import numpy as np# 提示用户输入关系矩阵n = int(input("请输入矩阵维度n："))print("请输入矩阵元素（以空格分隔，每行n个元素）：")matrix = []for i in range(n): row = input().split() matrix.append([int(x) for x in row])matrix = np.array(matrix)# 判断性质is_reflexive = np.all(np.diag(matrix))is_irreflexive = np.all(np.logical_not(np.diag(matrix)))is_symmetric = np.all(matrix == matrix.T)is_asymmetric = np.all(np.logical_not(matrix == matrix.T))is_transitive = np.all(np.logical_or(np.logical_not(matrix @ matrix == 0), np.diag(np.diag(matrix @ matrix))))# 输出结果if is_reflexive: print("该关系矩阵具有自反性。")if is_irreflexive: print("该关系矩阵具有反自反性。")if is_symmetric: print("该关系矩阵具有对称性。")if is_asymmetric: print("该关系矩阵具有非对称性。")if is_transitive: print("该关系矩阵具有传递性。")给我解释一下这段代码

python数据分析与可视化 import pandas as pd import numpy as np import m

import numpy as np import matplotlib.pyplot as plt # 创建示例数据 data = pd.DataFrame({ 'A': np.random.randn(1000), 'B': np.random.randn(1000), 'C': np.random.randn(1000), }) # 计算每列的平均值...

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 # 输入方阵的大小 n = int(input("请输入方阵的大小：")) # 生成随机方阵 matrix = np.random.randint(1, 10, (n, n)) print("随机生成的方阵：\n", matrix) # 生成列矩阵 col_matrix = np.array([['a{}'.format(i+1)] for i in range(n)]) print("生成的列矩阵：\n", col_matrix) # 拼接成增广矩阵 aug_matrix = np.concatenate((matrix, col_matrix), axis=1) print("生成的增广矩阵：\n", aug_matrix) # 初等变化，将方阵的部分化为单位矩阵 for i in range(n): # 首先将对角线上的元素变为1 aug_matrix[i] = np.divide(aug_matrix[i], aug_matrix[i][i], dtype=float) # 将其他行的元素变为0 for j in range(n): if i != j: aug_matrix[j] -= np.multiply(aug_matrix[j][i], aug_matrix[i], dtype=float) print("初等变化后的增广矩阵：\n", aug_matrix) # 提取增广矩阵的最后一列 last_col = aug_matrix[:, -1] print("增广矩阵的最后一列：\n", last_col) # 计算投影矩阵 proj_matrix = np.dot(col_matrix, last_col.T) / np.dot(last_col.T, last_col) print("计算得到的投影矩阵：\n", proj_matrix) # 计算逆矩阵 inv_matrix = np.linalg.inv(matrix) print("计算得到的逆矩阵：\n", inv_matrix)

这段代码是用 Python 实现的矩阵运算，主要实现了以下功能： ...需要输入方阵的大小，然后就可以输出随机生成的方阵、列矩阵、增广矩阵、初等变化后的增广矩阵、增广矩阵的最后一列、投影矩阵和逆矩阵。

import numpy as np n = int(input('输入每个维度的元素个数：')) a = np.arange(1,n**2+1).reshape(n,n) res = np.trace(a) print('矩阵的迹为',res)怎么把输出中的空格去掉

可以在print函数中使用参数sep...n = int(input('输入每个维度的元素个数：')) a = np.arange(1,n**2+1).reshape(n,n) res = np.trace(a) print('矩阵的迹为', res, sep='') 这样输出的结果中就不会再有空格了。

import numpy as np def MatrixChain(p,n): #存储最优值 m = np.zeros((n+1,n+1)) # 牺牲第0行，第0列 s = np.zeros((n+1,n+1)) # 牺牲第0行，第0列 # 单个矩阵连乘的次数 for i in range(n+1): #对角线值为0 m[i][i] = 0; s[i][i] = 0; # r表示子问题的规模，即连乘的矩阵个数，从两个矩阵开始，规模逐步放大到n for r in range(2,n+1): for i in range(1,n-r+2): j =i+r-1 # 当前的子问题 m[i][j] =m[i+1][j]+p[i-1]p[i]p[j] s[i][j] =i for k in range(i+1,j): t = m[i][k] + m[k + 1][j] + p[i - 1] * p[k] * p[j] if t<m[i][j]: m[i][j] = t s[i][j] = k return m,s # 备忘录 def Traceback(i,j,s): global res # 全局变量 if i == j: res.append('A'+ str(i)) else: res.append('(') Traceback(i,int(s[i][j]),s) Traceback(int(s[i][j])+1,j,s) res.append(')') if name == 'main': arr = [] for i in range(5): row = input(f"请输入第{i + 1}行，用空格分隔每个元素：") arr.append(row.split(" ")) n = int(len(arr)) res = [] # 处理矩阵的行和列 p = [] for i in range(n): if i==0: p.append(arr[0][0]) p.append(arr[0][1]) else: p.append(arr[i][1]) m,s = MatrixChain(p,n) Traceback(1,n,s) print(''.join(res))修改这段代码

row = input(f"请输入第{i+1}行，用空格分隔每个元素：") arr.append(list(map(int, row.split()))) n = len(arr) res = [] # 处理矩阵的行和列 p = [] for i in range(n): if i == 0: p.append(arr[0][0...

rom osgeo import gdal import copy import numpy as np from PIL import Image from tqdm import tqdm # read image def readTif(fileName): dataset = gdal.Open(fileName) if dataset == None: print(fileName + "文件无法打开") width = dataset.RasterXSize # 栅格矩阵的列数 height = dataset.RasterYSize # 栅格矩阵的行数 data = dataset.ReadAsArray(0, 0, width, height) return data, dataset # 保存tif文件函数 def writeTiff(im_data, im_geotrans, im_proj, path): if 'int8' in im_data.dtype.name: datatype = gdal.GDT_Byte elif 'int16' in im_data.dtype.name: datatype = gdal.GDT_UInt16 else: datatype = gdal.GDT_Float32 if len(im_data.shape) == 3: im_bands, im_height, im_width = im_data.shape elif len(im_data.shape) == 2: im_data = np.array([im_data]) im_bands, im_height, im_width = im_data.shape # 创建文件 driver = gdal.GetDriverByName("GTiff") dataset = driver.Create(path, int(im_width), int(im_height), int(im_bands), datatype) if (dataset != None): dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数 dataset.SetProjection(im_proj) # 写入投影 for i in range(im_bands): dataset.GetRasterBand(i + 1).WriteArray(im_data[i]) del dataset

你可以使用 Lua 的 io 库来读取和写入文件。以下是一个简单的 Lua 脚本，可以将 lvmvm 文件转换为 txt ...请将 "input.lvmvm" 替换为你要转换的 lvmvm 文件的路径，将 "output.txt" 替换为你要输出的 txt 文件的路径。

import numpy as np rows = int(input("输入行数：")) cols = int(input("输入列数：")) # 输入数据元素 data = [] for i in range(rows): row = [] for j in range(cols): value = float(input(f"输入元素[{i}, {j}]: ")) row.append(value) data.append(row) X0 = data m, n = X0.shape X1 = np.cumsum(X0) X1 = np.cumsum(X0) X2 = np.zeros((n-2, n)) for i in range(1, n-1): X2[i-1, i:] = X1[i] + X1[i+1] B = -0.5 * X2 t = np.ones((n-1, 1)) B = np.concatenate((B, t), axis=1) YN = X0[1:] P_t = YN / X1[:n-1] A = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), YN.T) a = A[0] u = A[1] c = u / a b = X0[0] - c X = str(b) + 'exp(' + str(-a) + 'k)' + str(c) equation = 'X(k+1)=' + X print(equation) k = np.arange(len(X0)) Y_k_1 = b * np.exp(-a * k) + c Y = Y_k_1[1:] - Y_k_1[:-1] XY = np.concatenate(([Y_k_1[0]], Y)) print(XY) CA = np.abs(XY - X0) Theta = CA XD_Theta = CA / X0 AV = np.mean(CA) R_k = (np.min(Theta) + 0.5 * np.max(Theta)) / (Theta + 0.5 * np.max(Theta)) P = 0.5 R = np.sum(R_k) / len(R_k) print(R) Temp0 = (CA - AV) 2 Temp1 = np.sum(Temp0) / len(CA) S2 = np.sqrt(Temp1) AV_0 = np.mean(X0) Temp_0 = (X0 - AV_0) 2 Temp_1 = np.sum(Temp_0) / len(CA) S1 = np.sqrt(Temp_1) TempC = S2 / S1 * 100 C = str(TempC) + '%' SS = 0.675 * S1 Delta = np.abs(CA - AV) TempN = np.where(Delta <= SS)[0] N1 = len(TempN) N2 = len(CA) TempP = N1 / N2 * 100 P = str(TempP) + '%'

具体来说，根据输入的行数和列数创建一个矩阵，并根据用户输入的元素值进行填充。然后，根据指数平滑模型的公式计算预测值，并将其打印出来。接下来，根据预测值和原始值计算预测误差、平均绝对误差、相对误差等指标...

import numpy as np n = int(input('请输入观测值个数：')) t = int(input('请输入待定点个数：')) h = np.mat([[5.654, 3.528, 10.273, 7.025, 2.727]]) Ha = np.mat([235.436]) l2 = int(-1000 * (h[0, 2] - h[0, 1] - h[0, 0])) l4 = int(-1000 * (h[0, 2] - h[0, 3] - h[0, 4])) B = np.mat([[1, 0, 0], [-1, 1, 0], [0, 1, 0], [0, 1, -1], [0, 0, 1]]) l = np.mat([[0, l2, 0, l4, 0]]) print('L\n',l) print('B\n',B) s = np.mat([[3.8, 2.5, 3.0, 4.0, 2.7]]) s1 = 10 / s[0, 0] s2 = 10 / s[0, 1] s3 = 10 / s[0, 2] s4 = 10 / s[0, 3] s5 = 10 / s[0, 4] P = np.diagflat(np.mat([[s1, s2, s3, s4, s5]])) print('权\n',P) N = (B.T) * P * B print('求得N值为：', np.around(N, decimals=4)) W = (B.T) * P * (l.T) print('求得W值为：', np.around(W, decimals=4)) x = np.linalg.inv(N) * W print('求得x值为(mm)：', np.around(x, decimals=2)) print('求得V值为(mm)：', np.around(V, decimals=4)) x1 = x[0] / 1000 x2 = x[1] / 1000 x3 = x[2] / 1000 a = np.array(h).reshape(-1, 1) h1 = a[0] h3 = a[2] h5 = a[4] X1 = x1 + h1 + Ha X2 = x2 + h3 + Ha X3 = x3 + h5 + Ha print('求得参数平差值X1值为：', np.around(X1, decimals=3)) print('求得参数平差值X2值为：', np.around(X2, decimals=3)) print('求得参数平差值X3值为：', np.around(X3, decimals=3)) H = a + V / 1000 print('求得观测值的平差值H值为：\n', np.around(H, decimals=4))

1. 输入观测值个数n和待定点个数t。 2. 定义高程观测值h和高程改正数Ha。 3. 计算l2和l4。 4. 定义B矩阵。 5. 计算l向量。 6. 定义s向量，并根据s向量计算权阵P。 7. 根据B矩阵和P，计算N矩阵。 8. 计算W向量...

测试点信息源代码源代码复制 import numpy as np n, m, k = map(int, input().split()) mat = np.zeros((n,n)) for i in range(m): x, y = map(int, input().split()) for j in range(x-2,x+3): if j >=0: mat[j-1][y-1] = 1 for j in range(y-2,y+3): if j >=0: mat[x-1][j-1] = 1 mat[x-2][y-2]=1 mat[x-2][y]=1 mat[x][y-2]=1 mat[x][y]=1 for i in range(k): o, p = map(int, input().split()) for j in range(o-3,o+2): for k in range(p-3,p+2): if j>=0 and k >=0: mat[j][k] = 1 cnt = 0 for i in range(n): for j in range(n): if mat[i][j] == 0: cnt +=1 print(cnt)优化代码，解决runtineerror

import numpy as np try: n, m, k = map(int, input().split()) mat = np.zeros((n, n)) for i in range(m): x, y = map(int, input().split()) for j in range(max(0, x - 2), min(n, x + 3)): mat[j - 1]...

import numpy as np import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit import time import torch # 1. 确定batch size大小，与导出的trt模型保持一致 BATCH_SIZE = 32 # 2. 选择是否采用FP16精度，与导出的trt模型保持一致 USE_FP16 = True target_dtype = np.float16 if USE_FP16 else np.float32 # 3. 创建Runtime，加载TRT引擎 f = open("resnet_engine.trt", "rb") # 读取trt模型 runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) # 创建一个Runtime(传入记录器Logger) engine = runtime.deserialize_cuda_engine(f.read()) # 从文件中加载trt引擎 context = engine.create_execution_context() # 创建context # 4. 分配input和output内存 input_batch = np.random.randn(BATCH_SIZE, 224, 224, 3).astype(target_dtype) output = np.empty([BATCH_SIZE, 1000], dtype = target_dtype) d_input = cuda.mem_alloc(1 * input_batch.nbytes) d_output = cuda.mem_alloc(1 * output.nbytes) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # 5. 创建predict函数 def predict(batch): # result gets copied into output # transfer input data to device cuda.memcpy_htod_async(d_input, batch, stream) # execute model context.execute_async_v2(bindings, stream.handle, None) # 此处采用异步推理。如果想要同步推理，需将execute_async_v2替换成execute_v2 # transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # syncronize threads stream.synchronize() return output # 6. 调用predict函数进行推理，并记录推理时间 def preprocess_input(input): # input_batch无法直接传给模型，还需要做一定的预处理 # 此处可以添加一些其它的预处理操作（如标准化、归一化等） result = torch.from_numpy(input).transpose(0,2).transpose(1,2) # 利用torch中的transpose,使(224,224,3)——>(3,224,224) return np.array(result, dtype=target_dtype) preprocessed_inputs = np.array([preprocess_input(input) for input in input_batch]) # (BATCH_SIZE,224,224,3)——>(BATCH_SIZE,3,224,224) print("Warming up...") pred = predict(preprocessed_inputs) print("Done warming up!") t0 = time.time() pred = predict(preprocessed_inputs) t = time.time() - t0 print("Prediction cost {:.4f}s".format(t)) 请将这部分代码，改成可以输入电脑摄像头视频的

其中，numpy是Python中用于快速处理大型数组和矩阵计算的库；tensorrt是NVIDIA推出的深度学习推理引擎，可以将训练好的模型转化为高效的推理模型；pycuda是一个Python接口，用于与CUDA（Compute Unified Device ...

import numpy as np from sklearn import datasets from sklearn.linear_model import LinearRegression np.random.seed(10) class Newton(object): def init(self,epochs=50): self.W = None self.epochs = epochs def get_loss(self, X, y, W,b): """ 计算损失 0.5sum(y_pred-y)^2 input: X(2 dim np.array):特征 y(1 dim np.array):标签 W(2 dim np.array):线性回归模型权重矩阵 output：损失函数值 """ #print(np.dot(X,W)) loss = 0.5np.sum((y - np.dot(X,W)-b)2) return loss def first_derivative(self,X,y): """ 计算一阶导数g = (y_pred - y)*x input: X(2 dim np.array):特征 y(1 dim np.array):标签 W(2 dim np.array):线性回归模型权重矩阵 output：损失函数值 """ y_pred = np.dot(X,self.W) + self.b g = np.dot(X.T, np.array(y_pred - y)) g_b = np.mean(y_pred-y) return g,g_b def second_derivative(self,X,y): """ 计算二阶导数 Hij = sum(X.T[i]X.T[j]) input: X(2 dim np.array):特征 y(1 dim np.array):标签 output：损失函数值 """ H = np.zeros(shape=(X.shape[1],X.shape[1])) H = np.dot(X.T, X) H_b = 1 return H, H_b def fit(self, X, y): """ 线性回归 y = WX + b拟合，牛顿法求解 input: X(2 dim np.array):特征 y(1 dim np.array):标签 output：拟合的线性回归 """ self.W = np.random.normal(size=(X.shape[1])) self.b = 0 for epoch in range(self.epochs): g,g_b = self.first_derivative(X,y) # 一阶导数 H,H_b = self.second_derivative(X,y) # 二阶导数 self.W = self.W - np.dot(np.linalg.pinv(H),g) self.b = self.b - 1/H_bg_b print("itration:{} ".format(epoch), "loss:{:.4f}".format( self.get_loss(X, y , self.W,self.b))) def predict(): """ 需要自己实现的代码 """ pass def normalize(x): return (x - np.min(x))/(np.max(x) - np.min(x)) if name == "main": np.random.seed(2) X = np.random.rand(100,5) y = np.sum(X3 + X**2,axis=1) print(X.shape, y.shape) # 归一化 X_norm = normalize(X) X_train = X_norm[:int(len(X_norm)0.8)] X_test = X_norm[int(len(X_norm)0.8):] y_train = y[:int(len(X_norm)0.8)] y_test = y[int(len(X_norm)0.8):] # 牛顿法求解回归问题 newton=Newton() newton.fit(X_train, y_train) y_pred = newton.predict(X_test,y_test) print(0.5np.sum((y_test - y_pred)2)) reg = LinearRegression().fit(X_train, y_train) y_pred = reg.predict(X_test) print(0.5np.sum((y_test - y_pred)2)) ——修改代码中的问题，并补全缺失的代码，实现牛顿最优化算法

import numpy as np from sklearn import datasets from sklearn.linear_model import LinearRegression np.random.seed(10) class Newton(object): def __init__(self, epochs=50): self.W = None self.b = ...

import jieba import pynlpir import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # 读取文本文件 with open('1.txt', 'r', encoding='utf-8') as f: text = f.read() # 对文本进行分词 word_list = list(jieba.cut(text, cut_all=False)) # 打开pynlpir分词器 pynlpir.open() # 对分词后的词语进行词性标注 pos_list = pynlpir.segment(text, pos_tagging=True) # 将词汇表映射成整数编号 vocab = set(word_list) vocab_size = len(vocab) word_to_int = {word: i for i, word in enumerate(vocab)} int_to_word = {i: word for i, word in enumerate(vocab)} # 将词语和词性标记映射成整数编号 pos_tags = set(pos for word, pos in pos_list) num_tags = len(pos_tags) tag_to_int = {tag: i for i, tag in enumerate(pos_tags)} int_to_tag = {i: tag for i, tag in enumerate(pos_tags)} # 将文本和标签转换成整数序列 X = np.array([word_to_int[word] for word in word_list]) y = np.array([tag_to_int[pos] for word, pos in pos_list]) # 将数据划分成训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # 定义模型参数 embedding_size = 128 rnn_size = 256 batch_size = 128 epochs = 10 # 定义RNN模型 model = tf.keras.Sequential([ tf.keras.layers.Embedding(vocab_size, embedding_size), tf.keras.layers.SimpleRNN(rnn_size), tf.keras.layers.Dense(num_tags, activation='softmax') ]) # 编译模型 model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test)) # 对测试集进行预测 y_pred = model.predict(X_test) y_pred = np.argmax(y_pred, axis=1) # 计算模型准确率 accuracy = np.mean(y_pred == y_test) print('Accuracy: {:.2f}%'.format(accuracy * 100)) # 将模型保存到文件中 model.save('model.h5')出现下述问题：ValueError: Found input variables with inconsistent numbers of samples:

import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split # 读取文本文件 with open('1.txt', 'r', encoding='utf-8') as f: text = f.read() # 对文本进行分词 ...

优化这段代码：n=int(input()) list_a=[] sum=0 for i in range(n): row = input().split( ) list_a.append([int(x) for x in row]) for j in range(n): for k in range(n): if j==k or j+k==n-1: sum+=list_a[j][k] print("{:.2f}".format(sum))

import numpy as np n = int(input()) list_a = np.zeros((n, n), dtype=int) for i in range(n): row = list(map(int, input().split())) list_a[i] = row sum = np.sum(list_a.diagonal()) + np.sum(np....

import sys, os sys.path.append(os.pardir) # 为了导入父目录而进行的设定 import numpy as np import matplotlib.pyplot as plt from catdog.deep_convnet import DeepConvNet, softmax from input_data import load_data x_train, t_train, x_test, t_test = load_data('F:\\2023\\archive\\train') network = DeepConvNet() network.load_params("deep_convnet_params.pkl") print("calculating test accuracy ... ") sampled = 1000 x_test = x_test[:sampled] t_test = t_test[:sampled] prediect_result = [] for i in x_test: i = np.expand_dims(i, 0) y = network.predict(i) _result = network.predict(i) _result = softmax(_result) result = np.argmax(_result) prediect_result.append(int(result))

- numpy：用于进行科学计算，例如数组操作和矩阵运算。 - matplotlib.pyplot：用于绘制数据和图形。 - DeepConvNet：一个自定义的卷积神经网络类，用于图像分类。 - softmax：一个自定义的softmax函数，用于将神经...

from keras.models import Sequential from keras.layers import Dense, Activation # 定义模型结构 model = Sequential() model.add(Dense(units=16, input_shape=(4,))) model.add(Activation('relu')) model.add(Dense(16)) model.add(Activation('relu')) model.add(Dense(3)) model.add(Activation('softmax')) #定义损失函数和优化器，并编译 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=["accuracy"]) import pandas as pd from sklearn.model_selection import train_test_split from keras.utils import np_utils filename = 'data\iris.data' data = pd.read_csv(filename, header = None) data.columns = ['sepal length','sepal width','petal length','petal width','class'] data.iloc[0:5,:] #数据预处理 #convert classname to integer data.loc[ data['class'] == 'Iris-setosa', 'class' ] = 0 data.loc[ data['class'] == 'Iris-versicolor', 'class' ] = 1 data.loc[ data['class'] == 'Iris-virginica', 'class' ] = 2 #data X = data.iloc[:,0:4].values.astype(float) y = data.iloc[:,4].values.astype(int) train_x, test_x, train_y, test_y = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0) #keras多分类问题需要将类型转化为独热矩阵 #与pd.get_dummies()函数作用一致 train_y_ohe = np_utils.to_categorical(train_y, 3) test_y_ohe = np_utils.to_categorical(test_y, 3) #print(test_y_ohe ) #训练模型 model.fit(train_x, train_y_ohe, epochs=50, batch_size=1, verbose=2, validation_data=(test_x,test_y_ohe)) # 评估模型 loss, accuracy = model.evaluate(test_x, test_y_ohe, verbose=2) print('loss = {},accuracy = {} '.format(loss,accuracy) ) # 查看预测结果 classes = model.predict(test_x, batch_size=1, verbose=2) print('测试样本数：',len(classes)) print("分类概率:\n",classes)

这段代码是一个使用Keras库进行机器学习的示例，主要针对鸢尾花数据集进行多分类预测。首先，使用Pandas读取数据集，并对类别进行处理，转化为数字标签。然后将数据集划分为训练集和测试集...import numpy as np

相关推荐

python数据分析与可视化 import pandas as pd import numpy as np import m

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

import numpy as np n = int(input('输入每个维度的元素个数：')) a = np.arange(1,n**2+1).reshape(n,n) res = np.trace(a) print('矩阵的迹为',res)怎么把输出中的空格去掉

优化这段代码：n=int(input()) list_a=[] sum=0 for i in range(n): row = input().split( ) list_a.append([int(x) for x in row]) for j in range(n): for k in range(n): if j==k or j+k==n-1: sum+=list_a[j][k] print("{:.2f}".format(sum))

numpy怎么接受用户输入矩阵

矩阵相加：提示用户输入一个数字n，为矩阵的行数，再提示用户输入一个数字m，为矩阵的列数，接下来，提示用户输入2nm个数字（每次输入一个数字）。输出C=A+B。

基于net的超市管理系统源代码（完整前后端+sqlserver+说明文档+LW）.zip

最新推荐

基于net的超市管理系统源代码（完整前后端+sqlserver+说明文档+LW）.zip

LABVIEW程序实例-公式节点.zip

Windows平台下的Fastboot工具使用指南

管理建模和仿真的文件

DLMS规约深度剖析：从基础到电力通信标准的全面掌握

修改代码，使其正确运行

Python机器学习基础入门与项目实践

"互动学习：行动中的多样性与论文攻读经历"

【Shell脚本进阶】：wc命令行数统计的高级用法及解决方案

python编写一个程序，使得根据输入的起点和终点坐标值计算出坐标方位角

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