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《机器学习》第一次作业——第一至三章学习记录和心得

《机器学习》第一次作业——第一至三章学习记录和心得

orz懒人直接上图了,真的好多作业好多考试啊啊啊啊啊啊啊啊啊啊

关于讨论区作业

机器学习的作业先做完了,所以可以参考这篇博客,看得出来实验班的大哥哥大姐姐们还是勤奋的,居然有40+访问量了……

1.复现MICD分类器的源码

请根据第二章的理论知识,尝试用Python、MATLAB等常见语言复现MICD分类器。

可以在讨论区内跟大家分享一下自己的代码

from sklearn import datasets
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import multivariate_normal as gaussian_cal

#使用np的数组方便向量运算
def getIrisLinear(data,iris_type,flag):
    data_linear = [data[i] for i in range(len(data)) if iris_type[i]!=flag]
    iris_type_linear = [iris_type[i] for i in range(len(iris_type)) if iris_type[i]!=flag]
    return np.asarray(data_linear,dtype="float64"),np.asarray(iris_type_linear,dtype="float64")


def hold_out_partition(data_linear,iris_type_linear):
    import random

    train_data = []
    train_type = []
    test_data = []
    test_type = []
    first_cur = []
    second_cur = []
    for i in range(len(data_linear)):
        if iris_type_linear[i] == 0:
            first_cur.append(i)
        else:
            second_cur.append(i)
    k = len(first_cur)-1
    #七三开训练集和测试集
    train_size = int(len(first_cur) * 7 / 10)
    test_size = int(len(first_cur) * 3 / 10)
    for i in range(0,train_size):
        cur = random.randint(0,k)
        train_data.append(data_linear[first_cur[cur]])
        train_type.append(iris_type_linear[first_cur[cur]])
        k = k - 1
        first_cur.remove(first_cur[cur])
    for i in range(len(first_cur)):
        test_data.append(data_linear[first_cur[i]])
        test_type.append(iris_type_linear[first_cur[i]])

    k = len(second_cur)-1
    train_size = int(len(second_cur) * 7 / 10)
    test_size = int(len(second_cur) * 3 / 10)
    for i in range(0, train_size):
        cur = random.randint(0, k)
        train_data.append(data_linear[second_cur[cur]])
        train_type.append(iris_type_linear[second_cur[cur]])
        k = k - 1
        second_cur.remove(second_cur[cur])
    for i in range(len(second_cur)):
        test_data.append(data_linear[second_cur[i]])
        test_type.append(iris_type_linear[second_cur[i]])

    return np.asarray(train_data,dtype="float64"),np.asarray(train_type,dtype="int16"),np.asarray(test_data,dtype="float64"),np.asarray(test_type,dtype="int16")


def MED_linear_classification(data,iris_type,t,f,flag):
    data_linear,iris_type_linear=getIrisLinear(data,iris_type,flag)
    train_data,train_type,test_data,test_type = hold_out_partition(data_linear,iris_type_linear)
    c1 = []
    c2 = []
    n1=0
    n2=0
    #计算均值
    for i in range(len(train_data)):
        if train_type[i] == 1:
            n1+=1
            c1.append(train_data[i])
        else:
            n2+=1
            c2.append(train_data[i])
    c1 = np.asarray(c1)
    c2 = np.asarray(c2)
    z1 = c1.sum(axis=0)/n1
    z2 = c2.sum(axis=0)/n2
    test_result = []
    for i in range(len(test_data)):
        result = np.dot(z2-z1,test_data[i]-(z1+z2)/2)
        test_result.append(np.sign(result))
    test_result = np.array(test_result)
    TP = 0
    FN = 0
    TN = 0
    FP = 0
    for i in range(len(test_result)):
        if(test_result[i]>=0 and test_type[i]==t):
            TP+=1
        elif(test_result[i]>=0 and test_type[i]==f):
            FN+=1
        elif(test_result[i]<0 and test_type[i]==t):
            FP+=1
        elif(test_result[i]<0 and test_type[i]==f):
            TN+=1
    Recall = TP/(TP+FN)
    Precision = TP/(TP+FP)
    print("Recall= %f"% Recall)
    print("Specify= %f"% (TN/(TN+FP)))
    print("Precision= %f"% Precision)
    print("F1 Score= %f"% (2*Recall*Precision/(Recall+Precision)))
    #开始画图
    xx = [[0, 1, 2], [1, 2, 3], [0, 2, 3], [0, 1, 3]]
    iris_name =['setosa','vesicolor','virginica']
    iris_color = ['r','g','b']
    iris_icon = ['o','x','^']
    fig = plt.figure(figsize=(20, 20))
    feature = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
    for i in range(4):
        ax = fig.add_subplot(221 + i, projection="3d")
        X = np.arange(test_data.min(axis=0)[xx[i][0]],test_data.max(axis=0)[xx[i][0]],1)
        Y = np.arange(test_data.min(axis=0)[xx[i][1]],test_data.max(axis=0)[xx[i][1]],1)
        X,Y = np.meshgrid(X,Y)
        m1 = [z1[xx[i][0]],z1[xx[i][1]],z1[xx[i][2]]]
        m2 = [z2[xx[i][0]], z2[xx[i][1]], z2[xx[i][2]]]
        m1 = np.array(m1)
        m2 = np.array(m2)
        m = m2-m1
        #公式化简可得
        Z = (np.dot(m,(m1+m2)/2)-m[0]*X-m[1]*Y)/m[2]
        ax.scatter(test_data[test_result >= 0, xx[i][0]], test_data[test_result>=0, xx[i][1]], test_data[test_result >= 0, xx[i][2]],
                   c=iris_color[t], marker=iris_icon[t], label=iris_name[t])
        ax.scatter(test_data[test_result < 0, xx[i][0]], test_data[test_result < 0, xx[i][1]],
                   test_data[test_result < 0, xx[i][2]],
                   c=iris_color[f], marker=iris_icon[f], label=iris_name[f])
        ax.set_zlabel(feature[xx[i][2]])
        ax.set_xlabel(feature[xx[i][0]])
        ax.set_ylabel(feature[xx[i][1]])
        ax.plot_surface(X,Y,Z,alpha=0.4)
        plt.legend(loc=0)
    plt.show()

def whiten_feature(data):
    Ex = np.cov(data,rowvar=False)#这个一定要加……因为我们计算的是特征的协方差
    a,w1 = np.linalg.eig(Ex)
    w1 = np.real(w1)
    module = []
    for i in range(w1.shape[1]):
        sum = 0
        for j in range(w1.shape[0]):
            sum += w1[i][j]**2
        module.append(sum**0.5)
    module = np.asarray(module,dtype="float64")
    w1 = w1/module
    a = np.real(a)
    a=a**(-0.5)
    w2 = np.diag(a)
    w = np.dot(w2,w1.transpose())
    for i in range(w.shape[0]):
        for j in range(w.shape[1]):
            if np.isnan(w[i][j]):
                w[i][j]=0
    #print(w)
    return np.dot(data,w)

def show_whiten_3D(data,iris_type):
    whiten_array = whiten_feature(data)
    show_3D(whiten_array,iris_type)

2.复现MICD分类器的源码

请根据第二章的理论知识,尝试用Python、MATLAB等常见语言复现MICD分类器。

可以在讨论区内跟大家分享一下自己的代码

from sklearn import datasets
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import multivariate_normal as gaussian_cal

#使用np的数组方便向量运算
def getIrisLinear(data,iris_type,flag):
    data_linear = [data[i] for i in range(len(data)) if iris_type[i]!=flag]
    iris_type_linear = [iris_type[i] for i in range(len(iris_type)) if iris_type[i]!=flag]
    return np.asarray(data_linear,dtype="float64"),np.asarray(iris_type_linear,dtype="float64")


def hold_out_partition(data_linear,iris_type_linear):
    import random

    train_data = []
    train_type = []
    test_data = []
    test_type = []
    first_cur = []
    second_cur = []
    for i in range(len(data_linear)):
        if iris_type_linear[i] == 0:
            first_cur.append(i)
        else:
            second_cur.append(i)
    k = len(first_cur)-1
    #七三开训练集和测试集
    train_size = int(len(first_cur) * 7 / 10)
    test_size = int(len(first_cur) * 3 / 10)
    for i in range(0,train_size):
        cur = random.randint(0,k)
        train_data.append(data_linear[first_cur[cur]])
        train_type.append(iris_type_linear[first_cur[cur]])
        k = k - 1
        first_cur.remove(first_cur[cur])
    for i in range(len(first_cur)):
        test_data.append(data_linear[first_cur[i]])
        test_type.append(iris_type_linear[first_cur[i]])

    k = len(second_cur)-1
    train_size = int(len(second_cur) * 7 / 10)
    test_size = int(len(second_cur) * 3 / 10)
    for i in range(0, train_size):
        cur = random.randint(0, k)
        train_data.append(data_linear[second_cur[cur]])
        train_type.append(iris_type_linear[second_cur[cur]])
        k = k - 1
        second_cur.remove(second_cur[cur])
    for i in range(len(second_cur)):
        test_data.append(data_linear[second_cur[i]])
        test_type.append(iris_type_linear[second_cur[i]])

    return np.asarray(train_data,dtype="float64"),np.asarray(train_type,dtype="int16"),np.asarray(test_data,dtype="float64"),np.asarray(test_type,dtype="int16")


def MED_linear_classification(data,iris_type,t,f,flag):
    data_linear,iris_type_linear=getIrisLinear(data,iris_type,flag)
    train_data,train_type,test_data,test_type = hold_out_partition(data_linear,iris_type_linear)
    c1 = []
    c2 = []
    n1=0
    n2=0
    #计算均值
    for i in range(len(train_data)):
        if train_type[i] == 1:
            n1+=1
            c1.append(train_data[i])
        else:
            n2+=1
            c2.append(train_data[i])
    c1 = np.asarray(c1)
    c2 = np.asarray(c2)
    z1 = c1.sum(axis=0)/n1
    z2 = c2.sum(axis=0)/n2
    test_result = []
    for i in range(len(test_data)):
        result = np.dot(z2-z1,test_data[i]-(z1+z2)/2)
        test_result.append(np.sign(result))
    test_result = np.array(test_result)
    TP = 0
    FN = 0
    TN = 0
    FP = 0
    for i in range(len(test_result)):
        if(test_result[i]>=0 and test_type[i]==t):
            TP+=1
        elif(test_result[i]>=0 and test_type[i]==f):
            FN+=1
        elif(test_result[i]<0 and test_type[i]==t):
            FP+=1
        elif(test_result[i]<0 and test_type[i]==f):
            TN+=1
    Recall = TP/(TP+FN)
    Precision = TP/(TP+FP)
    print("Recall= %f"% Recall)
    print("Specify= %f"% (TN/(TN+FP)))
    print("Precision= %f"% Precision)
    print("F1 Score= %f"% (2*Recall*Precision/(Recall+Precision)))
    #开始画图
    xx = [[0, 1, 2], [1, 2, 3], [0, 2, 3], [0, 1, 3]]
    iris_name =['setosa','vesicolor','virginica']
    iris_color = ['r','g','b']
    iris_icon = ['o','x','^']
    fig = plt.figure(figsize=(20, 20))
    feature = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
    for i in range(4):
        ax = fig.add_subplot(221 + i, projection="3d")
        X = np.arange(test_data.min(axis=0)[xx[i][0]],test_data.max(axis=0)[xx[i][0]],1)
        Y = np.arange(test_data.min(axis=0)[xx[i][1]],test_data.max(axis=0)[xx[i][1]],1)
        X,Y = np.meshgrid(X,Y)
        m1 = [z1[xx[i][0]],z1[xx[i][1]],z1[xx[i][2]]]
        m2 = [z2[xx[i][0]], z2[xx[i][1]], z2[xx[i][2]]]
        m1 = np.array(m1)
        m2 = np.array(m2)
        m = m2-m1
        #公式化简可得
        Z = (np.dot(m,(m1+m2)/2)-m[0]*X-m[1]*Y)/m[2]
        ax.scatter(test_data[test_result >= 0, xx[i][0]], test_data[test_result>=0, xx[i][1]], test_data[test_result >= 0, xx[i][2]],
                   c=iris_color[t], marker=iris_icon[t], label=iris_name[t])
        ax.scatter(test_data[test_result < 0, xx[i][0]], test_data[test_result < 0, xx[i][1]],
                   test_data[test_result < 0, xx[i][2]],
                   c=iris_color[f], marker=iris_icon[f], label=iris_name[f])
        ax.set_zlabel(feature[xx[i][2]])
        ax.set_xlabel(feature[xx[i][0]])
        ax.set_ylabel(feature[xx[i][1]])
        ax.plot_surface(X,Y,Z,alpha=0.4)
        plt.legend(loc=0)
    plt.show()

def whiten_feature(data):
    Ex = np.cov(data,rowvar=False)#这个一定要加……因为我们计算的是特征的协方差
    a,w1 = np.linalg.eig(Ex)
    w1 = np.real(w1)
    module = []
    for i in range(w1.shape[1]):
        sum = 0
        for j in range(w1.shape[0]):
            sum += w1[i][j]**2
        module.append(sum**0.5)
    module = np.asarray(module,dtype="float64")
    w1 = w1/module
    a = np.real(a)
    a=a**(-0.5)
    w2 = np.diag(a)
    w = np.dot(w2,w1.transpose())
    for i in range(w.shape[0]):
        for j in range(w.shape[1]):
            if np.isnan(w[i][j]):
                w[i][j]=0
    #print(w)
    return np.dot(data,w)

def show_whiten_3D(data,iris_type):
    whiten_array = whiten_feature(data)
    show_3D(whiten_array,iris_type)
posted @ 2021-04-30 00:45  Thewillman  阅读(171)  评论(0编辑  收藏  举报