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thieu1995 · Mar 27, 2024 · 54622d9 · 54622d9
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -30,7 +30,7 @@
 author = 'Thieu'
 
 # The full version, including alpha/beta/rc tags
-release = '1.1.0'
+release = '1.1.1'
 
 
 # -- General configuration ---------------------------------------------------

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -6,7 +6,7 @@
 Welcome to IntelELM's documentation!
 ====================================
 
-.. image:: https://img.shields.io/badge/release-1.1.0-yellow.svg
+.. image:: https://img.shields.io/badge/release-1.1.1-yellow.svg
    :target: https://github.com/thieu1995/intelelm/releases
 
 .. image:: https://img.shields.io/pypi/wheel/gensim.svg

diff --git a/docs/source/pages/quick_start.rst b/docs/source/pages/quick_start.rst
@@ -6,7 +6,7 @@ There are so many ways to install our library. For example:
 
 * Install from the `PyPI release <https://pypi.python.org/pypi/intelelm />`_::
 
-   $ pip install intelelm==1.1.0
+   $ pip install intelelm==1.1.1
 
 
 * Install directly from source code::
@@ -63,7 +63,7 @@ process consists of the following steps:
 	data.y_test = scaler_y.transform(np.reshape(data.y_test, (-1, 1)))
 
 	## Define the model
-	model = ElmRegressor(hidden_size=10, act_name="elu")
+	model = ElmRegressor(hidden_size=10, act_name="elu", seed=42)
 
 	## Test the model
 	model.fit(data.X_train, data.y_train)
@@ -95,7 +95,7 @@ If you want to use the Whale Optimization-based ELM (WO-ELM) model, you can chan
 
 	opt_paras = {"name": "WOA", "epoch": 100, "pop_size": 30}
 	model = MhaElmRegressor(hidden_size=10, act_name="elu", obj_name="MSE",
-			optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=False)
+			optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=False, seed=42)
 
 In the example above, I had to import the MhaElmRegressor class. This is the class that contains all Metaheuristics-based ELM models for regression problems.
 Then, I defined parameters for the Whale Optimization algorithm. And I defined parameters for the Whale Optimization-based ELM model.
@@ -110,7 +110,7 @@ MhaElmClassifier class (these are hybrid models combining metaheuristics algorit
 
 	from intelelm import ElmClassifier
 
-	model = ElmClassifier(hidden_size=10, act_name="elu")
+	model = ElmClassifier(hidden_size=10, act_name="elu", seed=42)
 
 
 
@@ -120,7 +120,7 @@ MhaElmClassifier class (these are hybrid models combining metaheuristics algorit
 
 	opt_paras = {"name": "GA", "epoch": 100, "pop_size": 30}
 	model = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="BSL",
-			optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False)
+			optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False, seed=42)
 
 
 -------------------
@@ -168,7 +168,7 @@ library to split and scale the data.
 	print(MhaElmClassifier.SUPPORTED_OPTIMIZERS)
 	print(MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES)
 	opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-	classifier = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="KLDL", optimizer="BaseGA", optimizer_paras=opt_paras)
+	classifier = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="KLDL", optimizer="BaseGA", optimizer_paras=opt_paras, seed=42)
 
 	#### Step 6: Traint the model
 	classifer.fit(X_train, y_train)
@@ -217,7 +217,7 @@ In the example below, we use the Whale Optimization-based ELM model as the base
 	# create model and selector
 	opt_paras = {"name": "GA", "epoch": 100, "pop_size": 30}
 	model = MhaElmRegressor(hidden_size=10, act_name="relu", obj_name="MSE",
-			optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False)
+			optimizer="BaseGA", optimizer_paras=opt_paras, verbose=False, seed=42)
 
 	selector = RFE(estimator=model)
 	selector.fit(X_train, y_train)