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（Setosa，Versicolour，Virginica）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 datasetsimport pandas as pdimport numpy as np data=datasets.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'], dtype='<U10'),'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%PLU@io.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 al"s 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_x=data["data"]data_y=data["target"] data_x.shape,data_y.shape ((150, 4), (150,)) data_y=data_y.reshape([len(data_y),1])data_y 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]]) #法1 ，用拼接的方法data=np.hstack([data_x,data_y]) #法二： 用插入的方法np.insert(data_x,data_x.shape[1],data_y,axis=1) 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. ]]) data=pd.DataFrame(data,columns=["F1","F2","F3","F4","target"])data F1F2F3F4target05.13.51.40.20.014.93.01.40.20.024.73.21.30.20.034.63.11.50.20.045.03.61.40.20.0..................1456.73.05.22.32.01466.32.55.01.92.01476.53.05.22.02.01486.23.45.42.32.01495.93.05.11.82.0150 rows × 5 columns
data.insert(0,"ones",1) data onesF1F2F3F4target015.13.51.40.20.0114.93.01.40.20.0214.73.21.30.20.0314.63.11.50.20.0415.03.61.40.20.0.....................14516.73.05.22.32.014616.32.55.01.92.014716.53.05.22.02.014816.23.45.42.32.014915.93.05.11.82.0150 rows × 6 columns
data["target"]=data["target"].astype("int32") data onesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3214616.32.55.01.9214716.53.05.22.0214816.23.45.42.3214915.93.05.11.82150 rows × 6 columns
1.2 训练数据的准备 data_x 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]]) data_x=np.insert(data_x,0,1,axis=1) 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
data1=data.copy() data1 onesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3214616.32.55.01.9214716.53.05.22.0214816.23.45.42.3214915.93.05.11.82150 rows × 6 columns
data
data1.loc[data["target"]!=0,"target"]=0data1.loc[data["target"]==0,"target"]=1 data1 onesF1F2F3F4target015.13.51.40.21114.93.01.40.21214.73.21.30.21314.63.11.50.21415.03.61.40.21.....................14516.73.05.22.3014616.32.55.01.9014716.53.05.22.0014816.23.45.42.3014915.93.05.11.80150 rows × 6 columns
data1_x=data1.iloc[:,:data1.shape[1]-1].valuesdata1_y=data1.iloc[:,data1.shape[1]-1].valuesdata1_x.shape,data1_y.shape ((150, 5), (150,)) #针对第二类，即第二个分类器的数据data2=data.copy()data2.loc[data["target"]==1,"target"]=1data2.loc[data["target"]!=1,"target"]=0data2["target"]==0 0 True1 True2 True3 True4 True... 145 True146 True147 True148 True149 TrueName: target, Length: 150, dtype: bool data2.shape[1] 6 data2.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_x=data2.iloc[:,:data2.shape[1]-1].valuesdata2_y=data2.iloc[:,data2.shape[1]-1].values #针对第三类，即第三个分类器的数据data3=data.copy()data3.loc[data["target"]==2,"target"]=1data3.loc[data["target"]!=2,"target"]=0data3 onesF1F2F3F4target015.13.51.40.20114.93.01.40.20214.73.21.30.20314.63.11.50.20415.03.61.40.20.....................14516.73.05.22.3114616.32.55.01.9114716.53.05.22.0114816.23.45.42.3114915.93.05.11.81150 rows × 6 columns
data3_x=data3.iloc[:,:data3.shape[1]-1].valuesdata3_y=data3.iloc[:,data3.shape[1]-1].values 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） def sigmoid(z):return 1 / (1 + np.exp(-z)) def h(X,w):z=X@wh=sigmoid(z)return h #代价函数构造def cost(X,w,y):#当X(m,n+1),y(m,),w(n+1,1)y_hat=sigmoid(X@w)right=np.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 cost def sigmoid(z):return 1 / (1 + np.exp(-z))def h(X,w):z=X@wh=sigmoid(z)return h#代价函数构造def cost(X,w,y):#当X(m,n+1),y(m,),w(n+1,1)y_hat=sigmoid(X@w)right=np.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):y=y.reshape((X.shape[0],1))w=np.zeros((X.shape[1],1))cost_lst=[] for i in range(iter_num):y_pred=h(X,w)-ytemp=np.zeros((X.shape[1],1))for j in range(X.shape[1]):right=np.multiply(y_pred.ravel(),X[:,j])gradient=1/(X.shape[0])*(np.sum(right))temp[j,0]=w[j,0]-alpha*gradientw=tempcost_lst.append(cost(X,w,y.ravel()))return w,cost_lst 1.4 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,alpha=600000,0.001 #训练第一个模型w1,cost_lst1=grandient(data1_x,data1_y,iter_num,alpha) import matplotlib.pyplot as pltplt.plot(range(iter_num),cost_lst1,"b-o") [<matplotlib.lines.Line2D at 0x2562630b100>] #训练第二个模型w2,cost_lst2=grandient(data2_x,data2_y,iter_num,alpha) import matplotlib.pyplot as pltplt.plot(range(iter_num),cost_lst2,"b-o") [<matplotlib.lines.Line2D at 0x25628114280>] #训练第三个模型w3,cost_lst3=grandient(data3_x,data3_y,iter_num,alpha) w3 array([[-3.22437049],[-3.50214058],[-3.50286355],[ 5.16580317],[ 5.89898368]]) import matplotlib.pyplot as pltplt.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_pred=pd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel()))multi_pred 01200.9992970.1080371.484454e-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-01150 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,axis=1) 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], dtype=int64) #每个样本的真实值data_y 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]]) 1.6 评估模型 np.argmax(multi_pred.values,axis=1)==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,axis=1)==data_y.ravel()) 145 np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data) 0.9666666666666667 1.7 试试sklearn from sklearn.linear_model import LogisticRegression#建立第一个模型clf1=LogisticRegression()clf1.fit(data1_x,data1_y)#建立第二个模型clf2=LogisticRegression()clf2.fit(data2_x,data2_y)#建立第三个模型clf3=LogisticRegression()clf3.fit(data3_x,data3_y) LogisticRegression() y_pred1=clf1.predict(data_x)y_pred2=clf2.predict(data_x)y_pred3=clf3.predict(data_x) #可视化各模型的预测结果multi_pred=pd.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............145001146011147001148001149001150 rows × 3 columns
#判断预测结果np.argmax(multi_pred.values,axis=1) 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], dtype=int64) 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,axis=1)==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_y=datasets.make_blobs(n_samples=200, n_features=6, centers=4,random_state=0) data_x.shape,data_y.shape ((200, 6), (200,)) 2.2 训练数据的准备 data=np.insert(data_x,data_x.shape[1],data_y,axis=1) data=pd.DataFrame(data,columns=["F1","F2","F3","F4","F5","F6","target"])data F1F2F3F4F5F6target02.1166327.972800-9.328969-8.224605-12.1784295.4984472.011.8864494.6210062.8415950.431245-2.4713502.5078330.022.3913296.464609-9.805900-7.289968-9.6509856.3884602.03-1.0347766.6268869.031235-0.8129085.4498550.1340621.04-0.4815938.1917537.504717-1.9756886.6490210.6368241.0........................1955.4348937.1284719.7895466.0613820.6341335.7570243.0196-0.4066257.5860019.322750-1.8373336.477815-0.9927251.01972.0314627.804427-8.539512-9.824409-10.0469356.9180852.01984.0818896.12768511.0911264.812011-0.0059155.3422113.01990.9857447.285737-8.395940-6.586471-9.6517656.6510122.0200 rows × 7 columns
data["target"]=data["target"].astype("int32") data F1F2F3F4F5F6target02.1166327.972800-9.328969-8.224605-12.1784295.498447211.8864494.6210062.8415950.431245-2.4713502.507833022.3913296.464609-9.805900-7.289968-9.6509856.38846023-1.0347766.6268869.031235-0.8129085.4498550.13406214-0.4815938.1917537.504717-1.9756886.6490210.6368241........................1955.4348937.1284719.7895466.0613820.6341335.7570243196-0.4066257.5860019.322750-1.8373336.477815-0.99272511972.0314627.804427-8.539512-9.824409-10.0469356.91808521984.0818896.12768511.0911264.812011-0.0059155.34221131990.9857447.285737-8.395940-6.586471-9.6517656.6510122200 rows × 7 columns
data.insert(0,"ones",1) data onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984472111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460231-1.0347766.6268869.031235-0.8129085.4498550.134062141-0.4815938.1917537.504717-1.9756886.6490210.6368241...........................19515.4348937.1284719.7895466.0613820.6341335.75702431961-0.4066257.5860019.322750-1.8373336.477815-0.992725119712.0314627.804427-8.539512-9.824409-10.0469356.918085219814.0818896.12768511.0911264.812011-0.0059155.342211319910.9857447.285737-8.395940-6.586471-9.6517656.6510122200 rows × 8 columns
#第一个类别的数据data1=data.copy()data1.loc[data["target"]==0,"target"]=1data1.loc[data["target"]!=0,"target"]=0data1 onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078331212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510120200 rows × 8 columns
data1_x=data1.iloc[:,:data1.shape[1]-1].valuesdata1_y=data1.iloc[:,data1.shape[1]-1].valuesdata1_x.shape,data1_y.shape ((200, 7), (200,)) #第二个类别的数据data2=data.copy()data2.loc[data["target"]==1,"target"]=1data2.loc[data["target"]!=1,"target"]=0data2 onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062141-0.4815938.1917537.504717-1.9756886.6490210.6368241...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725119712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510120200 rows × 8 columns
data2_x=data2.iloc[:,:data2.shape[1]-1].valuesdata2_y=data2.iloc[:,data2.shape[1]-1].values #第三个类别的数据data3=data.copy()data3.loc[data["target"]==2,"target"]=1data3.loc[data["target"]!=2,"target"]=0data3 onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984471111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460131-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702401961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085119814.0818896.12768511.0911264.812011-0.0059155.342211019910.9857447.285737-8.395940-6.586471-9.6517656.6510121200 rows × 8 columns
data3_x=data3.iloc[:,:data3.shape[1]-1].valuesdata3_y=data3.iloc[:,data3.shape[1]-1].values #第四个类别的数据data4=data.copy()data4.loc[data["target"]==3,"target"]=1data4.loc[data["target"]!=3,"target"]=0data4 onesF1F2F3F4F5F6target012.1166327.972800-9.328969-8.224605-12.1784295.4984470111.8864494.6210062.8415950.431245-2.4713502.5078330212.3913296.464609-9.805900-7.289968-9.6509856.388460031-1.0347766.6268869.031235-0.8129085.4498550.134062041-0.4815938.1917537.504717-1.9756886.6490210.6368240...........................19515.4348937.1284719.7895466.0613820.6341335.75702411961-0.4066257.5860019.322750-1.8373336.477815-0.992725019712.0314627.804427-8.539512-9.824409-10.0469356.918085019814.0818896.12768511.0911264.812011-0.0059155.342211119910.9857447.285737-8.395940-6.586471-9.6517656.6510120200 rows × 8 columns
data4_x=data4.iloc[:,:data4.shape[1]-1].valuesdata4_y=data4.iloc[:,data4.shape[1]-1].values 2.3 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):z=X@wh=sigmoid(z)return h #代价函数构造def cost(X,w,y):#当X(m,n+1),y(m,),w(n+1,1)y_hat=sigmoid(X@w)right=np.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 cost def grandient(X,y,iter_num,alpha):y=y.reshape((X.shape[0],1))w=np.zeros((X.shape[1],1))cost_lst=[] for i in range(iter_num):y_pred=h(X,w)-ytemp=np.zeros((X.shape[1],1))for j in range(X.shape[1]):right=np.multiply(y_pred.ravel(),X[:,j])gradient=1/(X.shape[0])*(np.sum(right))temp[j,0]=w[j,0]-alpha*gradientw=tempcost_lst.append(cost(X,w,y.ravel()))return w,cost_lst 2.4 学习这四个分类模型 import matplotlib.pyplot as plt #初始化超参数iter_num,alpha=600000,0.001 #训练第1个模型w1,cost_lst1=grandient(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_lst2=grandient(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_lst3=grandient(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_lst4=grandient(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_x array([[ 2.11663151e+00, 7.97280013e+00, -9.32896918e+00,-8.22460526e+00, -1.21784287e+01, 5.49844655e+00],[ 1.88644899e+00, 4.62100554e+00, 2.84159548e+00,4.31244563e-01, -2.47135027e+00, 2.50783257e+00],[ 2.39132949e+00, 6.46460915e+00, -9.80590050e+00,-7.28996786e+00, -9.65098460e+00, 6.38845956e+00],...,[ 2.03146167e+00, 7.80442707e+00, -8.53951210e+00,-9.82440872e+00, -1.00469351e+01, 6.91808489e+00],[ 4.08188906e+00, 6.12768483e+00, 1.10911262e+01,4.81201082e+00, -5.91530191e-03, 5.34221079e+00],[ 9.85744105e-01, 7.28573657e+00, -8.39593964e+00,-6.58647097e+00, -9.65176507e+00, 6.65101187e+00]]) data_x=np.insert(data_x,0,1,axis=1) data_x.shape (200, 7) w3.shape (7, 1) multi_pred=pd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel(),h(data_x,w4).ravel()))multi_pred 012300.0204364.556248e-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-24200 rows × 4 columns
2.6 计算准确率 np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data) 1.0
