合集-python数学建模

2.1
摘要：import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font.sans-s 阅读全文
posted @ 2024-10-28 11:54 2023310143015 阅读(6) 评论(0) 推荐(0)
2.2
摘要：2.2import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font.sans-s 阅读全文
posted @ 2024-10-28 11:55 2023310143015 阅读(10) 评论(0) 推荐(0)
2.3
摘要：代码 点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['f 阅读全文
posted @ 2024-10-28 12:36 2023310143015 阅读(12) 评论(0) 推荐(0)
2.4
摘要：代码 点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['f 阅读全文
posted @ 2024-10-28 12:45 2023310143015 阅读(13) 评论(0) 推荐(0)
2.5
摘要：代码 点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['f 阅读全文
posted @ 2024-10-28 12:48 2023310143015 阅读(11) 评论(0) 推荐(0)
2.7
摘要：代码 点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['f 阅读全文
posted @ 2024-10-28 12:49 2023310143015 阅读(11) 评论(0) 推荐(0)
2.7（2）
摘要：代码 点击查看代码 import numpy as np n=1000 A=np.zeros((n,n)) b=np.arange(1,n+1) np.fill_diagonal(A[1:,:-1],1) np.fill_diagonal(A[:-1,1:],1) np.fill_diagonal( 阅读全文
posted @ 2024-10-28 12:51 2023310143015 阅读(12) 评论(0) 推荐(0)
2.9
摘要：点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font 阅读全文
posted @ 2024-10-28 12:53 2023310143015 阅读(8) 评论(0) 推荐(0)
2.10
摘要：点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font 阅读全文
posted @ 2024-10-28 12:54 2023310143015 阅读(7) 评论(0) 推荐(0)
2.12
摘要：代码 点击查看代码 import numpy as np from sympy import Matrix, symbols # 定义一个矩阵 A = np.array([[-1,1,0],[-4,3,0],[1,0,2]]) # 使用numpy.linalg.eig求解特征值和特征向量 eigen 阅读全文
posted @ 2024-10-28 12:55 2023310143015 阅读(10) 评论(0) 推荐(0)
3.2
摘要：点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font 阅读全文
posted @ 2024-10-28 12:56 2023310143015 阅读(8) 评论(0) 推荐(0)
3.3
摘要：点击查看代码 import numpy as np import pandas as pd import sympy as sp sp.init_printing(use_unicode=True) import matplotlib.pyplot as plt plt.rcParams['font 阅读全文
posted @ 2024-10-28 12:57 2023310143015 阅读(7) 评论(0) 推荐(0)
5.3
摘要：代码 点击查看代码 from scipy.optimize import minimize import numpy as np c1=np.array([1,1,3,4,2]) c2=np.array([-8,-2,-3,-1,-2]) A=np.array([[1,1,1,1,1],[1,2,2 阅读全文
posted @ 2024-10-28 12:59 2023310143015 阅读(10) 评论(0) 推荐(0)
5.4
摘要：代码 点击查看代码 import numpy as np from scipy.optimize import minimize def objective(x): return -np.sqrt(x[0]) def constraint1(x): return x[0] - 10 def cons 阅读全文
posted @ 2024-10-28 12:59 2023310143015 阅读(10) 评论(0) 推荐(0)
5.5
摘要：点击查看代码 import numpy as np from scipy.optimize import minimize def objective(x): x1, x2, x3 = x return -(2 * x1 + 3 * x1 ** 2 + 3 * x2 + x2 ** 2 + x3) 阅读全文
posted @ 2024-10-28 13:00 2023310143015 阅读(18) 评论(0) 推荐(0)
6.1
摘要：代码 点击查看代码 import networkx as nx import matplotlib.pyplot as plt #创建一个无向图 G=nx.Graph() #添加节点 G.add_nodes_from([1,2,3,4,5,6]) #添加边 edges=[(1,2),(1,3),(1 阅读全文
posted @ 2024-10-28 13:01 2023310143015 阅读(7) 评论(0) 推荐(0)
6.1（2）
摘要：代码 点击查看代码 import networkx as nx import matplotlib.pyplot as plt G=nx.Graph() G.add_nodes_from([1,2,3,4,5,6]) edges_with_weights=[(1,2,7),(1,3,3),(1,4, 阅读全文
posted @ 2024-10-28 13:01 2023310143015 阅读(9) 评论(0) 推荐(0)
6.1（3）
摘要：点击查看代码 import networkx as nx import matplotlib.pyplot as plt G=nx.DiGraph() G.add_nodes_from([1,2,3,4,5,6]) edges_with_weights=[(1,3,3),(2,1,7),(2,3,1 阅读全文
posted @ 2024-10-28 13:02 2023310143015 阅读(9) 评论(0) 推荐(0)
6.3
摘要：代码 点击查看代码 import numpy as np import networkx as nx import pylab as plt L = [(1, 2, 20), (1, 5, 15), (2, 5, 25), (2, 3, 20), (2, 4, 60), (3, 5, 18), (3 阅读全文
posted @ 2024-10-28 13:03 2023310143015 阅读(13) 评论(0) 推荐(0)
6.4
摘要：*代码 点击查看代码 import numpy as np import networkx as nx a=np.zeros((5,5)) a[0,1:]=[0.8,2,3.8,6] a[1,2:]=[0.9,2.1,3.9] a[2,3:]=[1.1,2.3]; a[3,4]=1.4 G=nx.D 阅读全文
posted @ 2024-10-28 13:04 2023310143015 阅读(9) 评论(0) 推荐(0)
6.5
摘要：代码 点击查看代码 import numpy as np import networkx as nx import pandas as pd import pylab as plt n=6 node=['v'+str(i) for i in range(1,n+1)] A=np.zeros((n,n 阅读全文
posted @ 2024-10-28 13:05 2023310143015 阅读(8) 评论(0) 推荐(0)
7.3
摘要：代码 点击查看代码 import numpy as np from scipy.interpolate import interp1d, CubicSpline import matplotlib.pyplot as plt # 给定数据 T = np.array([700, 720, 740, 7 阅读全文
posted @ 2025-01-03 19:23 2023310143015 阅读(6) 评论(0) 推荐(0)
7.4
摘要：代码 点击查看代码 import numpy as np from scipy.interpolate import griddata import matplotlib.pyplot as plt # 定义函数 def f(x, y): return (x ** 2 - 2 * x) * np.e 阅读全文
posted @ 2025-01-03 19:24 2023310143015 阅读(3) 评论(0) 推荐(0)
7.7
摘要：代码 点击查看代码 import numpy as np from scipy.optimize import curve_fit, least_squares from scipy.linalg import lstsq import matplotlib.pyplot as plt def g( 阅读全文
posted @ 2025-01-03 19:26 2023310143015 阅读(9) 评论(0) 推荐(0)
7.10
摘要：代码 点击查看代码 import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.interpolate import interp1d, PchipInterpolator, CubicSplin 阅读全文
posted @ 2025-01-03 20:17 2023310143015 阅读(10) 评论(0) 推荐(0)
第八章
摘要：8.4代码 点击查看代码 import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp def system(t, state): x, y = state dxdt = -x*3 - 阅读全文
posted @ 2025-01-03 20:23 2023310143015 阅读(24) 评论(0) 推荐(0)
第九章
摘要：9.3 点击查看代码 import matplotlib.pyplot as plt data = [ [4.13, 3.86, 4.00, 3.88, 4.02, 4.02, 4.00], [4.07, 3.85, 4.02, 3.88, 3.95, 3.86, 4.02], [4.04, 4.0 阅读全文
posted @ 2025-01-03 20:38 2023310143015 阅读(16) 评论(0) 推荐(0)