import numpy as np # b = np.load("train_od_3936_109_109.npy") # print(b) c = np.load("X_od.npy") D = np.load("Y_od.npy") print(c.shape) print(D.shape) max=np.max(c) train_x=c[0:1000]/max train_y=D[0:1000]/max val_x=c[1000:1150]/max val_y=D[1000:1150]/max test_x=c[1150:]/max twst_y=D[1150:] print(train_x.shape) # print(D.shape) print(val_x.shape) # print(D.shape) print(test_x.shape) # print(D.shape) from keras.layers import Dense, LSTM, ConvLSTM2D, Dropout, Reshape from keras.models import Sequential model = Sequential() model.add(Reshape((5,109,109,1),input_shape=(5,109,109))) model.add(ConvLSTM2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same',input_shape=(5, 109, 109, 1))) # model.add(Dropout(0.2)) model.add(Dense(1)) # 在Dense层中，输出维度应该是(109, 109, 1)，而不是1 model.add(Reshape((109, 109))) # 在Reshape层中，输出维度应该是(109, 109)而不是(5, 109, 109)。 model.summary() model.compile(optimizer='adam', loss='mse') history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False)该段程序怎么改为以LSTM神经网络模型的地铁客流预测和预测结果可视化代码

补全程序，完成以下功能：创建一个存储了21~30的整数的Series对象，索引值为1~10，将其中小于等于25的元素赋值为0。import pandas as pd import numpy as np data = np._____(21, 31, 1, np.int32) index = np.arange(1, 11, 1) num = pd.Series(_, _) print(num) num[num.values <= _] = ___ print(num)

import pandas as pd import numpy as np data = np.arange(21, 31, 1, np.int32) index = np.arange(1, 11, 1) num = pd.Series(data, index) print(num) num[num.values <= 25] = 0 print(num)

import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense,LSTM,Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=64, input_shape=(5, 109))) model.add(Dropout(0.2)) model.add(Dense(units=109, activation='linear')) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何用返回序列修改这段程序

import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense, LSTM, Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据...

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(...

from sklearn.datasets import load_breast_cancer from sklearn.svm import SVC from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import numpy as np #导入乳腺癌数据集 cancers = load_breast_cancer() #下载乳腺癌数据集 X = cancers.data #获取特征

这是一些Python代码，导入了sklearn的数据集、支持向量机模型和模型选择方法，同时还导入了matplotlib库和numpy库。这些代码通常用于机器学习任务中的数据预处理、训练模型和可视化结果。其中，load_breast_cancer是...

import pickle import time from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import classification_report import numpy as np with open('体育.pkl', 'rb') as f: sports_news = pickle.load(f) # 加载行政新闻数据集 with open('从政.pkl', 'rb') as f: politics_news = pickle.load(f) # 将体育新闻和行政新闻合并成一个数据集 data = np.concatenate([politics_news, sports_news]) def load_variavle(filename): f = open(filename, 'rb') r = pickle.load(f) f.close() return r news = np.concatenate([politics_news, sports_news]) print(news.data[0]) print(news.target_names) print(news.target[0:20]) X_train,X_test,y_train,y_test=train_test_split(news.data,news.target,test_size=0.25,random_state=520) vec =TfidfVectorizer(stop_words='english') X_train=vec.fit_transform(X_train) X_test=vec.transform(X_test) time_start = time.perf_counter() mnb=MultinomialNB() mnb.fit(X_train,y_train) y_predict=mnb.predict(X_test) print('训练时间',time_start) print('准确率:',mnb.score(X_test,y_test)) print(classification_report(y_test,y_predict,target_names=news.target_names))

同时使用了TfidfVectorizer、train_test_split、MultinomialNB和classification_report等模块，用于数据的处理、模型的训练和评估。最后从名为“体育.pkl”的文件中读取了数据，并将其存储在变量sports_news中。

import pandas as pd import numpy as np from sklearn import datasets,discriminant_analysis from sklearn.model_selection import train_test_split iris=datasets.load_iris() x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target,train_size=0.8,stratify=iris.target) y_c = np.unique(iris.target) lda=pd.read_csv('pendigits.csv') lda=discriminant_analysis.LinearDiscriminantAnalysis() lda.fit(x_train,y_train) print('Coefficients:%s, intercept %s'%(lda.coef_,lda.intercept_))#输出权重向量和 b print('Score: %.2f' % lda.score(x_test, y_test))#测试集

null是一个表示空值或缺失值的特殊值，通常用于表示变量或对象没有被赋值或不存在。在编程中，null通常用于判断变量是否有值，或者作为函数的返回值来表示没有返回值。在JavaScript中，null是一个原始值，表示空对象...

import cv2 import numpy as np import matplotlib.pyplot as plt gray = cv2.imread('digits.png', 0) digits = [np.hsplit(r, 100) for r in np.vsplit(gray, 50)] # 分解数字：50行，100列 np_digits = np.array(digits) # 转换为numpy数组 # 准备训练数据，转换为二维数组，每个图像400个像素 train_data = np_digits.reshape(-1, 400).astype(np.float32) train_labels = np.repeat(np.arange(10), 500)[:, np.newaxis] # 定义标志 knn = cv2.ml.KNearest_create() # 创建kNN分类器 knn.train(train_data, cv2.ml.ROW_SAMPLE, train_labels) # 训练模型 # 用绘图工具创建手写数字5图像（大小20x20）进行测试（黑白二值图像） test = cv2.imread('d5.png', 0) # 打开测试图像 test = cv2.resize(test, (20, 20)) # 转换为20x20大小 test_data = test.reshape(1, 400).astype(np.float32) # 转换为测试数据 ret, result, neighbours, dist = knn.findNearest(test_data, k=3) # 执行测试 print(result.ravel()) print(neighbours.ravel()) # 拍摄图像数字3，进行测试（非黑白二值图像） img2 = cv2.imread('d3.png', 0) img2 = cv2.resize(img2, (20, 20)) ret, img2 = cv2.threshold(img2, 150, 255, cv2.THRESH_BINARY_INV) # 反二值化阈值处理 test_data = img2.reshape(1, 400).astype(np.float32) # 转换为测试数据 ret, result, neighbours, dist = knn.findNearest(test_data, k=3) # 执行测试 print(result.ravel()) print(neighbours.ravel()) 以上代码怎么优化

import numpy as np import matplotlib.pyplot as plt # 打开文件并读取灰度图像 with open('digits.png', 'rb') as f: gray = cv2.imdecode(np.frombuffer(f.read(), np.uint8), cv2.IMREAD_GRAYSCALE) # 分解...

import torch import numpy as np from torchvision import datasets, models, transforms from torchvision import transforms from PIL import Image transform = transforms.Compose([ transforms.Resize((28, 28), interpolation=2), transforms.ToTensor() ] ) a=np.load('./original_crystal_2d_graphs/mp-170_copy1_opt.vasp_subst_O-Se_V-Bi.npy') img = a[0:3, :] #数组形式 im = Image.fromarray(img) # img = Image.open(im) img_ = transform(im).unsqueeze(0) #拓展维度 img_.show()

这段代码的作用是，导入了PyTorch、NumPy和torchvision等工具库，并指定了一组数据转换操作（修改为28x28尺寸并转换为张量格式）。然后，使用NumPy库读取了一个二维晶体图形的数据，并取出前三行作为图像数据。

下面的代码哪里有问题，帮我改一下from future import print_function import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense,Dropout,Flatten from keras.layers import Conv2D,MaxPooling2D from keras import backend as K import tensorflow as tf import datetime import os np.random.seed(0) from sklearn.model_selection import train_test_split from PIL import Image import matplotlib.pyplot as plt from keras.datasets import mnist images = [] labels = [] (x_train,y_train),(x_test,y_test)=mnist.load_data() X = np.array(images) print (X.shape) y = np.array(list(map(int, labels))) print (y.shape) x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) print (x_train.shape) print (x_test.shape) print (y_train.shape) print (y_test.shape) ############################ ########## batch_size = 20 num_classes = 4 learning_rate = 0.0001 epochs = 10 img_rows,img_cols = 32 , 32 if K.image_data_format() =='channels_first': x_train =x_train.reshape(x_train.shape[0],1,img_rows,img_cols) x_test = x_test.reshape(x_test.shape[0],1,img_rows,img_cols) input_shape = (1,img_rows,img_cols) else: x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1) x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1) input_shape =(img_rows,img_cols,1) x_train =x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:',x_train.shape) print(x_train.shape[0],'train samples') print(x_test.shape[0],'test samples')

import numpy as np import tensorflow import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import ...

import tensorflow as tf import numpy as np from keras import Model in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") print(c1.shape) print(D1.shape) max=np.max(out_flow) train_in_flow=in_flow[0:200]/max val_in_flow=in_flow[200:260]/max test_in_flow=out_flow[260:]/max train_out_flow=out_flow[0:200]/max val_out_flow=out_flow[200:260]/max test_out_flow=out_flow[260:]/max train_c1=c1[0:200]/max val_c1=c1[200:260]/max test_c1=c1[260:]/max train_D1=D1[0:200]/max val_D1=D1[200:260]/max test_D1=D1[260:]/max print(train_c1.shape, train_in_flow.shape, train_in_flow.shape, train_D1.shape) from keras.layers import * input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error') history = model.fit([train_c1, train_in_flow,train_in_flow ],train_D1, validation_data=([val_c1,val_out_flow, val_in_flow], val_D1), epochs=100, batch_size=32) model.save("my_model.h10032") model.save_weights("my_model_weights.h10032") 根据上述程序利用保持好的模型预测并将预测结果可视化输出

import numpy as np from keras import Model from keras.models import load_model import matplotlib.pyplot as plt # 加载模型 model = load_model("my_model.h10032") # 加载数据 in_flow = np.load("X_in_30...

下述代码缺少什么import tensorflow as tf import numpy as np from keras import Model in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") print(train_in_flow.shape) print(val_in_flow.shape) print(test_in_flow.shape) from keras.layers import * input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error')

The code is missing the ...from keras.layers import Input, ConvLSTM2D, Dropout, Dense, Permute, LSTM, Multiply, Reshape, Add, Bidirectional These are necessary for defining the model architecture.

import tensorflow as tf import numpy as np from keras import Model from keras.layers import * from sklearn.model_selection import train_test_split in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error') 数据划分及其归一化，模型的训练和预测输出

X_train, X_test, y_train, y_test = train_test_split(in_flow, out_flow, test_size=0.2, random_state=42) # 归一化处理 scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler....

sys.path.append(os.pardir) import os import sys import numpy as np from dataset.mnist import load_mnist from PIL import Image def img_show(img): pil_img = Image.fromarray(np.uint8(img)) pil_img.show() (x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False) img = x_train[0] label = t_train[0] print(label) print(img.shape)#784 img = img.reshape(28, 28)#把图像形状变为原来的尺寸 print(img.shape)#（28，28） img_show(img)

load_mnist函数用于加载MNIST数据集，其中flatten=True表示将图像展开成一维数组，normalize=False表示不对图像进行归一化处理。接着从训练集中取出第一张图像和对应的标签，并打印出来。最后，调用img_show函数显示...

from 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('img.png') # 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=100, compactness=10) # 可视化超像素标记图 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)) # 保存超像素标记图 segment_img.save('segments.jpg') n_segments = np.max(segments) + 1 # 初始化超像素块的区域 segment_regions = np.zeros((n_segments, img_np.shape[0], img_np.shape[1])) # 遍历每个超像素块 for i in range(n_segments): # 获取当前超像素块的掩码 mask = (segments == i) # 将当前超像素块的掩码赋值给超像素块的区域 segment_regions[i][mask] = 1 # 保存超像素块的区域 np.save('segment_regions.npy', segment_regions) # 加载超像素块的区域 segment_regions = np.load('segment_regions.npy') # 取出第一个超像素块的区域 segment_region = segment_regions[0] # 显示超像素块的区域 plt.imshow(segment_region, cmap='gray') plt.show()，上述代码显示超像素块区域时显示的图像为全黑，怎么回事

出现这种情况可能是因为超像素...segment_region = (segment_region * 255).astype(np.uint8) # 显示超像素块的区域 plt.imshow(segment_region, cmap='gray') plt.show() 这样就可以正常显示超像素块的区域了。

import open3d as o3d import numpy as np np.set_printoptions(suppress=True) data = np.load('D:/Informer2020-main/results/informer_ETTh1_ftMS_sl192_ll96_pl48_dm512_nh8_el2_dl1_df2048_atprob_fc5_ebtimeF_dtTrue_mxTrue_test_0/pred.npy') b = np.array([1 , 1, 1]) np.savetxt('./scene.txt', data[:,:3]/b) pcd =o3d.io.read_point_cloud('./scene.txt', format='xyz') print(pcd)

其中，np.load()函数读取.npy文件并返回一个numpy数组。然后使用numpy的savetxt()函数将该数组的前三列 / 1 1 1 归一化并保存到文件scene.txt中。最后，使用Open3D库的read_point_cloud()函数从scene.txt文件中读取...

【java毕业设计】应急救援物资管理系统源码（springboot+vue+mysql+说明文档）.zip

项目经过测试均可完美运行！环境说明：开发语言：java jdk：jdk1.8 数据库：mysql 5.7+ 数据库工具：Navicat11+ 管理工具：maven 开发工具：idea/eclipse

基于java的音乐网站答辩PPT.pptx

相关推荐

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

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

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

from sklearn.datasets import load_breast_cancer from sklearn.svm import SVC from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import numpy as np #导入乳腺癌数据集 cancers = load_breast_cancer() #下载乳腺癌数据集 X = cancers.data #获取特征

【java毕业设计】应急救援物资管理系统源码（springboot+vue+mysql+说明文档）.zip

基于java的音乐网站答辩PPT.pptx

最新推荐

Python Numpy:找到list中的np.nan值方法

【java毕业设计】应急救援物资管理系统源码（springboot+vue+mysql+说明文档）.zip

基于java的音乐网站答辩PPT.pptx

基于Flexsim的公路交通仿真系统.zip

Android圆角进度条控件的设计与应用

管理建模和仿真的文件

【R语言lattice包实战】：从案例到技巧，图形制作不再难

输入正整数n.打出长度为n的菱形

mui框架实现带侧边栏的响应式布局

"互动学习：行动中的多样性与论文攻读经历"

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