from utils.utils import load_data
from utils.utils import from_csv_with_filenames
from utils.constants import Constants
from sklearn.externals import joblib
import os
import logging
import numpy as np
csv_path = os.path.join(Constants.DATA_FOLDER, '10classes', 'audio_data.csv')
LOAD = True
if __name__ == '__main__':
logging.info('Loading data')
xs, ys, filenames = from_csv_with_filenames(csv_path)
ys = [int(y) - 1000 for y in ys]
vect_size = len(xs[0])
audio_som = SOM(20,
30,
vect_size,
n_iterations=100,
checkpoint_dir=os.path.join(Constants.DATA_FOLDER,
'10classes', 'audio_model',
''))
if not LOAD:
audio_som.train(xs)
else:
logging.info('Training som')
audio_som.restore_trained()
#audio_som.plot_som(xs, ys, plot_name='audio_som.png')
showSom(audio_som, xs, ys, 1, 'Audio Map', filenames=filenames)
from models.som.SOM import SOM
from models.som.SOMTest import showSom
import numpy as np
from utils.constants import Constants
from utils.utils import from_csv_visual
from sklearn.preprocessing import MinMaxScaler
import os
import logging
visual_data_path = os.path.join(Constants.DATA_FOLDER, '10classes',
'VisualInputTrainingSet.csv')
N = 1000
lenExample = 2048
if __name__ == '__main__':
v_xs, v_ys = from_csv_visual(visual_data_path)
v_xs = MinMaxScaler().fit_transform(v_xs)
som = SOM(20,
30,
lenExample,
checkpoint_dir=os.path.join(Constants.DATA_FOLDER,
'visual_model_mine', ''),
n_iterations=100,
sigma=4.0)
som.restore_trained()
showSom(som, v_xs, v_ys, 1, 'Visual map')
nameInputs = list()
with open(fInput, 'r') as inp:
i = 0
for line in inp:
if len(line) > 2:
inputs[i] = (np.array(line.split(',')[1:])).astype(np.float)
nameInputs.append((line.split(',')[0]).split('/')[6])
i = i + 1
print(nameInputs[0])
#get the 20x30 SOM or train a new one (if the folder does not contain the model)
som = SOM(20,
30,
lenExample,
checkpoint_dir=os.path.join(Constants.DATA_FOLDER,
'VisualModel10classes/'),
n_iterations=20,
sigma=4.0)
loaded = som.restore_trained()
if not loaded:
logging.info('Training SOM')
som.train(inputs)
for k in range(len(nameInputs)):
nameInputs[k] = nameInputs[k].split('_')[0]
#shows the SOM
showSom(som, inputs, nameInputs, 1, 'Visual map')