def train_model(self, training_ffts, training_labels):
"""
Takes a set of training examples + corresponding true labels and returns a
trained SOM and kNN.
"""
som_size = self.params['som_size']
som_iterations = self.params['som_iterations']
som_learning_rate = self.params['som_learning_rate']
knn_k = self.params['knn_k']
som = SelfOrganizingMap(size=som_size,
n_iterations=som_iterations,
learning_rate=som_learning_rate)
column_vectors = np.hstack(training_ffts).T
np.random.shuffle(column_vectors)
som.fit(column_vectors)
training_sequences = [
self.sound_fft_to_string(sound_fft, som)
for sound_fft in training_ffts
]
knn_model = kNN.train(training_sequences, training_labels, knn_k)
return som, knn_model
