def denormalize(self, arr):
"""
denormalize the input
:param arr: (numpy Number) the normalized input
:return: (numpy Number) original input
"""
mean = reshape_for_broadcasting(self.mean, arr)
std = reshape_for_broadcasting(self.std, arr)
return mean + arr * std
def normalize(self, arr, clip_range=None):
"""
normalize the input
:param arr: (numpy Number) the input
:param clip_range: (float) the range to clip to [-clip_range, clip_range]
:return: (numpy Number) normalized input
"""
if clip_range is None:
clip_range = self.default_clip_range
mean = reshape_for_broadcasting(self.mean, arr)
std = reshape_for_broadcasting(self.std, arr)
return tf.clip_by_value((arr - mean) / std, -clip_range, clip_range)
def denormalize(self, v):
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return mean + v * std
def normalize(self, v, clip_range=None):
if clip_range is None:
clip_range = self.default_clip_range
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return tf.clip_by_value((v - mean) / std, -clip_range, clip_range)
def denormalize(self, v):
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return mean + v * std
def normalize(self, v, clip_range=None):
if clip_range is None:
clip_range = self.default_clip_range
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return tf.clip_by_value((v - mean) / std, -clip_range, clip_range)
