import numpy as np def pagerank(links, d=.85, max_iter=100, tol=1e-6): # 构建邻接矩阵 n = len(links) A = np.zeros((n, n)) for i in range(n): for j in links[i]: A[j][i] = 1 / len(links[i]) # 初始化PageRank值 pr = np.ones(n) / n # 迭代计算PageRank值 for _ in range(max_iter): pr_new = np.zeros(n) for i in range(n): for j in range(n): if A[j][i] != : pr_new[i] += A[j][i] * pr[j] pr_new = d * pr_new + (1 - d) / n if np.linalg.norm(pr_new - pr) < tol: break pr = pr_new # 输出结果 idx = np.argsort(pr)[::-1] for i in idx: print(f"Page {i}: {pr[i]}")

import numpy as np import tensorflow as tf import tflearn GAMMA = 0.99 A_DIM = 3 ENTROPY_WEIGHT = 4 ENTROPY_EPS = 1e-6 S_INFO = 4这段代码是什么意思

- import numpy as np: 导入NumPy库，并将其别名设置为np，以便在代码中更方便地使用NumPy中的函数和类。 - import tensorflow as tf: 导入TensorFlow库，以便在代码中使用TensorFlow中的函数和类。 - import...

from sklearn.datasets import load_iris data,target=load_iris(return_X_y=True) print('feature_value:',data.shape) print('target:',target) from sklearn.cluster import KMeans import numpy as np for i in range(0,30): kmeans=KMeans(n_clusters=3,max_iter=30,tol=0.0001, random_state=i).fit(data) label=kmeans.labels_ #print('label:',label) center=kmeans.cluster_centers_ #print('center:',center) predict=kmeans.predict(data) print('i=',i) print('predict:',predict) accuracy=np.mean(predict==target)100 print('accuracy',accuracy) #模型预测 predict=kmeans.predict(data) print('predict:',predict) import numpy as np accurancy=np.mean(predict==target)100 print('i=',i) print('accurancy',accurancy) import matplotlib.pyplot as plt import mglearn plt.figure(figsize=(10,8)) plt.subplot(221) mglearn.discrete_scatter(data[:,0],data[:,1],target,markers='^') plt.xlabel('data') plt.ylabel('origin') plt.subplot(222) mglearn.discrete_scatter(data[:,0],data[:,1],y_predict,markers='^') mglearn.discrete_scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],[0,1,2],markers='o',markeredgewidth=2) plt.xlabel('data') plt.ylabel('y_predict') plt.show()写一下注释，要非常详细

kmeans = KMeans(n_clusters=3, max_iter=30, tol=0.0001, random_state=i).fit(data) label = kmeans.labels_ center = kmeans.cluster_centers_ predict = kmeans.predict(data) print('i=',i) print('...

import numpy as np def sigmoid(z): return 1 / (1 + np.exp(-z)) def cost_function(theta, X, y): m = len(y) h = sigmoid(X @ theta) J = -(1/m) * (y.T @ np.log(h) + (1-y).T @ np.log(1-h)) grad = (1/m) * X.T @ (h - y) return J, grad def trust_region_newton_method(X, y, max_iter=100, eta=0.05, delta=0.1): n = X.shape[1] theta = np.zeros((n,1)) J, grad = cost_function(theta, X, y) H = np.eye(n) for i in range(max_iter): # solve trust region subproblem p = np.linalg.solve(H, -grad) if np.linalg.norm(p) <= delta: d = p else: d = delta * p / np.linalg.norm(p) # compute actual reduction and predicted reduction J_new, grad_new = cost_function(theta+d, X, y) actual_reduction = J - J_new predicted_reduction = -grad.T @ d - 0.5 * d.T @ H @ d # update trust region radius rho = actual_reduction / predicted_reduction if rho < 0.25: delta = 0.25 elif rho > 0.75 and np.abs(np.linalg.norm(d) - delta) < 1e-8: delta = min(2delta, eta*np.linalg.norm(theta)) # update parameters if rho > 0: theta += d J, grad = J_new, grad_new H += (grad_new - grad) @ (grad_new - grad).T / ((grad_new - grad).T @ d) # check convergence if np.linalg.norm(grad) < 1e-5: break return theta 修改此代码，让他运行出来

import numpy as np def sigmoid(z): return 1 / (1 + np.exp(-z)) def cost_function(theta, X, y): m = len(y) h = sigmoid(X @ theta) J = -(1/m) * (y.T @ np.log(h) + (1-y).T @ np.log(1-h)) grad = (1...

生成一段def deepfool(image, net, num_classes=10, overshoot=0.02, max_iter=50):的代码

x = x + (1 + overshoot) * torch.from_numpy(delta).to(x.device) # 限制像素值范围 x = torch.clamp(x, 0, 1) # 检查是否成功欺骗 if torch.argmax(net(x)) == k: pert_image = x adv_label = k break ...

import numpy as np class LinearReg(object): def init(self, indim=1, outdim=1):

import numpy as np class LinearReg(object): def __init__(self, indim=1, outdim=1): # 初始化线性回归模型参数 self.w = np.zeros((outdim, indim)) # 初始化权重参数为零行向量 self.b = np.zeros((outdim,...

导入波士顿房价数据集，计算房价的最小值，最大值，均值，中位数，方差，标准差，协方差。 import sklearn.datasets as datasets import pandas as pd import numpy as np boston=datasets.load_boston()#导入数据集

import numpy as np boston = datasets.load_boston() # 导入波士顿房价数据集 # 将数据集转化为DataFrame格式 boston_df = pd.DataFrame(boston.data,columns=boston.feature_names) # 添加目标值MEDV boston_df...

import pandas as pd import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn import preprocessing import matplotlib.pyplot as plt data_tr = pd.read_csv('BPdata_tr.txt') # 训练集样本 data_te = pd.read_csv('BPdata_te.txt') # 测试集样本 X=np.array([[0.0499853495508432],[0.334657078469172]]).reshape(1, -1) model = MLPRegressor(hidden_layer_sizes=(10,), activation='relu',random_state=10, max_iter=8000, learning_rate_init=0.3) # 构建模型,调用sklearn实现神经网络算法 model.fit(data_tr.iloc[:, :2], data_tr.iloc[:, 2]) # 模型训练 pre = model.predict(data_te.iloc[:, :2]) # 模型预测 pre1 = model.predict(X) err = np.abs(pre - data_te.iloc[:, 2]).mean() # 模型预测误差 print(pre,end='\n______________________________\n') print(pre1,end='\n++++++++++++++++++++++++++++++++\n') print('误差：',err)

import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn import preprocessing import matplotlib.pyplot as plt 2. 读取...

为代码添加注释import matplotlib.pyplot as plt import numpy as np x = np.random.rand(100) y = np.random.rand(100) plt.scatter(x,y) plt.show()

import numpy as np # 生成 100 个随机数 x = np.random.rand(100) y = np.random.rand(100) # 绘制散点图 plt.scatter(x,y) # 显示图形 plt.show() # 注释：上述代码使用 matplotlib.pyplot 和 numpy 模块生成...

import torch import numpy as np import matplotlib.pyplot as plt x = np.linspace(-10, 10, num=100) act = torch.nn.SiLU() pred = act(torch.from_numpy(x)) y = pred.detach().numpy() plt.plot(x, y) plt.show()使图像一直停留界面

import numpy as np import matplotlib.pyplot as plt x = np.linspace(-10, 10, num=100) act = torch.nn.SiLU() pred = act(torch.from_numpy(x)) y = pred.detach().numpy() plt.plot(x, y) plt.show() ...

import pandas as pd import numpy as np from sklearn.datasets import make_classification def decision_tree_binning(x_value: np.ndarray, y_value: np.ndarray, max_bin=10) -> list: '''利用决策树获得最优分箱的边界值列表''' from sklearn.tree import DecisionTreeClassif

构建决策树模型 clf = DecisionTreeClassification(criterion='entropy', max_leaf_nodes=max_bin) x = x_value.reshape(-1, 1) clf.fit(x, y_value) # 获得最优分箱的边界值列表 bin_edges = clf.tree_.threshold...

import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 加载 iris 数据 iris = load_iris() # 只选取两个特征和两个类别进行二分类 X = iris.data[(iris.target==0)|(iris.target==1), :2] y = iris.target[(iris.target==0)|(iris.target==1)] # 将标签转化为 0 和 1 y[y==0] = -1 # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 实现逻辑回归算法 class LogisticRegression: def init(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False): self.lr = lr self.num_iter = num_iter self.fit_intercept = fit_intercept self.verbose = verbose def __add_intercept(self, X): intercept = np.ones((X.shape[0], 1)) return np.concatenate((intercept, X), axis=1) def sigmoid(self, z): return 1 / (1 + np.exp(-z)) def loss(self, h, y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() def fit(self, X, y): if self.fit_intercept: X = self.__add_intercept(X) # 初始化参数 self.theta = np.zeros(X.shape[1]) for i in range(self.num_iter): # 计算梯度 z = np.dot(X, self.theta) h = self.sigmoid(z) gradient = np.dot(X.T, (h - y)) / y.size # 更新参数 self.theta -= self.lr * gradient # 打印损失函数 if self.verbose and i % 10000 == 0: z = np.dot(X, self.theta) h = self.sigmoid(z) loss = self.__loss(h, y) print(f"Loss: {loss} \t") def predict_prob(self, X): if self.fit_intercept: X = self.__add_intercept(X) return self.__sigmoid(np.dot(X, self.theta)) def predict(self, X, threshold=0.5): return self.predict_prob(X) >= threshold # 训练模型 model = LogisticRegressio

accuracy = np.sum(y_pred == y_test) / y_test.shape[0] print(f"Accuracy: {accuracy}") # 可视化 plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred) plt.show() 请问这段代码实现了什么功能？

如何对上述代码进行修改，要求通过max_iterations = 1000 # 最大迭代次数 tolerance = 1e-6 # 目标函数值的变化量阈值 mean_tol = 1e-6 # 均值向量变化量阈值 sigma_tol = 1e-6 # 标准差变化量阈值上述标准终止计算

import numpy as np def main(): # X坐标束条件 Min_pump_zcjj = 26 Max_pump_zcjj = 51 # Y坐标约束条件 Min_pump_bdljd = 26 Max_pump_bdljd = 51 # 靶点位置约束条件 Min_pump_bdwz = 0 Max_pump_bdwz ...

import pandas as pdimport numpy as npimport noldsimport matplotlib.pyplot as plt# 读取csv文件df = pd.read_csv('data.csv', header=None)# 转换为numpy数组data = np.array(df[0])# 计算最大lyapunov指数和数max_lyap, _ = nolds.lyap_e(data, emb_dim=10, matrix_dim=2)# 打印结果print("最大lyapunov指数：", max_lyap)# 绘制时间序列图plt.plot(data)plt.show()以上代码运行时报错如下OverflowError: cannot convert float infinity to integer，请改写一段新的代码以避免报错

import numpy as np import nolds import matplotlib.pyplot as plt # 读取csv文件 df = pd.read_csv('data.csv', header=None) # 转换为numpy数组 data = np.array(df[0]) # 计算最大lyapunov指数和数 max_lyap,...

def return_values(): import pandas as pd import numpy as np from sklearn.neural_network import MLPRegressor as MP data = pd.read_excel('4.xlsx') x_train=data.iloc[:,1:4] y_train=data.iloc[:,4:6] clf = MP(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=8, random_state=1) clf.fit(x_train, y_train); a=np.array([[73.39,3.9635,0.9880],[75.55,4.0975,1.0268]]) Y=clf.predict(a) return Y 解释代码

import numpy as np from sklearn.neural_network import MLPRegressor as MP 2. 读取Excel文件数据： python data = pd.read_excel('4.xlsx') 3. 提取训练数据集的特征和标签： python x_train=...

import numpy as np_SPI干旱识别_spi_SPI6_matlabimportnumpy_测试_源码.rar

标题 "import numpy as np_SPI干旱识别_spi_SPI6_matlabimportnumpy_测试_源码.rar" 暗示了这是一个关于使用Python的NumPy库进行SPI（标准降水指数）干旱识别的项目，SPI是评估气候干旱状况的一种统计方法。...

# Python中numpy库中，X,Y = np.meshgrid(x,y)最详细理解（附理解代码）

import numpy as np N = 3 M = 7 x = np.linspace(-2, 2, N) # [-2 0 2] y = np.linspace(-3, 3, M) # [-3 -2 1 0 1 2 3] 这里，np.linspace()函数用于生成等差序列，N和M分别表示x和y的长度。接...

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