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refactor/emcomposition_field_handling (#3122)
* • emcomposition.py
_parse_fields: clean up assignment of self.num_fields
• test_emcomposition.py
add test_assign_field_weights_and_0_vs_None()
add test_field_weights_all_None_and_or_0
• emcomposition.py
- revamp docstring to document new mods
- add fields arg to specify field_naes, field_weights, learn_field_weights
- implement fields arg to specify field_names, field_weights, learn_field_weights in dict format
- implement support for field-specific learn_field_weight specifications
- _identify_target_nodes():
refactor to use target_fields instead of learn_field_weights
- add target_fields to fields specification dict
- add dict spec for entries in fields arg
- start adding field_idx to all components
- add self._field_index_map
• pytorchEMcompositionwrapper.py
- store_memory(): use self._field_index_map to assign memories to fields
• test_emcomposition.py
- test_backpropagation_of_error_in_learning(): use EGO model to test for error backpropagation through EMCompoistion
- test_field_args_and_map_assignments(): flesh out _field_index_map validation
* • emcomposition.py
- update docstring figs
- add purge_by_field_weights Parameter
* • autodiffcomposition.py
- infer_backpropagation_learning_pathways(): add NodeRole.BIAS to pathways consructed for learning- Loading branch information
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54
Scripts/Models (Under Development)/EGO/Using EMComposition/Coffee Shop World/Environment.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,54 @@ | ||
| import numpy as np | ||
| import torch | ||
| from torch.utils.data import dataset | ||
| from random import randint | ||
|
|
||
| def one_hot_encode(labels, num_classes): | ||
| """ | ||
| One hot encode labels and convert to tensor. | ||
| """ | ||
| return torch.tensor((np.arange(num_classes) == labels[..., None]).astype(float),dtype=torch.float32) | ||
|
|
||
| class DeterministicCSWDataset(dataset.Dataset): | ||
| def __init__(self, n_samples_per_context, contexts_to_load) -> None: | ||
| super().__init__() | ||
| raw_xs = np.array([ | ||
| [[9,1,3,5,7],[9,2,4,6,8]], | ||
| [[10,1,4,5,8],[10,2,3,6,7]] | ||
| ]) | ||
|
|
||
| item_indices = np.random.choice(raw_xs.shape[1],sum(n_samples_per_context),replace=True) | ||
| task_names = [0,1] # Flexible so these can be renamed later | ||
| task_indices = [task_names.index(name) for name in contexts_to_load] | ||
|
|
||
| context_indices = np.repeat(np.array(task_indices),n_samples_per_context) | ||
| self.xs = one_hot_encode(raw_xs[context_indices,item_indices],11) | ||
|
|
||
| self.xs = self.xs.reshape((-1,11)) | ||
| self.ys = torch.cat([self.xs[1:],one_hot_encode(np.array([0]),11)],dim=0) | ||
| context_indices = np.repeat(np.array(task_indices),[x*5 for x in n_samples_per_context]) | ||
| self.contexts = one_hot_encode(context_indices, len(task_names)) | ||
|
|
||
| # Remove the last transition since there's no next state available | ||
| self.xs = self.xs[:-1] | ||
| self.ys = self.ys[:-1] | ||
| self.contexts = self.contexts[:-1] | ||
|
|
||
| def __len__(self): | ||
| return len(self.xs) | ||
|
|
||
| def __getitem__(self, idx): | ||
| return self.xs[idx], self.contexts[idx], self.ys[idx] | ||
|
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| def generate_dataset(condition='Blocked'): | ||
| # Generate the dataset for either the blocked or interleaved condition | ||
| if condition=='Blocked': | ||
| contexts_to_load = [0,1,0,1] + [randint(0,1) for _ in range(40)] | ||
| n_samples_per_context = [40,40,40,40] + [1]*40 | ||
| elif condition == 'Interleaved': | ||
| contexts_to_load = [0,1]*80 + [randint(0,1) for _ in range(40)] | ||
| n_samples_per_context = [1]*160 + [1]*40 | ||
| else: | ||
| raise ValueError(f'Unknown dataset condition: {condition}') | ||
|
|
||
| return DeterministicCSWDataset(n_samples_per_context, contexts_to_load) |
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/EGO/Using%20EMComposition/Coffee%20Shop%20World/Figures/EGO%20Paper%20Figure.jpg){kind=link}
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