# Source code for brainpy.initialize.regular_inits

```# -*- coding: utf-8 -*-

from brainpy import math as bm, tools
from .base import InterLayerInitializer

__all__ = [
'ZeroInit',
'Constant',
'OneInit',
'Identity',
]

[docs]class ZeroInit(InterLayerInitializer):
"""Zero initializer.

Initialize the weights with zeros.
"""

def __call__(self, shape, dtype=None):
shape = [tools.size2num(d) for d in shape]
return bm.zeros(shape, dtype=dtype)

def __repr__(self):
return self.__class__.__name__

[docs]class Constant(InterLayerInitializer):
"""Constant initializer.

Initialize the weights with the given values.

Parameters
----------
value : float, int, bm.ndarray
The value to specify.
"""

[docs]  def __init__(self, value=1.):
super(Constant, self).__init__()
self.value = value

def __call__(self, shape, dtype=None):
shape = [tools.size2num(d) for d in shape]
return bm.ones(shape, dtype=dtype) * self.value

def __repr__(self):
return f'{self.__class__.__name__}(value={self.value})'

[docs]class OneInit(Constant):
"""One initializer.
"""
pass

[docs]class Identity(InterLayerInitializer):
"""Returns the identity matrix.

This initializer was proposed in (Le, et al., 2015) _.

Parameters
----------
value : float
The optional scaling factor.

Returns
-------
shape: tuple of int
The weight shape/size.

References
----------
..  Le, Quoc V., Navdeep Jaitly, and Geoffrey E. Hinton. "A simple way to
initialize recurrent networks of rectified linear units." arXiv preprint
arXiv:1504.00941 (2015).
"""

[docs]  def __init__(self, value=1.):
super(Identity, self).__init__()
self.value = value

def __call__(self, shape, dtype=None):
if isinstance(shape, int):
shape = (shape,)
elif isinstance(shape, (tuple, list)):
if len(shape) > 2:
raise ValueError(f'Only support initialize 2D weights for {self.__class__.__name__}.')
else:
raise ValueError(f'Only support shape of int, or tuple/list of int '
f'in {self.__class__.__name__}, but we got {shape}.')
shape = [tools.size2num(d) for d in shape]
return bm.eye(*shape, dtype=dtype) * self.value

def __repr__(self):
return f'{self.__class__.__name__}(value={self.value})'
```