def _screenshot(data_obj):
global n
visu.plot_coords_label_color(data_obj)
visu.plot_centers_by_label_color(data_obj)
n += 1
plt.savefig("kmeans_frame_" + str(n) + ".png")
plt.close()
def test_on_random_sized():
data_obj = trivial_gen.balanced_random_size_gauss_blobs(
10.0, 2.0, 5, 100, 5, 0.5)
data_obj = partitioning.k_means(data_obj)
visu.plot_coords_label_color(data_obj)
visu.plot_centers_by_label_color(data_obj)
plt.show()
def croissants():
data = trivial_gen.croissants(100, 5.0, 0.5)
visu.plot_coords_label_color(data)
visu.plot_centers_by_label_color(data)
plt.show()
data = partitioning.k_means(data, initialisation="kmeans_++")
visu.plot_coords_label_color(data)
visu.plot_centers_by_label_color(data)
plt.show()
def pront(data_obj,name):
print("scikit_silhouette_score :"+str(standalone.scikit_silhouette_score(data_obj)))
print("scikit_calinski_harabaz_score :"+str(standalone.scikit_calinski_harabaz_score(data_obj)))
print("dunn_index :"+str(standalone.dunn_index(data_obj)))
print(result_to_string(standalone.general_evaluate_clustered_object(data_obj)))
visu.plot_coords_label_color(data_obj)
visu.plot_centers_by_label_color(data_obj)
plt.savefig(name+".png")
plt.close()
def showfiles():
for i in [
"balanced_gauss", "big_gauss", "difficult_gauss", "scars",
"different_sizes"
]:
for j in range(10):
data_obj = Coords()
data_obj.read_file("k_means_data" + "/" + i + "/data" + str(j))
visu.plot_coords_label_color(data_obj)
visu.plot_centers_by_label_color(data_obj)
plt.show()
def add_step(self, data_obj):
print("frame:" + str(self.step_count))
self.step_count = self.step_count + 1
name = str(self.step_count)
self.filenames.append(name)
if self.draw_labels:
visu.plot_coords_label_color(data_obj)
else:
visu.plot_coords_label_num(data_obj)
if self.draw_centers:
visu.plot_centers_by_label_color(data_obj)
plt.savefig(self.filename + "/" + name + ".png")
plt.close()
from py_osm_cluster.generator import trivial_gen as trivial_gen import py_osm_cluster.visualisation.visualisation as visu from py_osm_cluster.util.coords import Coords as C import matplotlib.pyplot as plt data_obj = trivial_gen.balanced_multiple_weighted_gauss_blobs(10.0,[0.5,2.0],50,5,[0.2,2.0]) visu.plot_coords_label_color(data_obj) visu.plot_centers_by_label_color(data_obj) plt.show()
def save(data_obj, name):
visu.plot_coords_label_color(data_obj)
plt.xlim((-0.5, 2.5))
plt.ylim((-0.5, 2.5))
plt.savefig(name + ".png")
#coords = trivial_gen.croissants(30,5.0,0.5)
anim_obj = anim.Animation("anim_1",True,True,0.8)
def pront(data_obj):
print("hello")
print(data_obj.c_positions)
newcoords = deepcopy(coords)
newcoords = partitioning.k_means(newcoords,on_step=anim_obj.add_step,initialisation="forgy")
anim_obj.compile()
#print(Std.statistics_distance_multi_cluster(newcoords))
#print(Std.triangulation_distance_within(newcoords.coords))
#visu.plot_coords(newcoords)
visu.plot_coords_label_color(newcoords)
visu.plot_centers_by_label_color(newcoords)
plt.show()
"""
coords = trivial_gen.balanced_multiple_weighted_gauss_blobs(
10.0, [1.0, 0.5], 20, 5, [1.5, 0.5])
#print(coords)
visu.plot_coords_label_color(coords)
visu.plot_centers_by_label_color(coords)
plt.show()
def visualise(data_obj, name):
#plt.xlabel(name)
visu.plot_coords_label_color(data_obj)
visu.plot_centers_by_label_color(data_obj)
plt.savefig(name + ".png")
plt.close()
import py_osm_cluster.visualisation.visualisation as visu
import py_osm_cluster.visualisation.animation as anim
import matplotlib.pyplot as plt
parser = Parser("osm_data/krakow/krakow_center.osm")
all_buildings = parser.get_buildings_data_obj()
all_buildings.labels = [0 for i in all_buildings.coords]
churches = Coords()
for i in list(parser.ways.values()):
if "building" in i.tags and i.tags["building"] == "church":
churches.coords.append(list(i.geom.centroid.coords)[0])
churches.labels = [1 for i in churches.coords]
#print(churches)
#print(all_buildings)
all_buildings.c_number = len(churches.coords)
all_buildings.c_positions = churches.coords
all_buildings = partitioning.k_means(all_buildings,
iterations=1,
initialisation="default_pos")
print(all_buildings)
#all_buildings = partitioning.k_means(all_buildings,iterations=20,initialisation="kmeans_++")
#all_buildings = hierarchical.agglomerative(all_buildings,linkage="c-link")
visu.plot_coords_label_color(all_buildings)
visu.plot_coords_label_color(churches)
plt.show()
from py_osm_cluster.parser.parser import Parser
from py_osm_cluster.cluster import scikit
from py_osm_cluster.cluster import partitioning
from py_osm_cluster.eval import comparative as comparative
from py_osm_cluster.eval import standalone as standalone
from py_osm_cluster.cluster import hierarchical
import py_osm_cluster.visualisation.visualisation as visu
import py_osm_cluster.visualisation.animation as anim
import matplotlib.pyplot as plt
parser = Parser("osm_data/near_krakow/wegrzce_bibice.osm")
churches = Coords()
for i in list(parser.ways.values()):
if "building" in i.tags and i.tags["building"]=="church":
churches.coords.append(list(i.geom.centroid.coords)[0])
churches.labels = [1 for i in churches.coords]
data = parser.get_buildings_data_obj()
data.c_number = 10
data.labels =[0 for i in data.coords]
data = partitioning.k_means(data,initialisation="kmeans_++",iterations=25)
print(data)
visu.plot_coords_label_color(data)
visu.plot_coords_label_color(churches)
plt.show()
from py_osm_cluster.util.coords import Coords as Coords from py_osm_cluster.generator import trivial_gen as trivial_gen from copy import deepcopy import math import os from py_osm_cluster.parser.parser import Parser from py_osm_cluster.cluster import scikit from py_osm_cluster.cluster import partitioning from py_osm_cluster.eval import comparative as comparative from py_osm_cluster.eval import standalone as standalone from py_osm_cluster.cluster import hierarchical import py_osm_cluster.visualisation.visualisation as visu import py_osm_cluster.visualisation.animation as anim import matplotlib.pyplot as plt data = trivial_gen.balanced_multiple_gauss_blobs(10.0, 2.0, 20, 5, 0.75) data = scikit.scikit_dbscan(data, max_dist=1.0) visu.plot_coords_label_color(data) plt.show()
def _save_iteration_picture(data_obj):
visu.plot_coords_label_color(data_obj)
global iteration
plt.savefig("hierarchical_" + str(iteration))
iteration += 1
plt.close()
def simple_demo():
data_obj = trivial_gen.croissants(100, 5.0, 0.5)
data_obj = hierarchical.agglomerative(data_obj, linkage="c-link")
visu.plot_coords_label_color(data_obj)
plt.show()
def _save(data_obj, name):
visu.plot_coords_label_color(data_obj)
plt.savefig(name + ".png")
plt.close()
