kopia lustrzana https://github.com/gabrielegilardi/SignalFilters
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# Feed-Forward Neural Network (FFNN) for Regression Problems
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# Signal Filtering
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## Reference
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- Mathematical background: ["Neural Networks and Deep Learning"](http://neuralnetworksanddeeplearning.com/index.html).
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- Datasets: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.php).
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## Characteristics
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- The code has been written and tested in Python 3.7.7.
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- Usage: *python test.py example*.
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## Parameters
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`example` Name of the example to run (wine, stock, wifi, pulsar.)
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`problem` Defines the type of problem. Equal to C specifies logistic regression, anything else specifies linear regression. The default value is `None`.
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## Examples
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There are four examples in *test.py*: wine, stock, wifi, pulsar. Since GDO is used, `use_grad` is set to `True`. For all examples `init_weights` is also set to `True`.
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### Single-label linear regression examples: wine
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```python
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data_file = 'wine_dataset.csv'
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n_features = 11
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hidden_layers = [20]
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split_factor = 0.7
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L2 = 0.0
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epochs = 50000
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alpha = 0.2
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d_alpha = 1.0
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tolX = 1.e-7
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tolF = 1.e-7
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```
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Original dataset: <https://archive.ics.uci.edu/ml/datasets/Wine+Quality>.
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The dataset has 11 features, 1 label, 4898 samples, 261 variables, and a layout of [11, 20, 1].
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Correlation predicted/actual values: 0.708 (training), 0.601 (test).
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Exit on `epochs` with `tolX` = 2.0e-4 and `tolF` = 1.1e-7.
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