合集-python数学建模课后习题
摘要：import numpy as np from scipy.optimize import minimize # 定义目标函数 def objective(x): return -np.sum(np.sqrt(x)) # 注意：scipy的minimize默认是最小化问题，所以这里取负号 # 定义约
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摘要：import numpy as np from scipy.optimize import minimize def objective(x): return 2*x[0] + 3*x[0]**2 + 3*x[1] + x[1]**2 + x[2] def constraint1(x): retur
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摘要：initial_costs = [2.5, 2.6, 2.8, 3.1] salvage_values = [2.0, 1.6, 1.3, 1.1] maintenance_costs = [0.3, 0.8, 1.5, 2.0] dp = [[float('inf')] * 2 for _ in
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摘要：import heapq def prim(graph, start): num_nodes = len(graph) visited = [False] * num_nodes min_heap = [(0, start, -1)] mst_cost = 0 mst_edges = [] whil
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摘要：edges = [ ("Pe", "T", 13), ("Pe", "N", 68), ("Pe", "M", 78), ("Pe", "L", 51), ("Pe", "Pa", 51), ("T", "N", 68), ("T", "M", 70), ("T", "L", 6
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摘要：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[0][0]
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摘要：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, 0.055
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摘要：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_half_ex
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摘要：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.linspace(
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摘要：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() # 循环
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摘要：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=(12, 8
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摘要：import numpy as np #单叶双曲面 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：#椭圆抛物面 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：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，并填充随机高程值 x
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摘要：import numpy as np # 定义系数矩阵A和常数项向量b A = np.array([[4, 2, -1], [3, -1, 2], [11, 3, 0]]) b = np.array([2, 10, 8]) # 使用numpy的lstsq求解最小二乘解 x, residuals, r
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摘要：import numpy as np # 定义系数矩阵A和常数项向量b A = np.array([[2, 3, 1], [1, -2, 4], [3, 8, -2], [4, -1, 9]]) b = np.array([4, -5, 13, -6]) # 使用numpy的lstsq函数求解最小二
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摘要：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 i < n-1
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摘要：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 = sp.solv
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摘要：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_quad, 1,
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摘要：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 scipy.op
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摘要：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 = eig(
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摘要：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) # 假设我们有一些初始猜测
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摘要：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：3008") 结果如下
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摘要：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, 0, 0],
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摘要：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, 1, 6]
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摘要：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, 0, 1,
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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 ((3*x**2 + 4*x + 6) * np.sin(x)) / (x**2 +
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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): x2 = x**2 return (x2 - 2*x) * np.exp(-x2 - y**2
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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 = -x - y # 假设这里应该是 -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：import numpy as np import matplotlib.pyplot as plt 初始化参数 a = 1 - 0.2 * (1 / 12) m = 1.109 * 10**5 w3 = 17.86 w4 = 22.99 X = [] Z = [] 计算X和Z的值 for k in
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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：import numpy as np from scipy.stats import shapiro data=[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.
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摘要：import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats 读取Excel文件 file_path = '9.3.xlsx' data = pd.read_exce
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摘要：import pandas as pd from statsmodels.formula.api import ols from statsmodels.stats.anova import anova_lm 定义列名 column_names = ['城市1', '城市2', '城市3', '城市
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摘要：import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt def check(data): # 提取数据的第一列和第二列 x = data[:, 0] y = data[:, 1] # 使用stat
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摘要：import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt # 加载数据 a = np.loadtxt('data10_2.txt') # 设置绘图参数 plt.rc('text', usetex=T
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摘要：import numpy as np import statsmodels.formula.api as smf import matplotlib.pyplot as plt # 加载数据 a = np.loadtxt('data10_3.txt') # 设置绘图参数 plt.rc('text',
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