def _get_filter_indices(seg, start_as_yes, prob_raw_yes, ms_per_input, model, transition_matrix, model_stats):
    """
    Runs a Markov Decision Process over the given `seg` in chunks of `ms_per_input`, yielding `True` if
    this `ms_per_input` chunk has been classified as positive (1) and `False` if this chunk has been
    classified as negative (0).

    :param seg:                 The AudioSegment to apply this algorithm to.
    :param start_as_yes:        If True, the first `ms_per_input` chunk will be classified as positive.
    :param prob_raw_yes:        The raw probability of finding the event in any given independently sampled `ms_per_input`.
    :param ms_per_input:        The number of ms of AudioSegment to be fed into the model at a time.
    :param model:               The model, which must hava predict() function, which takes an AudioSegment of `ms_per_input`
                                number of ms and which outputs 1 if the audio event is detected in that input, 0 if not.
    :param transition_matrix:   An iterable of the form: [p(yes->no), p(no->yes)].
    :param model_stats:         An iterable of the form: [p(reality=1|output=1), p(reality=1|output=0)].
    :yields:                    `True` if the event has been classified in this chunk, `False` otherwise.
    """
    filter_triggered = 1 if start_as_yes else 0
    prob_raw_no = 1.0 - prob_raw_yes
    for segment, _timestamp in seg.generate_frames_as_segments(ms_per_input):
        yield filter_triggered
        observation = int(round(model.predict(segment)))
        assert observation == 1 or observation == 0, "The given model did not output a 1 or a 0, output: "\
                + str(observation)
        prob_hyp_yes_given_last_hyp = 1.0 - transition_matrix[0] if filter_triggered else transition_matrix[1]
        prob_hyp_no_given_last_hyp  = transition_matrix[0] if filter_triggered else 1.0 - transition_matrix[1]
        prob_hyp_yes_given_data = model_stats[0] if observation == 1 else model_stats[1]
        prob_hyp_no_given_data = 1.0 - model_stats[0] if observation == 1 else 1.0 - model_stats[1]
        hypothesis_yes = prob_raw_yes * prob_hyp_yes_given_last_hyp * prob_hyp_yes_given_data
        hypothesis_no  = prob_raw_no * prob_hyp_no_given_last_hyp  * prob_hyp_no_given_data
        # make a list of ints - each is 0 or 1. The number of 1s is hypotheis_yes * 100
        # the number of 0s is hypothesis_no * 100
        distribution = [1 for i in range(int(round(hypothesis_yes * 100)))]
        distribution.extend([0 for i in range(int(round(hypothesis_no * 100)))])
        # shuffle
        random.shuffle(distribution)
        filter_triggered = random.choice(distribution)
def _get_filter_indices(seg, start_as_yes, prob_raw_yes, ms_per_input, model, transition_matrix, model_stats):
    """
    Runs a Markov Decision Process over the given `seg` in chunks of `ms_per_input`, yielding `True` if
    this `ms_per_input` chunk has been classified as positive (1) and `False` if this chunk has been
    classified as negative (0).

    :param seg:                 The AudioSegment to apply this algorithm to.
    :param start_as_yes:        If True, the first `ms_per_input` chunk will be classified as positive.
    :param prob_raw_yes:        The raw probability of finding the event in any given independently sampled `ms_per_input`.
    :param ms_per_input:        The number of ms of AudioSegment to be fed into the model at a time.
    :param model:               The model, which must hava predict() function, which takes an AudioSegment of `ms_per_input`
                                number of ms and which outputs 1 if the audio event is detected in that input, 0 if not.
    :param transition_matrix:   An iterable of the form: [p(yes->no), p(no->yes)].
    :param model_stats:         An iterable of the form: [p(reality=1|output=1), p(reality=1|output=0)].
    :yields:                    `True` if the event has been classified in this chunk, `False` otherwise.
    """
    filter_triggered = 1 if start_as_yes else 0
    prob_raw_no = 1.0 - prob_raw_yes
    for segment, _timestamp in seg.generate_frames_as_segments(ms_per_input):
        yield filter_triggered
        observation = int(round(model.predict(segment)))
        assert observation == 1 or observation == 0, "The given model did not output a 1 or a 0, output: "\
                + str(observation)
        prob_hyp_yes_given_last_hyp = 1.0 - transition_matrix[0] if filter_triggered else transition_matrix[1]
        prob_hyp_no_given_last_hyp  = transition_matrix[0] if filter_triggered else 1.0 - transition_matrix[1]
        prob_hyp_yes_given_data = model_stats[0] if observation == 1 else model_stats[1]
        prob_hyp_no_given_data = 1.0 - model_stats[0] if observation == 1 else 1.0 - model_stats[1]
        hypothesis_yes = prob_raw_yes * prob_hyp_yes_given_last_hyp * prob_hyp_yes_given_data
        hypothesis_no  = prob_raw_no * prob_hyp_no_given_last_hyp  * prob_hyp_no_given_data
        # make a list of ints - each is 0 or 1. The number of 1s is hypotheis_yes * 100
        # the number of 0s is hypothesis_no * 100
        distribution = [1 for i in range(int(round(hypothesis_yes * 100)))]
        distribution.extend([0 for i in range(int(round(hypothesis_no * 100)))])
        # shuffle
        random.shuffle(distribution)
        filter_triggered = random.choice(distribution)
def _get_filter_indices ( seg , start_as_yes , prob_raw_yes , ms_per_input , model , transition_matrix , model_stats ) : filter_triggered = 1 if start_as_yes else 0 prob_raw_no = 1.0 - prob_raw_yes for segment , _timestamp in seg . generate_frames_as_segments ( ms_per_input ) : yield filter_triggered observation = int ( round ( model . predict ( segment ) ) ) assert observation == 1 or observation == 0 , "The given model did not output a 1 or a 0, output: " + str ( observation ) prob_hyp_yes_given_last_hyp = 1.0 - transition_matrix [ 0 ] if filter_triggered else transition_matrix [ 1 ] prob_hyp_no_given_last_hyp = transition_matrix [ 0 ] if filter_triggered else 1.0 - transition_matrix [ 1 ] prob_hyp_yes_given_data = model_stats [ 0 ] if observation == 1 else model_stats [ 1 ] prob_hyp_no_given_data = 1.0 - model_stats [ 0 ] if observation == 1 else 1.0 - model_stats [ 1 ] hypothesis_yes = prob_raw_yes * prob_hyp_yes_given_last_hyp * prob_hyp_yes_given_data hypothesis_no = prob_raw_no * prob_hyp_no_given_last_hyp * prob_hyp_no_given_data # make a list of ints - each is 0 or 1. The number of 1s is hypotheis_yes * 100 # the number of 0s is hypothesis_no * 100 distribution = [ 1 for i in range ( int ( round ( hypothesis_yes * 100 ) ) ) ] distribution . extend ( [ 0 for i in range ( int ( round ( hypothesis_no * 100 ) ) ) ] ) # shuffle random . shuffle ( distribution ) filter_triggered = random . choice ( distribution )
Runs a Markov Decision Process over the given `seg` in chunks of `ms_per_input`, yielding `True` if
    this `ms_per_input` chunk has been classified as positive (1) and `False` if this chunk has been
    classified as negative (0).

    :param seg:                 The AudioSegment to apply this algorithm to.
    :param start_as_yes:        If True, the first `ms_per_input` chunk will be classified as positive.
    :param prob_raw_yes:        The raw probability of finding the event in any given independently sampled `ms_per_input`.
    :param ms_per_input:        The number of ms of AudioSegment to be fed into the model at a time.
    :param model:               The model, which must hava predict() function, which takes an AudioSegment of `ms_per_input`
                                number of ms and which outputs 1 if the audio event is detected in that input, 0 if not.
    :param transition_matrix:   An iterable of the form: [p(yes->no), p(no->yes)].
    :param model_stats:         An iterable of the form: [p(reality=1|output=1), p(reality=1|output=0)].
    :yields:                    `True` if the event has been classified in this chunk, `False` otherwise.
Runs a Markov Decision Process over the given seg in chunks of ms_per_input yielding True if this ms_per_input chunk has been classified as positive ( 1 ) and False if this chunk has been classified as negative ( 0 ) .