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
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
h, w, d = neurons.shape
hexmap = np.apply_along_axis(rgb2hex, 1, neurons.reshape(-1, 3) / 256)
index = np.arange(h * w).reshape(h, w)
pl.pcolor(index, cmap=ListedColormap(hexmap), norm=NoNorm())
train = np.array([[0, 0, 0], # black
[255, 255, 255], # white
[255, 0, 0], # red
[0, 255, 0], # green
[0, 0, 255], # blue
[255, 255, 0], # yellow
[0, 255, 255], # cyan
[255, 0, 255]]) # magenta
init = np.random.rand(16, 16, 3) * 255
pl.subplot(1, 2, 1, aspect='equal')
plot(init)
pl.title('Initial map')
som = SelfOrganizingMap(init, n_iterations=1024,
init='matrix', learning_rate=1)
som.fit(train)
pl.subplot(1, 2, 2, aspect='equal')
plot(som.neurons_)
pl.title('Organized Map')
F = pl.gcf()
F.set_size_inches((40, 20))
pl.show()
n_samples, n_features = data.shape
n_digits = len(np.unique(digits.target))
print "Digits dataset"
print "n_digits : %d" % n_digits
print "n_features : %d" % n_features
print "n_samples : %d" % n_samples
print
################################################################################
# Digits dataset clustering using Self-Organizing Map
print "Self-Organizing Map "
t0 = time()
grid_width = 4
som = SelfOrganizingMap(size=grid_width, n_iterations=n_samples*5,
learning_rate=1)
som.fit(data)
print "done in %0.3fs" % (time() - t0)
print
F = pseudo_F(data, som.labels_, som.neurons_)
print 'pseudo_F %0.2f | %0.2f%%' % (F, 100 * (F / (1 + F)))
print
################################################################################
# Digits dataset clustering using Kmeans
print "KMeans "
t0 = time()
km = KMeans(init='k-means++', k=grid_width**2, n_init=10)
km.fit(data)
