class Topics:
def __init__(self):
self.metadata = Metadata(True, 'lda')
self.dataProvider = DataProvider()
self.topicFile = 'tmp/topics.npy'
self.kMeansFile = 'tmp/kmeans.pkl.npy'
def gatherData(self, t):
self.clips = self.dataProvider.getClips()
self.model = self.metadata.createModel(self.clips, t)
def getTopic(self, clip):
vector = self.metadata.getVectors(clip)
result = self.kmeans.predict(vector.reshape(1, -1))
return result
# return np.argmax(vector)
def createTopics(self, clips, t, k):
self.gatherData(t)
self.createClusters(clips, k)
topics = []
for idx, clip in enumerate(clips):
topics.append(self.getTopic(clip))
self.topics = np.hstack(topics)
return self.topics
def save(self):
self.metadata.save()
np.save(self.topicFile, self.topics)
joblib.dump(self.kmeans, self.kMeansFile)
def load(self):
if not self.metadata.load():
return False
try:
self.topics = np.load(self.topicFile)
self.kmeans = joblib.load(self.kMeansFile)
return True
except (IOError):
return False
# ------------------------------------------------
# K Means on topic data...
def createClusters(self, clips, k):
vectors = []
for idx, clip in enumerate(clips):
vectors.append(self.metadata.getVectors(clip))
self.vectors = np.vstack(vectors)
print self.clusterKMeans(self.vectors, k)
def clusterKMeans(self, data, k):
self.kmeans = KMeans(n_clusters=k,
init='k-means++',
n_init=10,
max_iter=300,
tol=0.0001,
precompute_distances='auto',
verbose=1,
random_state=None,
copy_x=True,
n_jobs=1)
self.kmeans.fit(data)
return self.kmeans.inertia_
