Migrate documentation theme to PyData Sphinx theme

docs/examples/elu_variants/isrlu_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import isrlu
+
+x = torch.linspace(-3, 3, 200)
+y = isrlu(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("ISRLU (Inverse Square Root Linear Unit)")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/elu_variants/pelu_example.py

@@ -0,0 +1,45 @@
+"""Example demonstrating the PELU activation function."""
+
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import pelu
+
+
+def main() -> None:
+    """Plot the PELU activation function with different parameter values."""
+    x = torch.linspace(-5, 5, 1000)
+
+    # Different parameter combinations
+    params = [
+        {"alpha": 1.0, "beta": 1.0, "label": "α=1.0, β=1.0 (default)"},
+        {"alpha": 1.5, "beta": 1.0, "label": "α=1.5, β=1.0"},
+        {"alpha": 1.0, "beta": 1.5, "label": "α=1.0, β=1.5"},
+        {"alpha": 1.5, "beta": 1.5, "label": "α=1.5, β=1.5"},
+    ]
+
+    # Create the plot
+    plt.figure(figsize=(10, 6))
+
+    # Plot PELU with different parameter combinations
+    for param in params:
+        y_pelu = pelu(x, alpha=param["alpha"], beta=param["beta"])
+        plt.plot(x.numpy(), y_pelu.numpy(), label=param["label"], linewidth=2)
+
+    # Add vertical and horizontal lines at origin
+    plt.axhline(y=0, color="k", linestyle="-", alpha=0.3)
+    plt.axvline(x=0, color="k", linestyle="-", alpha=0.3)
+
+    # Configure the plot
+    plt.grid(True, alpha=0.3)
+    plt.xlabel("x")
+    plt.ylabel("f(x)")
+    plt.title("PELU Activation Function with Different Parameters")
+    plt.legend()
+    plt.tight_layout()
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()

docs/examples/relu_variants/dual_line_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import dual_line
+
+x = torch.linspace(-3, 3, 200)
+y = dual_line(x, a=1.0, b=0.01)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("Dual Line (a=1.0, b=0.01)")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/sigmoid_tanh_variants/aria2_example.py

@@ -0,0 +1,42 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import aria2
+
+x = torch.linspace(-5, 5, 200)
+
+# Default parameters (alpha=1.5, beta=0.5)
+y_default = aria2(x)
+
+# Different alpha values
+y_alpha_1 = aria2(x, alpha=1.0, beta=0.5)
+y_alpha_2 = aria2(x, alpha=2.0, beta=0.5)
+
+# Different beta values
+y_beta_1 = aria2(x, alpha=1.5, beta=0.2)
+y_beta_2 = aria2(x, alpha=1.5, beta=1.0)
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+
+# Plot different alpha values
+ax1.plot(x.numpy(), y_default.numpy(), label="Default (α=1.5, β=0.5)")
+ax1.plot(x.numpy(), y_alpha_1.numpy(), label="α=1.0, β=0.5")
+ax1.plot(x.numpy(), y_alpha_2.numpy(), label="α=2.0, β=0.5")
+ax1.set_title("ARiA2 with Different Alpha Values")
+ax1.set_xlabel("Input")
+ax1.set_ylabel("Output")
+ax1.grid(alpha=0.3)
+ax1.legend()
+
+# Plot different beta values
+ax2.plot(x.numpy(), y_default.numpy(), label="Default (α=1.5, β=0.5)")
+ax2.plot(x.numpy(), y_beta_1.numpy(), label="α=1.5, β=0.2")
+ax2.plot(x.numpy(), y_beta_2.numpy(), label="α=1.5, β=1.0")
+ax2.set_title("ARiA2 with Different Beta Values")
+ax2.set_xlabel("Input")
+ax2.set_ylabel("Output")
+ax2.grid(alpha=0.3)
+ax2.legend()
+
+plt.tight_layout()
+fig.show()

docs/examples/sigmoid_tanh_variants/isru_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import isru
+
+x = torch.linspace(-3, 3, 200)
+y = isru(x, alpha=1.0)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("ISRU (alpha=1.0)")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/sigmoid_tanh_variants/tanh_exp_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import tanh_exp
+
+x = torch.linspace(-3, 3, 200)
+y = tanh_exp(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("TanhExp")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()

docs/examples/specialized_variants/erf_act_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import erf_act
+
+x = torch.linspace(-3, 3, 200)
+y = erf_act(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("ErfAct")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/specialized_variants/hat_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import hat
+
+x = torch.linspace(-3, 3, 200)
+y = hat(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("Hat")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/specialized_variants/pserf_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import pserf
+
+x = torch.linspace(-3, 3, 200)
+y = pserf(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("Pserf")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/specialized_variants/resp_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import resp
+
+x = torch.linspace(-3, 3, 200)
+y = resp(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("Rectified Softplus (ReSP)")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/examples/tanh_variants/penalized_tanh_example.py

@@ -0,0 +1,14 @@
+import matplotlib.pyplot as plt
+import torch
+
+from activations_plus.simple import penalized_tanh
+
+x = torch.linspace(-3, 3, 200)
+y = penalized_tanh(x)
+fig, ax = plt.subplots()
+ax.plot(x.numpy(), y.numpy())
+ax.set_title("Penalized Tanh")
+ax.set_xlabel("Input")
+ax.set_ylabel("Output")
+ax.grid(alpha=0.3)
+fig.show()  # This will be mocked in tests

docs/source/conf.py

@@ -42,6 +42,21 @@
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = "sphinx_rtd_theme"
+html_theme = "pydata_sphinx_theme"
 master_doc = "index"
 html_static_path = ["_static"]
+
+# Theme options
+html_theme_options = {
+    "github_url": "https://github.com/DanielAvdar/activations-plus",
+    "use_edit_page_button": True,
+    "show_toc_level": 2,
+    "navbar_align": "left",
+}
+
+html_context = {
+    "github_user": "DanielAvdar",
+    "github_repo": "activations-plus",
+    "github_version": "main",
+    "doc_path": "docs/source",
+}

pyproject.toml

@@ -63,7 +63,7 @@ dev = [
 docs = [
     "sphinx>=8.2.0; python_version >= '3.11'",
     "sphinx>=7.0.0; python_version < '3.11'",
-    "sphinx-rtd-theme>=3.0.2",
+    "pydata-sphinx-theme>=0.13.0",
     "matplotlib",
 
 ]