Feature extraction¶
Basic feature extraction¶
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breze.learn.feature.rbf(X, n_centers)¶ Return a design matrix with features given by radial basis functions.
n_centers Gaussian kernels are placed along data dimension, equidistant between the minimum and the maximum along that dimension. The result then contains one column for each of the Kernels.
Parameters: - X – NxD sized array.
- n_centers – Amount of Kernels to use for each dimension.
Returns: Nx(n_centers * D) sized array.
Feature extraction for EMG and similar time series data¶
Module that holds various preprocessing routines for emg signals.
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breze.learn.feature.emg.integrated(X)¶ Return the sum of the absolute values of a signal.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.mean_absolute_value(X)¶ Return the mean absolute value of the signal.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.modified_mean_absolute_value_1(X)¶ Return a weighted version of the mean absolute value.
Instead of equal weight, the first and last quarter of the signal are only weighed half.
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breze.learn.feature.emg.modified_mean_absolute_value_2(X)¶ Return a weighted version of the mean absolute value.
The central half of the signal has weight one. The beginning and the last quarter increase/decrease their weight towards that.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.mean_absolute_value_slope(X)¶ Return the first derivative of the mean absolute value.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.variance(X)¶ Return the variance of the signals.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.root_mean_square(X)¶ Return the root mean square of the signals.
Parameters: X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels. Returns: An (n, d) array.
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breze.learn.feature.emg.zero_crossing(X, threshold=1e-08)¶ Return the amount of times the signal crosses the zero y-axis.
Parameters: - X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels.
- threshold – Changes below this value are ignored. Useful to surpress noise.
Returns: An (n, d) array.
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breze.learn.feature.emg.slope_sign_change(X, threshold=1e-08)¶ Return the amount of times the signal changes slope.
Parameters: - X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels.
- threshold – Changes below this value are ignored. Useful to surpress noise.
Returns: An (n, d) array.
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breze.learn.feature.emg.willison_amplitude(X, threshold=1e-08)¶ Return the amount of times the difference between two adjacent emg segments exceeds a threshold.
Parameters: - X – An (t, n, d) array where t is the number of time steps, n is the number of different signals and d is the number of channels.
- threshold – Changes below this value are ignored. Useful to surpress noise.
Returns: An (n, d) array.