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LD estimation strategies.py
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LD estimation strategies.py
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import numpy as np
import pandas as pd
import time
import math
import random
import matplotlib.pyplot as plt
from collections import Counter
from sklearn import metrics
from scipy.optimize import linear_sum_assignment as linear_assignment
from NaN import nan_2
from NaN import nan_3
def getDistanceMatrix(datas):
N, D = np.shape(datas)
dists = np.zeros([N, N])
for i in range(N):
for j in range(N):
vi = datas[i, :]
vj = datas[j, :]
dists[i, j] = np.sqrt(np.dot((vi - vj), (vi - vj)))
return dists
def select_dc(dists):
N = np.shape(dists)[0]
tt = np.reshape(dists, N * N)
percent = 0.02
position = int(N * (N - 1) * percent)
dc = np.sort(tt)[position + N]
return dc
def get_density(dists, dc, method=None):
N = np.shape(dists)[0]
rho = np.zeros(N)
for i in range(N):
if method == None:
rho[i] = np.where(dists[i, :] < dc)[0].shape[0] - 1
else:
rho[i] = np.sum(np.exp(-(dists[i, :] / dc) ** 2)) - 1
return rho
def KROD_getDistanceMatrix(datas, a):
N, D = np.shape(datas)
dists = np.zeros([N, N])
for i in range(N):
for j in range(N):
vi = datas[i, :]
vj = datas[j, :]
dists[i, j] = np.sqrt(np.dot((vi - vj), (vi - vj)))
dists = dists/(np.max(dists))
list = np.argsort(dists)
rows = np.argsort(list)
rodist = (rows + rows.T)
rodist = rodist / (np.max(rodist))
KROD_value = rodist*np.exp((dists * dists) / (a * a))
return KROD_value
if __name__ == "__main__":
txtDF1 = pd.read_csv(r'E:\Datasets\D31.csv')
col = txtDF1.shape[1]
df = txtDF1.iloc[:, 0:(col - 1)]
df_min_max = df.values
nat_neighbor1 = nan_3.Natural_Neighbor()
nat_neighbor1.load(r'E:\Datasets\D31_1.csv')
nane1, temp_density1 = nat_neighbor1.algorithm()
# -------------------------------------------------------------------------------------------
dists = getDistanceMatrix(df_min_max)
dc = select_dc(dists)
rho_ruan = get_density(dists, dc) # Equation(1)
rho_ying=get_density(dists, dc, method="Gaussion") # Equation(2)
# -------------------------------------------------------------------------------------------
den_2017=[]
for ele_2017 in range(len(df_min_max)):
den_2017.append(np.sort(dists[ele_2017, :])[nane1])
aver_knn=np.mean(den_2017)
dc_2017=aver_knn+np.sqrt(np.sum((np.array(den_2017)-aver_knn)**2)/(len(df_min_max)-1))
ture_den_2017=[] # Equation(3)
for ele_2017 in range(len(df_min_max)):
dis_2017=np.sort(dists[ele_2017, :])[1:nane1+1]
ture_den_2017.append(np.sum(np.exp(-(dis_2017/dc_2017)**2)))
# -------------------------------------------------------------------------------------------
dists_2020 = KROD_getDistanceMatrix(df_min_max, 0.1)
dc_2020 = select_dc(dists_2020)
rho_KROD = get_density(dists_2020, dc_2020) # Equation(7)
# -------------------------------------------------------------------------------------------
dists_2020_1 = KROD_getDistanceMatrix(df_min_max, 100)
dc_2020_1 = select_dc(dists_2020_1)
rho_KROD_1 = get_density(dists_2020_1, dc_2020_1)
# -------------------------------------------------------------------------------------------
ture_den_2016 = [] # Equation(8)
for ele_2016 in range(len(df_min_max)):
dis_2016 = np.sort(dists[ele_2016, :])[1:nane1 + 1]
ture_den_2016.append(np.exp(-np.mean(dis_2016*dis_2016)))
# -------------------------------------------------------------------------------------------
F_ture_den_2016 = [] # Equation(9)
for Fele_2016 in range(len(df_min_max)):
Fdis_2016 = np.sort(dists[Fele_2016, :])[1:nane1 + 1]
F_ture_den_2016.append(np.sum(np.exp(-Fdis_2016)))
# -------------------------------------------------------------------------------------------
ture_den_2019 = [] # Equation(11)
for nane_2019 in range(len(df_min_max)):
Fdis_2016 = np.sort(dists[nane_2019, :])[1:nane1 + 1]
ture_den_2019.append(nane1/np.sum(Fdis_2016))
#-------------------------------------------------------------------------------------------
ture_den_2022 = [] # Equation(12)
for ele_2022 in range(len(df_min_max)):
dis_2022 = np.sort(dists[ele_2022, :])[1:nane1 + 1]
ture_den_2022.append(np.sum(np.exp(-dis_2022*dis_2022)))
# -------------------------------------------------------------------------------------------
nat_neighbor2 = nan_2.Natural_Neighbor()
nat_neighbor2.load(r'E:\Datasets\D31_1.csv')
nane2, temp_density2 = nat_neighbor2.algorithm()
final_density1_our = []
for i in temp_density2:
final_density1_our.append(i / nane2) # NN-LD
# -------------------------------------------------------------------------------------------
font = {'family': 'Times New Roman', 'weight': 'bold', 'size': 48}
font1 = {'family': 'Times New Roman', 'weight': 'bold', 'size': 46}
hh = 32
dd = 3.5
plt.figure(figsize=(35, 14))
plt.subplot(251)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=rho_ruan, marker='o', cmap='YlGnBu',lw=dd)
plt.xlabel('(a) Equation(1)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(252)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=rho_ying, marker='o', cmap='YlGnBu',lw=dd)
plt.xlabel('(b) Equation(2)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(253)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(ture_den_2017), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(c) Equation(3)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(254)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=rho_KROD, marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(d_1) Equation(7)[0.1]', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------
plt.subplot(255)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=rho_KROD_1, marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(d_2) Equation(7)[100]', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(256)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(ture_den_2016), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(e) Equation(8)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(257)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(F_ture_den_2016), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(f) Equation(9)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(258)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(ture_den_2019), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(g) Equation(11)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(259)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(ture_den_2022), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(h) Equation(12)', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
# -------------------------------------------------------------------------------------------
plt.subplot(2,5,10)
plt.scatter(df_min_max[:, 0], df_min_max[:, 1], c=np.array(final_density1_our), marker='o', cmap='YlGnBu', lw=dd)
plt.xlabel('(i) NN-LD', font1)
plt.xticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.yticks(fontsize=hh, family='Times New Roman', weight="bold")
plt.tight_layout()
plt.savefig(r'E:\Datasets\D31.jpeg', dpi=600)
plt.show()