☀️机器学习入门☀️(四) PCA 和 LDA 降维算法 | 附加小练习（文末送书）

from PIL import Image
import numpy as np
import os
from ex1.clustering_performance import clusteringMetrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
def getImage(path):
    images = []
    for root, dirs, files in os.walk(path):
        if len(dirs) == 0:
            images.append([root + "\\" + x for x in files])
    return images
# 加载图片
images_files = getImage('face_images')
y = []
all_imgs = []
for i in range(len(images_files)):
    y.append(i)
    imgs = []
    for j in range(len(images_files[i])):
        img = np.array(Image.open(images_files[i][j]).convert("L"))  # 灰度
        # img = np.array(Image.open(images_files[i][j])) #RGB
        imgs.append(img)
    all_imgs.append(imgs)
# 可视化图片
w, h = 180, 200
pic_all = np.zeros((h * 10, w * 10))  # gray
for i in range(10):
    for j in range(10):
        pic_all[i * h:(i + 1) * h, j * w:(j + 1) * w] = all_imgs[i][j]
pic_all = np.uint8(pic_all)
pic_all = Image.fromarray(pic_all)
pic_all.show()
# 构造输入X
label = []
X = []
for i in range(len(all_imgs)):
    for j in all_imgs[i]:
        label.append(i)
        # temp = j.reshape(h * w, 3) #RGB
        temp = j.reshape(h * w)  # GRAY
        X.append(temp)
def keams_in(X_Data, k):
    kMeans1 = KMeans(k)
    y_p = kMeans1.fit_predict(X_Data)
    ACC, NMI, ARI = clusteringMetrics(label, y_p)
    t = "ACC:{},NMI:{:.4f},ARI:{:.4f}".format(ACC, NMI, ARI)
    print(t)
    return ACC, NMI, ARI
# PCA
def pca(X_Data, n_component, height, weight):
    X_Data = np.array(X_Data)
    pca1 = PCA(n_component)
    pca1.fit(X_Data)
    faces = pca1.components_
    faces = faces.reshape(n_component, height, weight)
    X_t = pca1.transform(X_Data)
    return faces, X_t
def draw(n_component, faces):
    plt.figure(figsize=(10, 4))
    plt.subplots_adjust(hspace=0, wspace=0)
    for i in range(n_component):
        plt.subplot(2, 5, i + 1)
        plt.imshow(faces[i], cmap='gray')
        plt.title(i + 1)
        plt.xticks(())
        plt.yticks(())
    plt.show()
score = []
for i in range(10):
    _, X_trans = pca(X, i + 1, h, w)
    acc, nmi, ari = keams_in(X_trans, 10)
    score.append([acc, nmi, ari])
score = np.array(score)
bar_width = 0.25
x = np.arange(1, 11)
plt.bar(x, score[:, 0], bar_width, align="center", color="orange", label="ACC", alpha=0.5)
plt.bar(x + bar_width, score[:, 1], bar_width, color="blue", align="center", label="NMI", alpha=0.5)
plt.bar(x + bar_width*2, score[:, 2], bar_width, color="red", align="center", label="ARI", alpha=0.5)
plt.xlabel("n_component")
plt.ylabel("精度")
plt.legend()
plt.show()

from sklearn import datasets#引入数据集
from sklearn.neighbors import KNeighborsClassifier #KNN
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt #plt用于显示图片
from matplotlib import offsetbox
def calLDA(k):
    # LDA
    lda = LinearDiscriminantAnalysis(n_components=k).fit(data,label) # n_components设置降维到n维度
    dataLDA = lda.transform(data)  # 将规则应用于训练集
    return dataLDA
def calPCA(k):
    # PCA
    pca = PCA(n_components=k).fit(data)
    # 返回测试集和训练集降维后的数据集
    dataPCA = pca.transform(data)
    return dataPCA
def draw():
    # matplotlib画图中中文显示会有问题，需要这两行设置默认字体
    fig = plt.figure('example', figsize=(11, 6))
    # plt.xlabel('X')
    # plt.ylabel('Y')
    # plt.xlim(xmax=9, xmin=-9)
    # plt.ylim(ymax=9, ymin=-9)
    color = ["red","yellow","blue","green","black","purple","pink","brown","gray","Orange"]
    colors = []
    for target in label:
        colors.append(color[target])
    plt.subplot(121)
    plt.title("LDA 降维可视化")
    plt.scatter(dataLDA.T[0], dataLDA.T[1], s=10,c=colors)
    plt.subplot(122)
    plt.title("PCA 降维可视化")
    plt.scatter(dataPCA.T[0], dataPCA.T[1], s=10, c=colors)
    #plt.legend()
    plt.show()
def plot_embedding(X,title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)  # 对每一个维度进行0-1归一化，注意此时X只有两个维度
    colors = ['#5dbe80', '#2d9ed8', '#a290c4', '#efab40', '#eb4e4f', '#929591','#ababab','#eeeeee','#aaaaaa','#213832']
    ax = plt.subplot()
    # 画出样本点
    for i in range(X.shape[0]):  # 每一行代表一个样本
        plt.text(X[i, 0], X[i, 1], str(label[i]),
                 # color=plt.cm.Set1(y[i] / 10.),
                 color=colors[label[i]],
                 fontdict={'weight': 'bold', 'size': 9})  # 在样本点所在位置画出样本点的数字标签
    # 在样本点上画出缩略图，并保证缩略图够稀疏不至于相互覆盖
    if hasattr(offsetbox, 'AnnotationBbox'):
        shown_images = np.array([[1., 1.]])  # 假设最开始出现的缩略图在(1,1)位置上
        for i in range(data.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)  # 算出样本点与所有展示过的图片（shown_images）的距离
            if np.min(dist) < 4e-3:  # 若最小的距离小于4e-3，即存在有两个样本点靠的很近的情况，则通过continue跳过展示该数字图片缩略图
                continue
            shown_images = np.r_[shown_images, [X[i]]]  # 展示缩略图的样本点通过纵向拼接加入到shown_images矩阵中
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(datasets.load_digits().images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    #plt.xticks([]), plt.yticks([])  # 不显示横纵坐标刻度
    if title is not None:
        plt.title(title)
    plt.show()
data = datasets.load_digits().data#一个数64维，1797个数
label = datasets.load_digits().target
dataLDA = calLDA(2)
dataPCA = calPCA(2)
#draw() #普通图
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plot_embedding(dataLDA,"LDA 降维可视化")
plot_embedding(dataPCA,"PCA 降维可视化")