points_neighbers_find.py

import numpy as np

np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;

pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import pylab as plt
import numpy.matlib

#import Utils as utl

def ismember(A,B):
    return [np.sum(a == B) for a in A]

def points_neighbers_find(x,xE,xU,Bin,Ain):
    [delta_general, index,x1] = mindis(x, np.concatenate((xE,xU ), axis=1) )
    
    active_cons = []
    b = Bin - np.dot(Ain,x)
    for i in range(len(b)):
        if b[i][0] < 1e-3:
            active_cons.append(i+1)
    active_cons = np.array(active_cons)
    
    active_cons1 = []
    b = Bin - np.dot(Ain,x1)
    for i in range(len(b)):
        if b[i][0] < 1e-3:
            active_cons1.append(i+1)
    active_cons1 = np.array(active_cons1)
    
    if len(active_cons) == 0 or min(ismember(active_cons,active_cons1)) == 1:
        newadd = 1
        success = 1
        if mindis(x,xU) == 0:
            newadd = 0
    else:
        success = 0
        newadd = 0
        xU = np.concatenate((xU,x),axis=0)
    return x, xE, xU, newadd, success	
    
def mindis(x, xi):
    # function [y,x1,index] = mindistance(x,xi)
    # % calculates the minimum distance from all the existing points
    # % xi all the previous points
    # % x the new point
    y = float('inf')
    N = xi.shape[1]
    for i in range(N):
        y1 = np.linalg.norm(x[:, 0] - xi[:, i])
        if y1 < y:
            y = np.copy(y1)
            x1 = np.copy(xi[:, i])
            index = np.copy(i)
    return y, index, x1