Upload Python script and training data used to create model

create_classifier_function.py

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+# Train a machine-learning model to classify Morse code input
+#
+# Requirements
+# - SciKit-Learn to create the model (tested with v1.2.0)
+# - MicroMLGen to export it in Arduino-usable C code (tested with v1.1.28)
+#
+# These dependencies can be installed from command-line as follows:
+# pip install -U scikit-learn
+# pip install micromlgen
+
+# For loading training dataset from .csv files
+import numpy as np
+from glob import glob
+from os.path import basename
+# Import chosen classifier function
+# (for alternatives see https://github.com/eloquentarduino/micromlgen)
+from sklearn.ensemble import RandomForestClassifier
+# For exporting model to Arduino C code
+from micromlgen import port
+
+# Load training dataset from csv files 
+def load_features(folder):
+    dataset = None
+    classmap = {}
+    for class_idx, filename in enumerate(glob('%s/*.csv' % folder)):
+        class_name = basename(filename)[:-4]
+        classmap[class_idx] = class_name
+        samples = np.loadtxt(filename, dtype=float, delimiter=',')
+        labels = np.ones((len(samples), 1)) * class_idx
+        samples = np.hstack((samples, labels))
+        dataset = samples if dataset is None else np.vstack((dataset, samples))
+    return dataset, classmap
+
+# Load data, apply classifier, output as Arduino format 
+if __name__ == '__main__':
+    # Load training data from the specified subfolder
+    features, classmap = load_features('training_data')
+    # Create classifier function from feature set
+    X, y = features[:, :-1], features[:, -1]
+    classifier = RandomForestClassifier(20, max_depth=10).fit(X, y)
+    # Use MicroMLgen to port classifier to Arduino C-code
+    c_code = port(classifier, classmap=classmap)
+    # Show generated code
+    print(c_code)

training_data/a.csv

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training_data/b.csv

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training_data/c.csv

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training_data/d.csv

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training_data/e.csv

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training_data/f.csv

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training_data/g.csv

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training_data/h.csv

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training_data/i.csv

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training_data/j.csv

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training_data/k.csv

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training_data/l.csv

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training_data/m.csv

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training_data/n.csv

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training_data/o.csv

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training_data/p.csv

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training_data/q.csv

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training_data/r.csv

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training_data/s.csv

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training_data/t.csv

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training_data/u.csv

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training_data/v.csv

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training_data/w.csv

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training_data/x.csv

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training_data/y.csv

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training_data/z.csv

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