xor_train.py

# python/host code

import emlearn
from emlearn.preprocessing import Quantizer
import numpy
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import get_scorer

# Generate simple dataset
def make_xor(lower=0.0, upper=1.0, threshold=0.5, samples=100, seed=42):
    rng = numpy.random.RandomState(seed)
    X = rng.uniform(lower, upper, size=(samples, 2))
    y = numpy.logical_xor(X[:, 0] > threshold, X[:, 1] > threshold)
    X = Quantizer(max_value=1.0).fit_transform(X) # convert to int16
    return X, y

X, y = make_xor()

# Train a model
estimator = RandomForestClassifier(n_estimators=3, max_depth=3, max_features=2, random_state=1)
estimator.fit(X, y)
score = get_scorer('f1')(estimator, X, y)
assert score > 0.90, score # verify that we learned the function

# Convert model using emlearn
cmodel = emlearn.convert(estimator, method='inline')

# Save as loadable .csv file
path = 'xor_model.csv'
cmodel.save(file=path, name='xor', format='csv')
print('Wrote model to', path)