(self, lo: int, hi: int, depth: int)
return self._noise[lo:hi, :]
Return pseudorandom noise with shape float[length, depth].

    Args:
      lo: where to start sampling
      hi: where to stop sampling
      depth: noise depth

    Returns:
      noise[lo:hi, :]: the noise, where noise.diff(axis=0) is i.i.d. U(-1,1)
Return pseudorandom noise with shape float[length, depth].
Return pseudorandom noise with shape float [ length depth ] .
def _get_noise(self, lo: int, hi: int, depth: int):
    """Return pseudorandom noise with shape float[length, depth].

    Args:
      lo: where to start sampling
      hi: where to stop sampling
      depth: noise depth

    Returns:
      noise[lo:hi, :]: the noise, where noise.diff(axis=0) is i.i.d. U(-1,1)
    """
    if self._noise is None or self._noise.shape[0] < hi:
      # Resize the noise:
      new_length = 1
      while new_length < hi:
        new_length *= 2
      noise = threefry_2x32_prange(self._noise_rng, 0, new_length * depth)
      noise = noise.reshape((new_length, depth, 2))[:, :, 0]
      # Normalize to [-sqrt(3), sqrt(3)]:
      noise = noise.astype(jnp.float32) / np.float32(2**31 - 1)
      noise = noise * 3**.5
      # TODO(tying): use multiscale noise for memory-efficient sampling
      noise = noise.cumsum(axis=0)
      self._noise = noise
    assert self._noise.shape[0] >= hi
    assert self._noise.shape[1] == depth
    return self._noise[lo:hi, :]
def _get_noise ( self , lo : int , hi : int , depth : int ) : if self . _noise is None or self . _noise . shape [ 0 ] < hi : # Resize the noise: new_length = 1 while new_length < hi : new_length *= 2 noise = threefry_2x32_prange ( self . _noise_rng , 0 , new_length * depth ) noise = noise . reshape ( ( new_length , depth , 2 ) ) [ : , : , 0 ] # Normalize to [-sqrt(3), sqrt(3)]: noise = noise . astype ( jnp . float32 ) / np . float32 ( 2 ** 31 - 1 ) noise = noise * 3 ** .5 # TODO(tying): use multiscale noise for memory-efficient sampling noise = noise . cumsum ( axis = 0 ) self . _noise = noise assert self . _noise . shape [ 0 ] >= hi assert self . _noise . shape [ 1 ] == depth return self . _noise [ lo : hi , : ]