mohamad maheri
2 years ago
2 changed files with 2 additions and 2 deletions
+ 1
- 1
fast_adapt.py
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| alpha = config['alpha'] | alpha = config['alpha'] | ||||
| beta = config['beta'] | beta = config['beta'] | ||||
| d = config['d'] | d = config['d'] | ||||
| a = torch.div(1, torch.add(1, torch.exp(torch.mul(-1, torch.mul(alpha, torch.sub(torch.mul(d, c), beta)))))) | |||||
| a = torch.div(1, torch.add(1, torch.exp(torch.mul(-1, torch.mul(alpha, torch.sub(torch.mul(d, c.squeeze()), beta)))))) | |||||
| # a = 1 / (1 + torch.exp((-1) * alpha * (d * c - beta))) | # a = 1 / (1 + torch.exp((-1) * alpha * (d * c - beta))) | ||||
| b = torch.mul(a, torch.mul(torch.sub(1, a), torch.sub(1, torch.mul(2, a)))) | b = torch.mul(a, torch.mul(torch.sub(1, a), torch.sub(1, torch.mul(2, a)))) | ||||
| # b = 1 * a * (1 - a) * (1 - 2 * a) | # b = 1 * a * (1 - a) * (1 - 2 * a) |
+ 1
- 1
learnToLearn.py
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| kmeans_model = KMeans(n_clusters=config['cluster_k'], init="k-means++").fit(user_embeddings) | kmeans_model = KMeans(n_clusters=config['cluster_k'], init="k-means++").fit(user_embeddings) | ||||
| tr.cluster_module.array.data = torch.Tensor(kmeans_model.cluster_centers_).cuda() | tr.cluster_module.array.data = torch.Tensor(kmeans_model.cluster_centers_).cuda() | ||||
| if iteration > (0): | |||||
| if iteration > 0: | |||||
| # indexes = data_batching(indexes, C_distribs, batch_size, training_set_size, config['cluster_k']) | # indexes = data_batching(indexes, C_distribs, batch_size, training_set_size, config['cluster_k']) | ||||
| # random.shuffle(indexes) | # random.shuffle(indexes) | ||||
| C_distribs = [] | C_distribs = [] |
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