# Source code for akro.box

```"""A Space representing a rectangular region of space."""
import warnings

import numpy as np

from akro.space import Space

[docs]class Box(Space):
"""A box in R^n.

Each coordinate is bounded above and below.
"""

def __init__(self, low, high, shape=None, dtype=np.float32):
"""
Initialize the Box.

Two kinds of bounds are supported, scalars and arrays:
Box(-1.0, 1.0, (3,4)) # low and high are scalars, and shape is
provided
Box(np.array([-1.0,-2.0]), np.array([2.0,4.0])) # low and high are
arrays of the same shape

If dtype is not specified, we assume dtype to be np.float32,
but when low=0 and high=255, it is very likely to be np.uint8.
We warn user. It is different from gym.Box,
where they warn user as long as dtype is not specified.
"""
if shape is None:
assert low.shape == high.shape
self.low = low
self.high = high
else:
assert np.isscalar(low) and np.isscalar(high)
self.low = low + np.zeros(shape)
self.high = high + np.zeros(shape)

if (self.low == 0).all() and (
self.high == 255).all() and dtype != np.uint8:
warnings.warn("Creating a akro.Box with low=0, high=255 and "
"dtype=np.float32.")

self._dtype = dtype

[docs]    def sample(self):
"""Uniformly randomly sample a random element of this space."""
if self.dtype == np.uint8:
# since np.random.randint() does not accept array as input
low = np.take(self.low, 0)
high = np.take(self.high, 0)
return np.random.randint(
low=low, high=high + 1, size=self.low.shape).astype(
self.dtype, copy=False)
else:
return np.random.uniform(
low=self.low, high=self.high, size=self.low.shape).astype(
self.dtype, copy=False)

[docs]    def contains(self, x):
"""Return boolean specifying if x is a valid member of this space."""
return x.shape == self.shape and (x >= self.low).all() and (
x <= self.high).all()

@property
def dtype(self):
"""Return the dtype of samples contained in this Space."""
return self._dtype

@property
def shape(self):
"""Return the shape of samples contained in this Space."""
return self.low.shape

@property
def flat_dim(self):
"""Return the length of the flattened vector of the space."""
return np.prod(self.low.shape)

@property
def bounds(self):
"""Return a 2-tuple containing the lower and upper bounds."""
return self.low, self.high

[docs]    def flatten(self, x):
"""
Return a flattened observation x.

Returns:
x (flattened)

"""
return np.asarray(x).flatten()

[docs]    def unflatten(self, x):
"""
Return an unflattened observation x.

Returns:
x (unflattened)

"""
return np.asarray(x).reshape(self.shape)

[docs]    def flatten_n(self, xs):
"""
Return flattened observations xs.

Returns:
xs (flattened)

"""
xs = np.asarray(xs)
return xs.reshape((xs.shape[0], -1))

[docs]    def unflatten_n(self, xs):
"""
Return unflattened observations xs.

Returns:
xs (unflattened)

"""
xs = np.asarray(xs)
return xs.reshape((xs.shape[0], ) + self.shape)

def __repr__(self):
"""Compute a representation of the Box Space."""
return "Box" + str(self.shape)

def __eq__(self, other):
"""Compare two Box Spaces for approximate equality."""
return isinstance(other, Box) \
and np.allclose(self.low, other.low) \
and np.allclose(self.high, other.high)

def __hash__(self):
"""Hash the Box Space by value."""
return hash((self.low, self.high))

[docs]    def new_tensor_variable(self, name, extra_dims):
"""
Create a tensor variable given the name and extra dimensions.

:param name: name of the variable
:param extra_dims: extra dimensions in the front
:return: the created tensor variable
"""
raise NotImplementedError
```