def dist_for_float(p1, p2): p1 = DTW.numpy_num_to_python_num(p1) p2 = DTW.numpy_num_to_python_num(p2) if (type(p1) == float or type(p1) == int) and \ (type(p2) == float or type(p2) == int): dist = float(abs(p1 - p2)) return dist else: sum_val = 0.0 for i in range(len(p1)): sum_val += pow(p1[i] - p2[i], 2) dist = pow(sum_val, 0.5) return dist请帮我把上面的欧式距离计算改为马氏距离计算

马氏距离的计算公式为：dist = sqrt((p1-p2) * S^(-1) * (p1-p2)^T)，其中S为协方差矩阵，^T表示矩阵的转置。因此，可以将原来的代码修改为：

import numpy as np

def mahalanobis_dist(p1, p2, cov):
p1 = np.array(p1)
p2 = np.array(p2)
diff = p1 - p2
inv_cov = np.linalg.inv(cov)
dist = np.sqrt(np.dot(np.dot(diff, inv_cov), diff.T))
return dist

# 使用示例
p1 = [1, 2, 3]
p2 = [4, 5, 6]
cov = np.array([[1, , ], [, 1, ], [, , 1]]) # 假设协方差矩阵为单位矩阵
dist = mahalanobis_dist(p1, p2, cov)
print(dist) # 输出结果为 5.196152422706632

相关问题

def dist_for_float(p1, p2, cov): p1 = DTW.numpy_num_to_python_num(p1) p2 = DTW.numpy_num_to_python_num(p2) if (type(p1) == float or type(p1) == int) and \ (type(p2) == float or type(p2) == int): dist = float(abs(p1 - p2)) return dist else: sum_val = 0.0 for i in range(len(p1)): sum_val += pow(p1[i] - p2[i], 2) dist = pow(sum_val, 0.5) return dist

这是一个计算两个点之间距离的函数，输入参数包括两个点p1和p2，以及一个协方差矩阵cov。该函数首先将输入的numpy类型转换为python类型，然后判断p1和p2是否为float或int类型，如果是，则直接计算两点之间的距离，如果不是，则按照欧氏距离公式计算两个点之间的距离。最后返回计算出的距离值。

def DSM_grid_sorting_masking_check(DSM,grid_size,threshold_angle): ''' 进行基于DSM格网排序的遮蔽检测方法 :param DSM: 输入的数字高程模型 :param grid_size: 格网大小 :param threshold_angle: 实现遮蔽的最大角度 :return: 遮蔽检测结果。True表示不遮蔽，False表示遮蔽 ''' width = DSM.RasterXSize height = DSM.RasterYSize #计算网格数量 grid_num_y =int(np.ceil(height/grid_size)) grid_num_x =int(np.ceil(width/grid_size)) #初始化遮蔽检测结果矩阵 result = np.ones((grid_num_y,grid_num_x),dtype=bool) #计算每个格网进行遮蔽检测 for i in range(grid_num_y): for j in range(grid_num_x): #当前格网内的点坐标 y_min = i*grid_size y_max = min((i+1)*grid_size,height) x_min = j * grid_size x_max = min((j+1)*grid_size,width) coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0) coords[:, 0] += y_min coords[:, 1] += x_min # 构建KD树 tree = cKDTree(coords) # 查询每个点的最邻近点 k = 2 dist, ind = tree.query(coords, k=k) # 计算每个点的法向量 normals = np.zeros(coords.shape) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] normals[l, :] = np.cross(p1 - p2, p1 - DSM.ReadAsArray(p1[1], p1[0], 1, 1)) # 计算每个点的可见性 visibilities = np.zeros(coords.shape[0]) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] angle = np.cross(np.dot(normals[l, :], (p2 - p1) / dist[l, 1])) * 180 / np.pi if angle <= threshold_angle: visibilities[l] = 1 # 判断当前格网是否遮蔽 if np.sum(visibilities) == 0: result[i, j] = False else: result[i, j] = True return result dsm_path = 'C:/yingxiang/output.tif' DSM = gdal.Open(dsm_path) result = DSM_grid_sorting_masking_check(DSM,grid_size=10,threshold_angle=10) print(result.shape)这段代码怎么改可以输出每个点是否被遮蔽

可以在函数中添加一个返回值，记录每个点是否被遮蔽。可以按照以下方式修改函数：

def DSM_grid_sorting_masking_check(DSM,grid_size,threshold_angle):
    ''' 进行基于DSM格网排序的遮蔽检测方法
    :param DSM: 输入的数字高程模型
    :param grid_size: 格网大小
    :param threshold_angle: 实现遮蔽的最大角度
    :return: 遮蔽检测结果。True表示不遮蔽，False表示遮蔽，以及每个点是否被遮蔽的矩阵，True表示不遮蔽，False表示遮蔽
    '''
    width = DSM.RasterXSize
    height = DSM.RasterYSize

    #计算网格数量
    grid_num_y =int(np.ceil(height/grid_size))
    grid_num_x =int(np.ceil(width/grid_size))

    #初始化遮蔽检测结果矩阵
    result = np.ones((grid_num_y,grid_num_x),dtype=bool)
    mask = np.zeros((height, width), dtype=bool)

    #计算每个格网进行遮蔽检测
    for i in range(grid_num_y):
        for j in range(grid_num_x):
            #当前格网内的点坐标
            y_min = i*grid_size
            y_max = min((i+1)*grid_size,height)
            x_min = j * grid_size
            x_max = min((j+1)*grid_size,width)

            coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0)
            coords[:, 0] += y_min
            coords[:, 1] += x_min

            # 构建KD树
            tree = cKDTree(coords)

            # 查询每个点的最邻近点
            k = 2
            dist, ind = tree.query(coords, k=k)

            # 计算每个点的法向量
            normals = np.zeros(coords.shape)
            for l in range(coords.shape[0]):
                if k == 2:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]
                else:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]

                normals[l, :] = np.cross(p1 - p2, p1 - DSM.ReadAsArray(p1[1], p1[0], 1, 1))

            # 计算每个点的可见性
            visibilities = np.zeros(coords.shape[0])
            for l in range(coords.shape[0]):
                if k == 2:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]
                else:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]

                angle = np.cross(np.dot(normals[l, :], (p2 - p1) / dist[l, 1])) * 180 / np.pi
                if angle <= threshold_angle:
                    visibilities[l] = 1

            # 判断当前格网是否遮蔽
            if np.sum(visibilities) == 0:
                result[i, j] = False
                mask[y_min:y_max, x_min:x_max] = True
            else:
                result[i, j] = True

    return result, mask

dsm_path = 'C:/yingxiang/output.tif'
DSM = gdal.Open(dsm_path)
result, mask = DSM_grid_sorting_masking_check(DSM,grid_size=10,threshold_angle=10)
print(result.shape)

使用该函数后，`mask` 矩阵的值即表示每个点是否被遮蔽，True 表示被遮蔽，False 表示没有被遮蔽。