def average(a, axis=None, weights=None, returned=False): # pylint: disable=missing-docstring
if axis is not None and not isinstance(axis, six.integer_types):
# TODO(wangpeng): Support tuple of ints as `axis`
raise ValueError('`axis` must be an integer. Tuple of ints is not '
'supported yet. Got type: %s' % type(axis))
a = array_ops.array(a)
if weights is None: # Treat all weights as 1
if not np.issubdtype(a.dtype, np.inexact):
a = a.astype(
utils.result_type(a.dtype, dtypes.default_float_type()))
avg = tf.reduce_mean(a.data, axis=axis)
if returned:
if axis is None:
weights_sum = tf.size(a.data)
else:
weights_sum = tf.shape(a.data)[axis]
weights_sum = tf.cast(weights_sum, a.data.dtype)
else:
if np.issubdtype(a.dtype, np.inexact):
out_dtype = utils.result_type(a.dtype, weights)
else:
out_dtype = utils.result_type(a.dtype, weights,
dtypes.default_float_type())
a = array_ops.array(a, out_dtype).data
weights = array_ops.array(weights, out_dtype).data
def rank_equal_case():
tf.debugging.Assert(
tf.reduce_all(tf.shape(a) == tf.shape(weights)),
[tf.shape(a), tf.shape(weights)])
weights_sum = tf.reduce_sum(weights, axis=axis)
avg = tf.reduce_sum(a * weights, axis=axis) / weights_sum
return avg, weights_sum
if axis is None:
avg, weights_sum = rank_equal_case()
else:
def rank_not_equal_case():
tf.debugging.Assert(tf.rank(weights) == 1, [tf.rank(weights)])
weights_sum = tf.reduce_sum(weights)
axes = tf.convert_to_tensor([[axis], [0]])
avg = tf.tensordot(a, weights, axes) / weights_sum
return avg, weights_sum
# We condition on rank rather than shape equality, because if we do the
# latter, when the shapes are partially unknown but the ranks are known
# and different, utils.cond will run shape checking on the true branch,
# which will raise a shape-checking error.
avg, weights_sum = utils.cond(
tf.rank(a) == tf.rank(weights), rank_equal_case,
rank_not_equal_case)
avg = array_ops.array(avg)
if returned:
weights_sum = array_ops.broadcast_to(weights_sum, tf.shape(avg.data))
return avg, weights_sum
return avg
def f(x1, x2):
if x1.dtype == tf.bool:
assert x2.dtype == tf.bool
float_ = dtypes.default_float_type()
x1 = tf.cast(x1, float_)
x2 = tf.cast(x2, float_)
return tf.math.truediv(x1, x2)
def heaviside(x1, x2):
def f(x1, x2):
return tf.where(x1 < 0, tf.constant(0, dtype=x2.dtype),
tf.where(x1 > 0, tf.constant(1, dtype=x2.dtype), x2))
y = _bin_op(f, x1, x2)
if not np.issubdtype(y.dtype, np.inexact):
y = y.astype(dtypes.default_float_type())
return y
def _reduce(tf_fn, a, axis=None, dtype=None, keepdims=None,
promote_int=_TO_INT64, tf_bool_fn=None, preserve_bool=False):
"""A general reduction function.
Args:
tf_fn: the TF reduction function.
a: the array to be reduced.
axis: (optional) the axis along which to do the reduction. If None, all
dimensions are reduced.
dtype: (optional) the dtype of the result.
keepdims: (optional) whether to keep the reduced dimension(s).
promote_int: how to promote integer and bool inputs. There are three
choices: (1) _TO_INT64: always promote them to int64 or uint64; (2)
_TO_FLOAT: always promote them to a float type (determined by
dtypes.default_float_type); (3) None: don't promote.
tf_bool_fn: (optional) the TF reduction function for bool inputs. It
will only be used if `dtype` is explicitly set to `np.bool_` or if `a`'s
dtype is `np.bool_` and `preserve_bool` is True.
preserve_bool: a flag to control whether to use `tf_bool_fn` if `a`'s dtype
is `np.bool_` (some reductions such as np.sum convert bools to
integers, while others such as np.max preserve bools.
Returns:
An ndarray.
"""
if dtype:
dtype = utils.result_type(dtype)
if keepdims is None:
keepdims = False
a = array_creation.asarray(a, dtype=dtype)
if ((dtype == np.bool_ or preserve_bool and a.dtype == np.bool_)
and tf_bool_fn is not None):
return utils.tensor_to_ndarray(
tf_bool_fn(input_tensor=a.data, axis=axis, keepdims=keepdims))
if dtype is None:
dtype = a.dtype
if np.issubdtype(dtype, np.integer) or dtype == np.bool_:
if promote_int == _TO_INT64:
# If a is an integer/bool type and whose bit width is less than 64,
# numpy up-casts it to 64-bit.
if dtype == np.bool_:
is_signed = True
width = 8 # We can use any number here that is less than 64
else:
is_signed = np.issubdtype(dtype, np.signedinteger)
width = np.iinfo(dtype).bits
if width < 64:
if is_signed:
dtype = np.int64
else:
dtype = np.uint64
a = a.astype(dtype)
elif promote_int == _TO_FLOAT:
a = a.astype(dtypes.default_float_type())
return utils.tensor_to_ndarray(
tf_fn(input_tensor=a.data, axis=axis, keepdims=keepdims))
def convert_to_tensor(value, dtype=None):
# A safer version of `tf.convert_to_tensor` to work around b/149876037.
# TODO(wangpeng): Remove this function once the bug is fixed.
if (dtype is None and isinstance(value, six.integer_types)
and value >= 2 ** 63):
dtype = tf.uint64
elif (dtype is None and isinstance(value, float)):
dtype = dtypes.default_float_type()
return tf.convert_to_tensor(value, dtype=dtype)
def f(x1, x2):
if x1.dtype == tf.bool:
assert x2.dtype == tf.bool
float_ = dtypes.default_float_type()
x1 = tf.cast(x1, float_)
x2 = tf.cast(x2, float_)
if not dtypes.is_allow_float64():
# tf.math.truediv in Python3 produces float64 when both inputs are int32
# or int64. We want to avoid that when is_allow_float64() is False.
x1, x2 = _avoid_float64(x1, x2)
return tf.math.truediv(x1, x2)
def around(a, decimals=0): # pylint: disable=missing-docstring
a = asarray(a)
dtype = a.dtype
factor = math.pow(10, decimals)
# Use float as the working dtype instead of a.dtype, because a.dtype can be
# integer and `decimals` can be negative.
float_dtype = dtypes.default_float_type()
a = a.astype(float_dtype).data
factor = tf.cast(factor, float_dtype)
a = tf.multiply(a, factor)
a = tf.round(a)
a = tf.math.divide(a, factor)
return utils.tensor_to_ndarray(a).astype(dtype)
def around(a, decimals=0): # pylint: disable=missing-docstring
a = asarray(a)
dtype = a.dtype
factor = math.pow(10, decimals)
if np.issubdtype(dtype, np.inexact):
factor = tf.cast(factor, dtype)
else:
# Use float as the working dtype when a.dtype is exact (e.g. integer),
# because `decimals` can be negative.
float_dtype = dtypes.default_float_type()
a = a.astype(float_dtype).data
factor = tf.cast(factor, float_dtype)
a = tf.multiply(a, factor)
a = tf.round(a)
a = tf.math.divide(a, factor)
return utils.tensor_to_ndarray(a).astype(dtype)
def _scalar(x, tf_fn):
"""Computes the tf_fn(x) for each element in `x`.
Args:
x: array_like. Could be an ndarray, a Tensor or any object that can
be converted to a Tensor using `tf.convert_to_tensor`.
tf_fn: function that takes a single Tensor argument.
Returns:
An ndarray with the same shape as `x`. The default output dtype is
determined by `dtypes.default_float_type`, unless x is an ndarray with a
floating point type, in which case the output type is same as x.dtype.
"""
x = array_creation.asarray(x)
if x.dtype not in (np.float16, np.float32, np.float64):
x = x.astype(dtypes.default_float_type())
return utils.tensor_to_ndarray(tf_fn(x.data))
def _scalar(tf_fn, x, promote_to_float=False):
"""Computes the tf_fn(x) for each element in `x`.
Args:
tf_fn: function that takes a single Tensor argument.
x: array_like. Could be an ndarray, a Tensor or any object that can
be converted to a Tensor using `tf.convert_to_tensor`.
promote_to_float: whether to cast the argument to a float dtype
(`dtypes.default_float_type`) if it is not already.
Returns:
An ndarray with the same shape as `x`. The default output dtype is
determined by `dtypes.default_float_type`, unless x is an ndarray with a
floating point type, in which case the output type is same as x.dtype.
"""
x = array_creation.asarray(x)
if promote_to_float and not np.issubdtype(x.dtype, np.floating):
x = x.astype(dtypes.default_float_type())
return utils.tensor_to_ndarray(tf_fn(x.data))
def tri(N, M=None, k=0, dtype=None): # pylint: disable=invalid-name,missing-docstring
M = M if M is not None else N
if dtype is not None:
dtype = utils.result_type(dtype)
else:
dtype = dtypes.default_float_type()
if k < 0:
lower = -k - 1
if lower > N:
r = tf.zeros([N, M], dtype)
else:
# Keep as tf bool, since we create an upper triangular matrix and invert
# it.
o = tf.ones([N, M], dtype=tf.bool)
r = tf.cast(tf.math.logical_not(tf.linalg.band_part(o, lower, -1)), dtype)
else:
o = tf.ones([N, M], dtype)
if k > M:
r = o
else:
r = tf.linalg.band_part(o, -1, k)
return utils.tensor_to_ndarray(r)
