np.random.shuffle(x2)

`np.random.shuffle(x2)`是NumPy中的一个函数，用于将数组x2中的元素随机打乱。具体使用方法如下所示：

import numpy as np

x2 = np.array([1, 2, 3, 4, 5])
np.random.shuffle(x2)
print(x2)

运行结果可能为：

[2 5 1 4 3]

以上代码将数组x2中的元素随机打乱，并输出打乱后的结果。

相关问题

翻译这段程序并自行赋值调用：import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

这段程序是一个分类模型的辅助函数，包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下：

import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model

def plot_decision_boundary(model, X, y):
    # 设置最小值和最大值，并给它们一些填充
    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
    h = 0.01

    # 生成一个网格，网格中点的距离为h
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

    # 对整个网格预测函数值
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    # 绘制轮廓和训练样本
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    plt.ylabel('x2')
    plt.xlabel('x1')
    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)

def sigmoid(x):
    s = 1 / (1 + np.exp(-x))
    return s

def load_planar_dataset():
    np.random.seed(1)
    m = 400  # 样本数量
    N = int(m / 2)  # 每个类的样本数量

    # 生成数据集
    D = 2  # 特征维度
    X = np.zeros((m, D))  # 特征矩阵
    Y = np.zeros((m, 1), dtype='uint8')  # 标签向量

    a = 4  # 花的最大半径

    for j in range(2):
        ix = range(N*j, N*(j+1))
        t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2  # theta
        r = a*np.sin(4*t) + np.random.randn(N)*0.2  # radius
        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
        Y[ix] = j

    X = X.T
    Y = Y.T

    return X, Y

def load_extra_datasets():
    N = 200
    noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
    noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
    blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
    gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
    no_structure = np.random.rand(N, 2), np.random.rand(N, 2)

    return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

这段程序中包含了以下函数：

- `plot_decision_boundary(model, X, y)`：绘制分类模型的决策边界，其中`model`是分类模型，`X`是特征矩阵，`y`是标签向量。
- `sigmoid(x)`：实现sigmoid函数。
- `load_planar_dataset()`：加载一个二维的花瓣数据集。
- `load_extra_datasets()`：加载五个其他数据集。

import scipy.io as sio from sklearn import svm import numpy as np import matplotlib.pyplot as plt data=sio.loadmat('AllData') labels=sio.loadmat('label') print(data) class1 = 0 class2 = 1 idx1 = np.where(labels['label']==class1)[0] idx2 = np.where(labels['label']==class2)[0] X1 = data['B007FFT0'] X2 = data['B014FFT0'] Y1 = labels['label'][idx1].reshape(-1, 1) Y2 = labels['label'][idx2].reshape(-1, 1) ## 随机选取训练数据和测试数据 np.random.shuffle(X1) np.random.shuffle(X2) # Xtrain = np.vstack((X1[:200,:], X2[:200,:])) # Xtest = np.vstack((X1[200:300,:], X2[200:300,:])) # Ytrain = np.vstack((Y1[:200,:], Y2[:200,:])) # Ytest = np.vstack((Y1[200:300,:], Y2[200:300,:])) # class1=data['B007FFT0'][0:1000, :] # class2=data['B014FFT0'][0:1000, :] train_data=np.vstack((X1[0:200, :],X2[0:200, :])) test_data=np.vstack((X1[200:300, :],X2[200:300, :])) train_labels=np.vstack((Y1[:200,:], Y2[:200,:])) test_labels=np.vstack((Y1[200:300,:], Y2[200:300,:])) ## 训练SVM模型 clf=svm.SVC(kernel='linear', C=1000) clf.fit(train_data,train_labels.reshape(-1)) ## 用测试数据测试模型准确率 train_accuracy = clf.score(train_data, train_labels) test_accuracy = clf.score(test_data, test_labels) # test_pred=clf.predict(test_data) # accuracy=np.mean(test_pred==test_labels) # print("分类准确率为:{:.2F}%".fromat(accuracy*100)) x_min,x_max=test_data[:,0].min()-1,test_data[:,0].max()+1 y_min,y_max=test_data[:,1].min()-1,test_data[:,1].max()+1 xx,yy=np.meshgrid(np.arange(x_min,x_max,0.02),np.arange(y_min,y_max,0.02)) # 生成一个由xx和yy组成的网格 # X, Y = np.meshgrid(xx, yy) # 将网格展平成一个二维数组xy xy = np.vstack([xx.ravel(), yy.ravel()]).T # Z = clf.decision_function(xy).reshape(xx.shape) # z=clf.predict(np.c_[xx.ravel(),yy.ravel()]) z=xy.reshape(xx.shape) plt.pcolormesh(xx.shape) plt.xlim(xx.min(),xx.max()) plt.ylim(yy.min(),yy.max()) plt.xtickes(()) plt.ytickes(()) # # 画出分界线 # axis.contour(X, Y, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--']) # axis.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,linewidth=1, facecolors='none') plt.scatter(test_data[:,0],test_data[:1],c=test_labels,cmap=plt.cm.Paired) plt.scatter(clf.support_vectors_[:,0],clf.support_vectors_[:,1],s=80,facecolors='none',linewidths=1.5,edgecolors='k') plt.show()处理一下代码出错问题

代码中出现了几个错误：

1. 在最后几行代码中，plt.pcolormesh(xx.shape)应该改为plt.pcolormesh(xx, yy, z)，因为需要绘制的是网格xy上对应的值z。

2. 在plt.xtickes(())和plt.ytickes(())中，xtickes和ytickes应该改为xticks和yticks，因为正确的函数名是xticks和yticks。

3. 在plt.scatter(test_data[:,0],test_data[:1],c=test_labels,cmap=plt.cm.Paired)中，test_data[:,0]和test_data[:1]应该改为test_data[:,0]和test_data[:,1]，因为需要绘制的是测试数据的两个特征值。

4. 在画出分界线的代码中，应该将注释的代码取消注释，即将axis.contour(X, Y, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])的注释去掉。