Downsampling (Downsample
)
Downsampling reduces the number of frames in an observation sequence according to a specified downsample factor and one of two methods: averaging and decimation.
This is an especially helpful preprocessing method for speeding up classification times.
API reference
- class sequentia.preprocessing.Downsample(factor, method='decimate')[source]
Downsamples an observation sequence (or multiple sequences) by either:
Decimating the next \(n-1\) observations
Averaging the current observation with the next \(n-1\) observations
- Parameters
- factor: int > 0
Downsample factor.
- method: {‘decimate’, ‘mean’}
The downsampling method.
Examples
>>> # Create some sample data >>> X = [np.random.random((10 * i, 3)) for i in range(1, 4)] >>> # Downsample the data with downsample factor 5 and decimation >>> X = Downsample(factor=5, method='decimate')(X)
- transform(x)[source]
Applies the transformation to a single observation sequence.
- Parameters
- X: numpy.ndarray (float)
An individual observation sequence.
- Returns
- transformed:class:numpy:numpy.ndarray (float)
The transformed input observation sequence.
- __call__(X, validate=True)
Applies the transformation to the observation sequence(s).
- Parameters
- X: numpy.ndarray (float) or list of numpy.ndarray (float)
An individual observation sequence or a list of multiple observation sequences.
- validate: bool
Whether or not to validate the input sequences.
- Returns
- transformed:class:numpy:numpy.ndarray (float) or list of
numpy.ndarray
(float) The transformed input observation sequence(s).
- transformed:class:numpy:numpy.ndarray (float) or list of