计算机专业前沿作业 Python

Date: 20240427
Design by JRNitre

03_pycharm_python_coursework_20240427/DecisionTree.py

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+from sklearn import tree
+
+# 特征数据 身高 体重
+# 1 - 有
+# 0 - 无
+features = [[178, 1], [155, 0], [180, 1]]
+# 特征值
+labels = ['male', 'female', 'male']
+
+def decision_tree_classifier():
+    # 创建分类器
+    clf = tree.DecisionTreeClassifier()
+    # 模型训练
+    clf = clf.fit(features, labels)
+    # 预测
+    r1 = clf.predict([[158, 0]])
+    print('Data[158, 0] is label for: ', r1)
+
+    r2 = clf.predict([[190, 1]])
+    print('Data[190, 1] is label for: ', r2)
+
+if __name__ == '__main__':
+    decision_tree_classifier()

03_pycharm_python_coursework_20240427/DeepLearn.py

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+import networkx as nx
+import matplotlib.pyplot as plt
+
+G = nx.DiGraph()
+
+G.add_node("输入层")
+G.add_node("隐藏层 1")
+G.add_node("隐藏层 2")
+G.add_node("输出层")
+
+G.add_edge("输入层", "隐藏层 1")
+G.add_edge("输入层", "隐藏层 2")
+G.add_edge("隐藏层 1", "输出层")
+G.add_edge("隐藏层 2", "输出层")
+
+pos = nx.spring_layout(G)
+nx.draw_networkx(G, pos)
+plt.rcParams['font.sans-serif']=['SimHei']
+plt.show()

03_pycharm_python_coursework_20240427/Kmeans.py

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+import numpy as np
+import  matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
+
+from sklearn.datasets import make_blobs
+from sklearn import  metrics
+
+n_samples = 1500
+x, y = make_blobs(n_samples=n_samples, centers=4, random_state=170)
+
+x = StandardScaler().fit_transform(x)
+
+KMeans = KMeans(n_clusters=4, n_init='auto', random_state=170)
+KMeans.fit(x)
+
+plt.figure(figsize=(12, 6))
+plt.subplot(121)
+plt.scatter(x[:, 0], x[:, 1], c='r')
+plt.title("Before clustering")
+plt.subplot(122)
+plt.scatter(x[:, 0], x[:, 1], c=KMeans.labels_)
+plt.title("After clustering")
+plt.show()

03_pycharm_python_coursework_20240427/LinearRegression.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+# 转化矩阵
+x = np.linspace(0, 10, 30).reshape(-1, 1)
+
+# 斜率和截距 随机生成
+w = np.random.randint(1, 5, 1)
+b = np.random.randint(1, 10, 1)
+
+# 根据一元一次方程计算目标值 y , 并加上 噪声, 数据上下有波动
+y = x * w + b + np.random.randn(30, 1)
+plt.scatter(x, y)
+
+# 重构 x, b 截距
+x = np.column_stack([x, np.full((30, 1), 1)])
+
+# 正规方程求解
+a = np.linalg.inv(x.T.dot(x)).dot(x.T).dot(y).round(2)
+
+print(w, b)
+print(a)
+
+# 输出回归线性图
+plt.plot(x[:, 0], x.dot(a), 'green')
+plt.show()

03_pycharm_python_coursework_20240427/NaiveBayesAlgorithm.py

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+import numpy as np
+from sklearn.naive_bayes import BernoulliNB
+
+x = np.array([[0, 1, 0, 1], [1, 1, 1, 1], [1, 1, 1, 0],
+              [0, 1, 1, 0], [0, 1, 0, 0], [0, 1, 0, 1],
+              [1, 1, 0, 1], [1, 0, 0, 1], [1, 1, 0, 1],
+              [0, 0, 0, 0]])
+
+# 有风-潮湿-多云-闷热
+y = np.array([1, 1, 1, 1, 0, 1, 0, 1, 1, 0])
+bnb = BernoulliNB()
+bnb.fit(x, y)
+day_pre = [[1, 0, 1, 0]]
+pre = bnb.predict(day_pre)
+print("预测结果如下:\n", "*" * 50)
+print("结果为: ", pre)
+print("*" * 50)
+
+# 进一步查看概率分析
+pre_pro = bnb.predict_proba(day_pre)
+print("不下雨的概率为: ", pre_pro[0][0], "\n下雨的概率为: ", pre_pro[0][1])

03_pycharm_python_coursework_20240427/RandomForest.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn import datasets
+
+# 载入红酒数据
+wine = datasets.load_wine()
+
+# 只选取前两个特征
+x = wine.data[:, :2]
+y = wine.target
+
+# 拆分训练集和数据集
+x_train, x_test, y_train, y_test = train_test_split(x, y)
+
+forest = RandomForestClassifier(n_estimators=6, random_state=3)
+
+# 拟合数据
+forest.fit(x_train, y_train)
+
+# 绘制图形
+cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
+cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
+
+x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
+y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
+
+xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))
+
+# 生成随机森林
+z = forest.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
+
+plt.figure()
+plt.pcolormesh(xx, yy, z, cmap=cmap_light)
+
+# 绘制训练数据和测试数据的散点图
+plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cmap_bold, edgecolor='k', s=20)
+plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cmap_bold, edgecolor='k', s=50, marker='x')
+
+plt.xlim(xx.min(), xx.max())
+plt.ylim(yy.min(), yy.max())
+plt.title("Random Forest Classification")
+plt.show()

03_pycharm_python_coursework_20240427/main.py

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+# This is a sample Python script.
+
+# Press Shift+F10 to execute it or replace it with your code.
+# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
+
+
+def print_hi(name):
+    # Use a breakpoint in the code line below to debug your script.
+    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    print_hi('PyCharm')
+
+# See PyCharm help at https://www.jetbrains.com/help/pycharm/

03_pycharm_python_coursework_20240427/opencv_01.py

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+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+# 读取图片
+img = cv2.imread('source/data_01.png')
+orb = cv2.ORB_create()
+
+keypoints, descriptors = orb.detectAndCompute(img, None)
+
+output_image = cv2.drawKeypoints(img, keypoints, None, color=(0, 255, 0), flags=0)
+cv2.imshow('Feature KeyPoints', output_image)
+cv2.waitKey(0)
+cv2.destroyWindow()

03_pycharm_python_coursework_20240427/opencv_02.py

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+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+img = cv2.imread('source/data_02.jpg')
+
+plt.subplot(3, 3, 1)
+plt.imshow(img)
+plt.axis('off')
+plt.title('BGR')
+
+# BGR -> RGB
+img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+plt.subplot(3, 3, 2)
+plt.imshow(img_RGB)
+plt.axis('off')
+plt.title('RGB')
+
+# 原图 -> 灰度
+img_GRAY = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+plt.subplot(3, 3, 3)
+plt.imshow(img_GRAY)
+plt.axis('off')
+plt.title('GRAY')
+
+# HSV
+img_HSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+plt.subplot(3, 3, 4)
+plt.imshow(img_HSV)
+plt.axis('off')
+plt.title('HSV')
+
+# TCrCb
+img_YcrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+plt.subplot(3, 3, 5)
+plt.imshow(img_YcrCb)
+plt.axis('off')
+plt.title('YcrCb')
+
+# HLS
+img_HLS = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
+plt.subplot(3, 3, 6)
+plt.imshow(img_HLS)
+plt.axis('off')
+plt.title('HLS')
+
+# XYZ
+img_XYZ = cv2.cvtColor(img, cv2.COLOR_BGR2XYZ)
+plt.subplot(3, 3, 7)
+plt.imshow(img_XYZ)
+plt.axis('off')
+plt.title('XYZ')
+
+# LAB
+img_LAB = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+plt.subplot(3, 3, 8)
+plt.imshow(img_LAB)
+plt.axis('off')
+plt.title('LAB')
+
+# YUV
+img_YUV = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
+plt.subplot(3, 3, 9)
+plt.imshow(img_YUV)
+plt.axis('off')
+plt.title('LAB')
+
+plt.show()

03_pycharm_python_coursework_20240427/opencv_03.py

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+import cv2
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+
+img_1 = cv2.imread('source/data_03.jpg')
+cv2.imshow("img1", img_1)
+img_2 = np.zeros(img_1.shape, dtype=np.uint8)
+img_2[200:400, 200:400] = 255
+cv2.imshow("img2", img_2)
+
+result_or = cv2.bitwise_or(img_1, img_2)
+result_and = cv2.bitwise_and(img_1, img_2)
+result_not = cv2.bitwise_not(img_1, img_2)
+result_xor = cv2.bitwise_xor(img_1, img_2)
+
+cv2.imshow("or", result_or)
+cv2.imshow("and", result_and)
+cv2.imshow("not", result_not)
+cv2.imshow("xor", result_xor)
+cv2.waitKey()
+cv2.destroyWindow()

03_pycharm_python_coursework_20240427/opencv_04.py

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+import cv2
+
+img = cv2.imread("source/data_02.jpg")
+
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+faces = faceCascade.detectMultiScale(gray, scaleFactor=1.09, minNeighbors=3, minSize=(5, 5))
+print(faces)
+print("该图一共有{0}人脸".format(len(faces)))
+for(x, y, w, h) in faces:
+    cv2.circle(img, (int((2 * x + w) / 2), int((2 * y + h) / 2)),
+               int(w / 2), (0, 255, 0), 2)
+cv2.imshow("result", img)
+cv2.waitKey()
+cv2.destroyWindow()

03_pycharm_python_coursework_20240427/pca.py

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+import numpy as np
+import  matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.datasets import load_iris
+
+# 加载数据
+iris = load_iris()
+x = iris.data
+
+# 执行 PCA
+pca = PCA(n_components=2)
+x_reduced = pca.fit_transform(x)
+
+# 绘制结果
+plt.figure(figsize=(10, 5))
+plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=iris.target)
+plt.xlabel('First Principal Component')
+plt.ylabel('Second Principal Component')
+plt.title('PCA of Iris Dataset')
+plt.show()