Add: noise_kwargs parameters to reservoirs to parameterize noise (#98)

reservoirpy/nodes/reservoirs/intrinsic_plasticity.py

@@ -9,7 +9,7 @@
     from typing import Literal
 
 from functools import partial
-from typing import Callable, Optional, Sequence, Union
+from typing import Callable, Optional, Sequence, Union, Dict
 
 import numpy as np
 
@@ -18,7 +18,7 @@
 from ...mat_gen import bernoulli, uniform
 from ...node import Unsupervised, _init_with_sequences
 from ...type import Weights
-from ...utils.random import noise
+from ...utils.random import noise, rand_generator
 from ...utils.validation import is_array
 from .base import forward_external
 from .base import initialize as initialize_base
@@ -211,6 +211,9 @@ class IPReservoir(Unsupervised):
     noise_type : str, default to "normal"
         Distribution of noise. Must be a Numpy random variable generator
         distribution (see :py:class:`numpy.random.Generator`).
+    noise_kwargs : dict, optional
+        Keyword arguments to pass to the noise generator, such as `low` and `high`
+        values of uniform distribution.
     input_scaling : float or array-like of shape (features,), default to 1.0.
         Input gain. An array of the same dimension as the inputs can be used to
         set up different input scaling for each feature.
@@ -318,6 +321,7 @@ def __init__(
         noise_in: float = 0.0,
         noise_fb: float = 0.0,
         noise_type: str = "normal",
+        noise_kwargs: Dict = None,
         input_scaling: Union[float, Sequence] = 1.0,
         bias_scaling: float = 1.0,
         fb_scaling: Union[float, Sequence] = 1.0,
@@ -347,6 +351,9 @@ def __init__(
                 "appliying intrinsic plasticity."
             )
 
+        rng = rand_generator(seed=seed)
+        noise_kwargs = dict() if noise_kwargs is None else noise_kwargs
+
         super(IPReservoir, self).__init__(
             fb_initializer=partial(
                 initialize_feedback,
@@ -387,7 +394,7 @@ def __init__(
                 ),
                 "fb_activation": fb_activation,
                 "units": units,
-                "noise_generator": partial(noise, seed=seed),
+                "noise_generator": partial(noise, rng=rng, **noise_kwargs),
             },
             forward=forward_external,
             initializer=partial(

reservoirpy/nodes/reservoirs/reservoir.py

@@ -9,13 +9,13 @@
     from typing import Literal
 
 from functools import partial
-from typing import Callable, Optional, Sequence, Union
+from typing import Callable, Optional, Sequence, Union, Dict
 
 from ...activationsfunc import get_function, identity, tanh
 from ...mat_gen import bernoulli, normal
 from ...node import Node
 from ...type import Weights
-from ...utils.random import noise
+from ...utils.random import noise, rand_generator
 from ...utils.validation import is_array
 from .base import forward_external, forward_internal, initialize, initialize_feedback
 
@@ -110,6 +110,9 @@ class Reservoir(Node):
     noise_type : str, default to "normal"
         Distribution of noise. Must be a Numpy random variable generator
         distribution (see :py:class:`numpy.random.Generator`).
+    noise_kwargs : dict, optional
+        Keyword arguments to pass to the noise generator, such as `low` and `high`
+        values of uniform distribution.
     input_scaling : float or array-like of shape (features,), default to 1.0.
         Input gain. An array of the same dimension as the inputs can be used to
         set up different input scaling for each feature.
@@ -221,6 +224,7 @@ def __init__(
         noise_in: float = 0.0,
         noise_fb: float = 0.0,
         noise_type: str = "normal",
+        noise_kwargs: Dict = None,
         input_scaling: Union[float, Sequence] = 1.0,
         bias_scaling: float = 1.0,
         fb_scaling: Union[float, Sequence] = 1.0,
@@ -259,6 +263,10 @@ def __init__(
         if type(fb_activation) is str:
             fb_activation = get_function(fb_activation)
 
+        rng = rand_generator(seed)
+
+        noise_kwargs = dict() if noise_kwargs is None else noise_kwargs
+
         super(Reservoir, self).__init__(
             fb_initializer=partial(
                 initialize_feedback,
@@ -290,7 +298,7 @@ def __init__(
                 "activation": activation,
                 "fb_activation": fb_activation,
                 "units": units,
-                "noise_generator": partial(noise, seed=seed),
+                "noise_generator": partial(noise, rng=rng, **noise_kwargs),
             },
             forward=forward,
             initializer=partial(

reservoirpy/nodes/tests/test_reservoir.py

@@ -10,7 +10,6 @@
 
 
 def test_reservoir_init():
-
     node = Reservoir(100, lr=0.8, input_bias=False)
 
     data = np.ones((1, 10))
@@ -38,7 +37,6 @@ def test_reservoir_init():
 
 
 def test_reservoir_init_from_matrices():
-
     Win = np.ones((100, 10))
 
     node = Reservoir(100, lr=0.8, Win=Win, input_bias=False)
@@ -108,7 +106,6 @@ def test_reservoir_init_from_matrices():
 
 
 def test_reservoir_bias():
-
     node = Reservoir(100, lr=0.8, input_bias=False)
 
     data = np.ones((1, 10))
@@ -169,7 +166,6 @@ def test_reservoir_run():
 
 
 def test_reservoir_chain():
-
     node1 = Reservoir(100, lr=0.8, input_bias=False)
     node2 = Reservoir(50, lr=1.0, input_bias=False)
 
@@ -185,7 +181,6 @@ def test_reservoir_chain():
 
 
 def test_reservoir_feedback():
-
     node1 = Reservoir(100, lr=0.8, input_bias=False)
     node2 = Reservoir(50, lr=1.0, input_bias=False)
 
@@ -226,3 +221,33 @@ def test_reservoir_feedback():
         node1 <<= node2
         data = np.ones((1, 10))
         res = (node1 >> node2)(data)
+
+
+def test_reservoir_noise():
+    node1 = Reservoir(100, seed=123, noise_rc=0.1, noise_in=0.5)
+    node2 = Reservoir(100, seed=123, noise_rc=0.1, noise_in=0.5)
+
+    data = np.ones((10, 10))
+
+    assert_array_equal(node1.run(data), node2.run(data))
+
+    node1 = Reservoir(
+        100,
+        seed=123,
+        noise_rc=0.1,
+        noise_in=0.5,
+        noise_type="uniform",
+        noise_kwargs={"low": -1, "high": 0.5},
+    )
+    node2 = Reservoir(
+        100,
+        seed=123,
+        noise_rc=0.1,
+        noise_in=0.5,
+        noise_type="uniform",
+        noise_kwargs={"low": -1, "high": 0.5},
+    )
+
+    data = np.ones((10, 10))
+
+    assert_array_equal(node1.run(data), node2.run(data))