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()