mutuoencoder.py - master - TolmachevM/MLLowLevelResearch

mutuoencoder.py
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TolmachevM
update mutuoencoder.py
12 июл 2025, 06:32
12 июл 2025, 06:324aee2e3
import numpy as np
import torch
from itertools import permutations

# Entropy_driven_learning4_Spirals_Mutuoencoders.ipynb

# На входе n точек, на выходе - n*(n-1) пар точек
# Использование:
# pairs = create_pairs(points)

def create_pairs(points):
    # Создаем массив для хранения пар
    n = len(points)
    pairs = np.empty((n * (n - 1), 2, 2))
    # Заполняем массив парами точек
    for index, (i, j) in enumerate(permutations(range(n), 2)):
        pairs[index, 0] = points[i]
        pairs[index, 1] = points[j]
    return torch.Tensor(pairs)

# Использование:
# points = shuffle_points(points)

def shuffle_points(points):
    return points[torch.randperm(points.size()[0])]

# Использование:
# s = similarity(points1, points2, alpha=10)

def similarity(p1, p2, alpha=10):
    n = len(p1)
    ###pairs = torch.Tensor([])
    ###for index, (i, j) in enumerate(permutations(range(n), 2)):
        ###pairs = torch.cat((pairs, torch.cat((p1[i].unsqueeze(1), p2[j].unsqueeze(1)), 1).unsqueeze(0)), 0)
    ###dist = torch.linalg.vector_norm(pairs[:, :, 0] - pairs[:, :, 1], dim=1)
    #print(pairs.shape)
    dist = torch.empty((n, n))
    for i in range(n):
        for j in range(n):
            if i == j:
                dist[i, j] = torch.inf
            else:
                dist[i, j] = torch.linalg.vector_norm(p1[i] - p2[j])
    #print(dist)
    return torch.exp(-alpha * dist)


# Использование
# loss1 = RangeUncoveryLoss(pred, real_points, reduction='max', gamma=10, alpha=10) #reduction='mean'

def RangeUncoveryLoss(points1, points2, reduction='max', alpha=10, gamma=10):
    s = similarity(points1, points2, alpha=alpha)
    if reduction == 'max':
        return 1 - (torch.sigmoid(gamma * s.max(dim=1).values)).mean()
    elif reduction == 'mean':
        return 1 - (torch.sigmoid(gamma * s.mean(dim=1))).mean()
    else:
        assert("No such type of reduction is supported.")