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Delta (normal).py
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697 lines (595 loc) · 30.8 KB
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class Delta_Equipartition:
def __init__(self,polygon,number_of_regions):
self.poligon = polygon
self.number_of_regions = number_of_regions
def punto_mas_cercano(puntos_iniciales, punto):
c=0
dist=(puntos_iniciales[0][0]-punto[0])**2+(puntos_iniciales[0][1]-punto[1])**2
for i in range(0,np.shape(puntos_iniciales)[0],1):
if (puntos_iniciales[i][0]-punto[0])**2+(puntos_iniciales[i][1]-punto[1])**2< dist:
c=i
dist = (puntos_iniciales[i][0]-punto[0])**2+(puntos_iniciales[i][1]-punto[1])**2
return(c)
def puntos_mas_cercanos(puntos_iniciales, punto):
c=0
v=[]
dist=(puntos_iniciales[0][0]-punto[0])**2+(puntos_iniciales[0][1]-punto[1])**2
for i in range(0,np.shape(puntos_iniciales)[0],1):
if (puntos_iniciales[i][0]-punto[0])**2+(puntos_iniciales[i][1]-punto[1])**2< dist:
c=i
dist = (puntos_iniciales[i][0]-punto[0])**2+(puntos_iniciales[i][1]-punto[1])**2
v.append(c)
for i in range(c+1,np.shape(puntos_iniciales)[0],1):
if (puntos_iniciales[i][0]-punto[0])**2+(puntos_iniciales[i][1]-punto[1])**2 == dist:
v.append(i)
return(v)
def puntos_en_region_n(puntos_iniciales, points,j):
v=[]
for i in range(0,np.shape(points)[0]):
if j in Delta_Equipartition.puntos_mas_cercanos(puntos_iniciales, points[i]):
v.append(i)
return(v)
def segment_segment_intersect(Ax1, Ay1, Ax2, Ay2, Bx1, By1, Bx2, By2):
d = (By2 - By1) * (Ax2 - Ax1) - (Bx2 - Bx1) * (Ay2 - Ay1)
if d:
uA = ((Bx2 - Bx1) * (Ay1 - By1) - (By2 - By1) * (Ax1 - Bx1)) / d
uB = ((Ax2 - Ax1) * (Ay1 - By1) - (Ay2 - Ay1) * (Ax1 - Bx1)) / d
else:
return
if not(0 <= uA <= 1 and 0 <= uB <= 1 ): # เหมือนตัว segment_line_intersect ตัวล่างต่างแค่บรรทัดนี้
return
x = Ax1 + uA * (Ax2 - Ax1)
y = Ay1 + uA * (Ay2 - Ay1)
return x, y
def segment_line_intersect(Ax1, Ay1, Ax2, Ay2, Bx1, By1, Bx2, By2):
d = (By2 - By1) * (Ax2 - Ax1) - (Bx2 - Bx1) * (Ay2 - Ay1)
if d:
uA = ((Bx2 - Bx1) * (Ay1 - By1) - (By2 - By1) * (Ax1 - Bx1)) / d
uB = ((Ax2 - Ax1) * (Ay1 - By1) - (Ay2 - Ay1) * (Ax1 - Bx1)) / d
else:
return
if not(0 <= uA <= 1 and 0 <= uB ):
return
x = Ax1 + uA * (Ax2 - Ax1)
y = Ay1 + uA * (Ay2 - Ay1)
return x, y
def weighted_ridge(pt1,pt2):
a=((sqrt((pt2[0]-pt1[0])**2+(pt2[1]-pt1[1])**2))**2)/(2*(sqrt((pt2[0]-pt1[0])**2+(pt2[1]-pt1[1])**2)))
uni=sqrt((pt2[0]-pt1[0])**2+(pt2[1]-pt1[1])**2)
ridge=np.array([pt1[0]+a*((pt2[0]-pt1[0])/uni),pt1[1]+a*((pt2[1]-pt1[1])/uni)])
dir = pt1-pt2
dir_perp=np.array([dir[1],-dir[0]])
vertice=ridge+dir_perp
return np.array([ridge,vertice])
def vect_intersect(line1,line2):
d = (line2[1][1] - line2[0][1]) * (line1[1][0] - line1[0][0]) - (line2[1][0] - line2[0][0]) * (line1[1][1] - line1[0][1])
if d:
uA = ((line2[1][0] - line2[0][0]) * (line1[0][1] - line2[0][1]) - (line2[1][1] - line2[0][1]) * (line1[0][0] - line2[0][0])) / d
uB = ((line1[1][0] - line1[0][0]) * (line1[0][1] - line2[0][1]) - (line1[1][1] - line1[0][1]) * (line1[0][0] - line2[0][0])) / d
x = line1[0][0] + uA * (line1[1][0] - line1[0][0])
y = line1[0][1] + uA * (line1[1][1] - line1[0][1])
return np.array([x, y])
else:
return
def triple_intersect(pt1,pt2,pt3):
return Delta_Equipartition.vect_intersect(Delta_Equipartition.weighted_ridge(pt1,pt2),Delta_Equipartition.weighted_ridge(pt2,pt3))
def area(R):
respuesta=0
region = np.append(R, np.array([R[0]]), axis=0)
for i in range(np.shape(R)[0]):
respuesta = respuesta + (region[i][0]*region[i+1][1]-region[i+1][0]*region[i][1])/2
return respuesta
def centroide(R):
centrox=0
centroy=0
region = np.append(R, np.array([R[0]]), axis=0)
for i in range(np.shape(R)[0]):
centrox = centrox + (region[i][0]+region[i+1][0])*(region[i][0]*region[i+1][1]-region[i+1][0]*region[i][1])
centroy = centroy + (region[i][1]+region[i+1][1])*(region[i][0]*region[i+1][1]-region[i+1][0]*region[i][1])
areatotal=Delta_Equipartition.area(R)
centrox = centrox/(6*areatotal)
centroy = centroy/(6*areatotal)
return np.array([[centrox,centroy]])
def internal_vertices(poligono,sitios):
poligon = ConvexHull(poligono)
poligon_path = Path( poligon.points[poligon.vertices] )
lista=[]
length_sitios=len(sitios)
for i in range (0,length_sitios-2,1):
for j in range (i+1,length_sitios-1,1):
for k in range (j+1,length_sitios,1):
interseccion= Delta_Equipartition.triple_intersect(sitios[i],sitios[j],sitios[k])
if poligon_path.contains_point(interseccion) == True:
if length_sitios==3:
lista.append([i,j,k,interseccion])
else:
new_sitios=np.delete(sitios,[i,j,k],0)
w=True
value = np.linalg.norm(interseccion-sitios[i])
for q in range(len(new_sitios)):
w=w*(value <np.linalg.norm(interseccion-new_sitios[q]))
if w==True:
lista.append([i,j,k,interseccion])
df = pd.DataFrame(lista, columns=['l','m','n','inter'])
if len(lista)==0 :
vertices = np.empty([0,2])
else :
vertices = np.vstack(df['inter'])
regiones = [ [] for i in range(len(sitios))]
ridge_vertices=[]
ridge_points=np.empty([0,2], dtype=np.int32)
for i in range(0,len(sitios),1):
regiones[i] = df[(df['l']==i)|(df['m']==i)|(df['n']==i)].index.to_list()
return vertices, regiones
def external_vertices(poligono, sitios):
number_of_regions = np.shape(sitios)[0]
regiones=[]
for i in range(0,number_of_regions,1):
regiones.append(Delta_Equipartition.puntos_en_region_n(sitios,poligono,i))
return poligono, regiones
def particion_vertices(external, intermediate, internal):
regiones=[]
areas=[]
perimetros=[]
convex=[]
todos_convexos=True
for n in range(0,len(external[1]),1):
a=np.empty((0,2))
a = np.append(a, external[0][external[1][n]],axis=0)
a = np.append(a, intermediate[0][intermediate[1][n]],axis=0)
a = np.append(a, internal[0][internal[1][n]],axis=0)
size = np.shape(a)[0]
if size != 0:
zona = ConvexHull(a)
a = zona.points[zona.vertices]
regiones.append(a)
areas.append(zona.volume)
perimetros.append(zona.area)
else:
regiones.append([])
areas.append(0)
perimetros.append(0)
lista_respuesta = []
lista_respuesta.append(regiones)
lista_respuesta.append(np.asarray(areas))
lista_respuesta.append(np.asarray(perimetros))
return lista_respuesta
def particion(poligono, sitios):
poligon = ConvexHull(poligono)
poligon_path = Path( poligon.points[poligon.vertices] )
number_of_regions = np.shape(sitios)[0]
vor = Voronoi(sitios, furthest_site=False)
regiones=[]
for i in range(0,number_of_regions,1):
list= vor.regions[vor.point_region[i]].copy()
if -1 in list:
list.remove(-1)
vert = vor.vertices[list].copy()
rows_to_remove=[]
for j in range(0,np.shape(vert)[0],1):
if poligon_path.contains_point(vert[j]) == False:
rows_to_remove.append(j)
vert = np.delete(vert,rows_to_remove,0)
a = np.vstack((vert,poligono[Delta_Equipartition.puntos_en_region_n(sitios,poligono,i)]))
regiones.append(a)
intermediate_vertices=np.empty((0,2))
regiones_intermediate=[ [] for i in range(len(sitios))]
sides_intermediate=[]
count=0
for row, side in enumerate(vor.ridge_vertices):
if side[0]!=-1:
v1x = vor.vertices[side[0]][0]
v1y = vor.vertices[side[0]][1]
v2x = vor.vertices[side[1]][0]
v2y = vor.vertices[side[1]][1]
for j in range(-1,np.shape(poligono)[0]-1,1):
w1x = poligono[j][0]
w1y = poligono[j][1]
w2x = poligono[j+1][0]
w2y = poligono[j+1][1]
if Delta_Equipartition.segment_segment_intersect(v1x,v1y,v2x,v2y,w1x,w1y,w2x,w2y) != None:
x , y = Delta_Equipartition.segment_segment_intersect(v1x,v1y,v2x,v2y,w1x,w1y,w2x,w2y)
new_intersection = np.array([[x,y]])
region_1_to_add = vor.ridge_points[row][0]
region_2_to_add = vor.ridge_points[row][1]
regiones[region_1_to_add] = np.vstack((regiones[region_1_to_add],new_intersection))
regiones[region_2_to_add] = np.vstack((regiones[region_2_to_add],new_intersection))
intermediate_vertices = np.append(intermediate_vertices, new_intersection,axis=0)
regiones_intermediate[region_1_to_add].append(count)
regiones_intermediate[region_2_to_add].append(count)
sides_intermediate.append([j,j+1])
count=count+1
else:
vertex = vor.vertices[side[1]]
point_1= vor.ridge_points[row][0]
point_2= vor.ridge_points[row][1]
normal = vor.points[point_1]-vor.points[point_2]
dir = np.array([normal[1],-normal[0]])
sitios_sin = np.delete(sitios,[point_1, point_2],axis=0)
cercano= Delta_Equipartition.punto_mas_cercano(sitios_sin,vertex)
if cercano>=min(point_1,point_2):
cercano = cercano+1
if cercano>=max(point_1,point_2):
cercano = cercano+1
if np.linalg.norm(vertex+dir-vor.points[cercano]) < np.linalg.norm(vertex+dir-vor.points[point_1]):
dir = -dir
v1x = vertex[0]
v1y = vertex[1]
v2x = (vertex+dir)[0]
v2y = (vertex+dir)[1]
for j in range(-1,np.shape(poligono)[0]-1,1):
w1x = poligono[j][0]
w1y = poligono[j][1]
w2x = poligono[j+1][0]
w2y = poligono[j+1][1]
if Delta_Equipartition.segment_line_intersect(w1x,w1y,w2x,w2y,v1x,v1y,v2x,v2y) != None:
x , y = Delta_Equipartition.segment_line_intersect(w1x,w1y,w2x,w2y,v1x,v1y,v2x,v2y)
new_intersection = np.array([[x,y]])
region_1_to_add = vor.ridge_points[row][0]
region_2_to_add = vor.ridge_points[row][1]
regiones[region_1_to_add] = np.vstack((regiones[region_1_to_add],new_intersection))
regiones[region_2_to_add] = np.vstack((regiones[region_2_to_add],new_intersection))
intermediate_vertices = np.append(intermediate_vertices, new_intersection,axis=0)
regiones_intermediate[region_1_to_add].append(count)
regiones_intermediate[region_2_to_add].append(count)
sides_intermediate.append([j,j+1])
count=count+1
areas=[]
perimetros=[]
for n in range(0,len(regiones),1):
if len(regiones[n].tolist()) != 0:
zona = ConvexHull(regiones[n])
regiones[n] = zona.points[zona.vertices]
areas.append(zona.volume)
perimetros.append(zona.area)
else:
areas.append(0)
perimetros.append(0)
areas = np.array(areas)
perimetros = np.array(perimetros)
lista_respuesta = []
lista_respuesta.append(regiones)
lista_respuesta.append(areas)
lista_respuesta.append(perimetros)
lista_respuesta.append(intermediate_vertices)
lista_respuesta.append(regiones_intermediate)
lista_respuesta.append(sides_intermediate)
return lista_respuesta
def AP(poligono,number_of_regions,external,intermediate,internal):
poligon = ConvexHull(poligono)
area_del_poligono = poligon.volume
parti=Delta_Equipartition.particion_vertices(external,intermediate,internal)
a = parti[1]-area_del_poligono/number_of_regions
b = parti[2]-parti[2].mean()
return np.array([np.append(a,b)]).transpose()
@classmethod
def get_jacap_timings(cls):
"""คืนผลลัพธ์สะสมจากทุกครั้งที่เรียก Jac_AP"""
return cls.cumulative_jacap_times
def Jac_AP(poligono, number_of_regions,external, intermediate, internal, delta):
t_start = time.process_time()
# 1) AP initial
t0 = time.process_time()
b = Delta_Equipartition.AP(poligono, number_of_regions,external, intermediate, internal)
t_ap = time.process_time() - t0
# 2) internal loop ใช้เวลาสูงสุกในอัลกอริทึม
X = np.array([internal[0].flatten()]).T
n = np.size(X)
m = np.size(b)
jac = np.empty((m, 0))
t1 = time.process_time()
# print(f'internal 0 : {internal[0]}')
# print(f'internal 1 : {internal[1]}')
for column in range(0,n,1):
base = np.zeros(n)
base[column] = 1
base = np.array([base]).transpose()
X_new = X+base*delta
internal_new=[]
internal_new.append(X_new.reshape((int(np.shape(X)[0]/2),2)))
internal_new.append(internal[1])
cambio = (Delta_Equipartition.AP(poligono,number_of_regions,external,intermediate,internal_new)-b)/delta
jac = np.append(jac,cambio,axis=1)
t_int = time.process_time() - t1
# 3) intermediate loop
p = intermediate[0].shape[0]
directions = np.empty((p,2))
t2 = time.process_time()
for row in range(p):
inicio=external[0][intermediate[2][row][0]]
final=external[0][intermediate[2][row][1]]
dir=(final-inicio)/np.linalg.norm(final-inicio)
base = np.zeros(np.shape(intermediate[0]))
base[row] = dir
directions[row] = dir
intermediate_new=[]
intermediate_new.append(intermediate[0]+base*delta)
intermediate_new.append(intermediate[1])
intermediate_new.append(intermediate[2])
cambio = (Delta_Equipartition.AP(poligono,number_of_regions,external,intermediate_new,internal)-b)/delta
jac = np.append(jac, cambio, axis=1)
t_intm = time.process_time() - t2
t_total = time.process_time() - t_start
# สะสมเวลาลงตัวแปรคลาส
ct = Delta_Equipartition.cumulative_jacap_times
ct['AP_initial'] += t_ap
ct['internal_loop'] += t_int
ct['intermediate_loop']+= t_intm
ct['total_cpu'] += t_total
ct['calls'] += 1
return jac, directions
cumulative_jacap_times = {
'AP_initial': 0.0,
'internal_loop': 0.0,
'intermediate_loop': 0.0,
'total_cpu': 0.0,
'calls': 0
}
def balanceo(poligono, arreglo):
distancia_max = 0
punto_1 = np.empty([1,2])
punto_2 = np.empty([1,2])
for i in range(0, len(poligono), 1):
for j in range(i+1, len(poligono), 1):
if (distancia_max < np.linalg.norm(poligono[i]-poligono[j])):
distancia_max = np.linalg.norm(poligono[i]-poligono[j])
punto_1 = poligono[i]
punto_2 = poligono[j]
vv= (punto_1-punto_2)/np.linalg.norm(punto_1-punto_2)
ww= np.array([-vv[1],vv[0]])
alt_pos =0
alt_neg =0
for i in range(0, len(poligono),1 ):
if (np.dot(poligono[i]-punto_1, ww) > alt_pos):
alt_pos = np.dot(poligono[i]-punto_1, ww)
if (np.dot(poligono[i]-punto_1, ww) < alt_neg):
alt_neg = np.dot(poligono[i]-punto_1, ww)
distancia_perpendicular = alt_pos - alt_neg
const = distancia_max/distancia_perpendicular
nuevo_arreglo=arreglo.copy()
for j in range(0,len(arreglo),1):
nuevo_arreglo[j]= arreglo[j] +np.dot(punto_1 - arreglo[j],ww)*(1-const)*ww
return nuevo_arreglo
def desbalanceo(poligono, arreglo):
distancia_max = 0
punto_1 = np.empty([1,2])
punto_2 = np.empty([1,2])
for i in range(0, len(poligono), 1):
for j in range(i+1, len(poligono), 1):
if (distancia_max < np.linalg.norm(poligono[i]-poligono[j])):
distancia_max = np.linalg.norm(poligono[i]-poligono[j])
punto_1 = poligono[i]
punto_2 = poligono[j]
vv= (punto_1-punto_2)/np.linalg.norm(punto_1-punto_2)
ww= np.array([-vv[1],vv[0]])
alt_pos =0
alt_neg =0
for i in range(0, len(poligono),1 ):
if (np.dot(poligono[i]-punto_1, ww) > alt_pos):
alt_pos = np.dot(poligono[i]-punto_1, ww)
if (np.dot(poligono[i]-punto_1, ww) < alt_neg):
alt_neg = np.dot(poligono[i]-punto_1, ww)
distancia_perpendicular = alt_pos - alt_neg
const = 1/(distancia_max/distancia_perpendicular)
nuevo_arreglo=arreglo.copy()
for j in range(0,len(arreglo),1):
nuevo_arreglo[j]= arreglo[j] +np.dot(punto_1 - arreglo[j],ww)*(1-const)*ww
return nuevo_arreglo
def save_regions_to_csv(regiones, filename,
trial_id,error_total_normalizado,
order,polygon,sigmaa,
repetition,save_csv,number_of_regions,t1,t,endlloyd,endnormal,eqq,endtime):
# ให้แน่ใจว่าโฟลเดอร์มีอยู่
os.makedirs(os.path.dirname(filename), exist_ok=True)
rows = []
rows.append({
'num_regions': number_of_regions,
'order': order,
'eqq': eqq,
'polygon': polygon,
'error_total': error_total_normalizado,
'time': endtime,
'time_lloyd':endlloyd,
'time_normal':endnormal,
'repetition': repetition,
'iteration_lloyd':t1,
'iteration_Newton': t,
'sigma': sigmaa,
'time_details':save_csv
})
df = pd.DataFrame(rows, columns=[
'num_regions','order','eqq','polygon','error_total','time','time_lloyd','time_normal','repetition',
'iteration_lloyd','iteration_Newton','sigma','time_details'
])
# ถ้าไฟล์ยังไม่มี ให้เขียน header ครั้งแรก (mode='a' append)
write_header = not os.path.exists(filename)
df.to_csv(filename,
index=False,
mode='a',
header=write_header)
print(f"Trial {order}: Appended {len(regiones)} regions + summary to {filename}")
def partition(polygon,number_of_regions,trial_id,round,order,seed):
os.makedirs('k99', exist_ok=True)
iteraciones_centroide = 100
iteraciones_Newton = 200
numero_de_repetitiones= 2500
balancear_poligono = True
alcanzo_resultado=False
eqq = ['central_diff','factor','muti_fuzz','svr_fuzz','elas_fuzz','nn_pred_fuzz','normal','delta']
repetition=0
poligon = ConvexHull(polygon)
area_del_poligono=poligon.volume
poligono = poligon.points[poligon.vertices]
area_del_poligono=poligon.volume
while (alcanzo_resultado==False) and (repetition < numero_de_repetitiones):
starttime = time.perf_counter()
start_cputime = time.process_time()
stscale = time.perf_counter()
stscalecpu = time.process_time()
if (balancear_poligono == True) :
poligono_original = poligono
nuevo_poligono = Delta_Equipartition.balanceo(poligono, poligono)
poligon = ConvexHull(nuevo_poligono)
poligono = poligon.points[poligon.vertices]
area_del_poligono=poligon.volume
rng_sitios = np.random.default_rng(seed[repetition])
bbox = [poligon.min_bound, poligon.max_bound]
poligon_path = Path( poligon.points[poligon.vertices] )
rand_points = np.empty((number_of_regions, 2))
for i in range(number_of_regions):
rand_points[i] = np.array([rng_sitios.uniform(bbox[0][0], bbox[1][0]), rng_sitios.uniform(bbox[0][1], bbox[1][1])])
while poligon_path.contains_point(rand_points[i]) == False:
rand_points[i] = np.array([rng_sitios.uniform(bbox[0][0], bbox[1][0]), rng_sitios.uniform(bbox[0][1], bbox[1][1])])
sitios = rand_points
coordenadas=sitios.copy()
part = Delta_Equipartition.particion(poligono,coordenadas)
regiones = part[0]
centros = np.empty(np.shape(coordenadas))
endscale = time.perf_counter() - stscale
endscalecpu = time.process_time() - stscalecpu
stlloyd = time.perf_counter()
stlloydcpu = time.process_time()
for i in range(len(regiones)):
centros[i]=Delta_Equipartition.centroide(regiones[i])[0]
coord = coordenadas
t1=0
while (t1 < iteraciones_centroide) and (np.linalg.norm(coord-centros) >10**-4):
part = Delta_Equipartition.particion(poligono,coordenadas)
regiones = part[0]
centros = np.empty(np.shape(coordenadas))
for i in range(len(regiones)):
centros[i]=Delta_Equipartition.centroide(regiones[i])[0]
coord = coordenadas
coordenadas = centros
t1=t1+1
max_progress = 100
bar_length = 20
percent = t1 / max_progress * 100
filled_len = int(percent / 100 * bar_length)
bar = '0' * filled_len + '-' * (bar_length - filled_len)
print(f"\r Tr : {order} Iterations lloyd : |{bar}| {percent:.1f}%", end='', flush=True)
endlloyd = time.perf_counter() - stlloyd
endlloydcpu = time.process_time() - stlloydcpu
stex = time.perf_counter()
stexcpu = time.process_time()
part = Delta_Equipartition.particion(poligono,coordenadas)
external=Delta_Equipartition.external_vertices(poligono,coordenadas)
intermediate=part[3:]
internal=Delta_Equipartition.internal_vertices(poligono,coordenadas)
endex = time.perf_counter() - stex
endexcpu = time.process_time() - stexcpu
strescale= time.perf_counter()
strescalecpu = time.process_time()
if (balancear_poligono == True) :
internal_new=[]
internal_new.append(Delta_Equipartition.desbalanceo(poligono_original, internal[0]))
internal_new.append(internal[1])
intermediate_new=[]
intermediate_new.append(Delta_Equipartition.desbalanceo(poligono_original, intermediate[0]))
intermediate_new.append(intermediate[1])
intermediate_new.append(intermediate[2])
external_new=[]
external_new.append(Delta_Equipartition.desbalanceo(poligono_original, external[0]))
external_new.append(external[1])
external =external_new
intermediate = intermediate_new
internal = internal_new
poligono = poligono_original
poligon = ConvexHull(poligono)
poligono = poligon.points[poligon.vertices]
area_del_poligono=poligon.volume
parti=Delta_Equipartition.particion_vertices(external,intermediate,internal)
perim=parti[2]
are=parti[1]
endrescale = time.perf_counter() - strescale
endrescalecpu = time.process_time() - strescalecpu
error_total_normalizado=sum((are/(area_del_poligono/number_of_regions)-1)**2)+sum((perim/perim.mean()-1)**2)
factor0 = 1
eta = 10**-4
t = 0
delta = 10**-4
count_delta = 0
stnormal = time.perf_counter()
stnormalcpu = time.process_time()
while (t < iteraciones_Newton) and ((error_total_normalizado >10**-16)):
rutina_factor_inicial_chico = True
rutina_puntos_en_el_interior = True
# rutina_achicamiento_vector = False #Rutina de Mayita
if count_delta == 14:
count_delta = 1
if (rutina_factor_inicial_chico == True):
if (t < 10):
factor = factor0*((t+1)/10)
else :
factor = factor0
else :
factor = factor0
if count_delta == 0:
delta = 0.5*(10**-3)
elif count_delta > 1 and count_delta <= 10:
delta = 10**-4
elif count_delta > 10:
delta = 5*(10**-5)
jacobian = Delta_Equipartition.Jac_AP(poligono,number_of_regions,external,intermediate,internal,delta)
AP_value = Delta_Equipartition.AP(poligono,number_of_regions,external,intermediate,internal)
X = np.array([internal[0].flatten()]).transpose()
n = np.size(X)
p= np.shape(intermediate[0])[0]
resp = np.linalg.lstsq(jacobian[0],-AP_value,rcond=None)
if (rutina_puntos_en_el_interior == True) :
puntos_intermedios = intermediate[0]+factor*(np.append(resp[0][n:n+p,:],resp[0][n:n+p,:],axis=1)*jacobian[1])
puntos_internos = internal[0]+factor*(resp[0][0:n].transpose().reshape((int(np.shape(X)[0]/2),2)))
while (np.prod(poligon_path.contains_points(puntos_intermedios, radius=10**-4))*np.prod(poligon_path.contains_points(puntos_internos, radius=10**-4)) == 0) :
factor = factor/2
puntos_intermedios = intermediate[0]+factor*(np.append(resp[0][n:n+p,:],resp[0][n:n+p,:],axis=1)*jacobian[1])
puntos_internos = internal[0]+factor*(resp[0][0:n].transpose().reshape((int(np.shape(X)[0]/2),2)))
intermediate_new=[]
intermediate_new.append(intermediate[0]+factor*(np.append(resp[0][n:n+p,:],resp[0][n:n+p,:],axis=1)*jacobian[1]))
intermediate_new.append(intermediate[1])
intermediate_new.append(intermediate[2])
internal_new=[]
internal_new.append(internal[0]+factor*(resp[0][0:n].transpose().reshape((int(np.shape(X)[0]/2),2))))
internal_new.append(internal[1])
parti = Delta_Equipartition.particion_vertices(external,intermediate_new,internal_new)
error_convexidad = sum(parti[1])-area_del_poligono
error_convexidad_normalizada = sum(parti[1])/area_del_poligono - 1
error_total_normalizado = sum((parti[1]/(area_del_poligono/number_of_regions)-1)**2)+sum((parti[2]/parti[2].mean()-1)**2)
internal = internal_new
intermediate = intermediate_new
progress = t + 1
max_progress = 200
bar_length = 20
percent = progress / max_progress * 100
filled_len = int(percent / 100 * bar_length)
bar = '0' * filled_len + '-' * (bar_length - filled_len)
print(f"\r Tr : {order} eqq : {eqq[7]} Iterations Normal Flow : |{bar}| {percent:.1f}%", end='', flush=True)
if (error_convexidad_normalizada > 1) or (factor < 10**-8): # น่าจะเช็คกรณีลู่ออก กับ ลู่เข้าน้อยเกินไป แต่มันมีด้วยหรอ
break
t = t+1
count_delta = count_delta+1
endnormal = time.perf_counter() - stnormal
endnormalcpu = time.process_time() - stnormalcpu
parti=Delta_Equipartition.particion_vertices(external,intermediate,internal)
error_convexidad = sum(parti[1])-area_del_poligono
sigmaa = 0
perim=parti[2]
are=parti[1]
error_total=sum((are-(area_del_poligono/number_of_regions))**2)+sum((perim-perim.mean())**2)
error_total_normalizado=sum((are/(area_del_poligono/number_of_regions)-1)**2)+sum((perim/perim.mean()-1)**2)
if ((error_total_normalizado <= 10**-16)):
alcanzo_resultado=True
print('Repetition {}, |%F^c|: Total Error= {},Convexity Error = {}'.format(repetition,error_total,error_convexidad))
repetition += 1
endtime = time.perf_counter()- starttime
end_cputime = time.process_time() - start_cputime
save_csv = (f'TR : {trial_id}_{round} Total_Time: {endtime} CPU_time : {end_cputime}\n',
f'Normal_Flow : {endnormal} Normal_FlowCPU : {endnormalcpu}\n',
f'LLoyd : {endlloyd} Lloyd_CPU : {endlloydcpu}\n',
f'External : {endex} External_CPU : {endexcpu}\n',
f'scale : {endscale} scale_CPU : {endscalecpu}\n',
f'rescale : {endrescale} rescale_CPU : {endrescalecpu}\n'
,Delta_Equipartition.get_jacap_timings())
Delta_Equipartition.save_regions_to_csv(parti[0],os.path.join('k99', f'regions_coordinates_{trial_id}_{round}.csv'),trial_id,
error_total_normalizado,order ,polygon ,sigmaa ,repetition ,save_csv ,number_of_regions ,t1 ,t
,endlloyd ,endnormal ,eqq[7] ,endtime)
return parti[0],parti[1],parti[2]