v0.1.4

README.md

@@ -77,6 +77,49 @@ prediction = Optimizer.predict(X_test)
 score = Optimizer.score(X_test, y_test)
 ```
 
+Example with a feedforward neural network in keras (experimental):
+```python
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+
+from hyperactive import ParticleSwarm_Optimizer
+
+breast_cancer_data = load_breast_cancer()
+X = breast_cancer_data.data
+y = breast_cancer_data.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
+
+# this defines the structure of the model and the search space in each layer
+search_config = {
+    "keras.compile.0": {"loss": ["binary_crossentropy"], "optimizer": ["adam"]},
+    "keras.fit.0": {"epochs": [5], "batch_size": [100]},
+    "keras.layers.Dense.1": {
+        "units": range(5, 15),
+        "activation": ["relu"],
+        "kernel_initializer": ["uniform"],
+    },
+    "keras.layers.Dense.2": {
+        "units": range(5, 15),
+        "activation": ["relu"],
+        "kernel_initializer": ["uniform"],
+    },
+    "keras.layers.Dense.3": {"units": [1], "activation": ["sigmoid"]},
+}
+Optimizer = ParticleSwarm_Optimizer(search_config, 3, cv=1)
+
+# search best hyperparameter for given data
+Optimizer.fit(X_train, y_train)
+
+# predict from test data
+prediction = Optimizer.predict(X_test)
+
+# calculate accuracy score
+score = Optimizer.score(X_test, y_test)
+```
+
+
+
 
 ## Hyperactive API
 

hyperactive/__init__.py

@@ -6,7 +6,7 @@
 from .simulated_annealing import SimulatedAnnealing_Optimizer
 from .particle_swarm_optimization import ParticleSwarm_Optimizer
 
-__version__ = "0.1.3"
+__version__ = "0.1.4"
 __license__ = "MIT"
 
 __all__ = [

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="hyperactive",
-    version="0.1.3",
+    version="0.1.4",
     author="Simon Blanke",
     author_email="[email protected]",
     license="MIT",