revised elements of utils to comply with docs

Author: Alexander Ororbia

docs/source/ngclearn.utils.feature_dictionaries.rst

@@ -0,0 +1,21 @@
+ngclearn.utils.feature\_dictionaries package
+============================================
+
+Submodules
+----------
+
+ngclearn.utils.feature\_dictionaries.polynomialLibrary module
+-------------------------------------------------------------
+
+.. automodule:: ngclearn.utils.feature_dictionaries.polynomialLibrary
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: ngclearn.utils.feature_dictionaries
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/ngclearn.utils.masks.rst

@@ -0,0 +1,21 @@
+ngclearn.utils.masks package
+============================
+
+Submodules
+----------
+
+ngclearn.utils.masks.multiblock2d module
+----------------------------------------
+
+.. automodule:: ngclearn.utils.masks.multiblock2d
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: ngclearn.utils.masks
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/ngclearn.utils.rst

@@ -10,6 +10,8 @@ Subpackages
    ngclearn.utils.analysis
    ngclearn.utils.density
    ngclearn.utils.diffeq
+   ngclearn.utils.feature_dictionaries
+   ngclearn.utils.masks
    ngclearn.utils.optim
    ngclearn.utils.viz
 

ngclearn/utils/diffeq/ode_utils.py

@@ -1,12 +1,13 @@
 """
 Routines and co-routines for ngc-learn's differential equation integration backend.
 
-Currently supported back-end forms of integration in ngc-learn include:
-0) Euler integration (RK-1);
-1) Midpoint method (RK-2);
-2) Heun's method (error-corrector RK-2);
-3) Ralston's method (error-corrector RK-2);
-4) 4th-order Runge-Kutta method (RK-4);
+| Currently supported back-end forms of integration in ngc-learn include:
+| 0) Euler integration (RK-1);
+| 1) Midpoint method (RK-2);
+| 2) Heun's method (error-corrector RK-2);
+| 3) Ralston's method (error-corrector RK-2);
+| 4) 4th-order Runge-Kutta method (RK-4);
+
 """
 
 from jax import numpy as jnp, random, jit #, nn

ngclearn/utils/diffeq/odes.py

@@ -1,3 +1,16 @@
+"""
+In-built dynamical systems built on differential equations. Note that these systems are designed such that they
+directly operzte with ngc-learn's ODE integration backend.
+
+| Currently in-built dynamical systems include:
+| 0) A continuous linear 2D system;
+| 1) A continuous cubic 2D system;
+| 2) A Lorenz attractor system;
+| 3) A continuous linear 3D system;
+| 4) A continuous oscillator system.
+
+"""
+
 import jax.numpy as jnp
 
 def linear_2D(t, x, params):

ngclearn/utils/feature_dictionaries/__init__.py

@@ -1,19 +1,16 @@
-import jax.numpy as jnp
-from jax import jit, random
-import jax.numpy as jnp
+from jax import jit, random, numpy as jnp
 from typing import List, Tuple, Union
 from dataclasses import dataclass
 
-
-
 @dataclass
 class PolynomialLibrary:
     """
     A class for creating polynomial feature libraries in 1D, 2D, or 3D.
 
-    Attributes:
-        poly_order (int): Maximum order of polynomial terms
-        include_bias (bool): Whether to include the bias term in the output
+    Args:
+        poly_order (int): Maximum order of polynomial terms (Attribute)
+
+        include_bias (bool): Whether to include the bias term in the output (Attribute)
     """
 
     poly_order: int = None

ngclearn/utils/feature_dictionaries/polynomialLibrary.py

@@ -93,9 +93,9 @@ def target(self, img: jnp.ndarray):
         plt.show()
 
 
-
-gen = PatchGenerator(patch_width=5, patch_height=5, horizontal_alignment='center', horizontal_stride=1)
-
-test_img = jnp.zeros((32, 32))
-
-gen.target(test_img)
+## testing code
+# gen = PatchGenerator(patch_width=5, patch_height=5, horizontal_alignment='center', horizontal_stride=1)
+#
+# test_img = jnp.zeros((32, 32))
+#
+# gen.target(test_img)

ngclearn/utils/masks/__init__.py

@@ -39,17 +39,19 @@ class Create_Patches:
 
     Args:
         img: jax array of size (H, W)
-        patched: (height_patch, width_patch)
-        overlap: (height_overlap, width_overlap)
 
-        add_frame: increases the img size by (height_patch - height_overlap, width_patch - width_overlap)
-        create_patches: creates small patches out of the image based on the provided attributes.
+        patch_shape: (height_patch, width_patch)
+
+        overlap_shape: (height_overlap, width_overlap)
 
     Returns:
-        jnp.array: Array containing the patches
-        shape: (num_patches, patch_height, patch_width)
+        jnp.array: Array containing the patches, shape: (num_patches, patch_height, patch_width)
 
     """
+    #patched: (height_patch, width_patch)
+    #overlap: (height_overlap, width_overlap)
+    #add_frame: increases the img size by (height_patch - height_overlap, width_patch - width_overlap)
+    #create_patches: creates small patches out of the image based on the provided attributes.
 
     def __init__(self, img, patch_shape, overlap_shape):
         self.img = img
@@ -90,6 +92,8 @@ def create_patches(self, add_frame=False, center=True):
         Keyword Args:
             add_frame: If true the function will add zero frames (increase the dimension) to the image
 
+            center:
+
         Returns:
             jnp.array: Array containing the patches
             shape: (num_patches, patch_height, patch_width)