"""simple docstring"""






from typing import Dict, Optional, Tuple

import torch



def     a_		(   lowerCamelCase			):
				UpperCAmelCase__			 =				boundaries[1] - boundaries[0]
				UpperCAmelCase__			 =				boundaries + step / 2
				UpperCAmelCase__			 =				torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1			)]   ,     dim=0			)
				return bin_centers



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     ):
				UpperCAmelCase__			 =				_calculate_bin_centers(lowerCamelCase			)
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers   ,     dim=-1			),
				    bin_centers[-1],
				)



def     a_		(   lowerCamelCase   ,     lowerCamelCase = 3_1   ,     lowerCamelCase = 6_4   ,     **lowerCamelCase   ,     ):
				UpperCAmelCase__			 =				torch.linspace(0   ,     lowerCamelCase   ,     steps=(no_bins - 1)   ,     device=logits.device			)

				UpperCAmelCase__			 =				torch.nn.functional.softmax(lowerCamelCase   ,     dim=-1			)
				UpperCAmelCase__ ,      UpperCAmelCase__			 =				_calculate_expected_aligned_error(
				    alignment_confidence_breaks=lowerCamelCase   ,     aligned_distance_error_probs=lowerCamelCase   ,     )

				return {
				    "aligned_confidence_probs": aligned_confidence_probs,
				    "predicted_aligned_error": predicted_aligned_error,
				    "max_predicted_aligned_error": max_predicted_aligned_error,
				}







def     a_		(   lowerCamelCase   ,     lowerCamelCase = None   ,     lowerCamelCase = 3_1   ,     lowerCamelCase = 6_4   ,     lowerCamelCase = 1e-8   ,     **lowerCamelCase   ,     ):
				if residue_weights is None:
								UpperCAmelCase__			 =				logits.new_ones(logits.shape[-2]			)

				UpperCAmelCase__			 =				torch.linspace(0   ,     lowerCamelCase   ,     steps=(no_bins - 1)   ,     device=logits.device			)

				UpperCAmelCase__			 =				_calculate_bin_centers(lowerCamelCase			)
				torch.sum(lowerCamelCase			)
				UpperCAmelCase__			 =				logits.shape[-2]
				UpperCAmelCase__			 =				max(lowerCamelCase   ,     1_9			)

				UpperCAmelCase__			 =				1.24 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

				UpperCAmelCase__			 =				torch.nn.functional.softmax(lowerCamelCase   ,     dim=-1			)

				UpperCAmelCase__			 =				1.0 / (1 + (bin_centers**2) / (da**2))
				UpperCAmelCase__			 =				torch.sum(probs * tm_per_bin   ,     dim=-1			)

				UpperCAmelCase__			 =				residue_weights / (eps + residue_weights.sum())
				UpperCAmelCase__			 =				torch.sum(predicted_tm_term * normed_residue_mask   ,     dim=-1			)

				UpperCAmelCase__			 =				per_alignment * residue_weights

				UpperCAmelCase__			 =				(weighted == torch.max(lowerCamelCase			)).nonzero()[0]
				return per_alignment[tuple(lowerCamelCase			)]




"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



