def dbscan_run_all():
pd.set_option('expand_frame_repr', True)
pd.set_option('max_rows', 100)
pd.set_option('max_columns', 250)
pd.set_option('display.width', 1000)
np.set_printoptions(precision=3, floatmode='fixed')
for fn in c.DB_TESTS:
e, pts = c.e_pts[fn]
df, class_id = parse_csv(fn)
clusters, outliers = dbscan(df, e, pts)
results = evaluate_clusters(clusters,
None,
verbose=False,
outliers=outliers)
totals = results.loc[:, [MAX, MIN, AVG, PTS, SSE]].sum()
totals[NUM_DROPPED] = '-'
totals[PCT_DROPPED] = '-'
totals.name = TOTALS
results = results.append(totals)
sfn = strip_file_path(fn)
print(f'\nSummary - {sfn}')
print(results.round(3))
centroids = np.array([cluster.mean() for cluster in clusters])
for idx, (cluster, centroid) in enumerate(zip(clusters, centroids)):
print(f'\nCluster {idx + 1}')
print(f'Centroid: {centroid}')
print(cluster)
print('\nOutliers')
print(outliers)
if 2 <= clusters[0].shape[1] <= 3:
plot_clusters(clusters, centroids, f'dbscan {sfn}')
def run(self):
self.read_input()
algo = self.options['algo']
params = self.options['params']
if algo == 'kmeans':
model = kmeans(self.doc, params)
elif algo == 'dbscan':
model = dbscan(self.doc, params)
elif algo == 'agglo':
model = agglo(self.doc, params)
elif algo == 'minib':
model = minib(self.doc, params)
model.evaluate()
def calculate_mbrs(points, epsilon, min_pts, debug=False):
"""
Find clusters using DBscan and then create a list of bounding rectangles
to return.
"""
mbrs = {}
clusters = dbscan(points,
epsilon,
min_pts,
distance=euclidean,
debug=debug)
extremes = {
'max_x': sys.maxint * -1,
'max_y': sys.maxint * -1,
'min_x': sys.maxint,
'min_y': sys.maxint
}
"""
Traditional dictionary iteration to populate mbr list
Does same as below
"""
for id, cpoints in clusters.items():
print(id)
xs = []
ys = []
for p in cpoints:
xs.append(p[0])
ys.append(p[1])
max_x = max(xs)
max_y = max(ys)
min_x = min(xs)
min_y = min(ys)
if max_x > extremes['max_x']:
extremes['max_x'] = max_x
if max_y > extremes['max_y']:
extremes['max_y'] = max_y
if min_x < extremes['min_x']:
extremes['min_x'] = min_x
if min_y < extremes['min_y']:
extremes['min_y'] = min_y
mbrs[id] = [(min_x, min_y), (max_x, min_y), (max_x, max_y),
(min_x, max_y), (min_x, min_y)]
mbrs['extremes'] = extremes
return mbrs
def dbscan_hypertuning(fn: str):
df, class_id = parse_csv(fn)
# best: k, pts, SSE
best = (0, 0, float('inf'))
max_dist = df.max().max()
step = min_dist = max(df.min().min() * 1.01, 1)
num_steps = int(max_dist // min_dist + 1)
for e in range(1, num_steps):
for pts in range(2, df.shape[0] // 2, 2):
clusters, noise = dbscan(df, e * step, pts)
if len(clusters) == 0:
continue
if check_pct_outliers(df, noise):
break
measures = evaluate_clusters(clusters, None, verbose=False)
if measures[SSE].sum() < best[2]:
best = (e * step, pts, measures[SSE].sum())
print(f'{fn}: e: {best[0]}, pts: {best[1]}, sse: {best[2]}')
return best[0], best[1]
def calculate_mbrs(self, points, epsilon, min_pts):
"""
Find clusters using DBscan and then create a list of bounding rectangles
to return.
"""
mbrs = []
clusters = dbscan(points, epsilon, min_pts)
"""
Traditional dictionary iteration to populate mbr list
Does same as below
"""
# for id,cpoints in clusters.items():
# xs = []
# ys = []
# for p in cpoints:
# xs.append(p[0])
# ys.append(p[1])
# max_x = max(xs)
# max_y = max(ys)
# min_x = min(xs)
# min_y = min(ys)
# mbrs.append([(min_x,min_y),(max_x,min_y),(max_x,max_y),(min_x,max_y),(min_x,min_y)])
# return mbrs
"""
Using list index value to iterate over the clusters dictionary
Does same as above
"""
for id in range(len(clusters) - 1):
xs = []
ys = []
for p in clusters[id]:
xs.append(p[0])
ys.append(p[1])
max_x = max(xs)
max_y = max(ys)
min_x = min(xs)
min_y = min(ys)
mbrs.append([(min_x, min_y), (max_x, min_y), (max_x, max_y),
(min_x, max_y), (min_x, min_y)])
print(mbrs)
return mbrs
def calculate_mbrs(points, epsilon, min_pts):
"""
Find clusters using DBscan and then create a list of bounding rectangles
to return.
"""
mbrs = []
clusters = dbscan(points, epsilon, min_pts)
"""
Using list index value to iterate over the clusters dictionary
Does same as above
"""
for id in range(len(clusters)-1):
xs = []
ys = []
for p in clusters[id]:
xs.append(p[0])
ys.append(p[1])
max_x = max(xs)
max_y = max(ys)
min_x = min(xs)
min_y = min(ys)
mbrs.append([(min_x,min_y),(max_x,min_y),(max_x,max_y),(min_x,max_y),(min_x,min_y)])
return mbrs
import numpy as np
from dbscan import *
from plot3d import *
encodedImgs = np.loadtxt('datasets/temp/encodedImgs', delimiter=',')
encodedImgsTrain = np.loadtxt('datasets/temp/encodedImgsTrain', delimiter=',')
labels = np.loadtxt('datasets/temp/labels', delimiter=',')
labels = labels.astype(int)
epsilon = 0.015
minSamps = 2
print("Training")
dbscan(encodedImgsTrain, labels, epsilon=epsilon, minSamples=minSamps)
print("Training")
dbscan(encodedImgs, labels, epsilon=epsilon, minSamples=minSamps)
datum['Time'], datum['temparature'], datum['humidity'],
datum['LPG'], datum['label']
])
if checkFileExist("%s.csv" % name) == True:
with open(output_file_name, 'a', encoding='utf-8') as output_file:
csvwriter = csv.writer(output_file)
csvwriter.writerow([
datum['Time'], datum['temparature'], datum['humidity'],
datum['LPG'], datum['label']
])
output_file.close()
if count >= 50 and count % 10 == 0:
label = dbscan(temp_list[count - 50:count], hum_list[count - 50:count],
gas_list[count - 50:count],
label_list[count - 50:count])
label = map(str, label)
data_s = " ".join(label)
conn.sendall(data_s.encode())
else:
reply = "send"
conn.sendall(reply.encode())
count += 1
conn.close()
s.close()
print('close')
import sys
import utils
from dbscan import *
if __name__ == "__main__":
filename = sys.argv[1]
points = utils.read_input(filename)
dbscan(points, 0.5) # it will labeled cluster of elements of points
utils.visualize(points)
import sys
import utils
from dbscan import *
if __name__ == "__main__":
filename = sys.argv[1]
points = utils.read_input(filename)
dbscan(points, 0.5) # it will labeled cluster of elements of points
utils.visualize(points)
#Creiamo la matrice x X = data[:, :data.shape[1]-1] #------------------------------------ NORMALIZAZZIONE FEATURE ---------------------------------# #------------------------------------- ZSCORE -----------------------------------# mu, sigma = muSigma(X) X = zScore(X, mu, sigma) #--------------------------------------------------------------------------------# #------------------------------------ MINMAX ------------------------------------# # min, diff, max = minmax(X) # X = Min_Max(X, min, diff) #--------------------------------------------------------------------------------# #-------------------------------- FEATURE SCALING -------------------------------# # min, diff, max = minmax(X) # X = Feat_Scaling(X, max) #--------------------------------------------------------------------------------# epsylon=2 punti_min=2 centroid=dbscan(X,epsylon,punti_min)
import arcpy
from dbscan import *
import numpy as np
from matplotlib import pyplot as plt
# curs = arcpy.SearchCursor("C:\Users\huangyixiu\Documents\Course\gisData\SH_hos\Hospital.shp")
curs = arcpy.SearchCursor("../Export_Output.shp")
pointx = []
pointy = []
c = 0
for row in curs:
pointx.append(row.Shape.firstPoint.X)
pointy.append(row.Shape.firstPoint.Y)
c += 1
if c%1000 ==0:
print "processing line %d"%c
m = np.matrix([pointx,pointy])
# 0.005 degree in wgs84, proximately 500m
eps = 0.005
min_points = 4
clusterlis = dbscan(m, eps, min_points)
res = open("../cluster2.csv","w")
res.write("x,y,clusterid\n")
for i in range(0,len(pointy)):
res.write("%f,%f,%s\n"%(pointx[i],pointy[i],clusterlis[i]))
res.close()
print "result written done!"