algo de knn naïf

3nar.py

@@ -1,5 +1,6 @@
-import matplotlib.pyplot as plt
 import numpy as np
+
+from point import *
 from function import *
 
 class Nnnar:
@@ -71,25 +72,10 @@ class Nnnar:
         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(3, 0, 3, 2)
 
 n.addPoint(np.array([1, 0.8,1.5]), [0.2,0.5])
 n.addPoint(np.array([0.2, 1.8,1.2]), [0.4,0.7])
 
-print(n.getValueOfPoint(np.array([1, 0.8, 1.5]), 1))
+print(n.getValueOfPoint(np.array([1, 0.8, 1.5]), 2))

knn.py

@@ -0,0 +1,54 @@
+import numpy as np
+
+from point import *
+
+
+class Knn:
+    def __init__(self):
+        self.space = np.array([])
+    
+    def addPoint(self, coord, value):
+        self.space = np.append(self.space, Point(coord, value))
+
+    def getValueOfPoint(self, coord,nbNearest):
+        points, dists = self.getNNearest(coord, nbNearest)
+        ttPart = 1/(dists[0]+1)
+        value = []
+        for i in points[0].value:
+            value.append(i * ttPart)
+        for idx in range(1,len(points)):
+            ttPart += 1/(dists[idx]+1)
+            tvalue = points[idx].value
+            for i in range(len(tvalue)):
+                value[i] += tvalue[i] * (1/(dists[idx]+1))
+        for i in range(len(value)):
+            value[i] = value[i] / ttPart
+        return value
+
+    def getNNearest(self, coord, nbNearest):
+        dist = np.copy(self.space)
+        dist = np.frompyfunc(lambda x: x.getDistFromCoord(coord), 1, 1)(dist)
+        if (nbNearest > len(dist)):
+            print("Error: not enough points")
+            return None, None
+        found = []
+        distance = []
+        for i in range(nbNearest):
+            found.append(self.space[i])
+            distance.append(dist[i])
+        maxDistIdx = np.argmax(distance)
+        for i in range(nbNearest, len(dist)):
+            if (dist[i] < distance[maxDistIdx]):
+                found[maxDistIdx] = self.space[i]
+                distance[maxDistIdx] = dist[i]
+                maxDistIdx = np.argmax(distance)
+        return found, distance
+    
+
+
+k = Knn()
+
+k.addPoint(np.array([1, 0.8,1.5]), [0.2,0.5])
+k.addPoint(np.array([0.2, 1.8,1.2]), [0.4,0.7])
+
+print(k.getValueOfPoint(np.array([1, 0.8, 1.5]), 2))

point.py

@@ -0,0 +1,15 @@
+import numpy as np
+
+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)