def testCalculatePoints(self): feat = featurization.featurization([]) self.assertTrue(not feat.data) feat = featurization.featurization(None) self.assertTrue(not feat.data) trip = Trip(None, None, None, None, None, None, None, None) data = [trip] try: feat = featurization.featurization(data) except AttributeError: self.assertTrue(True) except Exception: self.assertTrue(False) feat = featurization.featurization(self.data) self.assertTrue(len(feat.points) == len(feat.data)) for p in feat.points: self.assertTrue(None not in p)
def testCalculatePoints(self): feat = featurization.featurization([]) self.assertTrue(not feat.data) feat = featurization.featurization(None) self.assertTrue(not feat.data) trip = etatc._createTripEntry(self, None, None, None, None) data = [trip] try: feat = featurization.featurization(data) except AttributeError: self.assertTrue(True) except Exception: self.assertTrue(False) feat = featurization.featurization(self.data) self.assertTrue(len(feat.points) == len(feat.data)) for p in feat.points: self.assertTrue(None not in p)
def cluster(data, bins, old=True): if not data: return 0, [], [] feat = featurization.featurization(data, old=old) min = bins max = int(math.ceil(1.5 * bins)) feat.cluster(min_clusters=min, max_clusters=max) logging.debug('number of clusters: %d' % feat.clusters) return feat.clusters, feat.labels, feat.data
def cluster(data, bins): if not data: return 0, [], [] feat = featurization.featurization(data) min = bins max = int(math.ceil(1.5 * bins)) feat.cluster(min_clusters=min, max_clusters=max) logging.debug('number of clusters: %d' % feat.clusters) return feat.clusters, feat.labels, feat.data
def testCheckClusters(self): feat = featurization.featurization(self.data) a = feat.check_clusters() self.assertTrue(a == None) feat.cluster(min_clusters=2, max_clusters=10) try: feat.check_clusters() except Exception: self.assertTrue(False)
def __init__(self, *args, **kwargs): super(RepresentativesTests, self).__init__(*args, **kwargs) self.data = cp.read_data(size=100) #if len(self.data) == 0: # tg.create_fake_trips() # self.data = cp.read_data(size=100) print 'there are ' + str(len(self.data)) n = len(self.data)/5 self.labels = feat.featurization(self.data).cluster(min_clusters=n, max_clusters=n)
def cluster(data, bins): if not data: return 0, [], [] feat = featurization.featurization(data) min = bins max = int(math.ceil(1.5 * bins)) feat.cluster(min_clusters=min, max_clusters=max) print 'number of clusters: ' + str(feat.clusters) return feat.clusters, feat.labels, feat.data
def cluster(data, nBins): logging.debug("Calling cluster(%s, %d)" % (data, nBins)) if not data: return 0, [], [] feat = featurization.featurization(data) min = nBins max = int(math.ceil(1.5 * nBins)) feat.cluster(min_clusters=min, max_clusters=max) logging.debug('number of clusters: %d' % feat.clusters) return feat.clusters, feat.labels, feat.data, feat.points
def __init__(self, *args, **kwargs): super(RepresentativesTests, self).__init__(*args, **kwargs) self.data = cp.read_data(size=100) #if len(self.data) == 0: # tg.create_fake_trips() # self.data = cp.read_data(size=100) print 'there are ' + str(len(self.data)) n = len(self.data) / 5 self.labels = feat.featurization(self.data).cluster(min_clusters=n, max_clusters=n)
def testCluster(self): feat = featurization.featurization(self.data) feat.cluster(min_clusters=2, max_clusters=10) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) a = feat.cluster(name='kmeans', min_clusters=5, max_clusters=20) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) b = feat.cluster(name='nonname', min_clusters=5, max_clusters=20) self.assertTrue(a == b) #defaults to kmeans with invalid clustering method feat.cluster(min_clusters=len(self.data)+1) c = feat.cluster(min_clusters = 0, max_clusters=20) d = feat.cluster(min_clusters = 2, max_clusters=20) self.assertTrue(c == d) try: feat.cluster(min_clusters = 10, max_clusters=2) except ValueError: self.assertTrue(True) except Exception: self.assertTrue(False) data = [] start = Coordinate(47,-122) end = Coordinate(47,-123) for i in range(10): now = datetime.datetime.now() a = Trip(None, None, None, None, now, now, start, end) data.append(a) start = Coordinate(41,-74) end = Coordinate(42, -74) for i in range(10): now = datetime.datetime.now() a = Trip(None, None, None, None, now, now, start, end) data.append(a) feat = featurization.featurization(data) feat.cluster() self.assertTrue(len(set(feat.labels)) == 2)
def testCluster(self): feat = featurization.featurization(self.data) feat.cluster(min_clusters=2, max_clusters=10) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) a = feat.cluster(name='kmeans', min_clusters=5, max_clusters=20) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) b = feat.cluster(name='nonname', min_clusters=5, max_clusters=20) self.assertTrue( a == b) #defaults to kmeans with invalid clustering method feat.cluster(min_clusters=len(self.data) + 1) c = feat.cluster(min_clusters=0, max_clusters=20) d = feat.cluster(min_clusters=2, max_clusters=20) self.assertTrue(c == d) try: feat.cluster(min_clusters=10, max_clusters=2) except ValueError: self.assertTrue(True) except Exception: self.assertTrue(False) data = [] start = [-122, 47] end = [-123, 47] now = time.time() for i in range(10): a = etatc._createTripEntry(self, now, now, start, end) data.append(a) start = [-74, 41] end = [-74, 42] for i in range(10): a = etatc._createTripEntry(self, now, now, start, end) data.append(a) feat = featurization.featurization(data) feat.cluster() self.assertTrue(len(set(feat.labels)) == 2)
def testCluster(self): feat = featurization.featurization(self.data) feat.cluster(min_clusters=2, max_clusters=10) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) a = feat.cluster(name='kmeans', min_clusters=5, max_clusters=20) self.assertTrue(len(feat.labels) == len(feat.points)) self.assertTrue(feat.clusters == len(set(feat.labels))) b = feat.cluster(name='nonname', min_clusters=5, max_clusters=20) self.assertTrue(a == b) #defaults to kmeans with invalid clustering method feat.cluster(min_clusters=len(self.data)+1) c = feat.cluster(min_clusters = 0, max_clusters=20) d = feat.cluster(min_clusters = 2, max_clusters=20) self.assertTrue(c == d) try: feat.cluster(min_clusters = 10, max_clusters=2) except ValueError: self.assertTrue(True) except Exception: self.assertTrue(False) data = [] start = [-122, 47] end = [-123,47] now = time.time() for i in range(10): a = etatc._createTripEntry(self, now, now, start, end) data.append(a) start = [-74, 41] end = [-74, 42] for i in range(10): a = etatc._createTripEntry(self, now, now, start, end) data.append(a) feat = featurization.featurization(data) feat.cluster() self.assertTrue(len(set(feat.labels)) == 2)
def setUp(self): etatc._setup(self) n = len(self.data)/5 self.labels = feat.featurization(self.data).cluster(min_clusters=n, max_clusters=n)
def setUp(self): etatc._setup(self) n = old_div(len(self.data), 5) self.labels = feat.featurization(self.data).cluster(min_clusters=n, max_clusters=n)