import numpy as np # 定义参数 n_lags = 31 # 滞后阶数 n_vars = 6 # 变量数量 alpha = 0.05 # 置信水平 # 准备数据 data = np.array([820.95715,819.17877,801.60077,30,26164.9,11351.8], [265.5425,259.05476,257.48619,11.4,12525,4296.5], [696.9681,685.54114,663.32014,47.5,23790.484,8344.8], [4556.1091,440.58995,433.21995,24.6,12931.388,5575.4], [360.08693,353.75386,351.59186,26.9,11944.322,4523], [938.55919,922.25468,894.26468,35.3,27177.893,8287.4], [490.47837,477.35237,385.17474,24.5,14172.1,6650.4], [553.15463,452.35042,425.92277,32.9,16490.17,7795], [740.35759,721.68259,721.68259,15.5,26117.755,7511.7], [1581.99576,1579.50357,1571.23257,65.4,59386.7,15347.2], [1360.91636,1360.20825,1358.11425,66.4,57160.533,8080], [564.06146,560.91611,559.08711,35.2,22361.86,6165.4], [732.17283,727.25063,725.93863,29.7,22177.389,4393.2], [424.12777,424.10579,411.19979,21.6,14691.359,4695.6], [1439.38133,1437.85585,1436.67585,77.3,50123.672,15479], [961.92496,935.21589,931.28189,45.7,28073.9,11273.3], [881.92808,868.65804,832.44504,46.1,27409.15,11224.4], [713.32299,710.75882,707.42682,35.8,24887.111,5164.2], [2657.28891,2599.20299,2515.67859,92,94207.179,19066.4], [420.95033,418.22931,416.80631,25.6,13309.9,7020], [193.92636,193.84936,193.83836,10.9,6133,6139.5], [499.81565,493.73678,485.2468,20.9,13555.897,3412], [951.93942,939.58126,930.049,45.6,27245.608,7752.5], [309.88498,297.05055,295.69055,22.6,11929.038,3903.2], [411.87141,406.63838,389.29638,27.8,12197.085,3834.1], [45.53226,39.24379,55.34631667,7.5,1872.333333,564.3], [532.67282,524.78031,520.89851,24,18041.642,3902], [269.00374,266.96222,211.14422,20.3,7163.069,3515.4], [91.95276,88.77094,85.74583,7.7,1962.8,645.8], [120.60234,116.39872,113.85872,9.8,4227.003,1706.2], [362.98862,350.36495,318.70232,23.7,11615.383,5752.1]) # 计算VAR模型的系数 X = np.zeros((data.shape[0] - n_lags, n_lags * n_vars)) y = np.zeros((data.shape[0] - n_lags, n_vars)) for i in range(n_lags, data.shape[0]): X[i-n_lags, :] = data[i-n_lags:i, :].reshape(1, -1) y[i-n_lags, :] = data[i, :] coefficients = np.linalg.inv(X.T @ X) @ X.T @ y # 计算残差 residuals = y - X @ coefficients # 计算PVAR模型的紧贴矩阵 T = residuals[n_lags:, :] @ residuals[:-n_lags, :].T / (data.shape[0] - n_lags) # 计算PVAR模型的系数 u, s, vh = np.linalg.svd(T) S_inv = np.diag(np.sqrt(s[:n_vars])) @ np.linalg.inv(vh[:n_vars, :]) A = S_inv @ u[:, :n_vars].T @ residuals[n_lags:, :].T # 计算置信区间 t_value = np.abs(np.tinv(alpha/2, data.shape[0]-n_lags-n_vars)) se = np.sqrt((1/(data.shape[0]-n_lags-n_vars)) * (np.sum(residuals[n_lags:, :]**2) / (data.shape[0]-n_lags-n_vars-1))) conf_int = t_value * se print("PVAR模型的系数：\n", A) print("置信区间：[{:.4f}, {:.4f}]".format(A.mean() - conf_int, A.mean() + conf_int))这段代码有什么错误

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_SPI干旱识别_spi_SPI6_matlabimportnumpy_测试_源码.zip

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

import numpy as np coding: utf-8 n = 770 #数据数量 max_val = 4000 # 最大值 min_val = 2606 # 最小值 avg_val = 3300 # 平均值 half_n = n // 2 # 计算数据数量的一半，取整 # 生成前半部分数据 descending_data = np.linspace(max_val, min_val, half_n) # 生成后半部分数据 ascending_data = np.linspace(min_val, max_val, half_n) # 合并数据 data = np.concatenate((descending_data, ascending_data)) # 添加随机噪声，使平均值达到设定值 data += np.random.normal(avg_val - np.mean(data), 100, n) # 随机打乱数据 np.random.shuffle(data) print(data)

1. 定义了数据数量n、最大值max_val、最小值min_val、平均值avg_val等参数； 2. 计算出数据数量的一半half_n； 3. 使用np.linspace()函数生成前半部分数据descending_data，该函数会生成一定数量的数据，使得数据从...

详细分析代码“from future import absolute_import from future import division from future import print_function import argparse import sys import numpy as np import pandas as pd from sklearn import metrics import tensorflow as tf learn = tf.contrib.learn FLAGS = None # 文档最长长度 MAX_DOCUMENT_LENGTH = 100 # 最小词频数 MIN_WORD_FREQUENCE = 2 # 词嵌入的维度 EMBEDDING_SIZE = 20 # filter个数 N_FILTERS = 10 # 10个神经元 # 感知野大小 WINDOW_SIZE = 20 #filter的形状 FILTER_SHAPE1 = [WINDOW_SIZE, EMBEDDING_SIZE]#[20,20] FILTER_SHAPE2 = [WINDOW_SIZE, N_FILTERS] #[20,10] # 池化 POOLING_WINDOW = 4 POOLING_STRIDE = 2 n_words = 0 def cnn_model(features, target): ”详细分析每一句代码含义，作用

6. import numpy as np: 用于处理数值数组。 7. import pandas as pd: 用于处理数据表格。 8. from sklearn import metrics: 用于评估模型的性能。 9. import tensorflow as tf: 引入TensorFlow库。 10. ...

# Define DGP parameters np.random.seed(123) n_panels = 5000 # number of panels n_periods = 3 # number of time periods in each panel n_treatments = 2 # number of treatments in each period

import numpy as np np.random.seed(123) # 设置随机种子，以确保结果可复现 n_panels = 5000 # 面板数 n_periods = 3 # 每个面板中的时间段数 n_treatments = 2 # 每个时间段中的处理数其中，np.random....

import pandas as pd import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from arch import arch_model # 读取数据 data = pd.read_csv('三个-负标准化-二分.csv', index_col=0, parse_dates=True) data = data[['F4']] data = data.dropna() # 拆分训练集和测试集 train_data = data[:'2022-06-15'] test_data = data['2022-06-16':] # ARIMA模型 arima_model = sm.tsa.ARIMA(train_data, order=(10, 1, 0)).fit() # GARCH模型 garch_model = arch_model(arima_model.resid, vol='GARCH', p=1, q=1).fit() # 预测 forecast_mean = arima_model.forecast(steps=len(test_data))[0] forecast_vol = garch_model.forecast(horizon=len(test_data)) # 计算置信区间 forecast_upper = forecast_mean + 1.96 * forecast_vol.mean forecast_lower = forecast_mean - 1.96 * forecast_vol.mean # 绘制预测结果 plt.plot(test_data.index, test_data.values, label='Actual') plt.plot(test_data.index, forecast_mean, label='Forecast') plt.fill_between(test_data.index, forecast_upper, forecast_lower, color='gray', alpha=0.2) plt.legend() plt.show() # 数据检验 resid = arima_model.resid / garch_model.conditional_volatility sm.stats.acorr_ljungbox(resid, lags=[10])这段代码的问题

import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from arch import arch_model # 读取数据 data = pd.read_csv('三个-负标准化-二分.csv', index_col=0, parse_dates=True) data...

import numpy as np import matplotlib.pyplot as plt # 设置模拟参数 num_boids = 50 # 粒子数 max_speed = 0.03 # 最大速度 max_force = 0.05 # 最大受力 neighborhood_radius = 0.2 # 邻域半径 separation_distance = 0.05 # 分离距离 alignment_distance = 0.1 # 对齐距离 cohesion_distance = 0.2 # 凝聚距离 # 初始化粒子位置和速度 positions = np.random.rand(num_boids, 2) velocities = np.random.rand(num_boids, 2) * max_speed # 模拟循环 for i in range(1000): # 计算邻域距离 distances = np.sqrt(np.sum(np.square(positions[:, np.newaxis, :] - positions), axis=-1)) neighbors = np.logical_and(distances > 0, distances < neighborhood_radius) # 计算三个力 separation = np.zeros_like(positions) alignment = np.zeros_like(positions) cohesion = np.zeros_like(positions) for j in range(num_boids): # 计算分离力 separation_vector = positions[j] - positions[neighbors[j]] separation_distance_mask = np.linalg.norm(separation_vector, axis=-1) < separation_distance separation_vector = separation_vector[separation_distance_mask] separation[j] = np.sum(separation_vector, axis=0) # 计算对齐力 alignment_vectors = velocities[neighbors[j]] alignment_distance_mask = np.linalg.norm(separation_vector, axis=-1) < alignment_distance alignment_vectors = alignment_vectors[alignment_distance_mask] alignment[j] = np.sum(alignment_vectors, axis=0) # 计算凝聚力 cohesion_vectors = positions[neighbors[j]] cohesion_distance_mask = np.linalg.norm(separation_vector, axis=-1) < cohesion_distance cohesion_vectors = cohesion_vectors[cohesion_distance_mask] cohesion[j] = np.sum(cohesion_vectors, axis=0) # 计算总受力 total_force = separation + alignment + cohesion total_force = np.clip(total_force, -max_force, max_force) # 更新速度和位置 velocities += total_force velocities = np.clip(velocities, -max_speed, max_speed) positions += velocities # 绘制粒子 plt.clf() plt.scatter(positions[:, 0], positions[:, 1], s=5) plt.xlim(0, 1) plt.ylim(0, 1) plt.pause(0.01)

1. 设置模拟参数：定义粒子数、最大速度、最大受力、邻域半径、分离距离、对齐距离、凝聚距离等参数。 2. 初始化粒子位置和速度：使用numpy的rand()函数生成随机位置和速度。 3. 模拟循环：在每个时间步长内，计算...

基于以下代码，加入图像高斯模糊处理代码：import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torchvision import datasets,transforms import matplotlib.pyplot as plt import numpy as np import pylab %matplotlib inline # 定义超参数 input_size = 28 #图像的总尺寸28*28 num_classes = 10 #标签的种类数 num_epochs = 10 #训练的总循环周期 batch_size = 64 #一个撮（批次）的大小，64张图片 # 训练集 train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True) # 测试集 test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) # 构建batch数据 train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

可以将以下代码加入到定义超参数的代码块后面，实现对训练集和测试集图像的高斯模糊处理： # 定义高斯模糊的卷积核 blur_kernel = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 16. # 定义高斯模糊的函数...

import pydicom import numpy as np # 读取dcm文件 ds = pydicom.dcmread('5-1.dcm') # 将图像数据转换为numpy数组 img_array = ds.pixel_array # 将批量图像数据转换为单个图像 if len(img_array.shape) > 2: img_array = np.mean(img_array, axis=0) # 显示图像 import matplotlib.pyplot as plt plt.imshow(img_array, cmap=plt.cm.gray) plt.show() 请逐行解释这段代码的意思

1. import pydicom import numpy as np：导入 pydicom 和 numpy 库。 2. ds = pydicom.dcmread('5-1.dcm')：使用 dcmread() 函数读取名为 “5-1.dcm” 的 dcm 文件，并将其存储在变量 ds 中。 3. img_...

import cv2 import numpy as np # 定义线性变换函数 def linear_transform(image, alpha=1.0, beta=0): new_image = alpha * image + beta # 对像素值进行截断处理，保证像素值在0-255之间 new_image = np.clip(new_image, 0, 255).astype(np.uint8) return new_image # 读取图像并显示 image = cv2.imread('原图.jpg') cv2.imshow('original image', image) # 对比度调节 alpha = 2.0 # 对比度增强倍数，大于1表示增强，小于1表示降低 beta = 50 # 亮度调节参数，可以为负数，表示降低亮度 new_image = linear_transform(image, alpha=alpha, beta=beta) cv2.imshow('adjusted image', new_image) cv2.waitKey(0) cv2.destroyAllWindows()

- import cv2 和 import numpy as np 导入了需要用到的库。 - linear_transform 函数实现了对图像的线性变换，其中 alpha 表示对比度增强倍数，beta 表示亮度调节参数。函数的实现方式为：将每个像素的像素值乘上 ...

rom skimage.segmentation import slic, mark_boundaries import torchvision.transforms as transforms import numpy as np from PIL import Image import matplotlib.pyplot as plt # 加载图像 image = Image.open('3.jpg') # 转换为 PyTorch 张量 transform = transforms.ToTensor() img_tensor = transform(image).unsqueeze(0) # 将 PyTorch 张量转换为 Numpy 数组 img_np = img_tensor.numpy().transpose(0, 2, 3, 1)[0] # 使用 SLIC 算法生成超像素标记图 segments = slic(img_np, n_segments=60, compactness=10) # 可视化超像素索引映射 plt.imshow(segments, cmap='gray') plt.show() # 将超像素索引映射可视化 segment_img = mark_boundaries(img_np, segments) # 将 Numpy 数组转换为 PIL 图像 segment_img = Image.fromarray((segment_img * 255).astype(np.uint8)) # 保存超像素索引映射可视化 segment_img.save('segment_map.jpg') 将上述代码中引入超像素池化代码：import cv2 import numpy as np # 读取图像 img = cv2.imread('3.jpg') # 定义超像素分割器 num_segments = 60 # 超像素数目 slic = cv2.ximgproc.createSuperpixelSLIC(img, cv2.ximgproc.SLICO, num_segments) # 进行超像素分割 slic.iterate(10) # 获取超像素标签和数量 labels = slic.getLabels() num_label = slic.getNumberOfSuperpixels() # 对每个超像素进行池化操作，这里使用平均值池化 pooled = [] for i in range(num_label): mask = labels == i region = img[mask] pooled.append(region.mean(axis=0)) # 将池化后的特征图可视化 pooled = np.array(pooled, dtype=np.uint8) pooled_features = pooled.reshape(-1) pooled_img = cv2.resize(pooled_features, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST) print(pooled_img.shape) cv2.imshow('Pooled Image', pooled_img) cv2.waitKey(0)，并显示超像素池化后的特征图

import numpy as np from PIL import Image import matplotlib.pyplot as plt import cv2 # 加载图像 image = Image.open('3.jpg') # 转换为 PyTorch 张量 transform = transforms.ToTensor() img_tensor = ...

import numpy as np # define 'train' and 'valid' variables first # 将 preds_valid 转换为 NumPy 数组 preds_valid = np.array(preds_valid) # make predictions preds = [] for i in range(0,103): a = train['close'][len(train)-103+i:].sum() + sum(preds) b = a/103 preds.append(b) # calculate RMSE valid = [...] # define 'valid' variable first preds_np = np.array(preds) sum_preds = preds_np.sum() preds_valid = [] for i in range(0, len(preds) - 102): preds_slice = preds_np[i:i+103] sum_slice = preds_slice.sum() b = sum_slice / 103 preds_valid.append(b) rms = np.sqrt(np.mean(np.power((np.array(valid['close'])-preds_valid),2)))有错怎么解决

import numpy as np # 将 valid['close'] 和 preds_valid 都转换为 NumPy 数组 close_np = np.array(valid['close']) preds_valid_np = np.array(preds_valid) # 计算均方根误差 rms = np.sqrt(np.mean(np.power(...

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,...

ypeError Traceback (most recent call last) Cell In[100], line 7 4 import numpy as np 6 # 将 valid['close'] 和 preds_valid 都转换为 NumPy 数组 ----> 7 close_np = np.array(valid['close']) 8 preds_valid_np = np.array(preds_valid) 10 # 计算均方根误差 TypeError: list indices must be integers or slices, not str错误怎么修改

import numpy as np # 将 valid 列表转换为 Pandas 数据框 valid = pd.DataFrame(valid) # 将 valid['close'] 和 preds_valid 都转换为 NumPy 数组 close_np = np.array(valid['close']) preds_valid_np = np....

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 cv2 import numpy as np # 读取相机拍摄的图像 image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange(hsv, lower_red, upper_red)完善上述代码，显示结果

import numpy as np # 读取相机拍摄的图像 image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, ...

import numpy as np_FCM代码python_

算法流程随机初始化模糊矩阵U（描述每个点在不同类的隶属度）有了模糊矩阵U通过下面公式计算类中心在这里插入图片描述通过下面公式根据计算出的类中心，更新模糊矩阵U在这里插入图片描述当U的变化不大时结束迭代，...

yizt#numpy_neural_network#0_2_5-池化层的反向传播-MaxPooling、AveragePooli

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