# Copyright 2018 The RLgraph authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from rlgraph.spaces.box_space import BoxSpace
from rlgraph.utils.util import dtype as dtype_, LARGE_INTEGER
[docs]class IntBox(BoxSpace):
"""
A box in Z^n (only integers; each coordinate is bounded)
e.g. an image (w x h x RGB) where each color channel pixel can be between 0 and 255.
"""
def __init__(self, low=None, high=None, shape=None, dtype="int32", **kwargs):
"""
Three kinds of valid input:
IntBox(6) # only high is given -> low assumed to be 0 (0D scalar).
IntBox(0, 2) # low and high are given as scalars and shape is assumed to be 0D scalar.
IntBox(-1, 1, (3,4)) # low and high are scalars, and shape is provided.
IntBox(np.array([-1,-2]), np.array([2,4])) # low and high are arrays of the same shape (no shape given!)
NOTE: The `high` value for IntBoxes is excluded. Valid values thus are from the interval: [low,high[
"""
if low is None:
assert high is None, "ERROR: If `low` is None, `high` must be None as well!"
low = -LARGE_INTEGER
high = LARGE_INTEGER
# support calls like (IntBox(5) -> low=0, high=5)
elif high is None:
high = low
low = 0
dtype = dtype_(dtype, "np")
assert dtype in [np.int16, np.int32, np.int64, np.uint8], \
"ERROR: IntBox does not allow dtype '{}'!".format(dtype)
super(IntBox, self).__init__(low=low, high=high, shape=shape, dtype=dtype, **kwargs)
self.num_categories = None if self.global_bounds is False else self.global_bounds[1]
[docs] def get_shape(self, with_batch_rank=False, with_time_rank=False, **kwargs):
"""
Keyword Args:
with_category_rank (bool): Whether to include a category rank for this IntBox (if all dims have equal
lower/upper bounds).
"""
with_category_rank = kwargs.pop("with_category_rank", False)
shape = super(IntBox, self).get_shape(with_batch_rank=with_batch_rank, with_time_rank=with_time_rank, **kwargs)
if with_category_rank is not False:
return shape + ((self.num_categories,) if self.num_categories is not None else ())
return shape
@property
def flat_dim_with_categories(self):
"""
If we were to flatten this Space and also consider each single possible int value (assuming global bounds)
as one category, what would the dimension have to be to represent this Space.
"""
if self.global_bounds is False:
return None
return int(np.prod(self.shape) * self.global_bounds[1])
[docs] def sample(self, size=None, fill_value=None):
shape = self._get_np_shape(num_samples=size)
if fill_value is None:
sample_ = np.random.uniform(low=self.low, high=self.high, size=shape)
else:
sample_ = fill_value if shape == () or shape is None else np.full(shape=shape, fill_value=fill_value)
return np.asarray(sample_, dtype=self.dtype)
[docs] def contains(self, sample):
# If int: Check for int type in given sample.
if not np.equal(np.mod(sample, 1), 0).all():
return False
return super(IntBox, self).contains(sample)