def testBinData(self):
sim = similarity.similarity([], 300)
self.assertTrue(not sim.bins)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sum = 0
for bin in sim.bins:
sum += len(bin)
self.assertTrue(sum == len(sim.data))
testbins = set()
for bin in sim.bins:
for b in bin:
testbins.add(b)
self.assertTrue(len(testbins) == len(sim.data))
for i in range(len(sim.bins)-1):
self.assertTrue(len(sim.bins[i]) >= len(sim.bins[i+1]))
data = []
now = datetime.datetime.now()
start = Coordinate(47,-122)
end = Coordinate(47,-123)
for i in range(10):
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):
a = Trip(None, None, None, None, now, now, start, end)
data.append(a)
sim = similarity.similarity(data, 300)
sim.bin_data()
self.assertTrue(len(sim.bins) == 2)
def testBinData(self):
sim = similarity.similarity([], 300)
self.assertTrue(not sim.bins)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sum = 0
for bin in sim.bins:
sum += len(bin)
self.assertTrue(sum == len(sim.data))
testbins = set()
for bin in sim.bins:
for b in bin:
testbins.add(b)
self.assertTrue(len(testbins) == len(sim.data))
for i in range(len(sim.bins)-1):
self.assertTrue(len(sim.bins[i]) >= len(sim.bins[i+1]))
data = []
now = time.time()
start = [-122, 47]
end = [-123, 47]
for i in range(10):
data.append(etatc._createTripEntry(self, now, now, start, end))
start = [-74, 41]
end = [-74, 42]
for i in range(10):
data.append(etatc._createTripEntry(self, now, now, start, end))
sim = similarity.similarity(data, 300)
sim.bin_data()
self.assertTrue(len(sim.bins) == 2)
def testBinData(self):
sim = similarity.similarity([], 300)
self.assertTrue(not sim.bins)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sum = 0
for bin in sim.bins:
sum += len(bin)
self.assertTrue(sum == len(sim.data))
testbins = set()
for bin in sim.bins:
for b in bin:
testbins.add(b)
self.assertTrue(len(testbins) == len(sim.data))
for i in range(len(sim.bins) - 1):
self.assertTrue(len(sim.bins[i]) >= len(sim.bins[i + 1]))
data = []
now = datetime.datetime.now()
start = Coordinate(47, -122)
end = Coordinate(47, -123)
for i in range(10):
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):
a = Trip(None, None, None, None, now, now, start, end)
data.append(a)
sim = similarity.similarity(data, 300)
sim.bin_data()
self.assertTrue(len(sim.bins) == 2)
def testBinData(self):
sim = similarity.similarity([], 300)
self.assertTrue(not sim.bins)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sum = 0
for bin in sim.bins:
sum += len(bin)
self.assertTrue(sum == len(sim.data))
testbins = set()
for bin in sim.bins:
for b in bin:
testbins.add(b)
self.assertTrue(len(testbins) == len(sim.data))
for i in range(len(sim.bins) - 1):
self.assertTrue(len(sim.bins[i]) >= len(sim.bins[i + 1]))
data = []
now = time.time()
start = [-122, 47]
end = [-123, 47]
for i in range(10):
data.append(etatc._createTripEntry(self, now, now, start, end))
start = [-74, 41]
end = [-74, 42]
for i in range(10):
data.append(etatc._createTripEntry(self, now, now, start, end))
sim = similarity.similarity(data, 300)
sim.bin_data()
self.assertTrue(len(sim.bins) == 2)
def testEvaluateBins(self):
sim = similarity.similarity([], 300)
a = sim.evaluate_bins()
self.assertTrue(not a)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
c = sim.evaluate_bins()
if sim.data:
self.assertTrue(c)
def testEvaluateBins(self):
sim = similarity.similarity([], 300)
a = sim.evaluate_bins()
self.assertTrue(not a)
sim = similarity.similarity(self.data, 300)
sim.bin_data()
c = sim.evaluate_bins()
if sim.data:
self.assertTrue(c)
def testGraph(self):
if os.path.isfile('./histogram.png'):
os.remove('./histogram.png')
sim = similarity.similarity([], 300)
sim.bin_data()
# sim.graph()
sim = similarity.similarity(self.data, 300)
# sim.graph()
sim.bin_data()
# sim.graph()
sim.delete_bins()
def testGraph(self):
if os.path.isfile('./histogram.png'):
os.remove('./histogram.png')
sim = similarity.similarity([], 300)
sim.bin_data()
# sim.graph()
sim = similarity.similarity(self.data, 300)
# sim.graph()
sim.bin_data()
# sim.graph()
sim.delete_bins()
def testMatch(self):
sim = similarity.similarity(self.data, 300)
sim.bin_data()
for bin in sim.bins:
for b in bin:
for c in bin:
self.assertTrue(sim.distance_helper(b, c))
def testDeleteBins(self):
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sim.delete_bins()
b = len(sim.bins)
if sim.data:
self.assertTrue(b == sim.num)
def testMatch(self):
sim = similarity.similarity(self.data, 300)
sim.bin_data()
for bin in sim.bins:
for b in bin:
for c in bin:
self.assertTrue(sim.distance_helper(b,c))
def testDeleteBins(self):
sim = similarity.similarity(self.data, 300)
sim.bin_data()
sim.delete_bins()
b = len(sim.bins)
if sim.data:
self.assertTrue(b == sim.num)
def testInit(self):
try:
sim = similarity.similarity([], 'b')
except ValueError:
self.assertTrue(True)
except Exception:
self.assertTrue(False)
sim = similarity.similarity([], 100)
self.assertTrue(len(sim.data) == 0)
now = datetime.datetime.now()
start = Coordinate(47,-122)
end = Coordinate(47,-123)
t1 = Trip(None, None, None, None, now, now, start, start)
t2 = Trip(None, None, None, None, now, now, start, end)
sim = similarity.similarity([t1, t2], 100)
simmy = similarity.similarity([t2], 100)
self.assertTrue(sim.data == simmy.data)
def testInit(self):
try:
sim = similarity.similarity([], 'b')
except ValueError:
self.assertTrue(True)
except Exception:
self.assertTrue(False)
sim = similarity.similarity([], 100)
self.assertTrue(len(sim.data) == 0)
now = datetime.datetime.now()
start = Coordinate(47, -122)
end = Coordinate(47, -123)
t1 = Trip(None, None, None, None, now, now, start, start)
t2 = Trip(None, None, None, None, now, now, start, end)
sim = similarity.similarity([t1, t2], 100)
simmy = similarity.similarity([t2], 100)
self.assertTrue(sim.data == simmy.data)
def remove_noise(data, radius):
if not data:
return [], []
sim = similarity.similarity(data, radius)
sim.bin_data()
print 'number of bins before filtering: ' + str(len(sim.bins))
sim.delete_bins()
print 'number of bins after filtering: ' + str(len(sim.bins))
return sim.newdata, sim.bins
def remove_noise(data, radius, old=True):
if not data:
return [], []
sim = similarity.similarity(data, radius, old)
sim.bin_data()
logging.debug('number of bins before filtering: %d' % len(sim.bins))
sim.delete_bins()
logging.debug('number of bins after filtering: %d' % len(sim.bins))
return sim.newdata, sim.bins
def remove_noise(data, radius):
if not data:
return [], []
sim = similarity.similarity(data, radius)
sim.bin_data()
logging.debug('number of bins before filtering: %d' % len(sim.bins))
sim.delete_bins()
logging.debug('number of bins after filtering: %d' % len(sim.bins))
return sim.newdata, sim.bins
def testDistance(self):
start = Coordinate(-122.259447, 37.875174)
end1 = Coordinate(-122.259279, 37.875479)
end2 = Coordinate(-122.252287, 37.869569)
now = datetime.datetime.now()
t1 = Trip(None, None, None, None, now, now, start, end1)
t2 = Trip(None, None, None, None, now, now, start, end2)
sim = similarity.similarity(self.data, 300)
self.assertTrue(sim.distance(start.lat, start.lon, end1.lat, end1.lon))
self.assertTrue(not sim.distance(start.lat, start.lon, end2.lat, end2.lon))
def testDistance(self):
start = [-122.259447, 37.875174]
end1 = [-122.259279, 37.875479]
end2 = [-122.252287, 37.869569]
now = time.time()
t1 = etatc._createTripEntry(self, now, now, start, end1)
t2 = etatc._createTripEntry(self, now, now, start, end2)
sim = similarity.similarity(self.data, 300)
self.assertTrue(sim.distance(start[1], start[0], end1[1], end1[0]))
self.assertTrue(not sim.distance(start[1], start[0], end2[1], end2[0]))
def testDistance(self):
start = [-122.259447, 37.875174]
end1 = [-122.259279, 37.875479]
end2 = [-122.252287, 37.869569]
now = time.time()
t1 = etatc._createTripEntry(self, now, now, start, end1)
t2 = etatc._createTripEntry(self, now, now, start, end2)
sim = similarity.similarity(self.data, 300)
self.assertTrue(sim.distance(start[1], start[0], end1[1], end1[0]))
self.assertTrue(not sim.distance(start[1], start[0], end2[1], end2[0]))
def testElbowDistance(self):
start = [-122,47]
end = [-123,47]
now = time.time()
t = etatc._createTripEntry(self, now, now, start, end)
data = [t] * 11
bins = [[1,2,3,4], [5,6,10], [7], [8], [9], [0]]
sim = similarity.similarity(data, 300)
sim.bins = bins
sim.delete_bins()
self.assertTrue(sim.num == 2)
def testElbowDistance(self):
start = [-122, 47]
end = [-123, 47]
now = time.time()
t = etatc._createTripEntry(self, now, now, start, end)
data = [t] * 11
bins = [[1, 2, 3, 4], [5, 6, 10], [7], [8], [9], [0]]
sim = similarity.similarity(data, 300)
sim.bins = bins
sim.delete_bins()
self.assertTrue(sim.num == 2)
def testDistance(self):
start = Coordinate(-122.259447, 37.875174)
end1 = Coordinate(-122.259279, 37.875479)
end2 = Coordinate(-122.252287, 37.869569)
now = datetime.datetime.now()
t1 = Trip(None, None, None, None, now, now, start, end1)
t2 = Trip(None, None, None, None, now, now, start, end2)
sim = similarity.similarity(self.data, 300)
self.assertTrue(sim.distance(start.lat, start.lon, end1.lat, end1.lon))
self.assertTrue(
not sim.distance(start.lat, start.lon, end2.lat, end2.lon))
def testElbowDistance(self):
start = Coordinate(47, -122)
end = Coordinate(47, -123)
now = datetime.datetime.now()
t = Trip(None, None, None, None, now, now, start, end)
data = [t] * 11
bins = [[1, 2, 3, 4], [5, 6, 10], [7], [8], [9], [0]]
sim = similarity.similarity(data, 300)
sim.bins = bins
sim.delete_bins()
self.assertTrue(sim.num == 2)
def testElbowDistance(self):
start = Coordinate(47,-122)
end = Coordinate(47,-123)
now = datetime.datetime.now()
t = Trip(None, None, None, None, now, now, start, end)
data = [t] * 11
bins = [[1,2,3,4], [5,6,10], [7], [8], [9], [0]]
sim = similarity.similarity(data, 300)
sim.bins = bins
sim.delete_bins()
self.assertTrue(sim.num == 2)
def testInit(self):
try:
sim = similarity.similarity([], 'b')
except ValueError:
self.assertTrue(True)
except Exception:
self.assertTrue(False)
logging.debug("STARTING init test")
sim = similarity.similarity([], 100)
self.assertTrue(len(sim.data) == 0)
now = time.time()
start = [-122,47]
end = [-123,47]
t1 = etatc._createTripEntry(self, now, now, start, start)
t2 = etatc._createTripEntry(self, now, now, start, end)
sim = similarity.similarity([t1, t2], 100)
logging.debug("sim.data = %s" % sim.data)
simmy = similarity.similarity([t2], 100)
logging.debug("simmy.data = %s" % simmy.data)
self.assertTrue(sim.data == simmy.data)
def testInit(self):
try:
sim = similarity.similarity([], 'b')
except ValueError:
self.assertTrue(True)
except Exception:
self.assertTrue(False)
logging.debug("STARTING init test")
sim = similarity.similarity([], 100)
self.assertTrue(len(sim.data) == 0)
now = time.time()
start = [-122, 47]
end = [-123, 47]
t1 = etatc._createTripEntry(self, now, now, start, start)
t2 = etatc._createTripEntry(self, now, now, start, end)
sim = similarity.similarity([t1, t2], 100)
logging.debug("sim.data = %s" % sim.data)
simmy = similarity.similarity([t2], 100)
logging.debug("simmy.data = %s" % simmy.data)
self.assertTrue(sim.data == simmy.data)
def main(colors):
data = cp.read_data() #get the data
colors = get_colors(data, colors) #make colors the right format
data, bins = cp.remove_noise(data, .5, 300) #remove noise from data
###### the next few lines are to evaluate the binning
sim = similarity.similarity(data, .5, 300) #create a similarity object
sim.bins = bins #set the bins, since we calculated them above
sim.evaluate_bins() #evaluate them to create the labels
######
colors = update_colors(bins, colors) #update the colors to reflect deleted bins
labels = sim.labels #get labels
evaluate(numpy.array(colors), numpy.array(labels)) #evaluate the bins
clusters, labels, data = cp.cluster(data, len(bins)) #cluster
evaluate(numpy.array(colors), numpy.array(labels)) #evaluate clustering
map_clusters_by_groundtruth(data, labels, colors, map_individuals=False) #map clusters, make last parameter true to map individual clusters
def main(colors):
data = cp.read_data() #get the data
colors = get_colors(data, colors) #make colors the right format
data, bins = cp.remove_noise(data, .5, 300) #remove noise from data
###### the next few lines are to evaluate the binning
sim = similarity.similarity(data, .5, 300) #create a similarity object
sim.bins = bins #set the bins, since we calculated them above
sim.evaluate_bins() #evaluate them to create the labels
######
colors = update_colors(bins,
colors) #update the colors to reflect deleted bins
labels = sim.labels #get labels
evaluate(numpy.array(colors), numpy.array(labels)) #evaluate the bins
clusters, labels, data = cp.cluster(data, len(bins)) #cluster
evaluate(numpy.array(colors), numpy.array(labels)) #evaluate clustering
map_clusters_by_groundtruth(
data, labels, colors, map_individuals=False
) #map clusters, make last parameter true to map individual clusters
