摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt # 定义 x 的范围 x = np.linspace(-5, 5, 400) # 计算三个函数的值 y_cosh = np.cosh(x) y_sinh = np.sinh(x) y_
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摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt from scipy.integrate import quad def fun(t, x): return np.exp(-t) * (t ** (x - 1)) x = np.li
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摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt # 定义x的范围 x = np.linspace(-10, 10, 400) # 创建一个图形和坐标轴 plt.figure(figsize=(10, 6)) ax = plt.gca
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摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt # 定义x的范围 x = np.linspace(-10, 10, 400) # 创建一个2行3列的子图布局 fig, axs = plt.subplots(2, 3, figsize
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摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # 定义参数u和v u = np.linspace(-2, 2, 400) v = np.linspac
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摘要：点击查看代码 import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # 模拟高程数据（假设数据已经过某种方式插值或生成） # 这里我们创建一个简单的40x50网格，并填充随
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摘要：（1） 4x1+2x2-x3=2 3x1-x2+2x3=10 11x1+3x2=8 （2） 2x+3y+z=4 x-2y+4z=-5 3x+8y-2z=13 4x-y+9z=-6 点击查看代码 import numpy as np # 定义系数矩阵A和常数项向量b A = np.array([[4,
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摘要：点击查看代码 import numpy as np # 初始化系数矩阵A和常数项向量b n = 1000 A = np.zeros((n, n)) b = np.arange(1, n+1) # 填充系数矩阵A for i in range(n): A[i, i] = 4 # 对角线元素为4 if
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摘要：点击查看代码 import sympy as sp # 定义变量 x, y = sp.symbols('x y') # 定义方程组 equation1 = sp.Eq(x**2 - y - x, 3) equation2 = sp.Eq(x + 3*y, 2) # 解方程组 solutions =
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摘要：点击查看代码 from scipy.integrate import quad import numpy as np # 第一部分：抛物线旋转体（修正后） def V1_quad(y): return np.pi * (4*y - y**2) V1_corrected, _ = quad(V1_qu
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摘要：点击查看代码 import numpy as np def f(x): return (abs(x + 1) - abs(x - 1)) / 2 + np.sin(x) def g(x): return (abs(x + 3) - abs(x - 3)) / 2 + np.cos(x) from s
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摘要：点击查看代码 import numpy as np from scipy.linalg import eig # 定义矩阵 A = np.array([[-1, 1, 0], [-4, 3, 0], [1, 0, 2]]) # 计算特征值和特征向量 eigenvalues, eigenvectors
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摘要：点击查看代码 import numpy as np def f(x): return (abs(x + 1) - abs(x - 1)) / 2 + np.sin(x) def g(x): return (abs(x + 3) - abs(x - 3)) / 2 + np.cos(x) # 假设我们
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摘要：点击查看代码 def X(n): # 差分方程的解 return 2 * (-1)**(n + 1) n_values = [0, 1, 2, 3, 4, 5] for n in n_values: print(f"X({n}) = {X(n)}") print("学号：3004")
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摘要：点击查看代码 import numpy as np from scipy.sparse.linalg import eigs import pylab as plt w = np.array([[0, 1, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [1, 1, 0, 1,
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摘要：点击查看代码 import matplotlib.pyplot as plt import numpy as np import cvxpy as cp x=cp.Variable(6,pos=True) obj=cp.Minimize(x[5]) a1=np.array([0.025, 0.015
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摘要：点击查看代码 MAX_A = 15 MAX_B = 24 MAX_DEBUG = 5 products = [ {"name": "Ⅰ", "A_hours": 1, "B_hours": 6, "debug_hours": 1, "profit": 2}, # 假设产品Ⅰ至少使用1小时设备A {"
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摘要：学号后四位：“3004” 5.4 求解下列非线性规划： 点击查看代码 import numpy as np from scipy.optimize import minimize def objective(x): return -np.sum(np.sqrt(x) * np.arange(1, 1
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摘要：点击查看代码 import numpy as np demands = [40, 60, 80] max_production = 100 total_demand = sum(demands) dp = np.full((4, total_demand + 1), float('inf')) dp
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摘要：用python绘制一个无向图：v1在中间，v2、v3、v4、v5、v6在周围；v1与v2、v3、v4相连；v2与v3、v6、v1相连；v3与v1、v2、v4相连；v4与v1、v3、v5相连；v5与v4、v6相连；v6与v2、v5相连 点击查看代码 import networkx as nx impo
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