pefeatures.py 文件源码

def __call__(self, bytez):
        output = np.zeros((16, 16), dtype=np.int)
        a = np.frombuffer(bytez, dtype=np.uint8)
        if a.shape[0] < self.window:
            Hbin, c = self._entropy_bin_counts(a)
            output[Hbin, :] += c
        else:
            # strided trick from here: http://www.rigtorp.se/2011/01/01/rolling-statistics-numpy.html
            shape = a.shape[:-1] + (a.shape[-1] - self.window + 1, self.window)
            strides = a.strides + (a.strides[-1],)
            blocks = np.lib.stride_tricks.as_strided(
                a, shape=shape, strides=strides)[::self.step, :]

            # from the blocks, compute histogram
            for block in blocks:
                Hbin, c = self._entropy_bin_counts(block)
                output[Hbin, :] += c

        return output.flatten().astype(self.dtype)