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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摘要：点击查看代码 edges = [ ("Pe", "T", 13), ("Pe", "N", 68), ("Pe", "M", 78), ("Pe", "L", 51), ("Pe", "Pa", 51), ("T", "N", 68), ("T", "M", 70), ("T", "L&q
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摘要：点击查看代码 import heapq def prim(graph, start): num_nodes = len(graph) visited = [False] * num_nodes min_heap = [(0, start, -1)] mst_cost = 0 mst_edges =
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摘要：点击查看代码 import numpy as np distances = np.array([ [0, 2, 7, np.inf, np.inf, np.inf], [2, 0, 4, 6, 8, np.inf], [7, 4, 0, 1, 3, np.inf], [np.inf, 6, 1, 0
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摘要：点击查看代码 import numpy as np matches = np.array([ [0, 1, 0, 1, 1, 1], # 1队 [0, 0, 0, 1, 1, 1], # 2队 [1, 1, 0, 1, 0, 0], # 3队 [0, 0, 0, 0, 1, 1], # 4队 [0,
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摘要：import numpy as np import scipy.interpolate as spi import scipy.integrate as spi_integrate def g(x): return ((3x**2 + 4x + 6) * np.sin(x)) / (x**2 + 8
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import interp1d, CubicSpline T = np.array([700, 720, 740, 760, 780]) V = np.
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import griddata def f(x, y): return (x2 - 2*x) * np.exp(-x2 - y**2 - x*y) x_
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.optimize import curve_fit, leastsq, least_squares from scipy.constants import e def g(x,
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摘要：import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.interpolate import interp1d, PchipInterpolator, CubicSpline from sci
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp def system(t, state): x, y = state dxdt = -x3 - y dydt = x -
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摘要：import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp def model(t, y): f, df_dm, d2f_dm2, T, dT_dm = y d3f_dm3 = -3
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摘要：有四个年龄组的鱼。该鱼类在每年后4个月季节性集中产卵繁殖。按规定，捕捞作业只允许在前8个月进行，每年投入的捕捞能力固定不变。单位时间捕捞量鱼各年龄组鱼群条数的比例称为捕捞强度系。使用只能捕捞3、4龄鱼的13mm网眼的拉网，其两个捕捞强度系数比为0.42：1。各年龄组鱼的自然死亡率为0.8（1/年），
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摘要：def calculate_monthly_payment(P, annual_interest_rate, n_years): monthly_interest_rate = annual_interest_rate / 12 / 100 total_months = n_years * 12 M
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摘要：某车间生产滚珠，随机的抽出了50粒，测得他们的直径为（单位mm）15.0 15.8 15.2 15.1 15.9 14.7 14.8 15.5 15.6 15.3 15.1 15.3 15.0 15.6 15.7 14.8 14.5 14.2 14.9 14.9 15.2 15.0 15.3 15.
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摘要：import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats file_path = '9.3.xlsx' data = pd.read_excel(file_pat
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摘要：import pandas as pd from statsmodels.formula.api import ols from statsmodels.stats.anova import anova_lm import openpyxl column_names = ['城市1', '城市2',
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