brainpy.math.Parameter

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

The pointer to specify the parameter.

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

Methods

__init__(value_or_size[, dtype, batch_axis])
abs(*[, out])	rtype: Optional[Array]
abs_()	in-place version of Array.abs()
absolute(*[, out])	alias of Array.abs
absolute_()	alias of Array.abs_()
addr(vec1, vec2, *[, beta, alpha, out])	Performs the outer-product of vectors vec1 and vec2 and adds it to the matrix input.
addr_(vec1, vec2, *[, beta, alpha])	rtype: None
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.
arccos(*[, out])	rtype: Optional[Array]
arccos_()	rtype: None
arcsin(*[, out])	rtype: Optional[Array]
arcsin_()	rtype: None
arctan(*[, out])	rtype: Optional[Array]
arctan_()	rtype: None
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.
clamp([min_value, max_value, out])	return the value between min_value and max_value, if min_value is None, then no lower bound, if max_value is None, then no upper bound.
clamp_([min_value, max_value])	return the value between min_value and max_value, if min_value is None, then no lower bound, if max_value is None, then no upper bound.
clip([min, max, out])	Return an array whose values are limited to [min, max].
clip_([min_value, max_value])	alias for clamp_
clone()	rtype: Array
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.
copy_(src)	rtype: None
cos(*[, out])	rtype: Optional[Array]
cos_()	rtype: None
cosh(*[, out])	rtype: Optional[Array]
cosh_()	rtype: None
cov_with([y, rowvar, bias, ddof, fweights, ...])	rtype: 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.
expand(*sizes)	Expand an array to a new shape.
expand_as(array)	Expand an array to a shape of another array.
expand_dims(axis)
fill(value)	Fill the array with a scalar value.
flatten()
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.
outer(other)	rtype: Array
pow(index)
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.
sin(*[, out])	rtype: Optional[Array]
sin_()	rtype: None
sinh(*[, out])	rtype: Optional[Array]
sinh_()	rtype: None
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.
tan(*[, out])	rtype: Optional[Array]
tan_()	rtype: None
tanh(*[, out])	rtype: Optional[Array]
tanh_()	rtype: None
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()	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.
unsqueeze(dim)	equals Array.expand_dims(dim)
update(value)	Update the value of this Array.
var([axis, dtype, ddof, keepdims])	Returns the variance of the array elements, along given axis.
view(*args[, 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
nobatch_shape	Shape without batch axis.
real
shape	Variable shape.
size
value