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| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| class ResidualBlock(nn.Module): | |
| def __init__(self, latent_dim, inner_dim): | |
| super(ResidualBlock, self).__init__() | |
| self.fc1 = nn.Linear(latent_dim, inner_dim) | |
| self.fc2 = nn.Linear(inner_dim, inner_dim) |
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| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing_extratrees.py bench ~/bench_results pr_no_pythoncheck | |
| ``` | |
| 2. On main run: | |
| ```bash |
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| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing_extraforest.py bench ~/bench_results_forest pr | |
| ``` | |
| 2. On main run: | |
| ```bash |
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| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_randomforest.py bench ~/bench_results_forest pr | |
| ``` | |
| 2. On main run: | |
| ```bash |
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| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.datasets import make_classification | |
| n_samples = 10_000 | |
| n_features = 200 | |
| n_estimators = 200 | |
| # Generate synthetic classification dataset | |
| X, y = make_classification(n_samples=n_samples, n_features=n_features, n_classes=2, random_state=42) |
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| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing_extraforest.py bench ~/bench_results_forest pr | |
| ``` | |
| 2. On main run: | |
| ```bash |
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| """Instructions | |
| 1. Build this PR and run: | |
| ```bash | |
| python bench_missing_extratrees.py bench ~/bench_results pr_no_pythoncheck | |
| ``` | |
| 2. On main run: | |
| ```bash |
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| pragma solidity ^0.4.0; | |
| contract Ballot { | |
| struct Voter { | |
| uint weight; | |
| bool voted; | |
| uint8 vote; | |
| address delegate; | |
| } | |
| struct Proposal { |