brainpy.math.Variable

class brainpy.math.Variable(value_or_size, dtype=None, batch_axis=None)

The pointer to specify the dynamical variable.

Initializing an instance of Variable by two ways:

>>> import brainpy.math as bm
>>> # 1. init a Variable by the concreate data
>>> v1 = bm.Variable(bm.zeros(10))
>>> # 2. init a Variable by the data shape
>>> v2 = bm.Variable(10)

Note that when initializing a Variable by the data shape, all values in this Variable will be initialized as zeros.

Parameters

• value_or_size (Shape, Array, int) – The value or the size of the value.

• dtype – The type of the data.

• batch_axis (optional, int) – The batch axis.

__init__(value_or_size, dtype=None, batch_axis=None)

Methods

__init__(value_or_size[, dtype, batch_axis])
all([axis, keepdims])	Returns True if all elements evaluate to True.
any([axis, keepdims])	Returns True if any of the elements of a evaluate to True.
argmax([axis])	Return indices of the maximum values along the given axis.
argmin([axis])	Return indices of the minimum values along the given axis.
argpartition(kth[, axis, kind, order])	Returns the indices that would partition this array.
argsort([axis, kind, order])	Returns the indices that would sort this array.
as_variable()	As an instance of Variable.
astype(dtype)	Copy of the array, cast to a specified type.
block_host_until_ready(*args)
block_until_ready(*args)
byteswap([inplace])	Swap the bytes of the array elements
choose(choices[, mode])	Use an index array to construct a new array from a set of choices.
clip([min, max])	Return an array whose values are limited to [min, max].
compress(condition[, axis])	Return selected slices of this array along given axis.
conj()	Complex-conjugate all elements.
conjugate()	Return the complex conjugate, element-wise.
copy()	Return a copy of the array.
cumprod([axis, dtype])	Return the cumulative product of the elements along the given axis.
cumsum([axis, dtype])	Return the cumulative sum of the elements along the given axis.
device()
diagonal([offset, axis1, axis2])	Return specified diagonals.
dot(b)	Dot product of two arrays.
fill(value)	Fill the array with a scalar value.
flatten([order])
item(*args)	Copy an element of an array to a standard Python scalar and return it.
max([axis, keepdims])	Return the maximum along a given axis.
mean([axis, dtype, keepdims])	Returns the average of the array elements along given axis.
min([axis, keepdims])	Return the minimum along a given axis.
nonzero()	Return the indices of the elements that are non-zero.
numpy([dtype])	Convert to numpy.ndarray.
prod([axis, dtype, keepdims, initial, where])	Return the product of the array elements over the given axis.
ptp([axis, keepdims])	Peak to peak (maximum - minimum) value along a given axis.
put(indices, values)	Replaces specified elements of an array with given values.
ravel([order])	Return a flattened array.
repeat(repeats[, axis])	Repeat elements of an array.
reshape(*shape[, order])	Returns an array containing the same data with a new shape.
resize(new_shape)	Change shape and size of array in-place.
round([decimals])	Return a with each element rounded to the given number of decimals.
searchsorted(v[, side, sorter])	Find indices where elements should be inserted to maintain order.
sort([axis, kind, order])	Sort an array in-place.
split(indices_or_sections[, axis])	Split an array into multiple sub-arrays as views into ary.
squeeze([axis])	Remove axes of length one from a.
std([axis, dtype, ddof, keepdims])	Compute the standard deviation along the specified axis.
sum([axis, dtype, keepdims, initial, where])	Return the sum of the array elements over the given axis.
swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
take(indices[, axis, mode])	Return an array formed from the elements of a at the given indices.
tile(reps)	Construct an array by repeating A the number of times given by reps.
to_jax([dtype])	Convert to jax.numpy.ndarray.
to_numpy([dtype])	Convert to numpy.ndarray.
tobytes([order])	Construct Python bytes containing the raw data bytes in the array.
tolist()	Return the array as an a.ndim-levels deep nested list of Python scalars.
trace([offset, axis1, axis2, dtype])	Return the sum along diagonals of the array.
transpose(*axes)	Returns a view of the array with axes transposed.
update(value)	Update the value of this Array.
var([axis, dtype, ddof, keepdims])	Returns the variance of the array elements, along given axis.
view([dtype])	New view of array with the same data.

Attributes

T
at
batch_axis	rtype Optional[int]
batch_size	rtype Optional[int]
device_buffer
dtype	Variable dtype.
imag
is_brainpy_array
ndim
real
shape	Variable shape.
shape_nb	Shape without batch axis.
size
value