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)
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")