Esempio n. 1
0
def train_script_2():

    dbreader = DbReader(PATH, split_size=ONE_PERSON_SPLIT)
    training_commands = getting_commands_from_signals(
        dbreader.training_signals[:2], dbreader.training_text[:2])
    valid_commands = getting_commands_from_signals(
        dbreader.training_signals[2:], dbreader.training_text[2:])

    training_mfcc_data = simple_mfcc(training_commands)
    valid_mfcc_data = simple_mfcc(valid_commands)

    y_train = training_mfcc_data['command']
    X_train = training_mfcc_data.drop(columns=['command'])

    y_valid = valid_mfcc_data['command']
    X_valid = valid_mfcc_data.drop(columns=['command'])

    rf_model = RandomForestModel()

    model_to_fit = rf_model.gridsearchCV()
    model_to_fit.fit(X_train, y_train)
    rf_model.set_internal_model(model_to_fit.best_estimator_)
    print(model_to_fit.best_estimator_)
    rf_model.save_model()

    joblib.dump(dbreader, "dbreader.mdl")

    predictions = rf_model.predict(X_valid)
    plot_confusion_matrix(y_valid, predictions)
Esempio n. 2
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from Model import RandomForestModel

## Test 1
model = RandomForestModel(X_train=[[1, 2, 3], [11, 12, 13]], y_train = [0, 1], X_test=[[3, 4, 1],[14, 11, 17]], n_estimators=1)

model.fit()

out = list(model.predict())
desired_out = [0, 1]

print("Desired out:" + "\t" + str(desired_out))
print("Actual out:" + "\t" + str(out))
for index in range(0, len(out)):
    if out[index]!=desired_out[index]:
        print("Test 1 failed")
        exit(0)
print("Test 1 passed")