Code Generators

Classification

FogML allows generation of the C source code for simple classifiers trained in the scikit-learn library. The library supports:

• DecisionTreeClassifier

• GaussianNB

• MLPClassifier

• RandomForestClassifier

The example generates a classifier code for one of the selected methods:

from sklearn import datasets, tree
from fogml.generators import GeneratorFactory

iris = datasets.load_iris()
X = iris.data
y = iris.target

#clf = tree.DecisionTreeClassifier(random_state=3456)
#clf = naive_bayes.GaussianNB()
clf = MLPClassifier(hidden_layer_sizes=(4,), random_state=34, solver='adam', max_iter=1500)
#clf = RandomForestClassifier(n_estimators=10)

clf.fit(X, y)
print( 'accuracy: ',clf.score(X,y))

factory = GeneratorFactory()
generator = factory.get_generator(clf)
generator.generate()

The main classifier code will be generated in the current folder. Examples of generated files are located in the fogML/examples/simple/models directory.

Anomaly detection

The example uses the modification of the K-Means algorithm and z-score to detect anomalies - deviations from training data. Model generation for this purpose is described in Jupyter Notebook located in the tools directory.

from sklearn.preprocessing import MinMaxScaler
from fogml.anomaly import KMeansAnomalyDetector
from fogml.generators import GeneratorFactory

scaler = MinMaxScaler()
transformer = scaler.fit(spX)
data_norm = transformer.transform(spX)

anomalyDetector = KMeansAnomalyDetector(n_clusters=16)
anomalyDetector.fit(data_norm)

factory = GeneratorFactory()
generator = factory.get_generator(transformer)
generator.generate(fname="min_max_scaler_model.c")

generator = factory.get_generator(anomalyDetector)
generator.generate(fname="kmeans_anomaly_model.c")

Copy generated kmeans_anomaly_model.c and min_max_scaler_model.c to the folder fogml_generated in the MCU project.

Time-series data processing

Processing time series from sensors using FogML requires their pre-processing directly on the device. More information in the SDK and Examples section.

Reinforcement learning