diff --git a/0_python/README_EN.md b/0_python/README_ENG.md similarity index 100% rename from 0_python/README_EN.md rename to 0_python/README_ENG.md diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb index 59e94d7..fe9c9ef 100644 --- a/2_knn/knn_classification.ipynb +++ b/2_knn/knn_classification.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -325,18 +325,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'sklearn'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# load data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature dimensions: (1797, 64)\n", + "Label dimensions: (1797,)\n" ] } ], @@ -357,12 +354,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -386,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -402,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -417,7 +414,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/bushuhui/virtualenv/dl/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -444,8 +441,9 @@ "## 6. 深入思考\n", "\n", "* 如果输入的数据非常多，怎么快速进行距离计算？\n", - " - kd-tree\n", + " - [kd-tree](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html#sklearn.neighbors.KDTree) \n", " - Fast Library for Approximate Nearest Neighbors （FLANN）\n", + " - [PyNNDescent for fast Approximate Nearest Neighbors](https://pynndescent.readthedocs.io/en/latest/)\n", "* 如何选择最好的`k`？\n", " - https://zhuanlan.zhihu.com/p/143092725\n", "* kNN存在的问题？" @@ -477,7 +475,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/3_kmeans/1-k-means.ipynb b/3_kmeans/1-k-means.ipynb index ad3ca7d..7355cc1 100644 --- a/3_kmeans/1-k-means.ipynb +++ b/3_kmeans/1-k-means.ipynb @@ -11,9 +11,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 方法\n", "\n", - "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。***K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。***\n", + "根据训练样本中是否包含标签信息，机器学习可以分为`监督学习`和`无监督学习`。`聚类算法`是典型的无监督学习，其训练的样本中值包含`样本的特征`，**不包含样本的标签信息**。在聚类算法中，利用样本的特征，将具有相似特征空间分布的样本划分到同一类别中。\n", + "\n", + "\n", + "## 1. 方法\n", + "\n", + "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。***K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids）***:\n", + "* 移动到其包含成员的平均位置;\n", + "* 然后重新划分其内部成员。\n", "\n", "K是算法计算出的超参数，表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类。\n", "\n", @@ -26,12 +32,13 @@ "\n", "$u_k$是第$k$个类的重心位置，定义为：\n", "$$\n", - "u_k = \\frac{1}{|C_k|} \\sum_{x \\in C_k} x\n", + "u_k = \\frac{1}{|C_k|} \\sum_{i \\in C_k} x_i\n", "$$\n", "\n", "\n", "成本函数是各个类畸变程度（distortions）之和。每个类的畸变程度等于该类重心与其内部成员位置距离的平方和。若类内部的成员彼此间越紧凑则类的畸变程度越小，反之，若类内部的成员彼此间越分散则类的畸变程度越大。\n", "\n", + "## 2. 算法\n", "求解成本函数最小化的参数就是一个重复配置每个类包含的观测值，并不断移动类重心的过程。\n", "1. 首先，类的重心是随机确定的位置。实际上，重心位置等于随机选择的观测值的位置。\n", "2. 每次迭代的时候，K-Means会把观测值分配到离它们最近的类，然后把重心移动到该类全部成员位置的平均值那里。\n", @@ -40,13 +47,20 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 计算过程演示" + ] + }, + { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -84,12 +98,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -128,12 +142,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -171,12 +185,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - " sepal-length sepal-width petal-length petal-width class\n", - "0 5.1 3.5 1.4 0.2 Iris-setosa\n", - "1 4.9 3.0 1.4 0.2 Iris-setosa\n", - "2 4.7 3.2 1.3 0.2 Iris-setosa\n", - "3 4.6 3.1 1.5 0.2 Iris-setosa\n", - "4 5.0 3.6 1.4 0.2 Iris-setosa" + "
" ] }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -316,180 +256,79 @@ "%matplotlib inline\n", "\n", "# import librarys\n", - "from numpy import *\n", + "import numpy as np\n", + "from sklearn.datasets import make_blobs\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", "import random\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" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/virtualenv/dl/lib/python3.6/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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n", - "/home/bushuhui/virtualenv/dl/lib/python3.6/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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n", - "/home/bushuhui/virtualenv/dl/lib/python3.6/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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"\n" - ] - } - ], - "source": [ - "#对类别进行编码，3个类别分别赋值0，1，2\n", + "# 生成数据\n", + "centers = [(7, 0), (0, 0), (5, 5)]\n", + "n_samples = 500\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" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "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": 10, - "metadata": { - "lines_to_end_of_cell_marker": 2, - "scrolled": true - }, - "outputs": [ - { - "data": { 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "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)" + "X, y = make_blobs(n_samples=n_samples, n_features=2, \n", + " cluster_std=1.0, centers=centers, \n", + " shuffle=True, random_state=42)\n", + "\n", + "# 画出数据\n", + "plt.figure(figsize=(15, 9))\n", + "plt.scatter(X[:, 0], X[:, 1], c=y)\n", + "plt.colorbar()\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "import random\n", + "# k-means\n", + "\n", + "def calc_distance(v1, v2):\n", + " return np.sum(np.square(v1-v2))\n", "\n", - "def randChosenCent(dataSet,k):\n", + "def rand_cluster_cents(X, 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", + " m=np.shape(X)[0]\n", " \n", - " #生成类似于样本索引的列表\n", + " # 生成随机下标列表\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", + " 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": 12, - "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", + " # 返回随机的聚类中心\n", + " return X[centroidsIndex, :]\n", "\n", - "\n", - "def kMeans(dataSet, k):\n", + "def kmeans(X, k):\n", " # 样本总数\n", - " m = shape(dataSet)[0]\n", + " m = np.shape(X)[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", + " clusterAssment = np.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", + " centroids = rand_cluster_cents(X, 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", + " # 初始化迭代次数计数器\n", + " iterN = 0\n", " \n", " # 所有样本分配结果不再改变，迭代终止\n", - " while clusterChanged:\n", + " while True: \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 = False\n", - " \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", + " minDist = np.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", + " distJI = calc_distance(centroids[j, :], X[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", @@ -501,62 +340,59 @@ " clusterChanged = True\n", " clusterAssment[i, :] = minIndex, minDist ** 2 # 分配样本到最近的簇\n", " \n", - " iterTime += 1\n", + " iterN += 1\n", " sse = sum(clusterAssment[:, 1])\n", - " print('the SSE of %d' % iterTime + 'th iteration is %f' % sse)\n", + " print('the SSE of %d' % iterN + '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", + " ptsInClust = X[clusterAssment[:, 0] == cent, :]\n", " # 更新聚类中心：axis=0沿列方向求均值。\n", - " centroids[cent, :] = mean(ptsInClust, axis=0)\n", + " centroids[cent, :] = np.mean(ptsInClust, axis=0)\n", + " \n", + " # 如果聚类重心没有发生改变，则退出迭代\n", + " if not clusterChanged:\n", + " break\n", + " \n", " return centroids, clusterAssment\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "最初的中心= [[4.6 3.6]\n", - " [4.9 3. ]\n", - " [5.8 2.7]]\n", - "the SSE of 1th iteration is 93.220000\n", - "the SSE of 2th iteration is 50.840500\n", - "the SSE of 3th iteration is 47.318230\n", - "the SSE of 4th iteration is 45.338455\n", - "the SSE of 5th iteration is 44.384855\n", - "the SSE of 6th iteration is 43.591498\n", - "the SSE of 7th iteration is 41.904928\n", - "the SSE of 8th iteration is 39.066514\n", - "the SSE of 9th iteration is 38.316500\n", - "the SSE of 10th iteration is 37.912536\n", - "the SSE of 11th iteration is 37.423306\n", - "the SSE of 12th iteration is 37.136261\n", - "the SSE of 13th iteration is 37.123702\n" + "最初的中心= [[ 7.35701549 -0.6929096 ]\n", + " [ 0.95042384 -0.57690366]\n", + " [ 0.05572491 1.09419152]]\n", + "the SSE of 1th iteration is 239885.100070\n", + "the SSE of 2th iteration is 37348.235353\n", + "the SSE of 3th iteration is 8260.155429\n", + "the SSE of 4th iteration is 3635.163186\n", + "the SSE of 5th iteration is 3502.239035\n" ] } ], "source": [ "# 进行k-means聚类\n", "k = 3 # 用户定义聚类数\n", - "mycentroids, clusterAssment = kMeans(datamat, k)" + "mycentroids, clusterAssment = kmeans(X, k)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "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", + " num, dim = np.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", @@ -569,7 +405,7 @@ " 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", + " plt.plot(dataSet[i, 0], dataSet[i, 1], marksamples[markindex], markersize=6)\n", "\n", " # 绘中心点\n", " markcentroids = ['o', '*', '^'] # 聚类中心图形标记\n", @@ -578,8 +414,8 @@ " 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", + " plt.xlabel('feature 1')\n", + " plt.ylabel('feature 2')\n", "\n", " plt.title('k-means cluster result') # 标题\n", " plt.show()\n", @@ -589,21 +425,18 @@ "def trgartshow(dataSet, k, labels):\n", " from matplotlib import pyplot as plt\n", "\n", - " num, dim = shape(dataSet)\n", + " num, dim = np.shape(dataSet)\n", " label = ['0', '1', '2']\n", " marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '" ] @@ -629,7 +462,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -642,8 +475,8 @@ ], "source": [ "# 绘图显示\n", - "datashow(datamat, k, mycentroids, clusterAssment)\n", - "trgartshow(datamat, 3, labels)" + "datashow(X, k, mycentroids, clusterAssment)\n", + "trgartshow(X, 3, y)" ] }, { @@ -967,6 +800,14 @@ "source": [ "从上图可见，类簇数量从1降到2再降到3的过程，更改K值让整体聚类结构有很大改变，这意味着新的聚类数量让算法有更大的收敛空间，这样的K值不能反映真实的类簇数量。而当K=3以后再增大K，平均距离的下降速度显著变缓慢，这意味着进一步增加K值不再会有利于算法的收敛，同时也暗示着K=3是相对最佳的类簇数量。" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [机器学习聚类算法之K-Means](https://www.biaodianfu.com/k-means.html)" + ] } ], "metadata": { @@ -986,7 +827,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.7.9" } }, "nbformat": 4, diff --git a/references_tips/cheatsheet/scikit-learn_algorithm_cheat-sheet.jpg b/references_tips/cheatsheet/scikit-learn_algorithm_cheat-sheet.jpg new file mode 100644 index 0000000..1fa7305 Binary files /dev/null and b/references_tips/cheatsheet/scikit-learn_algorithm_cheat-sheet.jpg differ diff --git a/references_tips/datasets.ipynb b/references_tips/datasets.ipynb index 2f2034a..5cba373 100644 --- a/references_tips/datasets.ipynb +++ b/references_tips/datasets.ipynb @@ -246,7 +246,7 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "from sklearn.datasets import make_blobsb\n", + "from sklearn.datasets import make_blobs\n", "\n", "# Generate 3 blobs with 2 classes where the second blob contains\n", "# half positive samples and half negative samples. Probability in this\n", @@ -403,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.5" } }, "nbformat": 4,