Classifiers#
- class brainmaze_eeg.classifiers.KDEBayesianModel(fbands=[[0.5, 5], [4, 9], [8, 14], [11, 16], [14, 20], [20, 30]], segm_size=30, fs=200, bands_to_erase=[], filter_bands=True, nfft=12000, window_smooth_n=3, window_std=1, cat_bias={'AWAKE': 1, 'N2': 1, 'N3': 1, 'REM': 1}, Selector2=True)#
- extract_features(signal, return_names=False)#
- extract_features_bulk(list_of_signals, fsamp_list, return_names=False)#
- fit(X, y)#
- fit_transform(X, y)#
- predict(X)#
- predict_signal(signal, fs, datarate_threshold=0.85)#
- predict_signal_scores(signal, fs, datarate_threshold=0.85)#
- preprocess_signal(signal, fs, datarate_threshold=0.85)#
- scores(X)#
- transform(X)#
- class brainmaze_eeg.classifiers.KDEBayesianModelNC(fbands=[[0.5, 5], [4, 9], [8, 14], [11, 16], [14, 20], [20, 30]], segm_size=30, fs=200, bands_to_erase=[], filter_bands=True, filter_order=5001, nfft=12000, window_smooth_n=3, window_std=1, cat_bias={'AWAKE': 1, 'N2': 1, 'N3': 1, 'REM': 1}, Selector2=True)#
- extract_features(signal, return_names=False)#
- extract_features_bulk(list_of_signals, fsamp_list, return_names=False)#
- fit(X, y)#
- fit_transform(X, y)#
- predict(X)#
- predict_signal(signal, fs, datarate_threshold=0.85)#
- predict_signal_scores(signal, fs, datarate_threshold=0.85)#
- preprocess_signal(signal, fs, datarate_threshold=0.85)#
- scores(X)#
- transform(X)#
- class brainmaze_eeg.classifiers.MVGaussBayesianCausalModel(*args, **kwargs)#
- class brainmaze_eeg.classifiers.MVGaussBayesianModel(fbands=[[0.5, 5], [4, 9], [8, 14], [11, 16], [14, 20], [20, 30]], segm_size=30, fs=200, bands_to_erase=[], filter_bands=True, nfft=12000, window_smooth_n=3, window_std=1, cat_bias={'AWAKE': 1, 'N2': 1, 'N3': 1, 'REM': 1}, Selector2=True)#
- class brainmaze_eeg.classifiers.Mapper#
- create_template(x, y=None, name='Template')#
- fit_genetic(x, y=None, popsize=15)#
- fit_genetic_likelihood(x, y=None, popsize=15, model=None)#
- fit_map(x, y=None, bias={'REM': 2})#
- get_probabilities(x)#
- map(x, name, model=None)#
- class brainmaze_eeg.classifiers.MultiChannelMVGaussBayesClassifier(fbands=[[0.5, 5], [4, 9], [8, 14], [11, 16], [14, 20], [20, 30]], segm_size=30, fs=200, bands_to_erase=[], filter_bands=True, nfft=12000, window_smooth_n=3, window_std=1, cat_bias={'AWAKE': 1, 'N2': 1, 'N3': 1, 'REM': 1}, Selector2=True)#
- extract_features(signal, return_names=False)#
- extract_features_bulk(list_of_signals, fsamp_list, return_names=False)#
- fit(X, y)#
- fit_transform(X, y)#
- predict(X)#
- predict_signal(signal, fs, datarate_threshold=0.85)#
- predict_signal_scores(signal, fs, datarate_threshold=0.85)#
- preprocess_signal(signal, fs, datarate_threshold=0.85)#
- scores(X)#
- class brainmaze_eeg.classifiers.SleepClassifierWrapper#
- predict_signal(X, fs, stim_freq)#
- train(X, df)#
- class brainmaze_eeg.classifiers.SleepStageProbabilityMarkovChainFilter#
- correct_certainty(certainty: dict)#
- fit(scores, y)#
- fit_optimize(scores, y, Niter=200, popsize=10)#
- get_changing_state_posteriors(state, x_prob)#
- get_changing_state_priors(state)#
- get_changing_state_probabilities(state, x_prob)#
- get_prob_to_change(state, x_prob)#
- get_state_change_posterior(state, x_prob)#
- get_state_change_prior(state)#
- get_state_idx(state)#
- get_state_priors(state)#
- predict(scores, state='AWAKE')#
- remove_class(class_name)#
- reset_probabilities()#
- weight_probabilities(stability: ndarray)#
- class brainmaze_eeg.classifiers.SleepStructureClassifier(states=['WAKE', 'N1', 'N2', 'N3', 'REM'])#
- fit(x, y)#
- scores(x)#
- class brainmaze_eeg.classifiers.multivariate_normal_(X, allow_singular=False, seed=None)#
- pdf(X)#
Multivariate normal probability density function.
- Parameters:
x (array_like) – Quantiles, with the last axis of x denoting the components.
- Returns:
pdf – Probability density function evaluated at x
- Return type:
ndarray or scalar
Notes
See class definition for a detailed description of parameters.