import copy, inspect, textwrap
import numpy as np
from collections.abc import Callable
from nnodely.basic.relation import NeuObj, Relation
from nnodely.support.utils import check, enforce_types, NP_DTYPE
def is_numpy_float(var):
return isinstance(var, (np.float16, np.float32, np.float64))
[docs]
class Constant(NeuObj, Relation):
"""
Represents a constant value in the neural network model.
Parameters
----------
name : str
The name of the constant.
values : list, float, int, or np.ndarray
The values of the constant.
tw : float or int, optional
The time window for the constant. Default is None.
sw : int, optional
The sample window for the constant. Default is None.
Attributes
----------
name : str
The name of the constant.
dim : dict
A dictionary containing the dimensions of the constant.
json : dict
A dictionary containing the configuration of the constant.
Examples
--------
.. image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/parameter.ipynb
:alt: Open in Colab
Example - passing a custom scalar value -> g.dim = {'dim': 1}:
>>> g = Constant('gravity',values=9.81)
Example - passing a custom vector value -> n.dim = {'dim': 4}:
>>> n = Constant('numbers', values=[1,2,3,4])
Example - passing a custom vector value with single sample window -> n.dim = {'dim': 4, 'sw': 1}:
>>> n = Constant('numbers', values=[[1,2,3,4]])
Example - passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}:
>>> n = Constant('numbers', values=[[2,3,4],[1,2,3]])
Example - passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}.
If the value of the sw is differnt from the dimension of shape[0] an error will be raised.
>>> n = Constant('numbers', sw = 2, values=[[2,3,4],[1,2,3]])
Example - passing a custom vector value with time window -> n.dim = {'dim': 4, 'tw': 4}.
In this case the samplingtime must be 0.5 otherwise an error will be raised. If the Constant have a time dimension,
the input must have a len(shape) == 2.
>>> n = Constant('numbers', tw = 4, values=[[2,3,4],[1,2,3]])
"""
@enforce_types
def __init__(self, name:str,
values:list|float|int|np.ndarray, *,
tw:float|int|None = None,
sw:int|None = None):
NeuObj.__init__(self, name)
values = np.array(values, dtype=NP_DTYPE)
shape = values.shape
values = values.tolist()
self.dim = {}
if tw is not None:
check(len(shape) >= 2, ValueError, "The dimension must be at least 2 if tw is set.")
check(sw is None, ValueError, "If tw is set sw must be None")
dimensions = shape[1] if len(shape[1:]) == 1 else list(shape[1:])
self.dim['tw'] = tw
elif sw is not None:
check(len(shape) >= 2, ValueError, "The dimension must be at least 2 if sw is set.")
self.dim['sw'] = sw
check(shape[0] == self.dim['sw'],ValueError, f"The sw = {sw} is different from sw = {shape[0]} of the values.")
dimensions = shape[1] if len(shape[1:]) == 1 else list(shape[1:])
else:
dimensions = 1 if len(shape[0:]) == 0 else shape[0] if len(shape[0:]) == 1 else list(shape[0:])
self.dim['dim'] = dimensions
# deepcopy dimention information inside Parameters
self.json['Constants'][self.name] = copy.deepcopy(self.dim)
if type(values) in (float,int):
self.json['Constants'][self.name]['values'] = [values]
else:
self.json['Constants'][self.name]['values'] = values
[docs]
class Parameter(NeuObj, Relation):
"""
Represents a trainable parameter in the neural network model.
Notes
-----
.. note::
You can find some initialization functions for the 'init' and 'init_params' parameters inside the initializer module.
Parameters
----------
name : str
The name of the parameter.
dimensions : int, list, tuple, or None, optional
The dimensions of the parameter. Default is None.
tw : float or int, optional
The time window for the parameter. Default is None.
sw : int, optional
The sample window for the parameter. Default is None.
values : list, float, int, np.ndarray, or None, optional
The values by which initialize the parameter. Default is None.
init : Callable, optional
A callable for initializing the parameter values. Default is None.
init_params : dict, optional
A dictionary of parameters for the initializer. Default is None.
Attributes
----------
name : str
The name of the parameter.
dim : dict
A dictionary containing the dimensions of the parameter.
json : dict
A dictionary containing the configuration of the parameter.
Examples
--------
.. image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/parameter.ipynb
:alt: Open in Colab
Example - basic usage:
>>> k = Parameter('k', dimensions=3, tw=4)
Example - initialize a parameter with values:
>>> x = Input('x')
>>> gravity = Parameter('g', dimensions=(4,1),values=[[[1],[2],[3],[4]]])
>>> out = Output('out', Linear(W=gravity)(x.sw(3)))
Example - initialize a parameter with a function:
>>> x = Input('x').last()
>>> p = Parameter('param', dimensions=1, sw=1, init=init_constant, init_params={'value':1})
>>> relation = Fir(parameter=param)(x)
"""
@enforce_types
def __init__(self, name:str,
dimensions:int|list|tuple|None = None, *,
tw:float|int|None = None,
sw:int|None = None,
values:list|float|int|np.ndarray|None = None,
init:Callable|str|None = None,
init_params:dict|None = None):
NeuObj.__init__(self, name)
dimensions = list(dimensions) if type(dimensions) is tuple else dimensions
if values is None:
if dimensions is None:
dimensions = 1
self.dim = {'dim': dimensions}
if tw is not None:
check(sw is None, ValueError, "If tw is set sw must be None")
self.dim['tw'] = tw
elif sw is not None:
self.dim['sw'] = sw
# deepcopy dimention information inside Parameters
self.json['Parameters'][self.name] = copy.deepcopy(self.dim)
else:
values = np.array(values, dtype=NP_DTYPE)
shape = values.shape
values = values.tolist()
self.dim = {}
if tw is not None:
check(len(shape) >= 2, ValueError, "The dimension must be at least 2 if tw is set.")
check(sw is None, ValueError, "If tw is set sw must be None")
dimensions = shape[1] if len(shape[1:]) == 1 else list(shape[1:])
self.dim['tw'] = tw
elif sw is not None:
check(len(shape) >= 2, ValueError, "The dimension must be at least 2 if sw is set.")
self.dim['sw'] = sw
check(shape[0] == self.dim['sw'], ValueError,
f"The sw = {sw} is different from sw = {shape[0]} of the values.")
dimensions = shape[1] if len(shape[1:]) == 1 else list(shape[1:])
else:
dimensions = 1 if len(shape[0:]) == 0 else shape[0] if len(shape[0:]) == 1 else list(shape[0:])
self.dim['dim'] = dimensions
# deepcopy dimention information inside Parameters
self.json['Parameters'][self.name] = copy.deepcopy(self.dim)
if type(values) in (int, float):
self.json['Parameters'][self.name]['init_values'] = [values]
else:
self.json['Parameters'][self.name]['init_values'] = values
self.json['Parameters'][self.name]['values'] = self.json['Parameters'][self.name]['init_values']
if init is not None:
check('values' not in self.json['Parameters'][self.name], ValueError, f"The parameter {self.name} is already initialized.")
#check(inspect.isfunction(init), ValueError,f"The init parameter must be a function.")
if inspect.isfunction(init):
code = textwrap.dedent(inspect.getsource(init)).replace('\"', '\'')
self.json['Parameters'][self.name]['init_fun'] = { 'code' : code, 'name' : init.__name__}
elif type(init) is str:
self.json['Parameters'][self.name]['init_fun'] = { 'name' : init }
if init_params is not None:
self.json['Parameters'][self.name]['init_fun']['params'] = init_params
[docs]
class SampleTime():
"""
Represents a constant value that is equal to the sample time.
Attributes
----------
name : str
The name of the constant.
dim : dict
A dictionary containing the dimensions of the constant.
json : dict
A dictionary containing the configuration of the constant.
Example
-------
>>> dt = SampleTime()
"""
name = 'SampleTime'
g = Constant(name, values=0)
def __new__(cls):
SampleTime.g.dim = {'dim': 1}
SampleTime.g.json['Constants'][SampleTime.name] = copy.deepcopy(SampleTime.g.dim)
SampleTime.g.json['Constants'][SampleTime.name]['values'] = SampleTime.name
return SampleTime.g