1.向量机

from sklearn import svm#svm函数需要的
import numpy as np#numpy科学计算库
from sklearn import model_selection
import matplotlib.pyplot as plt#如果想要好的结果需要进行参数的调整，同时选取的用于训练的参数也可以增加到四个（本文用了前两个）#数据每一行一个鸢尾花的观察结果，前四个数据代表鸢尾花的生物属性比如大小等，
#最后一个数据是鸢尾花的类别，共三类。主要是对最后这个类别进行处理。
#–IrisSetosa
#–IrisVersicolour
#–IrisVirginica#定义一个函数,将三个类别分别与三个不同数字相对应
def iris_type(s):it={b'Iris-setosa':0,b'Iris-versicolor':1,b'Iris-virginica':2}return it[s]#导入数据
path ='C:/Users/HP/Desktop/模式识别大作业/IRIS/1.txt'  # 之前保存的文件路径
data = np.loadtxt(path,                          # 路径dtype=float,                   # 数据类型delimiter=',',                 # 数据以什么分割符号分割数据converters={4: iris_type})     # 对某一列数据（第四列）进行某种类型的转换（）#将原始数据分成训练集和测试集
X,y=np.split(data,(4,),axis=1)
x=X[:,0:2]
y=y.reshape((-1))
x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=1,test_size=0.3)
#np.split按照列（axis=1)进行分割，从第四列开始往后的作y数据，之前作为X数据。
#在X中我们取前两列作为特征（为了后面的可视化）。
#用train_test_split将数据分为训练集和测试集，测试集占总数据的30%（test_size=0.3),random_state是随机数种子
#（随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。
#比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。
#随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。）#搭建模型选择不同的核函数可能有不同的效果poly rbf sigmo idlinear
clf = svm.SVC(kernel='rbf',                      # 核函数gamma=0.1,decision_function_shape='ovo',     # one vs one 分类问题C=0.8)
clf.fit(x_train, y_train)#输出训练集的准确率
print(clf.score(x_train,y_train))#原始结果与训练集预测结果进行对比
y_train_hat=clf.predict(x_train)
y_train_1d=y_train.reshape((-1))
#zip把原始结果和预测结果放在一起
comp=zip(y_train_1d,y_train_hat)
print(list(comp))
print(clf.score(x_test,y_test))#训练好的模型对测试集的数据进行预测
y_test_hat=clf.predict(x_test)
y_test_1d=y_test.reshape((-1))
comp=zip(y_test_1d,y_test_hat)
print(list(comp))
#print(clf.score(x_test,y_test))#图像进行可视化左图为训练数据，右图为对训练数据的预测
plt.figure()
plt.subplot(121)
plt.scatter(x_train[:,0],x_train[:,1],c=y_train.reshape((-1)),edgecolors='k',s=50)plt.subplot(122)
plt.scatter(x_train[:,0],x_train[:,1],c=y_train_hat.reshape((-1)),edgecolors='k',s=50)
plt.show()

修改不同的核函数会有不同的结果

准确率 0.8380952380952381

2.贝叶斯决策

%贝叶斯
clc
clear
data=xlsread('C:/Users/HP/Desktop/模式识别大作业/IRIS/iris.csv');
Iris1=data(1:50,1:4);
Iris2=data(51:100,1:4);
Iris3=data(101:150,1:4);
%类均值向量
m1 = mean(Iris1);
m2 = mean(Iris2);
m3 = mean(Iris3);%各类内离散度矩阵
s1 = zeros(4);
s2 = zeros(4);
s3 = zeros(4);
for i=1:1:30s1 = s1 + (Iris1(i,:) - m1)'*(Iris1(i,:) - m1);
end
for i=1:1:30s2 = s2 + (Iris2(i,:) - m2)'*(Iris2(i,:) - m2);
end
for i=1:1:30s3 = s3 + (Iris3(i,:) - m3)'*(Iris3(i,:) - m3);
end%总类内离散矩阵
sw12 = s1 + s2;
sw13 = s1 + s3;
sw23 = s2 + s3;
%投影方向
w12 = ((sw12^-1)*(m1 - m2)')';
w13 = ((sw13^-1)*(m1 - m3)')';
w23 = ((sw23^-1)*(m2 - m3)')';
%判别函数以及阈值T（即w0）
T12 = -0.5 * (m1 + m2)*inv(sw12)*(m1 - m2)';
T13 = -0.5 * (m1 + m3)*inv(sw13)*(m1 - m3)';
T23 = -0.5 * (m2 + m3)*inv(sw23)*(m2 - m3)';%% 请补充输出判别函数%% 请给下面代码添加必要注释
kind1 = 0;
kind2 = 0;
kind3 = 0;
newiris1=[];
newiris2=[];
newiris3=[];
for i=31:50x = Iris1(i,:);g12 = w12 * x' + T12;g13 = w13 * x' + T13;g23 = w23 * x' + T23;if((g12 > 0)&(g13 > 0))newiris1=[newiris1;x];kind1=kind1+1;elseif((g12 < 0)&(g23 > 0))newiris2=[newiris2;x];elseif((g13 < 0)&(g23 < 0))newiris3=[newiris3;x];end
end
for i=31:50x = Iris2(i,:);g12 = w12 * x' + T12;g13 = w13 * x' + T13;g23 = w23 * x' + T23;if((g12 > 0)&(g13 > 0))newiris1=[newiris1;x];elseif((g12 < 0)&(g23 > 0))kind2=kind2+1;newiris2=[newiris2;x];elseif((g13 < 0)&(g23 < 0))newiris3=[newiris3;x];end
end
for i=31:50x = Iris3(i,:);g12 = w12 * x' + T12;g13 = w13 * x' + T13;g23 = w23 * x' + T23;if((g12 > 0)&(g13 > 0))newiris1=[newiris1;x];elseif((g12 < 0)&(g23 > 0))newiris2=[newiris2;x];elseif((g13 < 0)&(g23 < 0))kind3=kind3+1;newiris3=[newiris3;x];end
endcorrect=(kind1+kind2+kind3)/60;
fprintf('\n综合正确率：%.2f%%\n\n',correct* 100);

综合正确率：96.67%

3.决策树

#决策树分析   iris_data分为70%的训练，30%的进行预测 然后进行优化 输出准确率、召回率等
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris #导入数据集irisfrom sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metricsiris = load_iris() #载入数据集
x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target, test_size=0.3)
clf = DecisionTreeClassifier()
clf.fit(x_train,y_train)
predict_target = clf.predict(x_test)print(sum(predict_target == y_test)) #预测结果与真实结果比对
print(metrics.classification_report(y_test,predict_target))
print(metrics.confusion_matrix(y_test,predict_target))L1 = [n[0] for n in x_test]
L2 = [n[1] for n in x_test]
plt.scatter(L1,L2, c=predict_target,marker='x')
plt.title('DecisionTreeClassifier')
plt.show()

4.fisher准则

#fisher准则
#导入包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns# 导入数据集
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=0)Iris1 = df.values[0:50, 0:4]
Iris2 = df.values[50:100, 0:4]
Iris3 = df.values[100:150, 0:4]# 定义类均值向量
m1 = np.mean(Iris1, axis=0)
m2 = np.mean(Iris2, axis=0)
m3 = np.mean(Iris3, axis=0)# 定义类内离散度矩阵
s1 = np.zeros((4, 4))
s2 = np.zeros((4, 4))
s3 = np.zeros((4, 4))
for i in range(0, 30, 1):a = Iris1[i, :] - m1a = np.array([a])b = a.Ts1 = s1 + np.dot(b, a)
for i in range(0, 30, 1):c = Iris2[i, :] - m2c = np.array([c])d = c.Ts2 = s2 + np.dot(d, c)# s2=s2+np.dot((Iris2[i,:]-m2).T,(Iris2[i,:]-m2))
for i in range(0, 30, 1):a = Iris3[i, :] - m3a = np.array([a])b = a.Ts3 = s3 + np.dot(b, a)# 定义总类内离散矩阵
sw12 = s1 + s2;
sw13 = s1 + s3;
sw23 = s2 + s3;# 定义投影方向
a = np.array([m1 - m2])
sw12 = np.array(sw12, dtype='float')
sw13 = np.array(sw13, dtype='float')
sw23 = np.array(sw23, dtype='float')
# 判别函数以及阈值T（即w0）
a = m1 - m2
a = np.array([a])
a = a.T
b = m1 - m3
b = np.array([b])
b = b.T
c = m2 - m3
c = np.array([c])
c = c.T
w12 = (np.dot(np.linalg.inv(sw12), a)).T
w13 = (np.dot(np.linalg.inv(sw13), b)).T
w23 = (np.dot(np.linalg.inv(sw23), c)).T
T12 = -0.5 * (np.dot(np.dot((m1 + m2), np.linalg.inv(sw12)), a))
T13 = -0.5 * (np.dot(np.dot((m1 + m3), np.linalg.inv(sw13)), b))
T23 = -0.5 * (np.dot(np.dot((m2 + m3), np.linalg.inv(sw23)), c))
# 计算正确率
kind1 = 0
kind2 = 0
kind3 = 0
newiris1 = []
newiris2 = []
newiris3 = []
for i in range(30, 49):x = Iris1[i, :]x = np.array([x])g12 = np.dot(w12, x.T) + T12g13 = np.dot(w13, x.T) + T13g23 = np.dot(w23, x.T) + T23if g12 > 0 and g13 > 0:newiris1.extend(x)kind1 = kind1 + 1elif g12 < 0 and g23 > 0:newiris2.extend(x)elif g13 < 0 and g23 < 0:newiris3.extend(x)for i in range(30, 49):x = Iris2[i, :]x = np.array([x])g12 = np.dot(w12, x.T) + T12g13 = np.dot(w13, x.T) + T13g23 = np.dot(w23, x.T) + T23if g12 > 0 and g13 > 0:newiris1.extend(x)elif g12 < 0 and g23 > 0:newiris2.extend(x)kind2 = kind2 + 1elif g13 < 0 and g23 < 0:newiris3.extend(x)
for i in range(30, 49):x = Iris3[i, :]x = np.array([x])g12 = np.dot(w12, x.T) + T12g13 = np.dot(w13, x.T) + T13g23 = np.dot(w23, x.T) + T23if g12 > 0 and g13 > 0:newiris1.extend(x)elif g12 < 0 and g23 > 0:newiris2.extend(x)elif g13 < 0 and g23 < 0:newiris3.extend(x)kind3 = kind3 + 1
correct = (kind1 + kind2 + kind3) / 60print('判断出来的综合正确率：', correct * 100, '%')

判断出来的综合正确率： 91.66666666666666 %