2024年10月14日
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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 阅读全文
posted @ 2024-10-14 23:51
VVV1
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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 阅读全文
posted @ 2024-10-14 23:49
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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 阅读全文
posted @ 2024-10-14 23:41
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total_demand = sum(demands) dp = np.full((4, total_demand + 1), float('inf')) dp[0][0] = 0 prev_production = np.full((4, total_demand + 1), -1) for i 阅读全文
posted @ 2024-10-14 23:37
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import numpy as np from scipy.optimize import minimize def objective(x): return 2x[0] + 3x[0]2 + 3*x[1] + x[1]2 + x[2] def constraint1(x): return 10 - 阅读全文
posted @ 2024-10-14 23:35
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import numpy as np from scipy.optimize import minimize def objective(x): return -np.sum(np.sqrt(x) * np.arange(1, 101)) def constraint1(x): return x[1 阅读全文
posted @ 2024-10-14 23:33
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`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 {"name": "Ⅱ", "A_ho 阅读全文
posted @ 2024-10-14 23:28
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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], 阅读全文
posted @ 2024-10-14 23:03
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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：202331014 3005") 阅读全文
posted @ 2024-10-14 23:01
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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值（ 阅读全文
posted @ 2024-10-14 22:39
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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(A) 打 阅读全文
posted @ 2024-10-14 22:38
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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 阅读全文
posted @ 2024-10-14 22:38
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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, 3) 阅读全文
posted @ 2024-10-14 22:37
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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.solve((equ 阅读全文
posted @ 2024-10-14 22:37
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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: A[ 阅读全文
posted @ 2024-10-14 22:35
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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, rank, 阅读全文
posted @ 2024-10-14 22:34
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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.linspace(0, 2 * 阅读全文
posted @ 2024-10-14 22:34
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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 = np 阅读全文
posted @ 2024-10-14 22:34
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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)) 遍 阅读全文
posted @ 2024-10-14 22:33
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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() 循环绘制每条曲线 阅读全文
posted @ 2024-10-14 22:31
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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( 阅读全文
posted @ 2024-10-14 22:30
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from cProfile import label from re import X import matplotlib.pyplot as plt import numpy as np from matplotlib import font_manager 参数设置部分 先确定字体，以免无法识别 阅读全文
posted @ 2024-10-14 22:29
VVV1
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