un première partie des algos

3nar.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from function import *
+
+class Nnnar:
+    def __init__(self, nbDimensions, minCoord, maxCoord, nbSubdivisions):
+        self.nbDimensions = nbDimensions
+        self.minCoord = minCoord
+        self.maxCoord = maxCoord
+        self.nbSubdivisions = nbSubdivisions
+        self.delta = maxCoord - minCoord
+        self.unit = self.delta / nbSubdivisions
+        coord = np.zeros(nbDimensions,dtype=int) + self.nbSubdivisions
+        self.space = np.zeros(coord)
+        fillSpace = np.vectorize(lambda x: [], otypes=[object])
+        self.space = fillSpace(self.space)
+        self.calculatedSpaceAroundIdx = []
+    
+    def addPoint(self, coord, value):
+        if len(coord) != self.nbDimensions:
+            raise AttributeError("Error: wrong number of dimensions")
+        self.space[*self.getSpaceIdxFromCoord(coord)].append(Point(coord, value))
+
+    def getNNearest(self, coord, nbNearest):
+        idx = self.getSpaceIdxFromCoord(coord)
+        nbAround = 0
+        selected = []
+        while len(selected) < nbNearest:
+            selected = []
+            subSpaceIdxList = getSpaceIdxAround(idx, nbAround, self)
+            for subSpaceIdx in subSpaceIdxList:
+                selected += selectPointsInRange(self.space[subSpaceIdx],coord,nbAround*self.unit)
+            nbAround += 1
+        found = []
+        dist = []
+        i=0
+        while len(found) < nbNearest:
+            found += selected[i]
+            i+=1
+        maxDistIdx = foundMaxIndex(dist)
+        maxDist = max(dist)
+        for i in range(nbNearest, len(selected)):
+            if (selected[i].getDistFromCoord(coord) < maxDist):
+                found.pop(maxDistIdx)
+                dist.pop(maxDistIdx)
+                found.append(selected[i])
+                dist.append(selected[i].getDistFromCoord(coord))
+                maxDistIdx = foundMaxIndex(dist)
+                maxDist = max(dist)
+        return found
+
+    def getSpaceIdxFromCoord(self,coord):
+        coord = coord - self.minCoord
+        coord = coord / self.unit
+        coord = np.floor(coord).astype(int)
+        return coord
+
+class Point:
+    def __init__(self, coord, value):
+        self.coord = coord
+        self.value = value
+
+    def setvalue(self, value):
+        self.value = value
+
+    def getDist(self, point):
+        return np.linalg.norm(self.coord - point.coord)
+
+    def getDistFromCoord(self, coord):
+        return np.linalg.norm(self.coord - coord)
+
+
+
+
+n = Nnnar(2, 0, 3, 2)
+
+n.addPoint(np.array([1, 0.8]), 1)
+print(n.space[0][0][0].coord)

function.py

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+
+def getSpaceIdxAround(center, nbAround, space):
+    if (nbAround < len(space.calculatedSpaceAroundIdx)):
+        return space.calculatedSpaceAroundIdx[nbAround]
+    # TODO: implement l'algo pour trouver les indices subSapce à nbAround autour du centre
+
+def selectPointsInRange(points, coord, distance):
+    found = []
+    return found
+
+def foundMaxIndex(dist):
+    return dist.index(max(dist))