import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import neighbors, datasets n_neighbors = 15 iris = datasets.load_iris() X = iris.data[:, :2] y = iris.target h = .02 # step size in the mesh cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) weights = 'distance' clf = neighbors.KNeighborsClassifier(n_neighbors, weights)() clf.fit(X, y) 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, h),np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light) plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.title("3-Class classification (k = %i, weights = '%s')"% (n_neighbors,weights)) plt.show()

这段代码是一个示例，演示了如何使用 k-最近邻算法进行三类别的分类，并使用 matplotlib 绘制分类结果的决策边界和数据点。

首先，导入必要的库，包括 numpy、matplotlib 和 sklearn。然后，设置了一些参数，如 k 值、数据集（这里使用的是鸢尾花数据集）以及步长 h。接下来，定义了两个颜色映射，用于可视化分类结果。

然后，创建了一个 KNeighborsClassifier 对象 clf，传入了 k 值和权重参数。调用 fit() 方法拟合模型，传入特征数据 X 和标签数据 y。

接着，计算了决策边界的网格点坐标，并使用 predict() 方法对网格点进行预测，得到预测结果 Z。最后，将预测结果 Z 重塑为与网格点坐标相同的形状。

最后，使用 matplotlib 绘制了分类结果的决策边界和数据点。pcolormesh() 函数用于绘制决策边界的背景颜色，scatter() 函数用于绘制数据点。设置了坐标轴范围、标题等，并调用 show() 函数显示图形。

相关问题

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

要在Python中运行这些代码，你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。

首先，在命令行中运行以下命令来安装所需的包：

pip install scikit-learn pandas matplotlib numpy

然后，在你的Python脚本中导入所需的库：

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC

import random

请确保在运行这些代码之前，已经安装了所需的Python库。如果遇到任何问题，请确保已正确安装这些库，并且版本与代码兼容。

现有1500个二维空间的数据点，数据产生代码范例如下所示 import time as time import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.mplot3d.axes3d as p3 from sklearn.datasets import make_swiss_roll # Generate data (swiss roll dataset) n_samples = 1500 noise = 0.05 X, _ = make_swiss_roll(n_samples, noise=noise) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], cmap=plt.cm.Spectral) ，编写一个python程序不调用locally_linear_embedding，实现LLE降维

LLE（Locally Linear Embedding）是一种非线性降维方法，它可以将高维数据映射到低维空间中，并且保持数据之间的局部关系。下面是一个实现LLE降维的Python程序。

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors

def lle(X, n_neighbors, n_components):
    """
    LLE降维算法实现
    :param X: 数据矩阵，每一行代表一个样本
    :param n_neighbors: 邻居个数
    :param n_components: 降维后的维度
    :return: 降维后的数据矩阵
    """
    # 首先计算每个样本的k个邻居，保存邻居的索引
    knn = NearestNeighbors(n_neighbors=n_neighbors + 1).fit(X)
    _, indices = knn.kneighbors(X)

    # 计算每个样本的权重矩阵W
    W = np.zeros((X.shape[0], X.shape[0]))
    for i in range(X.shape[0]):
        xi = X[i]
        neighbors = X[indices[i]][1:]
        G = neighbors - xi
        GtG = np.dot(G, G.T)
        w = np.linalg.solve(GtG, np.ones(n_neighbors) / n_neighbors)
        w /= np.sum(w)
        for j, neighbor in zip(indices[i][1:], w):
            W[i, j] = neighbor

    # 计算重构权重矩阵M
    I = np.eye(X.shape[0])
    M = np.dot((I - W).T, (I - W))

    # 计算特征值和特征向量
    _, eig_vecs = np.linalg.eig(M)
    indices = np.argsort(np.abs(eig_vecs.real), axis=1)[:, :n_components]
    W = eig_vecs.real / eig_vecs.real.sum(axis=0)

    # 返回降维后的数据
    return W[:, 1:n_components+1]

# 生成数据
n_samples = 1500
noise = 0.05
X, _ = make_swiss_roll(n_samples, noise=noise)

# LLE降维
X_lle = lle(X, n_neighbors=12, n_components=2)

# 可视化
plt.scatter(X_lle[:, 0], X_lle[:, 1], cmap=plt.cm.Spectral)
plt.show()

在上述代码中，我们首先计算每个样本的k个邻居，并计算每个样本的权重矩阵W。然后，我们计算重构权重矩阵M，并计算特征值和特征向量。最后，我们返回降维后的数据矩阵。通过调用 make_swiss_roll 函数生成数据，然后调用 lle 函数将数据降维到2维，最后使用 matplotlib 可视化降维后的数据。