网页制作三剑客不包括,优化关键词推广,做外贸国外网站,购物网站功能介绍文章目录 多分类以及机器学习实践如何对多个类别进行分类1.1 数据的预处理1.2 训练数据的准备1.3 定义假设函数#xff0c;代价函数#xff0c;梯度下降算法#xff08;从实验3复制过来#xff09;1.4 调用梯度下降算法来学习三个分类模型的参数1.5 利用模型进行预测1.6 评… 文章目录 多分类以及机器学习实践如何对多个类别进行分类1.1 数据的预处理1.2 训练数据的准备1.3 定义假设函数代价函数梯度下降算法从实验3复制过来1.4 调用梯度下降算法来学习三个分类模型的参数1.5 利用模型进行预测1.6 评估模型1.7 试试sklearn 实验4(1) 请动手完成你们第一个多分类问题祝好运完成下面代码2.1 数据读取2.2 训练数据的准备2.3 定义假设函数、代价函数和梯度下降算法2.4 学习这四个分类模型2.5 利用模型进行预测2.6 计算准确率 多分类以及机器学习实践
如何对多个类别进行分类
Iris数据集是常用的分类实验数据集由Fisher, 1936收集整理。Iris也称鸢尾花卉数据集是一类多重变量分析的数据集。数据集包含150个数据样本分为3类每类50个数据每个数据包含4个属性。可通过花萼长度花萼宽度花瓣长度花瓣宽度4个属性预测鸢尾花卉属于SetosaVersicolourVirginica三个种类中的哪一类。
iris以鸢尾花的特征作为数据来源常用在分类操作中。该数据集由3种不同类型的鸢尾花的各50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的后两个种类是非线性可分离的。
该数据集包含了4个属性 Sepal.Length花萼长度单位是cm; Sepal.Width花萼宽度单位是cm; Petal.Length花瓣长度单位是cm; Petal.Width花瓣宽度单位是cm;
种类Iris Setosa山鸢尾、Iris Versicolour杂色鸢尾以及Iris Virginica维吉尼亚鸢尾。
1.1 数据的预处理
import sklearn.datasets as datasets
import pandas as pd
import numpy as npdatadatasets.load_iris()
data{data: array([[5.1, 3.5, 1.4, 0.2],[4.9, 3. , 1.4, 0.2],[4.7, 3.2, 1.3, 0.2],[4.6, 3.1, 1.5, 0.2],[5. , 3.6, 1.4, 0.2],[5.4, 3.9, 1.7, 0.4],[4.6, 3.4, 1.4, 0.3],[5. , 3.4, 1.5, 0.2],[4.4, 2.9, 1.4, 0.2],[4.9, 3.1, 1.5, 0.1],[5.4, 3.7, 1.5, 0.2],[4.8, 3.4, 1.6, 0.2],[4.8, 3. , 1.4, 0.1],[4.3, 3. , 1.1, 0.1],[5.8, 4. , 1.2, 0.2],[5.7, 4.4, 1.5, 0.4],[5.4, 3.9, 1.3, 0.4],[5.1, 3.5, 1.4, 0.3],[5.7, 3.8, 1.7, 0.3],[5.1, 3.8, 1.5, 0.3],[5.4, 3.4, 1.7, 0.2],[5.1, 3.7, 1.5, 0.4],[4.6, 3.6, 1. , 0.2],[5.1, 3.3, 1.7, 0.5],[4.8, 3.4, 1.9, 0.2],[5. , 3. , 1.6, 0.2],[5. , 3.4, 1.6, 0.4],[5.2, 3.5, 1.5, 0.2],[5.2, 3.4, 1.4, 0.2],[4.7, 3.2, 1.6, 0.2],[4.8, 3.1, 1.6, 0.2],[5.4, 3.4, 1.5, 0.4],[5.2, 4.1, 1.5, 0.1],[5.5, 4.2, 1.4, 0.2],[4.9, 3.1, 1.5, 0.2],[5. , 3.2, 1.2, 0.2],[5.5, 3.5, 1.3, 0.2],[4.9, 3.6, 1.4, 0.1],[4.4, 3. , 1.3, 0.2],[5.1, 3.4, 1.5, 0.2],[5. , 3.5, 1.3, 0.3],[4.5, 2.3, 1.3, 0.3],[4.4, 3.2, 1.3, 0.2],[5. , 3.5, 1.6, 0.6],[5.1, 3.8, 1.9, 0.4],[4.8, 3. , 1.4, 0.3],[5.1, 3.8, 1.6, 0.2],[4.6, 3.2, 1.4, 0.2],[5.3, 3.7, 1.5, 0.2],[5. , 3.3, 1.4, 0.2],[7. , 3.2, 4.7, 1.4],[6.4, 3.2, 4.5, 1.5],[6.9, 3.1, 4.9, 1.5],[5.5, 2.3, 4. , 1.3],[6.5, 2.8, 4.6, 1.5],[5.7, 2.8, 4.5, 1.3],[6.3, 3.3, 4.7, 1.6],[4.9, 2.4, 3.3, 1. ],[6.6, 2.9, 4.6, 1.3],[5.2, 2.7, 3.9, 1.4],[5. , 2. , 3.5, 1. ],[5.9, 3. , 4.2, 1.5],[6. , 2.2, 4. , 1. ],[6.1, 2.9, 4.7, 1.4],[5.6, 2.9, 3.6, 1.3],[6.7, 3.1, 4.4, 1.4],[5.6, 3. , 4.5, 1.5],[5.8, 2.7, 4.1, 1. ],[6.2, 2.2, 4.5, 1.5],[5.6, 2.5, 3.9, 1.1],[5.9, 3.2, 4.8, 1.8],[6.1, 2.8, 4. , 1.3],[6.3, 2.5, 4.9, 1.5],[6.1, 2.8, 4.7, 1.2],[6.4, 2.9, 4.3, 1.3],[6.6, 3. , 4.4, 1.4],[6.8, 2.8, 4.8, 1.4],[6.7, 3. , 5. , 1.7],[6. , 2.9, 4.5, 1.5],[5.7, 2.6, 3.5, 1. ],[5.5, 2.4, 3.8, 1.1],[5.5, 2.4, 3.7, 1. ],[5.8, 2.7, 3.9, 1.2],[6. , 2.7, 5.1, 1.6],[5.4, 3. , 4.5, 1.5],[6. , 3.4, 4.5, 1.6],[6.7, 3.1, 4.7, 1.5],[6.3, 2.3, 4.4, 1.3],[5.6, 3. , 4.1, 1.3],[5.5, 2.5, 4. , 1.3],[5.5, 2.6, 4.4, 1.2],[6.1, 3. , 4.6, 1.4],[5.8, 2.6, 4. , 1.2],[5. , 2.3, 3.3, 1. ],[5.6, 2.7, 4.2, 1.3],[5.7, 3. , 4.2, 1.2],[5.7, 2.9, 4.2, 1.3],[6.2, 2.9, 4.3, 1.3],[5.1, 2.5, 3. , 1.1],[5.7, 2.8, 4.1, 1.3],[6.3, 3.3, 6. , 2.5],[5.8, 2.7, 5.1, 1.9],[7.1, 3. , 5.9, 2.1],[6.3, 2.9, 5.6, 1.8],[6.5, 3. , 5.8, 2.2],[7.6, 3. , 6.6, 2.1],[4.9, 2.5, 4.5, 1.7],[7.3, 2.9, 6.3, 1.8],[6.7, 2.5, 5.8, 1.8],[7.2, 3.6, 6.1, 2.5],[6.5, 3.2, 5.1, 2. ],[6.4, 2.7, 5.3, 1.9],[6.8, 3. , 5.5, 2.1],[5.7, 2.5, 5. , 2. ],[5.8, 2.8, 5.1, 2.4],[6.4, 3.2, 5.3, 2.3],[6.5, 3. , 5.5, 1.8],[7.7, 3.8, 6.7, 2.2],[7.7, 2.6, 6.9, 2.3],[6. , 2.2, 5. , 1.5],[6.9, 3.2, 5.7, 2.3],[5.6, 2.8, 4.9, 2. ],[7.7, 2.8, 6.7, 2. ],[6.3, 2.7, 4.9, 1.8],[6.7, 3.3, 5.7, 2.1],[7.2, 3.2, 6. , 1.8],[6.2, 2.8, 4.8, 1.8],[6.1, 3. , 4.9, 1.8],[6.4, 2.8, 5.6, 2.1],[7.2, 3. , 5.8, 1.6],[7.4, 2.8, 6.1, 1.9],[7.9, 3.8, 6.4, 2. ],[6.4, 2.8, 5.6, 2.2],[6.3, 2.8, 5.1, 1.5],[6.1, 2.6, 5.6, 1.4],[7.7, 3. , 6.1, 2.3],[6.3, 3.4, 5.6, 2.4],[6.4, 3.1, 5.5, 1.8],[6. , 3. , 4.8, 1.8],[6.9, 3.1, 5.4, 2.1],[6.7, 3.1, 5.6, 2.4],[6.9, 3.1, 5.1, 2.3],[5.8, 2.7, 5.1, 1.9],[6.8, 3.2, 5.9, 2.3],[6.7, 3.3, 5.7, 2.5],[6.7, 3. , 5.2, 2.3],[6.3, 2.5, 5. , 1.9],[6.5, 3. , 5.2, 2. ],[6.2, 3.4, 5.4, 2.3],[5.9, 3. , 5.1, 1.8]]),target: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),frame: None,target_names: array([setosa, versicolor, virginica], dtypeU10),DESCR: .. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n \n :Summary Statistics:\n\n \n Min Max Mean SD Class Correlation\n \n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n \n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLUio.arc.nasa.gov)\n :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\s paper. Note that it\s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\s paper is a classic in the field and\nis referenced frequently to this day. (See Duda Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n - Fisher, R.A. The use of multiple measurements in taxonomic problems\n Annual Eugenics, 7, Part II, 179-188 (1936); also in Contributions to\n Mathematical Statistics (John Wiley, NY, 1950).\n - Duda, R.O., Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments. IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) The Reduced Nearest Neighbor Rule. IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et als AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...,feature_names: [sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)],filename: iris.csv,data_module: sklearn.datasets.data}data_xdata[data]
data_ydata[target]data_x.shape,data_y.shape((150, 4), (150,))data_ydata_y.reshape([len(data_y),1])
data_yarray([[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2]])#法1 用拼接的方法
datanp.hstack([data_x,data_y])#法二 用插入的方法
np.insert(data_x,data_x.shape[1],data_y,axis1)array([[5.1, 3.5, 1.4, ..., 2. , 2. , 2. ],[4.9, 3. , 1.4, ..., 2. , 2. , 2. ],[4.7, 3.2, 1.3, ..., 2. , 2. , 2. ],...,[6.5, 3. , 5.2, ..., 2. , 2. , 2. ],[6.2, 3.4, 5.4, ..., 2. , 2. , 2. ],[5.9, 3. , 5.1, ..., 2. , 2. , 2. ]])datapd.DataFrame(data,columns[F1,F2,F3,F4,target])
dataF1F2F3F4target05.13.51.40.20.014.93.01.40.20.024.73.21.30.20.034.63.11.50.20.045.03.61.40.20.0..................1456.73.05.22.32.01466.32.55.01.92.01476.53.05.22.02.01486.23.45.42.32.01495.93.05.11.82.0
150 rows × 5 columns
data.insert(0,ones,1)dataonesF1F2F3F4target015.13.51.40.20.0114.93.01.40.20.0214.73.21.30.20.0314.63.11.50.20.0415.03.61.40.20.0.....................14516.73.05.22.32.014616.32.55.01.92.014716.53.05.22.02.014816.23.45.42.32.014915.93.05.11.82.0
150 rows × 6 columns
data[target]data[target].astype(int32)dataonesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3214616.32.55.01.9214716.53.05.22.0214816.23.45.42.3214915.93.05.11.82
150 rows × 6 columns
1.2 训练数据的准备
data_xarray([[5.1, 3.5, 1.4, 0.2],[4.9, 3. , 1.4, 0.2],[4.7, 3.2, 1.3, 0.2],[4.6, 3.1, 1.5, 0.2],[5. , 3.6, 1.4, 0.2],[5.4, 3.9, 1.7, 0.4],[4.6, 3.4, 1.4, 0.3],[5. , 3.4, 1.5, 0.2],[4.4, 2.9, 1.4, 0.2],[4.9, 3.1, 1.5, 0.1],[5.4, 3.7, 1.5, 0.2],[4.8, 3.4, 1.6, 0.2],[4.8, 3. , 1.4, 0.1],[4.3, 3. , 1.1, 0.1],[5.8, 4. , 1.2, 0.2],[5.7, 4.4, 1.5, 0.4],[5.4, 3.9, 1.3, 0.4],[5.1, 3.5, 1.4, 0.3],[5.7, 3.8, 1.7, 0.3],[5.1, 3.8, 1.5, 0.3],[5.4, 3.4, 1.7, 0.2],[5.1, 3.7, 1.5, 0.4],[4.6, 3.6, 1. , 0.2],[5.1, 3.3, 1.7, 0.5],[4.8, 3.4, 1.9, 0.2],[5. , 3. , 1.6, 0.2],[5. , 3.4, 1.6, 0.4],[5.2, 3.5, 1.5, 0.2],[5.2, 3.4, 1.4, 0.2],[4.7, 3.2, 1.6, 0.2],[4.8, 3.1, 1.6, 0.2],[5.4, 3.4, 1.5, 0.4],[5.2, 4.1, 1.5, 0.1],[5.5, 4.2, 1.4, 0.2],[4.9, 3.1, 1.5, 0.2],[5. , 3.2, 1.2, 0.2],[5.5, 3.5, 1.3, 0.2],[4.9, 3.6, 1.4, 0.1],[4.4, 3. , 1.3, 0.2],[5.1, 3.4, 1.5, 0.2],[5. , 3.5, 1.3, 0.3],[4.5, 2.3, 1.3, 0.3],[4.4, 3.2, 1.3, 0.2],[5. , 3.5, 1.6, 0.6],[5.1, 3.8, 1.9, 0.4],[4.8, 3. , 1.4, 0.3],[5.1, 3.8, 1.6, 0.2],[4.6, 3.2, 1.4, 0.2],[5.3, 3.7, 1.5, 0.2],[5. , 3.3, 1.4, 0.2],[7. , 3.2, 4.7, 1.4],[6.4, 3.2, 4.5, 1.5],[6.9, 3.1, 4.9, 1.5],[5.5, 2.3, 4. , 1.3],[6.5, 2.8, 4.6, 1.5],[5.7, 2.8, 4.5, 1.3],[6.3, 3.3, 4.7, 1.6],[4.9, 2.4, 3.3, 1. ],[6.6, 2.9, 4.6, 1.3],[5.2, 2.7, 3.9, 1.4],[5. , 2. , 3.5, 1. ],[5.9, 3. , 4.2, 1.5],[6. , 2.2, 4. , 1. ],[6.1, 2.9, 4.7, 1.4],[5.6, 2.9, 3.6, 1.3],[6.7, 3.1, 4.4, 1.4],[5.6, 3. , 4.5, 1.5],[5.8, 2.7, 4.1, 1. ],[6.2, 2.2, 4.5, 1.5],[5.6, 2.5, 3.9, 1.1],[5.9, 3.2, 4.8, 1.8],[6.1, 2.8, 4. , 1.3],[6.3, 2.5, 4.9, 1.5],[6.1, 2.8, 4.7, 1.2],[6.4, 2.9, 4.3, 1.3],[6.6, 3. , 4.4, 1.4],[6.8, 2.8, 4.8, 1.4],[6.7, 3. , 5. , 1.7],[6. , 2.9, 4.5, 1.5],[5.7, 2.6, 3.5, 1. ],[5.5, 2.4, 3.8, 1.1],[5.5, 2.4, 3.7, 1. ],[5.8, 2.7, 3.9, 1.2],[6. , 2.7, 5.1, 1.6],[5.4, 3. , 4.5, 1.5],[6. , 3.4, 4.5, 1.6],[6.7, 3.1, 4.7, 1.5],[6.3, 2.3, 4.4, 1.3],[5.6, 3. , 4.1, 1.3],[5.5, 2.5, 4. , 1.3],[5.5, 2.6, 4.4, 1.2],[6.1, 3. , 4.6, 1.4],[5.8, 2.6, 4. , 1.2],[5. , 2.3, 3.3, 1. ],[5.6, 2.7, 4.2, 1.3],[5.7, 3. , 4.2, 1.2],[5.7, 2.9, 4.2, 1.3],[6.2, 2.9, 4.3, 1.3],[5.1, 2.5, 3. , 1.1],[5.7, 2.8, 4.1, 1.3],[6.3, 3.3, 6. , 2.5],[5.8, 2.7, 5.1, 1.9],[7.1, 3. , 5.9, 2.1],[6.3, 2.9, 5.6, 1.8],[6.5, 3. , 5.8, 2.2],[7.6, 3. , 6.6, 2.1],[4.9, 2.5, 4.5, 1.7],[7.3, 2.9, 6.3, 1.8],[6.7, 2.5, 5.8, 1.8],[7.2, 3.6, 6.1, 2.5],[6.5, 3.2, 5.1, 2. ],[6.4, 2.7, 5.3, 1.9],[6.8, 3. , 5.5, 2.1],[5.7, 2.5, 5. , 2. ],[5.8, 2.8, 5.1, 2.4],[6.4, 3.2, 5.3, 2.3],[6.5, 3. , 5.5, 1.8],[7.7, 3.8, 6.7, 2.2],[7.7, 2.6, 6.9, 2.3],[6. , 2.2, 5. , 1.5],[6.9, 3.2, 5.7, 2.3],[5.6, 2.8, 4.9, 2. ],[7.7, 2.8, 6.7, 2. ],[6.3, 2.7, 4.9, 1.8],[6.7, 3.3, 5.7, 2.1],[7.2, 3.2, 6. , 1.8],[6.2, 2.8, 4.8, 1.8],[6.1, 3. , 4.9, 1.8],[6.4, 2.8, 5.6, 2.1],[7.2, 3. , 5.8, 1.6],[7.4, 2.8, 6.1, 1.9],[7.9, 3.8, 6.4, 2. ],[6.4, 2.8, 5.6, 2.2],[6.3, 2.8, 5.1, 1.5],[6.1, 2.6, 5.6, 1.4],[7.7, 3. , 6.1, 2.3],[6.3, 3.4, 5.6, 2.4],[6.4, 3.1, 5.5, 1.8],[6. , 3. , 4.8, 1.8],[6.9, 3.1, 5.4, 2.1],[6.7, 3.1, 5.6, 2.4],[6.9, 3.1, 5.1, 2.3],[5.8, 2.7, 5.1, 1.9],[6.8, 3.2, 5.9, 2.3],[6.7, 3.3, 5.7, 2.5],[6.7, 3. , 5.2, 2.3],[6.3, 2.5, 5. , 1.9],[6.5, 3. , 5.2, 2. ],[6.2, 3.4, 5.4, 2.3],[5.9, 3. , 5.1, 1.8]])data_xnp.insert(data_x,0,1,axis1)data_x.shape,data_y.shape((150, 5), (150, 1))#训练数据的特征和标签
data_x,data_y(array([[1. , 5.1, 3.5, 1.4, 0.2],[1. , 4.9, 3. , 1.4, 0.2],[1. , 4.7, 3.2, 1.3, 0.2],[1. , 4.6, 3.1, 1.5, 0.2],[1. , 5. , 3.6, 1.4, 0.2],[1. , 5.4, 3.9, 1.7, 0.4],[1. , 4.6, 3.4, 1.4, 0.3],[1. , 5. , 3.4, 1.5, 0.2],[1. , 4.4, 2.9, 1.4, 0.2],[1. , 4.9, 3.1, 1.5, 0.1],[1. , 5.4, 3.7, 1.5, 0.2],[1. , 4.8, 3.4, 1.6, 0.2],[1. , 4.8, 3. , 1.4, 0.1],[1. , 4.3, 3. , 1.1, 0.1],[1. , 5.8, 4. , 1.2, 0.2],[1. , 5.7, 4.4, 1.5, 0.4],[1. , 5.4, 3.9, 1.3, 0.4],[1. , 5.1, 3.5, 1.4, 0.3],[1. , 5.7, 3.8, 1.7, 0.3],[1. , 5.1, 3.8, 1.5, 0.3],[1. , 5.4, 3.4, 1.7, 0.2],[1. , 5.1, 3.7, 1.5, 0.4],[1. , 4.6, 3.6, 1. , 0.2],[1. , 5.1, 3.3, 1.7, 0.5],[1. , 4.8, 3.4, 1.9, 0.2],[1. , 5. , 3. , 1.6, 0.2],[1. , 5. , 3.4, 1.6, 0.4],[1. , 5.2, 3.5, 1.5, 0.2],[1. , 5.2, 3.4, 1.4, 0.2],[1. , 4.7, 3.2, 1.6, 0.2],[1. , 4.8, 3.1, 1.6, 0.2],[1. , 5.4, 3.4, 1.5, 0.4],[1. , 5.2, 4.1, 1.5, 0.1],[1. , 5.5, 4.2, 1.4, 0.2],[1. , 4.9, 3.1, 1.5, 0.2],[1. , 5. , 3.2, 1.2, 0.2],[1. , 5.5, 3.5, 1.3, 0.2],[1. , 4.9, 3.6, 1.4, 0.1],[1. , 4.4, 3. , 1.3, 0.2],[1. , 5.1, 3.4, 1.5, 0.2],[1. , 5. , 3.5, 1.3, 0.3],[1. , 4.5, 2.3, 1.3, 0.3],[1. , 4.4, 3.2, 1.3, 0.2],[1. , 5. , 3.5, 1.6, 0.6],[1. , 5.1, 3.8, 1.9, 0.4],[1. , 4.8, 3. , 1.4, 0.3],[1. , 5.1, 3.8, 1.6, 0.2],[1. , 4.6, 3.2, 1.4, 0.2],[1. , 5.3, 3.7, 1.5, 0.2],[1. , 5. , 3.3, 1.4, 0.2],[1. , 7. , 3.2, 4.7, 1.4],[1. , 6.4, 3.2, 4.5, 1.5],[1. , 6.9, 3.1, 4.9, 1.5],[1. , 5.5, 2.3, 4. , 1.3],[1. , 6.5, 2.8, 4.6, 1.5],[1. , 5.7, 2.8, 4.5, 1.3],[1. , 6.3, 3.3, 4.7, 1.6],[1. , 4.9, 2.4, 3.3, 1. ],[1. , 6.6, 2.9, 4.6, 1.3],[1. , 5.2, 2.7, 3.9, 1.4],[1. , 5. , 2. , 3.5, 1. ],[1. , 5.9, 3. , 4.2, 1.5],[1. , 6. , 2.2, 4. , 1. ],[1. , 6.1, 2.9, 4.7, 1.4],[1. , 5.6, 2.9, 3.6, 1.3],[1. , 6.7, 3.1, 4.4, 1.4],[1. , 5.6, 3. , 4.5, 1.5],[1. , 5.8, 2.7, 4.1, 1. ],[1. , 6.2, 2.2, 4.5, 1.5],[1. , 5.6, 2.5, 3.9, 1.1],[1. , 5.9, 3.2, 4.8, 1.8],[1. , 6.1, 2.8, 4. , 1.3],[1. , 6.3, 2.5, 4.9, 1.5],[1. , 6.1, 2.8, 4.7, 1.2],[1. , 6.4, 2.9, 4.3, 1.3],[1. , 6.6, 3. , 4.4, 1.4],[1. , 6.8, 2.8, 4.8, 1.4],[1. , 6.7, 3. , 5. , 1.7],[1. , 6. , 2.9, 4.5, 1.5],[1. , 5.7, 2.6, 3.5, 1. ],[1. , 5.5, 2.4, 3.8, 1.1],[1. , 5.5, 2.4, 3.7, 1. ],[1. , 5.8, 2.7, 3.9, 1.2],[1. , 6. , 2.7, 5.1, 1.6],[1. , 5.4, 3. , 4.5, 1.5],[1. , 6. , 3.4, 4.5, 1.6],[1. , 6.7, 3.1, 4.7, 1.5],[1. , 6.3, 2.3, 4.4, 1.3],[1. , 5.6, 3. , 4.1, 1.3],[1. , 5.5, 2.5, 4. , 1.3],[1. , 5.5, 2.6, 4.4, 1.2],[1. , 6.1, 3. , 4.6, 1.4],[1. , 5.8, 2.6, 4. , 1.2],[1. , 5. , 2.3, 3.3, 1. ],[1. , 5.6, 2.7, 4.2, 1.3],[1. , 5.7, 3. , 4.2, 1.2],[1. , 5.7, 2.9, 4.2, 1.3],[1. , 6.2, 2.9, 4.3, 1.3],[1. , 5.1, 2.5, 3. , 1.1],[1. , 5.7, 2.8, 4.1, 1.3],[1. , 6.3, 3.3, 6. , 2.5],[1. , 5.8, 2.7, 5.1, 1.9],[1. , 7.1, 3. , 5.9, 2.1],[1. , 6.3, 2.9, 5.6, 1.8],[1. , 6.5, 3. , 5.8, 2.2],[1. , 7.6, 3. , 6.6, 2.1],[1. , 4.9, 2.5, 4.5, 1.7],[1. , 7.3, 2.9, 6.3, 1.8],[1. , 6.7, 2.5, 5.8, 1.8],[1. , 7.2, 3.6, 6.1, 2.5],[1. , 6.5, 3.2, 5.1, 2. ],[1. , 6.4, 2.7, 5.3, 1.9],[1. , 6.8, 3. , 5.5, 2.1],[1. , 5.7, 2.5, 5. , 2. ],[1. , 5.8, 2.8, 5.1, 2.4],[1. , 6.4, 3.2, 5.3, 2.3],[1. , 6.5, 3. , 5.5, 1.8],[1. , 7.7, 3.8, 6.7, 2.2],[1. , 7.7, 2.6, 6.9, 2.3],[1. , 6. , 2.2, 5. , 1.5],[1. , 6.9, 3.2, 5.7, 2.3],[1. , 5.6, 2.8, 4.9, 2. ],[1. , 7.7, 2.8, 6.7, 2. ],[1. , 6.3, 2.7, 4.9, 1.8],[1. , 6.7, 3.3, 5.7, 2.1],[1. , 7.2, 3.2, 6. , 1.8],[1. , 6.2, 2.8, 4.8, 1.8],[1. , 6.1, 3. , 4.9, 1.8],[1. , 6.4, 2.8, 5.6, 2.1],[1. , 7.2, 3. , 5.8, 1.6],[1. , 7.4, 2.8, 6.1, 1.9],[1. , 7.9, 3.8, 6.4, 2. ],[1. , 6.4, 2.8, 5.6, 2.2],[1. , 6.3, 2.8, 5.1, 1.5],[1. , 6.1, 2.6, 5.6, 1.4],[1. , 7.7, 3. , 6.1, 2.3],[1. , 6.3, 3.4, 5.6, 2.4],[1. , 6.4, 3.1, 5.5, 1.8],[1. , 6. , 3. , 4.8, 1.8],[1. , 6.9, 3.1, 5.4, 2.1],[1. , 6.7, 3.1, 5.6, 2.4],[1. , 6.9, 3.1, 5.1, 2.3],[1. , 5.8, 2.7, 5.1, 1.9],[1. , 6.8, 3.2, 5.9, 2.3],[1. , 6.7, 3.3, 5.7, 2.5],[1. , 6.7, 3. , 5.2, 2.3],[1. , 6.3, 2.5, 5. , 1.9],[1. , 6.5, 3. , 5.2, 2. ],[1. , 6.2, 3.4, 5.4, 2.3],[1. , 5.9, 3. , 5.1, 1.8]]),array([[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2]]))由于有三个类别那么在训练时三类数据要分开
data1data.copy()data1onesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3214616.32.55.01.9214716.53.05.22.0214816.23.45.42.3214915.93.05.11.82
150 rows × 6 columns
data
data1.loc[data[target]!0,target]0
data1.loc[data[target]0,target]1data1onesF1F2F3F4target015.13.51.40.21114.93.01.40.21214.73.21.30.21314.63.11.50.21415.03.61.40.21.....................14516.73.05.22.3014616.32.55.01.9014716.53.05.22.0014816.23.45.42.3014915.93.05.11.80
150 rows × 6 columns
data1_xdata1.iloc[:,:data1.shape[1]-1].values
data1_ydata1.iloc[:,data1.shape[1]-1].values
data1_x.shape,data1_y.shape((150, 5), (150,))#针对第二类即第二个分类器的数据
data2data.copy()
data2.loc[data[target]1,target]1
data2.loc[data[target]!1,target]0
data2[target]00 True
1 True
2 True
3 True
4 True...
145 True
146 True
147 True
148 True
149 True
Name: target, Length: 150, dtype: booldata2.shape[1]6data2.iloc[50:55,:]onesF1F2F3F4target5017.03.24.71.415116.43.24.51.515216.93.14.91.515315.52.34.01.315416.52.84.61.51
data2_xdata2.iloc[:,:data2.shape[1]-1].values
data2_ydata2.iloc[:,data2.shape[1]-1].values#针对第三类即第三个分类器的数据
data3data.copy()
data3.loc[data[target]2,target]1
data3.loc[data[target]!2,target]0
data3onesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3114616.32.55.01.9114716.53.05.22.0114816.23.45.42.3114915.93.05.11.81
150 rows × 6 columns
data3_xdata3.iloc[:,:data3.shape[1]-1].values
data3_ydata3.iloc[:,data3.shape[1]-1].values1.3 定义假设函数代价函数梯度下降算法从实验3复制过来
def sigmoid(z):return 1 / (1 np.exp(-z))def h(X,w):zXwhsigmoid(z)return h#代价函数构造
def cost(X,w,y):#当X(m,n1),y(m,),w(n1,1)y_hatsigmoid(Xw)rightnp.multiply(y.ravel(),np.log(y_hat).ravel())np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())cost-np.sum(right)/X.shape[0]return costdef sigmoid(z):return 1 / (1 np.exp(-z))def h(X,w):zXwhsigmoid(z)return h#代价函数构造
def cost(X,w,y):#当X(m,n1),y(m,),w(n1,1)y_hatsigmoid(Xw)rightnp.multiply(y.ravel(),np.log(y_hat).ravel())np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())cost-np.sum(right)/X.shape[0]return costdef grandient(X,y,iter_num,alpha):yy.reshape((X.shape[0],1))wnp.zeros((X.shape[1],1))cost_lst[] for i in range(iter_num):y_predh(X,w)-ytempnp.zeros((X.shape[1],1))for j in range(X.shape[1]):rightnp.multiply(y_pred.ravel(),X[:,j])gradient1/(X.shape[0])*(np.sum(right))temp[j,0]w[j,0]-alpha*gradientwtempcost_lst.append(cost(X,w,y.ravel()))return w,cost_lst1.4 调用梯度下降算法来学习三个分类模型的参数
#初始化超参数
iter_num,alpha600000,0.001#训练第一个模型
w1,cost_lst1grandient(data1_x,data1_y,iter_num,alpha)import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst1,b-o)[matplotlib.lines.Line2D at 0x2562630b100]#训练第二个模型
w2,cost_lst2grandient(data2_x,data2_y,iter_num,alpha)import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst2,b-o)[matplotlib.lines.Line2D at 0x25628114280]#训练第三个模型
w3,cost_lst3grandient(data3_x,data3_y,iter_num,alpha)w3array([[-3.22437049],[-3.50214058],[-3.50286355],[ 5.16580317],[ 5.89898368]])import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst3,b-o)[matplotlib.lines.Line2D at 0x2562e0f81c0]1.5 利用模型进行预测
h(data_x,w3)array([[1.48445441e-11],[1.72343968e-10],[1.02798153e-10],[5.81975546e-10],[1.48434710e-11],[1.95971176e-11],[2.18959639e-10],[5.01346874e-11],[1.40930075e-09],[1.12830635e-10],[4.31888744e-12],[1.69308343e-10],[1.35613372e-10],[1.65858883e-10],[7.89880725e-14],[4.23224675e-13],[2.48199140e-12],[2.67766642e-11],[5.39314286e-12],[1.56935848e-11],[3.47096426e-11],[4.01827075e-11],[7.63005509e-12],[8.26864773e-10],[7.97484594e-10],[3.41189783e-10],[2.73442178e-10],[1.75314894e-11],[1.48456174e-11],[4.84204982e-10],[4.84239990e-10],[4.01914238e-11],[1.18813180e-12],[3.14985611e-13],[2.03524473e-10],[2.14461446e-11],[2.18189955e-12],[1.16799745e-11],[5.92281641e-10],[3.53217554e-11],[2.26727669e-11],[8.74004884e-09],[2.93949962e-10],[6.26783110e-10],[2.23513465e-10],[4.41246960e-10],[1.45841303e-11],[2.44584721e-10],[6.13010507e-12],[4.24539165e-11],[1.64123143e-03],[8.55503211e-03],[1.65105645e-02],[9.87814122e-02],[3.97290777e-02],[1.11076040e-01],[4.19003715e-02],[2.88426221e-03],[6.27161978e-03],[7.67020481e-02],[2.27204861e-02],[2.08212169e-02],[4.58067633e-03],[9.90450665e-02],[1.19419048e-03],[1.41462060e-03],[2.22638069e-01],[2.68940904e-03],[3.66014737e-01],[6.97791873e-03],[5.78803255e-01],[2.32071970e-03],[5.28941621e-01],[4.57649874e-02],[2.69208900e-03],[2.84603646e-03],[2.20421076e-02],[2.07507605e-01],[9.10460936e-02],[2.44824946e-04],[8.37509821e-03],[2.78543808e-03],[3.11283202e-03],[8.89831833e-01],[3.65880536e-01],[3.03993844e-02],[1.18930239e-02],[4.99150151e-02],[1.10252946e-02],[5.15923462e-02],[1.43653056e-01],[4.41610209e-02],[7.37513950e-03],[2.88447014e-03],[5.07366744e-02],[7.24617687e-03],[1.83460602e-02],[5.40874928e-03],[3.87210511e-04],[1.55791816e-02],[9.99862942e-01],[9.89637526e-01],[9.86183040e-01],[9.83705644e-01],[9.98410187e-01],[9.97834502e-01],[9.84208537e-01],[9.85434538e-01],[9.94141336e-01],[9.94561329e-01],[7.20333384e-01],[9.70431293e-01],[9.62754456e-01],[9.96609064e-01],[9.99222270e-01],[9.83684437e-01],[9.26437633e-01],[9.83486260e-01],[9.99950496e-01],[9.39002061e-01],[9.88043323e-01],[9.88637702e-01],[9.98357641e-01],[7.65848930e-01],[9.73006160e-01],[8.76969899e-01],[6.61137141e-01],[6.97324053e-01],[9.97185846e-01],[6.11033594e-01],[9.77494647e-01],[6.58573810e-01],[9.98437920e-01],[5.24529693e-01],[9.70465066e-01],[9.87624920e-01],[9.97236435e-01],[9.26432706e-01],[6.61104746e-01],[8.84442100e-01],[9.96082862e-01],[8.40940308e-01],[9.89637526e-01],[9.96974990e-01],[9.97386310e-01],[9.62040470e-01],[9.52214579e-01],[8.96902215e-01],[9.90200940e-01],[9.28785160e-01]])#将数据输入三个模型的看看结果
multi_predpd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel()))
multi_pred01200.9992970.1080371.484454e-1110.9970610.2708141.723440e-1020.9986330.1647101.027982e-1030.9957740.2319105.819755e-1040.9994150.0852591.484347e-11............1450.0000070.1275749.620405e-011460.0000060.4963899.522146e-011470.0000100.2347458.969022e-011480.0000060.0584449.902009e-011490.0000140.2842959.287852e-01
150 rows × 3 columns
multi_pred.values[:3]array([[9.99297209e-01, 1.08037473e-01, 1.48445441e-11],[9.97060801e-01, 2.70813780e-01, 1.72343968e-10],[9.98632728e-01, 1.64709623e-01, 1.02798153e-10]])#每个样本的预测值
np.argmax(multi_pred.values,axis1)array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtypeint64)#每个样本的真实值
data_yarray([[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2],[2]])1.6 评估模型
np.argmax(multi_pred.values,axis1)data_y.ravel()array([ True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, False, True,True, True, True, True, True, True, True, True, True,True, True, False, False, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True, True, True, True,True, True, True, False, True, True, True, False, True,True, True, True, True, True, True, True, True, True,True, True, True, True, True, True])np.sum(np.argmax(multi_pred.values,axis1)data_y.ravel())145np.sum(np.argmax(multi_pred.values,axis1)data_y.ravel())/len(data)0.96666666666666671.7 试试sklearn
from sklearn.linear_model import LogisticRegression
#建立第一个模型
clf1LogisticRegression()
clf1.fit(data1_x,data1_y)
#建立第二个模型
clf2LogisticRegression()
clf2.fit(data2_x,data2_y)
#建立第三个模型
clf3LogisticRegression()
clf3.fit(data3_x,data3_y)LogisticRegression()y_pred1clf1.predict(data_x)
y_pred2clf2.predict(data_x)
y_pred3clf3.predict(data_x)#可视化各模型的预测结果
multi_predpd.DataFrame(zip(y_pred1,y_pred2,y_pred3),columns[模型1,模糊2,模型3])
multi_pred模型1模糊2模型301001100210031004100............145001146011147001148001149001
150 rows × 3 columns
#判断预测结果
np.argmax(multi_pred.values,axis1)array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,0, 1, 1, 1, 2, 0, 1, 1, 0, 0, 0, 2, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1,0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2,2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2], dtypeint64)data_y.ravel()array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])#计算准确率
np.sum(np.argmax(multi_pred.values,axis1)data_y.ravel())/data.shape[0]0.7333333333333333实验4(1) 请动手完成你们第一个多分类问题祝好运完成下面代码
2.1 数据读取
data_x,data_ydatasets.make_blobs(n_samples200, n_features6, centers4,random_state0)data_x.shape,data_y.shape((200, 6), (200,))2.2 训练数据的准备
datanp.insert(data_x,data_x.shape[1],data_y,axis1)datapd.DataFrame(data,columns[F1,F2,F3,F4,F5,F6,target])
dataF1F2F3F4F5F6target02.1166327.972800-9.328969-8.224605-12.1784295.4984472.011.8864494.6210062.8415950.431245-2.4713502.5078330.022.3913296.464609-9.805900-7.289968-9.6509856.3884602.03-1.0347766.6268869.031235-0.8129085.4498550.1340621.04-0.4815938.1917537.504717-1.9756886.6490210.6368241.0........................1955.4348937.1284719.7895466.0613820.6341335.7570243.0196-0.4066257.5860019.322750-1.8373336.477815-0.9927251.01972.0314627.804427-8.539512-9.824409-10.0469356.9180852.01984.0818896.12768511.0911264.812011-0.0059155.3422113.01990.9857447.285737-8.395940-6.586471-9.6517656.6510122.0
200 rows × 7 columns
data[target]data[target].astype(int32)dataF1F2F3F4F5F6target02.1166327.972800-9.328969-8.224605-12.1784295.498447211.8864494.6210062.8415950.431245-2.4713502.507833022.3913296.464609-9.805900-7.289968-9.6509856.38846023-1.0347766.6268869.031235-0.8129085.4498550.13406214-0.4815938.1917537.504717-1.9756886.6490210.6368241........................1955.4348937.1284719.7895466.0613820.6341335.7570243196-0.4066257.5860019.322750-1.8373336.477815-0.99272511972.0314627.804427-8.539512-9.824409-10.0469356.91808521984.0818896.12768511.0911264.812011-0.0059155.34221131990.9857447.285737-8.395940-6.586471-9.6517656.6510122
200 rows × 7 columns
data.insert(0,ones,1)dataonesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984472111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460231-1.0347766.6268869.031235-0.8129085.4498550.134062141-0.4815938.1917537.504717-1.9756886.6490210.6368241...........................19515.4348937.1284719.7895466.0613820.6341335.75702431961-0.4066257.5860019.322750-1.8373336.477815-0.992725119712.0314627.804427-8.539512-9.824409-10.0469356.918085219814.0818896.12768511.0911264.812011-0.0059155.342211319910.9857447.285737-8.395940-6.586471-9.6517656.6510122
200 rows × 8 columns
#第一个类别的数据
data1data.copy()
data1.loc[data[target]0,target]1
data1.loc[data[target]!0,target]0
data1onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078331212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510120
200 rows × 8 columns
data1_xdata1.iloc[:,:data1.shape[1]-1].values
data1_ydata1.iloc[:,data1.shape[1]-1].values
data1_x.shape,data1_y.shape((200, 7), (200,))#第二个类别的数据
data2data.copy()
data2.loc[data[target]1,target]1
data2.loc[data[target]!1,target]0
data2onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062141-0.4815938.1917537.504717-1.9756886.6490210.6368241...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725119712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510120
200 rows × 8 columns
data2_xdata2.iloc[:,:data2.shape[1]-1].values
data2_ydata2.iloc[:,data2.shape[1]-1].values#第三个类别的数据
data3data.copy()
data3.loc[data[target]2,target]1
data3.loc[data[target]!2,target]0
data3onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984471111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460131-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085119814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510121
200 rows × 8 columns
data3_xdata3.iloc[:,:data3.shape[1]-1].values
data3_ydata3.iloc[:,data3.shape[1]-1].values#第四个类别的数据
data4data.copy()
data4.loc[data[target]3,target]1
data4.loc[data[target]!3,target]0
data4onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702411961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211119910.9857447.285737-8.395940-6.586471-9.6517656.6510120
200 rows × 8 columns
data4_xdata4.iloc[:,:data4.shape[1]-1].values
data4_ydata4.iloc[:,data4.shape[1]-1].values2.3 定义假设函数、代价函数和梯度下降算法
def sigmoid(z):return 1 / (1 np.exp(-z))def h(X,w):zXwhsigmoid(z)return h#代价函数构造
def cost(X,w,y):#当X(m,n1),y(m,),w(n1,1)y_hatsigmoid(Xw)rightnp.multiply(y.ravel(),np.log(y_hat).ravel())np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())cost-np.sum(right)/X.shape[0]return costdef grandient(X,y,iter_num,alpha):yy.reshape((X.shape[0],1))wnp.zeros((X.shape[1],1))cost_lst[] for i in range(iter_num):y_predh(X,w)-ytempnp.zeros((X.shape[1],1))for j in range(X.shape[1]):rightnp.multiply(y_pred.ravel(),X[:,j])gradient1/(X.shape[0])*(np.sum(right))temp[j,0]w[j,0]-alpha*gradientwtempcost_lst.append(cost(X,w,y.ravel()))return w,cost_lst2.4 学习这四个分类模型
import matplotlib.pyplot as plt#初始化超参数
iter_num,alpha600000,0.001#训练第1个模型
w1,cost_lst1grandient(data1_x,data1_y,iter_num,alpha)plt.plot(range(iter_num),cost_lst1,b-o)[matplotlib.lines.Line2D at 0x25624eb08e0]#训练第2个模型
w2,cost_lst2grandient(data2_x,data2_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst2,b-o)[matplotlib.lines.Line2D at 0x25631b87a60]#训练第3个模型
w3,cost_lst3grandient(data3_x,data3_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst3,b-o)[matplotlib.lines.Line2D at 0x2562bcdfac0]#训练第4个模型
w4,cost_lst4grandient(data4_x,data4_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst4,b-o)[matplotlib.lines.Line2D at 0x25631ff4ee0]2.5 利用模型进行预测
data_xarray([[ 2.11663151e00, 7.97280013e00, -9.32896918e00,-8.22460526e00, -1.21784287e01, 5.49844655e00],[ 1.88644899e00, 4.62100554e00, 2.84159548e00,4.31244563e-01, -2.47135027e00, 2.50783257e00],[ 2.39132949e00, 6.46460915e00, -9.80590050e00,-7.28996786e00, -9.65098460e00, 6.38845956e00],...,[ 2.03146167e00, 7.80442707e00, -8.53951210e00,-9.82440872e00, -1.00469351e01, 6.91808489e00],[ 4.08188906e00, 6.12768483e00, 1.10911262e01,4.81201082e00, -5.91530191e-03, 5.34221079e00],[ 9.85744105e-01, 7.28573657e00, -8.39593964e00,-6.58647097e00, -9.65176507e00, 6.65101187e00]])data_xnp.insert(data_x,0,1,axis1)data_x.shape(200, 7)w3.shape(7, 1)multi_predpd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel(),h(data_x,w4).ravel()))
multi_pred012300.0204364.556248e-159.999975e-012.601227e-2710.8204884.180906e-053.551499e-055.908691e-0520.1093097.316201e-149.999978e-017.091713e-2430.0366089.999562e-011.048562e-095.724854e-0340.0030759.999292e-012.516742e-096.423038e-05...............1950.0172783.221293e-063.753372e-149.999943e-011960.0033699.999966e-016.673394e-102.281428e-031970.0006061.118174e-139.999941e-011.780212e-281980.0130724.999118e-059.811154e-149.996689e-011990.1515481.329623e-139.999447e-012.571989e-24
200 rows × 4 columns
2.6 计算准确率
np.sum(np.argmax(multi_pred.values,axis1)data_y.ravel())/len(data)1.0
