Feature Functions

This module has functions or callable objects that can be used to compute features from segmented time series data

Sets of these functions or callables can be passed in a dictionary object to initialize the FeatureRep transformer.

All functions follow the same template and process a single segmented time series instance:

>>> def compute_feature(X):
>>>     F = np.mean(X, axis = 1)
>>>     return F
X : array-like shape [n_segments, segment_width, n_variables]
F : array-like [n_segments, n_features]
The number of features returned (n_features) must be >= 1

Note

np.atleast_3d is used if accessing the third dimension, as some datasets will

have only a single time series variable. See hist4 as an example.

See hist for an example of a callable object

Examples

>>> from seglearn.feature_functions import all_features
>>> from seglearn.transform import FeatureRep
>>> FeatureTransform = FeatureRep(features=all_features())

seglearn.feature_functions.abs_energy(X)

absolute sum of squares for each variable

seglearn.feature_functions.abs_sum(X)

sum of absolute values

seglearn.feature_functions.all_features()

Returns dictionary of all features in the module

Note

Some of the features (hist4, corr) are relatively expensive to compute

seglearn.feature_functions.base_features()

Returns dictionary of some basic features that can be calculated for segmented time series data

seglearn.feature_functions.corr2(X)

computes correlations between all variable pairs in a segmented time series

Note

this feature is expensive to compute with the current implementation, and cannot be

used with univariate time series

seglearn.feature_functions.emg_features(threshold=0)

Return a dictionary of popular features used for EMG time series classification.

seglearn.feature_functions.emg_var(X)

variance (assuming a mean of zero) for each variable in the segmented time series (equals abs_energy divided by (seg_size - 1))

seglearn.feature_functions.gmean(X)

geometric mean for each variable

class seglearn.feature_functions.hist(bins=4)

histogram for each variable in a segmented time series

Note

this feature is expensive to compute with the current implementation

Methods

__call__(self, X)

Call self as a function.

seglearn.feature_functions.hmean(X)

harmonic mean for each variable

seglearn.feature_functions.hudgins_features(threshold=0)

Return a dict of Hudgin’s time domain features used for EMG time series classification.

seglearn.feature_functions.kurt(X)

kurtosis for each variable in a segmented time series

seglearn.feature_functions.maximum(X)

maximum value for each variable in a segmented time series

seglearn.feature_functions.mean(X)

statistical mean for each variable in a segmented time series

seglearn.feature_functions.mean_abs(X)

statistical mean of the absolute values for each variable in a segmented time series

seglearn.feature_functions.mean_crossings(X)

Computes number of mean crossings for each variable in a segmented time series

seglearn.feature_functions.mean_diff(X)

mean temporal derivative

seglearn.feature_functions.means_abs_diff(X)

mean absolute temporal derivative

seglearn.feature_functions.median(X)

statistical median for each variable in a segmented time series

seglearn.feature_functions.median_absolute_deviation(X)

median absolute deviation for each variable in a segmented time series

seglearn.feature_functions.minimum(X)

minimum value for each variable in a segmented time series

seglearn.feature_functions.mse(X)

computes mean spectral energy for each variable in a segmented time series

seglearn.feature_functions.root_mean_square(X)

root mean square for each variable in the segmented time series

seglearn.feature_functions.skew(X)

skewness for each variable in a segmented time series

class seglearn.feature_functions.slope_sign_changes(threshold=0)

number of changes between positive and negative slope among three consecutive samples above a certain threshold for each variable in the segmented time series

Methods

__call__(self, X)

Call self as a function.

seglearn.feature_functions.std(X)

statistical standard deviation for each variable in a segmented time series

seglearn.feature_functions.var(X)

statistical variance for each variable in a segmented time series

seglearn.feature_functions.variation(X)

coefficient of variation

seglearn.feature_functions.vec_sum(X)

vector sum of each variable

seglearn.feature_functions.waveform_length(X)

cumulative length of the waveform over a segment for each variable in the segmented time series

class seglearn.feature_functions.willison_amplitude(threshold=0)

the Willison amplitude for each variable in the segmented time series

Methods

__call__(self, X)

Call self as a function.

class seglearn.feature_functions.zero_crossing(threshold=0)

number of zero crossings among two consecutive samples above a certain threshold for each variable in the segmented time series

Methods

__call__(self, X)

Call self as a function.