import torch
import numpy as np


def euclidean_dist(x, y):
    m, n = x.size(0), y.size(0)
    xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
    yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
    dist = xx + yy
    dist.addmm_(1, -2, x, y.t())
    dist = dist.clamp(min=1e-12).sqrt()
    return dist


def euclidean_dist_pair(x):
    m = x.size(0)
    xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m)
    dist = xx + xx.t()
    dist.addmm_(1, -2, x, x.t())
    dist = dist.clamp(min=1e-12).sqrt()
    return dist

def euclidean_dist_np(x, y):
    (rowx, colx) = x.shape
    (rowy, coly) = y.shape
    xy = np.dot(x, y.T)
    x2 = np.repeat(np.reshape(np.sum(np.multiply(x, x), axis=1), (rowx, 1)), repeats=rowy, axis=1)
    y2 = np.repeat(np.reshape(np.sum(np.multiply(y, y), axis=1), (rowy, 1)), repeats=rowx, axis=1).T
    return np.sqrt(np.clip(x2 + y2 - 2. * xy, 1e-12, None))

#calculate the euclidean distance of each sample in x, return a N*N matrix, whose diag is zero
def euclidean_dist_pair_np(x):
    (rowx, colx) = x.shape
    xy = np.dot(x, x.T)
    x2 = np.repeat(np.reshape(np.sum(np.multiply(x, x), axis=1), (rowx, 1)), repeats=rowx, axis=1)
    return np.sqrt(np.clip(x2 + x2.T - 2. * xy, 1e-12, None))