freqanalysis.py 文件源码

def probs_to_classes(self, probabilities):
        """Takes a likelihood matrix produced by predict_proba and returns
        the classification for each entry

        Naive argmax returns a very noisy signal - windowing helps focus on
        strongly matching areas.
        """
        smooth_time = self.params.get('smooth_time', 0.1)
        dt = self.active_song.time[1] - self.active_song.time[0]
        windowsize = np.round(smooth_time / dt)
        window = signal.get_window('hamming', int(windowsize))
        window /= np.sum(window)

        num_classes = probabilities.shape[0]

        smooth_prbs = [np.convolve(
            probabilities[i, :], window, mode='same') for i in range(num_classes)]

        return np.argmax(np.stack(smooth_prbs, axis=0), axis=0)