import numpy as np def LU_decomposition(A): n = len(A) L = np.eye(n) U = A.copy() for i in range(n-1): for j in range(i+1, n): L[j, i] = U[j, i] / U[i, i] U[j, i:] -= L[j, i] * U[i, i:] return L, U def forward_substitution(L, b): n = len(b) y = np.zeros(n) for i in range(n): y[i] = b[i] - np.dot(L[i, :i], y[:i]) y[i] /= L[i, i] return y def back_substitution(U, y): n = len(y) x = np.zeros(n) for i in range(n-1, -1, -1): x[i] = y[i] - np.dot(U[i, i+1:], x[i+1:]) x[i] /= U[i, i] return x def inv(A): n = len(x) A_inv=np.zeros((n, n)) for i in range(n): # 将b中第i个元素设为1，其他元素设为0 bi = np.zeros((n, 1)) bi[i, 0] = 1 # 求解线性方程组Ax=bi，得到A_inv的第i列 ABi = np.hstack((A, bi)) for j in range(n): # 如果A[i, i]为0，则需要进行行交换 if ABi[j, j] == 0: for k in range(j+1, n): if ABi[k, j] != 0: ABi[[j, k]] = ABi[[k, j]] # 将A[i, i]消成1 ABi[j] = ABi[j] / ABi[j, j] # 将A[i, j]消成0（j != i） for k in range(n): if k != j: ABi[k] = ABi[k] - ABi[k, j] * ABi[j] A_inv[:, i] = ABi修改这段代码

import numpy as np def gauss_elimination(A): n = A.shape[0] U = A.copy() L = np.eye(n) for i in range(n): for j in range(i+1, n): k = U[j][i] / U[i][i] L[j][i] = k U[j][i:] -= k * U[i][i:] return L, U

import numpy as np def gauss_elimination(A): n = A.shape[0] U = A.copy() L = np.eye(n) for i in range(n): for j in range(i+1, n): k = U[j][i] / U[i][i] L[j][i] = k U[j][i:] -= k * U[i][i:] ...

解释已下代码import numpy as npdef generate_matrix(N): matrix = np.ones((N, N)) * (N - 1) np.fill_diagonal(matrix, N) return matrix

这段代码定义了一个名为generate_matrix的函数，该函数接受一个...具体来说，np.ones((N, N))生成了一个N*N的全1矩阵，然后用(N-1)乘以这个矩阵，最后使用np.fill_diagonal()函数将对角线上的元素设置为N。

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) #print(matrix) col_sum = np.sum(matrix, axis=0) print(col_sum)怎么在输出中输出逗号

你可以使用numpy的函数numpy.savetxt()来输出逗号分隔的矩阵数据。可以按照以下方式修改你的代码： python import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i...

修改代码　输出不要有逗号　import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0) output_list = [] for i in range(len(col_sum)): output_list.append(int(col_sum[i])) print(output_list)

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0) ...

输入形式】n个同学的考试成绩【输出形式】用数组存储且排好序的考试成绩。【样例输入】 98 100 96.5 64 59 58 65 78 85 【样例输出】 [ 58. 59. 64. 65. 78. 85. 96.5 98. 100. ] 【样例程序片段】 import numpy as np def main(): arr = input("") a = [float(x) for x in arr.split()] a = np.array(a) 排序代码 if name == 'main': main()

import numpy as np def main(): arr = input("请输入n个同学的考试成绩，以空格分隔：") a = [float(x) for x in arr.split()] a = np.array(a) a.sort() print(a) if __name__ == '__main__': main() ...

import numpy as np def get_prod(n): r=1 array1=np.arange(n) array2=[] for i in array1: array2.append((2i/(2i-1))(2i/(2i+1))) for j in array2: r=j print(r)哪里错了

import numpy as np def get_prod(n): array1 = np.arange(n) array2 = (2*array1/(2*array1-1))*(2*array1/(2*array1+1)) r = np.prod(array2) print(r) get_prod(10) # 测试代码，输出结果为 0....

修改代码　输出有中括号，括号中不要有逗号　import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0) output_list = [] for i in range(len(col_sum)): output_list.append(int(col_sum[i])) print(output_list)

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0) ...

import os from random import shuffle import pickle import librosa import tensorflow.keras as keras import librosa.display import numpy as np from matplotlib import pyplot as plt def load_files(audio_dir): files=os.listdir(audio_dir) wav_files=[] for wav in files: if not wav.endswith('.wav'): continue wav_files.append(wav) if not wav_files: print('未找到数据集') shuffle(wav_files) nfiles=len(wav_files) ntrain=int(nfiles*0.7) return wav_files[:ntrain],wav_files[ntrain:] audio_dir='../data/recordings/' dataset_pickle='../tmp/recordings.pkl' train_files,valid_files=load_files(audio_dir) print('训练集样本数为{}\n验证集样本数为{}'.format(len(train_files),load_files(valid_files)))帮我检查错误

import numpy as np from matplotlib import pyplot as plt def load_files(audio_dir): files = os.listdir(audio_dir) wav_files = [] for wav in files: if not wav.endswith('.wav'): continue wav_files...

修改代码　把a改成动态的import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) col_sum = np.sum(matrix, axis=0) output_list = [] for i in range(len(col_sum)): output_list.append(int(col_sum[i])) print('[{}]'.format(' '.join(map(str, output_list))))

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = int(input("请输入a的值：")) matrix = generate_matrix(a) col_sum ...

import numpy as np import pywt from scipy.fftpack import dct def nsst(image): # 将图像转换为灰度图像 def nsst(image): # 将图像转换为灰度图像 if len(image.shape) == 3: image = np.mean(image, axis=2) # 设置NSST参数 scales = 3 shearlet_system = pywt.ShearletSystem2D(image.shape, scales) # 计算图像的NSST分解系数 coeffss = [] for scale in range(scales): for shear in range(shearlet_system.shear_count): shearlet = shearlet_system.shearlets[scale][shear] shearlet_fft = np.fft.fft2(shearlet, image.shape) shearlet_fft_conj = np.conj(shearlet_fft) image_fft = np.fft.fft2(image) shearlet_coeff = np.fft.ifft2(shearlet_fft_conj * image_fft) coeffss.append(shearlet_coeff) # 将NSST分解系数组合成一个张量 coeffs = np.stack(coeffss, axis=-1) return coeffs coeffs1 = nsst_decomposition(image1, num_scales=4) coeffs2 = nsst_decomposition(image2, num_scales=4)

import numpy as np import pywt from scipy.fftpack import dct def nsst_decomposition(image): # 将图像转换为灰度图像 if len(image.shape) == 3: image = np.mean(image, axis=2) # 设置NSST参数 ...

import numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bounds): relaxed_A = A relaxed_b = b while True: res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x if all(int(val) == val for val in x): return x.astype(int) new_constraint = (relaxed_A @ x <= relaxed_b) relaxed_A = np.vstack((relaxed_A, new_constraint)) def get_bounds(): return [(0, None), (0, None)] def get_c(): return np.array([40, 90]) def get_A(): return np.array([[-9, -7], [-7, -20]]) def get_b(): return np.array([-56, -70]) if name == 'main': bounds = get_bounds() integer_cutting_plane(get_c(), get_A(), get_b(), bounds)以上代码运行报错ValueError: Invalid input for linprog: b_ub must be a 1-D array; b_ub must not have more than one non-singleton dimension and the number of rows in A_ub must equal the number of values in b_ub 请解决

import numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bounds): relaxed_A = A relaxed_b = b while True: res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b....

优化这段import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' def generate_signal(t_vec, A, phi, noise, freq): Omega = 2np.pifreq return A * np.sin(Omegat_vec + phi) + noise (2np.random.random def lock_in_measurement(signal, t_vec, ref_freq): Omega = 2np.piref_freq ref_0 = 2np.sin(Omegat_vec) ref_1 = 2np.cos(Omegat_vec) # signal_0 = signal ref_0 signal_1 = signal * ref_1 # X = np.mean(signal_0) Y = np.mean(signal_1) # A = np.sqrt(X2+Y2) phi = np.arctan2(Y,X) print("A=", A, "phi=", phi) # t_vec = np.linspace(0, 0.2, 1001) A = 1 phi = np.pi noise = 0.2 ref_freq = 17.77777 # signal = generate_signal(t_vec, A, phi, noise, ref_freq) # lock_in_measurement(signal, t_vec, ref_freq)

import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' def generate_signal(t_vec, A, phi, noise, freq): Omega = 2*np.pi*freq return A * np.sin(Omega*t_vec ...

import numpy as np X = np.arange(1, 6) D = len(X) def get_Penalized_1(X, a, k, m): Y = 0 U = 0 for i in range(D): if np.greater(X, a): u = k * np.power((X, a), m) elif np.less(X, a): u = k * np.power((-X - a), m) else: u = 0 U = U + u y = 1 + (X + 1) / 4 if np.equal(i, 0): Y1 = 10 * np.power((np.sin(np.pi * y)), 2) elif np.less_equal(i, D - 1): Y = Y + np.power((y_1 - 1), 2) * (1 + 10 * np.power((np.sin(np.pi * y), 2))) else: YD = np.power((y - 1), 2) y_1 = y F = np.pi / D * (Y1 + Y + YD) + U return F get_Penalized_1 = np.frompyfunc(get_Penalized_1, 4, 1) x = get_Penalized_1(X, 10, 100, 4) print(x)

import numpy as np X = np.arange(1, 6) D = len(X) def get_Penalized_1(X, a, k, m): Y = 0 U = 0 for i in range(D): if np.greater(X[i], a): u = k * np.power((X[i] - a), m) elif np.less(X[i], a):...

给出这段代码的示例用法import numpy as npdef haar_matrix(N): H = np.zeros((N,N)) n = int(np.log2(N)) H[0,:] = 1/np.sqrt(N) for j in range(n): for k in range(2j): p = 2(n-j) q = 2kp for i in range(p): H[i+q,j+1] = 1/np.sqrt(p) H[i+p+q,j+1] = -1/np.sqrt(p) return Hdef haar_wavelet(signal): n = signal.size H = haar_matrix(n) return H.dot(signal)

import numpy as np # 生成一个长度为 8 的随机信号 signal = np.random.rand(8) # 获取离散哈尔变换矩阵 H = haar_matrix(8) # 对信号进行离散哈尔变换 wavelet = haar_wavelet(signal) print('原始信号：', ...

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解释已下代码import numpy as npdef generate_matrix(N): matrix = np.ones((N, N)) * (N - 1) np.fill_diagonal(matrix, N) return matrix

import numpy as np def generate_matrix(a): arr = np.zeros((a,a)) for i in range(a): arr[i,:i+1] = np.arange(1,i+2) return arr a = 5 matrix = generate_matrix(a) #print(matrix) col_sum = np.sum(matrix, axis=0) print(col_sum)怎么在输出中输出逗号

import numpy as np def get_prod(n): r=1 array1=np.arange(n) array2=[] for i in array1: array2.append((2*i/(2*i-1))*(2*i/(2*i+1))) for j in array2: r*=j print(r)哪里错了

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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 def get_prod(n): r=1 array1=np.arange(n) array2=[] for i in array1: array2.append((2i/(2i-1))(2i/(2i+1))) for j in array2: r=j print(r)哪里错了