " print('sorry,your k is too large,please add length of the marksample!')\n", - " return 1\n", - " # 绘所有样本\n", - " for i in range(num):\n", - " markindex = int(clusterAssment[i, 0]) # 矩阵形式转为int值, 簇序号\n", - " # 特征维对应坐标轴x,y;样本图形标记及大小\n", - " plt.plot(dataSet.iat[i, 0], dataSet.iat[i, 1], marksamples[markindex], markersize=6)\n", - "\n", - " # 绘中心点\n", - " markcentroids = ['o', '*', '^'] # 聚类中心图形标记\n", - " label = ['0', '1', '2']\n", - " c = ['yellow', 'pink', 'red']\n", - " for i in range(k):\n", - " plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])\n", - " plt.legend(loc='upper left') #图例\n", - " plt.xlabel('sepal length')\n", - " plt.ylabel('sepal width')\n", - "\n", - " plt.title('k-means cluster result') # 标题\n", - " plt.show()\n", - " \n", - " \n", - "# 画出实际图像\n", - "def trgartshow(dataSet, k, labels):\n", - " from matplotlib import pyplot as plt\n", - "\n", - " num, dim = shape(dataSet)\n", - " label = ['0', '1', '2']\n", - " marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '" - ] + "text/plain": "
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\n" }, - "output_type": "display_data" + "metadata": { + "needs_background": "light" + } }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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\n" 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+ }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "迭代了 7 次\n" + ] } ], "source": [ - "# 绘图显示\n", - "datashow(datamat, k, mycentroids, clusterAssment)\n", - "trgartshow(datamat, 3, labels)" + "k = 3\n", + "# iris = KMeans(data=iris_df, k=k, feature='petal') # 根据花瓣分类\n", + "iris = KMeans(data=iris_df, k=k, feature='sepal') # 根据萼片分类\n", + "iris.result()\n", + "print('迭代了', iris.count, '次')\n" ] }, { @@ -955,9 +861,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.9-final" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/3_kmeans/1-k-means_EN.ipynb b/3_kmeans/1-k-means_EN.ipynb index 3d06d3e..e570c66 100644 --- a/3_kmeans/1-k-means_EN.ipynb +++ b/3_kmeans/1-k-means_EN.ipynb @@ -220,82 +220,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": {}, "outputs": [ { + 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" }, - "execution_count": 8, "metadata": {}, - "output_type": "execute_result" + "execution_count": 1 } ], "source": [ @@ -316,337 +246,333 @@ "# If you are using an old version of IPython, try using '%pylab inline' instead.\n", "%matplotlib inline\n", "\n", - "# import librarys\n", - "from numpy import *\n", - "import matplotlib.pyplot as plt\n", + "# import necessary libraries\n", "import pandas as pd\n", + "import numpy as np\n", "import random\n", + "from matplotlib import pyplot as plt\n", + "\n", "\n", - "# Load dataset\n", - "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n", - "dataset = pd.read_csv(\"iris.csv\", header=0, index_col=0)\n", - "dataset.head()\n" + "# 1 read from iris.csv\n", + "iris_df = pd.read_csv('./iris.csv', header=0, index_col=0)\n", + "iris_df.head()\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n", - "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " after removing the cwd from sys.path.\n", - "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \"\"\"\n" - ] + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 0\n", + "4 0\n", + " ..\n", + "145 2\n", + "146 2\n", + "147 2\n", + "148 2\n", + "149 2\n", + "Name: class, Length: 150, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 2 } ], "source": [ - "#Fixme-解决赋值的问题,参考https://www.jb51.net/article/138045.htm\n", - "#Coding the class and assign value 0, 1, 2 to each class.\n", - "dataset['class'][dataset['class']=='Iris-setosa']=0\n", - "dataset['class'][dataset['class']=='Iris-versicolor']=1\n", - "dataset['class'][dataset['class']=='Iris-virginica']=2" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "def originalDatashow(dataSet):\n", - " #Draw original sample point.\n", - " num,dim=shape(dataSet)\n", - " marksamples=['ob'] #Sample graphic marking\n", - " for i in range(num):\n", - " plt.plot(datamat.iat[i,0],datamat.iat[i,1],marksamples[0],markersize=5)\n", - " plt.title('original dataset')\n", - " plt.xlabel('sepal length')\n", - " plt.ylabel('sepal width') \n", - " plt.show()" + "# 2 label different kinds of iris to 0, 1, 2\n", + "\n", + "iris_df['class'].replace(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], [0, 1, 2], inplace=True)\n", + "iris_df['class']" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": { "lines_to_end_of_cell_marker": 2, "scrolled": true }, "outputs": [ { + "output_type": "display_data", "data": { - "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } } ], "source": [ - "#Get sample data\n", - "datamat = dataset.loc[:, ['sepal-length', 'sepal-width']]\n", - "#True label\n", - "labels = dataset.loc[:, ['class']]\n", - "#Show original data\n", - "originalDatashow(datamat)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", + "# 2 visualize iris data\n", + "def visualizeIris(iris_df, feature='sepal', form='ro', mark_size=5):\n", + " plt.plot(iris_df[feature+'-length'], iris_df[feature+'-width'], form, markersize=mark_size)\n", + " plt.title('original dataset')\n", + " plt.xlabel(feature+'-length')\n", + " plt.ylabel(feature+'-width')\n", + " plt.show()\n", "\n", - "def randChosenCent(dataSet,k):\n", - " \"\"\"Initialize cluster center:By randomly generating a value over the interval as a new central point \"\"\"\n", - "\n", - " # Sample numb\n", - " m=shape(dataSet)[0]\n", - " # initialize list\n", - " centroidsIndex=[]\n", - " \n", - " #Generate a list similar to the sample index\n", - " dataIndex=list(range(m))\n", - " if False:\n", - " for i in range(k):\n", - " #Generate random number\n", - " randIndex=random.randint(0,len(dataIndex))\n", - " #Put the sample index that generate randomly into centroidsIndex\n", - " centroidsIndex.append(dataIndex[randIndex])\n", - " #Delete the sample that has been choosen\n", - " del dataIndex[randIndex]\n", - " else:\n", - " random.shuffle(dataIndex)\n", - " centroidsIndex = dataIndex[:k]\n", - " \n", - " #Get the sample by index\n", - " centroids = dataSet.iloc[centroidsIndex]\n", - " return mat(centroids)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ "\n", - "def distEclud(vecA, vecB):\n", - " \"\"\"Calculate the Euclidean distance between two vector\"\"\"\n", - " return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)\n", - "\n", - "\n", - "def kMeans(dataSet, k):\n", - " # The total number of sample\n", - " m = shape(dataSet)[0]\n", - " # Allocate sample to the nearest culster: save as [cluster number, square of distance](m_row x 2_column)\n", - " clusterAssment = mat(zeros((m, 2)))\n", - "\n", - " # step1: Initialize cluster center by the sample point that generate randomly\n", - " centroids = randChosenCent(dataSet, k)\n", - " print('Original centers=', centroids)\n", - "\n", - " # Flag bit,if the result of sample classification before and after iteration has changed, the value is True\n", - " clusterChanged = True\n", - " # View the number of iterations\n", - " iterTime = 0\n", - " \n", - " # All sample assignment results are no longer changed and the iteration terminates\n", - " while clusterChanged:\n", - " clusterChanged = False\n", - " \n", - " # step2: Allocate to the nearest cluster corresponding to the nearest cluster center\n", - " for i in range(m):\n", - " # Initially define distance as infinite\n", - " minDist = inf;\n", - " # Initialize index value\n", - " minIndex = -1\n", - " # Calculate the distance of each sample and k centriods\n", - " for j in range(k):\n", - " # Calculate the distance between the ith smaple and jth centriods\n", - " distJI = distEclud(centroids[j, :], dataSet.values[i, :])\n", - " # Judeg if the distance if the minimum\n", - " if distJI < minDist:\n", - " # Update to get the minimum distance\n", - " minDist = distJI\n", - " # Get corresponding cluster numbers\n", - " minIndex = j\n", - " # If the result of sample classification is not the same,mark clusterChanged to True\n", - " if clusterAssment[i, 0] != minIndex:\n", - " clusterChanged = True\n", - " clusterAssment[i, :] = minIndex, minDist ** 2 # Allocate smaple to nearest cluster\n", - " \n", - " iterTime += 1\n", - " sse = sum(clusterAssment[:, 1])\n", - " print('the SSE of %d' % iterTime + 'th iteration is %f' % sse)\n", - " \n", - " # step3:Update cluster center\n", - " for cent in range(k): # When finished sample classification ,recalculate cluster center\n", - " # Get all sample point of this cluster,nonzero[0] represent the column of A == cent \n", - " #Without [0], column will also be shown\n", - " ptsInClust = dataSet.iloc[nonzero(clusterAssment[:, 0].A == cent)[0]]\n", - " # Update cluster center: calculate average value according to column direction, axis=0.\n", - " centroids[cent, :] = mean(ptsInClust, axis=0)\n", - " return centroids, clusterAssment\n" + "visualizeIris(iris_df, feature='sepal', form='ro')\n", + "visualizeIris(iris_df, feature='petal', form='bo')" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "最初的中心= [[4.8 3.4]\n", - " [5.5 2.4]\n", - " [5.3 3.7]]\n", - "the SSE of 1th iteration is 136.220000\n", - "the SSE of 2th iteration is 58.811902\n", - "the SSE of 3th iteration is 53.100129\n", - "the SSE of 4th iteration is 49.715722\n", - "the SSE of 5th iteration is 47.874761\n", - "the SSE of 6th iteration is 46.133064\n", - "the SSE of 7th iteration is 44.593439\n", - "the SSE of 8th iteration is 44.384855\n", - "the SSE of 9th iteration is 43.591498\n", - "the SSE of 10th iteration is 41.904928\n", - "the SSE of 11th iteration is 39.066514\n", - "the SSE of 12th iteration is 38.316500\n", - "the SSE of 13th iteration is 37.912536\n", - "the SSE of 14th iteration is 37.423306\n", - "the SSE of 15th iteration is 37.136261\n", - "the SSE of 16th iteration is 37.123702\n" - ] - } - ], - "source": [ - "# Perform k-means clustering\n", - "k = 3 # Cluster numbers designed by customer\n", - "mycentroids, clusterAssment = kMeans(datamat, k)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "def datashow(dataSet, k, centroids, clusterAssment): # Show cluster result in two dimensional space\n", - " from matplotlib import pyplot as plt\n", - " num, dim = shape(dataSet) # sample numbers:num ,dimension: dim\n", - "\n", - " if dim != 2:\n", - " print('sorry,the dimension of your dataset is not 2!')\n", - " return 1\n", - " marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', ' len(marksamples):\n", - " print('sorry,your k is too large,please add length of the marksample!')\n", - " return 1\n", - " # Draw all sample\n", - " for i in range(num):\n", - " markindex = int(clusterAssment[i, 0]) # Change value to int form, cluster number\n", - " # The characteristic dimension corresponds to x,y; Sample graphic marking and size\n", - " plt.plot(dataSet.iat[i, 0], dataSet.iat[i, 1], marksamples[markindex], markersize=6)\n", - "\n", - " # Draw center point\n", - " markcentroids = ['o', '*', '^'] # Cluster center graphic marking\n", - " label = ['0', '1', '2']\n", - " c = ['yellow', 'pink', 'red']\n", - " for i in range(k):\n", - " plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])\n", - " plt.legend(loc='upper left')\n", - " plt.xlabel('sepal length')\n", - " plt.ylabel('sepal width')\n", - "\n", - " plt.title('k-means cluster result') # Title\n", - " plt.show()\n", - " \n", - " \n", - "# Draw real graphic\n", - "def trgartshow(dataSet, k, labels):\n", - " from matplotlib import pyplot as plt\n", - "\n", - " num, dim = shape(dataSet)\n", - " label = ['0', '1', '2']\n", - " marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '" - ] + "text/plain": "
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\n" }, - "output_type": "display_data" + "metadata": { + "needs_background": "light" + } }, { + "output_type": "display_data", "data": { - "image/png": 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EPC7pr4BLgONq1DMMDENypTCTWDpFV1f9K4W89tmn9l/xRc51KGMfZta4PFcKP5B0yGx2EhG/Bb4FvHRS+aaIeDx9+mngyNnsx7avc1SvvL+/9usTy8uY6+D5FGbVVLdRkHSnpDuA5wO3SLo3vc0zVj4tSQvTKwQkPRl4MfDjSds8Y8LTk4B7ZnIQtt2KFTA0tP3KoKsreb5iRfL8uuumNgz9/Un5mDLmOng+hVk11e1oltQ73RsjosbF/w7vfzbJ7aAuksbnCxFxgaQLSHrBr5T0fpLGYCvwa5KO6B/XrRR3NJuZzUTejuY8o48ujYi/yCorixsFM7PGFTn6aPGkirvwvf+aipiMlTXxrIg68sQ522Mp4jgqo4B/2DwJYZw0xiqh3gQG4D3AwyS3dh5KHw8Dm4D355kE0YxHVSevFTEZK2viWRF15IlztsdSxHFURgH/sKvuWBU9y3uC8xh/9CzviVV3rGpoG7PZoMDJa++PiPc0tWVqQFVvHxUxGStr4lkRdeSJc7bHUsRxVEYB/7B5EsI4aYw1W97bR3XnKUg6Iv31ixN+HxcRt8wivjmniMlYRUw8y6ojT5yzPZYijqMyCviHzZMQxkljrCqm61P4cPr4BMm6RcPAP6W/f6L5obWXIpLb1Jtg1sjEs6w68sQ522Mp4jgqo4B/2DwJYZw0xqqibqMQEcdGxLHAL4AjImJJRBwJHA5sKCvAdlHEZKysiWdF1JEnztkeSxHHURkF/MPmSQjjpDFWGVmdDsDdecrKelS1ozki6Xvs7Y2Qkp8zWfFzaCiiqyvpz+zqmlnnbFYdeeKc7bEUcRyVUcA/7Ko7VkXvR3tD5yl6P9pbswM5zzZmM0WBHc2XkSyAtyotGiBZEvu1zWuq6qtqR7OZWZUVOU/h9cDdwNvTx5q0zCoqa1i9k9tU07KPLKX7XKHzRPe5YtlHamQ/anYMX11G9wXd6HzRfUE3y77azhNMbCacjnOOGRlJ7t1v3ry9rKdn+7pCWa9bayz7yFJWPnR9kppqTMDQrv2sOOO6uu8rNIavLmPl6pVTyoeWDLHihBWlxGDNM+tlLiR9ISJeLelOYMpGEfHs2YfZODcK08saVu/kNtXUfa4YrXHd3rUNtp5fzh9u3Rd0MxpTxw13qYut57TbBBObbNbzFEhuFQGcWExIVoasYfVOblNNo3Uy4tUrb0oMNRqE6cptbppuSOov0l+XAk+KiHUTH+WEZ43KGlZfxHwKK15XnYuBeuVNiUG1J5LUK7e5KU9H8z7ApyT9l6QvSnqrpMOaHZjNTNaweie3qabB3fqn3qSNtLysGI6sPZGkXrnNTZmNQkScGxHHkayW+l3gLODmZgdmM5OVvMbJbappxRnXMbRrP13bgEj6EsrsZAZYccIKhpYMjV8ZdKnLncwdKM88hb8FngfsDNwKfA/47oTbS6VyR7OZWeOKnKfwSmAP4DrgS8BXWtUgNFMRY/ez6igrx4DnITSoTU7YyMpl9J3VzbzzRN9Z3Yys3PEDVFY+hqz95IqjgP8Mzj/RJHmmPQO7Ai8DlgM/Ab6X533NeDRjmYsiciFk1VFWjoEijqWjtMkJW7ViKHrOZsd8C2cTq1YkH6Cy8jFk7SdXHAX8Z3D+icZR4DIXBwN/DLwQWAI8QHL76JwmtlV1NeP2URFj97PqKCvHgOchNKhNTljfWd2s23nqB6j3kS7WfmhrafkYsvaTK44C/jM4/0TjipinMOYDwH8A/wj8KCK2zDa4qili7H5WHWXlGPA8hAa1yQm7/ym1Pyhj5WXlY8jaT644CvjP4PwTzZNn9NGJEfHBiPjBXGwQoJix+1l1lJVjwPMQGtQmJ2yf39X+oIyVl5WPIWs/ueIo4D+D8080T56O5jmviLH7WXWUlWPA8xAa1CYnbPl+g/RM+pOsZ0tSDuXlY8jaT644CvjP4PwTTZSn46FKj2blUygiF0JWHWXlGCjiWDpKm5ywVSuGovfMrtC5RO+ZXeOdzOOvl5SPIWs/ueIo4D+D8080hqI6mqvG8xTMzBo363kKkq6SdGW9R7Hhzg1lzHVYujQZ2j32WFr+kvvWImWMyx85Yyl971QyF+KdYuSMxj9gVcgLYTM33dLZL5zujRHxnaZElKGqVwpF5CnIqmPpUrj++qnv6++H68pbDcFaYOTOEQavGmTzlu0fjp75PQy/fJiBQ4pZo2TkjKUMPvl6Nj9pe1nPEzD8aD8DH8n3AatCXgirbdb5FKqqqo1CGXMdNM0yym32z2gNKmNcft87xbrdp5b3/hbWfjTfB6wKeSGstsLmKUh6FvB+4CBgwVh5ROw3qwjnmDLmOljnKmNc/v27NVZeSxXyQtjs5BmS+s/ASmArcCzwOWBVM4NqR2XMdbDOVca4/H0ebKy8lirkhbDZydMoPDkirie51bQuIs4DTmhuWO2njLkO/XWW1q9XbnNHGePyl6ufnid2LOt5IinPqwp5IWx28jQKj0uaB/xU0lsk/SnJMto2QRF5CrLquO66qQ2AO5k7w8AhAwy/fJje3XoRone33kI7mQEGPnIdw4/20/tbUCR9CY10MkM18kLY7ORZEO85wD3A7sDfAbsBH4yIHzY/vKmq2tFsZlZlhXU0R8SP0grnAW+LiIdzBrCAZCG9ndL9XBER507aZieSPoojgU3AayJibZ76zcyseJm3jyQtkXQncAdwp6TbJR2Zo+7HgeMi4lDgMOClko6etM3pwG8iYn/go8CFjYWfT55JZVXJs5KVe6RtjqWIIPIkYilgP0Ukr8mqowxLP7cUna/xx9LP1Zg0lnG+8hxHKZPo8pzzCiTZaZc4G5K1DgZJY/DHE54/H7gjzxoaE97TA9wCHDWp/OvAMenv3cCvSG9p1Xs0uvZRnhwqVcmzkpV7pG2OpYgg8iRiKWA/RSSvyaqjDP2X9O+w/7FH/yX9Ew52+vOV5zjKSG6T65xXIMlOu8Q5hgKT7NwaEYdPKrslIo7IanAkdQE3A/sDn4iId096/S7gpRGxPn3+s7Th+FW9OhvtU8gzqawqeVayco+0zbEUEUSeRCwF7KeI5DVZdZRB59efCBDnpv/HM85XnuMoZRJdnnNegSQ77RLnmCJzNH9H0qckvUjSCyWtAL4t6QhJ0zYMETEaEYcBi4DnplncGiZpUNJqSas3btzY0HvzTAiryqSxrNwjbXMsRQSRJxFLAfspInlNVh2VkXG+8hxHKZPo8pzzCiTZaZc4G5WnUTgU+EPgXOA84EDgcODDwD/k2UlE/Bb4FvDSSS9tAPYGkNRNMrJpU433D0fEkohYsnDhwjy7HJdnQlhVJo1l5R5pm2MpIog8iVgK2E8RyWuy6qiMjPOV5zhKmUSX55xXIMlOu8TZqDyZ146d5nFcvfdJWihp9/T3JwMvBn48abMrgdelv78KuCGy7mc1KM+ksqrkWcnKPdI2x1JEEHkSsRSwnyKS12TVUYb+fWtPDtuhPON85TmOUibR5TnnFUiy0y5xNiyr0wH4feAzwNfS5wcBp+d437OBW0k6qu8CzknLLwBOSn9fAHwRuA+4Cdgvq96ZJNnJk0OlKnlWsnKPtM2xFBFEnkQsBeyniOQ1WXWUYXJn8w6dzOOBTn++8hxHGcltcp3zCiTZaZc4I4rtaP4ayfpHZ0fEoeltnlsj4pBCW6ecPHnNzKxxRXY07xkRXwC2AUTEVqBiPWizV4mx/bajqkzKKCKOHHVkjmcvI4vTHNJ28wOqIutSAvg2sAdwS/r8aOA7eS5DmvFoRo7mSozttx1VZVJGEXHkqCNzPHsRx9pBH/QqzQ+oCgq8fXQE8HHgYJK+gYXAqyLijqa1VNNoxu2jSozttx1VZVJGEXHkqCNzPHsZWZzmkCrND6iKQjOvpf0IB5Ak2bs3IrZkvKVpmtEozJtXO3OZBNu2FboryyvPP0oZ/3BFxJGjjnnnzyOmrDkNQmw7N18dhRzLHJF5PjtQYX0Kkv6MJKfC3cArgM9nTVprN5UY2287qsqkjCLiyFFH5nh2Z3FqSDvOD6iKPB3N/y8iHpb0fKCfZHjqyuaGVa5KjO23HVVlUkYRceSoI3M8exlZnOaQtpwfUBVZnQ4kw08hydP8fyaWteLRjI7miIqM7bcdVWVSRhFx5Kgjczx7EcfaQR/0qswPqAoK7Gi+mmQ5ihcDRwCPAjdFsiR26TxPwcyscUXOU3g1yRLXL4lkDaOnAmfNMj6zTLnGmWfkXChtrHoBcWRts+yry+i+oBudL7ov6GbZV2eQbKMIc2iuQ1VyQ1RKnsuJKj2adfvIqiXXOPOMnAuljVUvII6sbYauHqqZL2Ho6gaSbRRhDs11qEpuiLJQ1O2jqvHto86Qa5x5Rs6F0saqFxBH1jbdF3QzGlP30aUutp6TM9lGEebQXIeq5IYoS5G3j8xKl2sd+oycC6WtZV9AHFnb1GoQdigvK5FGJRJ2FKMquSGqxo2CVVKuceYZORdKG6teQBxZ23Sp9j7Gy8uagzCH5jpUJTdE1bhRsErKNc48I+dCaWPVC4gja5vBI2vvY7y8rDkIc2iuQ1VyQ1ROno6HKj3c0dw5co0zz8i5UNpY9QLiyNpm6Oqh6Dq/KziP6Dq/a3sn83gFJc1BmENzHaqSG6IMuKPZzMzGuKPZZqcKY9ELiGHZuxfTfY7QeaL7HLHs3YtbEkeu3WSMZ2+78e7WlrpbHYBV0MhIcj988+bk+bp12++bDwy0TQzL3r2YlU9ek6ztC4x2kTx/92JWXHh3aXHk2s2dIwxeNcjmLcl+1j24jsGrkv0MHDKQ+bpZUXz7yKaqwlj0AmLoPkeM1hi00zUKWy/I+bkv6VxkjWev0nh3a0++fWQzV4Wx6AXEMFrn012vvFlx5NpNxnj2dhzvbu3JjYJNVYWx6AXE0FUnl0q98mbFkWs3GePZ23G8u7UnNwo2VRXGohcQw+DjBzEl+Vak5SXGkWs3GePZ23K8u7UlNwo21cAADA8n982l5OfwcHmdzAXFsOLCuxl69CC6RoFI+hKGHj0ofydzQXHk2s0hAwy/fJje3XoRone3XoZfPjzeiZz1ullR3NFsZtYB3NFs1VfE+P+sOioyx8A6V7t9NjxPwVqjiPH/WXVUZI6Bda52/Gz49pG1RhHj/7PqqMgcA+tcVfps+PaRVVsR4/+z6qjIHAPrXO342XCjYK1RxPj/rDoqMsfAOlc7fjbcKFhrFDH+P6uOiswxsM7Vlp+NPOtrV+nhfApzSBHr8mfVUdLa/1VZM9+qpyqfDZxPwczMxrij2czMGta0RkHS3pK+JWmNpLslvb3GNi+S9KCk29LHOc2KZ64oZCJMFRLo5IkjR5ztNjFoOiMrl9F3VjfzzhN9Z3UzsnJZ+THMofNpM9PMyWtbgb+OiFsk7QLcLOmbEbFm0nbfjYgTmxjHnFHIRJgqJNDJE0eOONtxYlA9IyuXMbhhJZt3Tp6v23mUwQ0rYSUMDK0oJ4Y5dD5t5krrU5D0FeDiiPjmhLIXAWc20ih0cp9CIRNhqpBAJ08cOeKs0sSg2eo7q5t1O49OKe99pIu1H9paTgxz6HzaVJXqU5DUBxwO3Fjj5WMk3S7pa5JqJtCVNChptaTVGzdubGKk1VbIRJgqJNDJE0eOONtxYlA99z9laoMwXXlTYphD59NmrumNgqSdgX8D3hERD016+RagNyIOBT4O/HutOiJiOCKWRMSShQsXNjfgCitkIkwVEujkiSNHnO04MaiefX5XI2/oNOVNiWEOnU+buaY2CpLmkzQIIxHxpcmvR8RDEfFI+vs1wHxJezYzpnZWyESYKiTQyRNHjjjbcmJQHcv3G6Rny45lPVuS8tJimEPn02Yhz2SGmTwAAZ8DLppmm6ezvV/jucD9Y8/rPTp98lohE2FKmtA16zhyxFmViUFFWLViKHrP7AqdS/Se2RWrVgyVH8McOp+2I1o9eU3S84HvAncCY1lx3wvskzZGn5T0FmCIZKTSo8AZEfGD6ert5I5mM7OZanlHc0R8LyIUEc+OiMPSxzUR8cmI+GS6zcURsTgiDo2Io7MaBKM6cwyKsGwZdHcnaS67u5PnZtZSTrLTTqoyx6AIy5bBypXbn4+Obn++opxx+WY2ldc+aieb27pLAAAKwElEQVRVmWNQhO7upCGYrKsLtpYzLt+sk7T89pE1QVXmGBShVoMwXbmZlcKNQjupyhyDInTVGX9fr9zMSuFGoZ1UZY5BEQbrjL+vV25mpXCj0E4GBmB4OOlDkJKfw8Pt18kMSWfy0ND2K4OuruS5O5nNWsodzWZmHcAdzQVrq+kB7RJsu8RZFp8Pq4I8056r9GjFMherVkX09ETA9kdPT+tWh5hWuwTbLnGWxefDmoxWL3PRLK24fdRW0wPaJdh2ibMsPh/WZHlvH7lRyGHevORPt8kk2LZtanlLtUuw7RJnWXw+rMncp1Cgtpoe0C7BtkucZfH5sIpwo5BDW00PaJdg2yXOsvh8WEW4UcihraYHtEuw7RJnWXw+rCLcp2Bm1gHcp2BWkJGVy+g7q5t554m+s7oZWTmDvA+eg2Btwo2C2TRGVi5jcMNK1u08SgjW7TzK4IaVjTUMY3kw1q1LRhiN5cFww2AV5NtHZtPoO6ubdTtPXc6795Eu1n4oZ94Hz0GwCvDtI7MC3P+U2vkd6pXX3ngO5cGwOc+Ngtk09vld7fwO9cprb+w5CNY+3CiYTWP5foP0bNmxrGdLUp6/Es9BsPbhRsFsGgNDKxjea4jeR7pQJH0Jw3sNMTDUQN4Hz0GwNuKOZjOzDuCOZjMza5gbBTMzG+dGwczMxrlRMDOzcW4UzMxsnBsFMzMb50bBzMzGuVEwM7NxbhTMzGxc0xoFSXtL+pakNZLulvT2GttI0j9Kuk/SHZKOaFY8HcUJXcxshrqbWPdW4K8j4hZJuwA3S/pmRKyZsM3LgGelj6OAlelPm6mxhC6bNyfPxxK6gNfaMbNMTbtSiIhfRMQt6e8PA/cAe03a7GTgc5H4IbC7pGc0K6aOcPbZ2xuEMZs3J+VmZhlK6VOQ1AccDtw46aW9gAcmPF/P1IYDSYOSVktavXHjxmaFOTc4oYuZzULTGwVJOwP/BrwjIh6aSR0RMRwRSyJiycKFC4sNcK5xQhczm4WmNgqS5pM0CCMR8aUam2wA9p7wfFFaZjPlhC5mNgvNHH0k4DPAPRHxkTqbXQn8ZToK6WjgwYj4RbNi6ghO6GJms9DM0UfPA/4CuFPSbWnZe4F9ACLik8A1wPHAfcBm4PVNjKdzDAy4ETCzGWlaoxAR3wOUsU0Ab25WDGZm1hjPaDYzs3FuFMzMbJwbBTMzG+dGwczMxinp620fkjYC61oYwp7Ar1q4/0a0S6yOs1jtEie0T6xzIc7eiMic/dt2jUKrSVodEUtaHUce7RKr4yxWu8QJ7RNrJ8Xp20dmZjbOjYKZmY1zo9C44VYH0IB2idVxFqtd4oT2ibVj4nSfgpmZjfOVgpmZjXOjYGZm49woTENSl6RbJV1d47XTJG2UdFv6eGOLYlwr6c40htU1Xpekf5R0n6Q7JB3RijjTWLJifZGkByec03NaFOfukq6Q9GNJ90g6ZtLrlTinOeKsyvk8YEIMt0l6SNI7Jm3T8nOaM86qnNN3Srpb0l2SLpO0YNLrO0n6fHo+b0yzX+bSzKWz54K3k+SW3rXO65+PiLeUGE89x0ZEvQkrLwOelT6OAlamP1tlulgBvhsRJ5YWTW0fA66NiFdJehIwKWtRZc5pVpxQgfMZEfcCh0HyhxZJIq0vT9qs5ec0Z5zQ4nMqaS/gbcBBEfGopC8ApwL/MmGz04HfRMT+kk4FLgRek6d+XynUIWkRcALw6VbHMksnA5+LxA+B3SU9o9VBVZWk3YAXkCSIIiKeiIjfTtqs5ec0Z5xV1A/8LCImr0rQ8nM6Sb04q6IbeLKkbpI/Bv570usnA5ekv18B9KeJzzK5UajvIuBdwLZptjklvdS9QtLe02zXTAF8Q9LNkgZrvL4X8MCE5+vTslbIihXgGEm3S/qapMVlBpfaF9gI/HN66/DTkp4yaZsqnNM8cULrz+dkpwKX1SivwjmdqF6c0OJzGhEbgH8A7gd+QZKx8huTNhs/nxGxFXgQ2CNP/W4UapB0IvDLiLh5ms2uAvoi4tnAN9neKpft+RFxBMnl95slvaBFceSRFestJOuzHAp8HPj3sgMk+QvsCGBlRBwO/A74mxbEkSVPnFU4n+PSW1wnAV9sZRxZMuJs+TmV9HskVwL7As8EniLpz4uq341Cbc8DTpK0FrgcOE7SqokbRMSmiHg8ffpp4MhyQxyPY0P685ck9z+fO2mTDcDEq5hFaVnpsmKNiIci4pH092uA+ZL2LDnM9cD6iLgxfX4FyZfvRFU4p5lxVuR8TvQy4JaI+N8ar1XhnI6pG2dFzulS4OcRsTEitgBfAv5o0jbj5zO9xbQbsClP5W4UaoiI90TEoojoI7mMvCEidmiJJ93vPImkQ7pUkp4iaZex34E/Ae6atNmVwF+mozuOJrnU/EXJoeaKVdLTx+57Snouyecz1we5KBHxP8ADkg5Ii/qBNZM2a/k5zRNnFc7nJK+l/i2Zlp/TCerGWZFzej9wtKSeNJZ+pn7/XAm8Lv39VSTfYblmKnv0UQMkXQCsjogrgbdJOgnYCvwaOK0FIf0+8OX0M9oN/GtEXCvpTQAR8UngGuB44D5gM/D6FsSZN9ZXAUOStgKPAqfm/SAX7K3ASHob4b+A11f0nGbFWZXzOfaHwIuBv5pQVrlzmiPOlp/TiLhR0hUkt7K2ArcCw5O+nz4DXCrpPpLvp1Pz1u9lLszMbJxvH5mZ2Tg3CmZmNs6NgpmZjXOjYGZm49womJnZODcKZg1KV8qstXJuzfIC9vcKSQdNeP5tSZVPIm/tyY2CWfW9AjgocyuzArhRsDknnT391XTRsrskvSYtP1LSd9IF+b4+Nis9/cv7Y0rWx78rnamKpOdK+s90wbkfTJg9nDeGz0q6KX3/yWn5aZK+JOlaST+V9MEJ7zld0k/S9/yTpIsl/RHJjPkPpfH9Qbr5n6Xb/UTSHxd06sw8o9nmpJcC/x0RJ0CyzLSk+SQLmJ0cERvThmI58Ib0PT0RcVi6SN9ngYOBHwN/HBFbJS0F3geckjOGs0mWFniDpN2BmyRdl752GHA48Dhwr6SPA6PA/yNZv+hh4Abg9oj4gaQrgasj4or0eAC6I+K5ko4HziVZD8ds1two2Fx0J/BhSReSfJl+V9LBJF/030y/VLtIlh0ecxlARPyHpF3TL/JdgEskPYtk2e/5DcTwJySLKp6ZPl8A7JP+fn1EPAggaQ3QC+wJfCcifp2WfxH4w2nq/1L682agr4G4zKblRsHmnIj4iZJ0jscDfy/pepJVWe+OiGPqva3G878DvhURf6okneG3GwhDwClpNq/thdJRJFcIY0aZ2f/DsTpm+n6zmtynYHOOpGcCmyNiFfAhklsy9wILleYxljRfOyZIGet3eD7JCp0Pkiw3PLZ882kNhvF14K0TVtQ8PGP7HwEvlPR7SpY6nnib6mGSqxazpnOjYHPRIST38G8jud/+9xHxBMkKlxdKuh24jR3XoH9M0q3AJ0ny2wJ8EHh/Wt7oX+N/R3K76Q5Jd6fP60pzTbwPuAn4PrCWJFsWJDk9zko7rP+gdg1mxfAqqdbxJH0bODMiVrc4jp0j4pH0SuHLwGcjolbieLOm8ZWCWXWcl17d3AX8nBanz7TO5CsFMzMb5ysFMzMb50bBzMzGuVEwM7NxbhTMzGycGwUzMxv3/wE8oH4Q8PsYzwAAAABJRU5ErkJggg==\n", 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "iterated 11 times\n" + ] } ], "source": [ - "# The drawing shows\n", - "datashow(datamat, k, mycentroids, clusterAssment)\n", - "trgartshow(datamat, 3, labels)" + "k = 3\n", + "# iris = KMeans(data=iris_df, k=k, feature='petal') # cluster by petal\n", + "iris = KMeans(data=iris_df, k=k, feature='sepal') # clusetr by sepal\n", + "iris.result()\n", + "print('iterated', iris.count, 'times')" ] }, { @@ -1003,9 +929,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.9-final" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file