有如下一组运动员100米短跑成绩 Powell:9.74 Green:9.79 Bolt:9.69 Burrell:9.85 Montgomery:9.78 Lewis:9.86 编写一个程序，按名次输出排名，姓名和成绩

可以编写一个简单的Python程序来按成绩对运动员进行排序并输出前几名。首先，我们需要将运动员的成绩和名字存储在一个字典列表中，然后根据成绩值对这个列表进行降序排序，最后按照新的顺序打印出来。

# 运动员成绩数据
athletes = [
    {"name": "Powell", "time": 9.74},
    {"name": "Green", "time": 9.79},
    {"name": "Bolt", "time": 9.69},
    {"name": "Burrell", "time": 9.85},
    {"name": "Montgomery", "time": 9.78},
    {"name": "Lewis", "time": 9.86},
]

# 按照时间（成绩）从低到高排序
athletes.sort(key=lambda x: x["time"], reverse=True)

# 输出排名、姓名和成绩
for i, athlete in enumerate(athletes, start=1):
    print(f"{i}. {athlete['name']} - 时间: {athlete['time']} 秒")

#

相关问题

写出python代码实现：1.将点云数据划分为若干个体素；2.对每个体素内的点云数据进行高斯分布拟合；3.计算每个体素内的点云数据与目标点云数据的匹配度；4.根据匹配度进行点云配准。

以下是 Python 代码实现：

1. 将点云数据划分为若干个体素：

import numpy as np
from sklearn.cluster import MeanShift

def voxel_downsample(points, voxel_size):
    ms = MeanShift(bin_seeding=True, bandwidth=voxel_size)
    ms.fit(points)
    return ms.cluster_centers_

2. 对每个体素内的点云数据进行高斯分布拟合：

from scipy.stats import multivariate_normal

def fit_gaussian(points):
    mean = np.mean(points, axis=0)
    cov = np.cov(points.T)
    return multivariate_normal(mean=mean, cov=cov)

3. 计算每个体素内的点云数据与目标点云数据的匹配度：

def compute_matching_score(source_points, target_points, voxel_size):
    source_voxels = voxel_downsample(source_points, voxel_size)
    target_voxels = voxel_downsample(target_points, voxel_size)
    source_gaussians = [fit_gaussian(points) for points in source_voxels]
    target_gaussians = [fit_gaussian(points) for points in target_voxels]
    matching_score = 0
    for source_gaussian in source_gaussians:
        matching_score += max([source_gaussian.pdf(target_gaussian.mean) for target_gaussian in target_gaussians])
    return matching_score

4. 根据匹配度进行点云配准：

from scipy.optimize import minimize

def point_cloud_registration(source_points, target_points, voxel_size):
    def objective_function(x):
        transformed_points = np.dot(source_points, x[:3, :3].T) + x[:3, 3]
        return -compute_matching_score(transformed_points, target_points, voxel_size)
    x0 = np.eye(4)
    res = minimize(objective_function, x0, method='Powell')
    return res.x[:3, :]

以上是 Python 代码实现，希望能对你有所帮助。

解释：def conjugate_gradient(fun, grad, x0, iterations, tol): """ Minimization of scalar function of one or more variables using the conjugate gradient algorithm. Parameters ---------- fun : function Objective function. grad : function Gradient function of objective function. x0 : numpy.array, size=9 Initial value of the parameters to be estimated. iterations : int Maximum iterations of optimization algorithms. tol : float Tolerance of optimization algorithms. Returns ------- xk : numpy.array, size=9 Parameters wstimated by optimization algorithms. fval : float Objective function value at xk. grad_val : float Gradient value of objective function at xk. grad_log : numpy.array The record of gradient of objective function of each iteration. """ fval = None grad_val = None x_log = [] y_log = [] grad_log = [] x0 = asarray(x0).flatten() # iterations = len(x0) * 200 old_fval = fun(x0) gfk = grad(x0) k = 0 xk = x0 # Sets the initial step guess to dx ~ 1 old_old_fval = old_fval + np.linalg.norm(gfk) / 2 pk = -gfk x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) gnorm = np.amax(np.abs(gfk)) sigma_3 = 0.01 while (gnorm > tol) and (k < iterations): deltak = np.dot(gfk, gfk) cached_step = [None] def polak_ribiere_powell_step(alpha, gfkp1=None): xkp1 = xk + alpha * pk if gfkp1 is None: gfkp1 = grad(xkp1) yk = gfkp1 - gfk beta_k = max(0, np.dot(yk, gfkp1) / deltak) pkp1 = -gfkp1 + beta_k * pk gnorm = np.amax(np.abs(gfkp1)) return (alpha, xkp1, pkp1, gfkp1, gnorm) def descent_condition(alpha, xkp1, fp1, gfkp1): # Polak-Ribiere+ needs an explicit check of a sufficient # descent condition, which is not guaranteed by strong Wolfe. # # See Gilbert & Nocedal, "Global convergence properties of # conjugate gradient methods for optimization", # SIAM J. Optimization 2, 21 (1992). cached_step[:] = polak_ribiere_powell_step(alpha, gfkp1) alpha, xk, pk, gfk, gnorm = cached_step # Accept step if it leads to convergence. if gnorm <= tol: return True # Accept step if sufficient descent condition applies. return np.dot(pk, gfk) <= -sigma_3 * np.dot(gfk, gfk) try: alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ _line_search_wolfe12(fun, grad, xk, pk, gfk, old_fval, old_old_fval, c2=0.4, amin=1e-100, amax=1e100, extra_condition=descent_condition) except _LineSearchError: break # Reuse already computed results if possible if alpha_k == cached_step[0]: alpha_k, xk, pk, gfk, gnorm = cached_step else: alpha_k, xk, pk, gfk, gnorm = polak_ribiere_powell_step(alpha_k, gfkp1) k += 1 grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) fval = old_fval grad_val = grad_log[-1] return xk, fval, grad_val, x_log, y_log, grad_log

这是一个使用共轭梯度算法来最小化一个标量函数的函数。它的输入包括一个目标函数，一个梯度函数，一个初始值，迭代次数和容差。返回值包括参数的估计值，目标函数在该点的值，目标函数在该点的梯度值以及每次迭代中目标函数梯度的记录。这个算法使用了Polak-Ribiere共轭梯度法，并且在每次迭代中使用了Wolfe条件进行线搜索。算法的大致步骤是：首先计算梯度，然后使用Polak-Ribiere方向来计算下一步方向，然后使用Wolfe条件进行线搜索来确定步长。重复这个过程直到满足停止条件。