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94 lines
3.3 KiB
94 lines
3.3 KiB
import numpy as np
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from point import *
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from function import *
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class Nnnar:
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def __init__(self, nbDimensions, minCoord, maxCoord, nbSubdivisions):
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self.nbDimensions = nbDimensions
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self.minCoord = minCoord
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self.maxCoord = maxCoord
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self.nbSubdivisions = nbSubdivisions
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self.delta = maxCoord - minCoord
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self.unit = self.delta / nbSubdivisions
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coord = np.zeros(nbDimensions,dtype=int) + self.nbSubdivisions
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self.space = np.zeros(coord)
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fillSpace = np.vectorize(lambda x: [], otypes=[object])
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self.space = fillSpace(self.space)
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self.calculatedSpaceAroundIdx = []
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def reset(self):
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coord = np.zeros(self.nbDimensions,dtype=int) + self.nbSubdivisions
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self.space = np.zeros(coord)
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fillSpace = np.vectorize(lambda x: [], otypes=[object])
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self.space = fillSpace(self.space)
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def addPoint(self, coord, value):
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if len(coord) != self.nbDimensions:
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raise AttributeError("Error: wrong number of dimensions")
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self.space[*self.getSpaceIdxFromCoord(coord)].append(Point(coord, value))
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def getLabelOfPoint(self, coord,nbNearest):
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points, dists = self.getNNearest(coord, nbNearest)
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label = {}
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for idx in range(len(points)):
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tvalue = points[idx].value[0]
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if tvalue in label:
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label[tvalue] += 1/(dists[idx]+1)
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else:
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label[tvalue] = 1/(dists[idx]+1)
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value = max(label, key=label.get)
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return value
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def getValueOfPoint(self, coord,nbNearest):
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points, dists = self.getNNearest(coord, nbNearest)
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ttPart = 1/(dists[0]+1)
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value = []
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for i in points[0].value:
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value.append(i * ttPart)
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for idx in range(1,len(points)):
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ttPart += 1/(dists[idx]+1)
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tvalue = points[idx].value
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for i in range(len(tvalue)):
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value[i] += tvalue[i] * (1/(dists[idx]+1))
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for i in range(len(value)):
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value[i] = value[i] / ttPart
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return value
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def getNNearest(self, coord, nbNearest):
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idx = self.getSpaceIdxFromCoord(coord)
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nbAround = 0
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selected = []
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while len(selected) < nbNearest:
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selected = []
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subSpaceIdxList = getSpaceIdxAround(idx, nbAround, self)
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for subSpaceIdx in subSpaceIdxList:
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selected += selectPointsInRange(self.space[tuple(subSpaceIdx)],coord,nbAround*self.unit,self.space)
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nbAround += 1
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found = []
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dist = []
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i=0
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while len(found) < nbNearest:
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found.append(selected[i])
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dist.append(selected[i].getDistFromCoord(coord))
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i+=1
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maxDistIdx = foundMaxIndex(dist)
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maxDist = max(dist)
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while i != len(selected):
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if (selected[i].getDistFromCoord(coord) < maxDist):
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found.pop(maxDistIdx)
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dist.pop(maxDistIdx)
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found.append(selected[i])
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dist.append(selected[i].getDistFromCoord(coord))
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maxDistIdx = foundMaxIndex(dist)
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maxDist = max(dist)
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i+=1
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return found, dist
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def getSpaceIdxFromCoord(self,coord):
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coord = coord - self.minCoord
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coord = coord / self.unit
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coord = np.floor(coord).astype(int)
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return coord
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