def train(self, training_data, bins=100, bandwidth=3, smoothing=1e-2):
        self.ndim = training_data.shape[1]
        self.bins = bins
        self.hist, self.edges = np.histogramdd(
            training_data, bins=bins, normed=True)
        self.hist[smoothing < self.hist] = smoothing
        if bandwidth:
            self.hist = gaussian_filter(self.hist, sigma=bandwidth)
        #self.hist /= np.sum(self.hist)
        self.hist = np.log(self.hist)
dataset/ETHPy150Open seanbell/intrinsic/bell2014/density.py/ProbDensityHistogram.train/swapped operands of `<`