import numpy as np import open3d as o3d data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/test01.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:, :3]) # 创建一个新的点云属性对象 velocity_attr = o3d.utility.DoubleVector(data[:, 3]) scatter_attr = o3d.utility.DoubleVector(data[:, 4]) # 将点云属性作为点的属性添加到点云对象中 point_cloud.point['velocity'] = o3d.utility.Vector3dVector(velocity_attr) point_cloud.point['scatter'] = o3d.utility.Vector3dVector(scatter_attr) o3d.visualization.draw_geometries([point_cloud])发生异常: RuntimeError Unable to cast Python instance to C++ type (compile in debug mode for details) File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 14, in <module> point_cloud.point['velocity'] = o3d.utility.Vector3dVector(velocity_attr) RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details)

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

import numpy as np import matplotlib.pyplot as plt import math

import numpy as np import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes....

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

请解释以下代码： data = np.loadtxt('data/{}.txt'.format(dataset)) n, _ = data.shape idx = np.array([i for i in range(n)], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt(path, dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(n, n), dtype=np.float32)

1. data = np.loadtxt('data/{}.txt'.format(dataset))：读取文件，文件路径由变量dataset指定，将文件中的数据加载到一个 NumPy 数组 data 中。 2. n, _ = data.shape：获取 data 数组的形状，n 为数...

class DiabetesDataset(Dataset): def init(self, filepath): xy = np.loadtxt(filepath, delimiter=',', dtype=np.float32) self.len = xy.shape[0] # shape(多少行，多少列) self.x_data = torch.from_numpy(xy[:, :-1]) self.y_data = torch.from_numpy(xy[:, [-1]]) def getitem(self, index): return self.x_data[index], self.y_data[index] def len(self): return self.len

这是一个名为DiabetesDataset的...在构造函数中，使用numpy库的loadtxt函数从指定路径的文件中读取数据，数据以逗号分隔，数据类型为float32。读取的数据被存储在变量xy中。self.len被赋值为xy的行数，即数据集的大小。

import numpy as np import matplotlib.pyplot as plt from sklearn import svm from sklearn.datasets import make_blobs from sklearn import model_selection from sklearn.metrics import f1_score def show_svm(a, b, bt): plt.figure(bt) plt.title('SVM with ' + bt) # 建立图像坐标 axis = plt.gca() plt.scatter(a[:, 0], a[:, 1], c=b, s=30) xlim = [a[:, 0].min(), a[:, 0].max()] ylim = [a[:, 1].min(), a[:, 1].max()] # 生成两个等差数列 xx = np.linspace(xlim[0], xlim[1], 50) yy = np.linspace(ylim[0], ylim[1], 50) X, Y = np.meshgrid(xx, yy) xy = np.vstack([X.ravel(), Y.ravel()]).T Z = clf.decision_function(xy).reshape(X.shape) # 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=200, linewidths=1, facecolors='none') if name == 'main': # data = np.loadtxt('separable_data.txt', delimiter=',') # data = np.loadtxt('non_separable_data.txt', delimiter=',') # data = np.loadtxt('banknote.txt', delimiter=',') data = np.loadtxt('ionosphere.txt', delimiter=',') # data = np.loadtxt('wdbc.txt', delimiter=',') X = data[:, 0:-1] y = data[:, -1] """标签中有一类标签为1""" y = y + 1 ymin = min(y) if not (1 in set(y)): ll = max(list(set(y))) + 1 for i in range(len(y)): if y[i] == ymin: y[i] = 1 # 建立一个线性核（多项式核）的SVM clf = svm.SVC(kernel='linear') clf.fit(X, y) """显示所有数据用于训练后的可视化结果""" show_svm(X, y, 'all dataset') """divide the data into two sections: training and test datasets""" X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=42) """training""" clf = svm.SVC(kernel='linear')#线性内核 # clf = svm.SVC(kernel='poly')# 多项式内核 # clf = svm.SVC(kernel='sigmoid')# Sigmoid内核 clf.fit(X_train, y_train) # show_svm(X_train, y_train, 'training dataset') """predict""" pred = clf.predict(X_test) pred = np.array(pred) y_test = np.array(y_test) print(f'SVM 的预测结果 f1-score:{f1_score(y_test, pred)}') # plt.show()结果与分析

1. 导入需要使用的库，如 numpy、matplotlib、sklearn 等。 2. 定义一个名为 show_svm 的函数，用于对数据进行可视化展示。函数有三个参数：a 表示数据的特征值，b 表示数据的标签，bt 表示图像的标题。 3. 在...

发生异常: IndexError index 5 is out of bounds for axis 1 with size 5 File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 11, in <module> scatter_attr = o3d.utility.DoubleVector(data[:, 5]) IndexError: index 5 is out of bounds for axis 1 with size 5

data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/000000.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:,...

发生异常: AttributeError module 'open3d.cpu.pybind.utility' has no attribute 'VectorDouble' File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 14, in <module> point_cloud.point['velocity'] = o3d.utility.VectorDouble(velocity_attr) AttributeError: module 'open3d.cpu.pybind.utility' has no attribute 'VectorDouble'

data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/000000.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:,...

发生异常: AttributeError module 'open3d.cpu.pybind.geometry' has no attribute 'PointAttribute' File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 10, in <module> velocity_attr = o3d.geometry.PointAttribute() AttributeError: module 'open3d.cpu.pybind.geometry' has no attribute 'PointAttribute'

data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/000000.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:,...

发生异常: AttributeError 'open3d.cpu.pybind.geometry.PointCloud' object has no attribute 'point_attr' File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 10, in <module> point_cloud.point_attr['velocity'] = o3d.utility.DoubleVector(data[:, 4]) AttributeError: 'open3d.cpu.pybind.geometry.PointCloud' object has no attribute 'point_attr'

data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/000000.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:,...

from keras.models import Sequential from keras.layers import Dense from sklearn.preprocessing import MinMaxScaler import numpy as np from sklearn.model_selection import train_test_split # 加载数据集，18列数据 dataset = np.loadtxt(r'D:\python-learn\asd.csv', delimiter=",",skiprows=1) # 划分数据, 使用17列数据来预测最后一列 X = dataset[:,0:17] y = dataset[:,17] # 归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X = scaler.fit_transform(X) y = scaler.fit_transform(y.reshape(-1, 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=0) # 创建模型 model = Sequential() model.add(Dense(64, input_dim=17, activation='relu')) model.add(Dense(32, activation='relu')) model.add(Dense(16, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='linear')) # 编译模型, 选择MSE作为损失函数 model.compile(loss='mse', optimizer='adam') # 训练模型, 迭代1000次 model.fit(X_train, y_train, epochs=300, batch_size=32) score= model.evaluate(X_train, y_train) print('Test loss:', score) # 评估神经网络模型 score= model.evaluate(X_test,y_test) print('Test loss:', score) # 预测结果 dataset = np.loadtxt(r'D:\python-learn\testdata.csv', delimiter=",",skiprows=1) X = dataset[:,0:17] scaler = MinMaxScaler(feature_range=(0, 1)) X = scaler.fit_transform(X) y = scaler.fit_transform(y.reshape(-1, 1)) # pred_Y = model.predict(X) print("Predicted value:", pred_Y) from sklearn.metrics import mean_squared_error, r2_score # y_true是真实值，y_pred是预测值 # 计算均方误差 y_true = dataset[:,-1] mse = mean_squared_error(y_true, pred_Y) # 计算决定系数 r2 = r2_score(y_true, pred_Y) # 输出均方误差和决定系数 print("均方误差： %.2f" % mse) print("决定系数： %.2f" % r2) import matplotlib.pyplot as plt plt.scatter(y_true, pred_Y) # 添加x轴标签 plt.xlabel('真实值') # 添加y轴标签 plt.ylabel('预测值') # 添加图标题 plt.title('真实值与预测值的散点图') # 显示图像 plt.show()请你优化一下这段代码，尤其是归一化和反归一化过程

import numpy as np from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt # 加载数据集，18列数据 dataset = np....

使用Python中的loadtxt库从本地文件夹中读取Fashion-MNIST数据（以CSV格式读取，训练集和测试集分别读取），并使用PyTorch中的Dataset来构建数据集

import numpy as np # 读取训练集数据 train_data = np.loadtxt('path/to/train.csv', delimiter=',', skiprows=1) train_images = train_data[:, 1:].reshape(-1, 1, 28, 28) / 255.0 # 将图像数据转换成28x28的...

pytorch dataset

import numpy as np class DiabetesDataset(Dataset): def __init__(self, filePath): xy = np.loadtxt(filePath, delimiter=',', dtype=np.float32) self.len = xy.shape<span class="em">1</span><span class=...

我想你弄得有问题，第四行和第五行并不是点云数据，它只是该点的速度信息和散射强度信息，你是不是读取的时候读错了，是不是不能用o3d.utility.Vector3dVector（）来读

data = np.loadtxt('E:/radar_dataset_astyx-main/dataset_astyx_demo/radar_hires1/000000.txt', skiprows=1) point_cloud = o3d.geometry.PointCloud() point_cloud.points = o3d.utility.Vector3dVector(data[:,...

lstm多变量输入回归预测模型_Keras中的多变量时间序列预测-LSTMs

import numpy as np # 准备数据 def create_dataset(data, look_back): X, Y = [], [] for i in range(len(data)-look_back-1): X.append(data[i:(i+look_back), :]) Y.append(data[(i+look_back), 0]) return...

编写pytorch代码，读取时序数据集dataset，首先通过滑动窗口划分法整理数据集。然后按照比例为0.7：0.2：0.1划分为训练集、验证集和测试集。

import numpy as np class SlidingWindowDataset(Dataset): def __init__(self, data, window_size): self.data = data self.window_size = window_size def __len__(self): return len(self.data) - self....

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python 3.74 运行import numpy as np 报错lib\site-packages\numpy\__init__.py

import numpy as np import matplotlib.pyplot as plt import math

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

发生异常: IndexError index 5 is out of bounds for axis 1 with size 5 File "E:\迅雷下载\serial-port-to-database-master\import open3d as o3d.py", line 11, in <module> scatter_attr = o3d.utility.DoubleVector(data[:, 5]) IndexError: index 5 is out of bounds for axis 1 with size 5

point_attr也没有，这是上一个版本的代码

也没有vectorfloat，是不是我的open3D版本有问题

python dataset download

使用Python中的loadtxt库从本地文件夹中读取Fashion-MNIST数据（以CSV格式读取，训练集和测试集分别读取），并使用PyTorch中的Dataset来构建数据集

pytorch dataset

我想你弄得有问题，第四行和第五行并不是点云数据，它只是该点的速度信息和散射强度信息，你是不是读取的时候读错了，是不是不能用o3d.utility.Vector3dVector（）来读

lstm多变量输入回归预测模型_Keras中的多变量时间序列预测-LSTMs

编写pytorch代码，读取时序数据集dataset，首先通过滑动窗口划分法整理数据集。然后按照比例为0.7：0.2：0.1划分为训练集、验证集和测试集。

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